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Sample records for drugs predict subsequent

  1. A data-driven predictive approach for drug delivery using machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Li

    Full Text Available In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.

  2. Data-driven prediction of adverse drug reactions induced by drug-drug interactions.

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    Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Kumar, Kamal; Yu, Xueping; Wallqvist, Anders; Reifman, Jaques

    2017-06-08

    The expanded use of multiple drugs has increased the occurrence of adverse drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions are typically not observed in clinical drug-development studies because most of them focus on single-drug therapies. ADR reporting systems collect information on adverse health effects caused by both single drugs and DDIs. A major challenge is to unambiguously identify the effects caused by DDIs and to attribute them to specific drug interactions. A computational method that provides prospective predictions of potential DDI-induced ADRs will help to identify and mitigate these adverse health effects. We hypothesize that drug-protein interactions can be used as independent variables in predicting ADRs. We constructed drug pair-protein interaction profiles for ~800 drugs using drug-protein interaction information in the public domain. We then constructed statistical models to score drug pairs for their potential to induce ADRs based on drug pair-protein interaction profiles. We used extensive clinical database information to construct categorical prediction models for drug pairs that are likely to induce ADRs via synergistic DDIs and showed that model performance deteriorated only slightly, with a moderate amount of false positives and false negatives in the training samples, as evaluated by our cross-validation analysis. The cross validation calculations showed an average prediction accuracy of 89% across 1,096 ADR models that captured the deleterious effects of synergistic DDIs. Because the models rely on drug-protein interactions, we made predictions for pairwise combinations of 764 drugs that are currently on the market and for which drug-protein interaction information is available. These predictions are publicly accessible at http://avoid-db.bhsai.org . We used the predictive models to analyze broader aspects of DDI-induced ADRs, showing that ~10% of all combinations have the potential to induce ADRs

  3. Computational prediction of drug-drug interactions based on drugs functional similarities.

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    Ferdousi, Reza; Safdari, Reza; Omidi, Yadollah

    2017-06-01

    Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs. Copyright © 2017. Published by Elsevier Inc.

  4. QSAR Modeling and Prediction of Drug-Drug Interactions.

    Science.gov (United States)

    Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

    2016-02-01

    Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

  5. Potentially inappropriate medication: Association between the use of antidepressant drugs and the subsequent risk for dementia.

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    Heser, Kathrin; Luck, Tobias; Röhr, Susanne; Wiese, Birgitt; Kaduszkiewicz, Hanna; Oey, Anke; Bickel, Horst; Mösch, Edelgard; Weyerer, Siegfried; Werle, Jochen; Brettschneider, Christian; König, Hans-Helmut; Fuchs, Angela; Pentzek, Michael; van den Bussche, Hendrik; Scherer, Martin; Maier, Wolfgang; Riedel-Heller, Steffi G; Wagner, Michael

    2018-01-15

    Potentially inappropriate medication (PIM) is associated with an increased risk for detrimental health outcomes in elderly patients. Some antidepressant drugs are considered as PIM, but previous research on the association between antidepressants and subsequent dementia has been inconclusive. Therefore, we investigated whether the intake of antidepressants, particularly of those considered as PIM according to the Priscus list, would predict incident dementia. We used data of a prospective cohort study of non-demented primary care patients (n = 3239, mean age = 79.62) to compute Cox proportional hazards models. The risk for subsequent dementia was estimated over eight follow-ups up to 12 years depending on antidepressant intake and covariates. The intake of antidepressants was associated with an increased risk for subsequent dementia (HR = 1.53, 95% CI: 1.16-2.02, p = .003; age-, sex-, education-adjusted). PIM antidepressants (HR = 1.49, 95% CI: 1.06-2.10, p = .021), but not other antidepressants (HR = 1.04, 95% CI: 0.66-1.66, p = .863), were associated with an increased risk for subsequent dementia (in age-, sex-, education-, and depressive symptoms adjusted models). Significant associations disappeared after global cognition at baseline was controlled for. Methodological limitations such as selection biases and self-reported drug assessments might have influenced the results. Only antidepressants considered as PIM were associated with an increased subsequent dementia risk. Anticholinergic effects might explain this relationship. The association disappeared after the statistical control for global cognition at baseline. Nonetheless, physicians should avoid the prescription of PIM antidepressants in elderly patients whenever possible. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

    Science.gov (United States)

    Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

    2013-11-01

    The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

  7. Quantitative prediction of drug side effects based on drug-related features.

    Science.gov (United States)

    Niu, Yanqing; Zhang, Wen

    2017-09-01

    Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them. In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model. Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

  8. Deep-Learning-Based Drug-Target Interaction Prediction.

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    Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

    2017-04-07

    Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

  9. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

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    Huang, Cai; Mezencev, Roman; McDonald, John F; Vannberg, Fredrik

    2017-01-01

    Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

  10. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

    Directory of Open Access Journals (Sweden)

    Cai Huang

    Full Text Available Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM algorithm combined with a standard recursive feature elimination (RFE approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60. The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

  11. Mathematical modeling and computational prediction of cancer drug resistance.

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    Sun, Xiaoqiang; Hu, Bin

    2017-06-23

    Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of

  12. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

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    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Global brain dynamics during social exclusion predict subsequent behavioral conformity.

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    Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O'Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

    2018-02-01

    Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences.

  14. Predicting drug-target interactions using restricted Boltzmann machines.

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    Wang, Yuhao; Zeng, Jianyang

    2013-07-01

    In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are

  15. Predicting drug?drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

    OpenAIRE

    Takeda, Takako; Hao, Ming; Cheng, Tiejun; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    Drug?drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our a...

  16. Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

    DEFF Research Database (Denmark)

    Jonsdottir, Svava Osk; Jorgensen, FS; Brunak, Søren

    2005-01-01

    about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability......MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information...... of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described....

  17. DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

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    Wang, QuanQiu; Xu, Rong

    2015-01-01

    Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

  18. Drug-Target Interactions: Prediction Methods and Applications.

    Science.gov (United States)

    Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

    2018-01-01

    Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  19. Neural correlates of encoding processes predicting subsequent cued recall and source memory.

    Science.gov (United States)

    Angel, Lucie; Isingrini, Michel; Bouazzaoui, Badiâa; Fay, Séverine

    2013-03-06

    In this experiment, event-related potentials were used to examine whether the neural correlates of encoding processes predicting subsequent successful recall differed from those predicting successful source memory retrieval. During encoding, participants studied lists of words and were instructed to memorize each word and the list in which it occurred. At test, they had to complete stems (the first four letters) with a studied word and then make a judgment of the initial temporal context (i.e. list). Event-related potentials recorded during encoding were segregated according to subsequent memory performance to examine subsequent memory effects (SMEs) reflecting successful cued recall (cued recall SME) and successful source retrieval (source memory SME). Data showed a cued recall SME on parietal electrode sites from 400 to 1200 ms and a late inversed cued recall SME on frontal sites in the 1200-1400 ms period. Moreover, a source memory SME was reported from 400 to 1400 ms on frontal areas. These findings indicate that patterns of encoding-related activity predicting successful recall and source memory are clearly dissociated.

  20. Drug-target interaction prediction: A Bayesian ranking approach.

    Science.gov (United States)

    Peska, Ladislav; Buza, Krisztian; Koller, Júlia

    2017-12-01

    In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

  1. Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction.

    Science.gov (United States)

    Fukunishi, Yoshifumi

    2010-01-01

    For fragment-based drug development, both hit (active) compound prediction and docking-pose (protein-ligand complex structure) prediction of the hit compound are important, since chemical modification (fragment linking, fragment evolution) subsequent to the hit discovery must be performed based on the protein-ligand complex structure. However, the naïve protein-compound docking calculation shows poor accuracy in terms of docking-pose prediction. Thus, post-processing of the protein-compound docking is necessary. Recently, several methods for the post-processing of protein-compound docking have been proposed. In FBDD, the compounds are smaller than those for conventional drug screening. This makes it difficult to perform the protein-compound docking calculation. A method to avoid this problem has been reported. Protein-ligand binding free energy estimation is useful to reduce the procedures involved in the chemical modification of the hit fragment. Several prediction methods have been proposed for high-accuracy estimation of protein-ligand binding free energy. This paper summarizes the various computational methods proposed for docking-pose prediction and their usefulness in FBDD.

  2. Predicting client attendance at further treatment following drug and alcohol detoxification: Theory of Planned Behaviour and Implementation Intentions.

    Science.gov (United States)

    Kelly, Peter J; Leung, Joanne; Deane, Frank P; Lyons, Geoffrey C B

    2016-11-01

    Despite clinical recommendations that further treatment is critical for successful recovery following drug and alcohol detoxification, a large proportion of clients fail to attend treatment after detoxification. In this study, individual factors and constructs based on motivational and volitional models of health behaviour were examined as predictors of post-detoxification treatment attendance. The sample consisted of 220 substance-dependent individuals participating in short-term detoxification programs provided by The Australian Salvation Army. The Theory of Planned Behaviour and Implementation Intentions were used to predict attendance at subsequent treatment. Follow-up data were collected for 177 participants (81%), with 104 (80%) of those participants reporting that they had either attended further formal treatment (e.g. residential rehabilitation programs, outpatient counselling) or mutual support groups in the 2 weeks after leaving the detoxification program. Logistic regression examined the predictors of further treatment attendance. The full model accounted for 21% of the variance in treatment attendance, with attitude and Implementation Intentions contributing significantly to the prediction. Findings from the present study would suggest that assisting clients to develop a specific treatment plan, as well as helping clients to build positive perceptions about subsequent treatment, will promote greater attendance at further treatment following detoxification. [Kelly PJ, Leung J, Deane FP, Lyons GCB. Predicting client attendance at further treatment following drug and alcohol detoxification: Theory of Planned Behaviour and Implementation Intentions. Drug Alcohol Rev 2016;35:678-685]. © 2015 Australasian Professional Society on Alcohol and other Drugs.

  3. The Combination of GIS and Biphasic to Better Predict In Vivo Dissolution of BCS Class IIb Drugs, Ketoconazole and Raloxifene.

    Science.gov (United States)

    Tsume, Yasuhiro; Igawa, Naoto; Drelich, Adam J; Amidon, Gregory E; Amidon, Gordon L

    2018-01-01

    The formulation developments and the in vivo assessment of Biopharmaceutical Classification System (BCS) class II drugs are challenging due to their low solubility and high permeability in the human gastrointestinal (GI) tract. Since the GI environment influences the drug dissolution of BCS class II drugs, the human GI characteristics should be incorporated into the in vitro dissolution system to predict bioperformance of BCS class II drugs. An absorptive compartment may be important in dissolution apparatus for BCS class II drugs, especially for bases (BCS IIb) because of high permeability, precipitation, and supersaturation. Thus, the in vitro dissolution system with an absorptive compartment may help predicting the in vivo phenomena of BCS class II drugs better than compendial dissolution apparatuses. In this study, an absorptive compartment (a biphasic device) was introduced to a gastrointestinal simulator. This addition was evaluated if this in vitro system could improve the prediction of in vivo dissolution for BCS class IIb drugs, ketoconazole and raloxifene, and subsequent absorption. The gastrointestinal simulator is a practical in vivo predictive tool and exhibited an improved in vivo prediction utilizing the biphasic format and thus a better tool for evaluating the bioperformance of BCS class IIb drugs than compendial apparatuses. Copyright © 2018. Published by Elsevier Inc.

  4. A classification framework for drug relapse prediction | Salleh ...

    African Journals Online (AJOL)

    mining algorithms, Artificial Intelligence Neural Network (ANN) is one of the best algorithms to predict relapse among drug addicts. This may help the rehabilitation center to predict relapse individually and the prediction result is hoped to prevent drug addicts from relapse. Keywords: classification; artificial neural network; ...

  5. Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization.

    Science.gov (United States)

    Yu, Hui; Mao, Kui-Tao; Shi, Jian-Yu; Huang, Hua; Chen, Zhi; Dong, Kai; Yiu, Siu-Ming

    2018-04-11

    Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription. In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs. Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree

  6. Large-scale prediction of drug-target interactions using protein sequences and drug topological structures

    Energy Technology Data Exchange (ETDEWEB)

    Cao Dongsheng [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Liu Shao [Xiangya Hospital, Central South University, Changsha 410008 (China); Xu Qingsong [School of Mathematical Sciences and Computing Technology, Central South University, Changsha 410083 (China); Lu Hongmei; Huang Jianhua [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Hu Qiannan [Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071 (China); Liang Yizeng, E-mail: yizeng_liang@263.net [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China)

    2012-11-08

    Highlights: Black-Right-Pointing-Pointer Drug-target interactions are predicted using an extended SAR methodology. Black-Right-Pointing-Pointer A drug-target interaction is regarded as an event triggered by many factors. Black-Right-Pointing-Pointer Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. Black-Right-Pointing-Pointer Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug-target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug-target interactions in a timely manner. In this article, we aim at extending current structure-activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug-target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug

  7. In silico modeling to predict drug-induced phospholipidosis

    International Nuclear Information System (INIS)

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G.; Sadrieh, Nakissa

    2013-01-01

    Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL

  8. Neuroticism Predicts Subsequent Risk of Major Depression for Whites but Not Blacks

    Directory of Open Access Journals (Sweden)

    Shervin Assari

    2017-09-01

    Full Text Available Cultural and ethnic differences in psychosocial and medical correlates of negative affect are well documented. This study aimed to compare blacks and whites for the predictive role of baseline neuroticism (N on subsequent risk of major depressive episodes (MDD 25 years later. Data came from the Americans’ Changing Lives (ACL Study, 1986–2011. We used data on 1219 individuals (847 whites and 372 blacks who had data on baseline N in 1986 and future MDD in 2011. The main predictor of interest was baseline N, measured using three items in 1986. The main outcome was 12 months MDD measured using the Composite International Diagnostic Interview (CIDI at 2011. Covariates included baseline demographics (age and gender, socioeconomics (education and income, depressive symptoms [Center for Epidemiologic Studies Depression Scale (CES-D], stress, health behaviors (smoking and driking, and physical health [chronic medical conditions, obesity, and self-rated health (SRH] measured in 1986. Logistic regressions were used to test the predictive role of baseline N on subsequent risk of MDD 25 years later, net of covariates. The models were estimated in the pooled sample, as well as blacks and whites. In the pooled sample, baseline N predicted subsequent risk of MDD 25 years later (OR = 2.23, 95%CI = 1.14–4.34, net of covariates. We also found a marginally significant interaction between race and baseline N on subsequent risk of MDD (OR = 0.37, 95% CI = 0.12–1.12, suggesting a stronger effect for whites compared to blacks. In race-specific models, among whites (OR = 2.55; 95% CI = 1.22–5.32 but not blacks (OR = 0.90; 95% CI = 0.24–3.39, baseline N predicted subsequent risk of MDD. Black-white differences in socioeconomics and physical health could not explain the racial differences in the link between N and MDD. Blacks and whites differ in the salience of baseline N as a psychological determinant of MDD risk over a long period of time. This finding

  9. LSD-induced entropic brain activity predicts subsequent personality change.

    Science.gov (United States)

    Lebedev, A V; Kaelen, M; Lövdén, M; Nilsson, J; Feilding, A; Nutt, D J; Carhart-Harris, R L

    2016-09-01

    Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality. Hum Brain Mapp 37:3203-3213, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  10. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.

    Science.gov (United States)

    Luo, Yunan; Zhao, Xinbin; Zhou, Jingtian; Yang, Jinglin; Zhang, Yanqing; Kuang, Wenhua; Peng, Jian; Chen, Ligong; Zeng, Jianyang

    2017-09-18

    The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.

  11. Prediction of potential drug targets based on simple sequence properties

    Directory of Open Access Journals (Sweden)

    Lai Luhua

    2007-09-01

    Full Text Available Abstract Background During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets. Results Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research. Conclusion We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.

  12. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim; Fokoue, Achille; Hassanzadeh, Oktie; Zhang, Ping; Sadoghi, Mohammad

    2017-01-01

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  13. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim

    2017-06-12

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  14. Drug-target interaction prediction via class imbalance-aware ensemble learning.

    Science.gov (United States)

    Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

    2016-12-22

    Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was

  15. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

    Science.gov (United States)

    Xia, Zheng; Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T C

    2010-09-13

    Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

  16. Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

    Science.gov (United States)

    Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo

    2016-01-01

    The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

  17. Gaussian interaction profile kernels for predicting drug-target interaction.

    Science.gov (United States)

    van Laarhoven, Twan; Nabuurs, Sander B; Marchiori, Elena

    2011-11-01

    The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl. Supplementary data are available at Bioinformatics online.

  18. Does resident ranking during recruitment accurately predict subsequent performance as a surgical resident?

    Science.gov (United States)

    Fryer, Jonathan P; Corcoran, Noreen; George, Brian; Wang, Ed; Darosa, Debra

    2012-01-01

    While the primary goal of ranking applicants for surgical residency training positions is to identify the candidates who will subsequently perform best as surgical residents, the effectiveness of the ranking process has not been adequately studied. We evaluated our general surgery resident recruitment process between 2001 and 2011 inclusive, to determine if our recruitment ranking parameters effectively predicted subsequent resident performance. We identified 3 candidate ranking parameters (United States Medical Licensing Examination [USMLE] Step 1 score, unadjusted ranking score [URS], and final adjusted ranking [FAR]), and 4 resident performance parameters (American Board of Surgery In-Training Examination [ABSITE] score, PGY1 resident evaluation grade [REG], overall REG, and independent faculty rating ranking [IFRR]), and assessed whether the former were predictive of the latter. Analyses utilized Spearman correlation coefficient. We found that the URS, which is based on objective and criterion based parameters, was a better predictor of subsequent performance than the FAR, which is a modification of the URS based on subsequent determinations of the resident selection committee. USMLE score was a reliable predictor of ABSITE scores only. However, when we compared our worst residence performances with the performances of the other residents in this evaluation, the data did not produce convincing evidence that poor resident performances could be reliably predicted by any of the recruitment ranking parameters. Finally, stratifying candidates based on their rank range did not effectively define a ranking cut-off beyond which resident performance would drop off. Based on these findings, we recommend surgery programs may be better served by utilizing a more structured resident ranking process and that subsequent adjustments to the rank list generated by this process should be undertaken with caution. Copyright © 2012 Association of Program Directors in Surgery

  19. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

    Science.gov (United States)

    Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

    2015-07-15

    Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

    Science.gov (United States)

    Zhang, Wen; Chen, Yanlin; Li, Dingfang

    2017-11-25

    Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

  1. Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions.

    Science.gov (United States)

    La, Mary K; Sedykh, Alexander; Fourches, Denis; Muratov, Eugene; Tropsha, Alexander

    2018-06-06

    Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug-target-effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug-target-effect paradigm, can be integrated to facilitate the inference of relationships between these entities. This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature. Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C-T-E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson's paradigm to form C-T-E triangles. Missing C-E edges were then inferred as C-E relationships. Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C-E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale. The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C-E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C-E inferences, this workflow may provide an effective computational method for

  2. Prediction of Parkinson's disease subsequent to severe depression: a ten-year follow-up study.

    Science.gov (United States)

    Walter, Uwe; Heilmann, Robert; Kaulitz, Lara; Just, Tino; Krause, Bernd Joachim; Benecke, Reiner; Höppner, Jacqueline

    2015-06-01

    Major depressive disorder (MDD) has been associated with an increased risk of subsequent Parkinson's disease (PD) in case-control and cohort studies. However, depression alone is unlikely to be a useful marker of prodromal PD due to its low specificity. In this longitudinal observational study, we assessed whether the presence of other potential markers of prodromal PD predicts the subsequent development of PD in MDD patients. Of 57 patients with severe MDD but no diagnosis of PD who underwent a structured interview, olfactory and motor investigation and transcranial sonography at baseline, 46 (36 women; mean age 54.9 ± 11.7 years) could be followed for up to 11 (median, 10) years. Three patients (2 women; age 64, 65 and 70 years) developed definite PD after 1, 7, and 9 years, respectively. The combined finding of mild asymmetric motor slowing, idiopathic hyposmia, and substantia nigra hyperechogenicity predicted subsequent PD in all patients who could be followed for longer than 1 year. Out of the whole study cohort, only the subjects with subsequent PD presented with the triad of asymmetric motor slowing, idiopathic hyposmia, and substantia nigra hyperechogenicity in combination with at least two out of four reportable risk factors (family history of PD, current non-smoker, non-coffee drinker, constipation) at baseline investigation. Post-hoc analysis revealed that additional rating of eye and eye-lid motor abnormalities might further improve the prediction of PD in larger cohorts. Findings of this pilot-study suggest that MDD patients at risk of subsequent PD can be identified using an inexpensive non-invasive diagnostic battery.

  3. Novel Methods for Drug-Target Interaction Prediction using Graph Mining

    KAUST Repository

    Ba Alawi, Wail

    2016-08-31

    The problem of developing drugs that can be used to cure diseases is important and requires a careful approach. Since pursuing the wrong candidate drug for a particular disease could be very costly in terms of time and money, there is a strong interest in minimizing such risks. Drug repositioning has become a hot topic of research, as it helps reduce these risks significantly at the early stages of drug development by reusing an approved drug for the treatment of a different disease. Still, finding new usage for a drug is non-trivial, as it is necessary to find out strong supporting evidence that the proposed new uses of drugs are plausible. Many computational approaches were developed to narrow the list of possible candidate drug-target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all non-cyclic paths that connect a drug and a target, and using a function that we define, calculates a score from all the paths. This score describes our confidence that DTI is correct. We show that DASPfind significantly outperforms other state-of-the-art methods in predicting the top ranked target for each drug. We demonstrate the utility of DASPfind by predicting 15 novel DTIs over a set of ion channel proteins, and confirming 12 out of these 15 DTIs through experimental evidence reported in literature and online drug databases. The second method (DASPfind+) modifies DASPfind in order to increase the confidence and reliability of the resultant predictions. Based on the structure of the drug-target interaction (DTI) networks, we introduced an optimization scheme that incrementally alters the network structure locally for each drug to achieve more robust top 1 ranked predictions. Moreover, we explored effects of several similarity measures between the targets on the prediction

  4. Predicting Drug Recalls From Internet Search Engine Queries.

    Science.gov (United States)

    Yom-Tov, Elad

    2017-01-01

    Batches of pharmaceuticals are sometimes recalled from the market when a safety issue or a defect is detected in specific production runs of a drug. Such problems are usually detected when patients or healthcare providers report abnormalities to medical authorities. Here, we test the hypothesis that defective production lots can be detected earlier by monitoring queries to Internet search engines. We extracted queries from the USA to the Bing search engine, which mentioned one of the 5195 pharmaceutical drugs during 2015 and all recall notifications issued by the Food and Drug Administration (FDA) during that year. By using attributes that quantify the change in query volume at the state level, we attempted to predict if a recall of a specific drug will be ordered by FDA in a time horizon ranging from 1 to 40 days in future. Our results show that future drug recalls can indeed be identified with an AUC of 0.791 and a lift at 5% of approximately 6 when predicting a recall occurring one day ahead. This performance degrades as prediction is made for longer periods ahead. The most indicative attributes for prediction are sudden spikes in query volume about a specific medicine in each state. Recalls of prescription drugs and those estimated to be of medium-risk are more likely to be identified using search query data. These findings suggest that aggregated Internet search engine data can be used to facilitate in early warning of faulty batches of medicines.

  5. Drug-target interaction prediction from PSSM based evolutionary information.

    Science.gov (United States)

    Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-01-01

    The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. Cell-specific prediction and application of drug-induced gene expression profiles.

    Science.gov (United States)

    Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

    2018-01-01

    Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

  7. Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization.

    Science.gov (United States)

    Ezzat, Ali; Zhao, Peilin; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

    2017-01-01

    Experimental determination of drug-target interactions is expensive and time-consuming. Therefore, there is a continuous demand for more accurate predictions of interactions using computational techniques. Algorithms have been devised to infer novel interactions on a global scale where the input to these algorithms is a drug-target network (i.e., a bipartite graph where edges connect pairs of drugs and targets that are known to interact). However, these algorithms had difficulty predicting interactions involving new drugs or targets for which there are no known interactions (i.e., "orphan" nodes in the network). Since data usually lie on or near to low-dimensional non-linear manifolds, we propose two matrix factorization methods that use graph regularization in order to learn such manifolds. In addition, considering that many of the non-occurring edges in the network are actually unknown or missing cases, we developed a preprocessing step to enhance predictions in the "new drug" and "new target" cases by adding edges with intermediate interaction likelihood scores. In our cross validation experiments, our methods achieved better results than three other state-of-the-art methods in most cases. Finally, we simulated some "new drug" and "new target" cases and found that GRMF predicted the left-out interactions reasonably well.

  8. Prior nonhip limb fracture predicts subsequent hip fracture in institutionalized elderly people.

    Science.gov (United States)

    Nakamura, K; Takahashi, S; Oyama, M; Oshiki, R; Kobayashi, R; Saito, T; Yoshizawa, Y; Tsuchiya, Y

    2010-08-01

    This 1-year cohort study of nursing home residents revealed that historical fractures of upper limbs or nonhip lower limbs were associated with hip fracture (hazard ratio = 2.14), independent of activities of daily living (ADL), mobility, dementia, weight, and type of nursing home. Prior nonhip fractures are useful for predicting of hip fracture in institutional settings. The aim of this study was to evaluate the utility of fracture history for the prediction of hip fracture in nursing home residents. This was a cohort study with a 1-year follow-up. Subjects were 8,905 residents of nursing homes in Niigata, Japan (mean age, 84.3 years). Fracture histories were obtained from nursing home medical records. ADL levels were assessed by caregivers. Hip fracture diagnosis was based on hospital medical records. Subjects had fracture histories of upper limbs (5.0%), hip (14.0%), and nonhip lower limbs (4.6%). Among historical single fractures, only prior nonhip lower limbs significantly predicted subsequent fracture (adjusted hazard ratio, 2.43; 95% confidence interval (CI), 1.30-4.57). The stepwise method selected the best model, in which a combined historical fracture at upper limbs or nonhip lower limbs (adjusted hazard ratio, 2.14; 95% CI, 1.30-3.52), dependence, ADL levels, mobility, dementia, weight, and type of nursing home independently predicted subsequent hip fracture. A fracture history at upper or nonhip lower limbs, in combination with other known risk factors, is useful for the prediction of future hip fracture in institutional settings.

  9. Global Brain Dynamics During Social Exclusion Predict Subsequent Behavioral Conformity

    OpenAIRE

    Wasylyshyn, Nick; Hemenway, Brett; Garcia, Javier O.; Cascio, Christopher N.; O'Donnell, Matthew Brook; Bingham, C. Raymond; Simons-Morton, Bruce; Vettel, Jean M.; Falk, Emily B.

    2017-01-01

    Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (N = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI...

  10. Is the quality of brief motivational interventions for drug use in primary care associated with subsequent drug use?

    Science.gov (United States)

    Palfai, Tibor P; Cheng, Debbie M; Bernstein, Judith A; Palmisano, Joseph; Lloyd-Travaglini, Christine A; Goodness, Tracie; Saitz, Richard

    2016-05-01

    Although a number of brief intervention approaches for drug use are based on motivational interviewing (MI), relatively little is known about whether the quality of motivational interviewing skills is associated with intervention outcomes. The current study examined whether indices of motivational interviewing skill were associated with subsequent drug use outcomes following two different MI-based brief interventions delivered in primary care; a 15 min Brief Negotiated Interview (BNI) and a 45 min adaptation of motivational interviewing (MOTIV). Audio recordings from 351 participants in a randomized controlled trial for drug use in primary care were coded using the Motivational Interviewing Treatment Integrity Scale, (MITI Version 3.1.1). Separate negative binomial regression analyses, stratified by intervention condition, were used to examine the associations between six MITI skill variables and the number of days that the participant used his/her main drug 6 weeks after study entry. Only one of the MITI variables (% reflections to questions) was significantly associated with the frequency of drug use in the MOTIV condition and this was opposite to the hypothesized direction (global p=0.01, adjusted IRR 1.50, 95%CI: 1.03-2.20 for middle vs. lowest tertile [higher skill, more drug use]. None were significantly associated with drug use in the BNI condition. Secondary analyses similarly failed to find consistent predictors of better drug outcomes. Overall, this study provides little evidence to suggest that the level of MI intervention skills are linked with better drug use outcomes among people who use drugs and receive brief interventions in primary care. Findings should be considered in light of the fact that data from the study are from negative trial of SBI and was limited to primary care patients. Future work should consider alternative ways of examining these process variables (i.e., comparing thresholds of proficient versus non-proficient skills) or

  11. The Rise, Fall and Subsequent Triumph of Thalidomide: Lessons Learned in Drug Development

    Science.gov (United States)

    Rehman, Waqas; Arfons, Lisa M.; Lazarus, Hillard M.

    2011-01-01

    Perhaps no other drug in modern medicine rivals the dramatic revitalization of thalidomide. Originally marketed as a sedative, thalidomide gained immense popularity worldwide among pregnant women because of its effective anti-emetic properties in morning sickness. Mounting evidence of human teratogenicity marked a dramatic fall from grace and led to widespread social, legal and economic ramifications. Despite its tragic past thalidomide emerged several decades later as a novel and highly effective agent in the treatment of various inflammatory and malignant diseases. In 2006 thalidomide completed its remarkable renaissance becoming the first new agent in over a decade to gain approval for the treatment of plasma cell myeloma. The catastrophic collapse yet subsequent revival of thalidomide provides important lessons in drug development. Never entirely abandoned by the medical community, thalidomide resurfaced as an important drug once the mechanisms of action were further studied and better understood. Ongoing research and development of related drugs such as lenalidomide now represent a class of irreplaceable drugs in hematological malignancies. Further, the tragedies associated with this agent stimulated the legislation which revamped the FDA regulatory process, expanded patient informed consent procedures and mandated more transparency from drug manufacturers. Finally, we review recent clinical trials summarizing selected medical indications for thalidomide with an emphasis on hematologic malignancies. Herein, we provide a historic perspective regarding the up-and-down development of thalidomide. Using PubMed databases we conducted searches using thalidomide and associated keywords highlighting pharmacology, mechanisms of action, and clinical uses. PMID:23556097

  12. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.

    Science.gov (United States)

    Liu, Yong; Wu, Min; Miao, Chunyan; Zhao, Peilin; Li, Xiao-Li

    2016-02-01

    In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.

  13. Prediction of adverse drug reactions using decision tree modeling.

    Science.gov (United States)

    Hammann, F; Gutmann, H; Vogt, N; Helma, C; Drewe, J

    2010-07-01

    Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure-activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs (n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9-90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end-organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance.

  14. A unified frame of predicting side effects of drugs by using linear neighborhood similarity.

    Science.gov (United States)

    Zhang, Wen; Yue, Xiang; Liu, Feng; Chen, Yanlin; Tu, Shikui; Zhang, Xining

    2017-12-14

    Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. In this paper, we present a novel measure of drug-drug similarity named "linear neighborhood similarity", which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method "LNSM" and its extension "LNSM-SMI" to predict side effects of new drugs, and propose the method "LNSM-MSE" to predict unobserved side effect of approved drugs. We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.

  15. DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue

    OpenAIRE

    Wang, QuanQiu; Xu, Rong

    2015-01-01

    Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algori...

  16. Within-person Changes in Individual Symptoms of Depression Predict Subsequent Depressive Episodes in Adolescents: A Prospective Study

    Science.gov (United States)

    Kouros, Chrystyna D.; Morris, Matthew C.; Garber, Judy

    2015-01-01

    The current longitudinal study examined which individual symptoms of depression uniquely predicted a subsequent Major Depressive Episode (MDE) in adolescents, and whether these relations differed by sex. Adolescents (N=240) were first interviewed in grade 6 (M=11.86 years old; SD = 0.56; 54% female; 81.5% Caucasian) and then annually through grade 12 regarding their individual symptoms of depression as well as the occurrence of MDEs. Individual symptoms of depression were assessed with the Children’s Depression Rating Scale-Revised (CDRS-R) and depressive episodes were assessed with the Longitudinal Interval Follow-up Evaluation (LIFE). Results showed that within-person changes in sleep problems and low self-esteem/excessive guilt positively predicted an increased likelihood of an MDE for both boys and girls. Significant sex differences also were found. Within-person changes in anhedonia predicted an increased likelihood of a subsequent MDE among boys, whereas irritability predicted a decreased likelihood of a future MDE among boys, and concentration difficulties predicted a decreased likelihood of an MDE in girls. These results identified individual depressive symptoms that predicted subsequent depressive episodes in male and female adolescents, and may be used to guide the early detection, treatment, and prevention of depressive disorders in youth. PMID:26105209

  17. Angiogenic Factors in Cord Blood of Preterm Infants Predicts Subsequently Developing Bronchopulmonary Dysplasia

    Directory of Open Access Journals (Sweden)

    Wen-Chien Yang

    2015-12-01

    Conclusion: Cord blood level of PlGF, rather than VEGF or sFlt-1, was significantly increased in the BPD group. Consistent with our previous report, cord blood level of PlGF may be considered as a biomarker to predict subsequently developing BPD in preterm infants.

  18. An introduction to predictive modelling of drug concentration in anaesthesia monitors.

    Science.gov (United States)

    DeCou, J; Johnson, K

    2017-01-01

    A significant amount of anaesthetists' work involves the prediction of drug effects and interactions to produce a smooth general anaesthetic that minimises drug side effects and promotes rapid emergence. Successfully managing this process requires a basic understanding of drug effects, experience and inevitably some guesswork, since it is difficult (and in some cases impossible) to anticipate all relevant patient and surgical factors. Although data are generally available to allow calculation of plasma drug and effect site concentrations, this is often difficult to apply in complex clinical contexts, particularly when multiple drug types are used. In recent years, manufacturers have developed and incorporated into anaesthetic workstations technologies that use drug pharmacodynamic and pharmacokinetic data to predict drug effects and interactions. Such systems can predict the duration and effects of drugs during anaesthesia and assist the anaesthetist to understand complex drug interactions. With this information available, different drug types, doses and combinations may be tailored in a scientific way to maximise useful effects whilst minimising overdose and side-effects, particularly in high-risk patients. Examples are used to illustrate how such systems can be used in practice, and how drug effects and interactions can be simulated to "rehearse" an anaesthetic before any drugs are actually administered. At present only a small number of anaesthetic workstations use this technology, and as yet they are not able to manage all drugs used in anaesthetic practice. However, such systems have the potential to help anaesthetists manage the complexity of their work, and to provide information on predicted drug effects in a way that is useful and relevant to both experienced anaesthetists and trainees. © 2017 The Association of Anaesthetists of Great Britain and Ireland.

  19. An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge.

    Directory of Open Access Journals (Sweden)

    Qian Wan

    Full Text Available We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on supervised learning on genomic profiles. The genetic and epigenetic characterization of a cell line provides observations on various aspects of regulation including DNA copy number variations, gene expression, DNA methylation and protein abundance. To extract relevant information from the various data types, we applied a random forest based approach to generate sensitivity predictions from each type of data and combined the predictions in a linear regression model to generate the final drug sensitivity prediction. Our approach when applied to the NCI-DREAM drug sensitivity prediction challenge was a top performer among 47 teams and produced high accuracy predictions. Our results show that the incorporation of multiple genomic characterizations lowered the mean and variance of the estimated bootstrap prediction error. We also applied our approach to the Cancer Cell Line Encyclopedia database for sensitivity prediction and the ability to extract the top targets of an anti-cancer drug. The results illustrate the effectiveness of our approach in predicting drug sensitivity from heterogeneous genomic datasets.

  20. Capnography for assessing nocturnal hypoventilation and predicting compliance with subsequent noninvasive ventilation in patients with ALS.

    Directory of Open Access Journals (Sweden)

    Sung-Min Kim

    Full Text Available BACKGROUND: Patients with amyotrophic lateral sclerosis (ALS suffer from hypoventilation, which can easily worsen during sleep. This study evaluated the efficacy of capnography monitoring in patients with ALS for assessing nocturnal hypoventilation and predicting good compliance with subsequent noninvasive ventilation (NIV treatment. METHODS: Nocturnal monitoring and brief wake screening by capnography/pulse oximetry, functional scores, and other respiratory signs were assessed in 26 patients with ALS. Twenty-one of these patients were treated with NIV and had their treatment compliance evaluated. RESULTS: Nocturnal capnography values were reliable and strongly correlated with the patients' respiratory symptoms (R(2 = 0.211-0.305, p = 0.004-0.021. The duration of nocturnal hypercapnea obtained by capnography exhibited a significant predictive power for good compliance with subsequent NIV treatment, with an area-under-the-curve value of 0.846 (p = 0.018. In contrast, no significant predictive values for nocturnal pulse oximetry or functional scores for nocturnal hypoventilation were found. Brief waking supine capnography was also useful as a screening tool before routine nocturnal capnography monitoring. CONCLUSION: Capnography is an efficient tool for assessing nocturnal hypoventilation and predicting good compliance with subsequent NIV treatment of ALS patients, and may prove useful as an adjunctive tool for assessing the need for NIV treatment in these patients.

  1. iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.

    Science.gov (United States)

    Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen

    2014-03-19

    Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called "iNR-Drug" was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

  2. Pretraining Cortical Thickness Predicts Subsequent Perceptual Learning Rate in a Visual Search Task.

    Science.gov (United States)

    Frank, Sebastian M; Reavis, Eric A; Greenlee, Mark W; Tse, Peter U

    2016-03-01

    We report that preexisting individual differences in the cortical thickness of brain areas involved in a perceptual learning task predict the subsequent perceptual learning rate. Participants trained in a motion-discrimination task involving visual search for a "V"-shaped target motion trajectory among inverted "V"-shaped distractor trajectories. Motion-sensitive area MT+ (V5) was functionally identified as critical to the task: after 3 weeks of training, activity increased in MT+ during task performance, as measured by functional magnetic resonance imaging. We computed the cortical thickness of MT+ from anatomical magnetic resonance imaging volumes collected before training started, and found that it significantly predicted subsequent perceptual learning rates in the visual search task. Participants with thicker neocortex in MT+ before training learned faster than those with thinner neocortex in that area. A similar association between cortical thickness and training success was also found in posterior parietal cortex (PPC). © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  3. Design of a tripartite network for the prediction of drug targets

    Science.gov (United States)

    Kunimoto, Ryo; Bajorath, Jürgen

    2018-02-01

    Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.

  4. ADVERPred-Web Service for Prediction of Adverse Effects of Drugs.

    Science.gov (United States)

    Ivanov, Sergey M; Lagunin, Alexey A; Rudik, Anastasia V; Filimonov, Dmitry A; Poroikov, Vladimir V

    2018-01-22

    Application of structure-activity relationships (SARs) for the prediction of adverse effects of drugs (ADEs) has been reported in many published studies. Training sets for the creation of SAR models are usually based on drug label information which allows for the generation of data sets for many hundreds of drugs. Since many ADEs may not be related to drug consumption, one of the main problems in such studies is the quality of data on drug-ADE pairs obtained from labels. The information on ADEs may be included in three sections of the drug labels: "Boxed warning," "Warnings and Precautions," and "Adverse reactions." The first two sections, especially Boxed warning, usually contain the most frequent and severe ADEs that have either known or probable relationships to drug consumption. Using this information, we have created manually curated data sets for the five most frequent and severe ADEs: myocardial infarction, arrhythmia, cardiac failure, severe hepatotoxicity, and nephrotoxicity, with more than 850 drugs on average for each effect. The corresponding SARs were built with PASS (Prediction of Activity Spectra for Substances) software and had balanced accuracy values of 0.74, 0.7, 0.77, 0.67, and 0.75, respectively. They were implemented in a freely available ADVERPred web service ( http://www.way2drug.com/adverpred/ ), which enables a user to predict five ADEs based on the structural formula of compound. This web service can be applied for estimation of the corresponding ADEs for hits and lead compounds at the early stages of drug discovery.

  5. Concordance and predictive value of two adverse drug event data sets.

    Science.gov (United States)

    Cami, Aurel; Reis, Ben Y

    2014-08-22

    Accurate prediction of adverse drug events (ADEs) is an important means of controlling and reducing drug-related morbidity and mortality. Since no single "gold standard" ADE data set exists, a range of different drug safety data sets are currently used for developing ADE prediction models. There is a critical need to assess the degree of concordance between these various ADE data sets and to validate ADE prediction models against multiple reference standards. We systematically evaluated the concordance of two widely used ADE data sets - Lexi-comp from 2010 and SIDER from 2012. The strength of the association between ADE (drug) counts in Lexi-comp and SIDER was assessed using Spearman rank correlation, while the differences between the two data sets were characterized in terms of drug categories, ADE categories and ADE frequencies. We also performed a comparative validation of the Predictive Pharmacosafety Networks (PPN) model using both ADE data sets. The predictive power of PPN using each of the two validation sets was assessed using the area under Receiver Operating Characteristic curve (AUROC). The correlations between the counts of ADEs and drugs in the two data sets were 0.84 (95% CI: 0.82-0.86) and 0.92 (95% CI: 0.91-0.93), respectively. Relative to an earlier snapshot of Lexi-comp from 2005, Lexi-comp 2010 and SIDER 2012 introduced a mean of 1,973 and 4,810 new drug-ADE associations per year, respectively. The difference between these two data sets was most pronounced for Nervous System and Anti-infective drugs, Gastrointestinal and Nervous System ADEs, and postmarketing ADEs. A minor difference of 1.1% was found in the AUROC of PPN when SIDER 2012 was used for validation instead of Lexi-comp 2010. In conclusion, the ADE and drug counts in Lexi-comp and SIDER data sets were highly correlated and the choice of validation set did not greatly affect the overall prediction performance of PPN. Our results also suggest that it is important to be aware of the

  6. Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

    Science.gov (United States)

    Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

    2018-01-01

    Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

  7. Analysis of clinical drug-drug interaction data to predict uncharacterized interaction magnitudes between antiretroviral drugs and co-medications.

    Science.gov (United States)

    Stader, Felix; Kinvig, Hannah; Battegay, Manuel; Khoo, Saye; Owen, Andrew; Siccardi, Marco; Marzolini, Catia

    2018-04-23

    Despite their high potential for drug-drug-interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required.We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes such as CYP3A and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers and inhibitors of CYP3A, to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs.The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and co-medications were successfully quantified meaning 53%, 85% and 98% of the predictions were within 1.25-fold (0.80 - 1.25), 1.5-fold (0.66 - 1.48) and 2-fold (0.66 - 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or conversely minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs.The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs being particularly relevant in a HIV-setting giving the treatments complexity, high DDI risk and limited guidance on the management of DDIs. Copyright © 2018 American Society for Microbiology.

  8. Application of optical action potentials in human induced pluripotent stem cells-derived cardiomyocytes to predict drug-induced cardiac arrhythmias.

    Science.gov (United States)

    Lu, H R; Hortigon-Vinagre, M P; Zamora, V; Kopljar, I; De Bondt, A; Gallacher, D J; Smith, G

    2017-09-01

    Human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) are emerging as new and human-relevant source in vitro model for cardiac safety assessment that allow us to investigate a set of 20 reference drugs for predicting cardiac arrhythmogenic liability using optical action potential (oAP) assay. Here, we describe our examination of the oAP measurement using a voltage sensitive dye (Di-4-ANEPPS) to predict adverse compound effects using hiPS-CMs and 20 cardioactive reference compounds. Fluorescence signals were digitized at 10kHz and the records subsequently analyzed off-line. Cells were exposed to 30min incubation to vehicle or compound (n=5/dose, 4 doses/compound) that were blinded to the investigating laboratory. Action potential parameters were measured, including rise time (T rise ) of the optical action potential duration (oAPD). Significant effects on oAPD were sensitively detected with 11 QT-prolonging drugs, while oAPD shortening was observed with I Ca -antagonists, I Kr -activator or ATP-sensitive K + channel (K ATP )-opener. Additionally, the assay detected varied effects induced by 6 different sodium channel blockers. The detection threshold for these drug effects was at or below the published values of free effective therapeutic plasma levels or effective concentrations by other studies. The results of this blinded study indicate that OAP is a sensitive method to accurately detect drug-induced effects (i.e., duration/QT-prolongation, shortening, beat rate, and incidence of early after depolarizations) in hiPS-CMs; therefore, this technique will potentially be useful in predicting drug-induced arrhythmogenic liabilities in early de-risking within the drug discovery phase. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking

    Directory of Open Access Journals (Sweden)

    Yue-Nong Fan

    2014-03-01

    Full Text Available Nuclear receptors (NRs are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

  10. Externalizing Problems in Childhood and Adolescence Predict Subsequent Educational Achievement but for Different Genetic and Environmental Reasons

    Science.gov (United States)

    Lewis, Gary J.; Asbury, Kathryn; Plomin, Robert

    2017-01-01

    Background: Childhood behavior problems predict subsequent educational achievement; however, little research has examined the etiology of these links using a longitudinal twin design. Moreover, it is unknown whether genetic and environmental innovations provide incremental prediction for educational achievement from childhood to adolescence.…

  11. Bitterness prediction in-silico: A step towards better drugs.

    Science.gov (United States)

    Bahia, Malkeet Singh; Nissim, Ido; Niv, Masha Y

    2018-02-05

    Bitter taste is innately aversive and thought to protect against consuming poisons. Bitter taste receptors (Tas2Rs) are G-protein coupled receptors, expressed both orally and extra-orally and proposed as novel targets for several indications, including asthma. Many clinical drugs elicit bitter taste, suggesting the possibility of drugs re-purposing. On the other hand, the bitter taste of medicine presents a major compliance problem for pediatric drugs. Thus, efficient tools for predicting, measuring and masking bitterness of active pharmaceutical ingredients (APIs) are required by the pharmaceutical industry. Here we highlight the BitterDB database of bitter compounds and survey the main computational approaches to prediction of bitter taste based on compound's chemical structure. Current in silico bitterness prediction methods provide encouraging results, can be constantly improved using growing experimental data, and present a reliable and efficient addition to the APIs development toolbox. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Early perception of medication benefit predicts subsequent antipsychotic response in schizophrenia: "the consumer has a point" revisited.

    Science.gov (United States)

    Ascher-Svanum, Haya; Weiden, Peter; Nyhuis, Allen W; Faries, Douglas E; Stauffer, Virginia; Kollack-Walker, Sara; Kinon, Bruce J

    2014-07-01

    An easy-to-administer tool for predicting response to antipsychotic treatment could improve the acute management of patients with schizophrenia. We assessed whether a patient's perception of medication benefit early in treatment could predict subsequent response or nonresponse to continued use of the same treatment. This post hoc analysis used data from a randomized, open-label trial of antipsychotics for treatment of schizophrenia in which attitudes about medication adherence were assessed after two weeks of antipsychotic treatment using the Rating of Medication Influences (ROMI) scale. The analysis included 439 patients who had Positive and Negative Syndrome Scale (PANSS) and ROMI scale data at Weeks 2 and 8. Scores on the ROMI subscale Perceived Medication Benefit factor were used to predict subsequent antipsychotic response at Week 8, defined as a .20% reduction from baseline on the PANSS. Logistic regression was used to identify a cut-off score for the Perceived Medication Benefit factor that could accurately identify antipsychotic responders vs. nonresponders at Week 8. A score of .2.75 (equal to a mean subscale score of .11.00) on the ROMI scale Perceived Medication Benefit factor at Week 2 predicted response at Week 8 with high specificity (72%) and negative predictive value (70%), moderate sensitivity (44%) and positive predictive value (47%), and with a 38% misclassification rate. A brief assessment of the patient's perception of medication benefit at two weeks into treatment appears to be a good predictor of subsequent response and nonresponse after eight weeks of treatment with the same antipsychotic.

  13. Modulating effect of the nootropic drug, piracetam on stress- and subsequent morphine-induced prolactin secretion in male rats.

    OpenAIRE

    Matton, A.; Engelborghs, S.; Bollengier, F.; Finné, E.; Vanhaeist, L.

    1996-01-01

    1. The effect of the nootropic drug, piracetam on stress- and subsequent morphine-induced prolactin (PRL) secretion was investigated in vivo in male rats, by use of a stress-free blood sampling and drug administration method by means of a permanent indwelling catheter in the right jugular vein. 2. Four doses of piracetam were tested (20, 100, 200 and 400 mg kg-1), being given intraperitoneally 1 h before blood sampling; control rats received saline instead. After a first blood sample, rats we...

  14. Predicting Drug-Target Interactions Based on Small Positive Samples.

    Science.gov (United States)

    Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

    2018-01-01

    A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

  15. Drug response prediction in high-risk multiple myeloma

    DEFF Research Database (Denmark)

    Vangsted, A J; Helm-Petersen, S; Cowland, J B

    2018-01-01

    from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged...

  16. Fast prediction of cytochrome P450 mediated drug metabolism

    DEFF Research Database (Denmark)

    Rydberg, Patrik Åke Anders; Poongavanam, Vasanthanathan; Oostenbrink, Chris

    2009-01-01

    Cytochrome P450 mediated metabolism of drugs is one of the major determinants of their kinetic profile, and prediction of this metabolism is therefore highly relevant during the drug discovery and development process. A new rule-based method, based on results from density functional theory...... calculations, for predicting activation energies for aliphatic and aromatic oxidations by cytochromes P450 is developed and compared with several other methods. Although the applicability of the method is currently limited to a subset of P450 reactions, these reactions describe more than 90...

  17. Fixed drug eruption: topical provocation and subsequent phenomena

    Energy Technology Data Exchange (ETDEWEB)

    Mahboob, A; Haroon, T S [Shaikh Zayed FPGMI, Lahore (Pakistan). Dept. of Dermatology; Haroon, T S [King Edward Medical Univ., Lahore (Pakistan). Dept. of Dematology; Iqbal, Z; Iqbal, F [Shaikh Zayed FPGMI, Lahore (Pakistan). Dept. of Medicine

    2006-12-15

    To determine the usefulness of topical provocation in detecting the incriminated drug causing fixed eruption. Three hundred and five, clinically diagnosed cases of Fixed Drug Eruption (FDE) of either gender and of any age were subjected to topical provocation with different drugs by using concentration of 1% (n=203), 2% (n=210) and 5% (n=235) in white soft paraffin. Drug ointment of one strength was applied one at a time on normal skin of flexor surface of right or left forearm. The effects of tests on involved and uninvolved skin were observed for 48 hours. The changes in lesions like erythema, hyperpigmentation, itching, burning or appearance of new lesion were considered a positive response. In case of no change, the patients (n=5) were subjected to oral provocation test, by giving half to full therapeutic dose of the suspected drug depending upon the severity of the initial attack. A patient who exhibited see-sawing phenomenon with 5% metamizole TPT was given oral challenge with same drug. Control topical tests were repeated in equal number of normal persons with various drug ointments and in patients of FDE with white soft paraffin on normal and affected skin. One hundred and thirty-seven patients were males and one hundred and sixty-eight patients were females. Maximum number of patients belonged to third decade. With 1% drug preparations 12 out of 316, with 2% drug preparations 28 out of 422 and with 5% drug preparations, 312 out of 523 TPTs were positive. The comparison revealed a highly significant association (Chi-square 448.1 and p<0.000) among various strengths of preparations and positive response. Sulphamethoxazole was found to be the most commonly incriminated cause of FDE applied in 5% concentration yielded sensitivity rate of 91% compared to 4% with lower concentrations. Positive patch test was also observed with oxytetracycline. Five patients who were given oral provocation with different drugs were found to be positive to tinidazole, dapsone

  18. Fixed drug eruption: topical provocation and subsequent phenomena

    International Nuclear Information System (INIS)

    Mahboob, A.; Haroon, T.S.; Haroon, T.S.; Iqbal, Z.; Iqbal, F.

    2006-01-01

    To determine the usefulness of topical provocation in detecting the incriminated drug causing fixed eruption. Three hundred and five, clinically diagnosed cases of Fixed Drug Eruption (FDE) of either gender and of any age were subjected to topical provocation with different drugs by using concentration of 1% (n=203), 2% (n=210) and 5% (n=235) in white soft paraffin. Drug ointment of one strength was applied one at a time on normal skin of flexor surface of right or left forearm. The effects of tests on involved and uninvolved skin were observed for 48 hours. The changes in lesions like erythema, hyperpigmentation, itching, burning or appearance of new lesion were considered a positive response. In case of no change, the patients (n=5) were subjected to oral provocation test, by giving half to full therapeutic dose of the suspected drug depending upon the severity of the initial attack. A patient who exhibited see-sawing phenomenon with 5% metamizole TPT was given oral challenge with same drug. Control topical tests were repeated in equal number of normal persons with various drug ointments and in patients of FDE with white soft paraffin on normal and affected skin. One hundred and thirty-seven patients were males and one hundred and sixty-eight patients were females. Maximum number of patients belonged to third decade. With 1% drug preparations 12 out of 316, with 2% drug preparations 28 out of 422 and with 5% drug preparations, 312 out of 523 TPTs were positive. The comparison revealed a highly significant association (Chi-square 448.1 and p<0.000) among various strengths of preparations and positive response. Sulphamethoxazole was found to be the most commonly incriminated cause of FDE applied in 5% concentration yielded sensitivity rate of 91% compared to 4% with lower concentrations. Positive patch test was also observed with oxytetracycline. Five patients who were given oral provocation with different drugs were found to be positive to tinidazole, dapsone

  19. Predicting Hepatotoxicity of Drug Metabolites Via an Ensemble Approach Based on Support Vector Machine

    Science.gov (United States)

    Lu, Yin; Liu, Lili; Lu, Dong; Cai, Yudong; Zheng, Mingyue; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

    2017-11-20

    Drug-induced liver injury (DILI) is a major cause of drug withdrawal. The chemical properties of the drug, especially drug metabolites, play key roles in DILI. Our goal is to construct a QSAR model to predict drug hepatotoxicity based on drug metabolites. 64 hepatotoxic drug metabolites and 3,339 non-hepatotoxic drug metabolites were gathered from MDL Metabolite Database. Considering the imbalance of the dataset, we randomly split the negative samples and combined each portion with all the positive samples to construct individually balanced datasets for constructing independent classifiers. Then, we adopted an ensemble approach to make prediction based on the results of all individual classifiers and applied the minimum Redundancy Maximum Relevance (mRMR) feature selection method to select the molecular descriptors. Eventually, for the drugs in the external test set, a Bayesian inference method was used to predict the hepatotoxicity of a drug based on its metabolites. The model showed the average balanced accuracy=78.47%, sensitivity =74.17%, and specificity=82.77%. Five molecular descriptors characterizing molecular polarity, intramolecular bonding strength, and molecular frontier orbital energy were obtained. When predicting the hepatotoxicity of a drug based on all its metabolites, the sensitivity, specificity and balanced accuracy were 60.38%, 70.00%, and 65.19%, respectively, indicating that this method is useful for identifying the hepatotoxicity of drugs. We developed an in silico model to predict hepatotoxicity of drug metabolites. Moreover, Bayesian inference was applied to predict the hepatotoxicity of a drug based on its metabolites which brought out valuable high sensitivity and specificity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Improving Predictive Modeling in Pediatric Drug Development: Pharmacokinetics, Pharmacodynamics, and Mechanistic Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Slikker, William; Young, John F.; Corley, Rick A.; Dorman, David C.; Conolly, Rory B.; Knudsen, Thomas; Erstad, Brian L.; Luecke, Richard H.; Faustman, Elaine M.; Timchalk, Chuck; Mattison, Donald R.

    2005-07-26

    A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

  1. Some Remarks on Prediction of Drug-Target Interaction with Network Models.

    Science.gov (United States)

    Zhang, Shao-Wu; Yan, Xiao-Ying

    2017-01-01

    System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  2. A community effort to assess and improve drug sensitivity prediction algorithms.

    Science.gov (United States)

    Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

    2014-12-01

    Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

  3. Magnetic resonance imaging at primary diagnosis cannot predict subsequent contralateral slip in slipped capital femoral epiphysis

    Energy Technology Data Exchange (ETDEWEB)

    Wensaas, Anders [Akershus University Hospital, Department of Orthopaedic Surgery, Loerenskog (Norway); Wiig, Ola; Terjesen, Terje [Oslo University Hospital, Department of Orthopaedic Surgery, Rikshospitalet (Norway); Castberg Hellund, Johan; Khoshnewiszadeh, Behzad [Oslo University Hospital, Department of Radiology and Nuclear Medicine, Ullevaal (Norway)

    2017-12-15

    Prophylactic fixation of the contralateral hip in slipped capital femoral epiphysis (SCFE) is controversial, and no reliable method has been established to predict subsequent contralateral slip. The main purpose of this study was to evaluate if magnetic resonance imaging (MRI) performed at primary diagnosis could predict future contralateral slip. Twenty-two patients with unilateral SCFE were included, all had MRI of both hips taken before operative fixation. Six different parameters were measured on the MRI: the MRI slip angle, the greatest focal widening of the physis, the global widening of the physis measured at three locations (the midpoint of the physis and 1 cm lateral and medial to the midpoint), periphyseal (epiphyseal and metaphyseal) bone marrow edema, the presence of pathological joint effusion, and the amount of joint effusion measured from the lateral edge of the greater trochanter. Mean follow-up was 33 months (range, 16-63 months). Six patients were treated for contralateral slip during the follow-up time and a comparison of the MRI parameters of the contralateral hip in these six patients and in the 16 patients that remained unilateral was done to see if subsequent contralateral slip was possible to predict at primary diagnosis. All MRI parameters were significantly altered in hips with established SCFE compared with the contralateral hips. However, none of the MRI parameters showed any significant difference between patients who had a subsequent contralateral slip and those that remained unilateral. MRI taken at primary diagnosis could not predict future contralateral slip. (orig.)

  4. DISIS: prediction of drug response through an iterative sure independence screening.

    Directory of Open Access Journals (Sweden)

    Yun Fang

    Full Text Available Prediction of drug response based on genomic alterations is an important task in the research of personalized medicine. Current elastic net model utilized a sure independence screening to select relevant genomic features with drug response, but it may neglect the combination effect of some marginally weak features. In this work, we applied an iterative sure independence screening scheme to select drug response relevant features from the Cancer Cell Line Encyclopedia (CCLE dataset. For each drug in CCLE, we selected up to 40 features including gene expressions, mutation and copy number alterations of cancer-related genes, and some of them are significantly strong features but showing weak marginal correlation with drug response vector. Lasso regression based on the selected features showed that our prediction accuracies are higher than those by elastic net regression for most drugs.

  5. Predictive tools for the evaluation of microbial effects on drugs during gastrointestinal passage.

    Science.gov (United States)

    Pieper, Ines A; Bertau, Martin

    2010-06-01

    Predicting drug metabolism after oral administration is highly complex, yet indispensable. Hitherto, drug metabolism mainly focuses on hepatic processes. In the intestine, drug molecules encounter the metabolic activity of microorganisms prior to absorption through the gut wall. Drug biotransformation through the gastrointestinal microflora has the potential to evoke serious problems because the metabolites formed may cause unexpected and undesired side effects in patients. Hence, in the course of drug development, the question has to be addressed if microbially formed metabolites are physiologically active, pharmaceutically active or even toxic. In order to provide answers to these questions and to keep the number of laboratory tests needed low, predictive tools - in vivo as well as in silico - are invaluable. This review gives an outline of the current state of the art in the field of predicting the drug biotransformation through the gastrointestinal microflora on several levels of modelling. A comprehensive review of the literature with a thorough discussion on assets and drawbacks of the different modelling approaches. The impact of the gastrointestinal drug biotransformation on patients' health will grow with increasing complexity of drug entities. Predicting metabolic fates of drugs by combining in vitro and in silico models provides invaluable information which will be suitable to particularly reduce in vivo studies.

  6. EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to Improve Prediction Accuracy.

    Science.gov (United States)

    Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor; Irie, Hanna; Zhang, Bin

    2018-04-24

    Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. We first designed an expression weighted cosine method (EWCos) to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed EMUDRA (Ensemble of Multiple Drug Repositioning Approaches) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. The EMUDRA R package is available at doi:10.7303/syn11510888. bin.zhang@mssm.edu or zhangb@hotmail.com. Supplementary data are available at Bioinformatics online.

  7. Using human genetics to predict the effects and side-effects of drugs

    DEFF Research Database (Denmark)

    Stender, Stefan; Tybjærg-Hansen, Anne

    2016-01-01

    PURPOSE OF REVIEW: 'Genetic proxies' are increasingly being used to predict the effects of drugs. We present an up-to-date overview of the use of human genetics to predict effects and adverse effects of lipid-targeting drugs. RECENT FINDINGS: LDL cholesterol lowering variants in HMG-Coenzyme A re...

