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.

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.

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

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.

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.

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.

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.

In silico prediction of potential chemical reactions mediated by human enzymes.

Science.gov (United States)

Yu, Myeong-Sang; Lee, Hyang-Mi; Park, Aaron; Park, Chungoo; Ceong, Hyithaek; Rhee, Ki-Hyeong; Na, Dokyun

2018-06-13

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition. Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

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

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.

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

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.

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.

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

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

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

Science.gov (United States)

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.

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.

Predicting drug-target interactions using restricted Boltzmann machines.

Science.gov (United States)

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

Cumulative organic anion transporter-mediated drug-drug interaction potential of multiple components in salvia miltiorrhiza (danshen) preparations.

Science.gov (United States)

Wang, Li; Venitz, Jürgen; Sweet, Douglas H

2014-12-01

To evaluate organic anion transporter-mediated drug-drug interaction (DDI) potential for individual active components of Danshen (Salvia miltiorrhiza) vs. combinations using in vitro and in silico approaches. Inhibition profiles for single Danshen components and combinations were generated in stably-expressing human (h)OAT1 and hOAT3 cells. Plasma concentration-time profiles for compounds were estimated from in vivo human data using an i.v. two-compartment model (with first-order elimination). The cumulative DDI index was proposed as an indicator of DDI potential for combination products. This index was used to evaluate the DDI potential for Danshen injectables from 16 different manufacturers and 14 different lots from a single manufacturer. The cumulative DDI index predicted in vivo inhibition potentials, 82% (hOAT1) and 74% (hOAT3), comparable with those observed in vitro, 72 ± 7% (hOAT1) and 81 ± 10% (hOAT3), for Danshen component combinations. Using simulated unbound Cmax values, a wide range in cumulative DDI index between manufacturers, and between lots, was predicted. Many products exhibited a cumulative DDI index > 1 (50% inhibition). Danshen injectables will likely exhibit strong potential to inhibit hOAT1 and hOAT3 function in vivo. The proposed cumulative DDI index might improve prediction of DDI potential of herbal medicines or pharmaceutical preparations containing multiple components.

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.

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.

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.

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.

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

Science.gov (United States)

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.

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.

Availability of human induced pluripotent stem cell-derived cardiomyocytes in assessment of drug potential for QT prolongation

International Nuclear Information System (INIS)

Nozaki, Yumiko; Honda, Yayoi; Tsujimoto, Shinji; Watanabe, Hitoshi; Kunimatsu, Takeshi; Funabashi, Hitoshi

2014-01-01

Field potential duration (FPD) in human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs), which can express QT interval in an electrocardiogram, is reported to be a useful tool to predict K + channel and Ca 2+ channel blocker effects on QT interval. However, there is no report showing that this technique can be used to predict multichannel blocker potential for QT prolongation. The aim of this study is to show that FPD from MEA (Multielectrode array) of hiPS-CMs can detect QT prolongation induced by multichannel blockers. hiPS-CMs were seeded onto MEA and FPD was measured for 2 min every 10 min for 30 min after drug exposure for the vehicle and each drug concentration. I Kr and I Ks blockers concentration-dependently prolonged corrected FPD (FPDc), whereas Ca 2+ channel blockers concentration-dependently shortened FPDc. Also, the multichannel blockers Amiodarone, Paroxetine, Terfenadine and Citalopram prolonged FPDc in a concentration dependent manner. Finally, the I Kr blockers, Terfenadine and Citalopram, which are reported to cause Torsade de Pointes (TdP) in clinical practice, produced early afterdepolarization (EAD). hiPS-CMs using MEA system and FPDc can predict the effects of drug candidates on QT interval. This study also shows that this assay can help detect EAD for drugs with TdP potential. - Highlights: • We focused on hiPS-CMs to replace in vitro assays in preclinical screening studies. • hiPS-CMs FPD is useful as an indicator to predict drug potential for QT prolongation. • MEA assay can help detect EAD for drugs with TdP potentials. • MEA assay in hiPS-CMs is useful for accurately predicting drug TdP risk in humans

Availability of human induced pluripotent stem cell-derived cardiomyocytes in assessment of drug potential for QT prolongation

Energy Technology Data Exchange (ETDEWEB)

Nozaki, Yumiko, E-mail: yumiko-nozaki@ds-pharma.co.jp [Preclinical Research Laboratories, Dainippon Sumitomo Pharma. Co., Ltd., Suita, Osaka 564-0053 (Japan); Honda, Yayoi, E-mail: yayoi-honda@ds-pharma.co.jp [Preclinical Research Laboratories, Dainippon Sumitomo Pharma. Co., Ltd., Suita, Osaka 564-0053 (Japan); Tsujimoto, Shinji, E-mail: shinji-tsujimoto@ds-pharma.co.jp [Regenerative and Cellular Medicine Office, Dainippon Sumitomo Pharma. Co., Ltd., Chuo-ku, Tokyo 104-0031 (Japan); Watanabe, Hitoshi, E-mail: hitoshi-1-watanabe@ds-pharma.co.jp [Preclinical Research Laboratories, Dainippon Sumitomo Pharma. Co., Ltd., Suita, Osaka 564-0053 (Japan); Kunimatsu, Takeshi, E-mail: takeshi-kunimatsu@ds-pharma.co.jp [Preclinical Research Laboratories, Dainippon Sumitomo Pharma. Co., Ltd., Suita, Osaka 564-0053 (Japan); Funabashi, Hitoshi, E-mail: hitoshi-funabashi@ds-pharma.co.jp [Preclinical Research Laboratories, Dainippon Sumitomo Pharma. Co., Ltd., Suita, Osaka 564-0053 (Japan)

2014-07-01

Field potential duration (FPD) in human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs), which can express QT interval in an electrocardiogram, is reported to be a useful tool to predict K{sup +} channel and Ca{sup 2+} channel blocker effects on QT interval. However, there is no report showing that this technique can be used to predict multichannel blocker potential for QT prolongation. The aim of this study is to show that FPD from MEA (Multielectrode array) of hiPS-CMs can detect QT prolongation induced by multichannel blockers. hiPS-CMs were seeded onto MEA and FPD was measured for 2 min every 10 min for 30 min after drug exposure for the vehicle and each drug concentration. I{sub Kr} and I{sub Ks} blockers concentration-dependently prolonged corrected FPD (FPDc), whereas Ca{sup 2+} channel blockers concentration-dependently shortened FPDc. Also, the multichannel blockers Amiodarone, Paroxetine, Terfenadine and Citalopram prolonged FPDc in a concentration dependent manner. Finally, the I{sub Kr} blockers, Terfenadine and Citalopram, which are reported to cause Torsade de Pointes (TdP) in clinical practice, produced early afterdepolarization (EAD). hiPS-CMs using MEA system and FPDc can predict the effects of drug candidates on QT interval. This study also shows that this assay can help detect EAD for drugs with TdP potential. - Highlights: • We focused on hiPS-CMs to replace in vitro assays in preclinical screening studies. • hiPS-CMs FPD is useful as an indicator to predict drug potential for QT prolongation. • MEA assay can help detect EAD for drugs with TdP potentials. • MEA assay in hiPS-CMs is useful for accurately predicting drug TdP risk in humans.

Rapid Identification of Potential Drugs for Diabetic Nephropathy Using Whole-Genome Expression Profiles of Glomeruli

Directory of Open Access Journals (Sweden)

Jingsong Shi

2016-01-01

Full Text Available Objective. To investigate potential drugs for diabetic nephropathy (DN using whole-genome expression profiles and the Connectivity Map (CMAP. Methodology. Eighteen Chinese Han DN patients and six normal controls were included in this study. Whole-genome expression profiles of microdissected glomeruli were measured using the Affymetrix human U133 plus 2.0 chip. Differentially expressed genes (DEGs between late stage and early stage DN samples and the CMAP database were used to identify potential drugs for DN using bioinformatics methods. Results. (1 A total of 1065 DEGs (FDR 1.5 were found in late stage DN patients compared with early stage DN patients. (2 Piperlongumine, 15d-PGJ2 (15-delta prostaglandin J2, vorinostat, and trichostatin A were predicted to be the most promising potential drugs for DN, acting as NF-κB inhibitors, histone deacetylase inhibitors (HDACIs, PI3K pathway inhibitors, or PPARγ agonists, respectively. Conclusion. Using whole-genome expression profiles and the CMAP database, we rapidly predicted potential DN drugs, and therapeutic potential was confirmed by previously published studies. Animal experiments and clinical trials are needed to confirm both the safety and efficacy of these drugs in the treatment of DN.

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

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

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.

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; ...

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

Potential drug-drug interactions on in-patient medication ...

African Journals Online (AJOL)

Potential drug-drug interactions on in-patient medication prescriptions at Mbarara Regional Referral Hospital (MRRH) in western Uganda: prevalence, clinical importance and associated factors. SJ Lubinga, E Uwiduhaye ...

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

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.

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.

Mathematical modeling and computational prediction of cancer drug resistance.

Science.gov (United States)

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

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

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

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.

IN VITRO MODELS TO EVALUATE DRUG-INDUCED HYPERSENSITIVITY: POTENTIAL TEST BASED ON ACTIVATION OF DENDRITIC CELLS

Directory of Open Access Journals (Sweden)

Valentina Galbiati

2016-07-01

Full Text Available Hypersensitivity drug reactions (HDRs are the adverse effect of pharmaceuticals that clinically resemble allergy. HDRs account for approximately 1/6 of drug-induced adverse effects, and include immune-mediated ('allergic' and non immune-mediated ('pseudo allergic' reactions. In recent years, the severe and unpredicted drug adverse events clearly indicate that the immune system can be a critical target of drugs. Enhanced prediction in preclinical safety evaluation is, therefore, crucial. Nowadays, there are no validated in vitro or in vivo methods to screen the sensitizing potential of drugs in the pre-clinical phase. The problem of non-predictability of immunologically-based hypersensitivity reactions is related to the lack of appropriate experimental models rather than to the lack of -understanding of the adverse phenomenon.We recently established experimental conditions and markers to correctly identify drug associated with in vivo hypersensitivity reactions using THP-1 cells and IL-8 production, CD86 and CD54 expression. The proposed in vitro method benefits from a rationalistic approach with the idea that allergenic drugs share with chemical allergens common mechanisms of cell activation. This assay can be easily incorporated into drug development for hazard identification of drugs, which may have the potential to cause in vivo hypersensitivity reactions. The purpose of this review is to assess the state of the art of in vitro models to assess the allergenic potential of drugs based on the activation of dendritic cells.

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

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.

Indolealkylamines: biotransformations and potential drug-drug interactions.

Science.gov (United States)

Yu, Ai-Ming

2008-06-01

Indolealkylamine (IAA) drugs are 5-hydroxytryptamine (5-HT or serotonin) analogs that mainly act on the serotonin system. Some IAAs are clinically utilized for antimigraine therapy, whereas other substances are notable as drugs of abuse. In the clinical evaluation of antimigraine triptan drugs, studies on their biotransformations and pharmacokinetics would facilitate the understanding and prevention of unwanted drug-drug interactions (DDIs). A stable, principal metabolite of an IAA drug of abuse could serve as a useful biomarker in assessing intoxication of the IAA substance. Studies on the metabolism of IAA drugs of abuse including lysergic acid amides, tryptamine derivatives and beta-carbolines are therefore emerging. An important role for polymorphic cytochrome P450 2D6 (CYP2D6) in the metabolism of IAA drugs of abuse has been revealed by recent studies, suggesting that variations in IAA metabolism, pharmaco- or toxicokinetics and dynamics can arise from distinct CYP2D6 status, and CYP2D6 polymorphism may represent an additional risk factor in the use of these IAA drugs. Furthermore, DDIs with IAA agents could occur additively at the pharmaco/toxicokinetic and dynamic levels, leading to severe or even fatal serotonin toxicity. In this review, the metabolism and potential DDIs of these therapeutic and abused IAA drugs are described.

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.

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.

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.

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.

Prevalence of potential drug-drug interactions in cancer patients treated with oral anticancer drugs

NARCIS (Netherlands)

van Leeuwen, R. W. F.; Brundel, D. H. S.; Neef, C.; van Gelder, T.; Mathijssen, R. H. J.; Burger, D. M.; Jansman, F. G. A.

2013-01-01

Background: Potential drug-drug interactions (PDDIs) in patients with cancer are common, but have not previously been quantified for oral anticancer treatment. We assessed the prevalence and seriousness of potential PDDIs among ambulatory cancer patients on oral anticancer treatment. Methods: A

Prevalence of potential drug-drug interactions in cancer patients treated with oral anticancer drugs

NARCIS (Netherlands)

R.W.F. van Leeuwen (Roelof); D.H.S. Brundel (D. H S); C. Neef (Cees); T. van Gelder (Teun); A.H.J. Mathijssen (Ron); D.M. Burger (David); F.G.A. Jansman (Frank)

2013-01-01

textabstractBackground: Potential drug-drug interactions (PDDIs) in patients with cancer are common, but have not previously been quantified for oral anticancer treatment. We assessed the prevalence and seriousness of potential PDDIs among ambulatory cancer patients on oral anticancer treatment.

Potential drug-drug interactions with direct oral anticoagulants in elderly hospitalized patients.

Science.gov (United States)

Forbes, Heather L; Polasek, Thomas M

2017-10-01

To determine the prevalence and nature of potential drug-drug interactions (DDIs) with direct oral anticoagulants (DOACs) in elderly hospitalized patients. This was a retrospective observational study. Inclusion criteria were: aged over 65 years; taking apixaban, rivaroxaban or dabigatran; and admitted to the Repatriation General Hospital between April 2014 and July 2015. A list of clinically relevant 'perpetrator' drugs was compiled from product information, the Australian Medicines Handbook, the Australian National Prescribing Service resources, and local health network guidelines. The prevalence and nature of potential DDIs with DOACs was determined by comparing inpatient drug charts with the list of perpetrator drugs. There were 122 patients in the study with a mean age of 82 years. Most patients had nonvalvular atrial fibrillation and were taking DOACs to prevent thrombotic stroke (83%). Overall, 45 patients (37%) had a total of 54 potential DDIs. Thirty-five patients had potential pharmacodynamic DDIs with antidepressants, nonsteroidal anti-inflammatory drugs and antiplatelets (35/122, 29%). Nineteen patients had potential pharmacokinetic DDIs (19/122, 16%). Of these, 68% (13/19) were taking drugs that increase DOAC plasma concentrations (amiodarone, erythromycin, diltiazem or verapamil) and 32% (6/19) were taking drugs that decrease DOAC plasma concentrations (carbamazepine, primidone or phenytoin). There were no cases of patients taking contraindicated interacting drugs. Potential DDIs with DOACs in elderly hospital inpatients are relatively common, particularly interactions that may increase the risk of bleeding. The risk-benefit ratio of DOACs in elderly patients on polypharmacy should always be carefully considered.

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.

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

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.

Anticancer drugs in Portuguese surface waters - Estimation of concentrations and identification of potentially priority drugs.