  8. Prediction of drug synergy in cancer using ensemble-based machine learning techniques

    Science.gov (United States)

    Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

    2018-04-01

    Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

  9. In Vitro Drug Sensitivity Tests to Predict Molecular Target Drug Responses in Surgically Resected Lung Cancer.

    Directory of Open Access Journals (Sweden)

    Ryohei Miyazaki

    Full Text Available Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs and anaplastic lymphoma kinase (ALK inhibitors have dramatically changed the strategy of medical treatment of lung cancer. Patients should be screened for the presence of the EGFR mutation or echinoderm microtubule-associated protein-like 4 (EML4-ALK fusion gene prior to chemotherapy to predict their clinical response. The succinate dehydrogenase inhibition (SDI test and collagen gel droplet embedded culture drug sensitivity test (CD-DST are established in vitro drug sensitivity tests, which may predict the sensitivity of patients to cytotoxic anticancer drugs. We applied in vitro drug sensitivity tests for cyclopedic prediction of clinical responses to different molecular targeting drugs.The growth inhibitory effects of erlotinib and crizotinib were confirmed for lung cancer cell lines using SDI and CD-DST. The sensitivity of 35 cases of surgically resected lung cancer to erlotinib was examined using SDI or CD-DST, and compared with EGFR mutation status.HCC827 (Exon19: E746-A750 del and H3122 (EML4-ALK cells were inhibited by lower concentrations of erlotinib and crizotinib, respectively than A549, H460, and H1975 (L858R+T790M cells were. The viability of the surgically resected lung cancer was 60.0 ± 9.8 and 86.8 ± 13.9% in EGFR-mutants vs. wild types in the SDI (p = 0.0003. The cell viability was 33.5 ± 21.2 and 79.0 ± 18.6% in EGFR mutants vs. wild-type cases (p = 0.026 in CD-DST.In vitro drug sensitivity evaluated by either SDI or CD-DST correlated with EGFR gene status. Therefore, SDI and CD-DST may be useful predictors of potential clinical responses to the molecular anticancer drugs, cyclopedically.

  10. Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

    Science.gov (United States)

    Hao, Ming; Bryant, Stephen H; Wang, Yanli

    2018-02-06

    While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

  11. Prediction and Factor Extraction of Drug Function by Analyzing Medical Records in Developing Countries.

    Science.gov (United States)

    Hu, Min; Nohara, Yasunobu; Nakamura, Masafumi; Nakashima, Naoki

    2017-01-01

    The World Health Organization has declared Bangladesh one of 58 countries facing acute Human Resources for Health (HRH) crisis. Artificial intelligence in healthcare has been shown to be successful for diagnostics. Using machine learning to predict pharmaceutical prescriptions may solve HRH crises. In this study, we investigate a predictive model by analyzing prescription data of 4,543 subjects in Bangladesh. We predict the function of prescribed drugs, comparing three machine-learning approaches. The approaches compare whether a subject shall be prescribed medicine from the 21 most frequently prescribed drug functions. Receiver Operating Characteristics (ROC) were selected as a way to evaluate and assess prediction models. The results show the drug function with the best prediction performance was oral hypoglycemic drugs, which has an average AUC of 0.962. To understand how the variables affect prediction, we conducted factor analysis based on tree-based algorithms and natural language processing techniques.

  12. Species differences in drug glucuronidation: Humanized UDP-glucuronosyltransferase 1 mice and their application for predicting drug glucuronidation and drug-induced toxicity in humans.

    Science.gov (United States)

    Fujiwara, Ryoichi; Yoda, Emiko; Tukey, Robert H

    2018-02-01

    More than 20% of clinically used drugs are glucuronidated by a microsomal enzyme UDP-glucuronosyltransferase (UGT). Inhibition or induction of UGT can result in an increase or decrease in blood drug concentration. To avoid drug-drug interactions and adverse drug reactions in individuals, therefore, it is important to understand whether UGTs are involved in metabolism of drugs and drug candidates. While most of glucuronides are inactive metabolites, acyl-glucuronides that are formed from compounds with a carboxylic acid group can be highly toxic. Animals such as mice and rats are widely used to predict drug metabolism and drug-induced toxicity in humans. However, there are marked species differences in the expression and function of drug-metabolizing enzymes including UGTs. To overcome the species differences, mice in which certain drug-metabolizing enzymes are humanized have been recently developed. Humanized UGT1 (hUGT1) mice were created in 2010 by crossing Ugt1-null mice with human UGT1 transgenic mice in a C57BL/6 background. hUGT1 mice can be promising tools to predict human drug glucuronidation and acyl-glucuronide-associated toxicity. In this review article, studies of drug metabolism and toxicity in the hUGT1 mice are summarized. We further discuss research and strategic directions to advance the understanding of drug glucuronidation in humans. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

  13. Strategy for the Prediction of Steady-State Exposure of Digoxin to Determine Drug-Drug Interaction Potential of Digoxin With Other Drugs in Digitalization Therapy.

    Science.gov (United States)

    Srinivas, Nuggehally R

    2016-01-20

    Digoxin, a narrow therapeutic index drug, is widely used in congestive heart failure. However, the digitalization therapy involves dose titration and can exhibit drug-drug interaction. Ctrough versus area under the plasma concentration versus time curve in a dosing interval of 24 hours (AUC0-24h) and Cmax versus AUC0-24h for digoxin were established by linear regression. The predictions of digoxin AUC0-24h values were performed using published Ctrough or Cmax with appropriate regression lines. The fold difference, defined as the quotient of the observed/predicted AUC0-24h values, was evaluated. The mean square error and root mean square error, correlation coefficient (r), and goodness of the fold prediction were used to evaluate the models. Both Ctrough versus AUC0-24h (r = 0.9215) and Cmax versus AUC0-24h models for digoxin (r = 0.7781) showed strong correlations. Approximately 93.8% of the predicted digoxin AUC0-24h values were within 0.76-fold to 1.25-fold difference for Ctrough model. In sharp contrast, the Cmax model showed larger variability with only 51.6% of AUC0-24h predictions within 0.76-1.25-fold difference. The r value for observed versus predicted AUC0-24h for Ctrough (r = 0.9551; n = 177; P < 0.001) was superior to the Cmax (r = 0.6134; n = 275; P < 0.001) model. The mean square error and root mean square error (%) for the Ctrough model were 11.95% and 16.2% as compared to 67.17% and 42.3% obtained for the Cmax model. Simple linear regression models for Ctrough/Cmax versus AUC0-24h were derived for digoxin. On the basis of statistical evaluation, Ctrough was superior to Cmax model for the prediction of digoxin AUC0-24h and can be potentially used in a prospective setting for predicting drug-drug interaction or lack of it.

  14. A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).

    Science.gov (United States)

    Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

    2014-01-01

    Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

  15. Reward Prediction Errors in Drug Addiction and Parkinson's Disease: from Neurophysiology to Neuroimaging.

    Science.gov (United States)

    García-García, Isabel; Zeighami, Yashar; Dagher, Alain

    2017-06-01

    Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease. By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

  16. Cocrystal solubilization in biorelevant media and its prediction from drug solubilization

    Science.gov (United States)

    Lipert, Maya P.; Roy, Lilly; Childs, Scott L.

    2015-01-01

    This work examines cocrystal solubility in biorelevant media, (FeSSIF, fed state simulated intestinal fluid), and develops a theoretical framework that allows for the simple and quantitative prediction of cocrystal solubilization from drug solubilization. The solubilities of four hydrophobic drugs and seven cocrystals containing these drugs were measured in FeSSIF and in acetate buffer at pH 5.00. In all cases, the cocrystal solubility (Scocrystal) was higher than the drug solubility (Sdrug) in both buffer and FeSSIF; however, the solubilization ratio of drug, SRdrug = (SFeSSIF/Sbuffer)drug, was not the same as the solubilization ratio of cocrystal, SRcocrystal = (SFeSSIF/Sbuffer)cocrystal, meaning drug and cocrystal were not solubilized to the same extent in FeSSIF. This highlights the potential risk of anticipating cocrystal behavior in biorelevant media based on solubility studies in water. Predictions of SRcocrystal from simple equations based only on SRdrug were in excellent agreement with measured values. For 1:1 cocrystals, the cocrystal solubilization ratio can be obtained from the square root of the drug solubilization ratio. For 2:1 cocrystals, SRcocrystal is found from (SRdrug)2/3. The findings in FeSSIF can be generalized to describe cocrystal behavior in other systems involving preferential solubilization of a drug such as surfactants, lipids, and other drug solubilizing media. PMID:26390213

  17. Semen molecular and cellular features: these parameters can reliably predict subsequent ART outcome in a goat model

    Directory of Open Access Journals (Sweden)

    Mereu Paolo

    2009-11-01

    Full Text Available Abstract Currently, the assessment of sperm function in a raw or processed semen sample is not able to reliably predict sperm ability to withstand freezing and thawing procedures and in vivo fertility and/or assisted reproductive biotechnologies (ART outcome. The aim of the present study was to investigate which parameters among a battery of analyses could predict subsequent spermatozoa in vitro fertilization ability and hence blastocyst output in a goat model. Ejaculates were obtained by artificial vagina from 3 adult goats (Capra hircus aged 2 years (A, B and C. In order to assess the predictive value of viability, computer assisted sperm analyzer (CASA motility parameters and ATP intracellular concentration before and after thawing and of DNA integrity after thawing on subsequent embryo output after an in vitro fertility test, a logistic regression analysis was used. Individual differences in semen parameters were evident for semen viability after thawing and DNA integrity. Results of IVF test showed that spermatozoa collected from A and B lead to higher cleavage rates (0

  18. A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs.

    Directory of Open Access Journals (Sweden)

    Qifan Kuang

    Full Text Available Early and accurate identification of adverse drug reactions (ADRs is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs.In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper.Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

  19. Psychophysiological prediction of choice: relevance to insight and drug addiction

    Science.gov (United States)

    Moeller, Scott J.; Hajcak, Greg; Parvaz, Muhammad A.; Dunning, Jonathan P.; Volkow, Nora D.

    2012-01-01

    An important goal of addiction research and treatment is to predict behavioural responses to drug-related stimuli. This goal is especially important for patients with impaired insight, which can interfere with therapeutic interventions and potentially invalidate self-report questionnaires. This research tested (i) whether event-related potentials, specifically the late positive potential, predict choice to view cocaine images in cocaine addiction; and (ii) whether such behaviour prediction differs by insight (operationalized in this study as self-awareness of image choice). Fifty-nine cocaine abusers and 32 healthy controls provided data for the following laboratory components that were completed in a fixed-sequence (to establish prediction): (i) event-related potential recordings while passively viewing pleasant, unpleasant, neutral and cocaine images, during which early (400–1000 ms) and late (1000–2000 ms) window late positive potentials were collected; (ii) self-reported arousal ratings for each picture; and (iii) two previously validated tasks: one to assess choice for viewing these same images, and the other to group cocaine abusers by insight. Results showed that pleasant-related late positive potentials and arousal ratings predicted pleasant choice (the choice to view pleasant pictures) in all subjects, validating the method. In the cocaine abusers, the predictive ability of the late positive potentials and arousal ratings depended on insight. Cocaine-related late positive potentials better predicted cocaine image choice in cocaine abusers with impaired insight. Another emotion-relevant event-related potential component (the early posterior negativity) did not show these results, indicating specificity of the late positive potential. In contrast, arousal ratings better predicted respective cocaine image choice (and actual cocaine use severity) in cocaine abusers with intact insight. Taken together, the late positive potential could serve as a biomarker

  20. Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

    Science.gov (United States)

    Zong, Nansu; Kim, Hyeoneui; Ngo, Victoria; Harismendy, Olivier

    2017-08-01

    A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies. The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction . nazong@ucsd.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  1. Externalizing problems in childhood and adolescence predict subsequent educational achievement but for different genetic and environmental reasons.

    Science.gov (United States)

    Lewis, Gary J; Asbury, Kathryn; Plomin, Robert

    2017-03-01

    Childhood behavior problems predict subsequent educational achievement; however, little research has examined the etiology of these links using a longitudinal twin design. Moreover, it is unknown whether genetic and environmental innovations provide incremental prediction for educational achievement from childhood to adolescence. We examined genetic and environmental influences on parental ratings of behavior problems across childhood (age 4) and adolescence (ages 12 and 16) as predictors of educational achievement at age 16 using a longitudinal classical twin design. Shared-environmental influences on anxiety, conduct problems, and peer problems at age 4 predicted educational achievement at age 16. Genetic influences on the externalizing behaviors of conduct problems and hyperactivity at age 4 predicted educational achievement at age 16. Moreover, novel genetic and (to a lesser extent) nonshared-environmental influences acting on conduct problems and hyperactivity emerged at ages 12 and 16, adding to the genetic prediction from age 4. These findings demonstrate that genetic and shared-environmental factors underpinning behavior problems in early childhood predict educational achievement in midadolescence. These findings are consistent with the notion that early-childhood behavior problems reflect the initiation of a life-course persistent trajectory with concomitant implications for social attainment. However, we also find evidence that genetic and nonshared-environment innovations acting on behavior problems have implications for subsequent educational achievement, consistent with recent work arguing that adolescence represents a sensitive period for socioaffective development. © 2016 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.

  2. Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

    Science.gov (United States)

    Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

    2015-08-01

    Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

  3. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.

    Directory of Open Access Journals (Sweden)

    Xuan Xiao

    Full Text Available Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.

  4. Differential effect of IP- and IV-injected nitrogen mustard on subsequently-irradiated intestinal crypts: implications for 'dose-effect factors' predicted by experimental, combined modality therapy

    International Nuclear Information System (INIS)

    Moore, J.V.

    1984-01-01

    In experimental chemotherapy-radiotherapy, cytotoxic drugs are almost invariably injected by the intraperitoneal (IP) route. This contrasts with normal clinical practice, which is to employ the intravenous (IV) route. We have used a clonogenic assay of gastrointestinal (GI) injury in mice to show that a given administered dose of nitrogen mustard (HN 2 ), injected IP, results in a much greater reduction in the subsequent radiation dose required to achieve an isoeffect, than if the drug is injected IV. At an administered dose of 3.5 mg kg -1 of HN 2 (the animal LDsub(10/30) for IP injection), the radiation dose-reduction factor for 10% survival of intestinal crypts, was 1.94 for IP HN 2 and only 1.28 for IV HN 2 . Even the grossly-equitoxic (mouse LDsub(10/30)) dose of IV HN 2 resulted in a smaller predicted radiation dose reduction for GI injury, by a factor of 1.45. The validity of using the IP route in combined chemotherapy-radiotherapy studies designed to generate quantitative estimates of toxicity is discussed. (author)

  5. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

  6. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

  7. Statistical Analysis of a Method to Predict Drug-Polymer Miscibility

    DEFF Research Database (Denmark)

    Knopp, Matthias Manne; Olesen, Niels Erik; Huang, Yanbin

    2016-01-01

    In this study, a method proposed to predict drug-polymer miscibility from differential scanning calorimetry measurements was subjected to statistical analysis. The method is relatively fast and inexpensive and has gained popularity as a result of the increasing interest in the formulation of drug...... as provided in this study. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci....

  8. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    Energy Technology Data Exchange (ETDEWEB)

    Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

    2016-02-25

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  9. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    International Nuclear Information System (INIS)

    Hao, Ming; Wang, Yanli; Bryant, Stephen H.

    2016-01-01

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  10. Mathematical Model to Predict Skin Concentration after Topical Application of Drugs

    Directory of Open Access Journals (Sweden)

    Hiroaki Todo

    2013-12-01

    Full Text Available Skin permeation experiments have been broadly done since 1970s to 1980s as an evaluation method for transdermal drug delivery systems. In topically applied drug and cosmetic formulations, skin concentration of chemical compounds is more important than their skin permeations, because primary target site of the chemical compounds is skin surface or skin tissues. Furthermore, the direct pharmacological reaction of a metabolically stable drug that binds with specific receptors of known expression levels in an organ can be determined by Hill’s equation. Nevertheless, little investigation was carried out on the test method of skin concentration after topically application of chemical compounds. Recently we investigated an estimating method of skin concentration of the chemical compounds from their skin permeation profiles. In the study, we took care of “3Rs” issues for animal experiments. We have proposed an equation which was capable to estimate animal skin concentration from permeation profile through the artificial membrane (silicone membrane and animal skin. This new approach may allow the skin concentration of a drug to be predicted using Fick’s second law of diffusion. The silicone membrane was found to be useful as an alternative membrane to animal skin for predicting skin concentration of chemical compounds, because an extremely excellent extrapolation to animal skin concentration was attained by calculation using the silicone membrane permeation data. In this chapter, we aimed to establish an accurate and convenient method for predicting the concentration profiles of drugs in the skin based on the skin permeation parameters of topically active drugs derived from steady-state skin permeation experiments.

  11. In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions.

    Science.gov (United States)

    Ivanov, Sergey; Semin, Maxim; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir

    2017-07-01

    Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

    Science.gov (United States)

    Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

    2014-08-01

    Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. Predictive typing of drug-induced neurological sufferings from studies of the distribution of labelled drugs

    International Nuclear Information System (INIS)

    Takasu, T.

    1980-01-01

    A drug given to an animal becomes widely distributed throughout the body, acting on the living mechanisms or structures, and is gradually excreted. Some drugs can remain in some parts of the body for a long period. For example, 14 C-chloramphenical was found to remain preferentially in the salivary gland, liver and bone marrow of mice 24 hours after its oral administration. If such a drug is given repeatedly, it could possibly accumulate gradually in these organs. Thus, when its accumulation in a particular part of the body exceeds a certain level, the living mechanism or structure may possibly be injured. The harmful effects of a drug in repeated administration are called its chronic toxicity. The author discusses whether it is possible to predict the toxicity of a drug by studying its distribution in relation to time, and, if possible, the points in time. This problem is studied especially in relation to the nervous system. (Auth.)

  14. A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction.

    Science.gov (United States)

    Haider, Saad; Rahman, Raziur; Ghosh, Souparno; Pal, Ranadip

    2015-01-01

    Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships between different output responses. In this article, we propose a novel cost criterion that captures the dissimilarity in the output response structure between the training data and node samples as the difference in the two empirical copulas. We illustrate that copulas are suitable for capturing the multivariate structure of output responses independent of the marginal distributions and the copula based multivariate random forest framework can provide higher accuracy prediction and improved variable selection. The proposed framework has been validated on genomics of drug sensitivity for cancer and cancer cell line encyclopedia database.

  15. Predictive modeling of structured electronic health records for adverse drug event detection.

    Science.gov (United States)

    Zhao, Jing; Henriksson, Aron; Asker, Lars; Boström, Henrik

    2015-01-01

    The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and

  16. Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs.

    Science.gov (United States)

    Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra

    2017-11-15

    The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237-48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pK a ≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among

  17. Oral delivery of anticancer drugs

    DEFF Research Database (Denmark)

    Thanki, Kaushik; Gangwal, Rahul P; Sangamwar, Abhay T

    2013-01-01

    The present report focuses on the various aspects of oral delivery of anticancer drugs. The significance of oral delivery in cancer therapeutics has been highlighted which principally includes improvement in quality of life of patients and reduced health care costs. Subsequently, the challenges...... incurred in the oral delivery of anticancer agents have been especially emphasized. Sincere efforts have been made to compile the various physicochemical properties of anticancer drugs from either literature or predicted in silico via GastroPlus™. The later section of the paper reviews various emerging...... trends to tackle the challenges associated with oral delivery of anticancer drugs. These invariably include efflux transporter based-, functional excipient- and nanocarrier based-approaches. The role of drug nanocrystals and various others such as polymer based- and lipid based...

  18. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-05-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  19. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-04-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  20. Predicting abuse potential of stimulants and other dopaminergic drugs: overview and recommendations.

    Science.gov (United States)

    Huskinson, Sally L; Naylor, Jennifer E; Rowlett, James K; Freeman, Kevin B

    2014-12-01

    Examination of a drug's abuse potential at multiple levels of analysis (molecular/cellular action, whole-organism behavior, epidemiological data) is an essential component to regulating controlled substances under the Controlled Substances Act (CSA). We reviewed studies that examined several central nervous system (CNS) stimulants, focusing on those with primarily dopaminergic actions, in drug self-administration, drug discrimination, and physical dependence. For drug self-administration and drug discrimination, we distinguished between experiments conducted with rats and nonhuman primates (NHP) to highlight the common and unique attributes of each model in the assessment of abuse potential. Our review of drug self-administration studies suggests that this procedure is important in predicting abuse potential of dopaminergic compounds, but there were many false positives. We recommended that tests to determine how reinforcing a drug is relative to a known drug of abuse may be more predictive of abuse potential than tests that yield a binary, yes-or-no classification. Several false positives also occurred with drug discrimination. With this procedure, we recommended that future research follow a standard decision-tree approach that may require examining the drug being tested for abuse potential as the training stimulus. This approach would also allow several known drugs of abuse to be tested for substitution, and this may reduce false positives. Finally, we reviewed evidence of physical dependence with stimulants and discussed the feasibility of modeling these phenomena in nonhuman animals in a rational and practical fashion. This article is part of the Special Issue entitled 'CNS Stimulants'. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy.

    Science.gov (United States)

    Ma, Leyuan; Boucher, Jeffrey I; Paulsen, Janet; Matuszewski, Sebastian; Eide, Christopher A; Ou, Jianhong; Eickelberg, Garrett; Press, Richard D; Zhu, Lihua Julie; Druker, Brian J; Branford, Susan; Wolfe, Scot A; Jensen, Jeffrey D; Schiffer, Celia A; Green, Michael R; Bolon, Daniel N

    2017-10-31

    Developing tools to accurately predict the clinical prevalence of drug-resistant mutations is a key step toward generating more effective therapeutics. Here we describe a high-throughput CRISPR-Cas9-based saturated mutagenesis approach to generate comprehensive libraries of point mutations at a defined genomic location and systematically study their effect on cell growth. As proof of concept, we mutagenized a selected region within the leukemic oncogene BCR-ABL1 Using bulk competitions with a deep-sequencing readout, we analyzed hundreds of mutations under multiple drug conditions and found that the effects of mutations on growth in the presence or absence of drug were critical for predicting clinically relevant resistant mutations, many of which were cancer adaptive in the absence of drug pressure. Using this approach, we identified all clinically isolated BCR-ABL1 mutations and achieved a prediction score that correlated highly with their clinical prevalence. The strategy described here can be broadly applied to a variety of oncogenes to predict patient mutations and evaluate resistance susceptibility in the development of new therapeutics. Published under the PNAS license.

  2. A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

    International Nuclear Information System (INIS)

    Poulin, Patrick; Ekins, Sean; Theil, Frank-Peter

    2011-01-01

    A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V ss ) in humans under in vivo conditions. This correlation method demonstrated inaccurate predictions of V ss for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V ss of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.

  3. Prediction of Central Nervous System Side Effects Through Drug Permeability to Blood-Brain Barrier and Recommendation Algorithm.

    Science.gov (United States)

    Fan, Jun; Yang, Jing; Jiang, Zhenran

    2018-04-01

    Drug side effects are one of the public health concerns. Using powerful machine-learning methods to predict potential side effects before the drugs reach the clinical stages is of great importance to reduce time consumption and protect the security of patients. Recently, researchers have proved that the central nervous system (CNS) side effects of a drug are closely related to its permeability to the blood-brain barrier (BBB). Inspired by this, we proposed an extended neighborhood-based recommendation method to predict CNS side effects using drug permeability to the BBB and other known features of drug. To the best of our knowledge, this is the first attempt to predict CNS side effects considering drug permeability to the BBB. Computational experiments demonstrated that drug permeability to the BBB is an important factor in CNS side effects prediction. Moreover, we built an ensemble recommendation model and obtained higher AUC score (area under the receiver operating characteristic curve) and AUPR score (area under the precision-recall curve) on the data set of CNS side effects by integrating various features of drug.

  4. The Past Is Present: Representations of Parents, Friends, and Romantic Partners Predict Subsequent Romantic Representations.

    Science.gov (United States)

    Furman, Wyndol; Collibee, Charlene

    2018-01-01

    This study examined how representations of parent-child relationships, friendships, and past romantic relationships are related to subsequent romantic representations. Two-hundred 10th graders (100 female; M age  = 15.87 years) from diverse neighborhoods in a Western U.S. city were administered questionnaires and were interviewed to assess avoidant and anxious representations of their relationships with parents, friends, and romantic partners. Participants then completed similar questionnaires and interviews about their romantic representations six more times over the next 7.5 years. Growth curve analyses revealed that representations of relationships with parents, friends, and romantic partners each uniquely predicted subsequent romantic representations across development. Consistent with attachment and behavioral systems theory, representations of romantic relationships are revised by representations and experiences in other relationships. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

  5. PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties.

    Science.gov (United States)

    Borrel, Alexandre; Regad, Leslie; Xhaard, Henri; Petitjean, Michel; Camproux, Anne-Claude

    2015-04-27

    Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.

  6. Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

    Science.gov (United States)

    Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo

    2018-05-10

    Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

  7. A mechanistic framework for in vitro-in vivo extrapolation of liver membrane transporters: prediction of drug-drug interaction between rosuvastatin and cyclosporine.

    Science.gov (United States)

    Jamei, M; Bajot, F; Neuhoff, S; Barter, Z; Yang, J; Rostami-Hodjegan, A; Rowland-Yeo, K

    2014-01-01

    The interplay between liver metabolising enzymes and transporters is a complex process involving system-related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug-drug interactions (mDDIs and tDDIs) add to the complexity of this interplay. Thus, gaining meaningful insight into the impact of each element on the disposition of a drug and accurately predicting drug-drug interactions becomes very challenging. To address this, an in vitro-in vivo extrapolation (IVIVE)-linked mechanistic physiologically based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented within the Simcyp Simulator(®). In this article an IVIVE technique for liver transporters is described and a full-body PBPK model is developed. Passive and active (saturable) transport at both liver sinusoidal and canalicular membranes are accounted for and the impact of binding and ionisation processes is considered. The model also accommodates tDDIs involving inhibition of multiple transporters. Integrating prior in vitro information on the metabolism and transporter kinetics of rosuvastatin (organic-anion transporting polypeptides OATP1B1, OAT1B3 and OATP2B1, sodium-dependent taurocholate co-transporting polypeptide [NTCP] and breast cancer resistance protein [BCRP]) with one clinical dataset, the PBPK model was used to simulate the drug disposition of rosuvastatin for 11 reported studies that had not been used for development of the rosuvastatin model. The simulated area under the plasma concentration-time curve (AUC), maximum concentration (C max) and the time to reach C max (t max) values of rosuvastatin over the dose range of 10-80 mg, were within 2-fold of the observed data. Subsequently, the validated model was used to investigate the impact of

  8. In silico modeling predicts drug sensitivity of patient-derived cancer cells.

    Science.gov (United States)

    Pingle, Sandeep C; Sultana, Zeba; Pastorino, Sandra; Jiang, Pengfei; Mukthavaram, Rajesh; Chao, Ying; Bharati, Ila Sri; Nomura, Natsuko; Makale, Milan; Abbasi, Taher; Kapoor, Shweta; Kumar, Ansu; Usmani, Shahabuddin; Agrawal, Ashish; Vali, Shireen; Kesari, Santosh

    2014-05-21

    Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.

  9. Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

    Energy Technology Data Exchange (ETDEWEB)

    Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov [Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993–0002 (United States); Cross, Kevin P. [Leadscope, Inc., 1393 Dublin Road, Columbus, OH, 43215–1084 (United States)

    2012-05-01

    Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive

  10. Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

    International Nuclear Information System (INIS)

    Valerio, Luis G.; Cross, Kevin P.

    2012-01-01

    Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.

  11. Scientific Prediction and Prophetic Patenting in Drug Discovery.

    Science.gov (United States)

    Curry, Stephen H; Schneiderman, Anne M

    2015-01-01

    Pharmaceutical patenting involves writing claims based on both discoveries already made, and on prophesy of future developments in an ongoing project. This is necessitated by the very different timelines involved in the drug discovery and product development process on the one hand, and successful patenting on the other. If patents are sought too early there is a risk that patent examiners will disallow claims because of lack of enablement. If patenting is delayed, claims are at risk of being denied on the basis of existence of prior art, because the body of relevant known science will have developed significantly while the project was being pursued. This review examines the role of prophetic patenting in relation to the essential predictability of many aspects of drug discovery science, promoting the concepts of discipline-related and project-related prediction. This is especially directed towards patenting activities supporting commercialization of academia-based discoveries, where long project timelines occur, and where experience, and resources to pay for patenting, are limited. The need for improved collaborative understanding among project scientists, technology transfer professionals in, for example, universities, patent attorneys, and patent examiners is emphasized.

  12. A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions.

    Science.gov (United States)

    Cherkaoui-Rbati, Mohammed H; Paine, Stuart W; Littlewood, Peter; Rauch, Cyril

    2017-01-01

    All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

  13. A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions.

    Directory of Open Access Journals (Sweden)

    Mohammed H Cherkaoui-Rbati

    Full Text Available All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs of new chemical entities (NCEs and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit, located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level. A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

  14. Robust prediction of anti-cancer drug sensitivity and sensitivity-specific biomarker.

    Directory of Open Access Journals (Sweden)

    Heewon Park

    Full Text Available The personal genomics era has attracted a large amount of attention for anti-cancer therapy by patient-specific analysis. Patient-specific analysis enables discovery of individual genomic characteristics for each patient, and thus we can effectively predict individual genetic risk of disease and perform personalized anti-cancer therapy. Although the existing methods for patient-specific analysis have successfully uncovered crucial biomarkers, their performance takes a sudden turn for the worst in the presence of outliers, since the methods are based on non-robust manners. In practice, clinical and genomic alterations datasets usually contain outliers from various sources (e.g., experiment error, coding error, etc. and the outliers may significantly affect the result of patient-specific analysis. We propose a robust methodology for patient-specific analysis in line with the NetwrokProfiler. In the proposed method, outliers in high dimensional gene expression levels and drug response datasets are simultaneously controlled by robust Mahalanobis distance in robust principal component space. Thus, we can effectively perform for predicting anti-cancer drug sensitivity and identifying sensitivity-specific biomarkers for individual patients. We observe through Monte Carlo simulations that the proposed robust method produces outstanding performances for predicting response variable in the presence of outliers. We also apply the proposed methodology to the Sanger dataset in order to uncover cancer biomarkers and predict anti-cancer drug sensitivity, and show the effectiveness of our method.

  15. Herb-drug interactions: challenges and opportunities for improved predictions.

    Science.gov (United States)

    Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

    2014-03-01

    Supported by a usage history that predates written records and the perception that "natural" ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb-drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb-drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb-drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb-drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.

  16. Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

    International Nuclear Information System (INIS)

    Cao Dongsheng; Liu Shao; Xu Qingsong; Lu Hongmei; Huang Jianhua; Hu Qiannan; Liang Yizeng

    2012-01-01

    Highlights: ► Drug–target interactions are predicted using an extended SAR methodology. ► A drug–target interaction is regarded as an event triggered by many factors. ► Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. ► Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug–target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug–target interactions in a timely manner. In this article, we aim at extending current structure–activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug–target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug–target interactions, and show a general compatibility between the new scheme and current SAR

  17. Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.

    Science.gov (United States)

    Yadav, Jaydeep; Korzekwa, Ken; Nagar, Swati

    2018-05-07

    Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min -1 , the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min -1 . These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

  18. A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals

    International Nuclear Information System (INIS)

    Peyret, Thomas; Poulin, Patrick; Krishnan, Kannan

    2010-01-01

    The algorithms in the literature focusing to predict tissue:blood PC (P tb ) for environmental chemicals and tissue:plasma PC based on total (K p ) or unbound concentration (K pu ) for drugs differ in their consideration of binding to hemoglobin, plasma proteins and charged phospholipids. The objective of the present study was to develop a unified algorithm such that P tb , K p and K pu for both drugs and environmental chemicals could be predicted. The development of the unified algorithm was accomplished by integrating all mechanistic algorithms previously published to compute the PCs. Furthermore, the algorithm was structured in such a way as to facilitate predictions of the distribution of organic compounds at the macro (i.e. whole tissue) and micro (i.e. cells and fluids) levels. The resulting unified algorithm was applied to compute the rat P tb , K p or K pu of muscle (n = 174), liver (n = 139) and adipose tissue (n = 141) for acidic, neutral, zwitterionic and basic drugs as well as ketones, acetate esters, alcohols, aliphatic hydrocarbons, aromatic hydrocarbons and ethers. The unified algorithm reproduced adequately the values predicted previously by the published algorithms for a total of 142 drugs and chemicals. The sensitivity analysis demonstrated the relative importance of the various compound properties reflective of specific mechanistic determinants relevant to prediction of PC values of drugs and environmental chemicals. Overall, the present unified algorithm uniquely facilitates the computation of macro and micro level PCs for developing organ and cellular-level PBPK models for both chemicals and drugs.

  19. Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

    Science.gov (United States)

    LaBute, Montiago X; Zhang, Xiaohua; Lenderman, Jason; Bennion, Brian J; Wong, Sergio E; Lightstone, Felice C

    2014-01-01

    Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number

  20. Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

    Directory of Open Access Journals (Sweden)

    Montiago X LaBute

    Full Text Available Late-stage or post-market identification of adverse drug reactions (ADRs is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409 of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively. Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with

  1. Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2013-01-01

    Full Text Available Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1 chemical interaction between drugs, (2 protein interactions between drugs’ targets, and (3 target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

  2. Predicting Adolescent Drug Abuse: A Review of Issues, Methods and Correlates. Research Issues 11.

    Science.gov (United States)

    Lettieri, Dan J., Ed.

    Presented are 18 papers on predicting adolescent drug abuse. The papers have the following titles: "Current Issues in the Epidemiology of Drug Abuse as Related to Psychosocial Studies of Adolescent Drug Use"; "The Quest for Interpersonal Predictors of Marihuana Abuse in Adolescents"; "Assessing the Interpersonal Determinants of Adolescent Drug…

  3. Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes

    Directory of Open Access Journals (Sweden)

    Hall Ross S

    2010-04-01

    Full Text Available Abstract Background New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Saccharomyces cerevisiae, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset. Results Cross-species analyses showed that the evolutionary conservation of genes and the presence of essential orthologues are each strong predictors of essentiality in eukaryotes. Absence of paralogues was also found to be a general predictor of increased relative essentiality. By combining several orthology and essentiality criteria one can select gene sets with up to a five-fold enrichment in essential genes compared with a random selection. We show how quantitative application of such criteria can be used to predict a ranked list of potential drug targets from Ancylostoma caninum and Haemonchus contortus - two blood-feeding strongylid nematodes, for which there are presently limited sequence data but no functional genomic tools. Conclusions The present study demonstrates the utility of using orthology information from multiple, diverse eukaryotes to predict essential genes. The data also emphasize the challenge of identifying essential genes among those in a parasite that are absent from its host.

  4. Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

    Science.gov (United States)

    Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

    2014-01-01

    Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

  5. Alpha Oscillations during Incidental Encoding Predict Subsequent Memory for New "Foil" Information.

    Science.gov (United States)

    Vogelsang, David A; Gruber, Matthias; Bergström, Zara M; Ranganath, Charan; Simons, Jon S

    2018-05-01

    People can employ adaptive strategies to increase the likelihood that previously encoded information will be successfully retrieved. One such strategy is to constrain retrieval toward relevant information by reimplementing the neurocognitive processes that were engaged during encoding. Using EEG, we examined the temporal dynamics with which constraining retrieval toward semantic versus nonsemantic information affects the processing of new "foil" information encountered during a memory test. Time-frequency analysis of EEG data acquired during an initial study phase revealed that semantic compared with nonsemantic processing was associated with alpha decreases in a left frontal electrode cluster from around 600 msec after stimulus onset. Successful encoding of semantic versus nonsemantic foils during a subsequent memory test was related to decreases in alpha oscillatory activity in the same left frontal electrode cluster, which emerged relatively late in the trial at around 1000-1600 msec after stimulus onset. Across participants, left frontal alpha power elicited by semantic processing during the study phase correlated significantly with left frontal alpha power associated with semantic foil encoding during the memory test. Furthermore, larger left frontal alpha power decreases elicited by semantic foil encoding during the memory test predicted better subsequent semantic foil recognition in an additional surprise foil memory test, although this effect did not reach significance. These findings indicate that constraining retrieval toward semantic information involves reimplementing semantic encoding operations that are mediated by alpha oscillations and that such reimplementation occurs at a late stage of memory retrieval, perhaps reflecting additional monitoring processes.

  6. Predicting and detecting adverse drug reactions in old age: challenges and opportunities.

    Science.gov (United States)

    Mangoni, Arduino A

    2012-05-01

    Increased, often inappropriate, drug exposure, pharmacokinetic and pharmacodynamic changes, reduced homeostatic reserve and frailty increase the risk of adverse drug reactions (ADRs) in the older population, thereby imposing a significant public health burden. Predicting and diagnosing ADRs in old age presents significant challenges for the clinician, even when specific risk scoring systems are available. The picture is further compounded by the potential adverse impact of several drugs on more 'global' health indicators, for example, physical function and independence, and the fragmentation of care (e.g., increased number of treating doctors and care transitions) experienced by older patients during their clinical journey. The current knowledge of drug safety in old age is also curtailed by the lack of efficacy and safety data from pre-marketing studies. Moreover, little consideration is given to individual patients' experiences and reporting of specific ADRs, particularly in the presence of cognitive impairment. Pending additional data on these issues, the close review and monitoring of individual patients' drug prescribing, clinical status and biochemical parameters remain essential to predict and detect ADRs in old age. Recently developed strategies, for example, medication reconciliation and trigger tool methodology, have the potential for ADRs risk mitigation in this population. However, more information is required on their efficacy and applicability in different healthcare settings.

  7. New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples

    Directory of Open Access Journals (Sweden)

    Frank Christian Kischkel

    2017-03-01

    Full Text Available Background To find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR-Test®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods. Methods We established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy’s concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements. Results The established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor. Conclusion Our data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system.

  8. Accelerating early anti-tuberculosis drug discovery by creating mycobacterial indicator strains that predict mode of action

    KAUST Repository

    Boot, Maikel

    2018-04-13

    Due to the rise of drug resistant forms of tuberculosis there is an urgent need for novel antibiotics to effectively combat these cases and shorten treatment regimens. Recently, drug screens using whole cell analyses have been shown to be successful. However, current high-throughput screens focus mostly on stricto sensu life-death screening that give little qualitative information. In doing so, promising compound scaffolds or non-optimized compounds that fail to reach inhibitory concentrations are missed. To accelerate early TB drug discovery, we performed RNA sequencing on Mycobacterium tuberculosis and Mycobacterium marinum to map the stress responses that follow upon exposure to sub-inhibitory concentrations of antibiotics with known targets: ciprofloxacin, ethambutol, isoniazid, streptomycin and rifampicin. The resulting dataset comprises the first overview of transcriptional stress responses of mycobacteria to different antibiotics. We show that antibiotics can be distinguished based on their specific transcriptional stress fingerprint. Notably, this fingerprint was more distinctive in M. marinum. We decided to use this to our advantage and continue with this model organism. A selection of diverse antibiotic stress genes was used to construct stress reporters. In total, three functional reporters were constructed to respond to DNA damage, cell wall damage and ribosomal inhibition. Subsequently, these reporter strains were used to screen a small anti-TB compound library to predict the mode of action. In doing so, we could identify the putative mode of action for three novel compounds, which confirms our approach.

  9. Attendance Rates in a Workplace Predict Subsequent Outcome of Employment-Based Reinforcement of Cocaine Abstinence in Methadone Patients

    Science.gov (United States)

    Donlin, Wendy D.; Knealing, Todd W.; Needham, Mick; Wong, Conrad J.; Silverman, Kenneth

    2008-01-01

    This study assessed whether attendance rates in a workplace predicted subsequent outcome of employment-based reinforcement of cocaine abstinence. Unemployed adults in Baltimore methadone programs who used cocaine (N = 111) could work in a workplace for 4 hr every weekday and earn $10.00 per hour in vouchers for 26 weeks. During an induction…

  10. Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model.

    Science.gov (United States)

    Xue, Caifu; Zhang, Xunjie; Cai, Weimin

    2017-12-21

    The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites-hydroxybupropion, threohydrobupropion and erythrohydrobupropion-based on a mixed "bottom-up" and "top-down" approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (C max ) area under the concentration-time curve (AUC) values and C max and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and C max ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory

  11. Do Functional Movement Screen (FMS) composite scores predict subsequent injury? A systematic review with meta-analysis.

    Science.gov (United States)

    Moran, Robert W; Schneiders, Anthony G; Mason, Jesse; Sullivan, S John

    2017-12-01

    This paper aims to systematically review studies investigating the strength of association between FMS composite scores and subsequent risk of injury, taking into account both methodological quality and clinical and methodological diversity. Systematic review with meta-analysis. A systematic search of electronic databases was conducted for the period between their inception and 3 March 2016 using PubMed, Medline, Google Scholar, Scopus, Academic Search Complete, AMED (Allied and Complementary Medicine Database), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Health Source and SPORTDiscus. Inclusion criteria: (1) English language, (2) observational prospective cohort design, (3) original and peer-reviewed data, (4) composite FMS score, used to define exposure and non-exposure groups and (5) musculoskeletal injury, reported as the outcome. (1) data reported in conference abstracts or non-peer-reviewed literature, including theses, and (2) studies employing cross-sectional or retrospective study designs. 24 studies were appraised using the Quality of Cohort Studies assessment tool. In male military personnel, there was 'strong' evidence that the strength of association between FMS composite score (cut-point ≤14/21) and subsequent injury was 'small' (pooled risk ratio=1.47, 95% CI 1.22 to 1.77, p<0.0001, I 2 =57%). There was 'moderate' evidence to recommend against the use of FMS composite score as an injury prediction test in football (soccer). For other populations (including American football, college athletes, basketball, ice hockey, running, police and firefighters), the evidence was 'limited' or 'conflicting'. The strength of association between FMS composite scores and subsequent injury does not support its use as an injury prediction tool. PROSPERO registration number CRD42015025575. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted

  12. [Predictive factors of clinically significant drug-drug interactions among regimens based on protease inhibitors, non-nucleoside reverse transcriptase inhibitors and raltegravir].

    Science.gov (United States)

    Cervero, Miguel; Torres, Rafael; Jusdado, Juan José; Pastor, Susana; Agud, Jose Luis

    2016-04-15

    To determine the prevalence and types of clinically significant drug-drug interactions (CSDI) in the drug regimens of HIV-infected patients receiving antiretroviral treatment. retrospective review of database. Centre: Hospital Universitario Severo Ochoa, Infectious Unit. one hundred and forty-two participants followed by one of the authors were selected from January 1985 to December 2014. from their outpatient medical records we reviewed information from the last available visit of the participants, in relation to HIV infection, comorbidities, demographics and the drugs that they were receiving; both antiretroviral drugs and drugs not related to HIV infection. We defined CSDI from the information sheet and/or database on antiretroviral drug interactions of the University of Liverpool (http://www.hiv-druginteractions.org) and we developed a diagnostic tool to predict the possibility of CSDI. By multivariate logistic regression analysis and by estimating the diagnostic performance curve obtained, we identified a quick tool to predict the existence of drug interactions. Of 142 patients, 39 (29.11%) had some type of CSDI and in 11.2% 2 or more interactions were detected. In only one patient the combination of drugs was contraindicated (this patient was receiving darunavir/r and quetiapine). In multivariate analyses, predictors of CSDI were regimen type (PI or NNRTI) and the use of 3 or more non-antiretroviral drugs (AUC 0.886, 95% CI 0.828 to 0.944; P=.0001). The risk was 18.55 times in those receiving NNRTI and 27,95 times in those receiving IP compared to those taking raltegravir. Drug interactions, including those defined as clinically significant, are common in HIV-infected patients treated with antiretroviral drugs, and the risk is greater in IP-based regimens. Raltegravir-based prescribing, especially in patients who receive at least 3 non-HIV drugs could avoid interactions. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

  13. Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

    Science.gov (United States)

    Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

    2017-01-01

    Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

  14. Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

    Directory of Open Access Journals (Sweden)

    Elisa Passini

    2017-09-01

    Full Text Available Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC50/Hill coefficient. Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca2+-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs. Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca2+/late Na+ currents and Na+/Ca2+-exchanger, reduced Na+/K+-pump are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

  15. A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions.

    Directory of Open Access Journals (Sweden)

    Francesco Iorio

    Full Text Available We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound. This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising agent as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells-consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.

  16. Does subsequent criminal justice involvement predict foster care and termination of parental rights for children born to incarcerated women?

    Science.gov (United States)

    Kubiak, Sheryl Pimlott; Kasiborski, Natalie; Karim, Nidal; Schmittel, Emily

    2012-01-01

    This longitudinal study of 83 incarcerated women, who gave birth during incarceration and retained their parental rights through brief sentences, examines the intersection between subsequent criminal justice involvement postrelease and child welfare outcomes. Ten years of multiple state-level administrative data sets are used to determine if arrest or conviction predict foster care and/or termination of parental rights. Findings indicate that only felony arrest is a significant predictor of foster care involvement. Additionally, 69% of mothers retained legal custody, despite subsequent criminal involvement for many, suggesting supportive parenting programs and resources need to be available to these women throughout and after incarceration.

  17. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

    Directory of Open Access Journals (Sweden)

    Huthmacher Carola

    2010-08-01

    Full Text Available Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasite's metabolic network was embedded into that of its host (erythrocyte. Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment. Conclusions The results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development.

  18. Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans

    Directory of Open Access Journals (Sweden)

    Min JS

    2016-09-01

    Full Text Available Jee Sun Min,1 Doyun Kim,1 Jung Bae Park,1 Hyunjin Heo,1 Soo Hyeon Bae,2 Jae Hong Seo,1 Euichaul Oh,1 Soo Kyung Bae1 1Integrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon, 2Department of Pharmacology, College of Medicine, The Catholic University of Korea, Seocho-gu, Seoul, South Korea Background: Evaluating the potential risk of metabolic drug–drug interactions (DDIs is clinically important. Objective: To develop a physiologically based pharmacokinetic (PBPK model for sarpogrelate hydrochloride and its active metabolite, (R,S-1-{2-[2-(3-methoxyphenylethyl]-phenoxy}-3-(dimethylamino-2-propanol (M-1, in order to predict DDIs between sarpogrelate and the clinically relevant cytochrome P450 (CYP 2D6 substrates, metoprolol, desipramine, dextromethorphan, imipramine, and tolterodine. Methods: The PBPK model was developed, incorporating the physicochemical and pharmacokinetic properties of sarpogrelate hydrochloride, and M-1 based on the findings from in vitro and in vivo studies. Subsequently, the model was verified by comparing the predicted concentration-time profiles and pharmacokinetic parameters of sarpogrelate and M-1 to the observed clinical data. Finally, the verified model was used to simulate clinical DDIs between sarpogrelate hydrochloride and sensitive CYP2D6 substrates. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministering sarpogrelate hydrochloride and metoprolol. Results: The developed PBPK model accurately predicted sarpogrelate and M-1 plasma concentration profiles after single or multiple doses of sarpogrelate hydrochloride. The simulated ratios of area under the curve and maximum plasma concentration of metoprolol in the presence of sarpogrelate hydrochloride to baseline were in good agreement with the observed ratios. The predicted fold-increases in the area under the curve ratios of metoprolol

  19. Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy.

    Science.gov (United States)

    Honeyborne, Isobella; McHugh, Timothy D; Kuittinen, Iitu; Cichonska, Anna; Evangelopoulos, Dimitrios; Ronacher, Katharina; van Helden, Paul D; Gillespie, Stephen H; Fernandez-Reyes, Delmiro; Walzl, Gerhard; Rousu, Juho; Butcher, Philip D; Waddell, Simon J

    2016-04-07

    New treatment options are needed to maintain and improve therapy for tuberculosis, which caused the death of 1.5 million people in 2013 despite potential for an 86 % treatment success rate. A greater understanding of Mycobacterium tuberculosis (M.tb) bacilli that persist through drug therapy will aid drug development programs. Predictive biomarkers for treatment efficacy are also a research priority. Genome-wide transcriptional profiling was used to map the mRNA signatures of M.tb from the sputa of 15 patients before and 3, 7 and 14 days after the start of standard regimen drug treatment. The mRNA profiles of bacilli through the first 2 weeks of therapy reflected drug activity at 3 days with transcriptional signatures at days 7 and 14 consistent with reduced M.tb metabolic activity similar to the profile of pre-chemotherapy bacilli. These results suggest that a pre-existing drug-tolerant M.tb population dominates sputum before and after early drug treatment, and that the mRNA signature at day 3 marks the killing of a drug-sensitive sub-population of bacilli. Modelling patient indices of disease severity with bacterial gene expression patterns demonstrated that both microbiological and clinical parameters were reflected in the divergent M.tb responses and provided evidence that factors such as bacterial load and disease pathology influence the host-pathogen interplay and the phenotypic state of bacilli. Transcriptional signatures were also defined that predicted measures of early treatment success (rate of decline in bacterial load over 3 days, TB test positivity at 2 months, and bacterial load at 2 months). This study defines the transcriptional signature of M.tb bacilli that have been expectorated in sputum after two weeks of drug therapy, characterizing the phenotypic state of bacilli that persist through treatment. We demonstrate that variability in clinical manifestations of disease are detectable in bacterial sputa signatures, and that the changing M.tb m

  20. Predicting Drug Court Treatment Completion Using the MMPI-2-RF

    Science.gov (United States)

    Mattson, Curtis; Powers, Bradley; Halfaker, Dale; Akeson, Steven; Ben-Porath, Yossef

    2012-01-01

    We examined the ability of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008) substantive scales to predict Drug Court treatment completion in a sample of individuals identified as being at risk for failure to complete the program. Higher scores on MMPI-2-RF scales…

  1. Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

    Science.gov (United States)

    Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

    2017-09-01

    Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  2. PBPK Modeling - A Predictive, Eco-Friendly, Bio-Waiver Tool for Drug Research.

    Science.gov (United States)

    De, Baishakhi; Bhandari, Koushik; Mukherjee, Ranjan; Katakam, Prakash; Adiki, Shanta K; Gundamaraju, Rohit; Mitra, Analava

    2017-01-01

    The world has witnessed growing complexities in disease scenario influenced by the drastic changes in host-pathogen- environment triadic relation. Pharmaceutical R&Ds are in constant search of novel therapeutic entities to hasten transition of drug molecules from lab bench to patient bedside. Extensive animal studies and human pharmacokinetics are still the "gold standard" in investigational new drug research and bio-equivalency studies. Apart from cost, time and ethical issues on animal experimentation, burning questions arise relating to ecological disturbances, environmental hazards and biodiversity issues. Grave concerns arises when the adverse outcomes of continued studies on one particular disease on environment gives rise to several other pathogenic agents finally complicating the total scenario. Thus Pharma R&Ds face a challenge to develop bio-waiver protocols. Lead optimization, drug candidate selection with favorable pharmacokinetics and pharmacodynamics, toxicity assessment are vital steps in drug development. Simulation tools like Gastro Plus™, PK Sim®, SimCyp find applications for the purpose. Advanced technologies like organ-on-a chip or human-on-a chip where a 3D representation of human organs and systems can mimic the related processes and activities, thereby linking them to major features of human biology can be successfully incorporated in the drug development tool box. PBPK provides the State of Art to serve as an optional of animal experimentation. PBPK models can successfully bypass bio-equivalency studies, predict bioavailability, drug interactions and on hyphenation with in vitro-in vivo correlation can be extrapolated to humans thus serving as bio-waiver. PBPK can serve as an eco-friendly bio-waiver predictive tool in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  3. Increase in waist circumference over 6 years predicts subsequent cardiovascular disease and total mortality in nordic women

    DEFF Research Database (Denmark)

    Klingberg, Sofia; Mehlig, Kirsten; Lanfer, Anne

    2015-01-01

    -shaped association. Associations between increase in WC and outcomes were restricted to women with normal weight at baseline and to ever-smokers. CONCLUSIONS: In contrast to changes in HC which did not predict mortality and CVD, a 6-year increase in WC is strongly predictive, particularly among initially lean women...... and cardiovascular disease (CVD) mortality in women but that gain or loss in HC was unrelated to these outcomes. This study examines whether a 6-year change in waist circumference (WC) predicts mortality and CVD in the same study sample. METHODS: Baseline WC and 6-year change in WC as predictors of mortality and CVD...... were analyzed in 2,492 women from the Danish MONICA study and the Prospective Population Study of Women in Gothenburg, Sweden. RESULTS: Increase in WC was significantly associated with increased subsequent mortality and CVD adjusting for BMI and other covariates, with some evidence of a J...

  4. Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

    Science.gov (United States)

    Simm, Jaak; Klambauer, Günter; Arany, Adam; Steijaert, Marvin; Wegner, Jörg Kurt; Gustin, Emmanuel; Chupakhin, Vladimir; Chong, Yolanda T; Vialard, Jorge; Buijnsters, Peter; Velter, Ingrid; Vapirev, Alexander; Singh, Shantanu; Carpenter, Anne E; Wuyts, Roel; Hochreiter, Sepp; Moreau, Yves; Ceulemans, Hugo

    2018-05-17

    In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

    Science.gov (United States)

    Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

    2018-01-01

    Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  6. MARKETING PREDICTIONS IN ANTI-DRUG SOCIAL PROGRAMS: USE OF CAUSAL METHODS IN THE STUDY AND PREVENTION OF DRUG ABUSE

    Directory of Open Access Journals (Sweden)

    Serban Corina

    2010-12-01

    Full Text Available Drug use is one of the major challenges that todays society faces; its effects are felt at the level of various social, professional and age categories. Over 50 non-profit organizations are involved in the development of anti-drug social programs in Romania. Their role is to improve the degree of awareness of the target population concerning the risks associated with drug use, but also to steer consumers towards healthy areas, beneficial to their future. This paper aims to detail the issue of drug use in Romania, by making predictions based on the evolution of this phenomenon during the next five years. The obtained results have revealed the necessity to increase the number of programs preventing drug use, aswell as the need to continue social programs that have proved effective in previous years.

  7. In Silico Modeling of Gastrointestinal Drug Absorption: Predictive Performance of Three Physiologically Based Absorption Models.

    Science.gov (United States)

    Sjögren, Erik; Thörn, Helena; Tannergren, Christer

    2016-06-06

    Gastrointestinal (GI) drug absorption is a complex process determined by formulation, physicochemical and biopharmaceutical factors, and GI physiology. Physiologically based in silico absorption models have emerged as a widely used and promising supplement to traditional in vitro assays and preclinical in vivo studies. However, there remains a lack of comparative studies between different models. The aim of this study was to explore the strengths and limitations of the in silico absorption models Simcyp 13.1, GastroPlus 8.0, and GI-Sim 4.1, with respect to their performance in predicting human intestinal drug absorption. This was achieved by adopting an a priori modeling approach and using well-defined input data for 12 drugs associated with incomplete GI absorption and related challenges in predicting the extent of absorption. This approach better mimics the real situation during formulation development where predictive in silico models would be beneficial. Plasma concentration-time profiles for 44 oral drug administrations were calculated by convolution of model-predicted absorption-time profiles and reported pharmacokinetic parameters. Model performance was evaluated by comparing the predicted plasma concentration-time profiles, Cmax, tmax, and exposure (AUC) with observations from clinical studies. The overall prediction accuracies for AUC, given as the absolute average fold error (AAFE) values, were 2.2, 1.6, and 1.3 for Simcyp, GastroPlus, and GI-Sim, respectively. The corresponding AAFE values for Cmax were 2.2, 1.6, and 1.3, respectively, and those for tmax were 1.7, 1.5, and 1.4, respectively. Simcyp was associated with underprediction of AUC and Cmax; the accuracy decreased with decreasing predicted fabs. A tendency for underprediction was also observed for GastroPlus, but there was no correlation with predicted fabs. There were no obvious trends for over- or underprediction for GI-Sim. The models performed similarly in capturing dependencies on dose and

  8. Prediction of overall in vitro microsomal stability of drug candidates based on molecular modeling and support vector machines. Case study of novel arylpiperazines derivatives.

    Directory of Open Access Journals (Sweden)

    Szymon Ulenberg

    Full Text Available Other than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model and predict metabolic stability quantitatively is still lacking. This study proposes a workflow for developing quantitative metabolic stability-structure relationships, taking a set of 30 arylpiperazine derivatives as an example. The metabolic stability of the compounds was assessed in in vitro incubations in the presence of human liver microsomes and NADPH and subsequently quantified by liquid chromatography-mass spectrometry (LC-MS. Density functional theory (DFT calculations were used to obtain 30 models of the molecules, and Dragon software served as a source of structure-based molecular descriptors. For modeling structure-metabolic stability relationships, Support Vector Machines (SVM, a non-linear machine learning technique, were found to be more effective than a regression technique, based on the validation parameters obtained. Moreover, for the first time, general sites of metabolism for arylpiperazines bearing the 4-aryl-2H-pyrido[1,2-c]pyrimidine-1,3-dione system were defined by analysis of Q-TOF-MS/MS spectra. The results indicated that the application of one of the most advanced chemometric techniques combined with a simple and quick in vitro procedure and LC-MS analysis provides a novel and valuable tool for predicting metabolic half-life values. Given the reduced time and simplicity of analysis, together with the accuracy of the predictions obtained, this is a valid approach for predicting metabolic stability using structural data. The approach presented provides a novel, comprehensive and reliable tool

  9. Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration.

    Science.gov (United States)

    Wagner, Christian; Pan, Yuzhuo; Hsu, Vicky; Grillo, Joseph A; Zhang, Lei; Reynolds, Kellie S; Sinha, Vikram; Zhao, Ping

    2015-01-01

    The US Food and Drug Administration (FDA) has seen a recent increase in the application of physiologically based pharmacokinetic (PBPK) modeling towards assessing the potential of drug-drug interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, we evaluated the predictive performance of PBPK modeling in predicting cytochrome P450 (CYP)-mediated DDI. This evaluation was based on 15 substrate PBPK models submitted by nine sponsors between 2009 and 2013. For these 15 models, a total of 26 DDI studies (cases) with various CYP inhibitors were available. Sponsors developed the PBPK models, reportedly without considering clinical DDI data. Inhibitor models were either developed by sponsors or provided by PBPK software developers and applied with minimal or no modification. The metric for assessing predictive performance of the sponsors' PBPK approach was the R predicted/observed value (R predicted/observed = [predicted mean exposure ratio]/[observed mean exposure ratio], with the exposure ratio defined as [C max (maximum plasma concentration) or AUC (area under the plasma concentration-time curve) in the presence of CYP inhibition]/[C max or AUC in the absence of CYP inhibition]). In 81 % (21/26) and 77 % (20/26) of cases, respectively, the R predicted/observed values for AUC and C max ratios were within a pre-defined threshold of 1.25-fold of the observed data. For all cases, the R predicted/observed values for AUC and C max were within a 2-fold range. These results suggest that, based on the submissions to the FDA to date, there is a high degree of concordance between PBPK-predicted and observed effects of CYP inhibition, especially CYP3A-based, on the exposure of drug substrates.

  10. Prediction of Smoking, Alcohol, Drugs, and Psychoactive Drugs Abuse Based on Emotional Dysregulation and Child Abuse Experience in People with Borderline Personality Traits

    Directory of Open Access Journals (Sweden)

    M GannadiFarnood

    2014-12-01

    Full Text Available Objective: This research was an attempt to predict the tendency of people having borderline personality traits to smoking, drinking alcohol, and taking psychoactive drugs based on emotional dysregulation and child abuse. Method: This study employed a correlation method which is categorized in descriptive category. A sample including 600 male and female bachelor students of Tabriz University was selected by cluster sampling. Then, high risk behaviors scale, Emotional dysregulation Scale, Child abuse scale, and borderline personality scale (STB were distributed among this group. Findings: Stepwise multiple regression analysis suggested that emotional dysregulation and child abuse significantly predicted varying degrees of smoking, drug, and alcohol usage. Conclusion: The research findings suggest the basic role of initial biological vulnerability in terms of emotional regulation (dysregulation and invalidating family environment (child abuse in the prediction of catching the disorder of borderline personality traits and producing high riskbehaviorssuch as alcohol drink and drug usage.

  11. Interaction between hippocampal and striatal systems predicts subsequent consolidation of motor sequence memory.

    Directory of Open Access Journals (Sweden)

    Geneviève Albouy

    Full Text Available The development of fast and reproducible motor behavior is a crucial human capacity. The aim of the present study was to address the relationship between the implementation of consistent behavior during initial training on a sequential motor task (the Finger Tapping Task and subsequent sleep-dependent motor sequence memory consolidation, using functional magnetic resonance imaging (fMRI and total sleep deprivation protocol. Our behavioral results indicated significant offline gains in performance speed after sleep whereas performance was only stabilized, but not enhanced, after sleep deprivation. At the cerebral level, we previously showed that responses in the caudate nucleus increase, in parallel to a decrease in its functional connectivity with frontal areas, as performance became more consistent. Here, the strength of the competitive interaction, assessed through functional connectivity analyses, between the caudate nucleus and hippocampo-frontal areas during initial training, predicted delayed gains in performance at retest in sleepers but not in sleep-deprived subjects. Moreover, during retest, responses increased in the hippocampus and medial prefrontal cortex in sleepers whereas in sleep-deprived subjects, responses increased in the putamen and cingulate cortex. Our results suggest that the strength of the competitive interplay between the striatum and the hippocampus, participating in the implementation of consistent motor behavior during initial training, conditions subsequent motor sequence memory consolidation. The latter process appears to be supported by a reorganisation of cerebral activity in hippocampo-neocortical networks after sleep.

  12. A Network-Based Model of Oncogenic Collaboration for Prediction of Drug Sensitivity

    Directory of Open Access Journals (Sweden)

    Ted G Laderas

    2015-12-01

    Full Text Available Tumorigenesis is a multi-step process, involving the acquisition of multiple oncogenic mutations that transform cells, resulting in systemic dysregulation that enables proliferation, among other cancer hallmarks. High throughput omics techniques are used in precision medicine, allowing identification of these mutations with the goal of identifying treatments that target them. However, the multiplicity of oncogenes required for transformation, known as oncogenic collaboration, makes assigning effective treatments difficult. Motivated by this observation, we propose a new type of oncogenic collaboration where mutations in genes that interact with an oncogene may contribute to its dysregulation, a new genomic feature that we term surrogate oncogenes. By mapping mutations to a protein/protein interaction network, we can determine significance of the observed distribution using permutation-based methods. For a panel of 38 breast cancer cell lines, we identified significant surrogate oncogenes in oncogenes such as BRCA1 and ESR1. In addition, using Random Forest Classifiers, we show that these significant surrogate oncogenes predict drug sensitivity for 74 drugs in the breast cancer cell lines with a mean error rate of 30.9%. Additionally, we show that surrogate oncogenes are predictive of survival in patients. The surrogate oncogene framework incorporates unique or rare mutations on an individual level. Our model has the potential for integrating patient-unique mutations in predicting drug-sensitivity, suggesting a potential new direction in precision medicine, as well as a new approach for drug development. Additionally, we show the prevalence of significant surrogate oncogenes in multiple cancers within the Cancer Genome Atlas, suggesting that surrogate oncogenes may be a useful genomic feature for guiding pancancer analyses and assigning therapies across many tissue types.

  13. Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning.

    Science.gov (United States)

    Rathore, Saima; Akbari, Hamed; Doshi, Jimit; Shukla, Gaurav; Rozycki, Martin; Bilello, Michel; Lustig, Robert; Davatzikos, Christos

    2018-04-01

    Standard surgical resection of glioblastoma, mainly guided by the enhancement on postcontrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within the peritumoral edema region (ED). Subsequent radiotherapy typically delivers uniform radiation to peritumoral FLAIR-hyperintense regions, without attempting to target areas likely to be infiltrated more heavily. Noninvasive in vivo delineation of the areas of tumor infiltration and prediction of early recurrence in peritumoral ED could assist in targeted intensification of local therapies, thereby potentially delaying recurrence and prolonging survival. This paper presents a method for estimating peritumoral edema infiltration using radiomic signatures determined via machine learning methods, and tests it on 90 patients with de novo glioblastoma. The generalizability of the proposed predictive model was evaluated via cross-validation in a discovery cohort ([Formula: see text]) and was subsequently evaluated in a replication cohort ([Formula: see text]). Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. The cross-validated accuracy of our predictive infiltration model on the discovery and replication cohorts was 87.51% (odds ratio = 10.22, sensitivity = 80.65, and specificity = 87.63) and 89.54% (odds ratio = 13.66, sensitivity = 97.06, and specificity = 76.73), respectively. The radiomic signature of the recurrent tumor region revealed higher vascularity and cellularity when compared with the nonrecurrent region. The proposed model shows evidence that multiparametric pattern analysis from clinical MRI sequences can assist in in vivo estimation of the spatial extent and pattern of tumor recurrence in peritumoral edema, which may guide supratotal resection and/or intensification of postoperative radiation therapy.

  14. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

    Science.gov (United States)

    Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

    2018-02-01

    For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  15. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

    Directory of Open Access Journals (Sweden)

    Hongbin Yang

    2018-02-01

    Full Text Available During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  16. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts.

    Science.gov (United States)

    Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

    2018-01-01

    During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  17. Prediction of the overall renal tubular secretion and hepatic clearance of anionic drugs and a renal drug-drug interaction involving organic anion transporter 3 in humans by in vitro uptake experiments.

    Science.gov (United States)

    Watanabe, Takao; Kusuhara, Hiroyuki; Watanabe, Tomoko; Debori, Yasuyuki; Maeda, Kazuya; Kondo, Tsunenori; Nakayama, Hideki; Horita, Shigeru; Ogilvie, Brian W; Parkinson, Andrew; Hu, Zhuohan; Sugiyama, Yuichi

    2011-06-01

    The present study investigated prediction of the overall renal tubular secretion and hepatic clearances of anionic drugs based on in vitro transport studies. The saturable uptake of eight drugs, most of which were OAT3 substrates (rosuvastatin, pravastatin, pitavastatin, valsartan, olmesartan, trichlormethiazide, p-aminohippurate, and benzylpenicillin) by freshly prepared human kidney slices underestimated the overall intrinsic clearance of the tubular secretion; therefore, a scaling factor of 10 was required for in vitro-in vivo extrapolation. We examined the effect of gemfibrozil and its metabolites, gemfibrozil glucuronide and the carboxylic metabolite, gemfibrozil M3, on pravastatin uptake by human kidney slices. The inhibition study using human kidney slices suggests that OAT3 plays a predominant role in the renal uptake of pravastatin. Comparison of unbound concentrations and K(i) values (1.5, 9.1, and 4.0 μM, for gemfibrozil, gemfibrozil glucuronide, and gemfibrozil M3, respectively) suggests that the mechanism of the interaction is due mainly to inhibition by gemfibrozil and gemfibrozil glucuronide. Furthermore, extrapolation of saturable uptake by cryopreserved human hepatocytes predicts clearance comparable with the observed hepatic clearance although fluvastatin and rosuvastatin required a scaling factor of 11 and 6.9, respectively. This study suggests that in vitro uptake assays using human kidney slices and hepatocytes provide a good prediction of the overall tubular secretion and hepatic clearances of anionic drugs and renal drug-drug interactions. It is also recommended that in vitro-in vivo extrapolation be performed in animals to obtain more reliable prediction.

  18. Usefulness of Two-Compartment Model-Assisted and Static Overall Inhibitory-Activity Method for Prediction of Drug-Drug Interaction.

    Science.gov (United States)

    Iga, Katsumi; Kiriyama, Akiko

    2017-12-01

    Our study of drug-drug interaction (DDI) started with the clarification of unusually large DDI observed between ramelteon (RAM) and fluvoxamine (FLV). The main cause of this DDI was shown to be the extremely small hepatic availability of RAM (vF h ). Traditional DDI prediction assuming the well-stirred hepatic extraction kinetic ignores the relative increase of vF h by DDI, while we could solve this problem by use of the tube model. Ultimately, we completed a simple and useful method for prediction of DDI. Currently, DDI prediction becomes more complex and difficult when examining issues such as dynamic changes in perpetrator level, inhibitory metabolites, etc. The regulatory agents recommend DDI prediction by use of some sophisticated methods. However, they seem problematic in requiring plural in vitro data that reduce the flexibility and accuracy of the simulation. In contrast, our method is based on the static and two-compartment models. The two-compartment model has advantages in that it uses common pharmacokinetics (PK) parameters determined from the actual clinical data, guaranteeing the simulation of the reference standard in DDI. Our studies confirmed that dynamic changes in perpetrator level do not make a difference between static and dynamic methods. DDIs perpetrated by FLV and itraconazole were successfully predicted by use of the present method where two DDI predictors [perpetrator-specific inhibitory activities toward CYP isoforms (pA i, CYP s) and victim-specific fractional CYP-isoform contributions to the clearance (vf m, CYP s)] are determined successively as shown in the graphical abstract. Accordingly, this approach will accelerate DDI prediction over the traditional methods.

  19. The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness

    Science.gov (United States)

    Mangus, J Michael; Turner, Benjamin O

    2017-01-01

    Abstract While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between structures implicated in the encoding of sensory information, which disrupts message processing and thereby inhibits attitude change. However, persuasion is a multi-determined construct that results from both message features and audience characteristics. Therefore, persuasive messages should lead to specific functional connectivity patterns among a priori defined structures within the persuasion network. The present study exposed 28 subjects to anti-drug public service announcements where arousal, argument strength, and subject drug-use risk were systematically varied. Psychophysiological interaction analyses provide support for the affective-executive hypothesis but not for the encoding-disruption hypothesis. Secondary analyses show that video-level connectivity patterns among structures within the persuasion network predict audience responses in independent samples (one college-aged, one nationally representative). We propose that persuasion neuroscience research is best advanced by considering network-level effects while accounting for interactions between message features and target audience characteristics. PMID:29140500

  20. Prediction of phospholipidosis-inducing potential of drugs by in vitro biochemical and physicochemical assays followed by multivariate analysis.

    Science.gov (United States)

    Kuroda, Yukihiro; Saito, Madoka

    2010-03-01

    An in vitro method to predict phospholipidosis-inducing potential of cationic amphiphilic drugs (CADs) was developed using biochemical and physicochemical assays. The following parameters were applied to principal component analysis, as well as physicochemical parameters: pK(a) and clogP; dissociation constant of CADs from phospholipid, inhibition of enzymatic phospholipid degradation, and metabolic stability of CADs. In the score plot, phospholipidosis-inducing drugs (amiodarone, propranolol, imipramine, chloroquine) were plotted locally forming the subspace for positive CADs; while non-inducing drugs (chlorpromazine, chloramphenicol, disopyramide, lidocaine) were placed scattering out of the subspace, allowing a clear discrimination between both classes of CADs. CADs that often produce false results by conventional physicochemical or cell-based assay methods were accurately determined by our method. Basic and lipophilic disopyramide could be accurately predicted as a nonphospholipidogenic drug. Moreover, chlorpromazine, which is often falsely predicted as a phospholipidosis-inducing drug by in vitro methods, could be accurately determined. Because this method uses the pharmacokinetic parameters pK(a), clogP, and metabolic stability, which are usually obtained in the early stages of drug development, the method newly requires only the two parameters, binding to phospholipid, and inhibition of lipid degradation enzyme. Therefore, this method provides a cost-effective approach to predict phospholipidosis-inducing potential of a drug. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

  1. Improving the prediction of the brain disposition for orally administered drugs using BDDCS

    DEFF Research Database (Denmark)

    Broccatelli, Fabio; Larregieu, Caroline A.; Cruciani, Gabriele

    2012-01-01

    outcome. Passive permeability and P-glycoprotein (Pgp, ABCB1) efflux have been successfully recognized to impact xenobiotic extrusion from the brain, as Pgp is known to play a role in limiting the BBB penetration of oral drugs in humans. However, these two properties alone fail to explain the BBB...... penetration for a significant number of marketed central nervous system (CNS) agents. The Biopharmaceutics Drug Disposition Classification System (BDDCS) has proved useful in predicting drug disposition in the human body, particularly in the liver and intestine. Here we discuss the value of using BDDCS...

  2. Therapeutic drug monitoring in pregnancy.

    Science.gov (United States)

    Matsui, Doreen M

    2012-10-01

    Therapeutic drug monitoring (TDM) is commonly recommended to optimize drug dosing regimens of various medications. It has been proposed to guide therapy in pregnant women, in whom physiological changes may lead to altered pharmacokinetics resulting in difficulty in predicting the appropriate drug dosage. Ideally, TDM may play a role in enhancing the effectiveness of treatment while minimizing toxicity of both the mother and fetus. Monitoring of drug levels may also be helpful in assessing adherence to prescribed therapy in selected cases. Limitations exist as therapeutic ranges have only been defined for a limited number of drugs and are based on data obtained in nonpregnant patients. TDM has been suggested for anticonvulsants, antidepressants, and antiretroviral drugs, based on pharmacokinetic studies that have shown reduced drug concentrations. However, there is only relatively limited (and sometimes inconsistent) information regarding the clinical impact of these pharmacokinetic changes during pregnancy and the effect of subsequent dose adjustments. Further studies are required to determine whether implementation of TDM during pregnancy improves outcome and is associated with any benefit beyond that achieved by clinical judgment alone. The cost effectiveness of TDM programs during pregnancy also remains to be examined.

  3. Prediction of drug-related cardiac adverse effects in humans--B: use of QSAR programs for early detection of drug-induced cardiac toxicities.

    Science.gov (United States)

    Frid, Anna A; Matthews, Edwin J

    2010-04-01

    This report describes the use of three quantitative structure-activity relationship (QSAR) programs to predict drug-related cardiac adverse effects (AEs), BioEpisteme, MC4PC, and Leadscope Predictive Data Miner. QSAR models were constructed for 9 cardiac AE clusters affecting Purkinje nerve fibers (arrhythmia, bradycardia, conduction disorder, electrocardiogram, palpitations, QT prolongation, rate rhythm composite, tachycardia, and Torsades de pointes) and 5 clusters affecting the heart muscle (coronary artery disorders, heart failure, myocardial disorders, myocardial infarction, and valve disorders). The models were based on a database of post-marketing AEs linked to 1632 chemical structures, and identical training data sets were configured for three QSAR programs. Model performance was optimized and shown to be affected by the ratio of the number of active to inactive drugs. Results revealed that the three programs were complementary and predictive performances using any single positive, consensus two positives, or consensus three positives were as follows, respectively: 70.7%, 91.7%, and 98.0% specificity; 74.7%, 47.2%, and 21.0% sensitivity; and 138.2, 206.3, and 144.2 chi(2). In addition, a prospective study using AE data from the U.S. Food and Drug Administration's (FDA's) MedWatch Program showed 82.4% specificity and 94.3% sensitivity. Furthermore, an external validation study of 18 drugs with serious cardiotoxicity not considered in the models had 88.9% sensitivity. Published by Elsevier Inc.

  4. Experimental and computational prediction of glass transition temperature of drugs.

    Science.gov (United States)

    Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

    2014-12-22

    Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

  5. Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors.

    Science.gov (United States)

    Mitra, A K; Mukherjee, U K; Harding, T; Jang, J S; Stessman, H; Li, Y; Abyzov, A; Jen, J; Kumar, S; Rajkumar, V; Van Ness, B

    2016-05-01

    Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that have a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Although genome profiling studies have demonstrated heterogeneity in subclonal architecture that may ultimately lead to relapse, a gene expression-based prediction program that can identify, distinguish and quantify drug response in sub-populations within a bulk population of myeloma cells is lacking. In this study, we performed targeted transcriptome analysis on 528 pre-treatment single cells from 11 myeloma cell lines and 418 single cells from 8 drug-naïve MM patients, followed by intensive bioinformatics and statistical analysis for prediction of proteasome inhibitor sensitivity in individual cells. Using our previously reported drug response gene expression profile signature at the single-cell level, we developed an R Statistical analysis package available at https://github.com/bvnlabSCATTome, SCATTome (single-cell analysis of targeted transcriptome), that restructures the data obtained from Fluidigm single-cell quantitative real-time-PCR analysis run, filters missing data, performs scaling of filtered data, builds classification models and predicts drug response of individual cells based on targeted transcriptome using an assortment of machine learning methods. Application of SCATT should contribute to clinically relevant analysis of intratumor heterogeneity, and better inform drug choices based on subclonal cellular responses.

  6. Using multicriteria decision analysis during drug development to predict reimbursement decisions.

    Science.gov (United States)

    Williams, Paul; Mauskopf, Josephine; Lebiecki, Jake; Kilburg, Anne

    2014-01-01

    Pharmaceutical companies design clinical development programs to generate the data that they believe will support reimbursement for the experimental compound. The objective of the study was to present a process for using multicriteria decision analysis (MCDA) by a pharmaceutical company to estimate the probability of a positive recommendation for reimbursement for a new drug given drug and environmental attributes. The MCDA process included 1) selection of decisions makers who were representative of those making reimbursement decisions in a specific country; 2) two pre-workshop questionnaires to identify the most important attributes and their relative importance for a positive recommendation for a new drug; 3) a 1-day workshop during which participants undertook three tasks: i) they agreed on a final list of decision attributes and their importance weights, ii) they developed level descriptions for these attributes and mapped each attribute level to a value function, and iii) they developed profiles for hypothetical products 'just likely to be reimbursed'; and 4) use of the data from the workshop to develop a prediction algorithm based on a logistic regression analysis. The MCDA process is illustrated using case studies for three countries, the United Kingdom, Germany, and Spain. The extent to which the prediction algorithms for each country captured the decision processes for the workshop participants in our case studies was tested using a post-meeting questionnaire that asked the participants to make recommendations for a set of hypothetical products. The data collected in the case study workshops resulted in a prediction algorithm: 1) for the United Kingdom, the probability of a positive recommendation for different ranges of cost-effectiveness ratios; 2) for Spain, the probability of a positive recommendation at the national and regional levels; and 3) for Germany, the probability of a determination of clinical benefit. The results from the post

  7. Modulating effect of the nootropic drug, piracetam on stress- and subsequent morphine-induced prolactin secretion in male rats.

    Science.gov (United States)

    Matton, A.; Engelborghs, S.; Bollengier, F.; Finné, E.; Vanhaeist, L.

    1996-01-01

    1. The effect of the nootropic drug, piracetam on stress- and subsequent morphine-induced prolactin (PRL) secretion was investigated in vivo in male rats, by use of a stress-free blood sampling and drug administration method by means of a permanent indwelling catheter in the right jugular vein. 2. Four doses of piracetam were tested (20, 100, 200 and 400 mg kg-1), being given intraperitoneally 1 h before blood sampling; control rats received saline instead. After a first blood sample, rats were subjected to immobilization stress and received morphine, 6 mg kg-1, 90 min later. 3. Piracetam had no effect on basal plasma PRL concentration. 4. While in the non-piracetam-treated rats, stress produced a significant rise in plasma PRL concentration, in the piracetam-pretreated rats PRL peaks were attenuated, especially in the group given 100 mg kg-1 piracetam, where plasma PRL concentration was not significantly different from basal values. The dose-response relationship showed a U-shaped curve; the smallest dose had a minor inhibitory effect and the highest dose had no further effect on the PRL rise. 5. In unrestrained rats, morphine led to a significant elevation of plasma PRL concentration. After the application of immobilization stress it lost its ability to raise plasma PRL concentration in the control rats, but not in the piracetam-treated rats. This tolerance was overcome by piracetam in a significant manner but with a reversed dose-response curve; i.e. the smaller the dose of piracetam, the higher the subsequent morphine-induced PRL peak. 6. There is no simple explanation for the mechanism by which piracetam induces these contradictory effects. Interference with the excitatory amino acid system, which is also involved in opiate action, is proposed speculatively as a possible mediator of the effects of piracetam. PMID:8821540

  8. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.

    Science.gov (United States)

    Bai, Li-Yue; Dai, Hao; Xu, Qin; Junaid, Muhammad; Peng, Shao-Liang; Zhu, Xiaolei; Xiong, Yi; Wei, Dong-Qing

    2018-02-05

    Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

  9. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm

    Directory of Open Access Journals (Sweden)

    Li-Yue Bai

    2018-02-01

    Full Text Available Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.

  10. A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

    Science.gov (United States)

    Ye, Min; Nagar, Swati; Korzekwa, Ken

    2015-01-01

    Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

  11. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

    Science.gov (United States)

    Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A; Kim, Sungjoon; Wilson, Christopher J; Lehár, Joseph; Kryukov, Gregory V; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F; Monahan, John E; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H; Cheng, Jill; Yu, Guoying K; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P; Gabriel, Stacey B; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E; Weber, Barbara L; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L; Meyerson, Matthew; Golub, Todd R; Morrissey, Michael P; Sellers, William R; Schlegel, Robert; Garraway, Levi A

    2012-03-28

    The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

  12. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

    Science.gov (United States)

    Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A.; Kim, Sungjoon; Wilson, Christopher J.; Lehár, Joseph; Kryukov, Gregory V.; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F.; Monahan, John E.; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A.; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H.; Cheng, Jill; Yu, Guoying K.; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D.; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C.; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P.; Gabriel, Stacey B.; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E.; Weber, Barbara L.; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L.; Meyerson, Matthew; Golub, Todd R.; Morrissey, Michael P.; Sellers, William R.; Schlegel, Robert; Garraway, Levi A.

    2012-01-01

    The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available1. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens2. PMID:22460905

  13. BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

    Directory of Open Access Journals (Sweden)

    Tsafnat Guy

    2011-04-01

    Full Text Available Abstract Background The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP to help experts screen drugs that may have important clinical characteristics of interest. Results BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH and the PharmacoKinetic Interaction Screening (PKIS database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100% and 157 (of 197 minor drug classes (80% with areas under the receiver operating characteristic curve (AUC > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238 adverse events (73%, up to 12 (of 15 groups of clinically significant cytochrome P450 enzyme (CYP inducers or inhibitors (80%, and up to 11 (of 14 groups of narrow therapeutic index drugs (79%. Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task. Conclusions BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.

  14. An integrated approach to improved toxicity prediction for the safety assessment during preclinical drug development using Hep G2 cells

    International Nuclear Information System (INIS)

    Noor, Fozia; Niklas, Jens; Mueller-Vieira, Ursula; Heinzle, Elmar

    2009-01-01

    Efficient and accurate safety assessment of compounds is extremely important in the preclinical development of drugs especially when hepatotoxicty is in question. Multiparameter and time resolved assays are expected to greatly improve the prediction of toxicity by assessing complex mechanisms of toxicity. An integrated approach is presented in which Hep G2 cells and primary rat hepatocytes are compared in frequently used cytotoxicity assays for parent compound toxicity. The interassay variability was determined. The cytotoxicity assays were also compared with a reliable alternative time resolved respirometric assay. The set of training compounds consisted of well known hepatotoxins; amiodarone, carbamazepine, clozapine, diclofenac, tacrine, troglitazone and verapamil. The sensitivity of both cell systems in each tested assay was determined. Results show that careful selection of assay parameters and inclusion of a kinetic time resolved assay improves prediction for non-metabolism mediated toxicity using Hep G2 cells as indicated by a sensitivity ratio of 1. The drugs with EC 50 values 100 μM or lower were considered toxic. The difference in the sensitivity of the two cell systems to carbamazepine which causes toxicity via reactive metabolites emphasizes the importance of human cell based in-vitro assays. Using the described system, primary rat hepatocytes do not offer advantage over the Hep G2 cells in parent compound toxicity evaluation. Moreover, respiration method is non invasive, highly sensitive and allows following the time course of toxicity. Respiration assay could serve as early indicator of changes that subsequently lead to toxicity.

  15. Global Optimization of Ventricular Myocyte Model to Multi-Variable Objective Improves Predictions of Drug-Induced Torsades de Pointes

    Directory of Open Access Journals (Sweden)

    Trine Krogh-Madsen

    2017-12-01

    Full Text Available In silico cardiac myocyte models present powerful tools for drug safety testing and for predicting phenotypical consequences of ion channel mutations, but their accuracy is sometimes limited. For example, several models describing human ventricular electrophysiology perform poorly when simulating effects of long QT mutations. Model optimization represents one way of obtaining models with stronger predictive power. Using a recent human ventricular myocyte model, we demonstrate that model optimization to clinical long QT data, in conjunction with physiologically-based bounds on intracellular calcium and sodium concentrations, better constrains model parameters. To determine if the model optimized to congenital long QT data better predicts risk of drug-induced long QT arrhythmogenesis, in particular Torsades de Pointes risk, we tested the optimized model against a database of known arrhythmogenic and non-arrhythmogenic ion channel blockers. When doing so, the optimized model provided an improved risk assessment. In particular, we demonstrate an elimination of false-positive outcomes generated by the baseline model, in which simulations of non-torsadogenic drugs, in particular verapamil, predict action potential prolongation. Our results underscore the importance of currents beyond those directly impacted by a drug block in determining torsadogenic risk. Our study also highlights the need for rich data in cardiac myocyte model optimization and substantiates such optimization as a method to generate models with higher accuracy of predictions of drug-induced cardiotoxicity.

  16. In vitro dissolution methodology, mini-Gastrointestinal Simulator (mGIS), predicts better in vivo dissolution of a weak base drug, dasatinib.

    Science.gov (United States)

    Tsume, Yasuhiro; Takeuchi, Susumu; Matsui, Kazuki; Amidon, Gregory E; Amidon, Gordon L

    2015-08-30

    USP apparatus I and II are gold standard methodologies for determining the in vitro dissolution profiles of test drugs. However, it is difficult to use in vitro dissolution results to predict in vivo dissolution, particularly the pH-dependent solubility of weak acid and base drugs, because the USP apparatus contains one vessel with a fixed pH for the test drug, limiting insight into in vivo drug dissolution of weak acid and weak base drugs. This discrepancy underscores the need to develop new in vitro dissolution methodology that better predicts in vivo response to assure the therapeutic efficacy and safety of oral drug products. Thus, the development of the in vivo predictive dissolution (IPD) methodology is necessitated. The major goals of in vitro dissolution are to ensure the performance of oral drug products and the support of drug formulation design, including bioequivalence (BE). Orally administered anticancer drugs, such as dasatinib and erlotinib (tyrosine kinase inhibitors), are used to treat various types of cancer. These drugs are weak bases that exhibit pH-dependent and high solubility in the acidic stomach and low solubility in the small intestine (>pH 6.0). Therefore, these drugs supersaturate and/or precipitate when they move from the stomach to the small intestine. Also of importance, gastric acidity for cancer patients may be altered with aging (reduction of gastric fluid secretion) and/or co-administration of acid-reducing agents. These may result in changes to the dissolution profiles of weak base and the reduction of drug absorption and efficacy. In vitro dissolution methodologies that assess the impact of these physiological changes in the GI condition are expected to better predict in vivo dissolution of oral medications for patients and, hence, better assess efficacy, toxicity and safety concerns. The objective of this present study is to determine the initial conditions for a mini-Gastrointestinal Simulator (mGIS) to assess in vivo

  17. How good are publicly available web services that predict bioactivity profiles for drug repurposing?

    Science.gov (United States)

    Murtazalieva, K A; Druzhilovskiy, D S; Goel, R K; Sastry, G N; Poroikov, V V

    2017-10-01

    Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.

  18. Physical and relational aggression as predictors of drug use: gender differences among high school students.

    Science.gov (United States)

    Skara, Silvana; Pokhrel, Pallav; Weiner, Michelle D; Sun, Ping; Dent, Clyde W; Sussman, Steve

    2008-12-01

    The present study investigated the longitudinal relationships between physical and relational aggression and later drug use, as moderated by gender. Self-reported data were gathered from 2064 high school students at pretest and 1-year post-test to test the hypotheses that (1) males would engage in more physical aggression than females, whereas females would engage in more relational aggression than males; and (2) physical aggression would be a stronger drug use predictor for males and relational aggression a stronger predictor for females. Results indicated that males reported engaging in more physical aggression than females at baseline; however, females and males reported engaging in similar rates of relational aggression. After controlling for relational aggression, baseline drug use, and demographic variables, physical aggression at baseline was found to predict alcohol use 1-year later for males but not for females. After controlling for physical aggression, baseline drug use, and demographic variables, relational aggression was found to predict cigarette use and marijuana use for females but not for males. However, relational aggression was found to predict later alcohol and hard drug equally across gender. These findings suggest that both physical and relational aggression are predictive of subsequent drug use and have important implications for violence and drug use prevention intervention efforts.

  19. The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness.

    Science.gov (United States)

    Huskey, Richard; Mangus, J Michael; Turner, Benjamin O; Weber, René

    2017-12-01

    While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between structures implicated in the encoding of sensory information, which disrupts message processing and thereby inhibits attitude change. However, persuasion is a multi-determined construct that results from both message features and audience characteristics. Therefore, persuasive messages should lead to specific functional connectivity patterns among a priori defined structures within the persuasion network. The present study exposed 28 subjects to anti-drug public service announcements where arousal, argument strength, and subject drug-use risk were systematically varied. Psychophysiological interaction analyses provide support for the affective-executive hypothesis but not for the encoding-disruption hypothesis. Secondary analyses show that video-level connectivity patterns among structures within the persuasion network predict audience responses in independent samples (one college-aged, one nationally representative). We propose that persuasion neuroscience research is best advanced by considering network-level effects while accounting for interactions between message features and target audience characteristics. © The Author (2017). Published by Oxford University Press.

  20. A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

    Science.gov (United States)

    Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

    2017-12-21

    In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

  1. A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations

    NARCIS (Netherlands)

    Yamamoto, Yumi; Valitalo, Pyry A.; van den Berg, Dirk-Jan; Hartman, Robin; van den Brink, Willem; Wong, Yin Cheong; Huntjens, Dymphy R.; Proost, Johannes H.; Vermeulen, An; Krauwinkel, Walter; Bakshi, Suruchi; Aranzana-Climent, Vincent; Marchand, Sandrine; Dahyot-Fizelier, Claire; Couet, William; Danhof, Meindert; van Hasselt, Johan G. C.; de lange, Elizabeth C. M.

    Purpose Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human

  2. Attendance Rates in A Workplace Predict Subsequent Outcome of Employment-Based Reinforcement of Cocaine Abstinence in Methadone Patients

    OpenAIRE

    Donlin, Wendy D; Knealing, Todd W; Needham, Mick; Wong, Conrad J; Silverman, Kenneth

    2008-01-01

    This study assessed whether attendance rates in a workplace predicted subsequent outcome of employment-based reinforcement of cocaine abstinence. Unemployed adults in Baltimore methadone programs who used cocaine (N  =  111) could work in a workplace for 4 hr every weekday and earn $10.00 per hour in vouchers for 26 weeks. During an induction period, participants provided urine samples but could work independent of their urinalysis results. After the induction period, participants had to prov...

  3. The biowaiver extension for BCS class III drugs: the effect of dissolution rate on the bioequivalence of BCS class III immediate-release drugs predicted by computer simulation.

    Science.gov (United States)

    Tsume, Yasuhiro; Amidon, Gordon L

    2010-08-02

    The Biopharmaceutical Classification System (BCS) guidance issued by the FDA allows waivers for in vivo bioavailability and bioequivalence studies for immediate-release (IR) solid oral dosage forms only for BCS class I drugs. However, a number of drugs within BCS class III have been proposed to be eligible for biowaivers. The World Health Organization (WHO) has shortened the requisite dissolution time of BCS class III drugs on their Essential Medicine List (EML) from 30 to 15 min for extended biowaivers; however, the impact of the shorter dissolution time on AUC(0-inf) and C(max) is unknown. The objectives of this investigation were to assess the ability of gastrointestinal simulation software to predict the oral absorption of the BCS class I drugs propranolol and metoprolol and the BCS class III drugs cimetidine, atenolol, and amoxicillin, and to perform in silico bioequivalence studies to assess the feasibility of extending biowaivers to BCS class III drugs. The drug absorption from the gastrointestinal tract was predicted using physicochemical and pharmacokinetic properties of test drugs provided by GastroPlus (version 6.0). Virtual trials with a 200 mL dose volume at different drug release rates (T(85%) = 15 to 180 min) were performed to predict the oral absorption (C(max) and AUC(0-inf)) of the above drugs. Both BCS class I drugs satisfied bioequivalence with regard to the release rates up to 120 min. The results with BCS class III drugs demonstrated bioequivalence using the prolonged release rate, T(85%) = 45 or 60 min, indicating that the dissolution standard for bioequivalence is dependent on the intestinal membrane permeability and permeability profile throughout the gastrointestinal tract. The results of GastroPlus simulations indicate that the dissolution rate of BCS class III drugs could be prolonged to the point where dissolution, rather than permeability, would control the overall absorption. For BCS class III drugs with intestinal absorption patterns

  4. Adverse drug reactions in older patients during hospitalisation: are they predictable?

    LENUS (Irish Health Repository)

    O'Connor, Marie N

    2012-11-01

    adverse drug reactions (ADRs) are a major cause of morbidity and healthcare utilisation in older people. The GerontoNet ADR risk score aims to identify older people at risk of ADRs during hospitalisation. We aimed to assess the clinical applicability of this score and identify other variables that predict ADRs in hospitalised older people.

  5. Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors.

    Directory of Open Access Journals (Sweden)

    Anna Cichonska

    2017-08-01

    Full Text Available Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001 between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel

  6. Predicting Drug Safety and Communicating Risk: Benefits of a Bayesian Approach.

    Science.gov (United States)

    Lazic, Stanley E; Edmunds, Nicholas; Pollard, Christopher E

    2018-03-01

    Drug toxicity is a major source of attrition in drug discovery and development. Pharmaceutical companies routinely use preclinical data to predict clinical outcomes and continue to invest in new assays to improve predictions. However, there are many open questions about how to make the best use of available data, combine diverse data, quantify risk, and communicate risk and uncertainty to enable good decisions. The costs of suboptimal decisions are clear: resources are wasted and patients may be put at risk. We argue that Bayesian methods provide answers to all of these problems and use hERG-mediated QT prolongation as a case study. Benefits of Bayesian machine learning models include intuitive probabilistic statements of risk that incorporate all sources of uncertainty, the option to include diverse data and external information, and visualizations that have a clear link between the output from a statistical model and what this means for risk. Furthermore, Bayesian methods are easy to use with modern software, making their adoption for safety screening straightforward. We include R and Python code to encourage the adoption of these methods.

  7. Early gross motor skills predict the subsequent development of language in children with autism spectrum disorder.

    Science.gov (United States)

    Bedford, Rachael; Pickles, Andrew; Lord, Catherine

    2016-09-01

    Motor milestones such as the onset of walking are important developmental markers, not only for later motor skills but also for more widespread social-cognitive development. The aim of the current study was to test whether gross motor abilities, specifically the onset of walking, predicted the subsequent rate of language development in a large cohort of children with autism spectrum disorder (ASD). We ran growth curve models for expressive and receptive language measured at 2, 3, 5 and 9 years in 209 autistic children. Measures of gross motor, visual reception and autism symptoms were collected at the 2 year visit. In Model 1, walking onset was included as a predictor of the slope of language development. Model 2 included a measure of non-verbal IQ and autism symptom severity as covariates. The final model, Model 3, additionally covaried for gross motor ability. In the first model, parent-reported age of walking onset significantly predicted the subsequent rate of language development although the relationship became non-significant when gross motor skill, non-verbal ability and autism severity scores were included (Models 2 & 3). Gross motor score, however, did remain a significant predictor of both expressive and receptive language development. Taken together, the model results provide some evidence that early motor abilities in young children with ASD can have longitudinal cross-domain influences, potentially contributing, in part, to the linguistic difficulties that characterise ASD. Autism Res 2016, 9: 993-1001. © 2015 The Authors Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research. © 2015 The Authors Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research.

  8. Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

    International Nuclear Information System (INIS)

    Morton, M.J.; Armstrong, D.; Abi Gerges, N.; Bridgland-Taylor, M.; Pollard, C.E.; Bowes, J.; Valentin, J.-P.

    2014-01-01

    Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility

  9. Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

    Energy Technology Data Exchange (ETDEWEB)

    Morton, M.J., E-mail: michael.morton@astrazeneca.com [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Armstrong, D.; Abi Gerges, N. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Bridgland-Taylor, M. [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Pollard, C.E.; Bowes, J.; Valentin, J.-P. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom)

    2014-09-01

    Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility.

  10. Analysis of the value of post-radiation prostate biopsy in predicting subsequent disease progression

    International Nuclear Information System (INIS)

    Benda, R.; Shamsa, F.; Meetze, K.; Bolton, S.; Littrup, P.; Grignon, D.; Washington, T.; Forman, J.D.

    1997-01-01

    Purpose: To analyze the value of Transrectal ultrasound(TRUS), Color flow doppler(CFD) and Prostate specific antigen(PSA) in identifying residual disease in the prostate status post external beam radiation therapy and to determine the value of this pathologic information in predicting subsequent disease progression. Materials and Methods: As part of four prospective protocols, 146 patients had scheduled TRUS guided prostate biopsies 6-25 months status post radiation therapy. The stage distribution was: 13% T1, 51% T2, and 36% T3/T4. Fifty six percent had neo-adjuvant hormones. Conformal photon or mixed neutron/photon irradiation was given to a median 2 Gy/fraction equivalent dose of 77 Gy(range 74 to 84 Gy). Following treatment, patients were assessed by digital rectal exam (DRE), PSA and TRUS guided biopsies at 6, 12 and/or 18 months. The ultrasound and CFD results were scored as normal, suspicious or abnormal. Sextant biopsies were obtained as well as ultrasound guided biopsies from any abnormal ultrasound or doppler area. The biopsies, all read by one pathologist (DG), were graded as negative, marked, moderate, minimal therapeutic effect or positive. The median followup post radiation therapy was 33.6 months and post biopsy was 25.3 months. Comparisons were done by Kappa index with corresponding 95% CI, chi square and Fisher's exact tests. Results: Twenty-eight patients had biopsies at both six and 12-18 months. Overall 35% of patients had all negative cores, 30% had at least one core showing a marked therapeutic effect, and 35% had at least one core showing moderate or minimal therapeutic effect or were positive. Although CFD correlated with a positive biopsy in 9% and a suspicious doppler identified cancer in 15% of cases, an abnormal TRUS identified cancer in 29.5% biopsies ((49(166))). However, a serum PSA >1.5ng/ml at the time of biopsy predicted 61% of positive biopsies ((23(38))). A negative biopsy was associated with low stage (≤T2c, p=0.001), low pre

  11. PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins.

    Science.gov (United States)

    Hussein, Hiba Abi; Borrel, Alexandre; Geneix, Colette; Petitjean, Michel; Regad, Leslie; Camproux, Anne-Claude

    2015-07-01

    Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target identification phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose 'PockDrug-Server' to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  12. Development of a Unified Dissolution and Precipitation Model and Its Use for the Prediction of Oral Drug Absorption.

    Science.gov (United States)

    Jakubiak, Paulina; Wagner, Björn; Grimm, Hans Peter; Petrig-Schaffland, Jeannine; Schuler, Franz; Alvarez-Sánchez, Rubén

    2016-02-01

    Drug absorption is a complex process involving dissolution and precipitation, along with other kinetic processes. The purpose of this work was to (1) establish an in vitro methodology to study dissolution and precipitation in early stages of drug development where low compound consumption and high throughput are necessary, (2) develop a mathematical model for a mechanistic explanation of generated in vitro dissolution and precipitation data, and (3) extrapolate in vitro data to in vivo situations using physiologically based models to predict oral drug absorption. Small-scale pH-shift studies were performed in biorelevant media to monitor the precipitation of a set of poorly soluble weak bases. After developing a dissolution-precipitation model from this data, it was integrated into a simplified, physiologically based absorption model to predict clinical pharmacokinetic profiles. The model helped explain the consequences of supersaturation behavior of compounds. The predicted human pharmacokinetic profiles closely aligned with the observed clinical data. In summary, we describe a novel approach combining experimental dissolution/precipitation methodology with a mechanistic model for the prediction of human drug absorption kinetics. The approach unifies the dissolution and precipitation theories and enables accurate predictions of in vivo oral absorption by means of physiologically based modeling.

  13. Fasting Plasma Insulin at 5 Years of Age Predicted Subsequent Weight Increase in Early Childhood over a 5-Year Period—The Da Qing Children Cohort Study

    Science.gov (United States)

    Chen, Yan Yan; Wang, Jin Ping; Jiang, Ya Yun; Li, Hui; Hu, Ying Hua; Lee, Kok Onn; Li, Guang Wei

    2015-01-01

    Background The association between hyperinsulinemia and obesity is well known. However, it is uncertain especially in childhood obesity, if initial fasting hyperinsulinemia predicts obesity, or obesity leads to hyperinsulinemia through insulin resistance. Objective To investigate the predictive effect of fasting plasma insulin on subsequent weight change after a 5-year interval in childhood. Methods 424 Children from Da Qing city, China, were recruited at 5 years of age and followed up for 5 years. Blood pressure, anthropometric measurements, fasting plasma insulin, glucose and triglycerides were measured at baseline and 5 years later. Results Fasting plasma insulin at 5 years of age was significantly correlated with change of weight from 5 to 10 years (ΔWeight). Children in the lowest insulin quartile had ΔWeight of 13.08±0.73 kg compare to 18.39±0.86 in the highest insulin quartile (P<0.0001) in boys, and similarly 12.03±0.71 vs 15.80±0.60 kg (P<0.0001) in girls. Multivariate analysis showed that the predictive effect of insulin at 5 years of age on subsequent weight gain over 5 years remained statistically significant even after the adjustment for age, sex, birth weight, TV-viewing time and weight (or body mass index) at baseline. By contrast, the initial weight at 5 years of age did not predict subsequent changes in insulin level 5 years later. Children who had both higher fasting insulin and weight at 5 years of age showed much higher levels of systolic blood pressures, fasting plasma glucose, the homeostasis model assessment for insulin resistance (HOMA-IR) and triglycerides at 10 years of age. Conclusions Fasting plasma insulin at 5 years of age predicts weight gain and cardiovascular risk factors 5 year later in Chinese children of early childhood, but the absolute weight at 5 years of age did not predict subsequent change in fasting insulin. PMID:26047327

  14. Fasting Plasma Insulin at 5 Years of Age Predicted Subsequent Weight Increase in Early Childhood over a 5-Year Period-The Da Qing Children Cohort Study.

    Directory of Open Access Journals (Sweden)

    Yan Yan Chen

    Full Text Available The association between hyperinsulinemia and obesity is well known. However, it is uncertain especially in childhood obesity, if initial fasting hyperinsulinemia predicts obesity, or obesity leads to hyperinsulinemia through insulin resistance.To investigate the predictive effect of fasting plasma insulin on subsequent weight change after a 5-year interval in childhood.424 Children from Da Qing city, China, were recruited at 5 years of age and followed up for 5 years. Blood pressure, anthropometric measurements, fasting plasma insulin, glucose and triglycerides were measured at baseline and 5 years later.Fasting plasma insulin at 5 years of age was significantly correlated with change of weight from 5 to 10 years (ΔWeight. Children in the lowest insulin quartile had ΔWeight of 13.08±0.73 kg compare to 18.39±0.86 in the highest insulin quartile (P<0.0001 in boys, and similarly 12.03±0.71 vs 15.80±0.60 kg (P<0.0001 in girls. Multivariate analysis showed that the predictive effect of insulin at 5 years of age on subsequent weight gain over 5 years remained statistically significant even after the adjustment for age, sex, birth weight, TV-viewing time and weight (or body mass index at baseline. By contrast, the initial weight at 5 years of age did not predict subsequent changes in insulin level 5 years later. Children who had both higher fasting insulin and weight at 5 years of age showed much higher levels of systolic blood pressures, fasting plasma glucose, the homeostasis model assessment for insulin resistance (HOMA-IR and triglycerides at 10 years of age.Fasting plasma insulin at 5 years of age predicts weight gain and cardiovascular risk factors 5 year later in Chinese children of early childhood, but the absolute weight at 5 years of age did not predict subsequent change in fasting insulin.

  15. Predictive toxicology in drug safety

    National Research Council Canada - National Science Library

    Xu, Jinghai J; Urban, Laszlo

    2011-01-01

    .... It provides information on the present knowledge of drug side effects and their mitigation strategy during drug discovery, gives guidance for risk assessment, and promotes evidence-based toxicology...

  16. Feedback on Facebook Fails to Predict the User’s Subsequent Posting

    Directory of Open Access Journals (Sweden)

    Sherwin E. Balbuena

    2017-11-01

    Full Text Available Facebook use is a new and complex social behavior that has stimulated research interests in psychology. Due to a distinct lack of theoretical basis for this new communication phenomenon, a number of studies established the significant association between personality traits and Facebook use. This study investigated the motivational effect of friends’ feedback on the user’s subsequent Facebook posting and examined the correspondence between the user’s perceived motivation and actual motivation-behavior outcome using a new method. Results showed no significant association between the number of feedback and the number of subsequent posts, users’ perceived motivations were consistent with their actual motivation-behavior outcomes, users’ self-reports validated the new results and confirmed the previous findings that Facebook use is aimed at satisfying the individual’s needs for belongingness, self-presentation, and social information-seeking. It is suggested that the amount of feedback on Facebook is an ineffective determinant of the users’ frequency of subsequent postings.

  17. A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

    Science.gov (United States)

    Ye, Min; Nagar, Swati; Korzekwa, Ken

    2016-04-01

    Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  18. Prior methylphenidate self-administration alters the subsequent reinforcing effects of methamphetamine in rats.

    Science.gov (United States)

    Baladi, Michelle G; Nielsen, Shannon M; Umpierre, Anthony; Hanson, Glen R; Fleckenstein, Annette E

    2014-12-01

    Methylphenidate (MPD) is clinically effective in treating the symptoms of attention-deficit hyperactivity disorder; however, its relatively widespread availability has raised public health concerns on nonmedical use of MPD among certain adult populations. Most preclinical studies investigate whether presumed therapeutically relevant doses of MPD alter sensitivity to the reinforcing effects of other drugs, but it remains unclear whether doses of MPD likely exceeding therapeutic relevance impact the subsequent reinforcing effects of drugs. To begin to address this question, the effect of prior MPD self-administration (0.56 mg/kg/infusion) on the subsequent reinforcing effects of methamphetamine (METH, 0.032 or 0.1 mg/kg/infusion) was investigated in male Sprague-Dawley rats. For comparison, it was also determined whether prior experimenter-administered MPD, injected daily at a presumed therapeutically relevant dose (2 mg/kg), altered the subsequent reinforcing effects of METH. Results indicated that, under the current conditions, only a history of MPD self-administration increased sensitivity to the subsequent reinforcing effects of METH. Furthermore, MPD did not impact food-maintained responding, suggesting that the effect of MPD might be specific to drug reinforcers. These data suggest that short-term, nonmedical use of MPD might alter the positive reinforcing effects of METH in a manner relevant to vulnerability to drug use in humans.

  19. Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

    Science.gov (United States)

    Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

    2018-04-15

    Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen

  20. Some uses of predictive probability of success in clinical drug development

    Directory of Open Access Journals (Sweden)

    Mauro Gasparini

    2013-03-01

    Full Text Available Predictive probability of success is a (subjective Bayesian evaluation of the prob- ability of a future successful event in a given state of information. In the context of pharmaceutical clinical drug development, successful events relate to the accrual of positive evidence on the therapy which is being developed, like demonstration of su- perior efficacy or ascertainment of safety. Positive evidence will usually be obtained via standard frequentist tools, according to the regulations imposed in the world of pharmaceutical development.Within a single trial, predictive probability of success can be identified with expected power, i.e. the evaluation of the success probability of the trial. Success means, for example, obtaining a significant result of a standard superiority test.Across trials, predictive probability of success can be the probability of a successful completion of an entire part of clinical development, for example a successful phase III development in the presence of phase II data.Calculations of predictive probability of success in the presence of normal data with known variance will be illustrated, both for within-trial and across-trial predictions.

  1. Predictive performance of three practical approaches for grapefruit juice-induced 2-fold or greater increases in AUC of concomitantly administered drugs.

    Science.gov (United States)

    Takahashi, M; Onozawa, S; Ogawa, R; Uesawa, Y; Echizen, H

    2015-02-01

    Clinical pharmacists have a challenging task when answering patients' question about whether they can take specific drugs with grapefruit juice (GFJ) without risk of drug interaction. To identify the most practicable method for predicting clinically relevant changes in plasma concentrations of orally administered drugs caused by the ingestion of GFJ, we compared the predictive performance of three methods using data obtained from the literature. We undertook a systematic search of drug interactions associated with GFJ using MEDLINE and the Metabolism & Transport Drug Interaction Database (DIDB version 4.0). We considered an elevation of the area under the plasma concentration-time curve (AUC) of 2 or greater relative to the control value [AUC ratio (AUCR) ≥ 2.0] as a clinically significant interaction. The data from 74 drugs (194 data sets) were analysed. When the reported information of CYP3A involvement in the metabolism of a drug of interest was adopted as a predictive criterion for GFJ-drug interaction, the performance assessed by positive predictive value (PPV) was low (0.26), but that assessed by negative predictive value (NPV) and sensitivity was high (1.00 for both). When the reported oral bioavailability of ≤ 0.1 was used as a criterion, the PPV improved to 0.50 with an acceptable NPV of 0.81, but sensitivity was reduced to 0.21. When the reported AUCR was ≥ 10 after co-administration of a typical CYP3A inhibitor, the corresponding values were 0.64, 0.79 and 0.19, respectively. We consider that an oral bioavailability of ≤ 0.1 or an AUCR of ≥ 10 caused by a CYP3A inhibitor of a drug of interest may be a practical prediction criterion for avoiding significant interactions with GFJ. Information about the involvement of CYP3A in their metabolism should also be taken into account for drugs with narrow therapeutic ranges. © 2014 John Wiley & Sons Ltd.

  2. Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

    Science.gov (United States)

    Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

    2018-05-12

    Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

  3. Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome.

    Science.gov (United States)

    Mallory, Emily K; Acharya, Ambika; Rensi, Stefano E; Turnbaugh, Peter J; Bright, Roselie A; Altman, Russ B

    2018-01-01

    Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response. Discovery and validation of drug metabolism via bacterial enzymes has yielded >50 drugs after nearly a century of experimental research. However, there are limited computational tools for screening drugs for potential metabolism by the gut microbiome. We developed a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning. We applied this pipeline to chemical reaction data from MetaCyc to characterize the utility of vector representations for chemical reaction transformations. After clustering molecular and reaction vectors, we performed enrichment analyses and queries to characterize the space. We detected enriched enzyme names, Gene Ontology terms, and Enzyme Consortium (EC) classes within reaction clusters. In addition, we queried reactions against drug-metabolite transformations known to be metabolized by the human gut microbiome. The top results for these known drug transformations contained similar substructure modifications to the original drug pair. This work enables high throughput screening of drugs and their resulting metabolites against chemical reactions common to gut bacteria.

  4. Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design.

    Science.gov (United States)

    Grebner, Christoph; Iegre, Jessica; Ulander, Johan; Edman, Karl; Hogner, Anders; Tyrchan, Christian

    2016-04-25

    Computer-aided drug design plays an important role in medicinal chemistry to obtain insights into molecular mechanisms and to prioritize design strategies. Although significant improvement has been made in structure based design, it still remains a key challenge to accurately model and predict induced fit mechanisms. Most of the current available techniques either do not provide sufficient protein conformational sampling or are too computationally demanding to fit an industrial setting. The current study presents a systematic and exhaustive investigation of predicting binding modes for a range of systems using PELE (Protein Energy Landscape Exploration), an efficient and fast protein-ligand sampling algorithm. The systems analyzed (cytochrome P, kinase, protease, and nuclear hormone receptor) exhibit different complexities of ligand induced fit mechanisms and protein dynamics. The results are compared with results from classical molecular dynamics simulations and (induced fit) docking. This study shows that ligand induced side chain rearrangements and smaller to medium backbone movements are captured well in PELE. Large secondary structure rearrangements, however, remain challenging for all employed techniques. Relevant binding modes (ligand heavy atom RMSD PELE method within a few hours of simulation, positioning PELE as a tool applicable for rapid drug design cycles.

  5. Bigger Data, Collaborative Tools and the Future of Predictive Drug Discovery

    Science.gov (United States)

    Clark, Alex M.; Swamidass, S. Joshua; Litterman, Nadia; Williams, Antony J.

    2014-01-01

    Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas. PMID:24943138

  6. Flexing the PECs: Predicting environmental concentrations of veterinary drugs in Canadian agricultural soils.

    Science.gov (United States)

    Kullik, Sigrun A; Belknap, Andrew M

    2017-03-01

    Veterinary drugs administered to food animals primarily enter ecosystems through the application of livestock waste to agricultural land. Although veterinary drugs are essential for protecting animal health, their entry into the environment may pose a risk for nontarget organisms. A means to predict environmental concentrations of new veterinary drug ingredients in soil is required to assess their environmental fate, distribution, and potential effects. The Canadian predicted environmental concentrations in soil (PECsoil) for new veterinary drug ingredients for use in intensively reared animals is based on the approach currently used by the European Medicines Agency for VICH Phase I environmental assessments. The calculation for the European Medicines Agency PECsoil can be adapted to account for regional animal husbandry and land use practices. Canadian agricultural practices for intensively reared cattle, pigs, and poultry differ substantially from those in the European Union. The development of PECsoil default values and livestock categories representative of typical Canadian animal production methods and nutrient management practices culminates several years of research and an extensive survey and analysis of the scientific literature, Canadian agricultural statistics, national and provincial management recommendations, veterinary product databases, and producers. A PECsoil can be used to rapidly identify new veterinary drugs intended for intensive livestock production that should undergo targeted ecotoxicity and fate testing. The Canadian PECsoil model is readily available, transparent, and requires minimal inputs to generate a screening level environmental assessment for veterinary drugs that can be refined if additional data are available. PECsoil values for a hypothetical veterinary drug dosage regimen are presented and discussed in an international context. Integr Environ Assess Manag 2017;13:331-341. © 2016 Her Majesty the Queen in Right of Canada

  7. Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test).

    Science.gov (United States)

    Kischkel, Frank Christian; Meyer, Carina; Eich, Julia; Nassir, Mani; Mentze, Monika; Braicu, Ioana; Kopp-Schneider, Annette; Sehouli, Jalid

    2017-10-27

    In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients.

  8. Validation of the cephalosporin intradermal skin test for predicting immediate hypersensitivity: a prospective study with drug challenge.

    Science.gov (United States)

    Yoon, S-Y; Park, S Y; Kim, S; Lee, T; Lee, Y S; Kwon, H-S; Cho, Y S; Moon, H-B; Kim, T-B

    2013-07-01

    Cephalosporin is a major offending agent in terms of drug hypersensitivity along with penicillin. Cephalosporin intradermal skin tests (IDTs) have been widely used; however, their validity for predicting immediate hypersensitivity has not been studied. This study aimed to determine the predictive value of cephalosporin intradermal skin testing before administration of the drug. We prospectively conducted IDTs with four cephalosporins, one each of selected first-, second-, third-, or fourth-generation cephalosporins: ceftezol; cefotetan or cefamandole; ceftriaxone or cefotaxime; and flomoxef, respectively, as well as with penicillin G. After the skin test, whatever the result, one of the tested cephalosporins was administered intravenously and the patient was carefully observed. We recruited 1421 patients who required preoperative cephalosporins. Seventy-four patients (74/1421, 5.2%) were positive to at least one cephalosporin. However, none of responders had immediate hypersensitivity reactions after a challenge dose of the same or different cephalosporin, which were positive in the skin test. Four patients who suffered generalized urticaria and itching after challenge gave negative skin tests for the corresponding drug. The IDT for cephalosporin had a sensitivity of 0%, a specificity of 97.5%, a negative predictive value of 99.7%, and a positive predictive value (PPV) of 0%, when challenged with the same drugs that were positive in the skin test. Routine skin testing with a cephalosporin before its administration is not useful for predicting immediate hypersensitivity because of the extremely low sensitivity and PPV of the skin test (CRIS registration no. KCT0000455). © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  9. Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.

    Science.gov (United States)

    de Lange, Elizabeth C M; van den Brink, Willem; Yamamoto, Yumi; de Witte, Wilhelmus E A; Wong, Yin Cheong

    2017-12-01

    CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

  10. Mining predicted essential genes of Brugia malayi for nematode drug targets.

    Directory of Open Access Journals (Sweden)

    Sanjay Kumar

    Full Text Available We report results from the first genome-wide application of a rational drug target selection methodology to a metazoan pathogen genome, the completed draft sequence of Brugia malayi, a parasitic nematode responsible for human lymphatic filariasis. More than 1.5 billion people worldwide are at risk of contracting lymphatic filariasis and onchocerciasis, a related filarial disease. Drug treatments for filariasis have not changed significantly in over 20 years, and with the risk of resistance rising, there is an urgent need for the development of new anti-filarial drug therapies. The recent publication of the draft genomic sequence for B. malayi enables a genome-wide search for new drug targets. However, there is no functional genomics data in B. malayi to guide the selection of potential drug targets. To circumvent this problem, we have utilized the free-living model nematode Caenorhabditis elegans as a surrogate for B. malayi. Sequence comparisons between the two genomes allow us to map C. elegans orthologs to B. malayi genes. Using these orthology mappings and by incorporating the extensive genomic and functional genomic data, including genome-wide RNAi screens, that already exist for C. elegans, we identify potentially essential genes in B. malayi. Further incorporation of human host genome sequence data and a custom algorithm for prioritization enables us to collect and rank nearly 600 drug target candidates. Previously identified potential drug targets cluster near the top of our prioritized list, lending credibility to our methodology. Over-represented Gene Ontology terms, predicted InterPro domains, and RNAi phenotypes of C. elegans orthologs associated with the potential target pool are identified. By virtue of the selection procedure, the potential B. malayi drug targets highlight components of key processes in nematode biology such as central metabolism, molting and regulation of gene expression.

  11. Comparative analysis of three drug-drug interaction screening systems against probable clinically relevant drug-drug interactions: a prospective cohort study.

    Science.gov (United States)

    Muhič, Neža; Mrhar, Ales; Brvar, Miran

    2017-07-01

    Drug-drug interaction (DDI) screening systems report potential DDIs. This study aimed to find the prevalence of probable DDI-related adverse drug reactions (ADRs) and compare the clinical usefulness of different DDI screening systems to prevent or warn against these ADRs. A prospective cohort study was conducted in patients urgently admitted to medical departments. Potential DDIs were checked using Complete Drug Interaction®, Lexicomp® Online™, and Drug Interaction Checker®. The study team identified the patients with probable clinically relevant DDI-related ADRs on admission, the causality of which was assessed using the Drug Interaction Probability Scale (DIPS). Sensitivity, specificity, and positive and negative predictive values of screening systems to prevent or warn against probable DDI-related ADRs were evaluated. Overall, 50 probable clinically relevant DDI-related ADRs were found in 37 out of 795 included patients taking at least two drugs, most common of them were bleeding, hyperkalemia, digitalis toxicity, and hypotension. Complete Drug Interaction showed the best sensitivity (0.76) for actual DDI-related ADRs, followed by Lexicomp Online (0.50), and Drug Interaction Checker (0.40). Complete Drug Interaction and Drug Interaction Checker had positive predictive values of 0.07; Lexicomp Online had 0.04. We found no difference in specificity and negative predictive values among these systems. DDI screening systems differ significantly in their ability to detect probable clinically relevant DDI-related ADRs in terms of sensitivity and positive predictive value.

  12. Personality, Drug Preference, Drug Use, and Drug Availability

    Science.gov (United States)

    Feldman, Marc; Boyer, Bret; Kumar, V. K.; Prout, Maurice

    2011-01-01

    This study examined the relationship between drug preference, drug use, drug availability, and personality among individuals (n = 100) in treatment for substance abuse in an effort to replicate the results of an earlier study (Feldman, Kumar, Angelini, Pekala, & Porter, 2007) designed to test prediction derived from Eysenck's (1957, 1967)…

  13. Idea density measured in late life predicts subsequent cognitive trajectories: implications for the measurement of cognitive reserve.

    Science.gov (United States)

    Farias, Sarah Tomaszewski; Chand, Vineeta; Bonnici, Lisa; Baynes, Kathleen; Harvey, Danielle; Mungas, Dan; Simon, Christa; Reed, Bruce

    2012-11-01

    The Nun Study showed that lower linguistic ability in young adulthood, measured by idea density (ID), increased the risk of dementia in late life. The present study examined whether ID measured in late life continues to predict the trajectory of cognitive change. ID was measured in 81 older adults who were followed longitudinally for an average of 4.3 years. Changes in global cognition and 4 specific neuropsychological domains (episodic memory, semantic memory, spatial abilities, and executive function) were examined as outcomes. Separate random effects models tested the effect of ID on longitudinal change in outcomes, adjusted for age and education. Lower ID was associated with greater subsequent decline in global cognition, semantic memory, episodic memory, and spatial abilities. When analysis was restricted to only participants without dementia at the time ID was collected, results were similar. Linguistic ability in young adulthood, as measured by ID, has been previously proposed as an index of neurocognitive development and/or cognitive reserve. The present study provides evidence that even when ID is measured in old age, it continues to be associated with subsequent cognitive decline and as such may continue to provide a marker of cognitive reserve.

  14. Prefrontal recruitment during social rejection predicts greater subsequent self-regulatory imbalance and impairment: neural and longitudinal evidence.

    Science.gov (United States)

    Chester, David S; DeWall, C Nathan

    2014-11-01

    Social rejection impairs self-regulation, yet the neural mechanisms underlying this relationship remain unknown. The right ventrolateral prefrontal cortex (rVLPFC) facilitates self-regulation and plays a robust role in regulating the distress of social rejection. However, recruiting this region's inhibitory function during social rejection may come at a self-regulatory cost. As supported by prominent theories of self-regulation, we hypothesized that greater rVLPFC recruitment during rejection would predict a subsequent self-regulatory imbalance that favored reflexive impulses (i.e., cravings), which would then impair self-regulation. Supporting our hypotheses, rVLPFC activation during social rejection was associated with greater subsequent nucleus accumbens (NAcc) activation and lesser functional connectivity between the NAcc and rVLPFC to appetitive cues. Over seven days, the effect of daily felt rejection on daily self-regulatory impairment was exacerbated among participants who showed a stronger rVLPFC response to social rejection. This interactive effect was mirrored in the effect of daily felt rejection on heightened daily alcohol cravings. Our findings suggest that social rejection likely impairs self-regulation by recruiting the rVLPFC, which then tips the regulatory balance towards reward-based impulses. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Making connections: New Orleans Evacuees' experiences in obtaining drugs.