Science.gov (United States)

Santos, Mónica S F; Franquet-Griell, Helena; Lacorte, Silvia; Madeira, Luis M; Alves, Arminda

2017-10-01

Anticancer drugs, used in chemotherapy, have emerged as new water contaminants due to their increasing consumption trends and poor elimination efficiency in conventional water treatment processes. As a result, anticancer drugs have been reported in surface and even drinking waters, posing the environment and human health at risk. However, the occurrence and distribution of anticancer drugs depend on the area studied and the hydrological dynamics, which determine the risk towards the environment. The main objective of the present study was to evaluate the risk of anticancer drugs in Portugal. This work includes an extensive analysis of the consumption trends of 171 anticancer drugs, sold or dispensed in Portugal between 2007 and 2015. The consumption data was processed aiming at the estimation of predicted environmental loads of anticancer drugs and 11 compounds were identified as potentially priority drugs based on an exposure-based approach (PEC b > 10 ng L -1 and/or PEC c > 1 ng L -1 ). In a national perspective, mycophenolic acid and mycophenolate mofetil are suspected to pose high risk to aquatic biota. Moderate and low risk was also associated to cyclophosphamide and bicalutamide exposition, respectively. Although no evidences of risk exist yet for the other anticancer drugs, concerns may be associated with long term effects. Copyright © 2017 Elsevier Ltd. All rights reserved.

Potential drug-drug and drug-disease interactions in well-functioning community-dwelling older adults.

Science.gov (United States)

Hanlon, J T; Perera, S; Newman, A B; Thorpe, J M; Donohue, J M; Simonsick, E M; Shorr, R I; Bauer, D C; Marcum, Z A

2017-04-01

There are few studies examining both drug-drug and drug-disease interactions in older adults. Therefore, the objective of this study was to describe the prevalence of potential drug-drug and drug-disease interactions and associated factors in community-dwelling older adults. This cross-sectional study included 3055 adults aged 70-79 without mobility limitations at their baseline visit in the Health Aging and Body Composition Study conducted in the communities of Pittsburgh PA and Memphis TN, USA. The outcome factors were potential drug-drug and drug-disease interactions as per the application of explicit criteria drawn from a number of sources to self-reported prescription and non-prescription medication use. Over one-third of participants had at least one type of interaction. Approximately one quarter (25·1%) had evidence of had one or more drug-drug interactions. Nearly 10·7% of the participants had a drug-drug interaction that involved a non-prescription medication. % The most common drug-drug interaction was non-steroidal anti-inflammatory drugs (NSAIDs) affecting antihypertensives. Additionally, 16·0% had a potential drug-disease interaction with 3·7% participants having one involving non-prescription medications. The most common drug-disease interaction was aspirin/NSAID use in those with history of peptic ulcer disease without gastroprotection. Over one-third (34·0%) had at least one type of drug interaction. Each prescription medication increased the odds of having at least one type of drug interaction by 35-40% [drug-drug interaction adjusted odds ratio (AOR) = 1·35, 95% confidence interval (CI) = 1·27-1·42; drug-disease interaction AOR = 1·30; CI = 1·21-1·40; and both AOR = 1·45; CI = 1·34-1·57]. A prior hospitalization increased the odds of having at least one type of drug interaction by 49-84% compared with those not hospitalized (drug-drug interaction AOR = 1·49, 95% CI = 1·11-2·01; drug-disease interaction AOR = 1·69, CI = 1·15-2�

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.

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

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

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.

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.

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.

Clinically relevant potential drug-drug interactions among outpatients: A nationwide database study.

Science.gov (United States)

Jazbar, Janja; Locatelli, Igor; Horvat, Nejc; Kos, Mitja

2018-06-01

Adverse drug events due to drug-drug interactions (DDIs) represent a considerable public health burden, also in Slovenia. A better understanding of the most frequently occurring potential DDIs may enable safer pharmacotherapy and minimize drug-related problems. The aim of this study was to evaluate the prevalence and predictors of potential DDIs among outpatients in Slovenia. An analysis of potential DDIs was performed using health claims data on prescription drugs from a nationwide database. The Lexi-Interact Module was used as the reference source of interactions. The influence of patient-specific predictors on the risk of potential clinically relevant DDIs was evaluated using logistic regression model. The study population included 1,179,803 outpatients who received 15,811,979 prescriptions. The total number of potential DDI cases identified was 3,974,994, of which 15.6% were potentially clinically relevant. Altogether, 9.3% (N = 191,213) of the total population in Slovenia is exposed to clinically relevant potential DDIs, and the proportion is higher among women and the elderly. After adjustment for cofactors, higher number of medications and older age are associated with higher odds of clinically relevant potential DDIs. The burden of DDIs is highest with drug combinations that increase risk of bleeding, enhance CNS depression or anticholinergic effects or cause cardiovascular complications. The current study revealed that 1 in 10 individuals in the total Slovenian population is exposed to clinically relevant potential DDIs yearly. Taking into account the literature based conservative estimate that approximately 1% of potential DDIs result in negative health outcomes, roughly 1800 individuals in Slovenia experience an adverse health outcome each year as a result of clinically relevant potential interactions alone. Copyright © 2017 Elsevier Inc. All rights reserved.

Is Drug Use Related to the Choice of Potentially More Harmful Methods in Suicide Attempts?

Directory of Open Access Journals (Sweden)

Dartiu Xavier Da Silveira

2014-01-01

Full Text Available Objective To identify whether drug abuse is a risk factor for potentially more harmful methods of suicide attempts that could predict suicide completion in the future. Methods: The study involved the assessment of 86 patients who attempted suicide and who were admitted to the emergency ward of a Southwestern Brazilian general hospital. Results: Most patients were women (84.9%, young adults (30.53 ± 10.4 years, and single (61.6%. Recent drug use was reported by 53.5%, and 25.6% reported the use of drugs during the 24-hour period immediately before the suicide attempt. Most patients (75.6% ingested pills when attempting suicide–-a method considered potentially less harmful. Hanging, jumping, gas inhaling, and wrist cutting accounted for 22.2% of the attempts. Considering dual diagnoses, 54.7% presented with a depressive disorder, 8.1% with a disorder on the impulse control spectrum, and 26.7% reported an associated clinical condition. Recent drug use was predictive of the severity of the suicide attempt, as it was reported by 81% of those who engaged in more harmful attempts and by 46.2% of those who used less harmful methods ( P < 0.01; odds ratio = 4.96; confidence interval: 1.5–16.4. Conclusion: The identified variables associated with the use of potentially more harmful methods in suicide attempts were gender (male, presence of an impulsive control disorder, and recent use of psychoactive drugs.

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

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

21 CFR 314.104 - Drugs with potential for abuse.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 5 2010-04-01 2010-04-01 false Drugs with potential for abuse. 314.104 Section 314.104 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES... and Abbreviated Applications § 314.104 Drugs with potential for abuse. The Food and Drug...

Prediction of Thorough QT study results using action potential simulations based on ion channel screens.

Science.gov (United States)

Mirams, Gary R; Davies, Mark R; Brough, Stephen J; Bridgland-Taylor, Matthew H; Cui, Yi; Gavaghan, David J; Abi-Gerges, Najah

2014-01-01

Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms - IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration-effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1Hz and running to steady state, for a range of concentrations. We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥5ms was predicted with up to 79% sensitivity and 100% specificity. This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety

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.

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.

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.

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...

Potential intravenous drug interactions in intensive care

Directory of Open Access Journals (Sweden)

Maiara Benevides Moreira

Full Text Available Abstract OBJECTIVE To analyze potential intravenous drug interactions, and their level of severity associated with the administration of these drugs based on the prescriptions of an intensive care unit. METHOD Quantitative study, with aretrospective exploratory design, and descriptive statistical analysis of the ICU prescriptions of a teaching hospital from March to June 2014. RESULTS The sample consisted of 319 prescriptions and subsamples of 50 prescriptions. The mean number of drugs per patient was 9.3 records, and a higher probability of drug interaction inherent to polypharmacy was evidenced. The study identified severe drug interactions, such as concomitant administration of Tramadol with selective serotonin reuptake inhibitor drugs (e.g., Metoclopramide and Fluconazole, increasing the risk of seizures due to their epileptogenic actions, as well as the simultaneous use of Ranitidine-Fentanyl®, which can lead to respiratory depression. CONCLUSION A previous mapping of prescriptions enables the characterization of the drug therapy, contributing to prevent potential drug interactions and their clinical consequences.

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.

Risk factors for potential drug interactions in general practice

DEFF Research Database (Denmark)

Bjerrum, Lars; Gonzalez Lopez-Valcarcel, Beatriz; Petersen, Gert

2008-01-01

interactions during 1 year. Patient factors associated with increased risk of potential drug interactions were high age, a high number of concurrently used drugs, and a high number of prescribers. Practice factors associated with potential drug interactions were a high percentage of elderly patients and a low......Objective: To identify patient- and practice-related factors associated with potential drug interactions. Methods: A register analysis study in general practices in the county of Funen, Denmark. Prescription data were retrieved from a population-based prescription database (Odense University......, depending on the severity of outcome and the quality of documentation. A two-level random coefficient logistic regression model was used to investigate factors related to potential drug interactions. Results: One-third of the population was exposed to polypharmacy, and 6% were exposed to potential drug...

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

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.

Drug-drug interactions involving lysosomes: mechanisms and potential clinical implications.

Science.gov (United States)

Logan, Randall; Funk, Ryan S; Axcell, Erick; Krise, Jeffrey P

2012-08-01

Many commercially available, weakly basic drugs have been shown to be lysosomotropic, meaning they are subject to extensive sequestration in lysosomes through an ion trapping-type mechanism. The extent of lysosomal trapping of a drug is an important therapeutic consideration because it can influence both activity and pharmacokinetic disposition. The administration of certain drugs can alter lysosomes such that their accumulation capacity for co-administered and/or secondarily administered drugs is altered. In this review the authors explore what is known regarding the mechanistic basis for drug-drug interactions involving lysosomes. Specifically, the authors address the influence of drugs on lysosomal pH, volume and lipid processing. Many drugs are known to extensively accumulate in lysosomes and significantly alter their structure and function; however, the therapeutic and toxicological implications of this remain controversial. The authors propose that drug-drug interactions involving lysosomes represent an important potential source of variability in drug activity and pharmacokinetics. Most evaluations of drug-drug interactions involving lysosomes have been performed in cultured cells and isolated tissues. More comprehensive in vivo evaluations are needed to fully explore the impact of this drug-drug interaction pathway on therapeutic outcomes.

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

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.

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.

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.

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.

Intracranial self-stimulation to evaluate abuse potential of drugs.

Science.gov (United States)

Negus, S Stevens; Miller, Laurence L

2014-07-01

Intracranial self-stimulation (ICSS) is a behavioral procedure in which operant responding is maintained by pulses of electrical brain stimulation. In research to study abuse-related drug effects, ICSS relies on electrode placements that target the medial forebrain bundle at the level of the lateral hypothalamus, and experimental sessions manipulate frequency or amplitude of stimulation to engender a wide range of baseline response rates or response probabilities. Under these conditions, drug-induced increases in low rates/probabilities of responding maintained by low frequencies/amplitudes of stimulation are interpreted as an abuse-related effect. Conversely, drug-induced decreases in high rates/probabilities of responding maintained by high frequencies/amplitudes of stimulation can be interpreted as an abuse-limiting effect. Overall abuse potential can be inferred from the relative expression of abuse-related and abuse-limiting effects. The sensitivity and selectivity of ICSS to detect abuse potential of many classes of abused drugs is similar to the sensitivity and selectivity of drug self-administration procedures. Moreover, similar to progressive-ratio drug self-administration procedures, ICSS data can be used to rank the relative abuse potential of different drugs. Strengths of ICSS in comparison with drug self-administration include 1) potential for simultaneous evaluation of both abuse-related and abuse-limiting effects, 2) flexibility for use with various routes of drug administration or drug vehicles, 3) utility for studies in drug-naive subjects as well as in subjects with controlled levels of prior drug exposure, and 4) utility for studies of drug time course. Taken together, these considerations suggest that ICSS can make significant contributions to the practice of abuse potential testing. Copyright © 2014 by The American Society for Pharmacology and Experimental Therapeutics.

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.

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.

Drug discrimination: A versatile tool for characterization of CNS safety pharmacology and potential for drug abuse.

Science.gov (United States)

Swedberg, Michael D B

2016-01-01

Drug discrimination studies for assessment of psychoactive properties of drugs in safety pharmacology and drug abuse and drug dependence potential evaluation have traditionally been focused on testing novel compounds against standard drugs for which drug abuse has been documented, e.g. opioids, CNS stimulants, cannabinoids etc. (e.g. Swedberg & Giarola, 2015), and results are interpreted such that the extent to which the test drug causes discriminative effects similar to those of the standard training drug, the test drug would be further characterized as a potential drug of abuse. Regulatory guidance for preclinical assessment of abuse liability by the European Medicines Agency (EMA, 2006), the U.S. Food and Drug Administration (FDA, 2010), the International Conference of Harmonization (ICH, 2009), and the Japanese Ministry of Health Education and Welfare (MHLW, 1994) detail that compounds with central nervous system (CNS) activity, whether by design or not, need abuse and dependence liability assessment. Therefore, drugs with peripheral targets and a potential to enter the CNS, as parent or metabolite, are also within scope (see Swedberg, 2013, for a recent review and strategy). Compounds with novel mechanisms of action present a special challenge due to unknown abuse potential, and should be carefully assessed against defined risk criteria. Apart from compounds sharing mechanisms of action with known drugs of abuse, compounds intended for indications currently treated with drugs with potential for abuse and or dependence are also within scope, regardless of mechanism of action. Examples of such compounds are analgesics, anxiolytics, cognition enhancers, appetite control drugs, sleep control drugs and drugs for psychiatric indications. Recent results (Swedberg et al., 2014; Swedberg & Raboisson, 2014; Swedberg, 2015) on the metabotropic glutamate receptor type 5 (mGluR5) antagonists demonstrate that compounds causing hallucinatory effects in humans did not exhibit

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

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.

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.

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.

Pharmacogenetics in drug regulation: promise, potential and pitfalls

Science.gov (United States)

Shah, Rashmi R

2005-01-01

Pharmacogenetic factors operate at pharmacokinetic as well as pharmacodynamic levels—the two components of the dose–response curve of a drug. Polymorphisms in drug metabolizing enzymes, transporters and/or pharmacological targets of drugs may profoundly influence the dose–response relationship between individuals. For some drugs, although retrospective data from case studies suggests that these polymorphisms are frequently associated with adverse drug reactions or failure of efficacy, the clinical utility of such data remains unproven. There is, therefore, an urgent need for prospective data to determine whether pre-treatment genotyping can improve therapy. Various regulatory guidelines already recommend exploration of the role of genetic factors when investigating a drug for its pharmacokinetics, pharmacodynamics, dose–response relationship and drug interaction potential. Arising from the global heterogeneity in the frequency of variant alleles, regulatory guidelines also require the sponsors to provide additional information, usually pharmacogenetic bridging data, to determine whether data from one ethnic population can be extrapolated to another. At present, sponsors explore pharmacogenetic influences in early clinical pharmacokinetic studies but rarely do they carry the findings forward when designing dose–response studies or pivotal studies. When appropriate, regulatory authorities include genotype-specific recommendations in the prescribing information. Sometimes, this may include the need to adjust a dose in some genotypes under specific circumstances. Detailed references to pharmacogenetics in prescribing information and pharmacogenetically based prescribing in routine therapeutics will require robust prospective data from well-designed studies. With greater integration of pharmacogenetics in drug development, regulatory authorities expect to receive more detailed genetic data. This is likely to complicate the drug evaluation process as well as

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.

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.

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

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.

Is Drug Use Related to the Choice of Potentially More Harmful Methods in Suicide Attempts?

OpenAIRE

da Silveira, Dartiu Xavier; Fidalgo, Thiago Marques; Di Pietro, Monica; Santos, Jair Guilherme; Oliveira, Leonardo Q

2014-01-01

Objective To identify whether drug abuse is a risk factor for potentially more harmful methods of suicide attempts that could predict suicide completion in the future. Methods: The study involved the assessment of 86 patients who attempted suicide and who were admitted to the emergency ward of a Southwestern Brazilian general hospital. Results: Most patients were women (84.9%), young adults (30.53 ± 10.4 years), and single (61.6%). Recent drug use was reported by 53.5%, and 25.6% reported the...

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...

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...

Nanomaterials potentiating standard chemotherapy drugs' effect

Science.gov (United States)

Kazantsev, S. O.; Korovin, M. S.

2017-09-01

Application of antitumor chemotherapeutic drugs is hindered by a number of barriers, multidrug resistance that makes effective drug deposition inside cancer cells difficult is among them. Recent research shows that potential efficiency of anticancer drugs can be increased with nanoparticles. This review is devoted to the application of nanoparticles for cancer treatment. Various types of nanoparticles currently used in medicine are reviewed. The nanoparticles that have been used for cancer therapy and targeted drug delivery to damaged sites of organism are described. Also, the possibility of nanoparticles application for cancer diagnosis that could help early detection of tumors is discussed. Our investigations of antitumor activity of low-dimensional nanostructures based on aluminum oxides and hydroxides are briefly reviewed.

A Novel Method for Determining the Inhibitory Potential of Anti-HIV Drugs

Science.gov (United States)

Shen, Lin; Rabi, S. Alireza; Siliciano, Robert F.

2009-01-01

In the absence of a cure, most HIV-1-infected individuals will require life-long treatment. It is therefore essential to optimize highly active antiretroviral therapy. Recent research has shown that the slope parameter or Hill coefficient, which describes the steepness of a dose-response curve, is a critical missing dimension in the evaluation of antiviral drug activity. Based on this finding, the instantaneous inhibitory potential (IIP) has been derived as a new measure of antiviral drug activity. IIP incorporates the slope parameter and thus is a more accurate pharmacodynamic measure of antiviral activity than current measures such as IC50 and inhibitory quotient. However, it remains important to determine how to use IIP to predict the in vivo efficacy of anti-HIV-1 drugs. This article discusses recent advances in in vitro measures of antiviral activity and the therapeutic implications of the dose-response curve slope and IIP. PMID:19837466

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.

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.

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.

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.

Carbon nanotubes buckypapers for potential transdermal drug delivery

International Nuclear Information System (INIS)

Schwengber, Alex; Prado, Héctor J.; Zilli, Darío A.; Bonelli, Pablo R.

2015-01-01

Drug loaded buckypapers based on different types of carbon nanotubes (CNTs) were prepared and characterized in order to evaluate their potentialities for the design of novel transdermal drug delivery systems. Lab-synthesized CNTs as well as commercial samples were employed. Clonidine hydrochloride was used as model drug, and the influence of composition of the drug loaded buckypapers and processing variables on in vitro release profiles was investigated. To examine the influence of the drug nature the evaluation was further extended to buckypapers prepared with flurbiprofen and one type of CNTs, their selection being based on the results obtained with the former drug. Scanning electronic microscopy images indicated that the model drugs were finely dispersed on the CNTs. Differential scanning calorimetry, and X-ray diffraction pointed to an amorphous state of both drugs in the buckypapers. A higher degree of CNT–drug superficial interactions resulted in a slower release of the drug. These interactions were in turn affected by the type of CNTs employed (single wall or multiwall CNTs), their functionalization with hydroxyl or carboxyl groups, the chemical structure of the drug, and the CNT:drug mass ratio. Furthermore, the application of a second layer of drug free CNTs on the loaded buckypaper, led to decelerate the drug release and to reduce the burst effect. - Highlights: • Drug loaded buckypapers from carbon nanotubes were prepared and characterized. • Their potentialities for transdermal drug delivery applications were evaluated. • Characteristics of carbon nanotubes and the structure of the drug affected release • A higher carbon nanotube:drug mass ratio decelerated release • Up to one week controlled release profiles were obtained for the drug flurbiprofen

Carbon nanotubes buckypapers for potential transdermal drug delivery

Energy Technology Data Exchange (ETDEWEB)

Schwengber, Alex [PINMATE-Departamento de Industrias, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA Buenos Aires (Argentina); Prado, Héctor J. [PINMATE-Departamento de Industrias, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA Buenos Aires (Argentina); Cátedra de Tecnología Farmacéutica II, Departamento de Tecnología Farmacéutica, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Junín 956, C1113AAD Buenos Aires (Argentina); Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Rivadavia 1917, C1033AAJ Buenos Aires (Argentina); Zilli, Darío A. [PINMATE-Departamento de Industrias, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA Buenos Aires (Argentina); Bonelli, Pablo R. [PINMATE-Departamento de Industrias, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA Buenos Aires (Argentina); Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Rivadavia 1917, C1033AAJ Buenos Aires (Argentina); and others

2015-12-01

Drug loaded buckypapers based on different types of carbon nanotubes (CNTs) were prepared and characterized in order to evaluate their potentialities for the design of novel transdermal drug delivery systems. Lab-synthesized CNTs as well as commercial samples were employed. Clonidine hydrochloride was used as model drug, and the influence of composition of the drug loaded buckypapers and processing variables on in vitro release profiles was investigated. To examine the influence of the drug nature the evaluation was further extended to buckypapers prepared with flurbiprofen and one type of CNTs, their selection being based on the results obtained with the former drug. Scanning electronic microscopy images indicated that the model drugs were finely dispersed on the CNTs. Differential scanning calorimetry, and X-ray diffraction pointed to an amorphous state of both drugs in the buckypapers. A higher degree of CNT–drug superficial interactions resulted in a slower release of the drug. These interactions were in turn affected by the type of CNTs employed (single wall or multiwall CNTs), their functionalization with hydroxyl or carboxyl groups, the chemical structure of the drug, and the CNT:drug mass ratio. Furthermore, the application of a second layer of drug free CNTs on the loaded buckypaper, led to decelerate the drug release and to reduce the burst effect. - Highlights: • Drug loaded buckypapers from carbon nanotubes were prepared and characterized. • Their potentialities for transdermal drug delivery applications were evaluated. • Characteristics of carbon nanotubes and the structure of the drug affected release • A higher carbon nanotube:drug mass ratio decelerated release • Up to one week controlled release profiles were obtained for the drug flurbiprofen.

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

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.

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.

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

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.

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.

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...

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

International Nuclear Information System (INIS)

Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.; Contrera, Joseph F.

2007-01-01

Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest is MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals

Drug affordability-potential tool for comparing illicit drug markets.

Science.gov (United States)

Groshkova, Teodora; Cunningham, Andrew; Royuela, Luis; Singleton, Nicola; Saggers, Tony; Sedefov, Roumen

2018-06-01

-national comparisons of retail drug markets in Europe. Future work will need to examine other potential uses of the drug affordability tool. The limitations of this measure reflect primarily the limitations of the constituent data; in addition to issues inherent in collecting accurate data on illicit markets, analysis that relies on data collected from multiple countries is susceptible to discrepancies in data collection practices from country to country. Copyright © 2018 Elsevier B.V. All rights reserved.

Alignment-independent comparison of binding sites based on DrugScore potential fields encoded by 3D Zernike descriptors.

Science.gov (United States)

Nisius, Britta; Gohlke, Holger

2012-09-24

Analyzing protein binding sites provides detailed insights into the biological processes proteins are involved in, e.g., into drug-target interactions, and so is of crucial importance in drug discovery. Herein, we present novel alignment-independent binding site descriptors based on DrugScore potential fields. The potential fields are transformed to a set of information-rich descriptors using a series expansion in 3D Zernike polynomials. The resulting Zernike descriptors show a promising performance in detecting similarities among proteins with low pairwise sequence identities that bind identical ligands, as well as within subfamilies of one target class. Furthermore, the Zernike descriptors are robust against structural variations among protein binding sites. Finally, the Zernike descriptors show a high data compression power, and computing similarities between binding sites based on these descriptors is highly efficient. Consequently, the Zernike descriptors are a useful tool for computational binding site analysis, e.g., to predict the function of novel proteins, off-targets for drug candidates, or novel targets for known drugs.

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.

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.

In silico repositioning-chemogenomics strategy identifies new drugs with potential activity against multiple life stages of Schistosoma mansoni.

Directory of Open Access Journals (Sweden)

Bruno J Neves

2015-01-01

Full Text Available Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD, DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes.

Potential drug interactions in patients given antiretroviral therapy.

Science.gov (United States)

Santos, Wendel Mombaque Dos; Secoli, Silvia Regina; Padoin, Stela Maris de Mello

2016-11-21

to investigate potential drug-drug interactions (PDDI) in patients with HIV infection on antiretroviral therapy. a cross-sectional study was conducted on 161 adults with HIV infection. Clinical, socio demographic, and antiretroviral treatment data were collected. To analyze the potential drug interactions, we used the software Micromedex(r). Statistical analysis was performed by binary logistic regression, with a p-value of ≤0.05 considered statistically significant. of the participants, 52.2% were exposed to potential drug-drug interactions. In total, there were 218 potential drug-drug interactions, of which 79.8% occurred between drugs used for antiretroviral therapy. There was an association between the use of five or more medications and potential drug-drug interactions (p = 0.000) and between the time period of antiretroviral therapy being over six years and potential drug-drug interactions (p central nervous and cardiovascular systems, but also can interfere in tests used for detection of HIV resistance to antiretroviral drugs. investigar potenciais interações droga-droga (PDDI) em pacientes infectados com HIV em terapia de antirretroviral. um estudo de corte transversal foi conduzido em 161 pessoas infectadas com o HIV. Dados de tratamentos clínicos, sociodemográficos e antirretrovirais foram coletados. Para analisar a possível interação medicamentosa, nós usamos o software Micromedex(r). A análise estatística foi feita por regressão logística binária, com um valor P de ≤0.05, considerado estatisticamente significativo. dos participantes, 52.2% foram expostos a potenciais interações droga-droga. No total, houve 218 interações droga-droga, das quais 79.8% ocorreram entre drogas usadas para a terapia antirretroviral. Houve uma associação entre o uso de cinco ou mais medicamentos e possíveis interações droga-droga (p = 0.000), e entre o período de tempo de terapia antirretroviral acima de seis anos e possíveis interações droga

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.

Assessing the potential impact of artemisinin and partner drug resistance in sub-Saharan Africa.

Science.gov (United States)

Slater, Hannah C; Griffin, Jamie T; Ghani, Azra C; Okell, Lucy C

2016-01-06

Artemisinin and partner drug resistant malaria parasites have emerged in Southeast Asia. If resistance were to emerge in Africa it could have a devastating impact on malaria-related morbidity and mortality. This study estimates the potential impact of artemisinin and partner drug resistance on disease burden in Africa if it were to emerge. Using data from Asia and Africa, five possible artemisinin and partner drug resistance scenarios are characterized. An individual-based malaria transmission model is used to estimate the impact of each resistance scenario on clinical incidence and parasite prevalence across Africa. Artemisinin resistance is characterized by slow parasite clearance and partner drug resistance is associated with late clinical failure or late parasitological failure. Scenarios with high levels of recrudescent infections resulted in far greater increases in clinical incidence compared to scenarios with high levels of slow parasite clearance. Across Africa, it is estimated that artemisinin and partner drug resistance at levels similar to those observed in Oddar Meanchey province in Cambodia could result in an additional 78 million cases over a 5 year period, a 7% increase in cases compared to a scenario with no resistance. A scenario with high levels of slow clearance but no recrudescence resulted in an additional 10 million additional cases over the same period. Artemisinin resistance is potentially a more pressing concern than partner drug resistance due to the lack of viable alternatives. However, it is predicted that a failing partner drug will result in greater increases in malaria cases and morbidity than would be observed from artemisinin resistance only.

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.

Potential drug therapies for the treatment of fibromyalgia.

Science.gov (United States)

Lawson, Kim

2016-09-01

Fibromyalgia (FM) is a common, complex chronic widespread pain condition is characterized by fatigue, sleep disturbance and cognitive dysfunction. Treatment of FM is difficult, requiring both pharmacological and non-pharmacological approaches, with an empiric approach to drug therapy focused toward individual symptoms, particularly pain. The effectiveness of current medications is limited with many patients discontinuing use. A systemic database search has identified 26 molecular entities as potential emerging drug therapies. Advances in the understanding of the pathophysiology of FM provides clues to targets for new medications. Investigation of bioamine modulation and α2δ ligands and novel targets such as dopamine receptors, NMDA receptors, cannabinoid receptors, melatonin receptors and potassium channels has identified potential drug therapies. Modest improvement of health status in patients with FM has been observed with drugs targeting a diverse range of molecular mechanisms. No single drug, however, offered substantial efficacy against all the symptoms characteristic of FM. Identification of new and improved therapies for FM needs to address the heterogeneity of the condition, which suggests existence of patient subgroups, the relationship of central and peripheral aspects of the pathophysiology and a requirement of combination therapy with drugs targeting multiple molecular mechanisms.

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.

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.

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.

Potential candidate genomic biomarkers of drug induced vascular injury in the rat

International Nuclear Information System (INIS)

Dalmas, Deidre A.; Scicchitano, Marshall S.; Mullins, David; Hughes-Earle, Angela; Tatsuoka, Kay; Magid-Slav, Michal; Frazier, Kendall S.; Thomas, Heath C.

2011-01-01

Drug-induced vascular injury is frequently observed in rats but the relevance and translation to humans present a hurdle for drug development. Numerous structurally diverse pharmacologic agents have been shown to induce mesenteric arterial medial necrosis in rats, but no consistent biomarkers have been identified. To address this need, a novel strategy was developed in rats to identify genes associated with the development of drug-induced mesenteric arterial medial necrosis. Separate groups (n = 6/group) of male rats were given 28 different toxicants (30 different treatments) for 1 or 4 days with each toxicant given at 3 different doses (low, mid and high) plus corresponding vehicle (912 total rats). Mesentery was collected, frozen and endothelial and vascular smooth muscle cells were microdissected from each artery. RNA was isolated, amplified and Affymetrix GeneChip® analysis was performed on selectively enriched samples and a novel panel of genes representing those which showed a dose responsive pattern for all treatments in which mesenteric arterial medial necrosis was histologically observed, was developed and verified in individual endothelial cell- and vascular smooth muscle cell-enriched samples. Data were confirmed in samples containing mesentery using quantitative real-time RT-PCR (TaqMan™) gene expression profiling. In addition, the performance of the panel was also confirmed using similarly collected samples obtained from a timecourse study in rats given a well established vascular toxicant (Fenoldopam). Although further validation is still required, a novel gene panel has been developed that represents a strategic opportunity that can potentially be used to help predict the occurrence of drug-induced mesenteric arterial medial necrosis in rats at an early stage in drug development. -- Highlights: ► A gene panel was developed to help predict rat drug-induced mesenteric MAN. ► A gene panel was identified following treatment of rats with 28

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

The effect of membrane diffusion potential change on anionic drugs ...