    Science.gov (United States)

    Dunlap, Eloise; Johnson, Bruce D; Kotarba, Joseph A; Fackler, Jennifer

    2009-09-01

    Between August 29 and September 7, 2005, almost all New Orleans residents were evacuated from the area in the aftermath of Hurricane Katrina. News reports indicate that almost 130,000 New Orleans Evacuees (NOEs) were evacuated to Houston, Texas, the largest recipient of the civilian population from New Orleans. Some of these NOEs were active participants in the illicit drug market in New Orleans prior to the hurricane. The period between the flooding and the nearly complete evacuation of New Orleans as well as their subsequent displacement to Houston and other locations provided unique opportunities to study what occurs when illicit drug markets are disrupted, since populations of illicit drug users and purchasers could no longer routinely obtain their drugs in predictable ways. Utilizing qualitative data from in-depth interviews and focus groups, this article describes the ways NOEs (1) managed their drug acquisition and use following evacuation; (2) located new sources of drugs in Houston and elsewhere by tapping into shared drug culture; and (3) gained access to and learned the argot for drugs in the local drug market in new settings. This report contributes to the nascent literature on disrupted drug markets.

  16. Early follow-up data from seizure diaries can be used to predict subsequent seizures in same cohort by borrowing strength across participants

    Science.gov (United States)

    Hall, Charles B.; Lipton, Richard B.; Tennen, Howard; Haut, Sheryl R.

    2014-01-01

    Accurate prediction of seizures in persons with epilepsy offers opportunities for both precautionary measures and preemptive treatment. Previously identified predictors of seizures include patient-reported seizure anticipation, as well as stress, anxiety, and decreased sleep. In this study, we developed three models using 30 days of nightly seizure diary data in a cohort of 71 individuals with a history of uncontrolled seizures to predict subsequent seizures in the same cohort over a 30-day follow-up period. The best model combined the individual’s seizure history with that of the remainder of the cohort, resulting in 72% sensitivity for 80% specificity, and 0.83 area under the receiver operating characteristic curve. The possibility of clinically relevant prediction should be examined through electronic data capture and more specific and more frequent sampling, and with patient training to improve prediction. PMID:19138755

  17. Blinded prospective evaluation of computer-based mechanistic schizophrenia disease model for predicting drug response.

    Directory of Open Access Journals (Sweden)

    Hugo Geerts

    Full Text Available The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published 'Quantitative Systems Pharmacology' computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D(2 antagonist and ocaperidone, a very high affinity dopamine D(2 antagonist, using only pharmacology and human positron emission tomography (PET imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS total score and the higher extra-pyramidal symptom (EPS liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development.

  18. An investigation of classification algorithms for predicting HIV drug resistance without genotype resistance testing

    CSIR Research Space (South Africa)

    Brandt, P

    2014-01-01

    Full Text Available is limited in low-resource settings. In this paper we investigate machine learning techniques for drug resistance prediction from routine treatment and laboratory data to help clinicians select patients for confirmatory genotype testing. The techniques...

  19. An analysis of abstracts presented to the College on Problems of Drug Dependence meeting and subsequent publication in peer review journals

    Science.gov (United States)

    Valderrama-Zurián, Juan Carlos; Bolaños-Pizarro, Máxima; Bueno-Cañigral, Francisco Jesús; Álvarez, F Javier; Ontalba-Ruipérez, José Antonio; Aleixandre-Benavent, Rafael

    2009-01-01

    Background Subsequent publication rate of abstracts presented at meetings is seen as an indicator of the interest and quality of the meeting. We have analyzed characteristics and rate publication in peer-reviewed journals derived from oral communications and posters presented at the 1999 College on Problems of Drug Dependence (CPDD) meeting. Methods All 689 abstracts presented at the 1999 CPDD meeting were reviewed. In order to find the existence of publications derived from abstracts presented at that meeting, a set of bibliographical searches in the database Medline was developed in July 2006. Information was gathered concerning the abstracts, articles and journals in which they were published. Results 254 out of 689 abstracts (36.9%) gave rise to at least one publication. The oral communications had a greater likelihood of being published than did the posters (OR = 2.53, 95% CI 1.80-3.55). The average time lapse to publication of an article was 672.97 days. The number of authors per work in the subsequent publications was 4.55. The articles were published in a total of 84 journals, of which eight were indexed with the subject term Substance-Related Disorders. Psychopharmacology (37 articles, 14.5%) was the journal that published the greatest number of articles subsequent to the abstracts presented at the 1999 CPDD meeting. Conclusion One out of every three abstracts presented to the 1999 CPDD meeting were later published in peer-reviewed journals indexed in Medline. The subsequent publication of the abstracts presented in the CPDD meetings should be actively encouraged, as this maximizes the dissemination of the scientific research and therefore the investment. PMID:19889211

  20. Clinical Relevance and Predictive Value of Damage Biomarkers of Drug-Induced Kidney Injury.

    Science.gov (United States)

    Kane-Gill, Sandra L; Smithburger, Pamela L; Kashani, Kianoush; Kellum, John A; Frazee, Erin

    2017-11-01

    Nephrotoxin exposure accounts for up to one-fourth of acute kidney injury episodes in hospitalized patients, and the associated consequences are as severe as acute kidney injury due to other etiologies. As the use of nephrotoxic agents represents one of the few modifiable risk factors for acute kidney injury, clinicians must be able to identify patients at high risk for drug-induced kidney injury rapidly. Recently, significant advancements have been made in the field of biomarker utilization for the prediction and detection of acute kidney injury. Such biomarkers may have a role both for detection of drug-induced kidney disease and implementation of preventative and therapeutic strategies designed to mitigate injury. In this article, basic principles of renal biomarker use in practice are summarized, and the existing evidence for six markers specifically used to detect drug-induced kidney injury are outlined, including liver-type fatty acid binding protein, neutrophil gelatinase-associated lipocalin, tissue inhibitor of metalloproteinase-2 times insulin-like growth factor-binding protein 7 ([TIMP-2]·[IGFBP7]), kidney injury molecule-1 and N-acetyl-β-D-glucosaminidase. The results of the literature search for these six kidney damage biomarkers identified 29 unique articles with none detected for liver-type fatty acid binding protein and [TIMP-2]·[IGFBP7]. For three biomarkers, kidney injury molecule-1, neutrophil gelatinase-associated lipocalin and N-acetyl-β-D-glucosaminidase, the majority of the studies suggest utility in clinical practice. While many questions need to be answered to clearly articulate the use of biomarkers to predict drug-induced kidney disease, current data are promising.

  1. Individualized prediction of seizure relapse and outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery.

    Science.gov (United States)

    Lamberink, Herm J; Boshuisen, Kim; Otte, Willem M; Geleijns, Karin; Braun, Kees P J

    2018-03-01

    The objective of this study was to create a clinically useful tool for individualized prediction of seizure outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery. We used data from the European retrospective TimeToStop study, which included 766 children from 15 centers, to perform a proportional hazard regression analysis. The 2 outcome measures were seizure recurrence and seizure freedom in the last year of follow-up. Prognostic factors were identified through systematic review of the literature. The strongest predictors for each outcome were selected through backward selection, after which nomograms were created. The final models included 3 to 5 factors per model. Discrimination in terms of adjusted concordance statistic was 0.68 (95% confidence interval [CI] 0.67-0.69) for predicting seizure recurrence and 0.73 (95% CI 0.72-0.75) for predicting eventual seizure freedom. An online prediction tool is provided on www.epilepsypredictiontools.info/ttswithdrawal. The presented models can improve counseling of patients and parents regarding postoperative antiepileptic drug policies, by estimating individualized risks of seizure recurrence and eventual outcome. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

  2. Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance

    Science.gov (United States)

    Gonzalez de Castro, D; Clarke, P A; Al-Lazikani, B; Workman, P

    2013-01-01

    The progressive elucidation of the molecular pathogenesis of cancer has fueled the rational development of targeted drugs for patient populations stratified by genetic characteristics. Here we discuss general challenges relating to molecular diagnostics and describe predictive biomarkers for personalized cancer medicine. We also highlight resistance mechanisms for epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer. We envisage a future requiring the use of longitudinal genome sequencing and other omics technologies alongside combinatorial treatment to overcome cellular and molecular heterogeneity and prevent resistance caused by clonal evolution. PMID:23361103

  3. Microdose-Induced Drug-DNA Adducts as Biomarkers of Chemotherapy Resistance in Humans and Mice.

    Science.gov (United States)

    Zimmermann, Maike; Wang, Si-Si; Zhang, Hongyong; Lin, Tzu-Yin; Malfatti, Michael; Haack, Kurt; Ognibene, Ted; Yang, Hongyuan; Airhart, Susan; Turteltaub, Kenneth W; Cimino, George D; Tepper, Clifford G; Drakaki, Alexandra; Chamie, Karim; de Vere White, Ralph; Pan, Chong-Xian; Henderson, Paul T

    2017-02-01

    We report progress on predicting tumor response to platinum-based chemotherapy with a novel mass spectrometry approach. Fourteen bladder cancer patients were administered one diagnostic microdose each of [ 14 C]carboplatin (1% of the therapeutic dose). Carboplatin-DNA adducts were quantified by accelerator mass spectrometry in blood and tumor samples collected within 24 hours, and compared with subsequent chemotherapy response. Patients with the highest adduct levels were responders, but not all responders had high adduct levels. Four patient-derived bladder cancer xenograft mouse models were used to test the possibility that another drug in the regimen could cause a response. The mice were dosed with [ 14 C]carboplatin or [ 14 C]gemcitabine and the resulting drug-DNA adduct levels were compared with tumor response to chemotherapy. At least one of the drugs had to induce high drug-DNA adduct levels or create a synergistic increase in overall adducts to prompt a corresponding therapeutic response, demonstrating proof-of-principle for drug-DNA adducts as predictive biomarkers. Mol Cancer Ther; 16(2); 376-87. ©2016 AACR. ©2016 American Association for Cancer Research.

  4. Hypertensive Disorders in Pregnancy and the Risk of Subsequent Cardiovascular Disease.

    Science.gov (United States)

    Grandi, Sonia M; Vallée-Pouliot, Karine; Reynier, Pauline; Eberg, Maria; Platt, Robert W; Arel, Roxane; Basso, Olga; Filion, Kristian B

    2017-09-01

    Hypertensive disorders in pregnancy (HDP) have been shown to predict later risk of cardiovascular disease (CVD). However, previous studies have not accounted for subsequent pregnancies and their complications, which are potential confounders and intermediates of this association. A cohort of 146 748 women with a first pregnancy was constructed using the Clinical Practice Research Datalink. HDP was defined using diagnostic codes, elevated blood pressure readings, or new use of an anti-hypertensive drug between 18 weeks' gestation and 6 weeks post-partum. The study outcomes were incident CVD and hypertension. Marginal structural Cox models (MSM) were used to account for time-varying confounders and intermediates. Time-fixed exposure defined at the first pregnancy was used in secondary analyses. A total of 997 women were diagnosed with incident CVD, and 6812 women were diagnosed with hypertension or received a new anti-hypertensive medication during the follow-up period. Compared with women without HDP, those with HDP had a substantially higher rate of CVD (hazard ratio (HR) 2.2, 95% confidence interval (CI) 1.7, 2.7). In women with HDP, the rate of hypertension was five times that of women without a HDP (HR 5.6, 95% CI 5.1, 6.3). With overlapping 95% CIs, the time-fixed analysis and the MSM produced consistent results for both outcomes. Women with HDP are at increased risk of developing subsequent CVD and hypertension. Similar estimates obtained with the MSM and the time-fixed analysis suggests that subsequent pregnancies do not confound a first episode of HDP and later CVD. © 2017 John Wiley & Sons Ltd.

  5. Improvement of the Prediction of Drugs Demand Using Spatial Data Mining Tools.

    Science.gov (United States)

    Ramos, M Isabel; Cubillas, Juan José; Feito, Francisco R

    2016-01-01

    The continued availability of products at any store is the major issue in order to provide good customer service. If the store is a drugstore this matter reaches a greater importance, as out of stock of a drug when there is high demand causes problems and tensions in the healthcare system. There are numerous studies of the impact this issue has on patients. The lack of any drug in a pharmacy in certain seasons is very common, especially when some external factors proliferate favoring the occurrence of certain diseases. This study focuses on a particular drug consumed in the city of Jaen, southern Andalucia, Spain. Our goal is to determine in advance the Salbutamol demand. Advanced data mining techniques have been used with spatial variables. These last have a key role to generate an effective model. In this research we have used the attributes that are associated with Salbutamol demand and it has been generated a very accurate prediction model of 5.78% of mean absolute error. This is a very encouraging data considering that the consumption of this drug in Jaen varies 500% from one period to another.

  6. Mathematical modeling of antibody drug conjugates with the target and tubulin dynamics to predict AUC.

    Science.gov (United States)

    Byun, Jong Hyuk; Jung, Il Hyo

    2018-04-14

    Antibody drug conjugates (ADCs)are one of the most recently developed chemotherapeutics to treat some types of tumor cells. They consist of monoclonal antibodies (mAbs), linkers, and potent cytotoxic drugs. Unlike common chemotherapies, ADCs combine selectively with a target at the surface of the tumor cell, and a potent cytotoxic drug (payload) effectively prevents microtubule polymerization. In this work, we construct an ADC model that considers both the target of antibodies and the receptor (tubulin) of the cytotoxic payloads. The model is simulated with brentuximab vedotin, one of ADCs, and used to investigate the pharmacokinetic (PK) characteristics of ADCs in vivo. It also predicts area under the curve (AUC) of ADCs and the payloads by identifying the half-life. The results show that dynamical behaviors fairly coincide with the observed data and half-life and capture AUC. Thus, the model can be used for estimating some parameters, fitting experimental observations, predicting AUC, and exploring various dynamical behaviors of the target and the receptor. Copyright © 2018 Elsevier Ltd. All rights reserved.

  7. Identification of drug metabolites in human plasma or serum integrating metabolite prediction, LC-HRMS and untargeted data processing

    NARCIS (Netherlands)

    Jacobs, P.L.; Ridder, L.; Ruijken, M.; Rosing, H.; Jager, N.G.L.; Beijnen, J.H.; Bas, R.R.; Dongen, W.D. van

    2013-01-01

    Background: Comprehensive identification of human drug metabolites in first-in-man studies is crucial to avoid delays in later stages of drug development. We developed an efficient workflow for systematic identification of human metabolites in plasma or serum that combines metabolite prediction,

  8. Risk Factors at Birth Predictive of Subsequent Injury Among Japanese Preschool Children: A Nationwide 5-Year Cohort Study.

    Science.gov (United States)

    Morioka, Hisayoshi; Itani, Osamu; Jike, Maki; Nakagome, Sachi; Otsuka, Yuichiro; Ohida, Takashi

    2018-03-19

    To identify risk factors at birth that are predictive of subsequent injury among preschool children. Retrospective analysis of population-based birth cohort data from the "Longitudinal Survey of Babies Born in the 21st Century" was performed from 2001 through 2007 in Japan (n = 47,015). The cumulative incidence and the total number of hospitalizations or examinations conducted at medical facilities for injury among children from birth up to the age of 5 years were calculated. To identify risk factors at birth that are predictive of injury, multivariate analysis of data for hospitalization or admission because of injury during a 5-year period (age, 0-5 years) was performed using the total number of hospital examinations as the dependent variable. The cumulative incidence (95% confidence interval) of hospital examinations for injury over the 5-year period was 34.8% (34.2%-35.4%) for boys and 27.6% (27.0%-28.2%) for girls. The predictive risk factors at birth we identified for injury among preschool children were sex (boys), heavy birth weight, late birth order, no cohabitation with the grandfather or grandmother, father's long working hours, mother's high education level, and strong intensity of parenting anxiety. Based on the results of this study, we identified a number of predictive factors for injury in children. To reduce the risk of injury in the juvenile population as a whole, it is important to pursue a high-risk or population approach by focusing on the predictive factors we have identified.

  9. Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction.

    Science.gov (United States)

    Rahman, Raziur; Haider, Saad; Ghosh, Souparno; Pal, Ranadip

    2015-01-01

    Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows for analytical computation of confidence intervals (CIs), and the tree weight optimization is expected to provide stricter CIs with comparable performance in mean error. We approached the ensemble of probabilistic trees' prediction from the perspectives of a mixture distribution and as a weighted sum of correlated random variables. We applied our methodology to the drug sensitivity prediction problem on synthetic and cancer cell line encyclopedia dataset and illustrated that tree weights can be selected to reduce the average length of the CI without increase in mean error.

  10. The Role of Early Maladaptive Schemas in Prediction of Dysfunctional Attitudes toward Drug Abuse among Students of university

    Directory of Open Access Journals (Sweden)

    NedaNaeemi

    2016-07-01

    Full Text Available Drug addiction as the most serious social issue of the world has different sociological, psychological, legal, and political aspects. In this regard, the purpose of this study is to determine the role of early maladaptive schemas in prediction of dysfunctional attitudes toward drug abuse among students of Islamic Azad Universities in Tehran Province, Iran. Statistical population of this study includes all students of Islamic Azad Universities in Tehran Province during 2013 and sample size is equal to 300 members that are randomly chosen. First, the name of university branches in Tehran Province were determined then three branches were randomly chosen out of them and then 300 members were chosen from those branches using random sampling method. All sample members filled out Young Schema Questionnaire Short Form and Dysfunctional Attitude Scale (DAS toward drug. Data were analyzed through regression correlation method and SPSS22 software. The obtained findings indicated a significant relation (P<0/05 between early maladaptive schemas and dysfunctional attitude toward drug abuse among students. Early maladaptive schemas can predict dysfunctional attitudes toward drug among students.

  11. Designing Predictive Models for Beta-Lactam Allergy Using the Drug Allergy and Hypersensitivity Database.

    Science.gov (United States)

    Chiriac, Anca Mirela; Wang, Youna; Schrijvers, Rik; Bousquet, Philippe Jean; Mura, Thibault; Molinari, Nicolas; Demoly, Pascal

    Beta-lactam antibiotics represent the main cause of allergic reactions to drugs, inducing both immediate and nonimmediate allergies. The diagnosis is well established, usually based on skin tests and drug provocation tests, but cumbersome. To design predictive models for the diagnosis of beta-lactam allergy, based on the clinical history of patients with suspicions of allergic reactions to beta-lactams. The study included a retrospective phase, in which records of patients explored for a suspicion of beta-lactam allergy (in the Allergy Unit of the University Hospital of Montpellier between September 1996 and September 2012) were used to construct predictive models based on a logistic regression and decision tree method; a prospective phase, in which we performed an external validation of the chosen models in patients with suspicion of beta-lactam allergy recruited from 3 allergy centers (Montpellier, Nîmes, Narbonne) between March and November 2013. Data related to clinical history and allergy evaluation results were retrieved and analyzed. The retrospective and prospective phases included 1991 and 200 patients, respectively, with a different prevalence of confirmed beta-lactam allergy (23.6% vs 31%, P = .02). For the logistic regression method, performances of the models were similar in both samples: sensitivity was 51% (vs 60%), specificity 75% (vs 80%), positive predictive value 40% (vs 57%), and negative predictive value 83% (vs 82%). The decision tree method reached a sensitivity of 29.5% (vs 43.5%), specificity of 96.4% (vs 94.9%), positive predictive value of 71.6% (vs 79.4%), and negative predictive value of 81.6% (vs 81.3%). Two different independent methods using clinical history predictors were unable to accurately predict beta-lactam allergy and replace a conventional allergy evaluation for suspected beta-lactam allergy. Copyright © 2017 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

  12. Adolescent inhalant abuse leads to other drug use and impaired growth; implications for diagnosis.

    Science.gov (United States)

    Crossin, Rose; Cairney, Sheree; Lawrence, Andrew J; Duncan, Jhodie R

    2017-02-01

    Abuse of inhalants containing the volatile solvent toluene is a significant public health issue, especially for adolescent and Indigenous communities. Adolescent inhalant abuse can lead to chronic health issues and may initiate a trajectory towards further drug use. Identification of at-risk individuals is difficult and diagnostic tools are limited primarily to measurement of serum toluene. Our objective was to identify the effects of adolescent inhalant abuse on subsequent drug use and growth parameters, and to test the predictive power of growth parameters as a diagnostic measure for inhalant abuse. We retrospectively analysed drug use and growth data from 118 Indigenous males; 86 chronically sniffed petrol as adolescents. Petrol sniffing was the earliest drug used (mean 13 years) and increased the likelihood and earlier use of other drugs. Petrol sniffing significantly impaired height and weight and was associated with meeting 'failure to thrive' criteria; growth diagnostically out-performed serum toluene. Adolescent inhalant abuse increases the risk for subsequent and earlier drug use. It also impairs growth such that individuals meet 'failure to thrive' criteria, representing an improved diagnostic model for inhalant abuse. Implications for Public Health: Improved diagnosis of adolescent inhalant abuse may lead to earlier detection and enhanced health outcomes. © 2016 The Authors.

  13. Prediction of Relative In Vivo Metabolite Exposure from In Vitro Data Using Two Model Drugs: Dextromethorphan and Omeprazole

    Science.gov (United States)

    Lutz, Justin D.

    2012-01-01

    Metabolites can have pharmacological or toxicological effects, inhibit metabolic enzymes, and be used as probes of drug-drug interactions or specific cytochrome P450 (P450) phenotypes. Thus, better understanding and prediction methods are needed to characterize metabolite exposures in vivo. This study aimed to test whether in vitro data could be used to predict and rationalize in vivo metabolite exposures using two model drugs and P450 probes: dextromethorphan and omeprazole with their primary metabolites dextrorphan, 5-hydroxyomeprazole (5OH-omeprazole), and omeprazole sulfone. Relative metabolite exposures were predicted using metabolite formation and elimination clearances. For dextrorphan, the formation clearances of dextrorphan glucuronide and 3-hydroxymorphinan from dextrorphan in human liver microsomes were used to predict metabolite (dextrorphan) clearance. For 5OH-omeprazole and omeprazole sulfone, the depletion rates of the metabolites in human hepatocytes were used to predict metabolite clearance. Dextrorphan/dextromethorphan in vivo metabolite/parent area under the plasma concentration versus time curve ratio (AUCm/AUCp) was overpredicted by 2.1-fold, whereas 5OH-omeprazole/omeprazole and omeprazole sulfone/omeprazole were predicted within 0.75- and 1.1-fold, respectively. The effect of inhibition or induction of the metabolite's formation and elimination on the AUCm/AUCp ratio was simulated. The simulations showed that unless metabolite clearance pathways are characterized, interpretation of the metabolic ratios is exceedingly difficult. This study shows that relative in vivo metabolite exposure can be predicted from in vitro data and characterization of secondary metabolism of probe metabolites is critical for interpretation of phenotypic data. PMID:22010218

  14. Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds

    Directory of Open Access Journals (Sweden)

    Mengxuan Gao

    2017-02-01

    Full Text Available Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug development, it is difficult to predict seizure-inducing side effects. Here, we introduced a machine learning-based in vitro system designed to detect seizure-inducing side effects. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices, while 14 drugs were bath-perfused at 5 different concentrations each. For each experimental condition, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs that induced seizure-like events and identified diphenhydramine, enoxacin, strychnine and theophylline as “seizure-inducing” drugs, which indeed were reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to detect the seizure-inducing side effects of preclinical drugs.

  15. Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

    Science.gov (United States)

    Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

    2016-02-19

    Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

  16. Craving and subsequent opioid use among opioid dependent patients who initiate treatment with buprenorphine

    Science.gov (United States)

    Tsui, Judith I.; Anderson, Bradley J.; Strong, David R.; Stein, Michael D.

    2016-01-01

    Background Few studies have directly assessed associations between craving and subsequent opioid use among treated patients. Our objective was to prospectively evaluate the relative utility of two craving questionnaires to predict opioid use among opioid dependent patients in treatment. Method Opioid dependent patients (n=147) initiating buprenorphine treatment were assessed for three months. Craving was measured using: 1) the Desires for Drug Questionnaire (DDQ) and 2) the Penn Alcohol-Craving Scale adapted for opioid craving (PCS) for this study. Multi-level logistic regression models estimated the effects of craving on the likelihood of opioid use after adjusting for gender, age, ethnicity, education, opioid of choice, frequency of use, pain and depression. In these analyses craving assessed at time t was entered as a time-varying predictor of opioid use at time t+1. Results In adjusted regression models, a 1-point increase in PCS scores (on a 7-point scale) was associated with a significant increase in the odds of opioid use at the subsequent assessment (OR = 1.27, 95% CI 1.08; 1.49, p .05) or DDQ control (OR = 0.97, 95%CI 0.85; 1.11, p > .05) scores. Conclusion Self-reported craving for opioids was associated with subsequent lapse to opioid use among a cohort of patients treated with buprenorphine. PMID:24521036

  17. Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics

    International Nuclear Information System (INIS)

    West, Paul R.; Weir, April M.; Smith, Alan M.; Donley, Elizabeth L.R.; Cezar, Gabriela G.

    2010-01-01

    Teratogens, substances that may cause fetal abnormalities during development, are responsible for a significant number of birth defects. Animal models used to predict teratogenicity often do not faithfully correlate to human response. Here, we seek to develop a more predictive developmental toxicity model based on an in vitro method that utilizes both human embryonic stem (hES) cells and metabolomics to discover biomarkers of developmental toxicity. We developed a method where hES cells were dosed with several drugs of known teratogenicity then LC-MS analysis was performed to measure changes in abundance levels of small molecules in response to drug dosing. Statistical analysis was employed to select for specific mass features that can provide a prediction of the developmental toxicity of a substance. These molecules can serve as biomarkers of developmental toxicity, leading to better prediction of teratogenicity. In particular, our work shows a correlation between teratogenicity and changes of greater than 10% in the ratio of arginine to asymmetric dimethylarginine levels. In addition, this study resulted in the establishment of a predictive model based on the most informative mass features. This model was subsequently tested for its predictive accuracy in two blinded studies using eight drugs of known teratogenicity, where it correctly predicted the teratogenicity for seven of the eight drugs. Thus, our initial data shows that this platform is a robust alternative to animal and other in vitro models for the prediction of the developmental toxicity of chemicals that may also provide invaluable information about the underlying biochemical pathways.

  18. Microfluidic cell culture systems for drug research.

    Science.gov (United States)

    Wu, Min-Hsien; Huang, Song-Bin; Lee, Gwo-Bin

    2010-04-21

    In pharmaceutical research, an adequate cell-based assay scheme to efficiently screen and to validate potential drug candidates in the initial stage of drug discovery is crucial. In order to better predict the clinical response to drug compounds, a cell culture model that is faithful to in vivo behavior is required. With the recent advances in microfluidic technology, the utilization of a microfluidic-based cell culture has several advantages, making it a promising alternative to the conventional cell culture methods. This review starts with a comprehensive discussion on the general process for drug discovery and development, the role of cell culture in drug research, and the characteristics of the cell culture formats commonly used in current microfluidic-based, cell-culture practices. Due to the significant differences in several physical phenomena between microscale and macroscale devices, microfluidic technology provides unique functionality, which is not previously possible by using traditional techniques. In a subsequent section, the niches for using microfluidic-based cell culture systems for drug research are discussed. Moreover, some critical issues such as cell immobilization, medium pumping or gradient generation in microfluidic-based, cell-culture systems are also reviewed. Finally, some practical applications of microfluidic-based, cell-culture systems in drug research particularly those pertaining to drug toxicity testing and those with a high-throughput capability are highlighted.

  19. Prediction of resistance development against drug combinations by collateral responses to component drugs

    DEFF Research Database (Denmark)

    Munck, Christian; Gumpert, Heidi; Nilsson Wallin, Annika

    2014-01-01

    the genomes of all evolved E. coli lineages, we identified the mutational events that drive the differences in drug resistance levels and found that the degree of resistance development against drug combinations can be understood in terms of collateral sensitivity and resistance that occurred during...... adaptation to the component drugs. Then, using engineered E. coli strains, we confirmed that drug resistance mutations that imposed collateral sensitivity were suppressed in a drug pair growth environment. These results provide a framework for rationally selecting drug combinations that limit resistance......Resistance arises quickly during chemotherapeutic selection and is particularly problematic during long-term treatment regimens such as those for tuberculosis, HIV infections, or cancer. Although drug combination therapy reduces the evolution of drug resistance, drug pairs vary in their ability...

  20. Cellular mechanisms in drug - radiation interaction

    International Nuclear Information System (INIS)

    Trott, K.R.

    1979-01-01

    Some cytotoxic drugs, especially those belonging to the group of antibiotics and antimetabolites, sensitize the cells having survived drug treatment to the subsequent irradiation by either increasing the slope of the radiation dose response curves or by decreasing extrapolation number. Bleomycin was found to interact with radiation in L-cells and FM3A cells, but not in HeLa-cells. The data with EMT-6 cells suggest that the interaction depends on drug dose: no interaction occurred after the exposure to bleomycin which killed only 20 - 40% of the cells; yet the exposure to bleomycin which killed 90% of the cells in addition sensitized the surviving cells by the DMF of 1.3. The sensitization found 24 hr after the exposure of HeLa cells to methotrexate was due to cell synchronization. Other cytostatic drugs were found to synchronize proliferating cells even better. Therefore, the fluctuation of radiosensitivity has been commonly observed after the termination of exposure to these drugs. Preirradiation may lead to the change in drug dose response curves. The recruitment of resting cells into cycle occurs hours or days later, in some irradiated normal and malignant tissues. Since many cytostatic drugs are far more active in proliferating cells than in resting cells, the recruitment after irradiation may lead to the sudden increase in drug sensitivity, days after the irradiation. No single, simple theory seems to exist to classify and predict the cellular response to combined modality treatment. (Yamashita, S.)

  1. Chimeric mice with humanized liver: Application in drug metabolism and pharmacokinetics studies for drug discovery.

    Science.gov (United States)

    Naritomi, Yoichi; Sanoh, Seigo; Ohta, Shigeru

    2018-02-01

    Predicting human drug metabolism and pharmacokinetics (PK) is key to drug discovery. In particular, it is important to predict human PK, metabolite profiles and drug-drug interactions (DDIs). Various methods have been used for such predictions, including in vitro metabolic studies using human biological samples, such as hepatic microsomes and hepatocytes, and in vivo studies using experimental animals. However, prediction studies using these methods are often inconclusive due to discrepancies between in vitro and in vivo results, and interspecies differences in drug metabolism. Further, the prediction methods have changed from qualitative to quantitative to solve these issues. Chimeric mice with humanized liver have been developed, in which mouse liver cells are mostly replaced with human hepatocytes. Since human drug metabolizing enzymes are expressed in the liver of these mice, they are regarded as suitable models for mimicking the drug metabolism and PK observed in humans; therefore, these mice are useful for predicting human drug metabolism and PK. In this review, we discuss the current state, issues, and future directions of predicting human drug metabolism and PK using chimeric mice with humanized liver in drug discovery. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

  2. Brain Activation during Associative Short-Term Memory Maintenance is Not Predictive for Subsequent Retrieval

    Directory of Open Access Journals (Sweden)

    Heiko eBergmann

    2015-09-01

    Full Text Available Performance on working memory (WM tasks may partially be supported by long-term memory (LTM processing. Hence, brain activation recently being implicated in WM may actually have been driven by (incidental LTM formation. We examined which brain regions actually support successful WM processing, rather than being confounded by LTM processes, during the maintenance and probe phase of a WM task. We administered a four-pair (faces and houses associative delayed-match-to-sample (WM task using event-related fMRI and a subsequent associative recognition LTM task, using the same stimuli. This enabled us to analyze subsequent memory effects for both the WM and the LTM test by contrasting correctly recognized pairs with incorrect pairs for either task. Critically, with respect to the subsequent WM effect, we computed this analysis exclusively for trials that were forgotten in the subsequent LTM recognition task. Hence, brain activity associated with successful WM processing was less likely to be confounded by incidental LTM formation. The subsequent LTM effect, in contrast, was analyzed exclusively for pairs that previously had been correctly recognized in the WM task, disclosing brain regions involved in successful LTM formation after successful WM processing. Results for the subsequent WM effect showed no significantly activated brain areas for WM maintenance, possibly due to an insensitivity of fMRI to mechanisms underlying active WM maintenance. In contrast, a correct decision at WM probe was linked to activation in the retrieval success network (anterior and posterior midline brain structures. The subsequent LTM analyses revealed greater activation in left dorsolateral prefrontal cortex and posterior parietal cortex in the early phase of the maintenance stage. No supra-threshold activation was found during the WM probe. Together, we obtained clearer insights in which brain regions support successful WM and LTM without the potential confound of the

  3. Brain activation during associative short-term memory maintenance is not predictive for subsequent retrieval.

    Science.gov (United States)

    Bergmann, Heiko C; Daselaar, Sander M; Beul, Sarah F; Rijpkema, Mark; Fernández, Guillén; Kessels, Roy P C

    2015-01-01

    Performance on working memory (WM) tasks may partially be supported by long-term memory (LTM) processing. Hence, brain activation recently being implicated in WM may actually have been driven by (incidental) LTM formation. We examined which brain regions actually support successful WM processing, rather than being confounded by LTM processes, during the maintenance and probe phase of a WM task. We administered a four-pair (faces and houses) associative delayed-match-to-sample (WM) task using event-related functional MRI (fMRI) and a subsequent associative recognition LTM task, using the same stimuli. This enabled us to analyze subsequent memory effects for both the WM and the LTM test by contrasting correctly recognized pairs with incorrect pairs for either task. Critically, with respect to the subsequent WM effect, we computed this analysis exclusively for trials that were forgotten in the subsequent LTM recognition task. Hence, brain activity associated with successful WM processing was less likely to be confounded by incidental LTM formation. The subsequent LTM effect, in contrast, was analyzed exclusively for pairs that previously had been correctly recognized in the WM task, disclosing brain regions involved in successful LTM formation after successful WM processing. Results for the subsequent WM effect showed no significantly activated brain areas for WM maintenance, possibly due to an insensitivity of fMRI to mechanisms underlying active WM maintenance. In contrast, a correct decision at WM probe was linked to activation in the "retrieval success network" (anterior and posterior midline brain structures). The subsequent LTM analyses revealed greater activation in left dorsolateral prefrontal cortex and posterior parietal cortex in the early phase of the maintenance stage. No supra-threshold activation was found during the WM probe. Together, we obtained clearer insights in which brain regions support successful WM and LTM without the potential confound of

  4. Drug repurposing based on drug-drug interaction.

    Science.gov (United States)

    Zhou, Bin; Wang, Rong; Wu, Ping; Kong, De-Xin

    2015-02-01

    Given the high risk and lengthy procedure of traditional drug development, drug repurposing is gaining more and more attention. Although many types of drug information have been used to repurpose drugs, drug-drug interaction data, which imply possible physiological effects or targets of drugs, remain unexploited. In this work, similarity of drug interaction was employed to infer similarity of the physiological effects or targets for the drugs. We collected 10,835 drug-drug interactions concerning 1074 drugs, and for 700 of them, drug similarity scores based on drug interaction profiles were computed and rendered using a drug association network with 589 nodes (drugs) and 2375 edges (drug similarity scores). The 589 drugs were clustered into 98 groups with Markov Clustering Algorithm, most of which were significantly correlated with certain drug functions. This indicates that the network can be used to infer the physiological effects of drugs. Furthermore, we evaluated the ability of this drug association network to predict drug targets. The results show that the method is effective for 317 of 561 drugs that have known targets. Comparison of this method with the structure-based approach shows that they are complementary. In summary, this study demonstrates the feasibility of drug repurposing based on drug-drug interaction data. © 2014 John Wiley & Sons A/S.

  5. The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction

    Directory of Open Access Journals (Sweden)

    Montserrat Cases

    2014-11-01

    Full Text Available The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP protection and set up of adequate controlled vocabularies and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds. In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys are presented as decision support knowledge-based tools for drug development process at an early stage.

  6. A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk

    Directory of Open Access Journals (Sweden)

    Juan Guillermo eDiaz Ochoa

    2013-01-01

    Full Text Available In this study, we focus on a novel multi-scale modeling approach for spatiotemporal prediction of the distribution of substances and resulting hepatotoxicity by combining cellular models, a 2D liver model, and whole-body model. As a case study, we focused on predicting human hepatotoxicity upon treatment with acetaminophen based on in vitro toxicity data and potential inter-individual variability in gene expression and enzyme activities. By aggregating mechanistic, genome-based in silico cells to a novel 2D liver model and eventually to a whole body model, we predicted pharmacokinetic properties, metabolism, and the onset of hepatotoxicity in an in silico patient. Depending on the concentration of acetaminophen in the liver and the accumulation of toxic metabolites, cell integrity in the liver as a function of space and time as well as changes in the elimination rate of substances were estimated. We show that the variations in elimination rates also influence the distribution of acetaminophen and its metabolites in the whole body. Our results are in agreement with experimental results. What is more, the integrated model also predicted variations in drug toxicity depending on alterations of metabolic enzyme activities. Variations in enzyme activity, in turn, reflect genetic characteristics or diseases of individuals. In conclusion, this framework presents an important basis for efficiently integrating inter-individual variability data into models, paving the way for personalized or stratified predictions of drug toxicity and efficacy.

  7. In Vitro-In Vivo Predictive Dissolution-Permeation-Absorption Dynamics of Highly Permeable Drug Extended-Release Tablets via Drug Dissolution/Absorption Simulating System and pH Alteration.

    Science.gov (United States)

    Li, Zi-Qiang; Tian, Shuang; Gu, Hui; Wu, Zeng-Guang; Nyagblordzro, Makafui; Feng, Guo; He, Xin

    2018-05-01

    Each of dissolution and permeation may be a rate-limiting factor in the absorption of oral drug delivery. But the current dissolution test rarely took into consideration of the permeation property. Drug dissolution/absorption simulating system (DDASS) valuably gave an insight into the combination of drug dissolution and permeation processes happening in human gastrointestinal tract. The simulated gastric/intestinal fluid of DDASS was improved in this study to realize the influence of dynamic pH change on the complete oral dosage form. To assess the effectiveness of DDASS, six high-permeability drugs were chosen as model drugs, including theophylline (pK a1  = 3.50, pK a2  = 8.60), diclofenac (pK a  = 4.15), isosorbide 5-mononitrate (pK a  = 7.00), sinomenine (pK a  = 7.98), alfuzosin (pK a  = 8.13), and metoprolol (pK a  = 9.70). A general elution and permeation relationship of their commercially available extended-release tablets was assessed as well as the relationship between the cumulative permeation and the apparent permeability. The correlations between DDASS elution and USP apparatus 2 (USP2) dissolution and also between DDASS permeation and beagle dog absorption were developed to estimate the predictability of DDASS. As a result, the common elution-dissolution relationship was established regardless of some variance in the characteristic behavior between DDASS and USP2 for drugs dependent on the pH for dissolution. Level A in vitro-in vivo correlation between DDASS permeation and dog absorption was developed for drugs with different pKa. The improved DDASS will be a promising tool to provide a screening method on the predictive dissolution-permeation-absorption dynamics of solid drug dosage forms in the early-phase formulation development.

  8. Scaling predictive modeling in drug development with cloud computing.

    Science.gov (United States)

    Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

    2015-01-26

    Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

  9. In Vitro Dissolution of Fluconazole and Dipyridamole in Gastrointestinal Simulator (GIS), Predicting in Vivo Dissolution and Drug-Drug Interaction Caused by Acid-Reducing Agents.

    Science.gov (United States)

    Matsui, Kazuki; Tsume, Yasuhiro; Amidon, Gregory E; Amidon, Gordon L

    2015-07-06

    Weakly basic drugs typically exhibit pH-dependent solubility in the physiological pH range, displaying supersaturation or precipitation along the gastrointestinal tract. Additionally, their oral bioavailabilities may be affected by coadministration of acid-reducing agents that elevate gastric pH. The purpose of this study was to assess the feasibility of a multicompartmental in vitro dissolution apparatus, Gastrointestinal Simulator (GIS), in predicting in vivo dissolution of certain oral medications. In vitro dissolution studies of fluconazole, a BCS class I, and dipyridamole, a BCS class II weak bases (class IIb), were performed in the GIS as well as United States Pharmacopeia (USP) apparatus II and compared with the results of clinical drug-drug interaction (DDI) studies. In both USP apparatus II and GIS, fluconazole completely dissolved within 60 min regardless of pH, reflecting no DDI between fluconazole and acid-reducing agents in a clinical study. On the other hand, seven-fold and 15-fold higher concentrations of dipyridamole than saturation solubility were observed in the intestinal compartments in GIS with gastric pH 2.0. Precipitation of dipyridamole was also observed in the GIS, and the percentage of dipyridamole in solution was 45.2 ± 7.0%. In GIS with gastric pH 6.0, mimicking the coadministration of acid-reducing agents, the concentration of dipyridamole was equal to its saturation solubility, and the percentage of drug in solution was 9.3 ± 2.7%. These results are consistent with the clinical DDI study of dipyridamole with famotidine, which significantly reduced the Cmax and area under the curve. An In situ mouse infusion study combined with GIS revealed that high concentration of dipyridamole in the GIS enhanced oral drug absorption, which confirmed the supersaturation of dipyridamole. In conclusion, GIS was shown to be a useful apparatus to predict in vivo dissolution for BCS class IIb drugs.

  10. Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

    Science.gov (United States)

    Wacker, Soren; Noskov, Sergei Yu

    2018-05-01

    Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC 50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC 50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC 50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost

  11. The Open Form Inducer Approach for Structure-Based Drug Design.

    Directory of Open Access Journals (Sweden)

    Daniel Ken Inaoka

    Full Text Available Many open form (OF structures of drug targets were obtained a posteriori by analysis of co-crystals with inhibitors. Therefore, obtaining the OF structure of a drug target a priori will accelerate development of potent inhibitors. In addition to its small active site, Trypanosoma cruzi dihydroorotate dehydrogenase (TcDHODH is fully functional in its monomeric form, making drug design approaches targeting the active site and protein-protein interactions unrealistic. Therefore, a novel a priori approach was developed to determination the TcDHODH active site in OF. This approach consists of generating an "OF inducer" (predicted in silico to bind the target and cause steric repulsion with flexible regions proximal to the active site that force it open. We provide the first proof-of-concept of this approach by predicting and crystallizing TcDHODH in complex with an OF inducer, thereby obtaining the OF a priori with its subsequent use in designing potent and selective inhibitors. Fourteen co-crystal structures of TcDHODH with the designed inhibitors are presented herein. This approach has potential to encourage drug design against diseases where the molecular targets are such difficult proteins possessing small AS volume. This approach can be extended to study open/close conformation of proteins in general, the identification of allosteric pockets and inhibitors for other drug targets where conventional drug design approaches are not applicable, as well as the effective exploitation of the increasing number of protein structures deposited in Protein Data Bank.

  12. Identifying predictive features in drug response using machine learning: opportunities and challenges.

    Science.gov (United States)

    Vidyasagar, Mathukumalli

    2015-01-01

    This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

  13. Prediction of solubility and permeability class membership: provisional BCS classification of the world's top oral drugs.

    Science.gov (United States)

    Dahan, Arik; Miller, Jonathan M; Amidon, Gordon L

    2009-12-01

    The Biopharmaceutics Classification System (BCS) categorizes drugs into one of four biopharmaceutical classes according to their water solubility and membrane permeability characteristics and broadly allows the prediction of the rate-limiting step in the intestinal absorption process following oral administration. Since its introduction in 1995, the BCS has generated remarkable impact on the global pharmaceutical sciences arena, in drug discovery, development, and regulation, and extensive validation/discussion/extension of the BCS is continuously published in the literature. The BCS has been effectively implanted by drug regulatory agencies around the world in setting bioavailability/bioequivalence standards for immediate-release (IR) oral drug product approval. In this review, we describe the BCS scientific framework and impact on regulatory practice of oral drug products and review the provisional BCS classification of the top drugs on the global market. The Biopharmaceutical Drug Disposition Classification System and its association with the BCS are discussed as well. One notable finding of the provisional BCS classification is that the clinical performance of the majority of approved IR oral drug products essential for human health can be assured with an in vitro dissolution test, rather than empirical in vivo human studies.

  14. Pharmacogenetic approaches to the prediction of drug response

    International Nuclear Information System (INIS)

    Vesell, E.S.

    1986-01-01

    The following review of pharmacogenetic progress and methodology is offered to stimulate and suggest analogous studies on drugs of abuse. It is readily acknowledged that formidable methodological problems are posed by adapting to drugs of abuse these pharmacogenetic approaches based on the administration of single safe doses of various prescription drugs to normal subjects under carefully controlled environmental conditions. Results of similarly designed studies on drugs of abuse in addicts might be uninterpretable because of confounding by numerous environmental perturbations, including the smoking of cigarettes and/or marijuana, nutritional variations, and intake of other drugs such as ethanol. Ethical considerations render objectionable the administration to unaddicted subjects of drugs at dosage levels usually ingested by drug abusers. Other approaches would have to be taken in such normal subjects. Possibilities include administration of tracer doses of /sup 14/C- or /sup 13/C- labeled drugs or growth of normal cells in culture to investigate their pharmacokinetic and/or pharmacodynamic responses to various drugs of abuse

  15. Pharmacokinetics in Drug Discovery: An Exposure-Centred Approach to Optimising and Predicting Drug Efficacy and Safety.

    Science.gov (United States)

    Reichel, Andreas; Lienau, Philip

    2016-01-01

    The role of pharmacokinetics (PK) in drug discovery is to support the optimisation of the absorption, distribution, metabolism and excretion (ADME) properties of lead compounds with the ultimate goal to attain a clinical candidate which achieves a concentration-time profile in the body that is adequate for the desired efficacy and safety profile. A thorough characterisation of the lead compounds aiming at the identification of the inherent PK liabilities also includes an early generation of PK/PD relationships linking in vitro potency and target exposure/engagement with expression of pharmacological activity (mode-of-action) and efficacy in animal studies. The chapter describes an exposure-centred approach to lead generation, lead optimisation and candidate selection and profiling that focuses on a stepwise generation of an understanding between PK/exposure and PD/efficacy relationships by capturing target exposure or surrogates thereof and cellular mode-of-action readouts in vivo. Once robust PK/PD relationship in animal PD models has been constructed, it is translated to anticipate the pharmacologically active plasma concentrations in patients and the human therapeutic dose and dosing schedule which is also based on the prediction of the PK behaviour in human as described herein. The chapter outlines how the level of confidence in the predictions increases with the level of understanding of both the PK and the PK/PD of the new chemical entities (NCE) in relation to the disease hypothesis and the ability to propose safe and efficacious doses and dosing schedules in responsive patient populations. A sound identification of potential drug metabolism and pharmacokinetics (DMPK)-related development risks allows proposing of an effective de-risking strategy for the progression of the project that is able to reduce uncertainties and to increase the probability of success during preclinical and clinical development.

  16. Prenatal drug exposures sensitize noradrenergic circuits to subsequent disruption by chlorpyrifos.

    Science.gov (United States)

    Slotkin, Theodore A; Skavicus, Samantha; Seidler, Frederic J

    2015-12-02

    We examined whether nicotine or dexamethasone, common prenatal drug exposures, sensitize the developing brain to chlorpyrifos. We gave nicotine to pregnant rats throughout gestation at a dose (3mg/kg/day) producing plasma levels typical of smokers; offspring were then given chlorpyrifos on postnatal days 1-4, at a dose (1mg/kg) that produces minimally-detectable inhibition of brain cholinesterase activity. In a parallel study, we administered dexamethasone to pregnant rats on gestational days 17-19 at a standard therapeutic dose (0.2mg/kg) used in the management of preterm labor, followed by postnatal chlorpyrifos. We evaluated cerebellar noradrenergic projections, a known target for each agent, and contrasted the effects with those in the cerebral cortex. Either drug augmented the effect of chlorpyrifos, evidenced by deficits in cerebellar β-adrenergic receptors; the receptor effects were not due to increased systemic toxicity or cholinesterase inhibition, nor to altered chlorpyrifos pharmacokinetics. Further, the deficits were not secondary adaptations to presynaptic hyperinnervation/hyperactivity, as there were significant deficits in presynaptic norepinephrine levels that would serve to augment the functional consequence of receptor deficits. The pretreatments also altered development of cerebrocortical noradrenergic circuits, but with a different overall pattern, reflecting the dissimilar developmental stages of the regions at the time of exposure. However, in each case the net effects represented a change in the developmental trajectory of noradrenergic circuits, rather than simply a continuation of an initial injury. Our results point to the ability of prenatal drug exposure to create a subpopulation with heightened vulnerability to environmental neurotoxicants. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  17. A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity

    International Nuclear Information System (INIS)

    Kostadinova, Radina; Boess, Franziska; Applegate, Dawn; Suter, Laura; Weiser, Thomas; Singer, Thomas; Naughton, Brian; Roth, Adrian

    2013-01-01

    Drug-induced liver injury (DILI) is the major cause for liver failure and post-marketing drug withdrawals. Due to species-specific differences in hepatocellular function, animal experiments to assess potential liabilities of drug candidates can predict hepatotoxicity in humans only to a certain extent. In addition to animal experimentation, primary hepatocytes from rat or human are widely used for pre-clinical safety assessment. However, as many toxic responses in vivo are mediated by a complex interplay among different cell types and often require chronic drug exposures, the predictive performance of hepatocytes is very limited. Here, we established and characterized human and rat in vitro three-dimensional (3D) liver co-culture systems containing primary parenchymal and non-parenchymal hepatic cells. Our data demonstrate that cells cultured on a 3D scaffold have a preserved composition of hepatocytes, stellate, Kupffer and endothelial cells and maintain liver function for up to 3 months, as measured by the production of albumin, fibrinogen, transferrin and urea. Additionally, 3D liver co-cultures maintain cytochrome P450 inducibility, form bile canaliculi-like structures and respond to inflammatory stimuli. Upon incubation with selected hepatotoxicants including drugs which have been shown to induce idiosyncratic toxicity, we demonstrated that this model better detected in vivo drug-induced toxicity, including species-specific drug effects, when compared to monolayer hepatocyte cultures. In conclusion, our results underline the importance of more complex and long lasting in vitro cell culture models that contain all liver cell types and allow repeated drug-treatments for detection of in vivo-relevant adverse drug effects. - Highlights: ► 3D liver co-cultures maintain liver specific functions for up to three months. ► Activities of Cytochrome P450s remain drug- inducible accross three months. ► 3D liver co-cultures recapitulate drug-induced liver toxicity

  18. A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity

    Energy Technology Data Exchange (ETDEWEB)

    Kostadinova, Radina; Boess, Franziska [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland); Applegate, Dawn [RegeneMed, 9855 Towne Centre Drive Suite 200, San Diego, CA 92121 (United States); Suter, Laura; Weiser, Thomas; Singer, Thomas [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland); Naughton, Brian [RegeneMed, 9855 Towne Centre Drive Suite 200, San Diego, CA 92121 (United States); Roth, Adrian, E-mail: adrian_b.roth@roche.com [Non-Clinical Safety, Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Building 73 / Room 117b, 4070 Basel (Switzerland)

    2013-04-01

    Drug-induced liver injury (DILI) is the major cause for liver failure and post-marketing drug withdrawals. Due to species-specific differences in hepatocellular function, animal experiments to assess potential liabilities of drug candidates can predict hepatotoxicity in humans only to a certain extent. In addition to animal experimentation, primary hepatocytes from rat or human are widely used for pre-clinical safety assessment. However, as many toxic responses in vivo are mediated by a complex interplay among different cell types and often require chronic drug exposures, the predictive performance of hepatocytes is very limited. Here, we established and characterized human and rat in vitro three-dimensional (3D) liver co-culture systems containing primary parenchymal and non-parenchymal hepatic cells. Our data demonstrate that cells cultured on a 3D scaffold have a preserved composition of hepatocytes, stellate, Kupffer and endothelial cells and maintain liver function for up to 3 months, as measured by the production of albumin, fibrinogen, transferrin and urea. Additionally, 3D liver co-cultures maintain cytochrome P450 inducibility, form bile canaliculi-like structures and respond to inflammatory stimuli. Upon incubation with selected hepatotoxicants including drugs which have been shown to induce idiosyncratic toxicity, we demonstrated that this model better detected in vivo drug-induced toxicity, including species-specific drug effects, when compared to monolayer hepatocyte cultures. In conclusion, our results underline the importance of more complex and long lasting in vitro cell culture models that contain all liver cell types and allow repeated drug-treatments for detection of in vivo-relevant adverse drug effects. - Highlights: ► 3D liver co-cultures maintain liver specific functions for up to three months. ► Activities of Cytochrome P450s remain drug- inducible accross three months. ► 3D liver co-cultures recapitulate drug-induced liver toxicity

  19. Malaria overdiagnosis and subsequent overconsumption of antimalarial drugs in Angola: Consequences and effects on human health.

    Science.gov (United States)

    Manguin, Sylvie; Foumane, Vincent; Besnard, Patrick; Fortes, Filomeno; Carnevale, Pierre

    2017-07-01

    Microscopic blood smear examinations done in health centers of Angola demonstrated a large overdiagnosis of malaria cases with an average rate of errors as high as 85%. Overall 83% of patients who received Coartem ® had an inappropriate treatment. Overestimated malaria diagnosis was noticed even when specific symptoms were part of the clinical observation, antimalarial treatments being subsequently given. Then, malaria overdiagnosis has three main consequences, (i) the lack of data reliability is of great concern, impeding epidemiological records and evaluation of the actual influence of operations as scheduled by the National Malaria Control Programme; (ii) the large misuse of antimalarial drug can increase the selective pressure for resistant strain and can make a false consideration of drug resistant P. falciparum crisis; and (iii) the need of strengthening national health centers in term of human, with training in microscopy, and equipment resources to improve malaria diagnosis with a large scale use of rapid diagnostic tests associated with thick blood smears, backed up by a "quality control" developed by the national health authorities. Monitoring of malaria cases was done in three Angolan health centers of Alto Liro (Lobito town) and neighbor villages of Cambambi and Asseque (Benguéla Province) to evaluate the real burden of malaria. Carriers of Plasmodium among patients of newly-borne to 14 years old, with or without fever, were analyzed and compared to presumptive malaria cases diagnosed in these health centers. Presumptive malaria cases were diagnosed six times more than the positive thick blood smears done on the same children. In Alto Liro health center, the percentage of diagnosis error reached 98%, while in Cambambi and Asseque it was of 79% and 78% respectively. The percentage of confirmed malaria cases was significantly higher during the dry (20.2%) than the rainy (13.2%) season. These observations in three peripheral health centers confirmed what

  20. Detection of Adverse Reaction to Drugs in Elderly Patients through Predictive Modeling

    Directory of Open Access Journals (Sweden)

    Rafael San-Miguel Carrasco

    2016-03-01

    Full Text Available Geriatrics Medicine constitutes a clinical research field in which data analytics, particularly predictive modeling, can deliver compelling, reliable and long-lasting benefits, as well as non-intuitive clinical insights and net new knowledge. The research work described in this paper leverages predictive modeling to uncover new insights related to adverse reaction to drugs in elderly patients. The differentiation factor that sets this research exercise apart from traditional clinical research is the fact that it was not designed by formulating a particular hypothesis to be validated. Instead, it was data-centric, with data being mined to discover relationships or correlations among variables. Regression techniques were systematically applied to data through multiple iterations and under different configurations. The obtained results after the process was completed are explained and discussed next.

  1. Physical stabilization of low-molecular-weight amorphous drugs in the solid state: a material science approach.

    Science.gov (United States)

    Qi, Sheng; McAuley, William J; Yang, Ziyi; Tipduangta, Pratchaya

    2014-07-01

    Use of the amorphous state is considered to be one of the most effective approaches for improving the dissolution and subsequent oral bioavailability of poorly water-soluble drugs. However as the amorphous state has much higher physical instability in comparison with its crystalline counterpart, stabilization of amorphous drugs in a solid-dosage form presents a major challenge to formulators. The currently used approaches for stabilizing amorphous drug are discussed in this article with respect to their preparation, mechanism of stabilization and limitations. In order to realize the potential of amorphous formulations, significant efforts are required to enable the prediction of formulation performance. This will facilitate the development of computational tools that can inform a rapid and rational formulation development process for amorphous drugs.

  2. FUNCTIONAL SUBCLONE PROFILING FOR PREDICTION OF TREATMENT-INDUCED INTRA-TUMOR POPULATION SHIFTS AND DISCOVERY OF RATIONAL DRUG COMBINATIONS IN HUMAN GLIOBLASTOMA

    Science.gov (United States)

    Reinartz, Roman; Wang, Shanshan; Kebir, Sied; Silver, Daniel J.; Wieland, Anja; Zheng, Tong; Küpper, Marius; Rauschenbach, Laurèl; Fimmers, Rolf; Shepherd, Timothy M.; Trageser, Daniel; Till, Andreas; Schäfer, Niklas; Glas, Martin; Hillmer, Axel M.; Cichon, Sven; Smith, Amy A.; Pietsch, Torsten; Liu, Ying; Reynolds, Brent A.; Yachnis, Anthony; Pincus, David W.; Simon, Matthias; Brüstle, Oliver; Steindler, Dennis A.; Scheffler, Björn

    2016-01-01

    Purpose Investigation of clonal heterogeneity may be key to understanding mechanisms of therapeutic failure in human cancer. However, little is known on the consequences of therapeutic intervention on the clonal composition of solid tumors. Experimental Design Here, we used 33 single cell-derived subclones generated from five clinical glioblastoma specimens for exploring intra- and inter-individual spectra of drug resistance profiles in vitro. In a personalized setting, we explored whether differences in pharmacological sensitivity among subclones could be employed to predict drug-dependent changes to the clonal composition of tumors. Results Subclones from individual tumors exhibited a remarkable heterogeneity of drug resistance to a library of potential anti-glioblastoma compounds. A more comprehensive intra-tumoral analysis revealed that stable genetic and phenotypic characteristics of co-existing subclones could be correlated with distinct drug sensitivity profiles. The data obtained from differential drug response analysis could be employed to predict clonal population shifts within the naïve parental tumor in vitro and in orthotopic xenografts. Furthermore, the value of pharmacological profiles could be shown for establishing rational strategies for individualized secondary lines of treatment. Conclusions Our data provide a previously unrecognized strategy for revealing functional consequences of intra-tumor heterogeneity by enabling predictive modeling of treatment-related subclone dynamics in human glioblastoma. PMID:27521447

  3. Changes in psychiatric symptoms among persons with methamphetamine dependence predicts changes in severity of drug problems but not frequency of use.

    Science.gov (United States)

    Polcin, Douglas L; Korcha, Rachael; Bond, Jason; Galloway, Gantt; Nayak, Madhabika

    2016-01-01

    Few studies have examined how changes in psychiatric symptoms over time are associated with changes in drug use and severity of drug problems. No studies have examined these relationships among methamphetamine (MA)-dependent persons receiving motivational interviewing within the context of standard outpatient treatment. Two hundred seventeen individuals with MA dependence were randomly assigned to a standard single session of motivational interviewing (MI) or an intensive 9-session model of MI. Both groups received standard outpatient group treatment. The Addiction Severity Index (ASI) and timeline follow-back (TLFB) for MA use were administered at treatment entry and 2-, 4-, and 6-month follow-ups. Changes in ASI psychiatric severity between baseline and 2 months predicted changes in ASI drug severity during the same time period, but not changes on measures of MA use. Item analysis of the ASI drug scale showed that psychiatric severity predicted how troubled or bothered participants were by their drug us, how important they felt it was for them to get treatment, and the number of days they experienced drug problems. However, it did not predict the number days they used drugs in the past 30 days. These associations did not differ between study conditions, and they persisted when psychiatric severity and outcomes were compared across 4- and 6-month time periods. Results are among the first to track how changes in psychiatric severity over time are associated with changes in MA use and severity of drug problems. Treatment efforts targeting reduction of psychiatric symptoms among MA-dependent persons might be helpful in reducing the level of distress and problems associated with MA use but not how often it is used. There is a need for additional research describing the circumstances under which the experiences and perceptions of drug-related problems diverge from frequency of consumption.

  4. Do psychopathic traits assessed in mid-adolescence predict mental health, psychosocial, and antisocial, including criminal outcomes, over the subsequent 5 years?

    Science.gov (United States)

    Hemphälä, Malin; Hodgins, Sheilagh

    2014-01-01

    To determine whether psychopathic traits assessed in mid-adolescence predicted mental health, psychosocial, and antisocial (including criminal) outcomes 5 years later and would thereby provide advantages over diagnosing conduct disorder (CD). Eighty-six women and 61 men were assessed in mid-adolescence when they first contacted a clinic for substance misuse and were reassessed 5 years later. Assessments in adolescence include the Psychopathy Checklist-Youth Version (PCL-YV), and depending on their age, either the Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Aged Children or the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (SCID). Assessments in early adulthood included the SCID, self-reports of psychosocial functioning, aggressive behaviour, and criminality and official criminal records. The antisocial facet score positively predicted the number of anxiety symptoms and likelihood of receiving treatment for substance use disorders (SUDs). Lifestyle and antisocial facet scores negatively predicted Global Assessment of Functioning scores. By contrast, the interpersonal score and male sex independently and positively predicted the number of months worked or studied, as did the interaction of Lifestyle × Sex indicating that among men, but not women, an increase in lifestyle facet score was associated with less time worked or studied. Interpersonal and antisocial scores positively predicted school drop-out. Antisocial facet scores predicted the number of symptoms of antisocial personality disorder, alcohol and SUDs, and violent and nonviolent criminality but much more strongly among males than females. Predictions from numbers of CD symptoms were similar. Psychopathic traits among adolescents who misuse substances predict an array of outcomes over the subsequent 5 years. Information on the levels of these traits may be useful for planning treatment.