African Journals Online (AJOL)

The effect of membrane potential change on anionic drugs Indomethacin and barbitone induced human erythrocyte shape change and red cell uptake of drug has been studied using microscopy and spectrophotometry techniques respectively. The membrane potential was changed by reducing the extracellular chloride ...

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.

[Impact of potentially inappropriate drug usage on health insurance business results].

Science.gov (United States)

Kirschke, Malin; Böhme, Jacqueline

2014-09-01

In Germany a list was drawn up that included 83 potentially inappropriate drugs. The PRISCUS list published in 2010 was intended to highlight certain problems in the pharmakotherapy of elderly patients and serve as a support for improved medicine safety. Almost a third of the insurance portfolio of the HALLESCHE Krankenversicherung aged over 75 years takes drugs that are on the PRISCUS list. Benzodiazepine and Z-drugs are taken most frequently. The costs per insurant with potentially inappropriate medication are on average higher than for policyholders who do not take drugs on the PRISCUS list. The costs per insurant are rising, with an increase in the number of PRISCUS agents being taken as well. However, there is still no scientific proof that potentially inappropriate drugs lead to adverse drug events.

Perfluorocarbon (PFC) emulsions as potential drug carriers

International Nuclear Information System (INIS)

Yuhas, J.M.; Goodman, R.L.; Moore, R.E.

1984-01-01

PFC emulsions have excellent oxygen transporting properties and have been reported to enhance the response of murine tumors to both radiation and BCNU. While the presently available emulsions are far too toxic to the immune system to be used in cancer therapy, they can be used to investigate the overall potential of this approach. As an example, the authors have found that these emulsions can alter drug availability. The lipophilicity of both the PFC and the drug in question determine the partitioning of the drug between the organic and aqueous phases of an emulsion. In vitro, this can reduce drug effectiveness by reducing the amount of drug available to the cells. In vivo, however, this partitioning may produce sustained drug exposure, which could be of benefit in cancer therapy and other applications. In brief, as the drug is absorbed from the circulating aqueous phase, additional drug would leach from the PFC, thereby providing a sustained drug exposure similar to that obtained with liposomes. While a great deal more work will be required to evaluate the practicality of this approach, the existence of this phenomenon must be taken into account in both the design and interpretation of efficacy studies in which anesthetics, chemotherapeutics, etc are employed

Colloid electrochemistry of conducting polymer: towards potential-induced in-situ drug release

International Nuclear Information System (INIS)

Sankoh, Supannee; Vagin, Mikhail Yu.; Sekretaryova, Alina N.; Thavarungkul, Panote; Kanatharana, Proespichaya; Mak, Wing Cheung

2017-01-01

Highlights: • Pulsed electrode potential induced an in-situ drug release from dispersion of conducting polymer microcapsules. • Fast detection of the released drug within the colloid microenvironment. • Improved the efficiency of localized drug release at the electrode interface. - Abstract: Over the past decades, controlled drug delivery system remains as one of the most important area in medicine for various diseases. We have developed a new electrochemically controlled drug release system by combining colloid electrochemistry and electro-responsive microcapsules. The pulsed electrode potential modulation led to the appearance of two processes available for the time-resolved registration in colloid microenvironment: change of the electronic charge of microparticles (from 0.5 ms to 0.1 s) followed by the drug release associated with ionic equilibration (1–10 s). The dynamic electrochemical measurements allow the distinction of drug release associated with ionic relaxation and the change of electronic charge of conducting polymer colloid microparticles. The amount of released drug (methylene blue) could be controlled by modulating the applied potential. Our study demonstrated a surface-potential driven controlled drug release of dispersion of conducting polymer carrier at the electrode interfaces, while the bulk colloids dispersion away from the electrode remains as a reservoir to improve the efficiency of localized drug release. The developed new methodology creates a model platform for the investigations of surface potential-induced in-situ electrochemical drug release mechanism.

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

Potential drug–drug interactions in Alzheimer patients with behavioral symptoms

Directory of Open Access Journals (Sweden)

Pasqualetti G

2015-09-01

Full Text Available Giuseppe Pasqualetti, Sara Tognini, Valeria Calsolaro, Antonio Polini, Fabio Monzani Geriatrics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy Abstract: The use of multi drug regimens among the elderly population has increased tremendously over the last decade although the benefits of medications are always accompanied by potential harm, even when prescribed at recommended doses. The elderly populations are particularly at an increased risk of adverse drug reactions considering comorbidity, poly-therapy, physiological changes affecting the pharmacokinetics and pharmacodynamics of many drugs and, in some cases, poor compliance due to cognitive impairment and/or depression. In this setting, drug–drug interaction may represent a serious and even life-threatening clinical condition. Moreover, the inability to distinguish drug-induced symptoms from a definitive medical diagnosis often results in addition of yet another drug to treat the symptoms, which in turn increases drug–drug interactions. Cognitive enhancers, including acetylcholinesterase inhibitors and memantine, are the most widely prescribed agents for Alzheimer’s disease (AD patients. Behavioral and psychological symptoms of dementia, including psychotic symptoms and behavioral disorders, represent noncognitive disturbances frequently observed in AD patients. Antipsychotic drugs are at high risk of adverse events, even at modest doses, and may interfere with the progression of cognitive impairment and interact with several drugs including anti-arrhythmics and acetylcholinesterase inhibitors. Other medications often used in AD patients are represented by anxiolytic, like benzodiazepine, or antidepressant agents. These agents also might interfere with other concomitant drugs through both pharmacokinetic and pharmacodynamic mechanisms. In this review we focus on the most frequent drug–drug interactions, potentially harmful, in AD patients with

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.

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.

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....

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

Science.gov (United States)

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.

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.

The potential biomarkers of drug addiction: proteomic and metabolomics challenges.

Science.gov (United States)

Wang, Lv; Wu, Ning; Zhao, Tai-Yun; Li, Jin

2016-07-28

Drug addiction places a significant burden on society and individuals. Proteomics and metabolomics approaches pave the road for searching potential biomarkers to assist the diagnosis and treatment. This review summarized putative drug addiction-related biomarkers in proteomics and metabolomics studies and discussed challenges and prospects in future studies. Alterations of several hundred proteins and metabolites were reported when exposure to abused drug, which enriched in energy metabolism, oxidative stress response, protein modification and degradation, synaptic function and neurotrasmission, etc. Hsp70, peroxiredoxin-6 and α- and β-synuclein, as well as n-methylserotonin and purine metabolites, were promising as potential biomarker for drug addiction.

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.

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.

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.

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.

Effect of Zeta Potential on the Properties of Nano-Drug Delivery ...

African Journals Online (AJOL)

Zeta potential is a scientific term for electrokinetic potential in colloidal systems which has a major effect on the various properties of nano-drug delivery systems. Presently, colloidal nano-carriers are growing at a remarkable rate owing to their strong potential for overcoming old challenges such as poor drug solubility and ...

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.

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.

 The potential nephrotoxicity of antiretroviral drugs

Directory of Open Access Journals (Sweden)

Zofia Marchewka

2012-09-01

Full Text Available  The intensive studies carried out in many scientific laboratories and the efforts of numerous pharmaceutical companies have led to the development of drugs which are able to effectively inhibitHIV proliferation. At present, a number of antiretroviral agents with different mechanisms of actionare available. Unfortunately, long-term use of antiretroviral drugs, however, does not remainindifferent to the patient and can cause significant side effects.In the present work, the antiretroviral drugs with a nephrotoxicity potential most commonly usedin clinical practice are described. In the review attention has also been focused on the nephropathyresulting from the HIV infection alone and the influence of genetic factors on the occurrenceof pathological changes in the kidney.

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

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.)

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.

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.

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.

Drug-Drug Interactions Potential of Icariin and Its Intestinal Metabolites via Inhibition of Intestinal UDP-Glucuronosyltransferases

Directory of Open Access Journals (Sweden)

Yun-Feng Cao

2012-01-01

Full Text Available Icariin is known as an indicative constituent of the Epimedium genus, which has been commonly used in Chinese herbal medicine to enhance treat impotence and improve sexual function, as well as for several other indications for over 2000 years. In this study, we aimed to investigate the effects of icariin and its intestinal metabolites on the activities of human UDP-glucuronosyltransferase (UGT activities. Using a panel of recombinant human UGT isoforms, we found that icariin exhibited potent inhibition against UGT1A3. It is interesting that the intestinal metabolites of icariin exhibited a different inhibition profile compared with icariin. Different from icariin, icariside II was a potent inhibitor of UGT1A4, UGT1A7, UGT1A9, and UGT2B7, and icaritin was a potent inhibitor of UGT1A7 and UGT1A9. The potential for drug interactions in vivo was also quantitatively predicted and compared. The quantitative prediction of risks indicated that in vivo inhibition against intestinal UGT1A3, UGT1A4, and UGT1A7 would likely occur after oral administration of icariin products.

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

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.

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.

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.

Preferred drug lists: Potential impact on healthcare economics

Directory of Open Access Journals (Sweden)

Kimberly Ovsag

2008-04-01

Full Text Available Kimberly Ovsag, Sabrina Hydery, Shaker A MousaPharmaceutical Research Institute at Albany College of Pharmacy, Albany, New York, USAObjectives: To analyze the implementation of Medicaid preferred drug lists (PDLs in a number of states and determine its impact on quality of care and cost relative to other segments of healthcare.Methods: We reviewed research and case studies found by searching library databases, primarily MEDLINE and EBSCOHost, and searching pertinent journals. Keywords initially included “drug lists,” “prior authorization,” “prior approval,” and “Medicaid.” We added terms such as “influence use of other healthcare services,” “quality of care,” and “overall economic impact.” We mainly used primary sources.Results: Based on our literature review, we determined that there are a number of issues regarding Medicaid PDLs that need to be addressed. Some issues include: (a the potential for PDLs to influence the utilization of other healthcare services, (b criteria used by Medicaid for determining acceptance of drugs onto a PDL, (c the effect of PDL implementation on compliance to new regimens, (d the potential effects of restricting medication availability on quality of care, (e administrative costs associated with PDLs, and (f satisfaction rates among patients and medical providers. This review highlighted expected short-term cost savings with limited degree of compromised quality of PDL implementation, but raised the concern about the potential long-term decline in quality of care and overall economic impact.Conclusions: The number of concerns raised indicates that further studies are warranted regarding both short-term cost benefits as well as potential long-term effects of Medicaid PDL implementation. Objective analysis of these effects is necessary to ensure cost-effectiveness and quality of care.Keywords: preferred drug lists, medicaid, healthcare costs, managed care

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

Computational Identification of Potential Multi-drug Combinations for Reduction of Microglial Inflammation in Alzheimer Disease

Directory of Open Access Journals (Sweden)

Thomas J. Anastasio

2015-06-01

Full Text Available Like other neurodegenerative diseases, Alzheimer Disease (AD has a prominent inflammatory component mediated by brain microglia. Reducing microglial inflammation could potentially halt or at least slow the neurodegenerative process. A major challenge in the development of treatments targeting brain inflammation is the sheer complexity of the molecular mechanisms that determine whether microglia become inflammatory or take on a more neuroprotective phenotype. The process is highly multifactorial, raising the possibility that a multi-target/multi-drug strategy could be more effective than conventional monotherapy. This study takes a computational approach in finding combinations of approved drugs that are potentially more effective than single drugs in reducing microglial inflammation in AD. This novel approach exploits the distinct advantages of two different computer programming languages, one imperative and the other declarative. Existing programs written in both languages implement the same model of microglial behavior, and the input/output relationships of both programs agree with each other and with data on microglia over an extensive test battery. Here the imperative program is used efficiently to screen the model for the most efficacious combinations of 10 drugs, while the declarative program is used to analyze in detail the mechanisms of action of the most efficacious combinations. Of the 1024 possible drug combinations, the simulated screen identifies only 7 that are able to move simulated microglia at least 50% of the way from a neurotoxic to a neuroprotective phenotype. Subsequent analysis shows that of the 7 most efficacious combinations, 2 stand out as superior both in strength and reliability. The model offers many experimentally testable and therapeutically relevant predictions concerning effective drug combinations and their mechanisms of action.

Computational identification of potential multi-drug combinations for reduction of microglial inflammation in Alzheimer disease.

Science.gov (United States)

Anastasio, Thomas J

2015-01-01

Like other neurodegenerative diseases, Alzheimer Disease (AD) has a prominent inflammatory component mediated by brain microglia. Reducing microglial inflammation could potentially halt or at least slow the neurodegenerative process. A major challenge in the development of treatments targeting brain inflammation is the sheer complexity of the molecular mechanisms that determine whether microglia become inflammatory or take on a more neuroprotective phenotype. The process is highly multifactorial, raising the possibility that a multi-target/multi-drug strategy could be more effective than conventional monotherapy. This study takes a computational approach in finding combinations of approved drugs that are potentially more effective than single drugs in reducing microglial inflammation in AD. This novel approach exploits the distinct advantages of two different computer programming languages, one imperative and the other declarative. Existing programs written in both languages implement the same model of microglial behavior, and the input/output relationships of both programs agree with each other and with data on microglia over an extensive test battery. Here the imperative program is used efficiently to screen the model for the most efficacious combinations of 10 drugs, while the declarative program is used to analyze in detail the mechanisms of action of the most efficacious combinations. Of the 1024 possible drug combinations, the simulated screen identifies only 7 that are able to move simulated microglia at least 50% of the way from a neurotoxic to a neuroprotective phenotype. Subsequent analysis shows that of the 7 most efficacious combinations, 2 stand out as superior both in strength and reliability. The model offers many experimentally testable and therapeutically relevant predictions concerning effective drug combinations and their mechanisms of action.

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.

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.

Potential intravenous drug interactions in intensive care.

Science.gov (United States)

Moreira, Maiara Benevides; Mesquita, Maria Gefé da Rosa; Stipp, Marluci Andrade Conceição; Paes, Graciele Oroski

2017-07-20

To analyze potential intravenous drug interactions, and their level of severity associated with the administration of these drugs based on the prescriptions of an intensive care unit. Quantitative study, with aretrospective exploratory design, and descriptive statistical analysis of the ICU prescriptions of a teaching hospital from March to June 2014. The sample consisted of 319 prescriptions and subsamples of 50 prescriptions. The mean number of drugs per patient was 9.3 records, and a higher probability of drug interaction inherent to polypharmacy was evidenced. The study identified severe drug interactions, such as concomitant administration of Tramadol with selective serotonin reuptake inhibitor drugs (e.g., Metoclopramide and Fluconazole), increasing the risk of seizures due to their epileptogenic actions, as well as the simultaneous use of Ranitidine-Fentanyl®, which can lead to respiratory depression. A previous mapping of prescriptions enables the characterization of the drug therapy, contributing to prevent potential drug interactions and their clinical consequences. Analisar as potenciais interações medicamentosas intravenosas e seu grau de severidade associadas à administração desses medicamentos a partir das prescrições do Centro de Terapia Intensiva. Estudo quantitativo, tipologia retrospectiva exploratória, com análise estatística descritiva das prescrições medicamentosas do Centro de Terapia Intensiva de um Hospital Universitário, no período de março-junho/2014. A amostra foi composta de 319 prescrições e subamostras de 50 prescrições. Constatou-se que a média de medicamentos por paciente foi de 9,3 registros, e evidenciou-se maior probabilidade para ocorrência de interação medicamentosa inerente à polifarmácia. O estudo identificou interações medicamentosas graves, como a administração concomitante de Tramadol com medicamentos inibidores seletivos da recaptação da serotonina, (exemplo: Metoclopramida e Fluconazol

Potential applications for halloysite nanotubes based drug delivery systems

Science.gov (United States)

Sun, Lin

Drug delivery refers to approaches, formulations, technologies, and systems for transporting a drug in the body. The purpose is to enhance the drug efficacy and to reduce side reactions, which can significantly improve treatment outcomes. Halloysite is a naturally occurred alumino-silicate clay with a tubular structure. It is a biocompatible material with a big surface area which can be used for attachment of targeted molecules. Besides, loaded molecules can present a sustained release manner in solution. These properties make halloysite nanotubes (HNTs) a good option for drug delivery. In this study, a drug delivery system was built based on halloysite via three different fabrication methods: physical adsorption, vacuum loading and layer-by-layer coating. Methotrexate was used as the model drug. Factors that may affect performance in both drug loading and release were tested. Results showed that methotrexate could be incorporated within the HNTs system and released in a sustained manner. Layer-by-layer coating showed a better potential than the other two methods in both MTX loading and release. Besides, lower pH could greatly improve MTX loading and release while the increased number of polyelectrolytes bilayers had a limited impact. Osteosarcoma is the most common primary bone malignancy in children and adolescents. Postoperative recurrence and metastasis has become one of the leading causes for patient death after surgical remove of the tumor mass. A strategy could be a sustained release of chemotherapeutics directly at the primary tumor sites where recurrence would mostly occur. Then, this HNTs based system was tested with osteosarcoma cells in vitro to show the potential of delivering chemotherapeutics in the treatment of osteosarcoma. Methotrexate was incorporated within HNTs with a layer-bylayer coating technique, and drug coated HNTs were filled into nylon-6 which is a common material for surgical sutures in industry. Results showed that (1) methotrexate

Application of receiver operating characteristic analysis to refine the prediction of potential digoxin drug interactionss

NARCIS (Netherlands)

Ellens, H.; Deng, S.; Coleman, J.; Bentz, J.; Taub, M.E.; Ragueneau-Majlessi, I.; Chung, S.P.; Herédi-Szabó, K.; Neuhoff, S.; Palm, J.; Balimane, P.; Zhang, L.; Jamei, M.; Hanna, I.; O'connor, M.; Bednarczyk, D.; Forsgard, M.; Chu, X.; Funk, C.; Guo, A.; Hillgren, K.M.; Li, L.; Pak, A.Y.; Perloff, E.S.; Rajaraman, G.; Salphati, L.; Taur, J.-S.; Weitz, D.; Wortelboer, H.M.; Xia, C.Q.; Xiao, G.; Yamagata, T.; Lee, C.A.

2013-01-01

In the 2012 Food and Drug Administration (FDA) draft guidance on drug-drug interactions (DDIs), a new molecular entity that inhibits Pglycoprotein (P-gp) may need a clinical DDI study with a P-gp substrate such as digoxin when themaximumconcentration of inhibitor at steady state divided by IC50

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.

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.

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.

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.

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

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, E-mail: cmu4h-mhy@126.com [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); and others

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.

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.

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.

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.

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.

Multiplexed and Switchable Release of Distinct Fluids from Microneedle Platforms via Conducting Polymer Nanoactuators for Potential Drug Delivery

Science.gov (United States)

Valdés-Ramírez, Gabriela; Windmiller, Joshua R.; Claussen, Jonathan C.; Martinez, Alexandra G.; Kuralay, Filiz; Zhou, Ming; Zhou, Nandi; Polsky, Ronen; Miller, Philip R.; Narayan, Roger; Wang, Joseph

2013-01-01

We report on the development of a microneedle-based multiplexed drug delivery actuator that enables the controlled delivery of multiple therapeutic agents. Two individually-addressable channels on a single microneedle array, each paired with its own reservoir and conducting polymer nanoactuator, are used to deliver various permutations of two unique chemical species. Upon application of suitable redox potentials to the selected actuator, the conducting polymer is able to undergo reversible volume changes, thereby serving to release a model chemical agent in a controlled fashion through the corresponding microneedle channels. Time-lapse videos offer direct visualization and characterization of the membrane switching capability and, along with calibration investigations, confirm the ability of the device to alternate the delivery of multiple reagents from individual microneedles of the array with higher precision and temporal resolution than conventional drug delivery actuators. Analytical modeling offers prediction of the volumetric flow rate through a single microneedle and accordingly can be used to assist in the design of subsequent microneedle arrays. The robust solid-state design and lack of mechanical components circumvent reliability issues that challenge fragile conventional microelectromechanical drug delivery devices. This proof-of-concept study demonstrates the potential of the drug delivery actuator system to aid in the rapid administration of multiple therapeutic agents and indicates the potential to counteract diverse biomedical conditions. PMID:24174709

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

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

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.

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.

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.

Automated applications of sandwich-cultured hepatocytes in the evaluation of hepatic drug transport.

Science.gov (United States)

Perry, Cassandra H; Smith, William R; St Claire, Robert L; Brouwer, Kenneth R

2011-04-01

Predictions of the absorption, distribution, metabolism, excretion, and toxicity of compounds in pharmaceutical development are essential aspects of the drug discovery process. B-CLEAR is an in vitro system that uses sandwich-cultured hepatocytes to evaluate and predict in vivo hepatobiliary disposition (hepatic uptake, biliary excretion, and biliary clearance), transporter-based hepatic drug-drug interactions, and potential drug-induced hepatotoxicity. Automation of predictive technologies is an advantageous and preferred format in drug discovery. In this study, manual and automated studies are investigated and equivalence is demonstrated. In addition, automated applications using model probe substrates and inhibitors to assess the cholestatic potential of drugs and evaluate hepatic drug transport are examined. The successful automation of this technology provides a more reproducible and less labor-intensive approach, reducing potential operator error in complex studies and facilitating technology transfer.

Therapeutic potential of cannabis-related drugs.

Science.gov (United States)

Alexander, Stephen P H

2016-01-04

In this review, I will consider the dual nature of Cannabis and cannabinoids. The duality arises from the potential and actuality of cannabinoids in the laboratory and clinic and the 'abuse' of Cannabis outside the clinic. The therapeutic areas currently best associated with exploitation of Cannabis-related medicines include pain, epilepsy, feeding disorders, multiple sclerosis and glaucoma. As with every other medicinal drug of course, the 'trick' will be to maximise the benefit and minimise the cost. After millennia of proximity and exploitation of the Cannabis plant, we are still playing catch up with an understanding of its potential influence for medicinal benefit. Copyright © 2015 Elsevier Inc. All rights reserved.

Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews.

Science.gov (United States)

Romagnoli, Katrina M; Nelson, Scott D; Hines, Lisa; Empey, Philip; Boyce, Richard D; Hochheiser, Harry

2017-02-22

Drug information compendia and drug-drug interaction information databases are critical resources for clinicians and pharmacists working to avoid adverse events due to exposure to potential drug-drug interactions (PDDIs). Our goal is to develop information models, annotated data, and search tools that will facilitate the interpretation of PDDI information. To better understand the information needs and work practices of specialists who search and synthesize PDDI evidence for drug information resources, we conducted an inquiry that combined a thematic analysis of published literature with unstructured interviews. Starting from an initial set of relevant articles, we developed search terms and conducted a literature search. Two reviewers conducted a thematic analysis of included articles. Unstructured interviews with drug information experts were conducted and similarly coded. Information needs, work processes, and indicators of potential strengths and weaknesses of information systems were identified. Review of 92 papers and 10 interviews identified 56 categories of information needs related to the interpretation of PDDI information including drug and interaction information; study design; evidence including clinical details, quality and content of reports, and consequences; and potential recommendations. We also identified strengths/weaknesses of PDDI information systems. We identified the kinds of information that might be most effective for summarizing PDDIs. The drug information experts we interviewed had differing goals, suggesting a need for detailed information models and flexible presentations. Several information needs not discussed in previous work were identified, including temporal overlaps in drug administration, biological plausibility of interactions, and assessment of the quality and content of reports. Richly structured depictions of PDDI information may help drug information experts more effectively interpret data and develop recommendations

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.

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

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

Transmission of drug resistant HIV and its potential impact on mortality and treatment outcomes in resource-limited settings

DEFF Research Database (Denmark)

Cambiano, Valentina; Bertagnolio, Silvia; Jordan, Michael R

2013-01-01

is the most cost-effective. Mathematical models can contribute to answer these questions. In order to estimate the potential long-term impact of TDR on mortality in people on ART we used the Synthesis transmission model. TDR is predicted to have potentially significant impact on future HIV mortality...... periods of unrecognized viral failure, during which drug-resistant virus can be transmitted and this could compromise the long-term effectiveness of currently available first-line regimens. In response to this concern, the World Health Organization recommends population-based surveys to detect whether...... the prevalence of resistance in ART-naive people is reaching alerting levels. Whereas adherence counseling has to be an integral component of any treatment program, it is still unclear which threshold of transmitted drug resistance (TDR) should trigger additional targeted public health actions and which action...

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.

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.

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…

Improving drug policy: The potential of broader democratic participation.

Science.gov (United States)

Ritter, Alison; Lancaster, Kari; Diprose, Rosalyn

2018-05-01

Policies concerned with illicit drugs vex governments. While the 'evidence-based policy' paradigm argues that governments should be informed by 'what works', in practice policy makers rarely operate this way. Moreover the evidence-based policy paradigm fails to account for democratic participatory processes, particularly how community members and people who use drugs might be included. The aim of this paper is to explore the political science thinking about democratic participation and the potential afforded in 'deliberative democracy' approaches, such as Citizens Juries and other mini-publics for improved drug policy processes. Deliberative democracy, through its focus on inclusion, equality and reasoned discussion, shows potential for drug policy reform and shifts the focus from reliance on and privileging of experts and scientific evidence. But the very nature of this kind of 'deliberation' may delimit participation, notably through its insistence on authorised modes of communication. Other forms of participation beyond reasoned deliberation aligned with the ontological view that participatory processes themselves are constitutive of subject positions and policy problems, may generate opportunities for considering how the deleterious effects of authorised modes of communication might be overcome. Copyright © 2018 Elsevier B.V. All rights reserved.

A regulatory perspective on the abuse potential evaluation of novel stimulant drugs in the United States.

Science.gov (United States)

Calderon, Silvia N; Klein, Michael

2014-12-01

In the United States of America (USA), the abuse potential assessment of a drug is performed as part of the safety evaluation of a drug under development, and to evaluate if the drug needs to be subject to controls that would minimize the abuse of the drug once on the market. The assessment of the abuse potential of new drugs consists of a scientific and medical evaluation of all data related to abuse of the drug. This paper describes the regulatory framework for evaluating the abuse potential of new drugs, in general, including novel stimulants. The role of the United States Food and Drug Administration (FDA) in the evaluation of the abuse potential of drugs, and its role in drug control are also discussed. A definition of abuse potential, an overview of the currently accepted approaches to evaluating the abuse potential of a drug, as well as a description of the criteria that applies when recommending a specific level of control (i.e., a Schedule) for a drug under the Controlled Substances Act (CSA). This article is part of the Special Issue entitled 'CNS Stimulants'. Published by Elsevier Ltd.

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.

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.

Clinical Drug-Drug Pharmacokinetic Interaction Potential of Sucralfate with Other Drugs

DEFF Research Database (Denmark)

Sulochana, Suresh P; Syed, Muzeeb; Chandrasekar, Devaraj V

2016-01-01

of drugs. This review covers several category of drugs such as non-steroidal anti-inflammatory drugs, fluoroquinolones, histamine H2-receptor blockers, macrolides, anti-fungals, anti-diabetics, salicylic acid derivatives, steroidal anti-inflammatory drugs and provides pharmacokinetic data summary along...

[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.

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.

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.

Erosive and cariogenicity potential of pediatric drugs: study of physicochemical parameters.

Science.gov (United States)

Xavier, Alidianne Fábia C; Moura, Eline F F; Azevedo, Waldeneide F; Vieira, Fernando F; Abreu, Mauro H N G; Cavalcanti, Alessandro L

2013-12-10

Pediatric medications may possess a high erosive potential to dental tissues due to the existence of acid components in their formulations. The purpose was to determine the erosive and cariogenic potential of pediatric oral liquid medications through the analysis of their physicochemical properties in vitro. A total of 59 substances were selected from the drug reference list of the National Health Surveillance Agency (ANVISA), which belong to 11 therapeutic classes, as follows: analgesics, non-steroidal anti-inflammatory, corticosteroids, antihistamines, antitussives, bronchodilators, antibacterials, antiparasitics, antiemetics, anticonvulsants and antipsychotics. Measurement of pH was performed by potentiometry, using a digital pH meter. For the Total Titratable Acidity (TTA) chemical assay, a 0.1 N NaOH standard solution was used, which was titrated until drug pH was neutralized. The Total Soluble Solids Contents (TSSC) quantification was carried out by refractometry using Brix scale and the analysis of Total Sugar Content was performed according to Fehling's method. In addition, it was analyzed the information contained in the drug inserts with regard to the presence of sucrose and type of acid and sweetener added to the formulations. All drug classes showed acidic pH, and the lowest mean was found for antipsychotics (2.61 ± 0.08). There was a large variation in the TTA (0.1% - 1.18%) and SST (10.44% - 57.08%) values. High total sugar contents were identified in the antitussives (53.25%) and anticonvulsants (51.75%). As described in the drug inserts, sucrose was added in 47.5% of the formulations, as well as citric acid (39.0%), sodium saccharin (36.4%) and sorbitol (34.8%). The drugs analyzed herein showed physicochemical characteristics indicative of a cariogenic and erosive potential on dental tissues. Competent bodies' strategies should be implemented in order to broaden the knowledge of health professionals, drug manufacturers and general consuming public

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…

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.

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.

Cancer stem cells and drug resistance: the potential of nanomedicine

Science.gov (United States)

Vinogradov, Serguei; Wei, Xin

2012-01-01

Properties of the small group of cancer cells called tumor-initiating or cancer stem cells (CSCs) involved in drug resistance, metastasis and relapse of cancers can significantly affect tumor therapy. Importantly, tumor drug resistance seems to be closely related to many intrinsic or acquired properties of CSCs, such as quiescence, specific morphology, DNA repair ability and overexpression of antiapoptotic proteins, drug efflux transporters and detoxifying enzymes. The specific microenvironment (niche) and hypoxic stability provide additional protection against anticancer therapy for CSCs. Thus, CSC-focused therapy is destined to form the core of any effective anticancer strategy. Nanomedicine has great potential in the development of CSC-targeting drugs, controlled drug delivery and release, and the design of novel gene-specific drugs and diagnostic modalities. This review is focused on tumor drug resistance-related properties of CSCs and describes current nanomedicine approaches, which could form the basis of novel combination therapies for eliminating metastatic and CSCs. PMID:22471722

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.