  5. Intratumor heterogeneity alters most effective drugs in designed combinations.

    Science.gov (United States)

    Zhao, Boyang; Hemann, Michael T; Lauffenburger, Douglas A

    2014-07-22

    The substantial spatial and temporal heterogeneity observed in patient tumors poses considerable challenges for the design of effective drug combinations with predictable outcomes. Currently, the implications of tissue heterogeneity and sampling bias during diagnosis are unclear for selection and subsequent performance of potential combination therapies. Here, we apply a multiobjective computational optimization approach integrated with empirical information on efficacy and toxicity for individual drugs with respect to a spectrum of genetic perturbations, enabling derivation of optimal drug combinations for heterogeneous tumors comprising distributions of subpopulations possessing these perturbations. Analysis across probabilistic samplings from the spectrum of various possible distributions reveals that the most beneficial (considering both efficacy and toxicity) set of drugs changes as the complexity of genetic heterogeneity increases. Importantly, a significant likelihood arises that a drug selected as the most beneficial single agent with respect to the predominant subpopulation in fact does not reside within the most broadly useful drug combinations for heterogeneous tumors. The underlying explanation appears to be that heterogeneity essentially homogenizes the benefit of drug combinations, reducing the special advantage of a particular drug on a specific subpopulation. Thus, this study underscores the importance of considering heterogeneity in choosing drug combinations and offers a principled approach toward designing the most likely beneficial set, even if the subpopulation distribution is not precisely known.

  6. Predicting the Risk of Recurrence Before the Start of Antithyroid Drug Therapy in Patients With Graves' Hyperthyroidism

    NARCIS (Netherlands)

    Vos, Xander G.; Endert, Erik; Zwinderman, A. H.; Tijssen, Jan G. P.; Wiersinga, Wilmar M.

    2016-01-01

    Genotyping increases the accuracy of a clinical score (based on pretreatment age, goiter size, FT4, TBII) for predicting recurrence of Graves' hyperthyroidism after a course of antithyroid drugs: a prospective study

  7. Data-intensive drug development in the information age: applications of Systems Biology/Pharmacology/Toxicology.

    Science.gov (United States)

    Kiyosawa, Naoki; Manabe, Sunao

    2016-01-01

    Pharmaceutical companies continuously face challenges to deliver new drugs with true medical value. R&D productivity of drug development projects depends on 1) the value of the drug concept and 2) data and in-depth knowledge that are used rationally to evaluate the drug concept's validity. A model-based data-intensive drug development approach is a key competitive factor used by innovative pharmaceutical companies to reduce information bias and rationally demonstrate the value of drug concepts. Owing to the accumulation of publicly available biomedical information, our understanding of the pathophysiological mechanisms of diseases has developed considerably; it is the basis for identifying the right drug target and creating a drug concept with true medical value. Our understanding of the pathophysiological mechanisms of disease animal models can also be improved; it can thus support rational extrapolation of animal experiment results to clinical settings. The Systems Biology approach, which leverages publicly available transcriptome data, is useful for these purposes. Furthermore, applying Systems Pharmacology enables dynamic simulation of drug responses, from which key research questions to be addressed in the subsequent studies can be adequately informed. Application of Systems Biology/Pharmacology to toxicology research, namely Systems Toxicology, should considerably improve the predictability of drug-induced toxicities in clinical situations that are difficult to predict from conventional preclinical toxicology studies. Systems Biology/Pharmacology/Toxicology models can be continuously improved using iterative learn-confirm processes throughout preclinical and clinical drug discovery and development processes. Successful implementation of data-intensive drug development approaches requires cultivation of an adequate R&D culture to appreciate this approach.

  8. Outpatient Pain Predicts Subsequent One-Year Acute Health Care Utilization Among Adults With Sickle Cell Disease

    Science.gov (United States)

    Ezenwa, Miriam O.; Molokie, Robert E.; Wang, Zaijie Jim; Yao, Yingwei; Suarez, Marie L.; Angulo, Veronica; Wilkie, Diana J.

    2014-01-01

    Context Patient demographic and clinical factors have known associations with acute health care utilization (AHCU) among patients with sickle cell disease (SCD), but it is unknown if pain measured predominantly in an outpatient setting is a predictor of future AHCU in patients with SCD. Objectives To determine whether multidimensional pain scores obtained predominantly in an outpatient setting predicted subsequent one-year AHCU by 137 adults with SCD and whether the pain measured at a second visit also predicted AHCU. Methods Pain data included the Composite Pain Index (CPI), a single score representative of a multidimensional pain experience (number of pain sites, intensity, quality, and pattern). Based on the distribution of AHCU events, we divided patients into three groups: (1) zero events (Zero), (2) 1–3 events (Low), or (3) 4–23 events (High). Results The initial CPI scores differed significantly by the three groups (F(2,134)=7.38, P=0.001). Post hoc comparisons showed that the Zero group had lower CPI scores than both the Low group (Pgroup (Page, and CPI scores (at both measurement times) were statistically significant predictors of utilization events. Pain intensity scores at both measurement times were significant predictors of utilization, but other pain scores (number of pain sites, quality, and pattern) were not. Conclusion Findings support use of outpatient CPI scores or pain intensity and age to identify at-risk young adults with SCD who are likely to benefit from improved outpatient pain management plans. PMID:24636960

  9. Goal-directed mechanisms that constrain retrieval predict subsequent memory for new "foil" information.

    Science.gov (United States)

    Vogelsang, David A; Bonnici, Heidi M; Bergström, Zara M; Ranganath, Charan; Simons, Jon S

    2016-08-01

    To remember a previous event, it is often helpful to use goal-directed control processes to constrain what comes to mind during retrieval. Behavioral studies have demonstrated that incidental learning of new "foil" words in a recognition test is superior if the participant is trying to remember studied items that were semantically encoded compared to items that were non-semantically encoded. Here, we applied subsequent memory analysis to fMRI data to understand the neural mechanisms underlying the "foil effect". Participants encoded information during deep semantic and shallow non-semantic tasks and were tested in a subsequent blocked memory task to examine how orienting retrieval towards different types of information influences the incidental encoding of new words presented as foils during the memory test phase. To assess memory for foils, participants performed a further surprise old/new recognition test involving foil words that were encountered during the previous memory test blocks as well as completely new words. Subsequent memory effects, distinguishing successful versus unsuccessful incidental encoding of foils, were observed in regions that included the left inferior frontal gyrus and posterior parietal cortex. The left inferior frontal gyrus exhibited disproportionately larger subsequent memory effects for semantic than non-semantic foils, and significant overlap in activity during semantic, but not non-semantic, initial encoding and foil encoding. The results suggest that orienting retrieval towards different types of foils involves re-implementing the neurocognitive processes that were involved during initial encoding. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  10. A prediction model-based algorithm for computer-assisted database screening of adverse drug reactions in the Netherlands.

    Science.gov (United States)

    Scholl, Joep H G; van Hunsel, Florence P A M; Hak, Eelko; van Puijenbroek, Eugène P

    2018-02-01

    The statistical screening of pharmacovigilance databases containing spontaneously reported adverse drug reactions (ADRs) is mainly based on disproportionality analysis. The aim of this study was to improve the efficiency of full database screening using a prediction model-based approach. A logistic regression-based prediction model containing 5 candidate predictors was developed and internally validated using the Summary of Product Characteristics as the gold standard for the outcome. All drug-ADR associations, with the exception of those related to vaccines, with a minimum of 3 reports formed the training data for the model. Performance was based on the area under the receiver operating characteristic curve (AUC). Results were compared with the current method of database screening based on the number of previously analyzed associations. A total of 25 026 unique drug-ADR associations formed the training data for the model. The final model contained all 5 candidate predictors (number of reports, disproportionality, reports from healthcare professionals, reports from marketing authorization holders, Naranjo score). The AUC for the full model was 0.740 (95% CI; 0.734-0.747). The internal validity was good based on the calibration curve and bootstrapping analysis (AUC after bootstrapping = 0.739). Compared with the old method, the AUC increased from 0.649 to 0.740, and the proportion of potential signals increased by approximately 50% (from 12.3% to 19.4%). A prediction model-based approach can be a useful tool to create priority-based listings for signal detection in databases consisting of spontaneous ADRs. © 2017 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.

  11. Are predictions of cancer response to targeted drugs, based on effects in unrelated tissues, the 'Black Swan' events?

    Science.gov (United States)

    Kurbel, Beatrica; Golem, Ante Zvonimir; Kurbel, Sven

    2015-01-01

    Adverse effects of targeted drugs on normal tissues can predict the cancer response. Rash correlates with efficacy of erlotinib, cetuximab and gefitinib and onset of arterial hypertension with response to bevacizumab, sunitinib, axitinib and sorafenib, possible examples of 'Black Swan' events, unexpected scientific observations, as described by Karl Popper in 1935. The proposition is that our patients have individual intrinsic variants of cell growth control, important for tumor response and adverse effects on tumor-unrelated tissue. This means that the lack of predictive side effects in healthy tissue is linked with poor results of tumor therapy when tumor resistance is caused by mechanisms that protect all cells of that patient from the targeted drug effects.

  12. ABC gene-ranking for prediction of drug-induced cholestasis in rats

    Directory of Open Access Journals (Sweden)

    Yauheniya Cherkas

    drugs that behaved very differently, and were distinct from both non-cholestatic and cholestatic drugs (ketoconazole, dipyridamole, cyproheptadine and aniline, and many postulated human cholestatic drugs that in rat showed no evidence of cholestasis (chlorpromazine, erythromycin, niacin, captopril, dapsone, rifampicin, glibenclamide, simvastatin, furosemide, tamoxifen, and sulfamethoxazole. Most of these latter drugs were noted previously by other groups as showing cholestasis only in humans. The results of this work suggest that the ABC procedure and similar statistical approaches can be instrumental in combining data to compare toxicants across toxicogenomics databases, extract similarities among responses and reduce unexplained data varation. Keywords: Cluster analysis, Cholestasis, Gene signature, Microarray, Prediction, Toxicogenomics

  13. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

    OpenAIRE

    Huthmacher, Carola; Hoppe, Andreas; Bulik, Sascha; Holzh?tter, Hermann-Georg

    2010-01-01

    Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicte...

  14. DemQSAR: predicting human volume of distribution and clearance of drugs.

    Science.gov (United States)

    Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

    2011-12-01

    In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VD(ss)) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VD(ss) and CL is

  15. Appropriate experimental approaches for predicting abuse potential and addictive qualities in preclinical drug discovery.

    Science.gov (United States)

    Mead, Andy N

    2014-11-01

    Drug abuse is an increasing social and public health issue, putting the onus on drug developers and regulatory agencies to ensure that the abuse potential of novel drugs is adequately assessed prior to product launch. This review summarizes the core preclinical data that frequently contribute to building an understanding of abuse potential for a new molecular entity, in addition to highlighting models that can provide increased resolution regarding the level of risk. Second, an important distinction between abuse potential and addiction potential is drawn, with comments on how preclinical models can inform on each. While the currently adopted preclinical models possess strong predictive validity, there are areas for future refinement and research. These areas include a more refined use of self-administration models to assess relative reinforcement; and the need for open innovation in pursuing improvements. There is also the need for careful scientifically driven application of models rather than a standardization of methodologies, and the need to explore the opportunities that may exist for enhancing the value of physical dependence and withdrawal studies by focusing on withdrawal-induced drug seeking, rather than broad symptomology.

  16. Feedback on Facebook Fails to Predict the User’s Subsequent Posting

    OpenAIRE

    Sherwin E. Balbuena; Princess Z. Balbuena

    2017-01-01

    Facebook use is a new and complex social behavior that has stimulated research interests in psychology. Due to a distinct lack of theoretical basis for this new communication phenomenon, a number of studies established the significant association between personality traits and Facebook use. This study investigated the motivational effect of friends’ feedback on the user’s subsequent Facebook posting and examined the correspondence between the user’s perceived motivation and actual...

  17. Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods.

    Science.gov (United States)

    Li, Mengshan; Zhang, Huaijing; Chen, Bingsheng; Wu, Yan; Guan, Lixin

    2018-03-05

    The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

  18. The prediction of drug dependence from expectancy for hostility while intoxicated.

    Science.gov (United States)

    Walter, D; Nagoshi, C; Muntaner, C; Haertzen, C A

    1990-10-01

    Three hundred seventy-one male substance-abusing volunteers for drug studies were administered the Buss-Durkee Hostility Inventory (B-D). One hundred nineteen of these subjects were readministered the B-D with the instruction to answer the items in terms of their behavior while drinking alcohol, with 67 of these subjects also completing a heroin use condition. Expectancies for hostility under alcohol or heroin were generally uncorrelated with other measures of personality, psychopathology, antisocial personality, impulsiveness, or criminality; but expectancies for hostility under alcohol were predictive of diagnoses of alcohol, opioid, and marijuana abuse and dependence over and above the influence of these other measures.

  19. Pharmacogenetics and Predictive Testing of Drug Hypersensitivity Reactions.

    Science.gov (United States)

    Böhm, Ruwen; Cascorbi, Ingolf

    2016-01-01

    Adverse drug reactions adverse drug reaction (ADR) occur in approximately 17% of patients. Avoiding ADR is thus mandatory from both an ethical and an economic point of view. Whereas, pharmacogenetics changes of the pharmacokinetics may contribute to the explanation of some type A reactions, strong relationships of genetic markers has also been shown for drug hypersensitivity belonging to type B reactions. We present the classifications of ADR, discuss genetic influences and focus on delayed-onset hypersensitivity reactions, i.e., drug-induced liver injury, drug-induced agranulocytosis, and severe cutaneous ADR. A guidance how to read and interpret the contingency table is provided as well as an algorithm whether and how a test for a pharmacogenetic biomarker should be conducted.

  20. Development and validation of a general approach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks.

    Science.gov (United States)

    Pivetta, Tiziana; Isaia, Francesco; Trudu, Federica; Pani, Alessandra; Manca, Matteo; Perra, Daniela; Amato, Filippo; Havel, Josef

    2013-10-15

    The combination of two or more drugs using multidrug mixtures is a trend in the treatment of cancer. The goal is to search for a synergistic effect and thereby reduce the required dose and inhibit the development of resistance. An advanced model-free approach for data exploration and analysis, based on artificial neural networks (ANN) and experimental design is proposed to predict and quantify the synergism of drugs. The proposed method non-linearly correlates the concentrations of drugs with the cytotoxicity of the mixture, providing the possibility of choosing the optimal drug combination that gives the maximum synergism. The use of ANN allows for the prediction of the cytotoxicity of each combination of drugs in the chosen concentration interval. The method was validated by preparing and experimentally testing the combinations with the predicted highest synergistic effect. In all cases, the data predicted by the network were experimentally confirmed. The method was applied to several binary mixtures of cisplatin and [Cu(1,10-orthophenanthroline)2(H2O)](ClO4)2, Cu(1,10-orthophenanthroline)(H2O)2(ClO4)2 or [Cu(1,10-orthophenanthroline)2(imidazolidine-2-thione)](ClO4)2. The cytotoxicity of the two drugs, alone and in combination, was determined against human acute T-lymphoblastic leukemia cells (CCRF-CEM). For all systems, a synergistic effect was found for selected combinations. © 2013 Elsevier B.V. All rights reserved.

  1. Modelling irradiation by EM waves of multifunctionalized iron oxide nanoparticles and subsequent drug release

    International Nuclear Information System (INIS)

    Wang, Feng; Calvayrac, Florent; Montembault, Véronique; Fontaine, Laurent

    2015-01-01

    Thermal transport in the environment close to the periphery of the nanoparticle, from a few angstroms to less than a nanometer scale, is becoming increasingly important with the advent of several biomedical applications of multifunctional magnetic nanoparticles, including drug delivery, magnetic resonance imaging, and hyperthermia therapy. We present a multiscale and multiphysics model of the irradiation by electromagnetic waves of radiofrequency of iron oxide nanoparticles functionalized by drug-releasing polymers used as new multifunctional therapeutic compounds against tumors. We compute ab initio the thermal conductivity of the polymer chains as a function of the length, model the unfolding of the polymer after heat transfer from the nanoparticle by molecular mechanics, and develop a multiscale thermodynamic and heat transfer model including the surrounding medium (water) in order to model the drug release. (paper)

  2. Hepatobiliary Clearance Prediction: Species Scaling From Monkey, Dog, and Rat, and In Vitro-In Vivo Extrapolation of Sandwich-Cultured Human Hepatocytes Using 17 Drugs.

    Science.gov (United States)

    Kimoto, Emi; Bi, Yi-An; Kosa, Rachel E; Tremaine, Larry M; Varma, Manthena V S

    2017-09-01

    Hepatobiliary elimination can be a major clearance pathway dictating the pharmacokinetics of drugs. Here, we first compared the dose eliminated in bile in preclinical species (monkey, dog, and rat) with that in human and further evaluated single-species scaling (SSS) to predict human hepatobiliary clearance. Six compounds dosed in bile duct-cannulated (BDC) monkeys showed biliary excretion comparable to human; and the SSS of hepatobiliary clearance with plasma fraction unbound correction yielded reasonable predictions (within 3-fold). Although dog SSS also showed reasonable predictions, rat overpredicted hepatobiliary clearance for 13 of 24 compounds. Second, we evaluated the translatability of in vitro sandwich-cultured human hepatocytes (SCHHs) to predict human hepatobiliary clearance for 17 drugs. For drugs with no significant active uptake in SCHH studies (i.e., with or without rifamycin SV), measured intrinsic biliary clearance was directly scalable with good predictability (absolute average fold error [AAFE] = 1.6). Drugs showing significant active uptake in SCHH, however, showed improved predictability when scaled based on extended clearance term (AAFE = 2.0), which incorporated sinusoidal uptake along with a global scaling factor for active uptake and the canalicular efflux clearance. In conclusion, SCHH is a useful tool to predict human hepatobiliary clearance, whereas BDC monkey model may provide further confidence in the prospective predictions. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  3. Severe Spastic Contractures and Diabetes Mellitus Independently Predict Subsequent Minimal Trauma Fractures Among Long-Term Care Residents.

    Science.gov (United States)

    Lam, Kuen; Leung, Man Fuk; Kwan, Chi Wai; Kwan, Joseph

    2016-11-01

    The study aimed to examine the epidemiology of hypertonic contractures and its relationship with minimal trauma fracture (MTF), and to determine the incidence and predictors of (MTF) in long-term care residents. This was a longitudinal cohort study of prospectively collected data. Participants were followed from March 2007 to March 2016 or until death. A 300-bed long-term care hospital in Hong Kong. All long-term care residents who were in need of continuous medical and nursing care for their activities of daily living. Information on patients' demographic data, severe contracture defined as a decrease of 50% or more of the normal passive range of joint movement of the joint, and severe limb spasticity defined by the Modified Ashworth Scale higher than grade 3, medical comorbidities, functional status, cognitive status, nutritional status including body mass index and serum albumin, past history of fractures, were evaluated as potential risk factors for subsequent MTF. Three hundred ninety-six residents [148 males, mean ± standard deviation (SD), age = 79 ± 16 years] were included for analysis. The presence of severe contracture was highly prevalent among the study population: 91% of residents had at least 1 severe contracture, and 41% of residents had severe contractures involving all 4 limbs. Moreover, there were a significant proportion of residents who had severe limb spasticity with the elbow flexors (32.4%) and knee flexors (33.9%) being the most commonly involved muscles. Twelve residents (3%) suffered from subsequent MTF over a median follow-up of 33 (SD = 30) months. Seven out of these 12 residents died during the follow-up period, with a mean survival of 17.8 months (SD = 12.6) after the fracture event. The following 2 factors were found to independently predict subsequent MTF in a multivariate Cox regression: bilateral severe spastic knee contractures (hazard ratio = 16.5, P contractures are common morbidities in long-term care residents

  4. Predictivity of dog co-culture model, primary human hepatocytes and HepG2 cells for the detection of hepatotoxic drugs in humans

    International Nuclear Information System (INIS)

    Atienzar, Franck A.; Novik, Eric I.; Gerets, Helga H.; Parekh, Amit; Delatour, Claude; Cardenas, Alvaro; MacDonald, James; Yarmush, Martin L.; Dhalluin, Stéphane

    2014-01-01

    Drug Induced Liver Injury (DILI) is a major cause of attrition during early and late stage drug development. Consequently, there is a need to develop better in vitro primary hepatocyte models from different species for predicting hepatotoxicity in both animals and humans early in drug development. Dog is often chosen as the non-rodent species for toxicology studies. Unfortunately, dog in vitro models allowing long term cultures are not available. The objective of the present manuscript is to describe the development of a co-culture dog model for predicting hepatotoxic drugs in humans and to compare the predictivity of the canine model along with primary human hepatocytes and HepG2 cells. After rigorous optimization, the dog co-culture model displayed metabolic capacities that were maintained up to 2 weeks which indicates that such model could be also used for long term metabolism studies. Most of the human hepatotoxic drugs were detected with a sensitivity of approximately 80% (n = 40) for the three cellular models. Nevertheless, the specificity was low approximately 40% for the HepG2 cells and hepatocytes compared to 72.7% for the canine model (n = 11). Furthermore, the dog co-culture model showed a higher superiority for the classification of 5 pairs of close structural analogs with different DILI concerns in comparison to both human cellular models. Finally, the reproducibility of the canine system was also satisfactory with a coefficient of correlation of 75.2% (n = 14). Overall, the present manuscript indicates that the dog co-culture model may represent a relevant tool to perform chronic hepatotoxicity and metabolism studies. - Highlights: • Importance of species differences in drug development. • Relevance of dog co-culture model for metabolism and toxicology studies. • Hepatotoxicity: higher predictivity of dog co-culture vs HepG2 and human hepatocytes

  5. Predictivity of dog co-culture model, primary human hepatocytes and HepG2 cells for the detection of hepatotoxic drugs in humans

    Energy Technology Data Exchange (ETDEWEB)

    Atienzar, Franck A., E-mail: franck.atienzar@ucb.com [UCB Pharma SA, Non-Clinical Development, Chemin du Foriest, 1420 Braine-l' Alleud (Belgium); Novik, Eric I. [H mu rel Corporation, 675 U.S. Highway 1, North Brunswick, NJ 08902 (United States); Gerets, Helga H. [UCB Pharma SA, Non-Clinical Development, Chemin du Foriest, 1420 Braine-l' Alleud (Belgium); Parekh, Amit [H mu rel Corporation, 675 U.S. Highway 1, North Brunswick, NJ 08902 (United States); Delatour, Claude; Cardenas, Alvaro [UCB Pharma SA, Non-Clinical Development, Chemin du Foriest, 1420 Braine-l' Alleud (Belgium); MacDonald, James [Chrysalis Pharma Consulting, LLC, 385 Route 24, Suite 1G, Chester, NJ 07930 (United States); Yarmush, Martin L. [Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854 (United States); Dhalluin, Stéphane [UCB Pharma SA, Non-Clinical Development, Chemin du Foriest, 1420 Braine-l' Alleud (Belgium)

    2014-02-15

    Drug Induced Liver Injury (DILI) is a major cause of attrition during early and late stage drug development. Consequently, there is a need to develop better in vitro primary hepatocyte models from different species for predicting hepatotoxicity in both animals and humans early in drug development. Dog is often chosen as the non-rodent species for toxicology studies. Unfortunately, dog in vitro models allowing long term cultures are not available. The objective of the present manuscript is to describe the development of a co-culture dog model for predicting hepatotoxic drugs in humans and to compare the predictivity of the canine model along with primary human hepatocytes and HepG2 cells. After rigorous optimization, the dog co-culture model displayed metabolic capacities that were maintained up to 2 weeks which indicates that such model could be also used for long term metabolism studies. Most of the human hepatotoxic drugs were detected with a sensitivity of approximately 80% (n = 40) for the three cellular models. Nevertheless, the specificity was low approximately 40% for the HepG2 cells and hepatocytes compared to 72.7% for the canine model (n = 11). Furthermore, the dog co-culture model showed a higher superiority for the classification of 5 pairs of close structural analogs with different DILI concerns in comparison to both human cellular models. Finally, the reproducibility of the canine system was also satisfactory with a coefficient of correlation of 75.2% (n = 14). Overall, the present manuscript indicates that the dog co-culture model may represent a relevant tool to perform chronic hepatotoxicity and metabolism studies. - Highlights: • Importance of species differences in drug development. • Relevance of dog co-culture model for metabolism and toxicology studies. • Hepatotoxicity: higher predictivity of dog co-culture vs HepG2 and human hepatocytes.

  6. Drug-drug interactions among recently hospitalised patients--frequent but mostly clinically insignificant

    DEFF Research Database (Denmark)

    Glintborg, Bente; Andersen, Stig Ejdrup; Dalhoff, Kim

    2005-01-01

    OBJECTIVE: Patients use and store considerable amounts of drugs. The aim of the present study was to identify potential drug-drug interactions between drugs used by patients recently discharged from the hospital and, subsequently, to estimate the clinical implications of these interactions. METHODS......: Patients were visited within 1 week following their discharge from hospital and interviewed about their drug use. Stored products were inspected. We used a bibliography (Hansten and Horn; Wolters Kluwer Health, St. Louis, Mo., 2004) to identify and classify potential drug-drug interactions. RESULTS......: eight per patient; range: 1-24). With respect to those drugs used daily or on demand, 476 potential interactions were identified (126 patients); none were class 1 (always avoid drug combination) and 25 were class 2 (usually avoid combination; 24 patients). Eleven of the potential class 2 interactions...

  7. Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

    Science.gov (United States)

    Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

    2016-06-01

    The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. Copyright © 2016. Published by Elsevier Inc.

  8. Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors.

    Directory of Open Access Journals (Sweden)

    Chien-Wei Fu

    Full Text Available As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D are computed and classified using the support vector machine (SVM for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA- representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes.

  9. In silico prediction of harmful effects triggered by drugs and chemicals

    International Nuclear Information System (INIS)

    Vedani, Angelo; Dobler, Max; Lill, Markus A.

    2005-01-01

    used in the training set as well as to classify harmless compounds as being nontoxic. This suggests that our approach may be used for the prediction of adverse effects of drug molecules and chemicals. It is the aim to provide cost-covering access to this technology-particularly to universities, hospitals and regulatory bodies-as it bears a significant potential to recognize hazardous compounds early in the development process and hence improve resource and waste management as well as reduce animal testing. The Biographics Laboratory 3R is a non-profit-oriented organization aimed at reducing animal experimentation in the biomedical sciences by computational approaches (cf. http://www.biograf.ch)

  10. Prediction of suicidality and violence in hospitalized adolescents: comparisons by sex.

    Science.gov (United States)

    Becker, Daniel F; Grilo, Carlos M

    2007-09-01

    To examine psychological correlates of suicidality and violent behaviour in hospitalized adolescents and the extent to which these associations may be affected by their sex. A sample of 487 psychiatric inpatients (207 male, 280 female), aged 12 to 19 years, completed a battery of psychometrically sound self-report measures of psychological functioning, substance abuse, suicidality, and violent behaviour. We conducted multiple regression analyses to determine the joint and independent predictors of suicide risk and violence risk. In subsequent analyses, we examined these associations separately by sex. Multiple regression analysis revealed that 9 variables (sex, age, hopelessness, self-esteem, depression, impulsivity, alcohol abuse, drug abuse, and violence risk) jointly predicted suicide risk and that an analogous model predicted violence risk. However, we found several differences with respect to which variables made significant independent contributions to these 2 predictive models. Female sex, low self-esteem, depression, drug abuse, and violence risk made independent contributions to suicide risk. Male sex, younger age, hopelessness, impulsivity, drug abuse, and suicide risk made independent contributions to violence risk. We observed a few additional differences when we considered male and female subjects separately. We found overlapping but distinctive patterns of prediction for suicide risk and violence risk, as well as some differences between male and female subjects. These results may reflect distinct psychological and behavioural pathways for suicidality and violence in adolescent psychiatric patients and differing risk factors for each sex. Such differences have potential implications for prevention and treatment programs.

  11. Impact of speciation on the electron charge transfer properties of nanodiamond drug carriers.

    Science.gov (United States)

    Sun, Baichuan; Barnard, Amanda S

    2016-08-07

    Unpassivated diamond nanoparticles (bucky-diamonds) exhibit a unique surface reconstruction involving graphitization of certain crystal facets, giving rise to hybrid core-shell particles containing both aromatic and aliphatic carbon. Considerable effort is directed toward eliminating the aromatic shell, but persistent graphitization of subsequent subsurface-layers makes perdurable purification a challenge. In this study we use some simple statistical methods, in combination with electronic structure simulations, to predict the impact of different fractions of aromatic and aliphatic carbon on the charge transfer properties of the ensembles of bucky-diamonds. By predicting quality factors for a variety of cases, we find that perfect purification is not necessary to preserve selectivity, and there is a clear motivation for purifying samples to improve the sensitivity of charge transfer reactions. This may prove useful in designing drug delivery systems where the release of (selected) drugs needs to be sensitive to specific conditions at the point of delivery.

  12. Impulsive reactions to food-cues predict subsequent food craving.

    Science.gov (United States)

    Meule, Adrian; Lutz, Annika P C; Vögele, Claus; Kübler, Andrea

    2014-01-01

    Low inhibitory control has been associated with overeating and addictive behaviors. Inhibitory control can modulate cue-elicited craving in social or alcohol-dependent drinkers, and trait impulsivity may also play a role in food-cue reactivity. The current study investigated food-cue affected response inhibition and its relationship to food craving using a stop-signal task with pictures of food and neutral stimuli. Participants responded slower to food pictures as compared to neutral pictures. Reaction times in response to food pictures positively predicted scores on the Food Cravings Questionnaire - State (FCQ-S) after the task and particularly scores on its hunger subscale. Lower inhibitory performance in response to food pictures predicted higher FCQ-S scores and particularly those related to a desire for food and lack of control over consumption. Task performance was unrelated to current dieting or other measures of habitual eating behaviors. Results support models on interactive effects of top-down inhibitory control processes and bottom-up hedonic signals in the self-regulation of eating behavior, such that low inhibitory control specifically in response to appetitive stimuli is associated with increased craving, which may ultimately result in overeating. © 2013.

  13. Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration.

    Science.gov (United States)

    Agur, Zvia; Elishmereni, Moran; Kheifetz, Yuri

    2014-01-01

    Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology. © 2014 Wiley Periodicals, Inc.

  14. The role of water molecules in computational drug design.

    Science.gov (United States)

    de Beer, Stephanie B A; Vermeulen, Nico P E; Oostenbrink, Chris

    2010-01-01

    Although water molecules are small and only consist of two different atom types, they play various roles in cellular systems. This review discusses their influence on the binding process between biomacromolecular targets and small molecule ligands and how this influence can be modeled in computational drug design approaches. Both the structure and the thermodynamics of active site waters will be discussed as these influence the binding process significantly. Structurally conserved waters cannot always be determined experimentally and if observed, it is not clear if they will be replaced upon ligand binding, even if sufficient space is available. Methods to predict the presence of water in protein-ligand complexes will be reviewed. Subsequently, we will discuss methods to include water in computational drug research. Either as an additional factor in automated docking experiments, or explicitly in detailed molecular dynamics simulations, the effect of water on the quality of the simulations is significant, but not easily predicted. The most detailed calculations involve estimates of the free energy contribution of water molecules to protein-ligand complexes. These calculations are computationally demanding, but give insight in the versatility and importance of water in ligand binding.

  15. Oral administration of drugs with hypersensitivity potential induces germinal center hyperplasia in secondary lymphoid organ/tissue in Brown Norway rats, and this histological lesion is a promising candidate as a predictive biomarker for drug hypersensitivity occurrence in humans

    International Nuclear Information System (INIS)

    Tamura, Akitoshi; Miyawaki, Izuru; Yamada, Toru; Kimura, Juki; Funabashi, Hitoshi

    2013-01-01

    It is important to evaluate the potential of drug hypersensitivity as well as other adverse effects during the preclinical stage of the drug development process, but validated methods are not available yet. In the present study we examined whether it would be possible to develop a new predictive model of drug hypersensitivity using Brown Norway (BN) rats. As representative drugs with hypersensitivity potential in humans, phenytoin (PHT), carbamazepine (CBZ), amoxicillin (AMX), and sulfamethoxazole (SMX) were orally administered to BN rats for 28 days to investigate their effects on these animals by examinations including observation of clinical signs, hematology, determination of serum IgE levels, histology, and flow cytometric analysis. Skin rashes were not observed in any animals treated with these drugs. Increases in the number of circulating inflammatory cells and serum IgE level did not necessarily occur in the animals treated with these drugs. However, histological examination revealed that germinal center hyperplasia was commonly induced in secondary lymphoid organs/tissues in the animals treated with these drugs. In cytometric analysis, changes in proportions of lymphocyte subsets were noted in the spleen of the animals treated with PHT or CBZ during the early period of administration. The results indicated that the potential of drug hypersensitivity was identified in BN rat by performing histological examination of secondary lymphoid organs/tissues. Data obtained herein suggested that drugs with hypersensitivity potential in humans gained immune reactivity in BN rat, and the germinal center hyperplasia induced by administration of these drugs may serve as a predictive biomarker for drug hypersensitivity occurrence. - Highlights: • We tested Brown Norway rats as a candidate model for predicting drug hypersensitivity. • The allergic drugs did not induce skin rash, whereas D-penicillamine did so in the rats. • Some of allergic drugs increased

  16. Oral administration of drugs with hypersensitivity potential induces germinal center hyperplasia in secondary lymphoid organ/tissue in Brown Norway rats, and this histological lesion is a promising candidate as a predictive biomarker for drug hypersensitivity occurrence in humans

    Energy Technology Data Exchange (ETDEWEB)

    Tamura, Akitoshi, E-mail: akitoshi-tamura@ds-pharma.co.jp; Miyawaki, Izuru; Yamada, Toru; Kimura, Juki; Funabashi, Hitoshi

    2013-08-15

    It is important to evaluate the potential of drug hypersensitivity as well as other adverse effects during the preclinical stage of the drug development process, but validated methods are not available yet. In the present study we examined whether it would be possible to develop a new predictive model of drug hypersensitivity using Brown Norway (BN) rats. As representative drugs with hypersensitivity potential in humans, phenytoin (PHT), carbamazepine (CBZ), amoxicillin (AMX), and sulfamethoxazole (SMX) were orally administered to BN rats for 28 days to investigate their effects on these animals by examinations including observation of clinical signs, hematology, determination of serum IgE levels, histology, and flow cytometric analysis. Skin rashes were not observed in any animals treated with these drugs. Increases in the number of circulating inflammatory cells and serum IgE level did not necessarily occur in the animals treated with these drugs. However, histological examination revealed that germinal center hyperplasia was commonly induced in secondary lymphoid organs/tissues in the animals treated with these drugs. In cytometric analysis, changes in proportions of lymphocyte subsets were noted in the spleen of the animals treated with PHT or CBZ during the early period of administration. The results indicated that the potential of drug hypersensitivity was identified in BN rat by performing histological examination of secondary lymphoid organs/tissues. Data obtained herein suggested that drugs with hypersensitivity potential in humans gained immune reactivity in BN rat, and the germinal center hyperplasia induced by administration of these drugs may serve as a predictive biomarker for drug hypersensitivity occurrence. - Highlights: • We tested Brown Norway rats as a candidate model for predicting drug hypersensitivity. • The allergic drugs did not induce skin rash, whereas D-penicillamine did so in the rats. • Some of allergic drugs increased

  17. Mind the gap : predicting cardiovascular risk during drug development

    NARCIS (Netherlands)

    Chain, Anne S. Y.

    2012-01-01

    Cardiovascular safety issues, specifically drug-induced QT/QTc-interval prolongation, remain a major cause of drug attrition during clinical development and is one of the main causes for post-market drug withdrawals accounting for 15-34% of all drug discontinuation. Given the potentially fatal

  18. Associations between mental disorders and subsequent onset of hypertension

    Science.gov (United States)

    Stein, Dan J.; Aguilar-Gaxiola, Sergio; Alonso, Jordi; Bruffaerts, Ronny; de Jonge, Peter; Liu, Zharoui; Caldas-de-Almeida, Jose Miguel; O’Neill, Siobhan; Viana, Maria Carmen; Al-Hamzawi, Ali Obaid; Angermeyer, Mattias C.; Benjet, Corina; de Graaf, Ron; Ferry, Finola; Kovess-Masfety, Viviane; Levinson, Daphna; de Girolamo, Giovanni; Florescu, Silvia; Hu, Chiyi; Kawakami, Norito; Haro, Josep Maria; Piazza, Marina; Wojtyniak, Bogdan J; Xavier, Miguel; Lim, Carmen C.W.; Kessler, Ronald C.; Scott, Kate

    2013-01-01

    Background Previous work has suggested significant associations between various psychological symptoms (e.g. depression, anxiety, anger, alcohol abuse) and hypertension. However, the presence and extent of associations between common mental disorders and subsequent adult onset of hypertension remains unclear. Further, there is little data available on how such associations vary by gender or over life course. Methods Data from the World Mental Health Surveys (comprising 19 countries, and 52,095 adults) were used. Survival analyses estimated associations between first onset of common mental disorders and subsequent onset of hypertension, with and without psychiatric comorbidity adjustment. Variations in the strength of associations by gender and by life course stage of onset of both the mental disorder and hypertension were investigated. Results After psychiatric comorbidity adjustment, depression, panic disorder, social phobia, specific phobia, binge eating disorder, bulimia nervosa, alcohol abuse, and drug abuse were significantly associated with subsequent diagnosis of hypertension (with ORs ranging from 1.1 to 1.6). Number of lifetime mental disorders was associated with subsequent hypertension in a dose-response fashion. For social phobia and alcohol abuse, associations with hypertension were stronger for males than females. For panic disorder, the association with hypertension was particularly apparent in earlier onset hypertension. Conclusions Depression, anxiety, impulsive eating disorders, and substance use disorders disorders were significantly associated with the subsequent diagnosis of hypertension. These data underscore the importance of early detection of mental disorders, and of physical health monitoring in people with these conditions.. PMID:24342112

  19. Towards a better prediction of peak concentration, volume of distribution and half-life after oral drug administration in man, using allometry.

    Science.gov (United States)

    Sinha, Vikash K; Vaarties, Karin; De Buck, Stefan S; Fenu, Luca A; Nijsen, Marjoleen; Gilissen, Ron A H J; Sanderson, Wendy; Van Uytsel, Kelly; Hoeben, Eva; Van Peer, Achiel; Mackie, Claire E; Smit, Johan W

    2011-05-01

    It is imperative that new drugs demonstrate adequate pharmacokinetic properties, allowing an optimal safety margin and convenient dosing regimens in clinical practice, which then lead to better patient compliance. Such pharmacokinetic properties include suitable peak (maximum) plasma drug concentration (C(max)), area under the plasma concentration-time curve (AUC) and a suitable half-life (t(½)). The C(max) and t(½) following oral drug administration are functions of the oral clearance (CL/F) and apparent volume of distribution during the terminal phase by the oral route (V(z)/F), each of which may be predicted and combined to estimate C(max) and t(½). Allometric scaling is a widely used methodology in the pharmaceutical industry to predict human pharmacokinetic parameters such as clearance and volume of distribution. In our previous published work, we have evaluated the use of allometry for prediction of CL/F and AUC. In this paper we describe the evaluation of different allometric scaling approaches for the prediction of C(max), V(z)/F and t(½) after oral drug administration in man. Twenty-nine compounds developed at Janssen Research and Development (a division of Janssen Pharmaceutica NV), covering a wide range of physicochemical and pharmacokinetic properties, were selected. The C(max) following oral dosing of a compound was predicted using (i) simple allometry alone; (ii) simple allometry along with correction factors such as plasma protein binding (PPB), maximum life-span potential or brain weight (reverse rule of exponents, unbound C(max) approach); and (iii) an indirect approach using allometrically predicted CL/F and V(z)/F and absorption rate constant (k(a)). The k(a) was estimated from (i) in vivo pharmacokinetic experiments in preclinical species; and (ii) predicted effective permeability in man (P(eff)), using a Caco-2 permeability assay. The V(z)/F was predicted using allometric scaling with or without PPB correction. The t(½) was estimated from

  20. Drug versus sweet reward: greater attraction to and preference for sweet versus drug cues.

    Science.gov (United States)

    Madsen, Heather B; Ahmed, Serge H

    2015-05-01

    Despite the unique ability of addictive drugs to directly activate brain reward circuits, recent evidence suggests that drugs induce reinforcing and incentive effects that are comparable to, or even lower than some nondrug rewards. In particular, when rats have a choice between pressing a lever associated with intravenous cocaine or heroin delivery and another lever associated with sweet water delivery, most respond on the latter. This outcome suggests that sweet water is more reinforcing and attractive than either drug. However, this outcome may also be due to the differential ability of sweet versus drug levers to elicit Pavlovian feeding-like conditioned responses that can cause involuntary lever pressing, such as pawing and biting the lever. To test this hypothesis, rats first underwent Pavlovian conditioning to associate one lever with sweet water (0.2% saccharin) and a different lever with intravenous cocaine (0.25 mg) or heroin (0.01 mg). Choice between these two levers was then assessed under two operant choice procedures: one that permitted the expression of Pavlovian-conditioned lever press responses during choice, the other not. During conditioning, Pavlovian-conditioned lever press responses were considerably higher on the sweet lever than on either drug lever, and slightly greater on the heroin lever than on the cocaine lever. Importantly, though these differences in Pavlovian-conditioned behavior predicted subsequent preference for sweet water during choice, they were not required for its expression. Overall, this study confirms that rats prefer the sweet lever because sweet water is more reinforcing and attractive than cocaine or heroin. © 2014 Society for the Study of Addiction.

  1. Identifying Drug-Target Interactions with Decision Templates.

    Science.gov (United States)

    Yan, Xiao-Ying; Zhang, Shao-Wu

    2018-01-01

    During the development process of new drugs, identification of the drug-target interactions wins primary concerns. However, the chemical or biological experiments bear the limitation in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity, chemogenomic methods can be able to predict potential drug-target interactions (DTIs) on a large scale and have no luxurious need about target structures or ligand entries. In order to reflect the cases that the drugs having variant structures interact with common targets and the targets having dissimilar sequences interact with same drugs. In addition, though several other similarity metrics have been developed to predict DTIs, the combination of multiple similarity metrics (especially heterogeneous similarities) is too naïve to sufficiently explore the multiple similarities. In this paper, based on Gene Ontology and pathway annotation, we introduce two novel target similarity metrics to address above issues. More importantly, we propose a more effective strategy via decision template to integrate multiple classifiers designed with multiple similarity metrics. In the scenarios that predict existing targets for new drugs and predict approved drugs for new protein targets, the results on the DTI benchmark datasets show that our target similarity metrics are able to enhance the predictive accuracies in two scenarios. And the elaborate fusion strategy of multiple classifiers has better predictive power than the naïve combination of multiple similarity metrics. Compared with other two state-of-the-art approaches on the four popular benchmark datasets of binary drug-target interactions, our method achieves the best results in terms of AUC and AUPR for predicting available targets for new drugs (S2), and predicting approved drugs for new protein targets (S3).These results demonstrate that our method can effectively predict the drug-target interactions. The software package can

  2. Age at First Concussion Influences the Number of Subsequent Concussions.

    Science.gov (United States)

    Schmidt, Julianne D; Rizzone, Katherine; Hoffman, Nicole L; Weber, Michelle L; Jones, Courtney; Bazarian, Jeff; Broglio, Steven P; McCrea, Michael; McAllister, Thomas W

    2018-04-01

    Individuals who sustain their first concussion during childhood may be at greater risk of sustaining multiple concussions throughout their lifetime because of a longer window of vulnerability. This article aims to estimate the association between age at first concussion and number of subsequent concussions. A total of 23,582 collegiate athletes from 26 universities and military cadets from three military academies completed a concussion history questionnaire (65% males, age 19.9 ± 1.4 years). Participants self-reported concussions and age at time of each injury. Participants with a history of concussion (n = 3,647, 15.5%) were categorized as having sustained their first concussion during childhood (less than ten years old) or adolescence (≥10 and ≤18 years old). Poisson regression was used to model age group (childhood, adolescence) predicting the number of subsequent concussions (0, 1, 2+). A second Poisson regression was developed to determine whether age at first concussion predicted the number of subsequent concussions. Participants self-reporting their first concussion during childhood had an increased risk of subsequent concussions (rate ratio = 2.19, 95% confidence interval: 1.82, 2.64) compared with participants self-reporting their first concussion during adolescence. For every one-year increase in age at first concussion, we observed a 16% reduction in the risk of subsequent concussion (rate ratio = 0.84, 95% confidence interval: 0.82, 0.86). Individuals self-reporting a concussion at a young age sustained a higher number of concussions before age 18. Concussion prevention, recognition, and reporting strategies are of particular need at the youth level. Copyright © 2018 Elsevier Inc. All rights reserved.

  3. Altered drug metabolism during pregnancy: hormonal regulation of drug-metabolizing enzymes.

    Science.gov (United States)

    Jeong, Hyunyoung

    2010-06-01

    Medication use during pregnancy is prevalent, but pharmacokinetic information of most drugs used during pregnancy is lacking in spite of known effects of pregnancy on drug disposition. Accurate pharmacokinetic information is essential for optimal drug therapy in mother and fetus. Thus, understanding how pregnancy influences drug disposition is important for better prediction of pharmacokinetic changes of drugs in pregnant women. Pregnancy is known to affect hepatic drug metabolism, but the underlying mechanisms remain unknown. Physiological changes accompanying pregnancy are probably responsible for the reported alteration in drug metabolism during pregnancy. These include elevated concentrations of various hormones such as estrogen, progesterone, placental growth hormones and prolactin. This review covers how these hormones influence expression of drug-metabolizing enzymes (DMEs), thus potentially responsible for altered drug metabolism during pregnancy. The reader will gain a greater understanding of the altered drug metabolism in pregnant women and the regulatory effects of pregnancy hormones on expression of DMEs. In-depth studies in hormonal regulatory mechanisms as well as confirmatory studies in pregnant women are warranted for systematic understanding and prediction of the changes in hepatic drug metabolism during pregnancy.

  4. Elevated risk of incarceration among street-involved youth who initiate drug dealing

    Directory of Open Access Journals (Sweden)

    Carly Hoy

    2016-11-01

    Full Text Available Abstract Background Street-involved youth are known to be an economically vulnerable population that commonly resorts to risky activities such as drug dealing to generate income. While incarceration is common among people who use illicit drugs and associated with increased economic vulnerability, interventions among this population remain inadequate. Although previous research has documented the role of incarceration in further entrenching youth in both the criminal justice system and street life, less is known whether recent incarceration predicts initiating drug dealing among vulnerable youth. This study examines the relationship between incarceration and drug dealing initiation among street-involved youth. Methods Between September 2005 and November 2014, data were collected through the At-Risk Youth Study, a cohort of street-involved youth who use illicit drugs, in Vancouver, Canada. An extended Cox model with time-dependent variables was used to examine the relationship between recent incarceration and initiation into drug dealing, controlling for relevant confounders. Results Among 1172 youth enrolled, only 194 (16.6% were drug dealing naïve at baseline and completed at least one additional study visit to facilitate the assessment of drug dealing initiation. Among this sample, 56 (29% subsequently initiated drug dealing. In final multivariable Cox regression analysis, recent incarceration was significantly associated with initiating drug dealing (adjusted hazard ratio = 2.31; 95% confidence interval (CI 1.21–4.42, after adjusting for potential confounders. Measures of recent incarceration lagged to the prior study follow-up were not found to predict initiation of drug dealing (hazard ratio = 1.50; 95% CI 0.66–3.42. Conclusions These findings suggest that among this study sample, incarceration does not appear to significantly propel youth to initiate drug dealing. However, the initiation of drug dealing among youth coincides

  5. Inhibition of hippocampal β-adrenergic receptors impairs retrieval but not reconsolidation of cocaine-associated memory and prevents subsequent reinstatement.

    Science.gov (United States)

    Otis, James M; Fitzgerald, Michael K; Mueller, Devin

    2014-01-01

    Retrieval of drug-associated memories is critical for maintaining addictive behaviors, as presentation of drug-associated cues can elicit drug seeking and relapse. Recently, we and others have demonstrated that β-adrenergic receptor (β-AR) activation is necessary for retrieval using both rat and human memory models. Importantly, blocking retrieval with β-AR antagonists persistently impairs retrieval and provides protection against subsequent reinstatement. However, the neural locus at which β-ARs are required for maintaining retrieval and subsequent reinstatement is unclear. Here, we investigated the necessity of dorsal hippocampus (dHipp) β-ARs for drug-associated memory retrieval. Using a cocaine conditioned place preference (CPP) model, we demonstrate that local dHipp β-AR blockade before a CPP test prevents CPP expression shortly and long after treatment, indicating that dHipp β-AR blockade induces a memory retrieval disruption. Furthermore, this retrieval disruption provides long-lasting protection against cocaine-induced reinstatement. The effects of β-AR blockade were dependent on memory reactivation and were not attributable to reconsolidation disruption as blockade of β-ARs immediately after a CPP test had little effect on subsequent CPP expression. Thus, cocaine-associated memory retrieval is mediated by β-AR activity within the dHipp, and disruption of this activity could prevent cue-induced drug seeking and relapse long after treatment.

  6. High levels of C-reactive protein in the peripheral blood during visceral leishmaniasis predict subsequent development of post kala-azar dermal leishmaniasis

    DEFF Research Database (Denmark)

    Gasim, S; Theander, T G; ElHassan, A M

    2000-01-01

    Post kala-azar dermal leishmaniasis (PKDL) is a known sequel to visceral leishmaniasis in India and East Africa, and in Sudan about 50% of the kala-azar patients develop PKDL. In this study we followed kala-azar patients from diagnosis and up to 2 years after initiation of treatment. During...... and in keratinocytes during visceral leishmaniasis predict subsequent development of PKDL. The method however requires expensive equipment and reagents. The results of the present study indicate that kala-azar patients, who have a high risk of developing PKDL after treatment can be identified by measuring plasma CRP....

  7. Using pharmacokinetics to predict the effects of pregnancy and maternal-infant transfer of drugs during lactation.

    Science.gov (United States)

    Anderson, Gail D

    2006-12-01

    Knowledge of pharmacokinetics and the use of a mechanistic-based approach can improve our ability to predict the effects of pregnancy for medications when data are limited. Despite the many physiological changes that occur during pregnancy that could theoretically affect absorption, bioavailability does not appear to be altered. Decreased albumin and alpha(1)-acid glycoprotein concentrations during pregnancy will result in decreased protein binding for highly bound drugs. For drugs metabolised by the liver, this can result in misinterpretation of total plasma concentrations of low extraction ratio drugs and overdosing of high extraction ratio drugs administered by non-oral routes. Renal clearance and the activity of the CYP isozymes, CYP3A4, 2D6 and 2C9, and uridine 5'-diphosphate glucuronosyltransferase are increased during pregnancy. In contrast, CYP1A2 and 2C19 activity is decreased. The dose of a drug an infant receives during breastfeeding is dependent on the amount excreted into the breast milk, the daily volume of milk ingested and the average plasma concentration of the mother. The lipophilicity, protein binding and ionisation properties of a drug will determine how much is excreted into the breast milk. The milk to plasma concentration ratio has large inter- and intrasubject variability and is often not known. In contrast, protein binding is usually known. An extensive literature review was done to identify case reports including infant concentrations from breast-fed infants exposed to maternal drugs. For drugs that were at least 85% protein bound, measurable concentrations of drug in the infant did not occur if there was no placental exposure immediately prior to or during delivery. Knowledge of the protein binding properties of a drug can provide a quick and easy tool to estimate exposure of an infant to medication from breastfeeding.

  8. A predictive ligand-based Bayesian model for human drug-induced liver injury.

    Science.gov (United States)

    Ekins, Sean; Williams, Antony J; Xu, Jinghai J

    2010-12-01

    Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both preapproval and postapproval stages. There has been increased interest in developing predictive in vivo, in vitro, and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study, we applied machine learning, a Bayesian modeling method with extended connectivity fingerprints and other interpretable descriptors. The model that was developed and internally validated (using a training set of 295 compounds) was then applied to a large test set relative to the training set (237 compounds) for external validation. The resulting concordance of 60%, sensitivity of 56%, and specificity of 67% were comparable to results for internal validation. The Bayesian model with extended connectivity functional class fingerprints of maximum diameter 6 (ECFC_6) and interpretable descriptors suggested several substructures that are chemically reactive and may also be important for DILI-causing compounds, e.g., ketones, diols, and α-methyl styrene type structures. Using Smiles Arbitrary Target Specification (SMARTS) filters published by several pharmaceutical companies, we evaluated whether such reactive substructures could be readily detected by any of the published filters. It was apparent that the most stringent filters used in this study, such as the Abbott alerts, which captures thiol traps and other compounds, may be of use in identifying DILI-causing compounds (sensitivity 67%). A significant outcome of the present study is that we provide predictions for many compounds that cause DILI by using the knowledge we have available from previous studies. These computational models may represent cost-effective selection criteria before in vitro or in vivo experimental studies.

  9. Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

    Directory of Open Access Journals (Sweden)

    Andrew D. Revell

    2013-01-01

    Full Text Available Objective. Antiretroviral drug selection in resource-limited settings is often dictated by strict protocols as part of a public health strategy. The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings. Methods. The HIV-TRePS computational models were used to predict the probability of response to therapy for 206 cases of treatment change following failure in India. The models were used to identify alternative locally available 3-drug regimens, which were predicted to be effective. The costs of these regimens were compared to those actually used in the clinic. Results. The models predicted the responses to treatment of the cases with an accuracy of 0.64. The models identified alternative drug regimens that were predicted to result in improved virological response and lower costs than those used in the clinic in 85% of the cases. The average annual cost saving was $364 USD per year (41%. Conclusions. Computational models that do not require a genotype can predict and potentially avoid treatment failure and may reduce therapy costs. The use of such a system to guide therapeutic decision-making could confer health economic benefits in resource-limited settings.

  10. The impact of supersaturation level for oral absorption of BCS class IIb drugs, dipyridamole and ketoconazole, using in vivo predictive dissolution system: Gastrointestinal Simulator (GIS).

    Science.gov (United States)

    Tsume, Yasuhiro; Matsui, Kazuki; Searls, Amanda L; Takeuchi, Susumu; Amidon, Gregory E; Sun, Duxin; Amidon, Gordon L

    2017-05-01

    The development of formulations and the assessment of oral drug absorption for Biopharmaceutical Classification System (BCS) class IIb drugs is often a difficult issue due to the potential for supersaturation and precipitation in the gastrointestinal (GI) tract. The physiological environment in the GI tract largely influences in vivo drug dissolution rates of those drugs. Thus, those physiological factors should be incorporated into the in vitro system to better assess in vivo performance of BCS class IIb drugs. In order to predict oral bioperformance, an in vitro dissolution system with multiple compartments incorporating physiologically relevant factors would be expected to more accurately predict in vivo phenomena than a one-compartment dissolution system like USP Apparatus 2 because, for example, the pH change occurring in the human GI tract can be better replicated in a multi-compartmental platform. The Gastrointestinal Simulator (GIS) consists of three compartments, the gastric, duodenal and jejunal chambers, and is a practical in vitro dissolution apparatus to predict in vivo dissolution for oral dosage forms. This system can demonstrate supersaturation and precipitation and, therefore, has the potential to predict in vivo bioperformance of oral dosage forms where this phenomenon may occur. In this report, in vitro studies were performed with dipyridamole and ketoconazole to evaluate the precipitation rates and the relationship between the supersaturation levels and oral absorption of BCS class II weak base drugs. To evaluate the impact of observed supersaturation levels on oral absorption, a study utilizing the GIS in combination with mouse intestinal infusion was conducted. Supersaturation levels observed in the GIS enhanced dipyridamole and ketoconazole absorption in mouse, and a good correlation between their supersaturation levels and their concentration in plasma was observed. The GIS, therefore, appears to represent in vivo dissolution phenomena and

  11. Potential for Drug Abuse: the Predictive Role of Parenting Styles, Stress and Type D Personality

    Directory of Open Access Journals (Sweden)

    mahin soheili

    2015-06-01

    Full Text Available Objective: This study was an attempt to predict potential for drug abuse on the basis of three predictors of parenting style, stress and type D personality. Method: In this descriptive-correlational study, 200 students (100 males and 100 females of Islamic Azad University of Karaj were selected by convenience sampling. For data collection, perceived parenting styles questionnaire, perceived stress scale, type D personality scale, and addiction potential scale were used. Results: The results showed that rejecting/neglecting parenting style and emotional warmth were positively and negatively correlated with addiction potential, respectively. Conclusion: The child-parent relationship and also the relationship between stress and type D personality can be considered as predictive factors in addiction potential.

  12. Current status of prediction of drug disposition and toxicity in humans using chimeric mice with humanized liver.

    Science.gov (United States)

    Kitamura, Shigeyuki; Sugihara, Kazumi

    2014-01-01

    1. Human-chimeric mice with humanized liver have been constructed by transplantation of human hepatocytes into several types of mice having genetic modifications that injure endogenous liver cells. Here, we focus on liver urokinase-type plasminogen activator-transgenic severe combined immunodeficiency (uPA/SCID) mice, which are the most widely used human-chimeric mice. Studies so far indicate that drug metabolism, drug transport, pharmacological effects and toxicological action in these mice are broadly similar to those in humans. 2. Expression of various drug-metabolizing enzymes is known to be different between humans and rodents. However, the expression pattern of cytochrome P450, aldehyde oxidase and phase II enzymes in the liver of human-chimeric mice resembles that in humans, not that in the host mice. 3. Metabolism of various drugs, including S-warfarin, zaleplon, ibuprofen, naproxen, coumarin, troglitazone and midazolam, in human-chimeric mice is mediated by human drug-metabolizing enzymes, not by host mouse enzymes, and thus resembles that in humans. 4. Pharmacological and toxicological effects of various drugs in human-chimeric mice are also similar to those in humans. 5. The current consensus is that chimeric mice with humanized liver are useful to predict drug metabolism catalyzed by cytochrome P450, aldehyde oxidase and phase II enzymes in humans in vivo and in vitro. Some remaining issues are discussed in this review.

  13. Response to intravenous fentanyl infusion predicts subsequent response to transdermal fentanyl.

    Science.gov (United States)

    Hayashi, Norihito; Kanai, Akifumi; Suzuki, Asaha; Nagahara, Yuki; Okamoto, Hirotsugu

    2016-04-01

    Prediction of the response to transdermal fentanyl (FENtd) before its use for chronic pain is desirable. We tested the hypothesis that the response to intravenous fentanyl infusion (FENiv) can predict the response to FENtd, including the analgesic and adverse effects. The study subjects were 70 consecutive patients with chronic pain. The response to fentanyl at 0.1 mg diluted in 50 ml of physiological saline and infused over 30 min was tested. This was followed by treatment with FENtd (Durotep MT patch 2.1 mg) at a dose of 12.5 µg/h for 2 weeks. Pain intensity before and after FENiv and 2 weeks after FENtd, and the response to treatment, were assessed by the numerical rating scale (NRS), clinical global impression-improvement scale (CGI-I), satisfaction scale (SS), and adverse effects. The NRS score decreased significantly from 7 (4-9) [median (range)] at baseline to 3 (0-8) after FENiv (p 0.04, each). The analgesic and side effects after intravenous fentanyl infusion can be used to predict the response to short-term transdermal treatment with fentanyl.

  14. Computational Analysis of Epidermal Growth Factor Receptor Mutations Predicts Differential Drug Sensitivity Profiles toward Kinase Inhibitors.

    Science.gov (United States)

    Akula, Sravani; Kamasani, Swapna; Sivan, Sree Kanth; Manga, Vijjulatha; Vudem, Dashavantha Reddy; Kancha, Rama Krishna

    2018-05-01

    A significant proportion of patients with lung cancer carry mutations in the EGFR kinase domain. The presence of a deletion mutation in exon 19 or L858R point mutation in the EGFR kinase domain has been shown to cause enhanced efficacy of inhibitor treatment in patients with NSCLC. Several less frequent (uncommon) mutations in the EGFR kinase domain with potential implications in treatment response have also been reported. The role of a limited number of uncommon mutations in drug sensitivity was experimentally verified. However, a huge number of these mutations remain uncharacterized for inhibitor sensitivity or resistance. A large-scale computational analysis of clinically reported 298 point mutants of EGFR kinase domain has been performed, and drug sensitivity profiles for each mutant toward seven kinase inhibitors has been determined by molecular docking. In addition, the relative inhibitor binding affinity toward each drug as compared with that of adenosine triphosphate was calculated for each mutant. The inhibitor sensitivity profiles predicted in this study for a set of previously characterized mutants correlated well with the published clinical, experimental, and computational data. Both the single and compound mutations displayed differential inhibitor sensitivity toward first- and next-generation kinase inhibitors. The present study provides predicted drug sensitivity profiles for a large panel of uncommon EGFR mutations toward multiple inhibitors, which may help clinicians in deciding mutant-specific treatment strategies. Copyright © 2018 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

  15. FDA-approved drugs that are spermatotoxic in animals and the utility of animal testing for human risk prediction.

    Science.gov (United States)

    Rayburn, Elizabeth R; Gao, Liang; Ding, Jiayi; Ding, Hongxia; Shao, Jun; Li, Haibo

    2018-02-01

    This study reviews FDA-approved drugs that negatively impact spermatozoa in animals, as well as how these findings reflect on observations in human male gametes. The FDA drug warning labels included in the DailyMed database and the peer-reviewed literature in the PubMed database were searched for information to identify single-ingredient, FDA-approved prescription drugs with spermatotoxic effects. A total of 235 unique, single-ingredient, FDA-approved drugs reported to be spermatotoxic in animals were identified in the drug labels. Forty-nine of these had documented negative effects on humans in either the drug label or literature, while 31 had no effect or a positive impact on human sperm. For the other 155 drugs that were spermatotoxic in animals, no human data was available. The current animal models are not very effective for predicting human spermatotoxicity, and there is limited information available about the impact of many drugs on human spermatozoa. New approaches should be designed that more accurately reflect the findings in men, including more studies on human sperm in vitro and studies using other systems (ex vivo tissue culture, xenograft models, in silico studies, etc.). In addition, the present data is often incomplete or reported in a manner that prevents interpretation of their clinical relevance. Changes should be made to the requirements for pre-clinical testing, drug surveillance, and the warning labels of drugs to ensure that the potential risks to human fertility are clearly indicated.