Iontophoresis: A Potential Emergence of a Transdermal Drug Delivery System

Science.gov (United States)

Dhote, Vinod; Bhatnagar, Punit; Mishra, Pradyumna K.; Mahajan, Suresh C.; Mishra, Dinesh K.

2012-01-01

The delivery of drugs into systemic circulation via skin has generated much attention during the last decade. Transdermal therapeutic systems propound controlled release of active ingredients through the skin and into the systemic circulation in a predictive manner. Drugs administered through these systems escape first-pass metabolism and maintain a steady state scenario similar to a continuous intravenous infusion for up to several days. However, the excellent impervious nature of the skin offers the greatest challenge for successful delivery of drug molecules by utilizing the concepts of iontophoresis. The present review deals with the principles and the recent innovations in the field of iontophoretic drug delivery system together with factors affecting the system. This delivery system utilizes electric current as a driving force for permeation of ionic and non-ionic medications. The rationale behind using this technique is to reversibly alter the barrier properties of skin, which could possibly improve the penetration of drugs such as proteins, peptides and other macromolecules to increase the systemic delivery of high molecular weight compounds with controlled input kinetics and minimum inter-subject variability. Although iontophoresis seems to be an ideal candidate to overcome the limitations associated with the delivery of ionic drugs, further extrapolation of this technique is imperative for translational utility and mass human application. PMID:22396901

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.

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.

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.

Potentiation of E-4031-induced torsade de pointes by HMR1556 or ATX-II is not predicted by action potential short-term variability or triangulation.

Science.gov (United States)

Michael, G; Dempster, J; Kane, K A; Coker, S J

2007-12-01

Torsade de pointes (TdP) can be induced by a reduction in cardiac repolarizing capacity. The aim of this study was to assess whether IKs blockade or enhancement of INa could potentiate TdP induced by IKr blockade and to investigate whether short-term variability (STV) or triangulation of action potentials preceded TdP. Experiments were performed in open-chest, pentobarbital-anaesthetized, alpha 1-adrenoceptor-stimulated, male New Zealand White rabbits, which received three consecutive i.v. infusions of either the IKr blocker E-4031 (1, 3 and 10 nmol kg(-1) min(-1)), the IKs blocker HMR1556 (25, 75 and 250 nmol kg(-1) min(-1)) or E-4031 and HMR1556 combined. In a second study rabbits received either the same doses of E-4031, the INa enhancer, ATX-II (0.4, 1.2 and 4.0 nmol kg(-1)) or both of these drugs. ECGs and epicardial monophasic action potentials were recorded. HMR1556 alone did not cause TdP but increased E-4031-induced TdP from 25 to 80%. ATX-II alone caused TdP in 38% of rabbits, as did E-4031; 75% of rabbits receiving both drugs had TdP. QT intervals were prolonged by all drugs but the extent of QT prolongation was not related to the occurrence of TdP. No changes in STV were detected and triangulation was only increased after TdP occurred. Giving modulators of ion channels in combination substantially increased TdP but, in this model, neither STV nor triangulation of action potentials could predict TdP.

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

[Potential antimicrobial drug interactions in clinical practice: consequences of polypharmacy and multidrug resistance].

Science.gov (United States)

Martínez-Múgica, Cristina

2015-12-01

Polypharmacy is a growing problem nowadays, which can increase the risk of potential drug interactions, and result in a loss of effectiveness. This is particularly relevant to the anti-infective therapy, especially when infection is produced by resistant bacteria, because therapeutic options are limited and interactions can cause treatment failure. All antimicrobial prescriptions were retrospectively reviewed during a week in the Pharmacy Department, in order to detect potential drug-interactions and analysing their clinical significance. A total of 314 antimicrobial prescriptions from 151 patients were checked. There was at least one potential interaction detected in 40% of patients, being more frequent and severe in those infected with multidrug-resistant microorganisms. Drugs most commonly involved were quinolones, azoles, linezolid and vancomycin. Potential drug interactions with antimicrobial agents are a frequent problem that can result in a loss of effectiveness. This is why they should be detected and avoided when possible, in order to optimize antimicrobial therapy, especially in case of multidrug resistant infections.

Peptide-based soft materials as potential drug delivery vehicles.

Science.gov (United States)

Verma, Sandeep; Joshi, K B; Ghosh, Surajit

2007-11-01

Emerging concepts in the construction of nanostructures hold immense potential in the areas of drug delivery and targeting. Such nanoscopic assemblies/structures, similar to natural proteins and self-associating systems, may lead to the formation of programmable soft structures with expanded drug delivery options and the capability to circumvent first-pass metabolism. This article aims to illustrate key recent developments and innovative bioinspired design paradigms pertaining to peptide-containing self-assembled tubular and vesicular soft structures. Soft structures are composed of components that self-assemble to reveal diverse morphologies stabilized by weak, noncovalent interactions. Morphological properties of such structures and their ability to encapsulate drugs, biologicals and bioactive small molecules, with the promise of targeted delivery, are discussed.

VirtualToxLab — A platform for estimating the toxic potential of drugs, chemicals and natural products

International Nuclear Information System (INIS)

Vedani, Angelo; Dobler, Max; Smieško, Martin

2012-01-01

The VirtualToxLab is an in silico technology for estimating the toxic potential (endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity) of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of proteins, known or suspected to trigger adverse effects. The toxic potential, a non-linear function ranging from 0.0 (none) to 1.0 (extreme), is derived from the individual binding affinities of a compound towards currently 16 target proteins: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, and thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, and 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The interface to the technology allows building and uploading molecular structures, viewing and downloading results and, most importantly, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. The VirtualToxLab has been used to predict the toxic potential for over 2500 compounds: the results are posted on (http://www.virtualtoxlab.org). The free platform — the OpenVirtualToxLab — is accessible (in client–server mode) over the Internet. It is free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations. -- Highlights: ► In silico technology for estimating the toxic potential of drugs and chemicals. ► Simulation of binding towards 16 proteins suspected to trigger adverse effects. ► Mechanistic interpretation and real-time 3D visualization. ► Accessible over the Internet. ► Free of charge for universities, governmental agencies, regulatory bodies and NPOs.

VirtualToxLab — A platform for estimating the toxic potential of drugs, chemicals and natural products

Energy Technology Data Exchange (ETDEWEB)

Vedani, Angelo, E-mail: angelo.vedani@unibas.ch [Biographics Laboratory 3R, Klingelbergstrasse 50, 4056 Basel (Switzerland); Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel (Switzerland); Dobler, Max [Biographics Laboratory 3R, Klingelbergstrasse 50, 4056 Basel (Switzerland); Smieško, Martin [Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel (Switzerland)

2012-06-01

The VirtualToxLab is an in silico technology for estimating the toxic potential (endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity) of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of proteins, known or suspected to trigger adverse effects. The toxic potential, a non-linear function ranging from 0.0 (none) to 1.0 (extreme), is derived from the individual binding affinities of a compound towards currently 16 target proteins: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, and thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, and 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The interface to the technology allows building and uploading molecular structures, viewing and downloading results and, most importantly, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. The VirtualToxLab has been used to predict the toxic potential for over 2500 compounds: the results are posted on (http://www.virtualtoxlab.org). The free platform — the OpenVirtualToxLab — is accessible (in client–server mode) over the Internet. It is free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations. -- Highlights: ► In silico technology for estimating the toxic potential of drugs and chemicals. ► Simulation of binding towards 16 proteins suspected to trigger adverse effects. ► Mechanistic interpretation and real-time 3D visualization. ► Accessible over the Internet. ► Free of charge for universities, governmental agencies, regulatory bodies and NPOs.

Prevalence of Potential and Clinically Relevant Statin-Drug Interactions in Frail and Robust Older Inpatients.

Science.gov (United States)

Thai, Michele; Hilmer, Sarah; Pearson, Sallie-Anne; Reeve, Emily; Gnjidic, Danijela

2015-10-01

A significant proportion of older people are prescribed statins and are also exposed to polypharmacy, placing them at increased risk of statin-drug interactions. To describe the prevalence rates of potential and clinically relevant statin-drug interactions in older inpatients according to frailty status. A cross-sectional study of patients aged ≥65 years who were prescribed a statin and were admitted to a teaching hospital between 30 July and 10 October 2014 in Sydney, Australia, was conducted. Data on socio-demographics, comorbidities and medications were collected using a standardized questionnaire. Potential statin-drug interactions were defined if listed in the Australian Medicines Handbook and three international drug information sources: the British National Formulary, Drug Interaction Facts and Drug-Reax(®). Clinically relevant statin-drug interactions were defined as interactions with the highest severity rating in at least two of the three international drug information sources. Frailty was assessed using the Reported Edmonton Frail Scale. A total of 180 participants were recruited (median age 78 years, interquartile range 14), 35.0% frail and 65.0% robust. Potential statin-drug interactions were identified in 10% of participants, 12.7% of frail participants and 8.5% of robust participants. Clinically relevant statin-drug interactions were identified in 7.8% of participants, 9.5% of frail participants and 6.8% of robust participants. Depending on the drug information source used, the prevalence rates of potential and clinically relevant statin-drug interactions ranged between 14.4 and 35.6% and between 14.4 and 20.6%, respectively. In our study of frail and robust older inpatients taking statins, the overall prevalence of potential statin-drug interactions was low and varied significantly according to the drug information source used.

The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development.

Science.gov (United States)

Kunz, Meik; Liang, Chunguang; Nilla, Santosh; Cecil, Alexander; Dandekar, Thomas

2016-01-01

The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure-activity relationships.Database URL:http://drumpid.bioapps.biozentrum.uni-wuerzburg.de. © The Author(s) 2016. Published by Oxford University Press.

Predicting local field potentials with recurrent neural networks.

Science.gov (United States)

Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter

2016-08-01

We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

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

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

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...

Potential Role of Extracellular Vesicles in the Pathophysiology of Drug Addiction.

Science.gov (United States)

Rao, P S S; O'Connell, Kelly; Finnerty, Thomas Kyle

2018-01-23

Extracellular vesicles (EVs) are small vesicles secreted by cells and are known to carry sub-cellular components including microRNA, proteins, and lipids. Due to their ability to transport cargo between cells, EVs have been identified as important regulators of various pathophysiological conditions and can therefore influence treatment outcomes. In particular, the significance of microRNAs in EV-mediated cell-cell communication is well-documented. While the influence of EVs and the cargo delivered by EVs has been extensively reviewed in other neurological disorders, the available literature on the potential role of EVs in the pathophysiology of drug addiction has not been reviewed. Hence, in this article, the known effects of commonly abused drugs (ethanol, nicotine, opiates, cocaine, and cannabinoids) on EV secretion have been reviewed. In addition, the potential role of drugs of abuse in affecting the delivery of EV-packaged microRNAs, and the subsequent impact on neuronal health and continued drug dependence, has been discussed.

Therapeutic Potential of Foldamers: From Chemical Biology Tools To Drug Candidates?

Science.gov (United States)

Gopalakrishnan, Ranganath; Frolov, Andrey I; Knerr, Laurent; Drury, William J; Valeur, Eric

2016-11-10

Over the past decade, foldamers have progressively emerged as useful architectures to mimic secondary structures of proteins. Peptidic foldamers, consisting of various amino acid based backbones, have been the most studied from a therapeutic perspective, while polyaromatic foldamers have barely evolved from their nascency and remain perplexing for medicinal chemists due to their poor drug-like nature. Despite these limitations, this compound class may still offer opportunities to study challenging targets or provide chemical biology tools. The potential of foldamer drug candidates reaching the clinic is still a stretch. Nevertheless, advances in the field have demonstrated their potential for the discovery of next generation therapeutics. In this perspective, the current knowledge of foldamers is reviewed in a drug discovery context. Recent advances in the early phases of drug discovery including hit finding, target validation, and optimization and molecular modeling are discussed. In addition, challenges and focus areas are debated and gaps highlighted.

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.

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

Potential predictability of a Colombian river flow

Science.gov (United States)

Córdoba-Machado, Samir; Palomino-Lemus, Reiner; Quishpe-Vásquez, César; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

In this study the predictability of an important Colombian river (Cauca) has been analysed based on the use of climatic variables as potential predictors. Cauca River is considered one of the most important rivers of Colombia because its basin supports important productive activities related with the agriculture, such as the production of coffee or sugar. Potential relationships between the Cauca River seasonal streamflow anomalies and different climatic variables such as sea surface temperature (SST), precipitation (Pt), temperature over land (Tm) and soil water (Sw) have been analysed for the period 1949-2009. For this end, moving correlation analysis of 30 years have been carried out for lags from one to four seasons for the global SST, and from one to two seasons for South America Pt, Tm and Sw. Also, the stability of the significant correlations have been also studied, identifying the regions used as potential predictors of streamflow. Finally, in order to establish a prediction scheme based on the previous stable correlations, a Principal Component Analysis (PCA) applied on the potential predictor regions has been carried out in order to obtain a representative time series for each predictor field. Significant and stable correlations between the seasonal streamflow and the tropical Pacific SST (El Niño region) are found for lags from one to four (one-year) season. Additionally, some regions in the Indian and Atlantic Oceans also show significant and stable correlations at different lags, highlighting the importance that exerts the Atlantic SST on the hydrology of Colombia. Also significant and stable correlations are found with the Pt, Tm and Sw for some regions over South America, at lags of one and two seasons. The prediction of Cauca seasonal streamflow based on this scheme shows an acceptable skill and represents a relative improvement compared with the predictability obtained using the teleconnection indices associated with El Niño. Keywords

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.

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.

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.

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.

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.

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)

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

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...

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.

In silico machine learning methods in drug development.

Science.gov (United States)

Dobchev, Dimitar A; Pillai, Girinath G; Karelson, Mati

2014-01-01

Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.

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.

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.

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

Potential savings from an evidence-based consumer-oriented public education campaign on prescription drugs.

Science.gov (United States)

Donohue, Julie M; Fischer, Michael A; Huskamp, Haiden A; Weissman, Joel S

2008-10-01

To estimate potential savings associated with the Consumer Reports Best Buy Drugs program, a national educational program that provides consumers with price and effectiveness information on prescription drugs. National data on 2006 prescription sales and retail prices paid for angiotensin-converting enzyme inhibitors (ACEIs), β-blockers, calcium channel blockers, and 3-hydroxy-3-methylglutaryl coenzyme A (HMG-coA) reductase inhibitors (statins). We converted national data on aggregate unit sales of drugs in the four classes to defined daily doses (DDD) and estimated a range of potential savings from generic and therapeutic substitution. We estimated that $2.76 billion, or 7.83 percent of sales, could be saved if use of the drugs recommended by the educational program was increased. The recommended drugs' prices were 15-65 percent lower per DDD than their therapeutic alternatives. The majority (57.4 percent) of potential savings would be achieved through therapeutic substitution. Substantial savings can be achieved through greater use of comparatively effective and lower cost drugs recommended by a national consumer education program. However, barriers to dissemination of consumer-oriented drug information must be addressed before savings can be realized. © Health Research and Educational Trust.

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.

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.

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

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

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.

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

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.

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.

Financial Impact of Cancer Drug Wastage and Potential Cost Savings From Mitigation Strategies.

Science.gov (United States)

Leung, Caitlyn Y W; Cheung, Matthew C; Charbonneau, Lauren F; Prica, Anca; Ng, Pamela; Chan, Kelvin K W

2017-07-01

Cancer drug wastage occurs when a parenteral drug within a fixed vial is not administered fully to a patient. This study investigated the extent of drug wastage, the financial impact on the hospital budget, and the cost savings associated with current mitigation strategies. We conducted a cross-sectional study in three University of Toronto-affiliated hospitals of various sizes. We recorded the actual amount of drug wasted over a 2-week period while using current mitigation strategies. Single-dose vial cancer drugs with the highest wastage potentials were identified (14 drugs). To calculate the hypothetical drug wastage with no mitigation strategies, we determined how many vials of drugs would be needed to fill a single prescription. The total drug costs over the 2 weeks ranged from $50,257 to $716,983 in the three institutions. With existing mitigation strategies, the actual drug wastage over the 2 weeks ranged from $928 to $5,472, which was approximately 1% to 2% of the total drug costs. In the hypothetical model with no mitigation strategies implemented, the projected drug cost wastage would have been $11,232 to $149,131, which accounted for 16% to 18% of the total drug costs. As a result, the potential annual savings while using current mitigation strategies range from 15% to 17%. The financial impact of drug wastage is substantial. Mitigation strategies lead to substantial cost savings, with the opportunity to reinvest those savings. More research is needed to determine the appropriate methods to minimize risk to patients while using the cost-saving mitigation strategies.

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

Pharmacokinetic Interactions between Drugs and Botanical Dietary Supplements.

Science.gov (United States)

Sprouse, Alyssa A; van Breemen, Richard B

2016-02-01

The use of botanical dietary supplements has grown steadily over the last 20 years despite incomplete information regarding active constituents, mechanisms of action, efficacy, and safety. An important but underinvestigated safety concern is the potential for popular botanical dietary supplements to interfere with the absorption, transport, and/or metabolism of pharmaceutical agents. Clinical trials of drug-botanical interactions are the gold standard and are usually carried out only when indicated by unexpected consumer side effects or, preferably, by predictive preclinical studies. For example, phase 1 clinical trials have confirmed preclinical studies and clinical case reports that St. John's wort (Hypericum perforatum) induces CYP3A4/CYP3A5. However, clinical studies of most botanicals that were predicted to interact with drugs have shown no clinically significant effects. For example, clinical trials did not substantiate preclinical predictions that milk thistle (Silybum marianum) would inhibit CYP1A2, CYP2C9, CYP2D6, CYP2E1, and/or CYP3A4. Here, we highlight discrepancies between preclinical and clinical data concerning drug-botanical interactions and critically evaluate why some preclinical models perform better than others in predicting the potential for drug-botanical interactions. Gaps in knowledge are also highlighted for the potential of some popular botanical dietary supplements to interact with therapeutic agents with respect to absorption, transport, and metabolism. Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

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.

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.

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.

Extent of poly-pharmacy, occurrence and associated factors of drug-drug interaction and potential adverse drug reactions in Gondar Teaching Referral Hospital, North West Ethiopia

Directory of Open Access Journals (Sweden)

Endalkachew Admassie

2013-01-01

Full Text Available The aim of this study was to assess the extent of poly-pharmacy, occurrence, and associated factors for the occurrence of drug-drug interaction (DDI and potential adverse drug reaction (ADR in Gondar University Teaching Referral Hospital. Institutional-based retrospective cross-sectional study. This study was conducted on prescriptions of both in and out-patients for a period of 3 months at Gondar University Hospital. Both bivariate analysis and multivariate logistic regression were used to identify risk factors for the occurrence of DDI and possible ADRs. All the statistical calculations were performed using SPSS; software. A total of 12,334 prescriptions were dispensed during the study period of which, 2,180 prescriptions were containing two or more drugs per prescription. A total of 21,210 drugs were prescribed and the average number of drugs per prescription was 1.72. Occurrences of DDI of all categories (Major, Moderate, and Minor were analyzed and DDI were detected in 711 (32.6% prescriptions. Sex was not found to be a risk factor for the occurrence of DDI and ADR, while age and number of medications per prescription were found to be significant risk factors for the occurrence of DDI and ADR. The mean number of drugs per prescription was 1.72 and hence with regard to the WHO limit of drugs per prescription, Gondar hospital was able to maintain the limit and prescriptions containing multiple drugs supposed to be taken systemically. Numbers of drugs per prescription as well as older age were found to be predisposing factors for the occurrence of DDI and potential ADRs while sex was not a risk factor.

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

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

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.

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.

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.

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.

Natural Non-Mulberry Silk Nanoparticles for Potential-Controlled Drug Release

Science.gov (United States)

Wang, Juan; Yin, Zhuping; Xue, Xiang; Kundu, Subhas C.; Mo, Xiumei; Lu, Shenzhou

2016-01-01

Natural silk protein nanoparticles are a promising biomaterial for drug delivery due to their pleiotropic properties, including biocompatibility, high bioavailability, and biodegradability. Chinese oak tasar Antheraea pernyi silk fibroin (ApF) nanoparticles are easily obtained using cations as reagents under mild conditions. The mild conditions are potentially advantageous for the encapsulation of sensitive drugs and therapeutic molecules. In the present study, silk fibroin protein nanoparticles are loaded with differently-charged small-molecule drugs, such as doxorubicin hydrochloride, ibuprofen, and ibuprofen-Na, by simple absorption based on electrostatic interactions. The structure, morphology and biocompatibility of the silk nanoparticles in vitro are investigated. In vitro release of the drugs from the nanoparticles depends on charge-charge interactions between the drugs and the nanoparticles. The release behavior of the compounds from the nanoparticles demonstrates that positively-charged molecules are released in a more prolonged or sustained manner. Cell viability studies with L929 demonstrated that the ApF nanoparticles significantly promoted cell growth. The results suggest that Chinese oak tasar Antheraea pernyi silk fibroin nanoparticles can be used as an alternative matrix for drug carrying and controlled release in diverse biomedical applications. PMID:27916946

Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network

Science.gov (United States)

Li, Wan; Wei, Wenqing; Li, Yiran; Xie, Ruiqiang; Guo, Shanshan; Wang, Yahui; Jiang, Jing; Chen, Binbin; Lv, Junjie; Zhang, Nana; Chen, Lina; He, Weiming

2016-01-01

Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biological background, 1 PPDT-Module and 22 PCOS potential drug targets were identified, 21 of which were verified by literatures to be associated with the pathogenesis of PCOS. 42 drugs targeting to 13 PCOS potential drug targets were investigated experimentally or clinically for PCOS. Evaluated by independent datasets, the whole PPDT-Module and 22 PCOS potential drug targets could not only reveal the drug response, but also distinguish the statuses between normal and disease. Our identified PPDT-Module and PCOS potential drug targets would shed light on the treatment of PCOS. And our approach would provide valuable insights to research on the pathogenesis and drug response of other diseases. PMID:27191267

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

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.

In vitro cytochrome P450 inhibition potential of methylenedioxy-derived designer drugs studied with a two-cocktail approach.

Science.gov (United States)

Dinger, Julia; Meyer, Markus R; Maurer, Hans H

2016-02-01

In vitro cytochrome P450 (CYP) inhibition assays are common approaches for testing the inhibition potential of drugs for predicting potential interactions. In contrast to marketed medicaments, drugs of abuse, particularly the so-called novel psychoactive substances, were not tested before distribution and consumption. Therefore, the inhibition potential of methylenedioxy-derived designer drugs (MDD) of different drug classes such as aminoindanes, amphetamines, benzofurans, cathinones, piperazines, pyrrolidinophenones, and tryptamines should be elucidated. The FDA-preferred test substrates, split in two cocktails, were incubated with pooled human liver microsomes and analysed after protein precipitation using LC-high-resolution-MS/MS. IC50 values were determined of MDD showing more than 50 % inhibition in the prescreening. Values were calculated by plotting the relative metabolite concentration formed over the logarithm of the inhibitor concentration. All MDD showed inhibition against CYP2D6 activity and most of them in the range of the clinically relevant CYP2D6 inhibitors quinidine and fluoxetine. In addition, the beta-keto compounds showed inhibition of the activity of CYP2B6, 5,6-MD-DALT of CYP1A2 and CYP3A, and MDAI of CYP2A6, all in the range of clinically relevant inhibitors. In summary, all MDD showed inhibition of the activity of CYP2D6, six of CYP1A2, three of CYP2A6, 13 of CYP2B6, two of CYP2C9, six of CYP2C19, one of CYP2E1, and six of CYP3A. These results showed that the CYP inhibition by MDD might be clinically relevant, but further studies are needed for final conclusions.

Drug-induced liver injury

DEFF Research Database (Denmark)

Nielsen, Mille Bækdal; Ytting, Henriette; Skalshøi Kjær, Mette

2017-01-01

OBJECTIVE: The idiosyncratic subtype of drug-induced liver injury (DILI) is a rare reaction to medical treatment that in severe cases can lead to acute liver failure and death. The aim of this study was to describe the presentation and outcome of DILI and to identify potential predictive factors...... that DILI may be severe and run a fatal course, and that bilirubin and INR levels may predict poor outcome....

Microbial P450 Enzymes in Bioremediation and Drug Discovery: Emerging Potentials and Challenges.

Science.gov (United States)

Bhattacharya, Sukanta S; Yadav, Jagjit S

2018-01-01

Cytochrome P450 enzymes are a structurally conserved but functionally diverse group of heme-containing mixed function oxidases found across both prokaryotic and eukaryotic forms of the microbial world. Microbial P450s are known to perform diverse functions ranging from the synthesis of cell wall components to xenobiotic/drug metabolism to biodegradation of environmental chemicals. Conventionally, many microbial systems have been reported to mimic mammalian P450-like activation of drugs and were proposed as the in-vitro models of mammalian drug metabolism. Recent reports suggest that native or engineered forms of specific microbial P450s from these and other microbial systems could be employed for desired specific biotransformation reactions toward natural and synthetic (drug) compounds underscoring their emerging potential in drug improvement and discovery. On the other hand, microorganisms particularly fungi and actinomycetes have been shown to possess catabolic P450s with unusual potential to degrade toxic environmental chemicals including persistent organic pollutants (POPs). Wood-rotting basidiomycete fungi in particular have revealed the presence of exceptionally large P450 repertoire (P450ome) in their genomes, majority of which are however orphan (with no known function). Our pre- and post-genomic studies have led to functional characterization of several fungal P450s inducible in response to exposure to several environmental toxicants and demonstration of their potential in bioremediation of these chemicals. This review is an attempt to summarize the postgenomic unveiling of this versatile enzyme superfamily in microbial systems and investigation of their potential to synthesize new drugs and degrade persistent pollutants, among other biotechnological applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Plant water potential improves prediction of empirical stomatal models.

Directory of Open Access Journals (Sweden)

William R L Anderegg

Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

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.

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.

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

Potential of surface-eroding poly(ethylene carbonate) for drug delivery to macrophages

DEFF Research Database (Denmark)

Bohr, Adam; Water, Jorrit J; Wang, Yingya

2016-01-01

Films composed of poly(ethylene carbonate) (PEC), a biodegradable polymer, were compared with poly(lactide-co-glycolide) (PLGA) films loaded with and without the tuberculosis drug rifampicin to study the characteristics and performance of PEC as a potential carrier for controlled drug delivery...... to macrophages. All drug-loaded PLGA and PEC films were amorphous indicating good miscibility of the drug in the polymers, even at high drug loading (up to 50wt.%). Polymer degradation studies showed that PLGA degraded slowly via bulk erosion while PEC degraded more rapidly and near-linearly via enzyme mediated...... surface erosion (by cholesterol esterase). Drug release studies performed with polymer films indicated a diffusion/erosion dependent delivery behavior for PLGA while an almost zero-order drug release profile was observed from PEC due to the controlled polymer degradation process. When exposed to polymer...

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.

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...

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

Chemical structure-based predictive model for methanogenic anaerobic biodegradation potential.

Science.gov (United States)

Meylan, William; Boethling, Robert; Aronson, Dallas; Howard, Philip; Tunkel, Jay

2007-09-01

Many screening-level models exist for predicting aerobic biodegradation potential from chemical structure, but anaerobic biodegradation generally has been ignored by modelers. We used a fragment contribution approach to develop a model for predicting biodegradation potential under methanogenic anaerobic conditions. The new model has 37 fragments (substructures) and classifies a substance as either fast or slow, relative to the potential to be biodegraded in the "serum bottle" anaerobic biodegradation screening test (Organization for Economic Cooperation and Development Guideline 311). The model correctly classified 90, 77, and 91% of the chemicals in the training set (n = 169) and two independent validation sets (n = 35 and 23), respectively. Accuracy of predictions of fast and slow degradation was equal for training-set chemicals, but fast-degradation predictions were less accurate than slow-degradation predictions for the validation sets. Analysis of the signs of the fragment coefficients for this and the other (aerobic) Biowin models suggests that in the context of simple group contribution models, the majority of positive and negative structural influences on ultimate degradation are the same for aerobic and methanogenic anaerobic biodegradation.

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.

Inorganically modified diatomite as a potential prolonged-release drug carrier.

Science.gov (United States)

Janićijević, Jelena; Krajišnik, Danina; Calija, Bojan; Dobričić, Vladimir; Daković, Aleksandra; Krstić, Jugoslav; Marković, Marija; Milić, Jela

2014-09-01

Inorganic modification of diatomite was performed with the precipitation product of partially neutralized aluminum sulfate solution at three different mass ratios. The starting and the modified diatomites were characterized by SEM-EDS, FTIR, thermal analysis and zeta potential measurements and evaluated for drug loading capacity in adsorption batch experiments using diclofenac sodium (DS) as a model drug. In vitro drug release studies were performed in phosphate buffer pH6.8 from comprimates containing: the drug adsorbed onto the selected modified diatomite sample (DAMD), physical mixture of the drug with the selected modified diatomite sample (PMDMD) and physical mixture of the drug with the starting diatomite (PMDD). In vivo acute toxicity testing of the modified diatomite samples was performed on mice. High adsorbent loading of the selected modified diatomite sample (~250mg/g in 2h) enabled the preparation of comprimates containing adsorbed DS in the amount near to its therapeutic dose. Drug release studies demonstrated prolonged release of DS over a period of 8h from both DAMD comprimates (18% after 8h) and PMDMD comprimates (45% after 8h). The release kinetics for DAMD and PMDMD comprimates fitted well with Korsmeyer-Peppas and Bhaskar models, indicating that the release mechanism was a combination of non-Fickian diffusion and ion exchange process. Copyright © 2014 Elsevier B.V. All rights reserved.