  16. Can Google Searches Predict the Popularity and Harm of Psychoactive Agents?

    Science.gov (United States)

    Jankowski, Wojciech; Hoffmann, Marcin

    2016-02-25

    Predicting the popularity of and harm caused by psychoactive agents is a serious problem that would be difficult to do by a single simple method. However, because of the growing number of drugs it is very important to provide a simple and fast tool for predicting some characteristics of these substances. We were inspired by the Google Flu Trends study on the activity of the influenza virus, which showed that influenza virus activity worldwide can be monitored based on queries entered into the Google search engine. Our aim was to propose a fast method for ranking the most popular and most harmful drugs based on easily available data gathered from the Internet. We used the Google search engine to acquire data for the ranking lists. Subsequently, using the resulting list and the frequency of hits for the respective psychoactive drugs combined with the word "harm" or "harmful", we estimated quickly how much harm is associated with each drug. We ranked the most popular and harmful psychoactive drugs. As we conducted the research over a period of several months, we noted that the relative popularity indexes tended to change depending on when we obtained them. This suggests that the data may be useful in monitoring changes over time in the use of each of these psychoactive agents. Our data correlate well with the results from a multicriteria decision analysis of drug harms in the United Kingdom. We showed that Google search data can be a valuable source of information to assess the popularity of and harm caused by psychoactive agents and may help in monitoring drug use trends.

  17. Chick embryo chorioallantoic membrane (CAM): an alternative predictive model in acute toxicological studies for anti-cancer drugs.

    Science.gov (United States)

    Kue, Chin Siang; Tan, Kae Yi; Lam, May Lynn; Lee, Hong Boon

    2015-01-01

    The chick embryo chorioallantoic membrane (CAM) is a preclinical model widely used for vascular and anti-vascular effects of therapeutic agents in vivo. In this study, we examine the suitability of CAM as a predictive model for acute toxicology studies of drugs by comparing it to conventional mouse and rat models for 10 FDA-approved anticancer drugs (paclitaxel, carmustine, camptothecin, cyclophosphamide, vincristine, cisplatin, aloin, mitomycin C, actinomycin-D, melphalan). Suitable formulations for intravenous administration were determined before the average of median lethal dose (LD50) and median survival dose (SD(50)) in the CAM were measured and calculated for these drugs. The resultant ideal LD(50) values were correlated to those reported in the literature using Pearson's correlation test for both intravenous and intraperitoneal routes of injection in rodents. Our results showed moderate correlations (r(2)=0.42 - 0.68, PLD(50) values obtained using the CAM model with LD(50) values from mice and rats models for both intravenous and intraperitoneal administrations, suggesting that the chick embryo may be a suitable alternative model for acute drug toxicity screening before embarking on full toxicological investigations in rodents in development of anticancer drugs.

  18. Subsequent Vertebral Fractures Post Cement Augmentation of the Thoracolumbar Spine: Does it Correlate With Level-specific Bone Mineral Density Scores?

    Science.gov (United States)

    Hey, Hwee Weng Dennis; Hwee Weng, Dennis Hey; Tan, Jun Hao; Jun, Hao Tan; Tan, Chuen Seng; Chuen, Seng Tan; Tan, Hsi Ming Bryan; Ming, Bryan Tan Hsi; Lau, Puang Huh Bernard; Huh, Bernard Lau Puang; Hee, Hwan Tak; Hwan, Tak Hee

    2015-12-01

    A case-control study. In this study, we investigated the correlation between level-specific preoperative bone mineral density and subsequent vertebral fractures. We also identified factors associated with subsequent vertebral fractures. Complications of cement augmentation of the spine include subsequent vertebral fractures, leading to unnecessary morbidity and more treatment. Ability to predict at-risk vertebra will help guide management. We studied all patients with osteoporotic compression fractures who underwent cement augmentation in a single institution from November 2001 to December 2010 by a single surgeon. Association between level-specific bone mineral density T-scores and subsequent fractures was assessed. Multivariable analysis was performed to identify significant factors associated with subsequent vertebral fractures. 93 patients followed up for a mean duration of 25.1 months (12-96) had a mean age of 76.8 years (47-99). Vertebroplasty was performed in 58 patients (62.4%) on 68 levels and kyphoplasty in 35 patients (37.6%) on 44 levels. Refracture was seen in 16 patients (17.2%). The time to subsequent fracture post cement augmentation was 20.5 months (2-90). For refracture cases, 43.8% (7/16) fractured in the adjacent vertebrae. Subsequently fractured vertebra had a mean T-score of -2.860 (95% confidence interval -3.268 to -2.452) and nonfractured vertebra had a mean T-score of -2.180 (95% confidence interval -2.373 to -1.986). A T-score of -2.2 or lower is predictive of refracture at that vertebra (P = 0.047). Odds ratio increases with decreasing T-scores from -2.2 or lower to -2.6 or lower. A T-score of -2.6 or lower gives no additional predictive advantage. After multivariable analysis, age (P = 0.049) and loss of preoperative anterior vertebral height (P = 0.017) are associated with refracture. Level-specific T-scores are predictive of subsequent fractures and the odds ratio increases with lower T-scores from -2.2 or less to -2.6 or less. They

  19. XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

    Science.gov (United States)

    Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

    2013-12-23

    Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

  20. Incarceration and injection drug use in Baltimore, Maryland.

    Science.gov (United States)

    Genberg, Becky L; Astemborski, Jacquie; Vlahov, David; Kirk, Gregory D; Mehta, Shruti H

    2015-07-01

    There is limited longitudinal research examining incarceration and subsequent changes in drug use among people who inject drugs (PWID) in the United States. The objective of the current study was to characterize the frequency of incarceration and estimate the association between incarceration and subsequent injection drug use among current and former PWIDs in one US city. ALIVE (AIDS Linked to the Intravenous Experience) is a prospective cohort study of current and former PWIDs, with semi-annual follow-up occurring since 1988. Baltimore, Maryland, USA. A total of 3245 participants with 48 738 study visits were included. Participants enrolled from 1988 to 2012 with a median of 13 follow-up visits per participant (Interquartile range = 7-25). Incarcerations were defined as any self-reported jail or prison stays in the previous 6 months that were ≥7 days or longer. The primary outcome was defined as any self-reported injection drug use in the previous 6 months. At baseline, 29% were female, 90% African American and 33% HIV-positive. Fifty-seven per cent of participants experienced at least one incarceration episode. After adjusting for confounders, there was a positive association between incarceration and subsequent injection drug use [adjusted odds ratio (AOR) = 1.48, 95% confidence interval (CI) = 1.37-1.59]; however, stratified analysis showed that the effect was restricted to those who were not injecting at the time of incarceration (AOR = 2.11, 95% CI = 1.88-2.37). In the United States, incarceration of people who had previously stopped injecting drugs appears to be associated with an increased risk of subsequent injecting. © 2015 Society for the Study of Addiction.

  1. Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours.

    Science.gov (United States)

    Sharifi, N; Ozgoli, S; Ramezani, A

    2017-06-01

    Mixed immunotherapy and chemotherapy of tumours is one of the most efficient ways to improve cancer treatment strategies. However, it is important to 'design' an effective treatment programme which can optimize the ways of combining immunotherapy and chemotherapy to diminish their imminent side effects. Control engineering techniques could be used for this. The method of multiple model predictive controller (MMPC) is applied to the modified Stepanova model to induce the best combination of drugs scheduling under a better health criteria profile. The proposed MMPC is a feedback scheme that can perform global optimization for both tumour volume and immune competent cell density by performing multiple constraints. Although current studies usually assume that immunotherapy has no side effect, this paper presents a new method of mixed drug administration by employing MMPC, which implements several constraints for chemotherapy and immunotherapy by considering both drug toxicity and autoimmune. With designed controller we need maximum 57% and 28% of full dosage of drugs for chemotherapy and immunotherapy in some instances, respectively. Therefore, through the proposed controller less dosage of drugs are needed, which contribute to suitable results with a perceptible reduction in medicine side effects. It is observed that in the presence of MMPC, the amount of required drugs is minimized, while the tumour volume is reduced. The efficiency of the presented method has been illustrated through simulations, as the system from an initial condition in the malignant region of the state space (macroscopic tumour volume) transfers into the benign region (microscopic tumour volume) in which the immune system can control tumour growth. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. A high content screening assay to predict human drug-induced liver injury during drug discovery.

    Science.gov (United States)

    Persson, Mikael; Løye, Anni F; Mow, Tomas; Hornberg, Jorrit J

    2013-01-01

    Adverse drug reactions are a major cause for failures of drug development programs, drug withdrawals and use restrictions. Early hazard identification and diligent risk avoidance strategies are therefore essential. For drug-induced liver injury (DILI), this is difficult using conventional safety testing. To reduce the risk for DILI, drug candidates with a high risk need to be identified and deselected. And, to produce drug candidates without that risk associated, risk factors need to be assessed early during drug discovery, such that lead series can be optimized on safety parameters. This requires methods that allow for medium-to-high throughput compound profiling and that generate quantitative results suitable to establish structure-activity-relationships during lead optimization programs. We present the validation of such a method, a novel high content screening assay based on six parameters (nuclei counts, nuclear area, plasma membrane integrity, lysosomal activity, mitochondrial membrane potential (MMP), and mitochondrial area) using ~100 drugs of which the clinical hepatotoxicity profile is known. We find that a 100-fold TI between the lowest toxic concentration and the therapeutic Cmax is optimal to classify compounds as hepatotoxic or non-hepatotoxic, based on the individual parameters. Most parameters have ~50% sensitivity and ~90% specificity. Drugs hitting ≥2 parameters at a concentration below 100-fold their Cmax are typically hepatotoxic, whereas non-hepatotoxic drugs typically hit based on nuclei count, MMP and human Cmax, we identified an area without a single false positive, while maintaining 45% sensitivity. Hierarchical clustering using the multi-parametric dataset roughly separates toxic from non-toxic compounds. We employ the assay in discovery projects to prioritize novel compound series during hit-to-lead, to steer away from a DILI risk during lead optimization, for risk assessment towards candidate selection and to provide guidance of safe

  3. Condensational growth of combination drug-excipient submicrometer particles for targeted high efficiency pulmonary delivery: comparison of CFD predictions with experimental results.

    Science.gov (United States)

    Longest, P Worth; Hindle, Michael

    2012-03-01

    The objective of this study was to investigate the hygroscopic growth of combination drug and excipient submicrometer aerosols for respiratory drug delivery using in vitro experiments and a newly developed computational fluid dynamics (CFD) model. Submicrometer combination drug and excipient particles were generated experimentally using both the capillary aerosol generator and the Respimat inhaler. Aerosol hygroscopic growth was evaluated in vitro and with CFD in a coiled tube geometry designed to provide residence times and thermodynamic conditions consistent with the airways. The in vitro results and CFD predictions both indicated that the initially submicrometer particles increased in mean size to a range of 1.6-2.5 μm for the 50:50 combination of a non-hygroscopic drug (budesonide) and different hygroscopic excipients. CFD results matched the in vitro predictions to within 10% and highlighted gradual and steady size increase of the droplets, which will be effective for minimizing extrathoracic deposition and producing deposition deep within the respiratory tract. Enhanced excipient growth (EEG) appears to provide an effective technique to increase pharmaceutical aerosol size, and the developed CFD model will provide a powerful design tool for optimizing this technique to produce high efficiency pulmonary delivery.

  4. Single photon emission computed tomography imaging using 99Tcm-methoxyisobutylisonitrile predict the multi-drug resistance and chemotherapy efficacy of lung cancer

    International Nuclear Information System (INIS)

    Zhang Yiqiu; Shi Hongcheng

    2008-01-01

    Chemotherapy is one of the main comprehensive treatments for lung cancer, especially for non-small cell lung cancer (NSCIC) Multi-drug resistance of lung cancer plays an important role in the failure of chemotherapy. Early detection of multi-drug resistance (MDR) is essential for choosing a suitable chemotherapy regimen for the patients of lung cancer. In recent years lots of literature reports that MDR of lung cancer is related to many kinds of multi-drug resistance protein (MRP) expression in lung cancer. Some lipophilic chemotherapy drugs and 99 Tc m -methoxyisobutylisonitrile( 99 Tc m -MIBI)may be the same substrate for some MRP. These MRP can transport them out of the tumor cells, then the chemotherapy is invalid or non-radioactive concentration. The retention of 99 Tc m -MIBI in tumor cells is correlated with the expression of MRP, thus the prediction of the MRP expression before chemotherapy or monitoring MRP expression changes in the process of chemotherapy by using the noninvasive 99 Tc m -MIBI single photon emission computed tomography imaging is helpful to predict the MDR and chemotherapy efficacy of lung cancer. (authors)

  5. Predicting Subsequent Task Performance From Goal Motivation and Goal Failure

    Directory of Open Access Journals (Sweden)

    Laura Catherine Healy

    2015-07-01

    Full Text Available Recent research has demonstrated that the cognitive processes associated with goal pursuit can continue to interfere with unrelated tasks when a goal is unfulfilled. Drawing from the self-regulation and goal-striving literatures, the present study explored the impact of goal failure on subsequent cognitive and physical task performance. Furthermore, we examined if the autonomous or controlled motivation underpinning goal striving moderates the responses to goal failure. Athletes (75 male, 59 female, Mage = 19.90 years, SDage = 3.50 completed a cycling trial with the goal of covering a given distance in 8 minutes. Prior to the trial, their motivation was primed using a video. During the trial they were provided with manipulated performance feedback, thus creating conditions of goal success or failure. No differences emerged in the responses to goal failure between the primed motivation or performance feedback conditions. We make recommendations for future research into how individuals can deal with failure in goal striving.

  6. Whole genome transcript profiling of drug induced steatosis in rats reveals a gene signature predictive of outcome.

    Directory of Open Access Journals (Sweden)

    Nishika Sahini

    Full Text Available Drug induced steatosis (DIS is characterised by excess triglyceride accumulation in the form of lipid droplets (LD in liver cells. To explore mechanisms underlying DIS we interrogated the publically available microarray data from the Japanese Toxicogenomics Project (TGP to study comprehensively whole genome gene expression changes in the liver of treated rats. For this purpose a total of 17 and 12 drugs which are diverse in molecular structure and mode of action were considered based on their ability to cause either steatosis or phospholipidosis, respectively, while 7 drugs served as negative controls. In our efforts we focused on 200 genes which are considered to be mechanistically relevant in the process of lipid droplet biogenesis in hepatocytes as recently published (Sahini and Borlak, 2014. Based on mechanistic considerations we identified 19 genes which displayed dose dependent responses while 10 genes showed time dependency. Importantly, the present study defined 9 genes (ANGPTL4, FABP7, FADS1, FGF21, GOT1, LDLR, GK, STAT3, and PKLR as signature genes to predict DIS. Moreover, cross tabulation revealed 9 genes to be regulated ≥10 times amongst the various conditions and included genes linked to glucose metabolism, lipid transport and lipogenesis as well as signalling events. Additionally, a comparison between drugs causing phospholipidosis and/or steatosis revealed 26 genes to be regulated in common including 4 signature genes to predict DIS (PKLR, GK, FABP7 and FADS1. Furthermore, a comparison between in vivo single dose (3, 6, 9 and 24 h and findings from rat hepatocyte studies (2 h, 8 h, 24 h identified 10 genes which are regulated in common and contained 2 DIS signature genes (FABP7, FGF21. Altogether, our studies provide comprehensive information on mechanistically linked gene expression changes of a range of drugs causing steatosis and phospholipidosis and encourage the screening of DIS signature genes at the preclinical stage.

  7. A strategy for early-risk predictions of clinical drug-drug interactions involving the GastroPlusTM DDI module for time-dependent CYP inhibitors.

    Science.gov (United States)

    Sohlenius-Sternbeck, Anna-Karin; Meyerson, Gabrielle; Hagbjörk, Ann-Louise; Juric, Sanja; Terelius, Ylva

    2018-04-01

    1. A set of reference compounds for time-dependent inhibition (TDI) of cytochrome P450 with available literature data for k inact and K I was used to predict clinical implications using the GastroPlus TM software. Comparisons were made to in vivo literature interaction data. 2. The predicted AUC ratios (AUC +inhibitor /AUC control ) could be compared with the observed ratios from literature for all compounds with detailed information about in vivo administration, pharmacokinetics and in vivo interactions (N = 21). For this dataset, the difference between predicted and observed AUC ratios for interactions with midazolam was within twofold for all compounds except one (telaprevir, for which non-CYP-mediated metabolism likely plays a role after multiple dosing). 3. The sensitivity, specificity and accuracy of the GastroPlus TM predictions using a binary classification as no-to-weak interaction versus moderate-to-strong interaction for all compounds with available in vivo interaction data, were 80%, 82% and 81%, respectively (N = 31). 4. As a result of our evaluations of the DDI module in GastroPlus TM , we have implemented an early TDI risk assessment decision tree for our drug discovery projects involving in vitro screening and early GastroPlus TM predictions. Shifted IC 50 values are determined and k inact /K I estimated (by using a regression line established with in house-shifted IC 50 values and literature k inact /K I ratios), followed by GastroPlus TM predictions.

  8. Ribonucleotide reductase as a drug target against drug resistance Mycobacterium leprae: A molecular docking study.

    Science.gov (United States)

    Mohanty, Partha Sarathi; Bansal, Avi Kumar; Naaz, Farah; Gupta, Umesh Datta; Dwivedi, Vivek Dhar; Yadava, Umesh

    2018-06-01

    Leprosy is a chronic infection of skin and nerve caused by Mycobacterium leprae. The treatment is based on standard multi drug therapy consisting of dapsone, rifampicin and clofazamine. The use of rifampicin alone or with dapsone led to the emergence of rifampicin-resistant Mycobacterium leprae strains. The emergence of drug-resistant leprosy put a hurdle in the leprosy eradication programme. The present study aimed to predict the molecular model of ribonucleotide reductase (RNR), the enzyme responsible for biosynthesis of nucleotides, to screen new drugs for treatment of drug-resistant leprosy. The study was conducted by retrieving RNR of M. leprae from GenBank. A molecular 3D model of M. leprae was predicted using homology modelling and validated. A total of 325 characters were included in the analysis. The predicted 3D model of RNR showed that the ϕ and φ angles of 251 (96.9%) residues were positioned in the most favoured regions. It was also conferred that 18 α-helices, 6 β turns, 2 γ turns and 48 helix-helix interactions contributed to the predicted 3D structure. Virtual screening of Food and Drug Administration approved drug molecules recovered 1829 drugs of which three molecules, viz., lincomycin, novobiocin and telithromycin, were taken for the docking study. It was observed that the selected drug molecules had a strong affinity towards the modelled protein RNR. This was evident from the binding energy of the drug molecules towards the modelled protein RNR (-6.10, -6.25 and -7.10). Three FDA-approved drugs, viz., lincomycin, novobiocin and telithromycin, could be taken for further clinical studies to find their efficacy against drug resistant leprosy. Copyright © 2018 Elsevier B.V. All rights reserved.

  9. Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

    Science.gov (United States)

    Chen, Lei; Zeng, Wei-Ming; Cai, Yu-Dong; Feng, Kai-Yan; Chou, Kuo-Chen

    2012-01-01

    The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels. PMID:22514724

  10. Kidney-on-a-Chip: a New Technology for Predicting Drug Efficacy, Interactions, and Drug-induced Nephrotoxicity.

    Science.gov (United States)

    Lee, Jeonghwan; Kim, Sejoong

    2018-03-08

    The kidneys play a pivotal role in most drug-removal processes and are important when evaluating drug safety. Kidney dysfunction resulting from various drugs is an important issue in clinical practice and during the drug development process. Traditional in vivo animal experiments are limited with respect to evaluating drug efficacy and nephrotoxicity due to discrepancies in drug pharmacokinetics and pharmacodynamics between humans and animals, and static cell culture experiments cannot fully reflect the actual microphysiological environment in humans. A kidney-on-a-chip is a microfluidic device that allows the culture of living renal cells in 3-dimensional channels and mimics the human microphysiological environment, thus simulating the actual drug filtering, absorption, and secretion process.. In this review, we discuss recent developments in microfluidic culturing technique and describe current and future kidney-on-a-chip applications. We focus on pharmacological interactions and drug-induced nephrotoxicity, and additionally discuss the development of multi-organ chips and their possible applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  11. Context Sensitive Modeling of Cancer Drug Sensitivity.

    Directory of Open Access Journals (Sweden)

    Bo-Juen Chen

    Full Text Available Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER (Contextual Heterogeneity Enabled Regression, an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should-and should not-be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features.

  12. Vaginal drug distribution modeling.

    Science.gov (United States)

    Katz, David F; Yuan, Andrew; Gao, Yajing

    2015-09-15

    This review presents and applies fundamental mass transport theory describing the diffusion and convection driven mass transport of drugs to the vaginal environment. It considers sources of variability in the predictions of the models. It illustrates use of model predictions of microbicide drug concentration distribution (pharmacokinetics) to gain insights about drug effectiveness in preventing HIV infection (pharmacodynamics). The modeling compares vaginal drug distributions after different gel dosage regimens, and it evaluates consequences of changes in gel viscosity due to aging. It compares vaginal mucosal concentration distributions of drugs delivered by gels vs. intravaginal rings. Finally, the modeling approach is used to compare vaginal drug distributions across species with differing vaginal dimensions. Deterministic models of drug mass transport into and throughout the vaginal environment can provide critical insights about the mechanisms and determinants of such transport. This knowledge, and the methodology that obtains it, can be applied and translated to multiple applications, involving the scientific underpinnings of vaginal drug distribution and the performance evaluation and design of products, and their dosage regimens, that achieve it. Copyright © 2015 Elsevier B.V. All rights reserved.

  13. Prediction of phenotypic susceptibility to antiretroviral drugs using physiochemical properties of the primary enzymatic structure combined with artificial neural networks

    DEFF Research Database (Denmark)

    Kjaer, J; Høj, L; Fox, Z

    2008-01-01

    OBJECTIVES: Genotypic interpretation systems extrapolate observed associations in datasets to predict viral susceptibility to antiretroviral drugs (ARVs) for given isolates. We aimed to develop and validate an approach using artificial neural networks (ANNs) that employ descriptors...

  14. The Brain Activity in Brodmann Area 17: A Potential Bio-Marker to Predict Patient Responses to Antiepileptic Drugs.

    Directory of Open Access Journals (Sweden)

    Yida Hu

    Full Text Available In this study, we aimed to predict newly diagnosed patient responses to antiepileptic drugs (AEDs using resting-state functional magnetic resonance imaging tools to explore changes in spontaneous brain activity. We recruited 21 newly diagnosed epileptic patients, 8 drug-resistant (DR patients, 11 well-healed (WH patients, and 13 healthy controls. After a 12-month follow-up, 11 newly diagnosed epileptic patients who showed a poor response to AEDs were placed into the seizures uncontrolled (SUC group, while 10 patients were enrolled in the seizure-controlled (SC group. By calculating the amplitude of fractional low-frequency fluctuations (fALFF of blood oxygen level-dependent signals to measure brain activity during rest, we found that the SUC patients showed increased activity in the bilateral occipital lobe, particularly in the cuneus and lingual gyrus compared with the SC group and healthy controls. Interestingly, DR patients also showed increased activity in the identical cuneus and lingual gyrus regions, which comprise Brodmann's area 17 (BA17, compared with the SUC patients; however, these abnormalities were not observed in SC and WH patients. The receiver operating characteristic (ROC curves indicated that the fALFF value of BA17 could differentiate SUC patients from SC patients and healthy controls with sufficient sensitivity and specificity prior to the administration of medication. Functional connectivity analysis was subsequently performed to evaluate the difference in connectivity between BA17 and other brain regions in the SUC, SC and control groups. Regions nearby the cuneus and lingual gyrus were found positive connectivity increased changes or positive connectivity changes with BA17 in the SUC patients, while remarkably negative connectivity increased changes or positive connectivity decreased changes were found in the SC patients. Additionally, default mode network (DMN regions showed negative connectivity increased changes or

  15. Compulsory drug detention and injection drug use cessation and relapse in Bangkok, Thailand.

    Science.gov (United States)

    Fairbairn, Nadia; Hayashi, Kanna; Ti, Lianping; Kaplan, Karyn; Suwannawong, Paisan; Wood, Evan; Kerr, Thomas

    2015-01-01

    Strategies to promote the reduction and cessation of injection drug use are central to human immunodeficiency virus prevention and treatment efforts globally. Though drug use cessation is a major focus of drug policy in Thailand, little is known about factors associated with injection cessation and relapse in this setting. A cross-sectional study was conducted between July and October 2011 of a community-recruited sample of people who inject drugs in Bangkok, Thailand. Using multivariate logistic regression, we examined the prevalence and correlates of injection drug use cessation with subsequent relapse. Among 422 participants, 209 (49.5%) reported a period of injection drug use cessation of at least one year. In multivariate analyses, incarceration (adjusted odds ratio [AOR] 13.07), voluntary drug treatment (AOR 2.75), midazolam injection (AOR 2.48) and number of years since first injection (AOR 1.07) were positively associated with injection cessation of duration greater than a year (all P Thailand. © 2014 Australasian Professional Society on Alcohol and other Drugs.

  16. The value of 18F-FDG PET before and after induction chemotherapy for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy in oesophageal adenocarcinoma

    International Nuclear Information System (INIS)

    Rossum, Peter S.N. van; Fried, David V.; Zhang, Lifei; Court, Laurence E.; Hofstetter, Wayne L.; Ho, Linus; Meijer, Gert J.; Carter, Brett W.; Lin, Steven H.

    2017-01-01

    The purpose of our study was to determine the value of 18 F-FDG PET before and after induction chemotherapy in patients with oesophageal adenocarcinoma for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy (CRT). In 70 consecutive patients receiving a three-step treatment strategy of induction chemotherapy and preoperative chemoradiotherapy for oesophageal adenocarcinoma, 18 F-FDG PET scans were performed before and after induction chemotherapy (before preoperative CRT). SUV max , SUV mean , metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were determined at these two time points. The predictive potential of (the change in) these parameters for a poor pathologic response, progression-free survival (PFS) and overall survival (OS) was assessed. A poor pathologic response after induction chemotherapy and preoperative CRT was found in 27 patients (39 %). Patients with a poor pathologic response experienced less of a reduction in TLG after induction chemotherapy (p < 0.01). The change in TLG was predictive for a poor pathologic response at a threshold of -26 % (sensitivity 67 %, specificity 84 %, accuracy 77 %, PPV 72 %, NPV 80 %), yielding an area-under-the-curve of 0.74 in ROC analysis. Also, patients with a decrease in TLG lower than 26 % had a significantly worse PFS (p = 0.02), but not OS (p = 0.18). 18 F-FDG PET appears useful to predict a poor pathologic response as well as PFS early after induction chemotherapy in patients with oesophageal adenocarcinoma undergoing a three-step treatment strategy. As such, the early 18 F-FDG PET response after induction chemotherapy could aid in individualizing treatment by modification or withdrawal of subsequent preoperative CRT in poor responders. (orig.)

  17. Treatment default amongst patients with tuberculosis in urban Morocco: predicting and explaining default and post-default sputum smear and drug susceptibility results.

    Science.gov (United States)

    Cherkaoui, Imad; Sabouni, Radia; Ghali, Iraqi; Kizub, Darya; Billioux, Alexander C; Bennani, Kenza; Bourkadi, Jamal Eddine; Benmamoun, Abderrahmane; Lahlou, Ouafae; Aouad, Rajae El; Dooley, Kelly E

    2014-01-01

    Public tuberculosis (TB) clinics in urban Morocco. Explore risk factors for TB treatment default and develop a prediction tool. Assess consequences of default, specifically risk for transmission or development of drug resistance. Case-control study comparing patients who defaulted from TB treatment and patients who completed it using quantitative methods and open-ended questions. Results were interpreted in light of health professionals' perspectives from a parallel study. A predictive model and simple tool to identify patients at high risk of default were developed. Sputum from cases with pulmonary TB was collected for smear and drug susceptibility testing. 91 cases and 186 controls enrolled. Independent risk factors for default included current smoking, retreatment, work interference with adherence, daily directly observed therapy, side effects, quick symptom resolution, and not knowing one's treatment duration. Age >50 years, never smoking, and having friends who knew one's diagnosis were protective. A simple scoring tool incorporating these factors was 82.4% sensitive and 87.6% specific for predicting default in this population. Clinicians and patients described additional contributors to default and suggested locally-relevant intervention targets. Among 89 cases with pulmonary TB, 71% had sputum that was smear positive for TB. Drug resistance was rare. The causes of default from TB treatment were explored through synthesis of qualitative and quantitative data from patients and health professionals. A scoring tool with high sensitivity and specificity to predict default was developed. Prospective evaluation of this tool coupled with targeted interventions based on our findings is warranted. Of note, the risk of TB transmission from patients who default treatment to others is likely to be high. The commonly-feared risk of drug resistance, though, may be low; a larger study is required to confirm these findings.

  18. Treatment Default amongst Patients with Tuberculosis in Urban Morocco: Predicting and Explaining Default and Post-Default Sputum Smear and Drug Susceptibility Results

    Science.gov (United States)

    Ghali, Iraqi; Kizub, Darya; Billioux, Alexander C.; Bennani, Kenza; Bourkadi, Jamal Eddine; Benmamoun, Abderrahmane; Lahlou, Ouafae; Aouad, Rajae El; Dooley, Kelly E.

    2014-01-01

    Setting Public tuberculosis (TB) clinics in urban Morocco. Objective Explore risk factors for TB treatment default and develop a prediction tool. Assess consequences of default, specifically risk for transmission or development of drug resistance. Design Case-control study comparing patients who defaulted from TB treatment and patients who completed it using quantitative methods and open-ended questions. Results were interpreted in light of health professionals’ perspectives from a parallel study. A predictive model and simple tool to identify patients at high risk of default were developed. Sputum from cases with pulmonary TB was collected for smear and drug susceptibility testing. Results 91 cases and 186 controls enrolled. Independent risk factors for default included current smoking, retreatment, work interference with adherence, daily directly observed therapy, side effects, quick symptom resolution, and not knowing one’s treatment duration. Age >50 years, never smoking, and having friends who knew one’s diagnosis were protective. A simple scoring tool incorporating these factors was 82.4% sensitive and 87.6% specific for predicting default in this population. Clinicians and patients described additional contributors to default and suggested locally-relevant intervention targets. Among 89 cases with pulmonary TB, 71% had sputum that was smear positive for TB. Drug resistance was rare. Conclusion The causes of default from TB treatment were explored through synthesis of qualitative and quantitative data from patients and health professionals. A scoring tool with high sensitivity and specificity to predict default was developed. Prospective evaluation of this tool coupled with targeted interventions based on our findings is warranted. Of note, the risk of TB transmission from patients who default treatment to others is likely to be high. The commonly-feared risk of drug resistance, though, may be low; a larger study is required to confirm these findings

  19. Treatment default amongst patients with tuberculosis in urban Morocco: predicting and explaining default and post-default sputum smear and drug susceptibility results.

    Directory of Open Access Journals (Sweden)

    Imad Cherkaoui

    Full Text Available Public tuberculosis (TB clinics in urban Morocco.Explore risk factors for TB treatment default and develop a prediction tool. Assess consequences of default, specifically risk for transmission or development of drug resistance.Case-control study comparing patients who defaulted from TB treatment and patients who completed it using quantitative methods and open-ended questions. Results were interpreted in light of health professionals' perspectives from a parallel study. A predictive model and simple tool to identify patients at high risk of default were developed. Sputum from cases with pulmonary TB was collected for smear and drug susceptibility testing.91 cases and 186 controls enrolled. Independent risk factors for default included current smoking, retreatment, work interference with adherence, daily directly observed therapy, side effects, quick symptom resolution, and not knowing one's treatment duration. Age >50 years, never smoking, and having friends who knew one's diagnosis were protective. A simple scoring tool incorporating these factors was 82.4% sensitive and 87.6% specific for predicting default in this population. Clinicians and patients described additional contributors to default and suggested locally-relevant intervention targets. Among 89 cases with pulmonary TB, 71% had sputum that was smear positive for TB. Drug resistance was rare.The causes of default from TB treatment were explored through synthesis of qualitative and quantitative data from patients and health professionals. A scoring tool with high sensitivity and specificity to predict default was developed. Prospective evaluation of this tool coupled with targeted interventions based on our findings is warranted. Of note, the risk of TB transmission from patients who default treatment to others is likely to be high. The commonly-feared risk of drug resistance, though, may be low; a larger study is required to confirm these findings.

  20. Luminal flow amplifies stent-based drug deposition in arterial bifurcations.

    Directory of Open Access Journals (Sweden)

    Vijaya B Kolachalama

    2009-12-01

    Full Text Available Treatment of arterial bifurcation lesions using drug-eluting stents (DES is now common clinical practice and yet the mechanisms governing drug distribution in these complex morphologies are incompletely understood. It is still not evident how to efficiently determine the efficacy of local drug delivery and quantify zones of excessive drug that are harbingers of vascular toxicity and thrombosis, and areas of depletion that are associated with tissue overgrowth and luminal re-narrowing.We constructed two-phase computational models of stent-deployed arterial bifurcations simulating blood flow and drug transport to investigate the factors modulating drug distribution when the main-branch (MB was treated using a DES. Simulations predicted extensive flow-mediated drug delivery in bifurcated vascular beds where the drug distribution patterns are heterogeneous and sensitive to relative stent position and luminal flow. A single DES in the MB coupled with large retrograde luminal flow on the lateral wall of the side-branch (SB can provide drug deposition on the SB lumen-wall interface, except when the MB stent is downstream of the SB flow divider. In an even more dramatic fashion, the presence of the SB affects drug distribution in the stented MB. Here fluid mechanic effects play an even greater role than in the SB especially when the DES is across and downstream to the flow divider and in a manner dependent upon the Reynolds number.The flow effects on drug deposition and subsequent uptake from endovascular DES are amplified in bifurcation lesions. When only one branch is stented, a complex interplay occurs - drug deposition in the stented MB is altered by the flow divider imposed by the SB and in the SB by the presence of a DES in the MB. The use of DES in arterial bifurcations requires a complex calculus that balances vascular and stent geometry as well as luminal flow.

  1. Condensational Growth of Combination Drug-Excipient Submicrometer Particles for Targeted High Efficiency Pulmonary Delivery: Comparison of CFD Predictions with Experimental Results

    Science.gov (United States)

    Hindle, Michael

    2011-01-01

    Purpose The objective of this study was to investigate the hygroscopic growth of combination drug and excipient submicrometer aerosols for respiratory drug delivery using in vitro experiments and a newly developed computational fluid dynamics (CFD) model. Methods Submicrometer combination drug and excipient particles were generated experimentally using both the capillary aerosol generator and the Respimat inhaler. Aerosol hygroscopic growth was evaluated in vitro and with CFD in a coiled tube geometry designed to provide residence times and thermodynamic conditions consistent with the airways. Results The in vitro results and CFD predictions both indicated that the initially submicrometer particles increased in mean size to a range of 1.6–2.5 µm for the 50:50 combination of a non-hygroscopic drug (budesonide) and different hygroscopic excipients. CFD results matched the in vitro predictions to within 10% and highlighted gradual and steady size increase of the droplets, which will be effective for minimizing extrathoracic deposition and producing deposition deep within the respiratory tract. Conclusions Enhanced excipient growth (EEG) appears to provide an effective technique to increase pharmaceutical aerosol size, and the developed CFD model will provide a powerful design tool for optimizing this technique to produce high efficiency pulmonary delivery. PMID:21948458

  2. Prediction of drug-related cardiac adverse effects in humans--A: creation of a database of effects and identification of factors affecting their occurrence.

    Science.gov (United States)

    Matthews, Edwin J; Frid, Anna A

    2010-04-01

    This is the first of two reports that describes the compilation of a database of drug-related cardiac adverse effects (AEs) that was used to construct quantitative structure-activity relationship (QSAR) models to predict these AEs, to identify properties of pharmaceuticals correlated with the AEs, and to identify plausible mechanisms of action (MOAs) causing the AEs. This database of 396,985 cardiac AE reports was linked to 1632 approved drugs and their chemical structures, 1851 clinical indications (CIs), 997 therapeutic targets (TTs), 432 pharmacological MOAs, and 21,180 affinity coefficients (ACs) for the MOA receptors. AEs were obtained from the Food and Drug Administration's (FDA's) Spontaneous Reporting System (SRS) and Adverse Event Reporting System (AERS) and publicly available medical literature. Drug TTs were obtained from Integrity; drug MOAs and ACs were predicted by BioEpisteme. Significant cardiac AEs and patient exposures were estimated based on the proportional reporting ratios (PRRs) for each drug and each AE endpoint as a percentage of the total AEs. Cardiac AE endpoints were bundled based on toxicological mechanism and concordance of drug-related findings. Results revealed that significant cardiac AEs formed 9 clusters affecting Purkinje nerve fibers (arrhythmia, bradycardia, conduction disorder, electrocardiogram, palpitations, QT prolongation, rate rhythm composite, tachycardia, and Torsades de pointes), and 5 clusters affecting the heart muscle (coronary artery disorders, heart failure, myocardial disorders, myocardial infarction, and valve disorders). Based on the observation that each drug had one TT and up to 9 off-target MOAs, cardiac AEs were highly correlated with drugs affecting cardiovascular and cardioneurological functions and certain MOAs (e.g., alpha- and beta-adeno, dopamine, and hydroxytryptomine receptors). Copyright 2010. Published by Elsevier Inc.

  3. The influence of children's pain memories on subsequent pain experience.

    Science.gov (United States)

    Noel, Melanie; Chambers, Christine T; McGrath, Patrick J; Klein, Raymond M; Stewart, Sherry H

    2012-08-01

    Healthy children are often required to repeatedly undergo painful medical procedures (eg, immunizations). Although memory is often implicated in children's reactions to future pain, there is a dearth of research directly examining the relationship between the 2. The current study investigated the influence of children's memories for a novel pain stimulus on their subsequent pain experience. One hundred ten healthy children (60 boys) between the ages of 8 and 12 years completed a laboratory pain task and provided pain ratings. Two weeks later, children provided pain ratings based on their memories as well as their expectancies about future pain. One month following the initial laboratory visit, children again completed the pain task and provided pain ratings. Results showed that children's memory of pain intensity was a better predictor of subsequent pain reporting than their actual initial reporting of pain intensity, and mediated the relationship between initial and subsequent pain reporting. Children who had negatively estimated pain memories developed expectations of greater pain prior to a subsequent pain experience and showed greater increases in pain ratings over time than children who had accurate or positively estimated pain memories. These findings highlight the influence of pain memories on healthy children's expectations of future pain and subsequent pain experiences and extend predictive models of subsequent pain reporting. Copyright © 2012 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

  4. Large-scale exploration and analysis of drug combinations.

    Science.gov (United States)

    Li, Peng; Huang, Chao; Fu, Yingxue; Wang, Jinan; Wu, Ziyin; Ru, Jinlong; Zheng, Chunli; Guo, Zihu; Chen, Xuetong; Zhou, Wei; Zhang, Wenjuan; Li, Yan; Chen, Jianxin; Lu, Aiping; Wang, Yonghua

    2015-06-15

    Drug combinations are a promising strategy for combating complex diseases by improving the efficacy and reducing corresponding side effects. Currently, a widely studied problem in pharmacology is to predict effective drug combinations, either through empirically screening in clinic or pure experimental trials. However, the large-scale prediction of drug combination by a systems method is rarely considered. We report a systems pharmacology framework to predict drug combinations (PreDCs) on a computational model, termed probability ensemble approach (PEA), for analysis of both the efficacy and adverse effects of drug combinations. First, a Bayesian network integrating with a similarity algorithm is developed to model the combinations from drug molecular and pharmacological phenotypes, and the predictions are then assessed with both clinical efficacy and adverse effects. It is illustrated that PEA can predict the combination efficacy of drugs spanning different therapeutic classes with high specificity and sensitivity (AUC = 0.90), which was further validated by independent data or new experimental assays. PEA also evaluates the adverse effects (AUC = 0.95) quantitatively and detects the therapeutic indications for drug combinations. Finally, the PreDC database includes 1571 known and 3269 predicted optimal combinations as well as their potential side effects and therapeutic indications. The PreDC database is available at http://sm.nwsuaf.edu.cn/lsp/predc.php. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  5. Non-destructive prediction of enteric coating layer thickness and drug dissolution rate by near-infrared spectroscopy and X-ray computed tomography.

    Science.gov (United States)

    Ariyasu, Aoi; Hattori, Yusuke; Otsuka, Makoto

    2017-06-15

    The coating layer thickness of enteric-coated tablets is a key factor that determines the drug dissolution rate from the tablet. Near-infrared spectroscopy (NIRS) enables non-destructive and quick measurement of the coating layer thickness, and thus allows the investigation of the relation between enteric coating layer thickness and drug dissolution rate. Two marketed products of aspirin enteric-coated tablets were used in this study, and the correlation between the predicted coating layer thickness and the obtained drug dissolution rate was investigated. Our results showed correlation for one product; the drug dissolution rate decreased with the increase in enteric coating layer thickness, whereas, there was no correlation for the other product. Additional examination of the distribution of coating layer thickness by X-ray computed tomography (CT) showed homogenous distribution of coating layer thickness for the former product, whereas the latter product exhibited heterogeneous distribution within the tablet, as well as inconsistent trend in the thickness distribution between the tablets. It was suggested that this heterogeneity and inconsistent trend in layer thickness distribution contributed to the absence of correlation between the layer thickness of the face and side regions of the tablets, which resulted in the loss of correlation between the coating layer thickness and drug dissolution rate. Therefore, the predictability of drug dissolution rate from enteric-coated tablets depended on the homogeneity of the coating layer thickness. In addition, the importance of micro analysis, X-ray CT in this study, was suggested even if the macro analysis, NIRS in this study, are finally applied for the measurement. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Strategy for Hepatotoxicity Prediction Induced by Drug Reactive Metabolites Using Human Liver Microsome and Online 2D-Nano-LC-MS Analysis.

    Science.gov (United States)

    Zhuo, Yue; Wu, Jian-Lin; Yan, Xiaojing; Guo, Ming-Quan; Liu, Ning; Zhou, Hua; Liu, Liang; Li, Na

    2017-12-19

    Hepatotoxicity is a leading cause of drug withdrawal from the market; thus, the assessment of potential drug induced liver injury (DILI) in preclinical trials is necessary. More and more research has shown that the covalent modification of drug reactive metabolites (RMs) for cellular proteins is a possible reason for DILI. Unfortunately, so far no appropriate method can be employed to evaluate this kind of DILI due to the low abundance of RM-protein adducts in complex biological samples. In this study, we proposed a mechanism-based strategy to solve this problem using human liver microsomes (HLMs) and online 2D nano-LC-MS analysis. First, RM modification patterns and potential modified AA residues are determined using HLM and model amino acids (AAs) by UHPLC-Q-TOF-MS. Then, a new online 2D-nano-LC-Q-TOF-MS method is established and applied to separate the digested modified microsomal peptides from high abundance peptides followed by identification of RM-modified proteins using Mascot, in which RM modification patterns on specific AA residues are added. Finally, the functions and relationship with hepatotoxicity of the RM-modified proteins are investigated using ingenuity pathway analysis (IPA) to predict the possible DILI. Using this strategy, 21 proteins were found to be modified by RMs of toosendanin, a hepatotoxic drug with complex structure, and some of them have been reported to be associated with hepatotoxicity. This strategy emphasizes the identification of drug RM-modified proteins in complex biological samples, and no pretreatment is required for the drugs. Consequently, it may serve as a valuable method to predict potential DILI, especially for complex compounds.

  7. Use of sexual risk assessment and feedback at intake to promote counselor awareness of subsequent client risk behavior during early treatment.

    Science.gov (United States)

    Hartzler, Bryan; Beadnell, Blair; Calsyn, Donald A

    2014-08-01

    Sexual risk is an important, oft-neglected area in addiction treatment. This report examines computerized sexual risk assessment and client feedback at intake as means of enhancing counselor awareness of client risk behavior during early treatment, as well as any clinical impact of that counselor awareness. In 2009-2011, new clients at both opiate treatment and drug-free treatment programs endorsed in a computer-assisted assessment at intake 90-day retrospective indices for: being sexually active, having multiple partners, having sex under drug influence, and inconsistently using condoms. Clients were randomly assigned in a 2:1 ratio to receive or not receive a personal feedback report, and those receiving a report chose if a counselor copy was also distributed. Ninety days later, retained clients (N = 79) repeated the assessment and their counselors concurrently reported perceptions of recent client risk behavior. Based on client reports, pretreatment risk behaviors were prevalent among men and women and remained so during treatment. A general linear model revealed greater counselor awareness of subsequent client risk behavior with mutual distribution of intake feedback reports to client and counselor, and at the opiate treatment program. A repeated-measures analysis of variance indicated that counselor awareness did not predict change in temporally stable patterns of sexual risk behavior. CONCLUSIONS/IMPORTANCE: Findings document that computerized intake assessment of sexual risk and mutually distributed feedback reports prompt greater counselor awareness of clients' subsequent risk behavior. Future research is needed to determine how best to prepare counselors to use such awareness to effectively prompt risk reduction in routine care.

  8. Hepatic transporter drug-drug interactions: an evaluation of approaches and methodologies.

    Science.gov (United States)

    Williamson, Beth; Riley, Robert J

    2017-12-01

    Drug-drug interactions (DDIs) continue to account for 5% of hospital admissions and therefore remain a major regulatory concern. Effective, quantitative prediction of DDIs will reduce unexpected clinical findings and encourage projects to frontload DDI investigations rather than concentrating on risk management ('manage the baggage') later in drug development. A key challenge in DDI prediction is the discrepancies between reported models. Areas covered: The current synopsis focuses on four recent influential publications on hepatic drug transporter DDIs using static models that tackle interactions with individual transporters and in combination with other drug transporters and metabolising enzymes. These models vary in their assumptions (including input parameters), transparency, reproducibility and complexity. In this review, these facets are compared and contrasted with recommendations made as to their application. Expert opinion: Over the past decade, static models have evolved from simple [I]/k i models to incorporate victim and perpetrator disposition mechanisms including the absorption rate constant, the fraction of the drug metabolised/eliminated and/or clearance concepts. Nonetheless, models that comprise additional parameters and complexity do not necessarily out-perform simpler models with fewer inputs. Further, consideration of the property space to exploit some drug target classes has also highlighted the fine balance required between frontloading and back-loading studies to design out or 'manage the baggage'.

  9. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

    Science.gov (United States)

    Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

    2016-01-01

    Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

  10. Chimeric mice transplanted with human hepatocytes as a model for prediction of human drug metabolism and pharmacokinetics.

    Science.gov (United States)

    Sanoh, Seigo; Ohta, Shigeru

    2014-03-01

    Preclinical studies in animal models are used routinely during drug development, but species differences of pharmacokinetics (PK) between animals and humans have to be taken into account in interpreting the results. Human hepatocytes are also widely used to examine metabolic activities mediated by cytochrome P450 (P450) and other enzymes, but such in vitro metabolic studies also have limitations. Recently, chimeric mice with humanized liver (h-chimeric mice), generated by transplantation of human donor hepatocytes, have been developed as a model for the prediction of metabolism and PK in humans, using both in vitro and in vivo approaches. The expression of human-specific metabolic enzymes and metabolic activities was confirmed in humanized liver of h-chimeric mice with high replacement ratios, and several reports indicate that the profiles of P450 and non-P450 metabolism in these mice adequately reflect those in humans. Further, the combined use of h-chimeric mice and r-chimeric mice, in which endogenous hepatocytes are replaced with rat hepatocytes, is a promising approach for evaluation of species differences in drug metabolism. Recent work has shown that data obtained in h-chimeric mice enable the semi-quantitative prediction of not only metabolites, but also PK parameters, such as hepatic clearance, of drug candidates in humans, although some limitations remain because of differences in the metabolic activities, hepatic blood flow and liver structure between humans and mice. In addition, fresh h-hepatocytes can be isolated reproducibly from h-chimeric mice for metabolic studies. Copyright © 2013 John Wiley & Sons, Ltd.

  11. Prediction and validation of diffusion coefficients in a model drug delivery system using microsecond atomistic molecular dynamics simulation and vapour sorption analysis.

    Science.gov (United States)

    Forrey, Christopher; Saylor, David M; Silverstein, Joshua S; Douglas, Jack F; Davis, Eric M; Elabd, Yossef A

    2014-10-14

    Diffusion of small to medium sized molecules in polymeric medical device materials underlies a broad range of public health concerns related to unintended leaching from or uptake into implantable medical devices. However, obtaining accurate diffusion coefficients for such systems at physiological temperature represents a formidable challenge, both experimentally and computationally. While molecular dynamics simulation has been used to accurately predict the diffusion coefficients, D, of a handful of gases in various polymers, this success has not been extended to molecules larger than gases, e.g., condensable vapours, liquids, and drugs. We present atomistic molecular dynamics simulation predictions of diffusion in a model drug eluting system that represent a dramatic improvement in accuracy compared to previous simulation predictions for comparable systems. We find that, for simulations of insufficient duration, sub-diffusive dynamics can lead to dramatic over-prediction of D. We present useful metrics for monitoring the extent of sub-diffusive dynamics and explore how these metrics correlate to error in D. We also identify a relationship between diffusion and fast dynamics in our system, which may serve as a means to more rapidly predict diffusion in slowly diffusing systems. Our work provides important precedent and essential insights for utilizing atomistic molecular dynamics simulations to predict diffusion coefficients of small to medium sized molecules in condensed soft matter systems.

  12. Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

    Science.gov (United States)

    Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

    2016-07-05

    Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

  13. Aptamers as Both Drugs and Drug-Carriers

    Directory of Open Access Journals (Sweden)

    Md. Ashrafuzzaman

    2014-01-01

    Full Text Available Aptamers are short nucleic acid oligos. They may serve as both drugs and drug-carriers. Their use as diagnostic tools is also evident. They can be generated using various experimental, theoretical, and computational techniques. The systematic evolution of ligands by exponential enrichment which uses iterative screening of nucleic acid libraries is a popular experimental technique. Theory inspired methodology entropy-based seed-and-grow strategy that designs aptamer templates to bind specifically to targets is another one. Aptamers are predicted to be highly useful in producing general drugs and theranostic drugs occasionally for certain diseases like cancer, Alzheimer’s disease, and so on. They bind to various targets like lipids, nucleic acids, proteins, small organic compounds, and even entire organisms. Aptamers may also serve as drug-carriers or nanoparticles helping drugs to get released in specific target regions. Due to better target specific physical binding properties aptamers cause less off-target toxicity effects. Therefore, search for aptamer based drugs, drug-carriers, and even diagnostic tools is expanding fast. The biophysical properties in relation to the target specific binding phenomena of aptamers, energetics behind the aptamer transport of drugs, and the consequent biological implications will be discussed. This review will open up avenues leading to novel drug discovery and drug delivery.

  14. Effects of Mother-Infant Social Interactions on Infants' Subsequent Contingency Task Performance.

    Science.gov (United States)

    Dunham, Philip; Dunham, Frances

    1990-01-01

    Infants participated in a nonsocial contingency task immediately after a social interaction with their mothers. The amount of time mothers and infants spent in a state of vocal turn-taking predicted individual differences in infants' subsequent performance on the contingency task. (PCB)

  15. Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy

    Science.gov (United States)

    2013-03-01

    Neratinib Versus Lapatinib Plus Capecitabine For ErbB2 Positive Advanced Breast Cancer Active, not recruiting No Results Available YES neratinib -9...Drug: Neratinib |Drug: Lapatinib|Drug: Capecitabine Efficacy and Safety of BMS-690514 in Combination With Letrozole to Treat Metastatic Breast Cancer

  16. The associations of earlier trauma exposures and history of mental disorders with PTSD after subsequent traumas.

    Science.gov (United States)

    Kessler, R C; Aguilar-Gaxiola, S; Alonso, J; Bromet, E J; Gureje, O; Karam, E G; Koenen, K C; Lee, S; Liu, H; Pennell, B-E; Petukhova, M V; Sampson, N A; Shahly, V; Stein, D J; Atwoli, L; Borges, G; Bunting, B; de Girolamo, G; Gluzman, S F; Haro, J M; Hinkov, H; Kawakami, N; Kovess-Masfety, V; Navarro-Mateu, F; Posada-Villa, J; Scott, K M; Shalev, A Y; Ten Have, M; Torres, Y; Viana, M C; Zaslavsky, A M

    2017-09-19

    Although earlier trauma exposure is known to predict posttraumatic stress disorder (PTSD) after subsequent traumas, it is unclear whether this association is limited to cases where the earlier trauma led to PTSD. Resolution of this uncertainty has important implications for research on pretrauma vulnerability to PTSD. We examined this issue in the World Health Organization (WHO) World Mental Health (WMH) Surveys with 34 676 respondents who reported lifetime trauma exposure. One lifetime trauma was selected randomly for each respondent. DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th Edition) PTSD due to that trauma was assessed. We reported in a previous paper that four earlier traumas involving interpersonal violence significantly predicted PTSD after subsequent random traumas (odds ratio (OR)=1.3-2.5). We also assessed 14 lifetime DSM-IV mood, anxiety, disruptive behavior and substance disorders before random traumas. We show in the current report that only prior anxiety disorders significantly predicted PTSD in a multivariate model (OR=1.5-4.3) and that these disorders interacted significantly with three of the earlier traumas (witnessing atrocities, physical violence victimization and rape). History of witnessing atrocities significantly predicted PTSD after subsequent random traumas only among respondents with prior PTSD (OR=5.6). Histories of physical violence victimization (OR=1.5) and rape after age 17 years (OR=17.6) significantly predicted only among respondents with no history of prior anxiety disorders. Although only preliminary due to reliance on retrospective reports, these results suggest that history of anxiety disorders and history of a limited number of earlier traumas might usefully be targeted in future prospective studies as distinct foci of research on individual differences in vulnerability to PTSD after subsequent traumas.Molecular Psychiatry advance online publication, 19 September 2017; doi:10.1038/mp.2017.194.

  17. A Case of Microstomia Subsequent to Toxic Epidermal Necrolysis Surgically Treated by Simple Technique

    Directory of Open Access Journals (Sweden)

    Takanobu Mashiko, MD

    2013-06-01

    Full Text Available Summary: Toxic epidermal necrolysis (TEN is a rare but severe adverse dermatitis that is an autoimmune reaction to drugs such as nonsteroidal anti-inflammatory drugs. TEN most severely affects the mucous membranes including the mouth and could develop into microstomia; however, microstomia in relation to TEN has rarely been reported in the literature. We describe an adult female patient who developed microstomia due to scar contracture of the bilateral oral commissures subsequent to TEN and was successfully treated by a simple surgical technique consisting solely of transverse incision of the commissure and longitudinal closure.

  18. Combined Transcriptomics and Metabolomics in a Rhesus Macaque Drug Administration Study

    Directory of Open Access Journals (Sweden)

    Kevin J. Lee

    2014-10-01

    Full Text Available We describe a multi-omic approach to understanding the effects that the anti-malarial drug pyrimethamine has on immune physiology in rhesus macaques (Macaca mulatta. Whole blood and bone marrow RNA-Seq and plasma metabolome profiles (each with over 15,000 features have been generated for five naïve individuals at up to seven time-points before, during and after three rounds of drug administration. Linear modelling and Bayesian network analyses are both considered, alongside investigations of the impact of statistical modeling strategies on biological inference. Individual macaques were found to be a major source of variance for both omic data types, and factoring individuals into subsequent modelling increases power to detect temporal effects. A major component of the whole blood transcriptome follows the bone marrow with a time-delay, while other components of variation are unique to each compartment. We demonstrate that pyrimethamine administration does impact both compartments throughout the experiment, but very limited perturbation of transcript or metabolite abundance following each round of drug exposure is observed. New insights into the mode of action of the drug are presented in the context of pyrimethamine’s predicted effect on suppression of cell division and metabolism in the immune system.

  19. The White Adolescent's Drug Odyssey.

    Science.gov (United States)

    Lipton, Douglas S.; Marel, Rozanne

    1980-01-01

    Presents a "typical" case history of a White middle-class teenager who becomes involved with marihuana and subsequently begins to abuse other drugs. Sociological findings from other research are interspersed in the anecdotal account. (GC)

  20. ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

    Science.gov (United States)

    Hou, Tingjun; Xu, Xiaojie

    2002-12-01

    In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

  1. Prediction of interindividual variation in drug plasma levels in vivo from individual enzyme kinetic data and physiologically based pharmacokinetic modeling

    NARCIS (Netherlands)

    Bogaards, J.J.P.; Hissink, E.M.; Briggs, M.; Weaver, R.; Jochemsen, R.; Jackson, P.; Bertrand, M.; Bladeren, P. van

    2000-01-01

    A strategy is presented to predict interindividual variation in drug plasma levels in vivo by the use of physiologically based pharmacokinetic modeling and human in vitro metabolic parameters, obtained through the combined use of microsomes containing single cytochrome P450 enzymes and a human liver

  2. Biodegradable microcontainers as an oral drug delivery system for poorly soluble drugs

    DEFF Research Database (Denmark)

    Nielsen, Line Hagner; Nagstrup, Johan; Keller, Stephan Sylvest

    2013-01-01

    PURPOSE: To fabricate microcontainers in biodegradable polylactic acid (PLLA) polymer films using hot embossing, and investigate the application of fabricated microcontainers as an oral drug delivery system for a poorly soluble drug. METHODS: For fabrication of the PLLA microcontainers, a film...... (produced by spray drying) using a simplified version of a screen printing technique. An enteric-resistant lid of Eudragit L-100 was subsequently spray coated onto the cavity of the microcontainers. Release of amorphous furosemide salt from the coated microcontainers was investigated using a μ-Diss profiler...... release from microcontainers in gastric medium, and facilitated an immediate release in the intestinal medium. The fabricated microcontainers therefore show considerable future potential as oral drug delivery systems....

  3. Alcohol as a Gateway Drug: A Study of US 12th Graders

    Science.gov (United States)

    Kirby, Tristan; Barry, Adam E.

    2012-01-01

    Background: The Gateway Drug Theory suggests that licit drugs, such as tobacco and alcohol, serve as a "gateway" toward the use of other, illicit drugs. However, there remains some discrepancy regarding which drug--alcohol, tobacco, or even marijuana--serves as the initial "gateway" drug subsequently leading to the use of…

  4. Prediction methods and databases within chemoinformatics

    DEFF Research Database (Denmark)

    Jónsdóttir, Svava Osk; Jørgensen, Flemming Steen; Brunak, Søren

    2005-01-01

    MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information...... about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability...

  5. The Role of Drug Metabolites in the Inhibition of Cytochrome P450 Enzymes.

    Science.gov (United States)

    Mikov, Momir; Đanić, Maja; Pavlović, Nebojša; Stanimirov, Bojan; Goločorbin-Kon, Svetlana; Stankov, Karmen; Al-Salami, Hani

    2017-12-01

    Following the drug administration, patients are exposed not only to the parent drug itself, but also to the metabolites generated by drug-metabolizing enzymes. The role of drug metabolites in cytochrome P450 (CYP) inhibition and subsequent drug-drug interactions (DDIs) have recently become a topic of considerable interest and scientific debate. The list of metabolites that were found to significantly contribute to clinically relevant DDIs is constantly being expanded and reported in the literature. New strategies have been developed for better understanding how different metabolites of a drug candidate contribute to its pharmacokinetic properties and pharmacological as well as its toxicological effects. However, the testing of the role of metabolites in CYP inhibition is still not routinely performed during the process of drug development, although the evaluation of time-dependent CYP inhibition during the clinical candidate selection process may provide information on possible effects of metabolites in CYP inhibition. Due to large number of compounds to be tested in the early stages of drug discovery, the experimental approaches for assessment of CYP-mediated metabolic profiles are particularly resource demanding. Consequently, a large number of in silico or computational tools have been developed as useful complement to experimental approaches. In summary, circulating metabolites may be recognized as significant CYP inhibitors. Current data may suggest the need for an optimized effort to characterize the inhibitory potential of parent drugs metabolites on CYP, as well as the necessity to develop the advanced in vitro models that would allow a better quantitative predictive value of in vivo studies.

  6. Drug-Target Kinetics in Drug Discovery.

    Science.gov (United States)

    Tonge, Peter J

    2018-01-17

    The development of therapies for the treatment of neurological cancer faces a number of major challenges including the synthesis of small molecule agents that can penetrate the blood-brain barrier (BBB). Given the likelihood that in many cases drug exposure will be lower in the CNS than in systemic circulation, it follows that strategies should be employed that can sustain target engagement at low drug concentration. Time dependent target occupancy is a function of both the drug and target concentration as well as the thermodynamic and kinetic parameters that describe the binding reaction coordinate, and sustained target occupancy can be achieved through structural modifications that increase target (re)binding and/or that decrease the rate of drug dissociation. The discovery and deployment of compounds with optimized kinetic effects requires information on the structure-kinetic relationships that modulate the kinetics of binding, and the molecular factors that control the translation of drug-target kinetics to time-dependent drug activity in the disease state. This Review first introduces the potential benefits of drug-target kinetics, such as the ability to delineate both thermodynamic and kinetic selectivity, and then describes factors, such as target vulnerability, that impact the utility of kinetic selectivity. The Review concludes with a description of a mechanistic PK/PD model that integrates drug-target kinetics into predictions of drug activity.

  7. Maintenance treatment with azathioprine in ulcerative colitis: outcome and predictive factors after drug withdrawal.

    Science.gov (United States)

    Cassinotti, Andrea; Actis, Giovanni C; Duca, Piergiorgio; Massari, Alessandro; Colombo, Elisabetta; Gai, Elisa; Annese, Vito; D'Albasio, Giuseppe; Manes, Gianpiero; Travis, Simon; Porro, Gabriele Bianchi; Ardizzone, Sandro

    2009-11-01

    Whether the duration of maintenance treatment with azathioprine (AZA) affects the outcome of ulcerative colitis (UC) is unclear. We investigated clinical outcomes and any predictive factors after withdrawal of AZA in UC. In this multicenter observational retrospective study, 127 Italian UC patients, who were in steroid-free remission at the time of withdrawal of AZA, were followed-up for a median of 55 months or until relapse. The frequency of clinical relapse or colectomy after AZA withdrawal was analyzed according to demographic, clinical, and endoscopic variables. After drug withdrawal, a third of the patients relapsed within 12 months, half within 2 years and two-thirds within 5 years. After multivariable analysis, predictors of relapse after drug withdrawal were lack of sustained remission during AZA maintenance (hazard ratio, HR 2.350, confidence interval, CI 95% 1.434-3.852; P=0.001), extensive colitis (HR 1.793, CI 95% 1.064-3.023, P=0.028 vs. left-sided colitis; HR 2.024, CI 95% 1.103-3.717, P=0.023 vs. distal colitis), and treatment duration, with short treatments (3-6 months) more disadvantaged than >48-month treatments (HR 2.783, CI 95% 1.267-6.114, P=0.008). Concomitant aminosalicylates were the only predictors of sustained remission during AZA therapy (P=0.009). The overall colectomy rate was 10%. Predictors of colectomy were drug-related toxicity as the cause of AZA withdrawal (P=0.041), no post-AZA drug therapy (P=0.031), and treatment duration (P<0.0005). Discontinuation of AZA while UC is in remission is associated with a high relapse rate. Disease extent, lack of sustained remission during AZA, and discontinuation due to toxicity could stratify relapse risk. Concomitant aminosalicylates were advantageous. Prospective randomized controlled trials are needed to confirm whether treatment duration is inversely associated with outcome.

  8. Modelled in vivo HIV fitness under drug selective pressure and estimated genetic barrier towards resistance are predictive for virological response

    DEFF Research Database (Denmark)

    Deforche, Koen; Cozzi-Lepri, Alessandro; Theys, Kristof

    2008-01-01

    landscapes (nelfinavir [NFV] and zidovudine [AZT] plus lamivudine [3TC]) to predict week 12 viral load (VL) change for 176 treatment change episodes (TCEs) and probability of week 48 virological failure for 90 TCEs, in treatment experienced patients starting these drugs in combination. RESULTS: A higher...

  9. Quantitative self-assembly prediction yields targeted nanomedicines

    Science.gov (United States)

    Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

    2018-02-01

    Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

  10. Comparison of self-administration behavior and responsiveness to drug-paired cues in rats running an alley for intravenous heroin and cocaine.

    Science.gov (United States)

    Su, Zu-In; Wenzel, Jennifer; Baird, Rebeccah; Ettenberg, Aaron

    2011-04-01

    Evidence suggests that responsiveness to a drug-paired cue is predicted by the reinforcing magnitude of the drug during prior self-administration. It remains unclear, however, if this principle holds true when comparisons are made across drug reinforcers. The current study was therefore devised to test the hypothesis that differences in the animals' responsiveness to a cocaine- or heroin-paired cue presented during extinction would reflect differences in the patterns of prior cocaine and heroin runway self-administration. Rats ran a straight alley for single intravenous injections of either heroin (0.1 mg/kg/inj) or cocaine (1.0 mg/kg/inj) each paired with a distinct olfactory cue. Animals experienced 15 trials with each drug reinforcer in a counterbalanced manner. Start latencies, run times, and retreat behaviors (a form of approach-avoidance conflict) provided behavioral indices of the subjects' motivation to seek the reinforcer on each trial. Responsiveness to each drug-paired cue was assessed after 7, 14, or 21 days of non-reinforced extinction trials. Other animals underwent conditioned place preference (CPP) testing to ensure that the two drug reinforcers were capable of producing drug-cue associations. While both drugs produced comparable CPPs, heroin served as a stronger incentive stimulus in the runway as evidenced by faster start and run times and fewer retreats. In contrast, cocaine- but not heroin-paired cues produced increases in drug-seeking behavior during subsequent extinction trials. The subjects' responsiveness to drug-paired cues during extinction was not predicted by differences in the motivation to seek heroin versus cocaine during prior drug self-administration.

  11. Encoded exposure to tobacco use in social media predicts subsequent smoking behavior.

    Science.gov (United States)

    Depue, Jacob B; Southwell, Brian G; Betzner, Anne E; Walsh, Barbara M

    2015-01-01

    Assessing the potential link between smoking behavior and exposure to mass media depictions of smoking on social networking Web sites. A representative longitudinal panel of 200 young adults in Connecticut. Telephone surveys were conducted by using computer assisted telephone interviewing technology and electronic dialing for random digit dialing and listed samples. Connecticut residents aged 18 to 24 years. To measure encoded exposure, respondents were asked whether or not they had smoked a cigarette in the past 30 days and about how often they had seen tobacco use on television, in movies, and in social media content. Respondents were also asked about cigarette use in the past 30 days, and a series of additional questions that have been shown to be predictive of tobacco use. Logistic regression was used to test for our main prediction that reported exposure to social media tobacco depictions at time 1 would influence time 2 smoking behavior. Encoded exposure to social media tobacco depictions (B = .47, p media depictions of tobacco use predict future smoking tendency, over and above the influence of TV and movie depictions of smoking. This is the first known study to specifically assess the role of social media in informing tobacco behavior.

  12. Does early improvement in depressive symptoms predict subsequent remission in patients with depression who are treated with duloxetine?

    Directory of Open Access Journals (Sweden)

    Sueki A

    2016-05-01

    Full Text Available Akitsugu Sueki, Eriko Suzuki, Hitoshi Takahashi, Jun Ishigooka Department of Neuropsychiatry, Tokyo Women’s Medical University, Tokyo, Japan Purpose: In this prospective study, we examined whether early reduction in depressive symptoms predicts later remission to duloxetine in the treatment of depression, as monitored using the Montgomery–Asberg Depression Rating Scale (MADRS. Patients and methods: Among the 106 patients who were enrolled in this study, 67 were included in the statistical analysis. A clinical evaluation using the MADRS was performed at weeks 0, 4, 8, 12, and 16 after commencing treatment. For each time point, the MADRS total score was separated into three components: dysphoria, retardation, and vegetative scores. Results: Remission was defined as an MADRS total score of ≤10 at end point. From our univariate logistic regression analysis, we found that improvements in both the MADRS total score and the dysphoria score at week 4 had a significant interaction with subsequent remission. Furthermore, age and sex were significant predictors of remission. There was an increase of approximately 4% in the odds of remission for each unit increase in age, and female sex had an odds of remission of 0.318 times that of male sex (remission rate for men was 73.1% [19/26] and for women 46.3% [19/41]. However, in the multivariate model using the change from baseline in the total MADRS, dysphoria, retardation, and vegetative scores at week 4, in which age and sex were included as covariates, only sex retained significance, except for an improvement in the dysphoria score. Conclusion: No significant interaction was found between early response to duloxetine and eventual remission in this study. Sex difference was found to be a predictor of subsequent remission in patients with depression who were treated with duloxetine, with the male sex having greater odds of remission. Keywords: antidepressant, early response, sex difference, serotonin

  13. Cancer in silico drug discovery: a systems biology tool for identifying candidate drugs to target specific molecular tumor subtypes.

    Science.gov (United States)

    San Lucas, F Anthony; Fowler, Jerry; Chang, Kyle; Kopetz, Scott; Vilar, Eduardo; Scheet, Paul

    2014-12-01

    Large-scale cancer datasets such as The Cancer Genome Atlas (TCGA) allow researchers to profile tumors based on a wide range of clinical and molecular characteristics. Subsequently, TCGA-derived gene expression profiles can be analyzed with the Connectivity Map (CMap) to find candidate drugs to target tumors with specific clinical phenotypes or molecular characteristics. This represents a powerful computational approach for candidate drug identification, but due to the complexity of TCGA and technology differences between CMap and TCGA experiments, such analyses are challenging to conduct and reproduce. We present Cancer in silico Drug Discovery (CiDD; scheet.org/software), a computational drug discovery platform that addresses these challenges. CiDD integrates data from TCGA, CMap, and Cancer Cell Line Encyclopedia (CCLE) to perform computational drug discovery experiments, generating hypotheses for the following three general problems: (i) determining whether specific clinical phenotypes or molecular characteristics are associated with unique gene expression signatures; (ii) finding candidate drugs to repress these expression signatures; and (iii) identifying cell lines that resemble the tumors being studied for subsequent in vitro experiments. The primary input to CiDD is a clinical or molecular characteristic. The output is a biologically annotated list of candidate drugs and a list of cell lines for in vitro experimentation. We applied CiDD to identify candidate drugs to treat colorectal cancers harboring mutations in BRAF. CiDD identified EGFR and proteasome inhibitors, while proposing five cell lines for in vitro testing. CiDD facilitates phenotype-driven, systematic drug discovery based on clinical and molecular data from TCGA. ©2014 American Association for Cancer Research.

  14. The value of {sup 18}F-FDG PET before and after induction chemotherapy for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy in oesophageal adenocarcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Rossum, Peter S.N. van [The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, TX (United States); University Medical Center Utrecht, Department of Radiation Oncology, Utrecht (Netherlands); Fried, David V.; Zhang, Lifei; Court, Laurence E. [The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX (United States); Hofstetter, Wayne L. [The University of Texas MD Anderson Cancer Center, Department of Thoracic and Cardiovascular Surgery, Houston, TX (United States); Ho, Linus [The University of Texas MD Anderson Cancer Center, Department of Gastrointestinal Medical Oncology, Houston, TX (United States); Meijer, Gert J. [University Medical Center Utrecht, Department of Radiation Oncology, Utrecht (Netherlands); Carter, Brett W. [The University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, TX (United States); Lin, Steven H. [The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, TX (United States)

    2017-01-15

    The purpose of our study was to determine the value of {sup 18}F-FDG PET before and after induction chemotherapy in patients with oesophageal adenocarcinoma for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy (CRT). In 70 consecutive patients receiving a three-step treatment strategy of induction chemotherapy and preoperative chemoradiotherapy for oesophageal adenocarcinoma, {sup 18}F-FDG PET scans were performed before and after induction chemotherapy (before preoperative CRT). SUV{sub max}, SUV{sub mean}, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were determined at these two time points. The predictive potential of (the change in) these parameters for a poor pathologic response, progression-free survival (PFS) and overall survival (OS) was assessed. A poor pathologic response after induction chemotherapy and preoperative CRT was found in 27 patients (39 %). Patients with a poor pathologic response experienced less of a reduction in TLG after induction chemotherapy (p < 0.01). The change in TLG was predictive for a poor pathologic response at a threshold of -26 % (sensitivity 67 %, specificity 84 %, accuracy 77 %, PPV 72 %, NPV 80 %), yielding an area-under-the-curve of 0.74 in ROC analysis. Also, patients with a decrease in TLG lower than 26 % had a significantly worse PFS (p = 0.02), but not OS (p = 0.18). {sup 18}F-FDG PET appears useful to predict a poor pathologic response as well as PFS early after induction chemotherapy in patients with oesophageal adenocarcinoma undergoing a three-step treatment strategy. As such, the early {sup 18}F-FDG PET response after induction chemotherapy could aid in individualizing treatment by modification or withdrawal of subsequent preoperative CRT in poor responders. (orig.)

  15. Evaluation of the use of Göttingen minipigs to predict food effects on the oral absorption of drugs in humans

    DEFF Research Database (Denmark)

    Christiansen, Martin Lau; Müllertz, Anette; Garmer, Mats

    2015-01-01

    This study investigated the oral absorption of drugs in minipigs to predict food effects in man. The protocol was based on a previously described model in dogs and further investigated the food source (i.e., US FDA breakfast or a nutritional drink) and food quantities. Two poorly soluble compounds...

  16. Modelling drug flux through microporated skin.

    Science.gov (United States)

    Rzhevskiy, Alexey S; Guy, Richard H; Anissimov, Yuri G

    2016-11-10

    A simple mathematical equation has been developed to predict drug flux through microporated skin. The theoretical model is based on an approach applied previously to water evaporation through leaf stomata. Pore density, pore radius and drug molecular weight are key model parameters. The predictions of the model were compared with results derived from a simple, intuitive method using porated area alone to estimate the flux enhancement. It is shown that the new approach predicts significantly higher fluxes than the intuitive analysis, with transport being proportional to the total pore perimeter rather than area as intuitively anticipated. Predicted fluxes were in good general agreement with experimental data on drug delivery from the literature, and were quantitatively closer to the measured values than those derived from the intuitive, area-based approach. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains.

    Science.gov (United States)

    Schroeder, Scott R; Salomon, Meghan M; Galanter, William L; Schiff, Gordon D; Vaida, Allen J; Gaunt, Michael J; Bryson, Michelle L; Rash, Christine; Falck, Suzanne; Lambert, Bruce L

    2017-05-01

    Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors. Published by the BMJ

  18. Use of machine learning approaches for novel drug discovery.

    Science.gov (United States)

    Lima, Angélica Nakagawa; Philot, Eric Allison; Trossini, Gustavo Henrique Goulart; Scott, Luis Paulo Barbour; Maltarollo, Vinícius Gonçalves; Honorio, Kathia Maria

    2016-01-01

    The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds. This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening. Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

  19. Modulation of fusiform cortex activity by cholinesterase inhibition predicts effects on subsequent memory.

    Science.gov (United States)

    Bentley, P; Driver, J; Dolan, R J

    2009-09-01

    Cholinergic influences on memory are likely to be expressed at several processing stages, including via well-recognized effects of acetylcholine on stimulus processing during encoding. Since previous studies have shown that cholinesterase inhibition enhances visual extrastriate cortex activity during stimulus encoding, especially under attention-demanding tasks, we tested whether this effect correlates with improved subsequent memory. In a within-subject physostigmine versus placebo design, we measured brain activity with functional magnetic resonance imaging while healthy and mild Alzheimer's disease subjects performed superficial and deep encoding tasks on face (and building) visual stimuli. We explored regions in which physostigmine modulation of face-selective neural responses correlated with physostigmine effects on subsequent recognition performance. In healthy subjects physostigmine led to enhanced later recognition for deep- versus superficially-encoded faces, which correlated across subjects with a physostigmine-induced enhancement of face-selective responses in right fusiform cortex during deep- versus superficial-encoding tasks. In contrast, the Alzheimer's disease group showed neither a depth of processing effect nor restoration of this with physostigmine. Instead, patients showed a task-independent improvement in confident memory with physostigmine, an effect that correlated with enhancements in face-selective (but task-independent) responses in bilateral fusiform cortices. Our results indicate that one mechanism by which cholinesterase inhibitors can improve memory is by enhancing extrastriate cortex stimulus selectivity at encoding, in a manner that for healthy people but not in Alzheimer's disease is dependent upon depth of processing.

  20. Sex-dependent independent prediction of incident diabetes by depressive symptoms.

    Science.gov (United States)

    Akbaş-Şimşek, Tuğba; Onat, Altan; Kaya, Adnan; Tusun, Eyyup; Yüksel, Hüsniye; Can, Günay

    2017-12-01

    To study the predictive value of depressive symptoms (DeprSs) in a general population of Turkey for type 2 diabetes. Responses to three questions served to assess the sense of depression. Cox regression analyses were used regarding risk estimates for incident diabetes, after exclusion of prevalent cases of diabetes. Mean follow-up consisted of 5.15 (±1.4) years. Depressive symptoms were present at baseline in 16.2% of the whole study sample, threefold in women than men. Reduced physical activity grade was the only significant covariate at baseline in men, while younger age and lower blood pressure were significantly different in women compared with those without DeprS. In men, presence of DeprS predicted incident diabetes at a significant 2.58-fold relative risk (95% confidence interval 1.03; 6.44), after adjustment for age, systolic blood pressure, and antidepressant drug usage. When further covariates were added, waist circumference remained the only significant predictor, while DepS was attenuated to a relative risk of 2.12 (95% confidence interval 0.83; 5.40). DeprS was not associated with diabetes in women, whereas antidepressant drug usage only tended to be positively associated. Gender difference existed in the relationship between DeprS and incident diabetes. DeprS predicted subsequent development of diabetes in men alone, not in women. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  1. Drug-drug interactions of antifungal agents and implications for patient care.

    Science.gov (United States)

    Gubbins, Paul O; Amsden, Jarrett R

    2005-10-01

    Drug interactions in the gastrointestinal tract, liver and kidneys result from alterations in pH, ionic complexation, and interference with membrane transport proteins and enzymatic processes involved in intestinal absorption, enteric and hepatic metabolism, renal filtration and excretion. Azole antifungals can be involved in drug interactions at all the sites, by one or more of the above mechanisms. Consequently, azoles interact with a vast array of compounds. Drug-drug interactions associated with amphotericin B formulations are predictable and result from the renal toxicity and electrolyte disturbances associated with these compounds. The echinocandins are unknown cytochrome P450 substrates and to date are relatively devoid of significant drug-drug interactions. This article reviews drug interactions involving antifungal agents that affect other agents and implications for patient care are highlighted.

  2. The Contribution of Africentric Values and Racial Identity to the Prediction of Drug Knowledge, Attitudes, and Use among African American Youth.

    Science.gov (United States)

    Belgrave, Faye Z.; Brome, Deborah Ridley; Hampton, Carl

    2000-01-01

    Investigated the contribution of cultural variables, particularly Africentric values and racial identity, to the prediction of drug use, knowledge, and attitudes among African American youths, highlighting individual, peer, and family variables. Data from upper elementary students who participated in a prevention program indicated that Africentric…

  3. Posttransplant sCD30 as a biomarker to predict kidney graft outcome.

    Science.gov (United States)

    Süsal, Caner; Opelz, Gerhard

    2012-09-08

    In current clinical praxis, monitoring of immunosuppressive agents in organ transplantation is restricted to measurement of drug blood levels and does not consider the drug's variable effect on the individual patient's immune system. Establishment of biological markers that measure the biological effect of immunosuppressive drugs is desirable and would enable the identification of patients who are at risk of developing rejection, or patients who are suitable for minimization or weaning of immunosuppressive therapy. Several studies demonstrated that the technically simple posttransplant measurement in serum of the T cell activation marker soluble CD30 (sCD30) allows prediction of subsequent graft loss in kidney transplant recipients. sCD30 is a relatively large molecule and therefore an attractive biological marker which is resistant to repeated thawing cycles and temperature differences and easily determined using commercial ELISA. Whether sCD30-based prospective adjustment of immunosuppressive therapy can prevent irreversible graft damage and improve long-term graft outcome awaits evaluation in randomized controlled trials. Copyright © 2011 Elsevier B.V. All rights reserved.

  4. Design, Characterization, and Optimization of Controlled Drug Delivery System Containing Antibiotic Drug/s

    Directory of Open Access Journals (Sweden)

    Apurv Patel

    2016-01-01

    Full Text Available The objective of this work was design, characterization, and optimization of controlled drug delivery system containing antibiotic drug/s. Osmotic drug delivery system was chosen as controlled drug delivery system. The porous osmotic pump tablets were designed using Plackett-Burman and Box-Behnken factorial design to find out the best formulation. For screening of three categories of polymers, six independent variables were chosen for Plackett-Burman design. Osmotic agent sodium chloride and microcrystalline cellulose, pore forming agent sodium lauryl sulphate and sucrose, and coating agent ethyl cellulose and cellulose acetate were chosen as independent variables. Optimization of osmotic tablets was done by Box-Behnken design by selecting three independent variables. Osmotic agent sodium chloride, pore forming agent sodium lauryl sulphate, and coating agent cellulose acetate were chosen as independent variables. The result of Plackett-Burman and Box-Behnken design and ANOVA studies revealed that osmotic agent and pore former had significant effect on the drug release up to 12 hr. The observed independent variables were found to be very close to predicted values of most satisfactory formulation which demonstrates the feasibility of the optimization procedure in successful development of porous osmotic pump tablets containing antibiotic drug/s by using sodium chloride, sodium lauryl sulphate, and cellulose acetate as key excipients.

  5. A proposal for a pharmacokinetic interaction significance classification system (PISCS) based on predicted drug exposure changes and its potential application to alert classifications in product labelling.

    Science.gov (United States)

    Hisaka, Akihiro; Kusama, Makiko; Ohno, Yoshiyuki; Sugiyama, Yuichi; Suzuki, Hiroshi

    2009-01-01

    Pharmacokinetic drug-drug interactions (DDIs) are one of the major causes of adverse events in pharmacotherapy, and systematic prediction of the clinical relevance of DDIs is an issue of significant clinical importance. In a previous study, total exposure changes of many substrate drugs of cytochrome P450 (CYP) 3A4 caused by coadministration of inhibitor drugs were successfully predicted by using in vivo information. In order to exploit these predictions in daily pharmacotherapy, the clinical significance of the pharmacokinetic changes needs to be carefully evaluated. The aim of the present study was to construct a pharmacokinetic interaction significance classification system (PISCS) in which the clinical significance of DDIs was considered with pharmacokinetic changes in a systematic manner. Furthermore, the classifications proposed by PISCS were compared in a detailed manner with current alert classifications in the product labelling or the summary of product characteristics used in Japan, the US and the UK. A matrix table was composed by stratifying two basic parameters of the prediction: the contribution ratio of CYP3A4 to the oral clearance of substrates (CR), and the inhibition ratio of inhibitors (IR). The total exposure increase was estimated for each cell in the table by associating CR and IR values, and the cells were categorized into nine zones according to the magnitude of the exposure increase. Then, correspondences between the DDI significance and the zones were determined for each drug group considering the observed exposure changes and the current classification in the product labelling. Substrate drugs of CYP3A4 selected from three therapeutic groups, i.e. HMG-CoA reductase inhibitors (statins), calcium-channel antagonists/blockers (CCBs) and benzodiazepines (BZPs), were analysed as representative examples. The product labelling descriptions of drugs in Japan, US and UK were obtained from the websites of each regulatory body. Among 220

  6. Role of laboratory biomarkers in monitoring and prediction of the effectiveness of treatment of rheumatic diseases using genetically engineered drugs

    Directory of Open Access Journals (Sweden)

    Elena Nikolayevna Aleksandrova

    2014-03-01

    Full Text Available Significant progress in treating immunoinflammatory rheumatic diseases (RD is related to the design of a novel family of drugs, genetically engineered (GE drugs. Molecular and cellular biomarkers (antibodies, indicators of acute inflammation, cytokines, chemokines, growth factors, endothelial activation markers, immunoglobulins, cryoglobulins, T- and B-cell subpopulations, products of bone and cartilage metabolism, genetic and metabolic markers that allow one to conduct immunological monitoring and prediction of the effectiveness of RD therapy using tumor necrosis factor α inhibitors (infliximab, adalimumab, golimumab, etanercept, anti-B-cell drugs (rituximab, belimumab, interleukin-6 receptor antagonist (tocilizumab, and T-cell costimulation blocker (abatacept have been detected in blood, synovial fluid, urine, and bioptates of the affected tissues. In addition to the conventional uniplex immunodiagnostics techniques, multiplex analysis of marker, which is based on genetic, transcriptomic and proteomic technologies using DNA and protein microarrays, polymerase chain reaction, and flow cytometry, is becoming increasingly widespread. The search for and validation of immunological predictors of the effective response to GE drug therapy make it possible to optimize and reduce the cost of therapy using these drugs in future.

  7. Role of laboratory biomarkers in monitoring and prediction of the effectiveness of treatment of rheumatic diseases using genetically engineered drugs

    Directory of Open Access Journals (Sweden)

    Elena Nikolayevna Aleksandrova

    2014-01-01

    Full Text Available Significant progress in treating immunoinflammatory rheumatic diseases (RD is related to the design of a novel family of drugs, genetically engineered (GE drugs. Molecular and cellular biomarkers (antibodies, indicators of acute inflammation, cytokines, chemokines, growth factors, endothelial activation markers, immunoglobulins, cryoglobulins, T- and B-cell subpopulations, products of bone and cartilage metabolism, genetic and metabolic markers that allow one to conduct immunological monitoring and prediction of the effectiveness of RD therapy using tumor necrosis factor α inhibitors (infliximab, adalimumab, golimumab, etanercept, anti-B-cell drugs (rituximab, belimumab, interleukin-6 receptor antagonist (tocilizumab, and T-cell costimulation blocker (abatacept have been detected in blood, synovial fluid, urine, and bioptates of the affected tissues. In addition to the conventional uniplex immunodiagnostics techniques, multiplex analysis of marker, which is based on genetic, transcriptomic and proteomic technologies using DNA and protein microarrays, polymerase chain reaction, and flow cytometry, is becoming increasingly widespread. The search for and validation of immunological predictors of the effective response to GE drug therapy make it possible to optimize and reduce the cost of therapy using these drugs in future.

  8. A drug cost model for injuries due to road traffic accidents.

    Directory of Open Access Journals (Sweden)

    Riewpaiboon A

    2008-03-01

    Full Text Available Objective: This study aimed to develop a drug cost model for injuries due to road traffic accidents for patients receiving treatment at a regional hospital in Thailand. Methods: The study was designed as a retrospective, descriptive analysis. The cases were all from road traffic accidents receiving treatment at a public regional hospital in the fiscal year 2004. Results: Three thousand seven hundred and twenty-three road accident patients were included in the study. The mean drug cost per case was USD18.20 (SD=73.49, median=2.36. The fitted drug cost model had an adjusted R2 of 0.449. The positive significant predictor variables of drug costs were prolonged length of stay, age over 30 years old, male, Universal Health Coverage Scheme, time of accident during 18:00-24:00 o’clock, and motorcycle comparing to bus. To forecast the drug budget for 2006, there were two approaches identified, the mean drug cost and the predicted average drug cost. The predicted average drug cost was calculated based on the forecasted values of statistically significant (p<0.05 predictor variables included in the fitted model; predicted total drug cost was USD44,334. Alternatively, based on the mean cost, predicted total drug cost in 2006 was USD63,408. This was 43% higher than the figure based on the predicted cost approach.Conclusions: The planned budget of drug cost based on the mean cost and predicted average cost were meaningfully different. The application of a predicted average cost model could result in a more accurate budget planning than that of a mean statistic approach.

  9. Drug repositioning for enzyme modulator based on human metabolite-likeness.

    Science.gov (United States)

    Lee, Yoon Hyeok; Choi, Hojae; Park, Seongyong; Lee, Boah; Yi, Gwan-Su

    2017-05-31

    Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs. We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness. In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for

  10. Evaluation of limited sampling models for prediction of oral midazolam AUC for CYP3A phenotyping and drug interaction studies.

    Science.gov (United States)

    Mueller, Silke C; Drewelow, Bernd

    2013-05-01

    The area under the concentration-time curve (AUC) after oral midazolam administration is commonly used for cytochrome P450 (CYP) 3A phenotyping studies. The aim of this investigation was to evaluate a limited sampling strategy for the prediction of AUC with oral midazolam. A total of 288 concentration-time profiles from 123 healthy volunteers who participated in four previously performed drug interaction studies with intense sampling after a single oral dose of 7.5 mg midazolam were available for evaluation. Of these, 45 profiles served for model building, which was performed by stepwise multiple linear regression, and the remaining 243 datasets served for validation. Mean prediction error (MPE), mean absolute error (MAE) and root mean squared error (RMSE) were calculated to determine bias and precision The one- to four-sampling point models with the best coefficient of correlation were the one-sampling point model (8 h; r (2) = 0.84), the two-sampling point model (0.5 and 8 h; r (2) = 0.93), the three-sampling point model (0.5, 2, and 8 h; r (2) = 0.96), and the four-sampling point model (0.5,1, 2, and 8 h; r (2) = 0.97). However, the one- and two-sampling point models were unable to predict the midazolam AUC due to unacceptable bias and precision. Only the four-sampling point model predicted the very low and very high midazolam AUC of the validation dataset with acceptable precision and bias. The four-sampling point model was also able to predict the geometric mean ratio of the treatment phase over the baseline (with 90 % confidence interval) results of three drug interaction studies in the categories of strong, moderate, and mild induction, as well as no interaction. A four-sampling point limited sampling strategy to predict the oral midazolam AUC for CYP3A phenotyping is proposed. The one-, two- and three-sampling point models were not able to predict midazolam AUC accurately.

  11. Different slopes for different folks: alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks.

    Science.gov (United States)

    Mathewson, Kyle E; Basak, Chandramallika; Maclin, Edward L; Low, Kathy A; Boot, Walter R; Kramer, Arthur F; Fabiani, Monica; Gratton, Gabriele

    2012-12-01

    We hypothesized that control processes, as measured using electrophysiological (EEG) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event-related spectral perturbations (ERSPs), and event-related brain potentials during early training of the Space Fortress task, and correlated these measures with subsequent learning rate and performance in transfer tasks. Once initial score was partialled out, the best predictors were frontal alpha power and alpha and delta ERSPs, but not P300. By combining these predictors, we could explain about 50% of the learning rate variance and 10%-20% of the variance in transfer to other tasks using only pretraining EEG measures. Thus, control processes, as indexed by alpha and delta EEG oscillations, can predict learning and skill improvements. The results are of potential use to optimize training regimes. Copyright © 2012 Society for Psychophysiological Research.

  12. Working memory overload: fronto-limbic interactions and effects on subsequent working memory function.

    Science.gov (United States)

    Yun, Richard J; Krystal, John H; Mathalon, Daniel H

    2010-03-01

    The human working memory system provides an experimentally useful model for examination of neural overload effects on subsequent functioning of the overloaded system. This study employed functional magnetic resonance imaging in conjunction with a parametric working memory task to characterize the behavioral and neural effects of cognitive overload on subsequent cognitive performance, with particular attention to cognitive-limbic interactions. Overloading the working memory system was associated with varying degrees of subsequent decline in performance accuracy and reduced activation of brain regions central to both task performance and suppression of negative affect. The degree of performance decline was independently predicted by three separate factors operating during the overload condition: the degree of task failure, the degree of amygdala activation, and the degree of inverse coupling between the amygdala and dorsolateral prefrontal cortex. These findings suggest that vulnerability to overload effects in cognitive functioning may be mediated by reduced amygdala suppression and subsequent amygdala-prefrontal interaction.

  13. Does brain slices from pentylenetetrazole-kindled mice provide a more predictive screening model for antiepileptic drugs?

    DEFF Research Database (Denmark)

    Hansen, Suzanne L.; Sterjev, Zoran; Werngreen, Marie

    2012-01-01

    The cortical wedge is a commonly applied model for in vitro screening of new antiepileptic drugs (AEDs) and has been extensively used in characterization of well-known AEDs. However, the predictive validity of this model as a screening model has been questioned as, e.g., carbamazepine has been...... screening model for AEDs. To this end, we compared the in vitro and in vivo pharmacological profile of several selected AEDs (phenobarbital, phenytoin, tiagabine, fosphenytoin, valproate, and carbamazepine) along with citalopram using the PTZ-kindled model and brain slices from naïve, saline...

  14. Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

    Directory of Open Access Journals (Sweden)

    Antonio M Rezende

    Full Text Available The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks

  15. Subsequent donation requests among 2472 unrelated hematopoietic progenitor cell donors are associated with bone marrow harvest

    Science.gov (United States)

    Lown, Robert N.; Tulpule, Sameer; Russell, Nigel H.; Craddock, Charles F.; Roest, Rochelle; Madrigal, J. Alejandro; Shaw, Bronwen E.

    2013-01-01

    Approximately 1 in 20 unrelated donors are asked to make a second donation of hematopoietic progenitor cells, the majority for the same patient. Anthony Nolan undertook a study of subsequent hematopoietic progenitor cell donations made by its donors from 2005 to 2011, with the aims of predicting those donors more likely to be called for a second donation, assessing rates of serious adverse reactions and examining harvest yields. This was not a study of factors predictive of second allografts. During the study period 2591 donations were made, of which 120 (4.6%) were subsequent donations. The median time between donations was 179 days (range, 21–4016). Indications for a second allogeneic transplant included primary graft failure (11.7%), secondary graft failure (53.2%), relapse (30.6%) and others (1.8%). On multivariate analysis, bone marrow harvest at first donation was associated with subsequent donation requests (odds ratio 2.00, P=0.001). The rate of serious adverse reactions in donors making a subsequent donation appeared greater than the rate in those making a first donation (relative risk=3.29, P=0.005). Harvest yields per kilogram recipient body weight were equivalent between donations, although females appeared to have a lower yield at the subsequent donation. Knowledge of these factors will help unrelated donor registries to counsel their donors. PMID:23812935

  16. Sex, drugs and moral goals: reproductive strategies and views about recreational drugs

    Science.gov (United States)

    Kurzban, Robert; Dukes, Amber; Weeden, Jason

    2010-01-01

    Humans, unlike most other species, show intense interest in the activities of conspecifics, even when the activities in question pose no obvious fitness threat or opportunity. Here, we investigate one content domain in which people show substantial interest, the use of drugs for non-medical purposes. Drawing from two subject populations—one undergraduate and one Internet-based—we look at the relationships among (i) abstract political commitments; (ii) attitudes about sexuality; and (iii) views surrounding recreational drugs. Whereas some theories suggest that drug views are best understood as the result of abstract political ideology, we suggest that these views can be better understood in the context of reproductive strategy. We show that, as predicted by a strategic construal, drug attitudes are best predicted by sexual items rather than abstract political commitments and, further, that the relationship between factors such as political ideology and drugs, while positive, are reduced to zero or nearly zero when items assessing sexuality are controlled for. We conclude that considering morality from the standpoint of strategic interests is a potentially useful way to understand why humans care about third party behaviour. PMID:20554547

  17. Prolonged Drug-Drug Interaction between Terbinafine and Perphenazine.

    Science.gov (United States)

    Park, Young-Min

    2012-12-01

    I report here an elderly woman receiving perphenazine together with terbinafine. After 1 week of terbinafine treatment she experienced extrapyramidal symptoms and, in particular, akathisia. Her symptoms did not disappear for 6 weeks, and so at 2 weeks prior to this most recent admission she had stopped taking terbinafine. However, these symptoms persisted for 3 weeks after discontinuing terbinafine. It is well known that terbinafine inhibits CYP2D6 and that perphenazine is metabolized mainly by CYP2D6. Thus, when terbinafine and perphenazine are coadministrated, the subsequent increase in the concentration of perphenazine may induce extrapyramidal symptoms. Thus, terbinafine therapy may be associated with the induction and persistence of extrapyramidal symptoms, including akathisia. This case report emphasizes the importance of monitoring drug-drug interactions in patients undergoing terbinafine and perphenazine therapy.

  18. [Artificial Intelligence in Drug Discovery].

    Science.gov (United States)

    Fujiwara, Takeshi; Kamada, Mayumi; Okuno, Yasushi

    2018-04-01

    According to the increase of data generated from analytical instruments, application of artificial intelligence(AI)technology in medical field is indispensable. In particular, practical application of AI technology is strongly required in "genomic medicine" and "genomic drug discovery" that conduct medical practice and novel drug development based on individual genomic information. In our laboratory, we have been developing a database to integrate genome data and clinical information obtained by clinical genome analysis and a computational support system for clinical interpretation of variants using AI. In addition, with the aim of creating new therapeutic targets in genomic drug discovery, we have been also working on the development of a binding affinity prediction system for mutated proteins and drugs by molecular dynamics simulation using supercomputer "Kei". We also have tackled for problems in a drug virtual screening. Our developed AI technology has successfully generated virtual compound library, and deep learning method has enabled us to predict interaction between compound and target protein.

  19. Trusted Allies with New Benefits: Repositioning Existing Drugs

    KAUST Repository

    Gao, Xin

    2016-01-25

    The classical assumption that one drug cures a single disease by binding to a single drug-target has been shown to be inaccurate. Recent studies estimate that each drug on average binds to at least six known and several unknown targets. Identifying the “off-targets” can help understand the side effects and toxicity of the drug. Moreover, off-targets for a given drug may inspire “drug repositioning”, where a drug already approved for one condition is redirected to treat another condition, thereby overcoming delays and costs associated with clinical trials and drug approval. In this talk, I will introduce our work along this direction. We have developed a structural alignment method that can precisely identify structural similarities between arbitrary types of interaction interfaces, such as the drug-target interaction. We have further developed a novel computational framework, iDTP that constructs the structural signatures of approved and experimental drugs, based on which we predict new targets for these drugs. Our method combines information from several sources including sequence independent structural alignment, sequence similarity, drug-target tissue expression data, and text mining. In a cross-validation study, we used iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments.

  20. Preclinical models used for immunogenicity prediction of therapeutic proteins.

    Science.gov (United States)

    Brinks, Vera; Weinbuch, Daniel; Baker, Matthew; Dean, Yann; Stas, Philippe; Kostense, Stefan; Rup, Bonita; Jiskoot, Wim

    2013-07-01

    All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.

  1. Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

    OpenAIRE

    Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor, Maureen

    2014-01-01

    We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial i...

  2. Clinical and laboratory assessment of a combination of drugs with radiation. Coordinated programme on improvement in radiotherapy of cancer using modifiers of radiosensitivity of cells

    International Nuclear Information System (INIS)

    Bleehen, N.

    1982-01-01

    Applications were clinically studied of misonidazole (MISO) as a hypoxic cell radiosensitizer with pharmacokinetic studies. Work with desmethylmisonidazole was focused on its penetration into the CNS because its low lipophilicity would predict poorer access than MISO. Laboratory work was focused on the interaction of hyperthermia with drugs. Cytotoxicity of MISO induced by hyperthermia was studied. Heat response following hypoxic pretreatment with MISO of EMT6 spheroids showed marked enhancement of subsequent heat killing dependent on the duration of the hypoxic pretreatment. The effect was studied in vitro of preheat temperature at modest temperatures (39 to 43 0 C) on thermal tolerance and subsequent hyperthermic (43 to 44 0 C) interaction with bleomycin, adriamycin and BCNU. Interaction between several cytotoxic drugs and two potentially critical normal tissues, skin and bone marrow was studied in the mouse. No increase in the heat reaction in the skin of the mouse foot was observed following single injections of adriamycin, bleomycin or 5 daily doses of bleomycin together with a single heat treatment. Single doses of BCNU and CTX increased the heat reaction. The radioprotector WR2721 failed to protect against either the heat or heat drug reactions from CTX and BCNU

  3. DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail

    2016-03-16

    Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind

  4. Drug-radiopharmaceutical interactions

    International Nuclear Information System (INIS)

    Hladik, W.B.; Ponto, J.A.; Stathis, V.J.

    1985-01-01

    Patients seen in the nuclear medicine department have a wide variety of disorders and, consequently, may be receiving any number of therapeutic drugs. For this reason, nuclear medicine professionals should be aware of the potential effects that these pharmacologic agents may have on the bio-distribution of subsequently administered radiopharmaceuticals, commonly referred to as ''drug-radiopharmaceutical interactions.'' Compared with the quantity of literature written about interactions between various therapeutic drugs, the information available on drug-radiopharmaceutical interactions is scarce. However, there has been increasing interest in this subject, particularly during the past five years. Some of the reported interactions are used intentionally to add a new dimension to the nuclear medicine study and increase its diagnostic capabilities, i.e., pharmacologic intervention. These beneficial ''interactions'' are discussed in detail in several other chapters of this book. Other interactions, however, cause changes in the normal distribution of radiopharmaceuticals, which may interfere with the diagnostic utility of various nuclear medicine procedures. The latter group of interactions is the focus of this chapter

  5. Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results

    Science.gov (United States)

    Jarrett, Angela M.; Hormuth, David A.; Barnes, Stephanie L.; Feng, Xinzeng; Huang, Wei; Yankeelov, Thomas E.

    2018-05-01

    Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used—obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety–Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from the DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p  <  0.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and

  6. Predicting Free Recalls

    Science.gov (United States)

    Laming, Donald

    2006-01-01

    This article reports some calculations on free-recall data from B. Murdock and J. Metcalfe (1978), with vocal rehearsal during the presentation of a list. Given the sequence of vocalizations, with the stimuli inserted in their proper places, it is possible to predict the subsequent sequence of recalls--the predictions taking the form of a…

  7. Breeding phenology and winter activity predict subsequent breeding success in a trans-global migratory seabird.

    Science.gov (United States)

    Shoji, A; Aris-Brosou, S; Culina, A; Fayet, A; Kirk, H; Padget, O; Juarez-Martinez, I; Boyle, D; Nakata, T; Perrins, C M; Guilford, T

    2015-10-01

    Inter-seasonal events are believed to connect and affect reproductive performance (RP) in animals. However, much remains unknown about such carry-over effects (COEs), in particular how behaviour patterns during highly mobile life-history stages, such as migration, affect RP. To address this question, we measured at-sea behaviour in a long-lived migratory seabird, the Manx shearwater (Puffinus puffinus) and obtained data for individual migration cycles over 5 years, by tracking with geolocator/immersion loggers, along with 6 years of RP data. We found that individual breeding and non-breeding phenology correlated with subsequent RP, with birds hyperactive during winter more likely to fail to reproduce. Furthermore, parental investment during one year influenced breeding success during the next, a COE reflecting the trade-off between current and future RP. Our results suggest that different life-history stages interact to influence RP in the next breeding season, so that behaviour patterns during winter may be important determinants of variation in subsequent fitness among individuals. © 2015 The Authors.

  8. Possibility of Predicting Serotonin Transporter Occupancy From the In Vitro Inhibition Constant for Serotonin Transporter, the Clinically Relevant Plasma Concentration of Unbound Drugs, and Their Profiles for Substrates of Transporters.

    Science.gov (United States)

    Yahata, Masahiro; Chiba, Koji; Watanabe, Takao; Sugiyama, Yuichi

    2017-09-01

    Accurate prediction of target occupancy facilitates central nervous system drug development. In this review, we discuss the predictability of serotonin transporter (SERT) occupancy in human brain estimated from in vitro K i values for human SERT and plasma concentrations of unbound drug (C u,plasma ), as well as the impact of drug transporters in the blood-brain barrier. First, the geometric means of in vitro K i values were compared with the means of in vivo K i values (K i,u,plasma ) which were calculated as C u,plasma values at 50% occupancy of SERT obtained from previous clinical positron emission tomography/single photon emission computed tomography imaging studies for 6 selective serotonin transporter reuptake inhibitors and 3 serotonin norepinephrine reuptake inhibitors. The in vitro K i values for 7 drugs were comparable to their in vivo K i,u,plasma values within 3-fold difference. SERT occupancy was overestimated for 5 drugs (P-glycoprotein substrates) and underestimated for 2 drugs (presumably uptake transporter substrates, although no evidence exists as yet). In conclusion, prediction of human SERT occupancy from in vitro K i values and C u,plasma was successful for drugs that are not transporter substrates and will become possible in future even for transporter substrates, once the transporter activities will be accurately estimated from in vitro experiments. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  9. Improving the prediction of in-sewer transformation of illicit drug biomarkers by identifying a new modelling framework

    DEFF Research Database (Denmark)

    Ramin, Pedram; Brock, Andreas Libonati; Polesel, Fabio

    -3-β-D-glucuronide; codeine and its metabolite norcodeine; methadone and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP); mephedrone; and tetrahydrocannabinol (THC) and its metabolites 11-hydroxy-Δ9-THC (THCOH), and 11-nor-9-carboxy-Δ9-THC (THCCOOH). All the transformation....... Furthermore, abiotic transformation was found to be the main transformation mechanism for THC (aerobic conditions); mephedrone, methadone, cocaine, ecgonine methyl ester, cocaethylene, THCOH and THCCOOH (anaerobic conditions). By use of the proposed model the uncertainty of predicting illicit drug...

  10. Adverse trajectories of mental health problems predict subsequent burnout and work-family conflict - a longitudinal study of employed women with children followed over 18 years.

    Science.gov (United States)

    Nilsen, Wendy; Skipstein, Anni; Demerouti, Evangelia

    2016-11-08

    The long-term consequence of experiencing mental health problems may lead to several adverse outcomes. The current study aims to validate previous identified trajectories of mental health problems from 1993 to 2006 in women by examining their implications on subsequent work and family-related outcomes in 2011. Employed women (n = 439) with children were drawn from the Tracking Opportunities and Problems-Study (TOPP), a community-based longitudinal study following Norwegian families across 18 years. Previous identified latent profiles of mental health trajectories (i.e., High; Moderate; Low-rising and Low levels of mental health problems over time) measured at six time points between 1993 and 2006 were examined as predictors of burnout (e.g., exhaustion and disengagement from work) and work-family conflict in 2011 in univariate and multivariate analyses of variance adjusted for potential confounders (age, job demands, and negative emotionality). We found that having consistently High and Moderate symptoms as well as Low-Rising symptoms from 1993 to 2006 predicted higher levels of exhaustion, disengagement from work and work-family conflict in 2011. Findings remained unchanged when adjusting for several potential confounders, but when adjusting for current mental health problems only levels of exhaustion were predicted by the mental health trajectories. The study expands upon previous studies on the field by using a longer time span and by focusing on employed women with children who experience different patterns of mental health trajectories. The long-term effect of these trajectories highlight and validate the importance of early identification and prevention in women experiencing adverse patterns of mental health problems with regards to subsequent work and family-related outcomes.

  11. Predicting biopharmaceutical performance of oral drug candidates - Extending the volume to dissolve applied dose concept.

    Science.gov (United States)

    Muenster, Uwe; Mueck, Wolfgang; van der Mey, Dorina; Schlemmer, Karl-Heinz; Greschat-Schade, Susanne; Haerter, Michael; Pelzetter, Christian; Pruemper, Christian; Verlage, Joerg; Göller, Andreas H; Ohm, Andreas

    2016-05-01

    The purpose of the study was to experimentally deduce pH-dependent critical volumes to dissolve applied dose (VDAD) that determine whether a drug candidate can be developed as immediate release (IR) tablet containing crystalline API, or if solubilization technology is needed to allow for sufficient oral bioavailability. pH-dependent VDADs of 22 and 83 compounds were plotted vs. the relative oral bioavailability (AUC solid vs. AUC solution formulation, Frel) in humans and rats, respectively. Furthermore, in order to investigate to what extent Frel rat may predict issues with solubility limited absorption in human, Frel rat was plotted vs. Frel human. Additionally, the impact of bile salts and lecithin on in vitro dissolution of poorly soluble compounds was tested and data compared to Frel rat and human. Respective in vitro - in vivo and in vivo - in vivo correlations were generated and used to build developability criteria. As a result, based on pH-dependent VDAD, Frel rat and in vitro dissolution in simulated intestinal fluid the IR formulation strategy within Pharmaceutical Research and Development organizations can be already set at late stage of drug discovery. Copyright © 2016 Elsevier B.V. All rights reserved.

  12. Exploiting Large-Scale Drug-Protein Interaction Information for Computational Drug Repurposing

    Science.gov (United States)

    2014-06-20

    studies that have reported antimalarial activities of azole compounds [39-43] lend support to our model predictions. The highest-scored non-malarial...Table 4, verapamil and cimetidine, do not have antimal- arial activities themselves but exhibit synergism when used in combination with antimalarial ... activators . Because of their high frequencies among the antimalarial drugs, according to Eq. 3, the drug-protein interactions contributing most to the

  13. Interplay of biopharmaceutics, biopharmaceutics drug disposition and salivary excretion classification systems

    Science.gov (United States)

    Idkaidek, Nasir M.

    2013-01-01

    The aim of this commentary is to investigate the interplay of Biopharmaceutics Classification System (BCS), Biopharmaceutics Drug Disposition Classification System (BDDCS) and Salivary Excretion Classification System (SECS). BCS first classified drugs based on permeability and solubility for the purpose of predicting oral drug absorption. Then BDDCS linked permeability with hepatic metabolism and classified drugs based on metabolism and solubility for the purpose of predicting oral drug disposition. On the other hand, SECS classified drugs based on permeability and protein binding for the purpose of predicting the salivary excretion of drugs. The role of metabolism, rather than permeability, on salivary excretion is investigated and the results are not in agreement with BDDCS. Conclusion The proposed Salivary Excretion Classification System (SECS) can be used as a guide for drug salivary excretion based on permeability (not metabolism) and protein binding. PMID:24493977

  14. Expression changes in the stroma of prostate cancer predict subsequent relapse.

    Directory of Open Access Journals (Sweden)

    Zhenyu Jia

    Full Text Available Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year, and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001. We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.

  15. Polymeric micelles for drug targeting.

    Science.gov (United States)

    Mahmud, Abdullah; Xiong, Xiao-Bing; Aliabadi, Hamidreza Montazeri; Lavasanifar, Afsaneh

    2007-11-01

    Polymeric micelles are nano-delivery systems formed through self-assembly of amphiphilic block copolymers in an aqueous environment. The nanoscopic dimension, stealth properties induced by the hydrophilic polymeric brush on the micellar surface, capacity for stabilized encapsulation of hydrophobic drugs offered by the hydrophobic and rigid micellar core, and finally a possibility for the chemical manipulation of the core/shell structure have made polymeric micelles one of the most promising carriers for drug targeting. To date, three generations of polymeric micellar delivery systems, i.e. polymeric micelles for passive, active and multifunctional drug targeting, have arisen from research efforts, with each subsequent generation displaying greater specificity for the diseased tissue and/or targeting efficiency. The present manuscript aims to review the research efforts made for the development of each generation and provide an assessment on the overall success of polymeric micellar delivery system in drug targeting. The emphasis is placed on the design and development of ligand modified, stimuli responsive and multifunctional polymeric micelles for drug targeting.

  16. The predictive power of family history measures of alcohol and drug problems and internalizing disorders in a college population.

    Science.gov (United States)

    Kendler, Kenneth S; Edwards, Alexis; Myers, John; Cho, Seung Bin; Adkins, Amy; Dick, Danielle

    2015-07-01

    A family history (FH) of psychiatric and substance use problems is a potent risk factor for common internalizing and externalizing disorders. In a large web-based assessment of mental health in college students, we developed a brief set of screening questions for a FH of alcohol problems (AP), drug problems (DP) and depression-anxiety in four classes of relatives (father, mother, aunts/uncles/grandparents, and siblings) as reported by the student. Positive reports of a history of AP, DP, and depression-anxiety were substantially correlated within relatives. These FH measures predicted in the student, in an expected pattern, dimensions of personality and impulsivity, alcohol consumption and problems, smoking and nicotine dependence, use of illicit drugs, and symptoms of depression and anxiety. Using the mean score from the four classes of relatives was more predictive than using a familial/sporadic dichotomy. Interactions were seen between the FH of AP, DP, and depression-anxiety and peer deviance in predicting symptoms of alcohol and tobacco dependence. As the students aged, the FH of AP became a stronger predictor of alcohol problems. While we cannot directly assess the validity of these FH reports, the pattern of findings suggest that our brief screening items were able to assess, with some accuracy, the FH of substance misuse and internalizing psychiatric disorders in relatives. If correct, these measures can play an important role in the creation of developmental etiologic models for substance and internalizing psychiatric disorders which constitute one of the central goals of the overall project. © 2015 Wiley Periodicals, Inc.

  17. Prediction error, ketamine and psychosis: An updated model.

    Science.gov (United States)

    Corlett, Philip R; Honey, Garry D; Fletcher, Paul C

    2016-11-01

    In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.

  18. The Predictive Role of Difficulties in Emotion Regulation and Self-Control with Susceptibility to Addiction in Drug-Dependent Individuals

    Directory of Open Access Journals (Sweden)

    Mahmoud Shirazi

    2015-06-01

    Full Text Available Objective: The present study aimed to examine the predictive role of difficulties in emotion regulation and self-control in potential for addiction among drug-dependent individuals. Method: This was a correlational study which falls within the category of descriptive studies. The statistical population of the current study included all patients under treatment in outpatient health centers in Bam, among whom 315 individuals were selected through cluster sampling method as the participants of the study. Difficulties in Emotion Regulation Scale, Self-Control Scale, and Addiction Susceptibility Questionnaire were used for data collection purposes. Results: The results indicated that difficulties engaging in goal directed behavior, impulse control difficulties, lack of emotional awareness, and lack of emotional clarity (dimensions of difficulties in emotion regulation had a significant positive correlation with potential for addiction. In addition, there was a negative significant relationship between self-control and potential for addiction among drug-dependent individuals. Conclusion: In addition to common methods of abstinence from drug dependence, teaching self-control and emotional control techniques to addicted patients can help them reduce their dependence.

  19. Predictive Value of Gene Polymorphisms on Recurrence after the Withdrawal of Antithyroid Drugs in Patients with Graves’ Disease

    Directory of Open Access Journals (Sweden)

    Jia Liu

    2017-09-01

    Full Text Available Graves’ disease (GD is one of the most common endocrine diseases. Antithyroid drugs (ATDs treatment is frequently used as the first-choice therapy for GD patients in most countries due to the superiority in safety and tolerance. However, GD patients treated with ATD have a relatively high recurrence rate after drug withdrawal, which is a main limitation for ATD treatment. It is of great importance to identify some predictors of the higher recurrence risk for GD patients, which may facilitate an appropriate therapeutic approach for a given patient at the time of GD diagnosis. The genetic factor was widely believed to be an important pathogenesis for GD. Increasing studies were conducted to investigate the relationship between gene polymorphisms and the recurrence risk in GD patients. In this article, we updated the current literatures to highlight the predictive value of gene polymorphisms on recurrence risk in GD patients after ATD withdrawal. Some gene polymorphisms, such as CTLA4 rs231775, human leukocyte antigen polymorphisms (DRB1*03, DQA1*05, and DQB1*02 might be associated with the high recurrence risk in GD patients. Further prospective studies on patients of different ethnicities, especially studies with large sample sizes, and long-term follow-up, should be conducted to confirm the predictive roles of gene polymorphism.

  20. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

    Science.gov (United States)

    Lin, Frank P Y; Pokorny, Adrian; Teng, Christina; Dear, Rachel; Epstein, Richard J

    2016-12-01

    Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations. The predictions so derived were duly compared with those based on published (ESMO and NCCN) cancer guidelines. Machine learning more accurately predicted adjuvant chemotherapy MDT decisions than did simple application of guidelines. No differences were found between MDT- vs. ESMO/NCCN- based decisions to prescribe either adjuvant endocrine (97%, p = 0.44/0.74) or biologic/targeted therapies (98%, p = 0.82/0.59). In contrast, significant discrepancies were evident between MDT- and guideline-based decisions to prescribe chemotherapy (87%, p machine learning models. A machine learning approach based on clinicopathologic characteristics can predict MDT decisions about adjuvant breast cancer drug therapies. The discrepancy between MDT- and guideline-based decisions regarding adjuvant chemotherapy implies that certain non-clincopathologic criteria, such as patient preference and resource availability, are factored into clinical decision-making by local experts but not captured by guidelines.

  1. Mathematical modeling for novel cancer drug discovery and development.

    Science.gov (United States)

    Zhang, Ping; Brusic, Vladimir

    2014-10-01

    Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

  2. The evaluation of the abuse liability of drugs.

    Science.gov (United States)

    Johanson, C E

    1990-01-01

    In order to place appropriate restrictions upon the availability of certain therapeutic agents to limit their abuse, it is important to assess abuse liability, an important aspect of drug safety evaluation. However, the negative consequences of restriction must also be considered. Drugs most likely to be tested are psychoactive compounds with therapeutic indications similar to known drugs of abuse. Methods include assays of pharmacological profile, drug discrimination procedures, self-administration procedures, and measures of drug-induced toxicity including evaluations of tolerance and physical dependence. Furthermore, the evaluation of toxicity using behavioural end-points is an important component of the assessment, and it is generally believed that the most valid procedure in this evaluation is the measurement of drug self-administration. However, even this method rarely predicts the extent of abuse of a specific drug. Although methods are available which appear to measure relative abuse liability, these procedures are not validated for all drug classes. Thus, additional strategies, including abuse liability studies in humans, modelled after those used with animals, must be used in order to make a more informed prediction. Although there is pressure to place restrictions on new drugs at the time of marketing, in light of the difficulty of predicting relative abuse potential, a better strategy might be to market a drug without restrictions, but require postmarketing surveillance in order to obtain more accurate information on which to base a final decision.

  3. Drug-like and non drug-like pattern classification based on simple topology descriptor using hybrid neural network.

    Science.gov (United States)

    Wan-Mamat, Wan Mohd Fahmi; Isa, Nor Ashidi Mat; Wahab, Habibah A; Wan-Mamat, Wan Mohd Fairuz

    2009-01-01

    An intelligent prediction system has been developed to discriminate drug-like and non drug-like molecules pattern. The system is constructed by using the application of advanced version of standard multilayer perceptron (MLP) neural network called Hybrid Multilayer Perceptron (HMLP) neural network and trained using Modified Recursive Prediction Error (MRPE) training algorithm. In this work, a well understood and easy excess Rule of Five + Veber filter properties are selected as the topological descriptor. The main idea behind the selection of this simple descriptor is to assure that the system could be used widely, beneficial and more advantageous regardless at all user level within a drug discovery organization.

  4. Discontinuation of Preventive Drugs in General Practice

    DEFF Research Database (Denmark)

    Andersen, John Sahl; Lindberg, Laura Maria Glahder; Nixon, Michael Simon

    Introduction: In Denmark about 600,000 persons are treated for hypertension and more than 300,000 people are receiving cholesterol lowering drugs. The prevalence of hypertension in people aged 80 years is 70%. For antidepressants the defined daily doses/1000 aged >80 years/day exceed 200. By far...... the most preventive drugs are prescribed in general practice. Special considerations exist in relation to medication of elderly patients. The prevalence of polypharmacy and the subsequent increased risk of side effects and drug interactions is high. Drug-related problems represent the fifth leading cause...... of death in the United States. The public expenses to drug treatment are constantly increasing. The possibility to withdraw the medication must be taken into account but the decision to discontinue drugs is complex and poorly understood. Planned studies: 1. Patients’ views upon discontinuation...

  5. Hepatic drug clearance following traumatic injury.

    Science.gov (United States)

    Slaughter, R L; Hassett, J M

    1985-11-01

    Trauma is a complex disease state associated with physiologic changes that have the potential to alter hepatic drug clearance mechanisms. These responses include alterations in hepatic blood flow, reduction in hepatic microsomal activity, reduction in hepatic excretion processes, and changes in protein binding. Hepatic blood flow is influenced by sympathomimetic activity. Both animal and human studies demonstrate an initial reduction and subsequent increase in hepatic blood flow, which coincides with an observed increase and subsequent return to normal in serum catecholamine concentrations. Unfortunately, there are no human studies that address the importance these findings may have to the clearance processes of high intrinsic clearance compounds. Animal studies of trauma indicate that hepatic microsomal activity is depressed during the post-traumatic period. Reduction in the hepatic clearance of antipyrine, a model low intrinsic compound, has also been demonstrated in animal models of trauma. In addition to these effects, hepatic excretion of substances such as indocyanine green and bilirubin have been demonstrated to be impaired in both traumatized animals and humans. Finally, substantial increases in the serum concentration of the binding protein alpha 1-acid glycoprotein occur in trauma patients. This has been reported to be associated with subsequent decreases in the free fraction of lidocaine and quinidine. In addition to changing serum drug concentration/response relationships, the pharmacokinetic behavior of drugs bound to alpha 1-acid glycoprotein should also change. Preliminary observations in our laboratory in a dog model of surgically-induced trauma have shown a reduction in the total clearance of lidocaine and reduction in free lidocaine concentration.(ABSTRACT TRUNCATED AT 250 WORDS)

  6. Prediction of individual patient response to chemotherapy by the fluorometric microculture cytotoxicity assay (FMCA) using drug specific cut-off limits and a Bayesian model.

    Science.gov (United States)

    Larsson, R; Nygren, P

    1993-01-01

    The semiautomated fluorometric microculture cytotoxicity assay (FMCA) based on the measurement of fluorescence generated from cellular hydrolysis of fluorescein diacetate (FDA) to fluorescein in microtiter plates, has been used for determination of cytotoxic drug resistance of tumor cells from patients with hematological and solid tumors. In the present study we describe a calibration procedure based on statistically derived cut-off limits and assay-predicted response probabilities using Bayes' theorem. Test results at a specified drug concentration were divided into three categories: low, intermediate or extreme drug resistance (LDR, IDR and EDR, respectively) using the median and median +1 standard deviation as the cut-off limits. When correlated with clinical outcome, LDR samples showed a higher response rate than expected, IDR a lower and EDR samples no response at all. The sensitivity and specificity of the test, using the median as cut-off limit, were 0.92 and 0.69 respectively. By fitting these test characteristics to a statistical model based on Bayes' theorem it is possible to calculate response probabilities for each individual patient taking into consideration not only the test characteristics and the particular assay result, but also the clinical and patient specific characteristics influencing the pre-test probability of response. EDR predicts clinical drug resistance with high specificity and is also observed in tumor types with high response rate.

  7. BDDCS Class Prediction for New Molecular Entities

    DEFF Research Database (Denmark)

    Broccatelli, Fabio; Cruciani, Gabriele; Benet, Leslie Z.

    2012-01-01

    M) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated...... chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction....... descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction...

  8. Biomarker-guided repurposing of chemotherapeutic drugs for cancer therapy: a novel strategy in drug development

    Directory of Open Access Journals (Sweden)

    Jan eStenvang

    2013-12-01

    Full Text Available Cancer is a leading cause of mortality worldwide and matters are only set to worsen as its incidence continues to rise. Traditional approaches to combat cancer include improved prevention, early diagnosis, optimized surgery, development of novel drugs and honing regimens of existing anti-cancer drugs. Although discovery and development of novel and effective anti-cancer drugs is a major research area, it is well known that oncology drug development is a lengthy process, extremely costly and with high attrition rates. Furthermore, those drugs that do make it through the drug development mill are often quite expensive, laden with severe side-effects and, unfortunately, to date, have only demonstrated minimal increases in overall survival. Therefore, a strong interest has emerged to identify approved non-cancer drugs that possess anti-cancer activity, thus shortcutting the development process. This research strategy is commonly known as drug repurposing or drug repositioning and provides a faster path to the clinics. We have developed and implemented a modification of the standard drug repurposing strategy that we review here; rather than investigating target-promiscuous non-cancer drugs for possible anti-cancer activity, we focus on the discovery of novel cancer indications for already approved chemotherapeutic anti-cancer drugs. Clinical implementation of this strategy is normally commenced at clinical phase II trials and includes pre-treated patients. As the response rates to any non-standard chemotherapeutic drug will be relatively low in such a patient cohort it is a pre-requisite that such testing is based on predictive biomarkers. This review describes our strategy of biomarker-guided repurposing of chemotherapeutic drugs for cancer therapy, taking the repurposing of topoisomerase I inhibitors and topoisomerase I as a potential predictive biomarker as case in point.

  9. Reducing patient drug acquisition costs can lower diabetes health claims.

    Science.gov (United States)

    Mahoney, John J

    2005-08-01

    Concerned about rising prevalence and costs of diabetes among its employees, Pitney Bowes Inc recently revamped its drug benefit design to synergize with ongoing efforts in its disease management and patient education programs. Specifically, based on a predictive model showing that low medication adherence was linked to subsequent increases in healthcare costs in patients with diabetes, the company shifted all diabetes drugs and devices from tier 2 or 3 formulary status to tier 1. The rationale was that reducing patient out-of-pocket costs would eliminate financial barriers to preventive care, and thereby increase adherence, reduce costly complications, and slow the overall rate of rising healthcare costs. This single change in pharmaceutical benefit design immediately made critical brand-name drugs available to most Pitney Bowes employees and their covered dependents for 10% coinsurance, the same coinsurance level as for generic drugs, versus the previous cost share of 25% to 50%. After 2 to 3 years, preliminary results in plan participants with diabetes indicate that medication possession rates have increased significantly, use of fixed-combination drugs has increased (possibly related to easier adherence), average total pharmacy costs have decreased by 7%, and emergency department visits have decreased by 26%. Hospital admission rates, although increasing slightly, remain below the demographically adjusted Medstat benchmark. Overall direct healthcare costs per plan participant with diabetes decreased by 6%. In addition, the rate of increase in overall per-plan-participant health costs at Pitney Bowes has slowed markedly, with net per-plan-participant costs in 2003 at about 4000 dollars per year versus 6500 dollars for the industry benchmark. This recent moderation in overall corporate health costs may be related to these strategic changes in drug benefit design for diabetes, asthma, and hypertension and also to ongoing enhancements in the company's disease

  10. Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations

    Science.gov (United States)

    Mey, Antonia S. J. S.; Jiménez, Jordi Juárez; Michel, Julien

    2018-01-01

    The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.

  11. In silico prediction of drug dissolution and absorption with variation in intestinal pH for BCS class II weak acid drugs: ibuprofen and ketoprofen.

    Science.gov (United States)

    Tsume, Yasuhiro; Langguth, Peter; Garcia-Arieta, Alfredo; Amidon, Gordon L

    2012-10-01

    The FDA Biopharmaceutical Classification System guidance allows waivers for in vivo bioavailability and bioequivalence studies for immediate-release solid oral dosage forms only for BCS class I. Extensions of the in vivo biowaiver for a number of drugs in BCS class III and BCS class II have been proposed, in particular, BCS class II weak acids. However, a discrepancy between the in vivo BE results and in vitro dissolution results for BCS class II acids was recently observed. The objectives of this study were to determine the oral absorption of BCS class II weak acids via simulation software and to determine if the in vitro dissolution test with various dissolution media could be sufficient for in vitro bioequivalence studies of ibuprofen and ketoprofen as models of carboxylic acid drugs. The oral absorption of these BCS class II acids from the gastrointestinal tract was predicted by GastroPlus™. Ibuprofen did not satisfy the bioequivalence criteria at lower settings of intestinal pH of 6.0. Further the experimental dissolution of ibuprofen tablets in a low concentration phosphate buffer at pH 6.0 (the average buffer capacity 2.2 mmol l (-1) /pH) was dramatically reduced compared with the dissolution in SIF (the average buffer capacity 12.6 mmol l (-1) /pH). Thus these predictions for the oral absorption of BCS class II acids indicate that the absorption patterns depend largely on the intestinal pH and buffer strength and must be considered carefully for a bioequivalence test. Simulation software may be a very useful tool to aid the selection of dissolution media that may be useful in setting an in vitro bioequivalence dissolution standard. Copyright © 2012 John Wiley & Sons, Ltd.

  12. In Silico Prediction of Drug Dissolution and Absorption with variation in Intestinal pH for BCS Class II Weak Acid Drugs: Ibuprofen and Ketoprofen§

    Science.gov (United States)

    Tsume, Yasuhiro; Langguth, Peter; Garcia-Arieta, Alfredo; Amidon, Gordon L.

    2012-01-01

    The FDA Biopharmaceutical Classification System guidance allows waivers for in vivo bioavailability and bioequivalence studies for immediate-release solid oral dosage forms only for BCS class I. Extensions of the in vivo biowaiver for a number of drugs in BCS Class III and BCS class II have been proposed, particularly, BCS class II weak acids. However, a discrepancy between the in vivo- BE results and in vitro- dissolution results for a BCS class II acids was recently observed. The objectives of this study were to determine the oral absorption of BCS class II weak acids via simulation software and to determine if the in vitro dissolution test with various dissolution media could be sufficient for in vitro bioequivalence studies of ibuprofen and ketoprofen as models of carboxylic acid drugs. The oral absorption of these BCS class II acids from the gastrointestinal tract was predicted by GastroPlus™. Ibuprofen did not satisfy the bioequivalence criteria at lower settings of intestinal pH=6.0. Further the experimental dissolution of ibuprofen tablets in the low concentration phosphate buffer at pH 6.0 (the average buffer capacity 2.2 mmol L-1/pH) was dramatically reduced compared to the dissolution in SIF (the average buffer capacity 12.6 mmol L -1/pH). Thus these predictions for oral absorption of BCS class II acids indicate that the absorption patterns largely depend on the intestinal pH and buffer strength and must be carefully considered for a bioequivalence test. Simulation software may be very useful tool to aid the selection of dissolution media that may be useful in setting an in vitro bioequivalence dissolution standard. PMID:22815122

  13. Swedish high-school pupils’ attitudes towards drugs in relation to drug usage, impulsiveness and other risk factors

    Directory of Open Access Journals (Sweden)

    Fariba Mousavi

    2014-06-01

    Full Text Available Background. Illicit drug use influences people’s lives and elicits unwanted behaviour. Current research shows that there is an increase in young people’s drug use in Sweden. The aim was to investigate Swedish high-school pupils’ attitudes, impulsiveness and gender differences linked to drug use. Risk and protective factors relative to drug use were also a focus of interest.Method. High school pupils (n = 146 aged 17–21 years, responded to the Adolescent Health and Development Inventory, Barratt Impulsiveness Scale and Knowledge, and the Attitudes and Beliefs. Direct logistic, multiple regression analyses, and Multivariate Analysis of Variance were used to analyze the data.Results. Positive Attitudes towards drugs were predicted by risk factors (odds ratio = 37.31 and gender (odds ratio = .32. Risk factors (odds ratio = 46.89, positive attitudes towards drugs (odds ratio = 4.63, and impulsiveness (odds ratio = 1.11 predicted drug usage. Risk factors dimensions Family, Friends and Individual Characteristic were positively related to impulsiveness among drug users. Moreover, although boys reported using drugs to a greater extent, girls expressed more positive attitude towards drugs and even reported more impulsiveness than boys.Conclusion. This study reinforces the notion that research ought to focus on gender differences relative to pro-drug attitudes along with testing for differences in the predictors of girls’ and boys’ delinquency and impulsiveness. Positive attitudes towards drugs among adolescents seem to be part of a vicious circle including risk factors, such as friendly drug environments (e.g., friends who use drugs and unsupportive family environments, individual characteristics, and impulsiveness.

  14. The Predictive Role of Difficulties in Emotion Regulation and Self-Control with Susceptibility to Addiction in Drug-Dependent Individuals

    OpenAIRE

    Mahmoud Shirazi; Monavar Janfaza

    2015-01-01

    Objective: The present study aimed to examine the predictive role of difficulties in emotion regulation and self-control in potential for addiction among drug-dependent individuals. Method: This was a correlational study which falls within the category of descriptive studies. The statistical population of the current study included all patients under treatment in outpatient health centers in Bam, among whom 315 individuals were selected through cluster sampling method as the participants of t...

  15. Evaluation of transporters in drug development: Current status and contemporary issues.

    Science.gov (United States)

    Lee, Sue-Chih; Arya, Vikram; Yang, Xinning; Volpe, Donna A; Zhang, Lei

    2017-07-01

    Transporters govern the access of molecules to cells or their exit from cells, thereby controlling the overall distribution of drugs to their intracellular site of action. Clinically relevant drug-drug interactions mediated by transporters are of increasing interest in drug development. Drug transporters, acting alone or in concert with drug metabolizing enzymes, can play an important role in modulating drug absorption, distribution, metabolism and excretion, thus affecting the pharmacokinetics and/or pharmacodynamics of a drug. The drug interaction guidance documents from regulatory agencies include various decision criteria that may be used to predict the need for in vivo assessment of transporter-mediated drug-drug interactions. Regulatory science research continues to assess the prediction performances of various criteria as well as to examine the strength and limitations of each prediction criterion to foster discussions related to harmonized decision criteria that may be used to facilitate global drug development. This review discusses the role of transporters in drug development with a focus on methodologies in assessing transporter-mediated drug-drug interactions, challenges in both in vitro and in vivo assessments of transporters, and emerging transporter research areas including biomarkers, assessment of tissue concentrations, and effect of diseases on transporters. Published by Elsevier B.V.

  16. In silico and in vitro prediction of gastrointestinal absorption from potential drug eremantholide C.

    Science.gov (United States)

    Caldeira, Tamires G; Saúde-Guimarães, Dênia A; Dezani, André B; Serra, Cristina Helena Dos Reis; de Souza, Jacqueline

    2017-11-01

    Analysis of the biopharmaceutical properties of eremantholide C, sesquiterpene lactone with proven pharmacological activity and low toxicity, is required to evaluate its potential to become a drug. Preliminary analysis of the physicochemical characteristics of eremantholide C was performed in silico. Equilibrium solubility was evaluated using the shake-flask method, at 37.0 °C, 100 rpm during 72 h in biorelevant media. The permeability was analysed using parallel artificial membrane permeability assay, at 37.0 °C, 50 rpm for 5 h. The donor compartment was composed of an eremantholide C solution in intestinal fluid simulated without enzymes, while the acceptor compartment consisted of phosphate buffer. Physicochemical characteristics predicted in silico indicated that eremantholide C has a low solubility and high permeability. In-vitro data of eremantholide C showed low solubility, with values for the dose/solubility ratio (ml): 9448.82, 10 389.61 e 15 000.00 for buffers acetate (pH 4.5), intestinal fluid simulated without enzymes (pH 6.8) and phosphate (pH 7.4), respectively. Also, it showed high permeability, with effective permeability of 30.4 × 10 -6 cm/s, a higher result compared with propranolol hydrochloride (9.23 × 10 -6 cm/s). The high permeability combined with its solubility, pharmacological activity and low toxicity demonstrate the importance of eremantholide C as a potential drug candidate. © 2017 Royal Pharmaceutical Society.

  17. Incorporation of the Time-Varying Postprandial Increase in Splanchnic Blood Flow into a PBPK Model to Predict the Effect of Food on the Pharmacokinetics of Orally Administered High-Extraction Drugs.

    Science.gov (United States)

    Rose, Rachel H; Turner, David B; Neuhoff, Sibylle; Jamei, Masoud

    2017-07-01

    Following a meal, a transient increase in splanchnic blood flow occurs that can result in increased exposure to orally administered high-extraction drugs. Typically, physiologically based pharmacokinetic (PBPK) models have incorporated this increase in blood flow as a time-invariant fed/fasted ratio, but this approach is unable to explain the extent of increased drug exposure. A model for the time-varying increase in splanchnic blood flow following a moderate- to high-calorie meal (TV-Q Splanch ) was developed to describe the observed data for healthy individuals. This was integrated within a PBPK model and used to predict the contribution of increased splanchnic blood flow to the observed food effect for two orally administered high-extraction drugs, propranolol and ibrutinib. The model predicted geometric mean fed/fasted AUC and C max ratios of 1.24 and 1.29 for propranolol, which were within the range of published values (within 1.0-1.8-fold of values from eight clinical studies). For ibrutinib, the predicted geometric mean fed/fasted AUC and C max ratios were 2.0 and 1.84, respectively, which was within 1.1-fold of the reported fed/fasted AUC ratio but underestimated the reported C max ratio by up to 1.9-fold. For both drugs, the interindividual variability in fed/fasted AUC and C max ratios was underpredicted. This suggests that the postprandial change in splanchnic blood flow is a major mechanism of the food effect for propranolol and ibrutinib but is insufficient to fully explain the observations. The proposed model is anticipated to improve the prediction of food effect for high-extraction drugs, but should be considered with other mechanisms.

  18. Factors affecting drug-induced liver injury: antithyroid drugs as instances

    Directory of Open Access Journals (Sweden)

    Reza Heidari

    2014-09-01

    Full Text Available Methimazole and propylthiouracil have been used in the management of hyperthyroidism for more than half a century. However, hepatotoxicity is one of the most deleterious side effects associated with these medications. The mechanism(s of hepatic injury induced by antithyroid agents is not fully recognized yet. Furthermore, there are no specific tools for predicting the occurrence of hepatotoxicity induced by these drugs. The purpose of this article is to give an overview on possible susceptibility factors in liver injury induced by antithyroid agents. Age, gender, metabolism characteristics, alcohol consumption, underlying diseases, immunologic mechanisms, and drug interactions are involved in enhancing antithyroid drugs-induced hepatic damage. An outline on the clinically used treatments for antithyroid drugs-induced hepatotoxicity and the potential therapeutic strategies found to be effective against this complication are also discussed.

  19. Predicting pharmacy syringe sales to people who inject drugs: Policy, practice and perceptions.

    Science.gov (United States)

    Meyerson, Beth E; Davis, Alissa; Agley, Jon D; Shannon, David J; Lawrence, Carrie A; Ryder, Priscilla T; Ritchie, Karleen; Gassman, Ruth

    2018-06-01

    Pharmacies have much to contribute to the health of people who inject drugs (PWID) and to community efforts in HIV and hepatitis C (HCV) prevention through syringe access. However, little is known about what predicts pharmacy syringe sales without a prescription. To identify factors predicting pharmacy syringes sales to PWID. A hybrid staggered online survey of 298 Indiana community pharmacists occurred from July-September 2016 measuring pharmacy policy, practice, and pharmacist perceptions about syringe sales to PWID. Separate bivariate logistical regressions were followed by multivariable logistic regression to predict pharmacy syringe sales and pharmacist comfort dispensing syringes to PWID. Half (50.5%) of Indiana pharmacies sold syringes without a prescription to PWID. Pharmacy syringe sales was strongly associated with pharmacist supportive beliefs about syringe access by PWID and their comfort level selling syringes to PWID. Notably, pharmacies located in communities with high rates of opioid overdose mortality were 56% less likely to sell syringes without a prescription than those in communities with lower rates. Pharmacist comfort dispensing syringes was associated with being male, working at a pharmacy that sold syringes to PWID and one that stocked naloxone, having been asked about syringe access by medical providers, and agreement that PWID should be able to buy syringes without a prescription. As communities with high rates of opioid overdose mortality were less likely to have pharmacies that dispensed syringes to PWID, a concerted effort with these communities and their pharmacies should be made to understand opportunities to increase syringe access. Future studies should explore nuances between theoretical support for syringe access by PWID without a prescription and actual dispensing behaviors. Addressing potential policy conflicts and offering continuing education on non-prescription syringe distribution for pharmacists may improve comfort

  20. [Treatment approaches for synthetic drug addiction].

    Science.gov (United States)

    Kobayashi, Ohji

    2015-09-01

    In Japan, synthetic drugs have emerged since late 2000s, and cases of emergency visits and fatal traffic accidents due to acute intoxication have rapidly increased. The synthetic drugs gained popularity mainly because they were cheap and thought to be "legal". The Japanese government restricted not only production and distribution, but also its possession and use in April 2014. As the synthetic drug dependent patients have better social profiles compared to methamphetamine abusers, this legal sanction may have triggered the decrease in the number of synthetic drug dependent patient visits observed at Kanagawa Psychiatric Center since July 2014. Treatment of the synthetic drug dependent patients should begin with empathic inquiry into the motives and positive psychological effects of the drug use. In the maintenance phase, training patients to trust others and express their hidden negative emotions through verbal communications is essential. The recovery is a process of understanding the relationship between psychological isolation and drug abuse, and gaining trust in others to cope with negative emotions that the patients inevitably would face in their subsequent lives.

  1. Drug use trajectory patterns among older drug users

    Directory of Open Access Journals (Sweden)

    Tyndall B

    2011-05-01

    Full Text Available Miriam Boeri, Thor Whalen, Benjamin Tyndall, Ellen BallardKennesaw State University, Department of Sociology and Criminal Justice, Kennesaw GA, USAAbstract: To better understand patterns of drug use trajectories over time, it is essential to have standard measures of change. Our goal here is to introduce measures we developed to quantify change in drug use behaviors. A secondary goal is to provide effective visualizations of these trajectories for applied use. We analyzed data from a sample of 92 older drug users (ages 45 to 65 to identify transition patterns in drug use trajectories across the life course. Data were collected for every year since birth using a mixed methods design. The community-drawn sample of active and former users were 40% female, 50% African American, and 60% reporting some college or greater. Their life histories provided retrospective longitudinal data on the diversity of paths taken throughout the life course and changes in drug use patterns that occurred over time. Bayesian analysis was used to model drug trajectories displayed by innovative computer graphics. The mathematical techniques and visualizations presented here provide the foundation for future models using Bayesian analysis. In this paper we introduce the concepts of transition counts, transition rates and relapse/remission rates, and we describe how these measures can help us better understand drug use trajectories. Depicted through these visual tools, measurements of discontinuous patterns provide a succinct view of individual drug use trajectories. The measures we use on drug use data will be further developed to incorporate contextual influences on the drug trajectory and build predictive models that inform rehabilitation efforts for drug users. Although the measures developed here were conceived to better examine drug use trajectories, the applications of these measures can be used with other longitudinal datasets.Keywords: drug use, trajectory patterns

  2. Matching Subsequences in Trees

    DEFF Research Database (Denmark)

    Bille, Philip; Gørtz, Inge Li

    2009-01-01

    Given two rooted, labeled trees P and T the tree path subsequence problem is to determine which paths in P are subsequences of which paths in T. Here a path begins at the root and ends at a leaf. In this paper we propose this problem as a useful query primitive for XML data, and provide new...

  3. Systematic identification of proteins that elicit drug side effects

    DEFF Research Database (Denmark)

    Kuhn, Michael; Al Banchaabouchi, Mumna; Campillos, Monica

    2013-01-01

    Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large-scale analysis to systematically predict and characterize proteins...... that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug-target relations to identify overrepresented protein-side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause......) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations....

  4. The SMARTCyp cytochrome P450 metabolism prediction server

    DEFF Research Database (Denmark)

    Rydberg, Patrik; Gloriam, David Erik Immanuel; Olsen, Lars

    2010-01-01

    The SMARTCyp server is the first web application for site of metabolism prediction of cytochrome P450-mediated drug metabolism.......The SMARTCyp server is the first web application for site of metabolism prediction of cytochrome P450-mediated drug metabolism....

  5. Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.

    Science.gov (United States)

    Chetty, Manoranjenni; Rose, Rachel H; Abduljalil, Khaled; Patel, Nikunjkumar; Lu, Gaohua; Cain, Theresa; Jamei, Masoud; Rostami-Hodjegan, Amin

    2014-01-01

    This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

  6. Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.

    Directory of Open Access Journals (Sweden)

    Manoranjenni eChetty

    2014-11-01

    Full Text Available This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK models with pharmacodynamics (PD models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PK and PD compared favourably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release formulation (CR were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modelling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

  7. Nucleus accumbens response to food cues predicts subsequent snack consumption in women and increased body mass index in those with reduced self-control.

    Science.gov (United States)

    Lawrence, Natalia S; Hinton, Elanor C; Parkinson, John A; Lawrence, Andrew D

    2012-10-15

    Individuals have difficulty controlling their food consumption, which is due in part to the ubiquity of tempting food cues in the environment. Individual differences in the propensity to attribute incentive (motivational) salience to and act on these cues may explain why some individuals eat more than others. Using fMRI in healthy women, we found that food cue related activity in the nucleus accumbens, a key brain region for food motivation and reward, was related to subsequent snack food consumption. However, both nucleus accumbens activation and snack food consumption were unrelated to self-reported hunger, or explicit wanting and liking for the snack. In contrast, food cue reactivity in the ventromedial prefrontal cortex was associated with subjective hunger/appetite, but not with consumption. Whilst the food cue reactivity in the nucleus accumbens that predicted snack consumption was not directly related to body mass index (BMI), it was associated with increased BMI in individuals reporting low self-control. Our findings reveal a neural substrate underpinning automatic environmental influences on consumption in humans and demonstrate how self-control interacts with this response to predict BMI. Our data provide support for theoretical models that advocate a 'dual hit' of increased incentive salience attribution to food cues and poor self-control in determining vulnerability to overeating and overweight. Copyright © 2012 Elsevier Inc. All rights reserved.

  8. A computational approach to finding novel targets for existing drugs.

    Directory of Open Access Journals (Sweden)

    Yvonne Y Li

    2011-09-01

    Full Text Available Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM, suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.

  9. Melanin targeting for intracellular drug delivery: Quantification of bound and free drug in retinal pigment epithelial cells.

    Science.gov (United States)

    Rimpelä, Anna-Kaisa; Hagström, Marja; Kidron, Heidi; Urtti, Arto

    2018-05-31

    Melanin binding affects drug distribution and retention in pigmented ocular tissues, thereby affecting drug response, duration of activity and toxicity. Therefore, it is a promising possibility for drug targeting and controlled release in the pigmented cells and tissues. Intracellular unbound drug concentrations determine pharmacological and toxicological actions, but analyses of unbound vs. total drug concentrations in pigmented cells are lacking. We studied intracellular binding and cellular drug uptake in pigmented retinal pigment epithelial cells and in non-pigmented ARPE-19 cells with five model drugs (chloroquine, propranolol, timolol, diclofenac, methotrexate). The unbound drug fractions in pigmented cells were 0.00016-0.73 and in non-pigmented cells 0.017-1.0. Cellular uptake (i.e. distribution ratio Kp), ranged from 1.3 to 6300 in pigmented cells and from 1.0 to 25 in non-pigmented cells. Values for intracellular bioavailability, F ic , were similar in both cells types (although larger variation in pigmented cells). In vitro melanin binding parameters were used to predict intracellular unbound drug fraction and cell uptake. Comparison of predictions with experimental data indicates that other factors (e.g. ion-trapping, lipophilicity-related binding to other cell components) also play a role. Melanin binding is a major factor that leads to cellular uptake and unbound drug fractions of a range of 3-4 orders of magnitude indicating that large reservoirs of melanin bound drug can be generated in the cells. Understanding melanin binding has important implications on retinal drug targeting, efficacy and toxicity. Copyright © 2017. Published by Elsevier B.V.

  10. Therapeutic drug monitoring: how to improve drug dosage and patient safety in tuberculosis treatment

    Directory of Open Access Journals (Sweden)

    Giovanni Sotgiu

    2015-03-01

    Full Text Available In this article we describe the key role of tuberculosis (TB treatment, the challenges (mainly the emergence of drug resistance, and the opportunities represented by the correct approach to drug dosage, based on the existing control and elimination strategies. In this context, the role and contribution of therapeutic drug monitoring (TDM is discussed in detail. Treatment success in multidrug-resistant (MDR TB cases is low (62%, with 7% failing or relapsing and 9% dying and in extensively drug-resistant (XDR TB cases is even lower (40%, with 22% failing or relapsing and 15% dying. The treatment of drug-resistant TB is also more expensive (exceeding €50 000 for MDR-TB and €160 000 for XDR-TB and more toxic if compared to that prescribed for drug-susceptible TB. Appropriate dosing of first- and second-line anti-TB drugs can improve the patient's prognosis and lower treatment costs. TDM is based on the measurement of drug concentrations in blood samples collected at appropriate times and subsequent dose adjustment according to the target concentration. The ‘dried blood spot’ technique offers additional advantages, providing the rationale for discussions regarding a possible future network of selected, quality-controlled reference laboratories for the processing of dried blood spots of difficult-to-treat patients from reference TB clinics around the world.

  11. Subsequence Automata with Default Transitions

    DEFF Research Database (Denmark)

    Bille, Philip; Gørtz, Inge Li; Skjoldjensen, Frederik Rye

    2016-01-01

    of states and transitions) of the subsequence automaton is O(nσ) and that this bound is asymptotically optimal. In this paper, we consider subsequence automata with default transitions, that is, special transitions to be taken only if none of the regular transitions match the current character, and which do...... not consume the current character. We show that with default transitions, much smaller subsequence automata are possible, and provide a full trade-off between the size of the automaton and the delay, i.e., the maximum number of consecutive default transitions followed before consuming a character......(nσ) and delay O(1), thus matching the bound for the standard subsequence automaton construction. The key component of our result is a novel hierarchical automata construction of independent interest....

  12. Successful treatment of thyroid storm presenting as recurrent cardiac arrest and subsequent multiorgan failure by continuous renal replacement therapy

    Directory of Open Access Journals (Sweden)

    Han Soo Park

    2017-03-01

    Full Text Available Thyroid storm is a rare and potentially life-threatening medical emergency. We experienced a case of thyroid storm associated with sepsis caused by pneumonia, which had a catastrophic course including recurrent cardiac arrest and subsequent multiple organ failure (MOF. A 22-year-old female patient with a 10-year history of Graves’ disease was transferred to our emergency department (ED. She had a cardiac arrest at her home and a second cardiac arrest at the ED. Her heart recovered after 20 min of cardiac resuscitation. She was diagnosed with thyroid storm associated with hyperthyroidism complicated by pneumonia and sepsis. Although full conventional medical treatment was given, she had progressive MOF and hemodynamic instability consisting of hyperthermia, tachycardia and hypotension. Because of hepatic and renal failure with refractory hypotension, we reduced the patient’s dose of beta-blocker and antithyroid drug, and she was started on continuous veno-venous renal replacement therapy (CRRT with intravenous albumin and plasma supplementation. Subsequently, her body temperature and pulse rate began to stabilize within 1 h, and her blood pressure reached 120/60 mmHg after 6 h. We discontinued antithyroid drug 3 days after admission because of aggravated hyperbilirubinemia. The patient exhibited progressive improvement in thyroid function even after cessation of antithyroid drug, and she successfully recovered from thyroid storm and MOF. This is the first case of thyroid storm successfully treated by CRRT in a patient considered unfit for antithyroid drug treatment.

  13. Integrated Theory of Planned Behavior with Extrinsic Motivation to Predict Intention Not to Use Illicit Drugs by Fifth-Grade Students in Taiwan

    Science.gov (United States)

    Liao, Jung-Yu; Chang, Li-Chun; Hsu, Hsiao-Pei; Huang, Chiu-Mieh; Huang, Su-Fei; Guo, Jong-Long

    2017-01-01

    This study assessed the effects of a model that integrated the theory of planned behavior (TPB) with extrinsic motivation (EM) in predicting the intentions of fifth-grade students to not use illicit drugs. A cluster-sampling design was adopted in a cross-sectional survey (N = 571). The structural equation modeling results showed that the model…

  14. Does Immediate Pain Relief After an Injection into the Sacroiliac Joint with Anesthetic and Corticosteroid Predict Subsequent Pain Relief?

    Science.gov (United States)

    Schneider, Byron J; Huynh, Lisa; Levin, Josh; Rinkaekan, Pranathip; Kordi, Ramin; Kennedy, David J

    2018-02-01

    To determine if immediate pain response following an injection with local anesthetic and corticosteroid predicts subsequent relief. Prospective observational cohort. An institutional review board-approved prospective study from a single academic medical center. Patients with clinical diagnosis of sacroiliac (SIJ) pain and referred for SIJ injection were enrolled; 1 cc of 2% lidocaine and 1 cc of triamcinolone 40 mg/mL were injected into the SIJ. Pain score on 0-10 numeric rating scale (NRS) during provocation maneuvers was recorded immediately before injection, immediately after injection, and at two and four weeks of follow-up. Oswestry Disability Index (ODI) was also recorded. Various cutoffs were identified to establish positive anesthetic response and successful outcomes at follow-up. These were used to calculated likelihood ratios. Of those with 100% anesthetic response, six of 11 (54.5%, 95% confidence interval [CI]+/-29.4%, +LR 2.6, 95% CI = 1.1-5.9) demonstrated 50% or greater pain relief at follow-up, and four of 11 (36.5%, 95% CI+/-28.4%, +LR 3.00, 95% CI = 1.4-5.1) had 100% relief at two to four weeks. Fourteen of 14 (100%, 95% CI+/-21.5%, -LR 0.0, 95% CI = 0.0-2.1) with an initial negative block failed to achieve 100% relief at follow-up. Patients who fail to achieve initial relief after SIJ injection with anesthetic and steroid are very unlikely to achieve significant pain relief at follow-up; negative likelihood ratios (LR) in this study, based on how success is defined, range between 0 and 0.9. Clinically significant positive likelihood ratios of anesthetic response to SIJ injection are more limited and less robust, but are valuable in predicting 50% relief or 100% relief at two to four weeks. © 2017 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  15. Supplementary Material for: DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail

    2016-01-01

    Abstract Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery

  16. Adverse drug reactions monitoring of psychotropic drugs: a tertiary care centre study

    Directory of Open Access Journals (Sweden)

    Hemendra Singh

    2017-06-01

    Full Text Available Background: Many new psychotropic drugs/ agents have been developed and found to be effective in the treatment of psychiatric disorders. However, these drugs also exhibit adverse drug reactions (ADRs which may affect compliance in psychiatric patients. Hence the present study was aimed at monitoring and assessing ADRs caused by psychotropic drugs. Methods: A hospital based prospective observational study was carried out in the psychiatry outpatient department of a tertiary care teaching hospital for the duration of six months. Two hundred and two patients were included in the study and ADRs were documented using a predesigned data collection form. The causality assessment was carried out as per the criteria of both the World Health Organization- Uppsala Monitoring Centre (WHO-UMC and Naranjo scale. Severity and predictability assessment of ADRs were also performed. Results: A total of 106 ADRs were observed during the study period with majority of them occurring in 25-35 years of age group (40.56%. Weight gain (18.86% followed by sedation (16.03% and insomnia (11.32% were found to be the commonest ADRs. Risperidone (19.8% and escitalopram (12.3% were the drugs responsible for majority of the ADRs. Causality assessment showed that most of ADRs were possible and probable. 94.33% of ADRs were found to be mild and 89% of them were predictable. Conclusion: A wide range of ADRs affecting central nervous and metabolic systems were reported with psychotropic drugs. The study findings necessitate the need for an active pharmacovigilance programme for the safe and effective use of psychotropics.

  17. 76 FR 64868 - Orphan Drug Regulations

    Science.gov (United States)

    2011-10-19

    ...: Pharmacological Property: The mechanism of action is a common principle for limiting the investigation and use of... drug, even in the face of a holder's exclusive marketing rights, if the subsequent sponsor advances a... appropriate, for those additional subsets from the date of such additional marketing approval(s). Before...

  18. Phase separation in coamorphous systems: in silico prediction and the experimental challenge of detection.

    Science.gov (United States)

    Pajula, Katja; Wittoek, Lieke; Lehto, Vesa-Pekka; Ketolainen, Jarkko; Korhonen, Ossi

    2014-07-07

    Combinatorial chemistry has enabled the production of very potent drugs that might otherwise suffer from poor solubility and low oral bioavailability. One approach to increase solubility is to make the drug amorphous, which leads to problems associated with drug stability. To improve stability, one option is to molecularly disperse the drug in a matrix. However, the primary reason for the failed stabilization with this approach is phase separation, which has been carefully studied in polymeric systems. Nevertheless, the amorphous-amorphous phase separation in coamorphous small molecule mixtures has not yet been reported. The goal of the present study was to experimentally detect the amorphous-amorphous phase separation between two small molecules. A modified in silico method for predicting miscibility by the Flory-Huggins interaction parameter is presented, where conformational variations of the studied molecules were taken into account. A series of drug-drug mixtures, with different mixture ratios, were analyzed by conventional differential scanning calorimetry (DSC(conv)) to detect possible amorphous-amorphous phase separations. The phase separation of coamorphous drug-drug mixtures was also monitored by temperature modulated DSC (MDSC) and Fourier transform infrared (FT-IR) imaging at temperatures above Tg for prolonged time periods. Amorphous-amorphous phase separation was not detected with DSC(conv), probably due to the slow kinetics of phase separation. However, the melting of the separated and subsequently crystallized phases was detected by MDSC. Furthermore, FT-IR imaging was able to detect the separation of the two amorphous phases, which demonstrates the ability of this method to detect small molecule phase separations.

  19. Predicting Dyspnea Inducers by Molecular Topology

    Directory of Open Access Journals (Sweden)

    María Gálvez-Llompart

    2013-01-01

    Full Text Available QSAR based on molecular topology (MT is an excellent methodology used in predicting physicochemical and biological properties of compounds. This approach is applied here for the development of a mathematical model capable to recognize drugs showing dyspnea as a side effect. Using linear discriminant analysis, it was found a four-variable regression equations enabling a predictive rate of about 81% and 73% in the training and test sets of compounds, respectively. These results demonstrate that QSAR-MT is an efficient tool to predict the appearance of dyspnea associated with drug consumption.

  20. Biomarkers of adverse drug reactions.

    Science.gov (United States)

    Carr, Daniel F; Pirmohamed, Munir

    2018-02-01

    Adverse drug reactions can be caused by a wide range of therapeutics. Adverse drug reactions affect many bodily organ systems and vary widely in severity. Milder adverse drug reactions often resolve quickly following withdrawal of the casual drug or sometimes after dose reduction. Some adverse drug reactions are severe and lead to significant organ/tissue injury which can be fatal. Adverse drug reactions also represent a financial burden to both healthcare providers and the pharmaceutical industry. Thus, a number of stakeholders would benefit from development of new, robust biomarkers for the prediction, diagnosis, and prognostication of adverse drug reactions. There has been significant recent progress in identifying predictive genomic biomarkers with the potential to be used in clinical settings to reduce the burden of adverse drug reactions. These have included biomarkers that can be used to alter drug dose (for example, Thiopurine methyltransferase (TPMT) and azathioprine dose) and drug choice. The latter have in particular included human leukocyte antigen (HLA) biomarkers which identify susceptibility to immune-mediated injuries to major organs such as skin, liver, and bone marrow from a variety of drugs. This review covers both the current state of the art with regard to genomic adverse drug reaction biomarkers. We also review circulating biomarkers that have the potential to be used for both diagnosis and prognosis, and have the added advantage of providing mechanistic information. In the future, we will not be relying on single biomarkers (genomic/non-genomic), but on multiple biomarker panels, integrated through the application of different omics technologies, which will provide information on predisposition, early diagnosis, prognosis, and mechanisms. Impact statement • Genetic and circulating biomarkers present significant opportunities to personalize patient therapy to minimize the risk of adverse drug reactions. ADRs are a significant heath issue

  1. DRUG REACTION WITH HERBAL SUPPLEMENT: A POSSIBLE CASE OF DRUG INDUCED LUPUS ERYTHEMATOSUS

    Directory of Open Access Journals (Sweden)

    AZIZ NA

    2010-01-01

    Full Text Available A 24-year-old lady presented with four days history of fever, non-pruritic rash, ankle pain and swelling. She had consumed herbal supplement five days before the onset of symptoms. Examinations revealed erythematous maculo-papular lesions of varying sizes on sun exposed areas. Patient was suspected to have Drug Induced Lupus Erythematosus (DILE and subsequently symptoms subsided rapidly on withholding the herbal medication.

  2. Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.

    Directory of Open Access Journals (Sweden)

    Guillaume Chevereau

    Full Text Available The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the "morbidostat", a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations-an almost paradoxical behavior since this drug causes DNA damage and increases the mutation

  3. Prelude to passion: limbic activation by "unseen" drug and sexual cues.

    Directory of Open Access Journals (Sweden)

    Anna Rose Childress

    2008-01-01

    Full Text Available The human brain responds to recognizable signals for sex and for rewarding drugs of abuse by activation of limbic reward circuitry. Does the brain respond in similar way to such reward signals even when they are "unseen", i.e., presented in a way that prevents their conscious recognition? Can the brain response to "unseen" reward cues predict the future affective response to recognizable versions of such cues, revealing a link between affective/motivational processes inside and outside awareness?We exploited the fast temporal resolution of event-related functional magnetic resonance imaging (fMRI to test the brain response to "unseen" (backward-masked cocaine, sexual, aversive and neutral cues of 33 milliseconds duration in male cocaine patients (n = 22. Two days after scanning, the affective valence for visible versions of each cue type was determined using an affective bias (priming task. We demonstrate, for the first time, limbic brain activation by "unseen" drug and sexual cues of only 33 msec duration. Importantly, increased activity in an large interconnected ventral pallidum/amygdala cluster to the "unseen" cocaine cues strongly predicted future positive affect to visible versions of the same cues in subsequent off-magnet testing, pointing both to the functional significance of the rapid brain response, and to shared brain substrates for appetitive motivation within and outside awareness.These findings represent the first evidence that brain reward circuitry responds to drug and sexual cues presented outside awareness. The results underscore the sensitivity of the brain to "unseen" reward signals and may represent the brain's primordial signature for desire. The limbic brain response to reward cues outside awareness may represent a potential vulnerability in disorders (e.g., the addictions for whom poorly-controlled appetitive motivation is a central feature.

  4. PSPP: a protein structure prediction pipeline for computing clusters.

    Directory of Open Access Journals (Sweden)

    Michael S Lee

    2009-07-01

    Full Text Available Protein structures are critical for understanding the mechanisms of biological systems and, subsequently, for drug and vaccine design. Unfortunately, protein sequence data exceed structural data by a factor of more than 200 to 1. This gap can be partially filled by using computational protein structure prediction. While structure prediction Web servers are a notable option, they often restrict the number of sequence queries and/or provide a limited set of prediction methodologies. Therefore, we present a standalone protein structure prediction software package suitable for high-throughput structural genomic applications that performs all three classes of prediction methodologies: comparative modeling, fold recognition, and ab initio. This software can be deployed on a user's own high-performance computing cluster.The pipeline consists of a Perl core that integrates more than 20 individual software packages and databases, most of which are freely available from other research laboratories. The query protein sequences are first divided into domains either by domain boundary recognition or Bayesian statistics. The structures of the individual domains are then predicted using template-based modeling or ab initio modeling. The predicted models are scored with a statistical potential and an all-atom force field. The top-scoring ab initio models are annotated by structural comparison against the Structural Classification of Proteins (SCOP fold database. Furthermore, secondary structure, solvent accessibility, transmembrane helices, and structural disorder are predicted. The results are generated in text, tab-delimited, and hypertext markup language (HTML formats. So far, the pipeline has been used to study viral and bacterial proteomes.The standalone pipeline that we introduce here, unlike protein structure prediction Web servers, allows users to devote their own computing assets to process a potentially unlimited number of queries as well as perform

  5. Herb-drug interaction prediction based on the high specific inhibition of andrographolide derivatives towards UDP-glucuronosyltransferase (UGT) 2B7.

    Science.gov (United States)

    Ma, Hai-Ying; Sun, Dong-Xue; Cao, Yun-Feng; Ai, Chun-Zhi; Qu, Yan-Qing; Hu, Cui-Min; Jiang, Changtao; Dong, Pei-Pei; Sun, Xiao-Yu; Hong, Mo; Tanaka, Naoki; Gonzalez, Frank J; Ma, Xiao-Chi; Fang, Zhong-Ze

    2014-05-15

    Herb-drug interaction strongly limits the clinical application of herbs and drugs, and the inhibition of herbal components towards important drug-metabolizing enzymes (DMEs) has been regarded as one of the most important reasons. The present study aims to investigate the inhibition potential of andrographolide derivatives towards one of the most important phase II DMEs UDP-glucuronosyltransferases (UGTs). Recombinant UGT isoforms (except UGT1A4)-catalyzed 4-methylumbelliferone (4-MU) glucuronidation reaction and UGT1A4-catalyzed trifluoperazine (TFP) glucuronidation were employed to firstly screen the andrographolide derivatives' inhibition potential. High specific inhibition of andrographolide derivatives towards UGT2B7 was observed. The inhibition type and parameters (Ki) were determined for the compounds exhibiting strong inhibition capability towards UGT2B7, and human liver microsome (HLMs)-catalyzed zidovudine (AZT) glucuronidation probe reaction was used to furtherly confirm the inhibition behavior. In combination of inhibition parameters (Ki) and in vivo concentration of andrographolide and dehydroandrographolide, the potential in vivo inhibition magnitude was predicted. Additionally, both the in vitro inhibition data and computational modeling results provide important information for the modification of andrographolide derivatives as selective inhibitors of UGT2B7. Taken together, data obtained from the present study indicated the potential herb-drug interaction between Andrographis paniculata and the drugs mainly undergoing UGT2B7-catalyzed metabolic elimination, and the andrographolide derivatives as potential candidates for the selective inhibitors of UGT2B7. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

    Directory of Open Access Journals (Sweden)

    Nicholas Ekow Thomford

    2018-05-01

    Full Text Available The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of “active compound” has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of ‘organ-on chip’ and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug

  7. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.

    Science.gov (United States)

    Thomford, Nicholas Ekow; Senthebane, Dimakatso Alice; Rowe, Arielle; Munro, Daniella; Seele, Palesa; Maroyi, Alfred; Dzobo, Kevin

    2018-05-25

    The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review

  8. Identification of designer drug 2C-E (4-ethyl-2, 5-dimethoxy-phenethylamine) in urine following a drug overdose.

    Science.gov (United States)

    Van Vrancken, Michael J; Benavides, Raul; Wians, Frank H

    2013-01-01

    In recent years, access to information regarding acquisition and synthesis of newer designer drugs has been at an all-time high due largely to the Internet. As these drugs have become more prevalent, laboratory techniques have been developed and refined to identify and screen for this burgeoning population of drugs. This provides a unique opportunity for learning about many of these methods. Laboratory testing techniques and instrumentation are obscure to many health care professionals, yet their results are crucial. Here, we present a case of an overdose of an uncommon designer drug (2C-E) and discuss the basics of liquid chromatography and mass spectrometry, two important techniques used in isolating and identifying the drug. Although often overlooked and taken for granted, these techniques can play a pivotal role in the diagnosis and subsequent management of select patients.

  9. Subsequence automata with default transitions

    DEFF Research Database (Denmark)

    Bille, Philip; Gørtz, Inge Li; Skjoldjensen, Frederik Rye

    2017-01-01

    of states and transitions) of the subsequence automaton is O(nσ) and that this bound is asymptotically optimal. In this paper, we consider subsequence automata with default transitions, that is, special transitions to be taken only if none of the regular transitions match the current character, and which do...... not consume the current character. We show that with default transitions, much smaller subsequence automata are possible, and provide a full trade-off between the size of the automaton and the delay, i.e., the maximum number of consecutive default transitions followed before consuming a character......(1), thus matching the bound for the standard subsequence automaton construction. Finally, we generalize the result to multiple strings. The key component of our result is a novel hierarchical automata construction of independent interest....

  10. Brain atrophy and lesion load predict long term disability in multiple sclerosis

    DEFF Research Database (Denmark)

    Popescu, Veronica; Agosta, Federica; Hulst, Hanneke E

    2013-01-01

    To determine whether brain atrophy and lesion volumes predict subsequent 10 year clinical evolution in multiple sclerosis (MS).......To determine whether brain atrophy and lesion volumes predict subsequent 10 year clinical evolution in multiple sclerosis (MS)....

  11. Predicting Inpatient Detoxification Outcome of Alcohol and Drug Dependent Patients: The Influence of Sociodemographic Environment, Motivation, Impulsivity, and Medical Comorbidities

    Directory of Open Access Journals (Sweden)

    Yvonne Sofin

    2017-01-01

    Full Text Available Aims. This prospective study aims to identify patient characteristics as predictors for treatment outcome during inpatient detoxification treatment for drug and alcohol dependent patients. Methods. A mixed gender sample of 832 consecutively admitted drug and alcohol dependent patients were interviewed by an experienced physician. The impact of a variety of factors concerning social environment, therapy motivation, impulsivity related variables, medical history, and addiction severity on treatment outcome was examined. Results. 525 (63.1% of the patients completed detoxification treatment whereas 307 (36.9% dropped out prematurely. Being female, living in a partnership, having children, being employed, and having good education were predictive for a positive outcome. Family, health, the fear of losing the job, prosecution, and emergency admission were significant motivational predictors for treatment outcome. Being younger, history of imprisonment, and the number of previous drop-outs were predictive for a negative outcome. Conclusions. Variables concerning social environment and the number of previous drop-outs have been identified as best predictors for treatment outcome. Socially stable patients benefit from the current treatment setting and treatment shall be adapted for patients with negative predictors. Treatment may consequently be tailored with respect to intervention type, duration, and intensity to improve the outcome for those patients that fulfil criteria with negative impact on treatment retention.

  12. Prediction of drug-packaging interactions via molecular dynamics (MD) simulations.

    Science.gov (United States)

    Feenstra, Peter; Brunsteiner, Michael; Khinast, Johannes

    2012-07-15

    The interaction between packaging materials and drug products is an important issue for the pharmaceutical industry, since during manufacturing, processing and storage a drug product is continuously exposed to various packaging materials. The experimental investigation of a great variety of different packaging material-drug product combinations in terms of efficacy and safety can be a costly and time-consuming task. In our work we used molecular dynamics (MD) simulations in order to evaluate the applicability of such methods to pre-screening of the packaging material-solute compatibility. The solvation free energy and the free energy of adsorption of diverse solute/solvent/solid systems were estimated. The results of our simulations agree with experimental values previously published in the literature, which indicates that the methods in question can be used to semi-quantitatively reproduce the solid-liquid interactions of the investigated systems. Copyright © 2012 Elsevier B.V. All rights reserved.

  13. Using PEGylated magnetic nanoparticles to describe the EPR effect in tumor for predicting therapeutic efficacy of micelle drugs.

    Science.gov (United States)

    Chen, Ling; Zang, Fengchao; Wu, Haoan; Li, Jianzhong; Xie, Jun; Ma, Ming; Gu, Ning; Zhang, Yu

    2018-01-25

    Micelle drugs based on a polymeric platform offer great advantages over liposomal drugs for tumor treatment. Although nearly all of the nanomedicines approved in the clinical use can passively target to the tumor tissues on the basis of an enhanced permeability and retention (EPR) effect, the nanodrugs have shown heterogenous responses in the patients. This phenomenon may be traced back to the EPR effect of tumor, which is extremely variable in the individuals from extensive studies. Nevertheless, there is a lack of experimental data describing the EPR effect and predicting its impact on therapeutic efficacy of nanoagents. Herein, we developed 32 nm magnetic iron oxide nanoparticles (MION) as a T 2 -weighted contrast agent to describe the EPR effect of each tumor by in vivo magnetic resonance imaging (MRI). The MION were synthesized by a thermal decomposition method and modified with DSPE-PEG2000 for biological applications. The PEGylated MION (Fe 3 O 4 @PEG) exhibited high r 2 of 571 mM -1 s -1 and saturation magnetization (M s ) of 94 emu g -1 Fe as well as long stability and favorable biocompatibility through the in vitro studies. The enhancement intensities of the tumor tissue from the MR images were quantitatively measured as TNR (Tumor/Normal tissue signal Ratio) values, which were correlated with the delay of tumor growth after intravenous administration of the PLA-PEG/PTX micelle drug. The results demonstrated that the group with the smallest TNR values (TNR EPR effect in patients for accurate medication guidance of micelle drugs in the future treatment of tumors.

  14. In Silico Predictions of hERG Channel Blockers in Drug Discovery

    DEFF Research Database (Denmark)

    Taboureau, Olivier; Sørensen, Flemming Steen

    2011-01-01

    The risk for cardiotoxic side effects represents a major problem in clinical studies of drug candidates and regulatory agencies have explicitly recommended that all new drug candidates should be tested for blockage of the human Ether-a-go-go Related-Gene (hERG) potassium channel. Indeed, several ...

  15. Realizing drug repositioning by adapting a recommendation system to handle the process.

    Science.gov (United States)

    Ozsoy, Makbule Guclin; Özyer, Tansel; Polat, Faruk; Alhajj, Reda

    2018-04-12

    Drug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases. In this study, we contribute to the ongoing research efforts by combining multiple features, namely chemical structures, protein interactions and side-effects to predict new indications of target drugs. To achieve our target, we realize drug repositioning as a recommendation process and this leads to a new perspective in tackling the problem. The utilized recommendation method is based on Pareto dominance and collaborative filtering. It can also integrate multiple data-sources and multiple features. For the computation part, we applied several settings and we compared their performance. Evaluation results show that the proposed method can achieve more concentrated predictions with high precision, where nearly half of the predictions are true. Compared to other state of the art methods described in the literature, the proposed method is better at making right predictions by having higher precision. The reported results demonstrate the applicability and effectiveness of recommendation methods for drug repositioning.

  16. Clinical utility of therapeutic drug monitoring in biological disease modifying anti-rheumatic drug treatment of rheumatic disorders: a systematic narrative review.

    Science.gov (United States)

    Van Herwaarden, Noortje; Van Den Bemt, Bart J F; Wientjes, Maike H M; Kramers, Cornelis; Den Broeder, Alfons A

    2017-08-01

    Biological Disease Modifying Anti-Rheumatic Drugs (bDMARDs) have improved the treatment outcomes of inflammatory rheumatic diseases including Rheumatoid Arthritis and spondyloarthropathies. Inter-individual variation exists in (maintenance of) response to bDMARDs. Therapeutic Drug Monitoring (TDM) of bDMARDs could potentially help in optimizing treatment for the individual patient. Areas covered: Evidence of clinical utility of TDM in bDMARD treatment is reviewed. Different clinical scenarios will be discussed, including: prediction of response after start of treatment, prediction of response to a next bDMARD in case of treatment failure of the first, prediction of successful dose reduction or discontinuation in case of low disease activity, prediction of response to dose-escalation in case of active disease and prediction of response to bDMARD in case of flare in disease activity. Expert opinion: The limited available evidence does often not report important outcomes for diagnostic studies, such as sensitivity and specificity. In most clinical relevant scenarios, predictive value of serum (anti-) drug levels is absent, therefore the use of TDM of bDMARDs cannot be advocated. Well-designed prospective studies should be done to further investigate the promising scenarios to determine the place of TDM in clinical practice.

  17. The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.

    Science.gov (United States)

    Vilar, Santiago; Hripcsak, George

    2017-07-01

    Explosion of the availability of big data sources along with the development in computational methods provides a useful framework to study drugs' actions, such as interactions with pharmacological targets and off-targets. Databases related to protein interactions, adverse effects and genomic profiles are available to be used for the construction of computational models. In this article, we focus on the description of biological profiles for drugs that can be used as a system to compare similarity and create methods to predict and analyze drugs' actions. We highlight profiles constructed with different biological data, such as target-protein interactions, gene expression measurements, adverse effects and disease profiles. We focus on the discovery of new targets or pathways for drugs already in the pharmaceutical market, also called drug repurposing, in the interaction with off-targets responsible for adverse reactions and in drug-drug interaction analysis. The current and future applications, strengths and challenges facing all these methods are also discussed. Biological profiles or signatures are an important source of data generation to deeply analyze biological actions with important implications in drug-related studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  18. Integration of in vitro biorelevant dissolution and in silico PBPK model of carvedilol to predict bioequivalence of oral drug products.

    Science.gov (United States)

    Ibarra, Manuel; Valiante, Cristian; Sopeña, Patricia; Schiavo, Alejandra; Lorier, Marianela; Vázquez, Marta; Fagiolino, Pietro

    2018-06-15

    Bioequivalence implementation in developing countries where a high proportion of similar drug products are being marketed has found several obstacles, impeding regulatory agencies to move forward with this policy. Biopharmaceutical quality of these products, several of which are massively prescribed, remains unknown. In this context, an in vitro-in silico-in vivo approach is proposed as a mean to screen product performance and target specific formulations for bioequivalence assessment. By coupling in vitro biorelevant dissolution testing in USP-4 Apparatus (flow-through cell) with physiologically-based pharmacokinetic (PBPK) modeling in PK-Sim® software (Bayer, Germany), the performance of seven similar products of carvedilol tablets containing 25 mg available in the Uruguayan market were compared with the brand-name drug Dilatrend®. In silico simulations for Dilatrend® were compared with published results of bioequivalence studies performed in fasting conditions allowing model development through a learning and confirming process. Single-dose pharmacokinetic profiles were then simulated for the brand-name drug and two similar drug products selected according to in vitro observations, in a virtual Caucasian population of 1000 subjects (50% male, aged between 18 and 50 years with standard body-weights). Population bioequivalence ratios were estimated revealing that in vitro differences in drug release would have a major impact in carvedilol maximum plasma concentration, leading to a non-bioequivalence outcome. Predictions support the need to perform in vivo bioequivalence for these products of extensive use. Application of the in vitro-in silico-in vivo approach stands as an interesting alternative to tackle and reduce drug product variability in biopharmaceutical quality. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. Herb–drug interaction prediction based on the high specific inhibition of andrographolide derivatives towards UDP-glucuronosyltransferase (UGT) 2B7

    Energy Technology Data Exchange (ETDEWEB)

    Ma, Hai-Ying [The Fourth Affiliated Hospital of China Medical University, Shenyang 110032 (China); Sun, Dong-Xue [School of Traditional Chinese Medicine, Shenyang Pharmaceutical University, No. 103, Wenhua Road, Shenyang 110016 (China); Cao, Yun-Feng [The First Affiliated Hospital of Liaoning Medical University, Jinzhou 121001 (China); Joint Center for Translational Medicine, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Zhongshan Road, Dalian 116023 (China); Ai, Chun-Zhi [Joint Center for Translational Medicine, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Zhongshan Road, Dalian 116023 (China); Qu, Yan-Qing [Thyroid Surgery, Yantaishan Hospital, Yantai, Shandong (China); Hu, Cui-Min [Joint Center for Translational Medicine, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Zhongshan Road, Dalian 116023 (China); Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC 20057 (United States); Jiang, Changtao [Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892 (United States); Dong, Pei-Pei [Academy of Integrative Medicine, Dalian Medical University, Dalian 116044 (China); Sun, Xiao-Yu; Hong, Mo [Joint Center for Translational Medicine, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Zhongshan Road, Dalian 116023 (China); Tanaka, Naoki; Gonzalez, Frank J. [Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892 (United States); others, and

    2014-05-15

    Herb–drug interaction strongly limits the clinical application of herbs and drugs, and the inhibition of herbal components towards important drug-metabolizing enzymes (DMEs) has been regarded as one of the most important reasons. The present study aims to investigate the inhibition potential of andrographolide derivatives towards one of the most important phase II DMEs UDP-glucuronosyltransferases (UGTs). Recombinant UGT isoforms (except UGT1A4)-catalyzed 4-methylumbelliferone (4-MU) glucuronidation reaction and UGT1A4-catalyzed trifluoperazine (TFP) glucuronidation were employed to firstly screen the andrographolide derivatives' inhibition potential. High specific inhibition of andrographolide derivatives towards UGT2B7 was observed. The inhibition type and parameters (K{sub i}) were determined for the compounds exhibiting strong inhibition capability towards UGT2B7, and human liver microsome (HLMs)-catalyzed zidovudine (AZT) glucuronidation probe reaction was used to furtherly confirm the inhibition behavior. In combination of inhibition parameters (K{sub i}) and in vivo concentration of andrographolide and dehydroandrographolide, the potential in vivo inhibition magnitude was predicted. Additionally, both the in vitro inhibition data and computational modeling results provide important information for the modification of andrographolide derivatives as selective inhibitors of UGT2B7. Taken together, data obtained from the present study indicated the potential herb–drug interaction between Andrographis paniculata and the drugs mainly undergoing UGT2B7-catalyzed metabolic elimination, and the andrographolide derivatives as potential candidates for the selective inhibitors of UGT2B7. - Highlights: • Specific inhibition of andrographolide derivatives towards UGT2B7. • Herb-drug interaction related withAndrographis paniculata. • Guidance for design of UGT2B7 specific inhibitors.

  20. Herb–drug interaction prediction based on the high specific inhibition of andrographolide derivatives towards UDP-glucuronosyltransferase (UGT) 2B7

    International Nuclear Information System (INIS)

    Ma, Hai-Ying; Sun, Dong-Xue; Cao, Yun-Feng; Ai, Chun-Zhi; Qu, Yan-Qing; Hu, Cui-Min; Jiang, Changtao; Dong, Pei-Pei; Sun, Xiao-Yu; Hong, Mo; Tanaka, Naoki; Gonzalez, Frank J.

    2014-01-01

    Herb–drug interaction strongly limits the clinical application of herbs and drugs, and the inhibition of herbal components towards important drug-metabolizing enzymes (DMEs) has been regarded as one of the most important reasons. The present study aims to investigate the inhibition potential of andrographolide derivatives towards one of the most important phase II DMEs UDP-glucuronosyltransferases (UGTs). Recombinant UGT isoforms (except UGT1A4)-catalyzed 4-methylumbelliferone (4-MU) glucuronidation reaction and UGT1A4-catalyzed trifluoperazine (TFP) glucuronidation were employed to firstly screen the andrographolide derivatives' inhibition potential. High specific inhibition of andrographolide derivatives towards UGT2B7 was observed. The inhibition type and parameters (K i ) were determined for the compounds exhibiting strong inhibition capability towards UGT2B7, and human liver microsome (HLMs)-catalyzed zidovudine (AZT) glucuronidation probe reaction was used to furtherly confirm the inhibition behavior. In combination of inhibition parameters (K i ) and in vivo concentration of andrographolide and dehydroandrographolide, the potential in vivo inhibition magnitude was predicted. Additionally, both the in vitro inhibition data and computational modeling results provide important information for the modification of andrographolide derivatives as selective inhibitors of UGT2B7. Taken together, data obtained from the present study indicated the potential herb–drug interaction between Andrographis paniculata and the drugs mainly undergoing UGT2B7-catalyzed metabolic elimination, and the andrographolide derivatives as potential candidates for the selective inhibitors of UGT2B7. - Highlights: • Specific inhibition of andrographolide derivatives towards UGT2B7. • Herb-drug interaction related withAndrographis paniculata. • Guidance for design of UGT2B7 specific inhibitors