Potential and problems in ultrasound-responsive drug delivery systems

Directory of Open Access Journals (Sweden)

Zhao YZ

2013-04-01

Full Text Available Ying-Zheng Zhao,1,3 Li-Na Du,2 Cui-Tao Lu,1 Yi-Guang Jin,2 Shu-Ping Ge3 1Wenzhou Medical College, Wenzhou City, Zhejiang Province, 2Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, Beijing, People’s Republic of China; 3St Christopher’s Hospital for Children/Drexel University College of Medicine, Philadelphia, PA, USA Abstract: Ultrasound is an important local stimulus for triggering drug release at the target tissue. Ultrasound-responsive drug delivery systems (URDDS have become an important research focus in targeted therapy. URDDS include many different formulations, such as microbubbles, nanobubbles, nanodroplets, liposomes, emulsions, and micelles. Drugs that can be loaded into URDDS include small molecules, biomacromolecules, and inorganic substances. Fields of clinical application include anticancer therapy, treatment of ischemic myocardium, induction of an immune response, cartilage tissue engineering, transdermal drug delivery, treatment of Huntington’s disease, thrombolysis, and disruption of the blood–brain barrier. This review focuses on recent advances in URDDS, and discusses their formulations, clinical application, and problems, as well as a perspective on their potential use in the future. Keywords: ultrasound, targeted therapy, clinical application

Botanical-drug interactions: a scientific perspective.

Science.gov (United States)

de Lima Toccafondo Vieira, Manuela; Huang, Shiew-Mei

2012-09-01

There is a continued predisposition of concurrent use of drugs and botanical products. A general lack of knowledge of the interaction potential together with an under-reporting of botanical use poses a challenge for the health care providers and a safety concern for patients. Botanical-drug interactions increase the patient risk, especially with regard to drugs with a narrow therapeutic index (e.g., warfarin, cyclosporine, and digoxin). Examples of case reports and clinical studies evaluating botanical-drug interactions of commonly used botanicals in the US are presented. The potential pharmacokinetic and pharmacodynamic bases of such interactions are discussed, as well as the challenges associated with the interpretation of the available data and prediction of botanical-drug interactions. Recent FDA experiences with botanical products and interactions including labeling implications as a risk management strategy are highlighted. Georg Thieme Verlag KG Stuttgart · New York.

Muscarinic Acetylcholine Receptor Subtypes as Potential Drug Targets for the Treatment of Schizophrenia, Drug Abuse and Parkinson's Disease

DEFF Research Database (Denmark)

Dencker, Ditte; Thomsen, Morgane; Wörtwein, Gitta

2011-01-01

's disease and drug abuse. Dopaminergic systems are regulated by cholinergic, especially muscarinic, input. Not surprisingly, increasing evidence implicates muscarinic acetylcholine receptor-mediated pathways as potential targets for the treatment of these disorders classically viewed as "dopamine based...... site. Such agents may lead to the development of novel classes of drugs useful for the treatment of psychosis, drug abuse and Parkinson's disease. The present review highlights recent studies carried out using muscarinic receptor knock-out mice and new subtype-selective allosteric ligands to assess...... the roles of M(1), M(4), and M(5) receptors in various central processes that are under strong dopaminergic control. The outcome of these studies opens new perspectives for the use of novel muscarinic drugs for several severe disorders of the CNS....

NMR characterisation and transdermal drug delivery potential of microemulsion systems

DEFF Research Database (Denmark)

Kreilgaard, Mads; Pedersen, E J; Jaroszewski, J W

2000-01-01

The purpose of this study was to investigate the influence of structure and composition of microemulsions (Labrasol/Plurol Isostearique/isostearylic isostearate/water) on their transdermal delivery potential of a lipophilic (lidocaine) and a hydrophilic model drug (prilocaine hydrochloride), and ...

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

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.

Properties of Protein Drug Target Classes

Science.gov (United States)

Bull, Simon C.; Doig, Andrew J.

2015-01-01

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme. PMID

A mapping of drug space from the viewpoint of small molecule metabolism.

Directory of Open Access Journals (Sweden)

James Corey Adams

2009-08-01

Full Text Available Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the "effect space" comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.

A mapping of drug space from the viewpoint of small molecule metabolism.

Science.gov (United States)

Adams, James Corey; Keiser, Michael J; Basuino, Li; Chambers, Henry F; Lee, Deok-Sun; Wiest, Olaf G; Babbitt, Patricia C

2009-08-01

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the "effect space" comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.

Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times.

Science.gov (United States)

Mollica, Luca; Theret, Isabelle; Antoine, Mathias; Perron-Sierra, Françoise; Charton, Yves; Fourquez, Jean-Marie; Wierzbicki, Michel; Boutin, Jean A; Ferry, Gilles; Decherchi, Sergio; Bottegoni, Giovanni; Ducrot, Pierre; Cavalli, Andrea

2016-08-11

Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times.

Are zebrafish larvae suitable for assessing the hepatotoxicity potential of drug candidates?

NARCIS (Netherlands)

Mesens, N.; Crawfordb, A.D.; Menke, A.; Hung, P.D.; Goethem, F. van; Nuyts, R.; Hansen, E.; Wolterbeek, A.; Gompel, J. van; Witte, P. de; Esguerra, C.V.

2015-01-01

Drug-induced liver injury (DILI) is poorly predicted by single-cell-based assays, probably because of the lack of physiological interactions with other cells within the liver. An intact whole liver system such as one present in zebrafish larvae could provide added value in a screening strategy for

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.

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.

Differentiating drugs by harm potential: the rational versus the feasible.

Science.gov (United States)

Kalant, H

1999-01-01

In an ideal harm reduction model, drugs would be ranked according to their potential to cause harm, with varying implications for control policies and interventions. In such a public health oriented approach, the maximum protection of the public from harm would be balanced with the least possible restriction of freedom. In reality, however, the accuracy and completeness of the necessary information for such a ranking is highly limited. Many other factors not readily incorporated in a rational model, such as values, beliefs, and traditions, also affect drug policy decisions. Thus, rather than relying on acquisition of the necessary knowledge, it may be preferable to focus efforts on developing effective nonlegal measures to reduce drug use and harm. [Translations are provided in the International Abstracts Section of this issue.

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.

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

Frequency of potential interactions between drugs in medical prescriptions in a city in southern Brazil

Directory of Open Access Journals (Sweden)

Genici Weyh Bleich

Full Text Available CONTEXT AND OBJECTIVE: Drug interactions form part of current clinical practice and they affect between 3 and 5% of polypharmacy patients. The aim of this study was to identify the frequency of potential drug-drug interactions in prescriptions for adult and elderly patients. TYPE OF STUDY AND SETTING: Cross-sectional pharmacoepidemiological survey in the Parque Verde housing project, municipality of Cascavel, Paraná, Brazil, between December 2006 and February 2007. METHODS: Stratified cluster sampling, proportional to the total number of homes in the housing project, was used. The sample consisted of 95 homes and 96 male or female patients aged 19 or over, with medical prescriptions for at least two pharmaceutical drugs. Interactions were identified using DrugDigest, Medscape and Micromedex softwares. RESULTS: Most of the patients were female (69.8%, married (59.4% and in the age group of 60 years or over (56.3%, with an income less than or equal to three minimum monthly salaries (81.3% and less than eight years of schooling (69.8%; 90.6% of the patients were living with another person. The total number of pharmaceutical drugs was 406 (average of 4.2 medications per patient. The drugs most prescribed were antihypertensives (47.5%. The frequency of drug interactions was 66.6%. Among the 154 potential drug interactions, 4.6% were classified as major, 65.6% as moderate and 20.1% as minor. CONCLUSION: The high frequency of drug prescriptions with a potential for differentiated interactions indicates a situation that has so far been little explored, albeit a reality in household surveys.

Strategies to enhance the bioavailability of curcumin: a potential antitumor drug

Science.gov (United States)

Kumar, Abhishek; Chittigori, Joshna; Li, Lian; Samuelson, Lynne; Sandman, Daniel; Kumar, Jayant

2012-02-01

Curcumin is a polyphenol which has elicited considerable interest for its antioxidant and anti tumor properties. Although curcumin may be used as potential therapeutic drug, it is very sparingly soluble in water which makes it less bioavailable under physiological conditions. We report two approaches to make curcumin more bioavailable. The first approach involves fabricating colloidal dispersions of curcumin in the range of tens of nanometers. The second approach involves functionalization of curcumin with polyethylene glycol (PEG) to render it water dispersible or soluble. Since curcumin is a fluorescent molecule as well as a potential drug, its interactions with cells have been investigated using one and two photon confocal fluorescence imaging. We have also observed strong interaction between curcumin and metal ions, which may have physiological implications.

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.

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.

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.

Analysis of Potential Drug-Drug Interactions and Its Clinical Manifestation of Pediatric Prescription on 2 Pharmacies in Bandung

Directory of Open Access Journals (Sweden)

Melisa I. Barliana

2013-09-01

Full Text Available The potential of Drug-Drug Interactions (DDI in prescription have high incidence around the world, including Indonesia. However, scientific evidence regarding DDI in Indonesia is not available. Therefore, in this study we have conducted survey in 2 pharmacies in Bandung against pediatric prescription given by pediatrician. These prescriptions then analyzed the potential for DDI contained in the prescription and clinical manifestation. The analysis showed that in pharmacy A, there are 33 prescriptions (from a total of 155 prescriptions that have potential DDI, or approximately 21.19% (2 prescriptions have the potential DDI major categories, 23 prescriptions categorized as moderate, and 8 prescriptions as minor. In Pharmacy B, there are 6 prescriptions (from a total of 40 prescriptions or 15% of potential DDI (4 prescriptions categorized as moderate and 2 prescriptions as minor. This result showed that potential DDI happened less than 50% in pediatric prescription from both pharmacies. However, this should get attention because DDI should not happen in a prescription considering its clinical manifestations caused by DDI. Moreover, current pharmaceutical care refers to patient oriented than product oriented. In addition, further study for the pediatric prescription on DDI incidence in large scale need to be investigated.

Microencapsulation of indocyanine green for potential applications in image-guided drug delivery.

Science.gov (United States)

Zhu, Zhiqiang; Si, Ting; Xu, Ronald X

2015-02-07

We present a novel process to encapsulate indocyanine green (ICG) in liposomal droplets at high concentration for potential applications in image-guided drug delivery. The microencapsulation process follows two consecutive steps of droplet formation by liquid-driven coaxial flow focusing (LDCFF) and solvent removal by oil phase dewetting. These biocompatible lipid vesicles may have important applications in drug delivery and fluorescence imaging.

A regulatory science viewpoint on botanical–drug interactions

Directory of Open Access Journals (Sweden)

Manuela Grimstein

2018-04-01

Full Text Available There is a continued predisposition of concurrent use of drugs and botanical products. Consumers often self-administer botanical products without informing their health care providers. The perceived safety of botanical products with lack of knowledge of the interaction potential poses a challenge for providers and both efficacy and safety concerns for patients. Botanical–drug combinations can produce untoward effects when botanical constituents modulate drug metabolizing enzymes and/or transporters impacting the systemic or tissue exposure of concomitant drugs. Examples of pertinent scientific literature evaluating the interaction potential of commonly used botanicals in the US are discussed. Current methodologies that can be applied to advance our efforts in predicting drug interaction liability is presented. This review also highlights the regulatory science viewpoint on botanical–drug interactions and labeling implications. Keywords: Drug interaction, Botanical product, St. John's wort, Fruit juices, Regulatory science

Potential drug development candidates for human soil-transmitted helminthiases.

Directory of Open Access Journals (Sweden)

Piero Olliaro

2011-06-01

Full Text Available Few drugs are available for soil-transmitted helminthiasis (STH; the benzimidazoles albendazole and mebendazole are the only drugs being used for preventive chemotherapy as they can be given in one single dose with no weight adjustment. While generally safe and effective in reducing intensity of infection, they are contra-indicated in first-trimester pregnancy and have suboptimal efficacy against Trichuris trichiura. In addition, drug resistance is a threat. It is therefore important to find alternatives.We searched the literature and the animal health marketed products and pipeline for potential drug development candidates. Recently registered veterinary products offer advantages in that they have undergone extensive and rigorous animal testing, thus reducing the risk, cost and time to approval for human trials. For selected compounds, we retrieved and summarised publicly available information (through US Freedom of Information (FoI statements, European Public Assessment Reports (EPAR and published literature. Concomitantly, we developed a target product profile (TPP against which the products were compared.The paper summarizes the general findings including various classes of compounds, and more specific information on two veterinary anthelmintics (monepantel, emodepside and nitazoxanide, an antiprotozoal drug, compiled from the EMA EPAR and FDA registration files.Few of the compounds already approved for use in human or animal medicine qualify for development track decision. Fast-tracking to approval for human studies may be possible for veterinary compounds like emodepside and monepantel, but additional information remains to be acquired before an informed decision can be made.

Potential Risks of Ecological Momentary Assessment Among Persons Who Inject Drugs.

Science.gov (United States)

Roth, Alexis M; Rossi, John; Goldshear, Jesse L; Truong, Quan; Armenta, Richard F; Lankenau, Stephen E; Garfein, Richard S; Simmons, Janie

2017-06-07

Ecological momentary assessment (EMA)-which often involves brief surveys delivered via mobile technology-has transformed our understanding of the individual and contextual micro-processes associated with legal and illicit drug use. However, little empirical research has focused on participant's perspective on the probability and magnitude of potential risks in EMA studies. To garner participant perspectives on potential risks common to EMA studies of illicit drug use. We interviewed 38 persons who inject drugs living in San Diego (CA) and Philadelphia (PA), United States. They completed simulations of an EMA tool and then underwent a semi-structured interview that systematically explored domains of risk considered within the proposed revisions to the Federal Policy for the Protection of Human Subjects or the "Common Rule." Interviews were transcribed verbatim and coded systematically to explore psychological, physical, social, legal, and informational risks from participation. Participants perceived most risks to be minimal. Some indicated that repetitive questioning about mood or drug use could cause psychological (i.e., anxiety) or behavioral risks (i.e., drug use relapse). Ironically, the questions that were viewed as risky were considered motivational to engage in healthy behaviors. The most cited risks were legal and social risks stemming from participant concerns about data collection and security. Improving our understanding of these issues is an essential first step to protect human participants in future EMA research. We provide a brief set of recommendations that can aid in the design and ethics review of the future EMA protocol with substance using populations.

Potential of Continuous Manufacturing for Liposomal Drug Products.

Science.gov (United States)

Worsham, Robert D; Thomas, Vaughan; Farid, Suzanne S

2018-05-21

Over the last several years, continuous manufacturing of pharmaceuticals has evolved from bulk APIs and solid oral dosages into the more complex realm of biologics. The development of continuous downstream processing techniques has allowed biologics manufacturing to realize the benefits (e.g. improved economics, more consistent quality) that come with continuous processing. If relevant processing techniques and principles are selected, the opportunity arises to develop continuous manufacturing designs for additional pharmaceutical products including liposomal drug formulations. Liposome manufacturing has some inherent aspects that make it favorable for a continuous process. Other aspects such as formulation refinement, materials of construction, and aseptic processing need development, but present an achievable challenge. This paper reviews the current state of continuous manufacturing technology applicable to liposomal drug product manufacturing and an assessment of the challenges and potential of this application. This article is protected by copyright. All rights reserved.

[Potential of cell penetrating peptides for cell drug delivery].

Science.gov (United States)

Poillot, Cathy; De Waard, Michel

2011-05-01

The interest of the scientific community for cell penetrating peptides (CPP) has been growing exponentially for these last years, and the list of novel CPP is increasing. These peptides are powerful tools for the delivery of cargoes to their site of action. Indeed, several drugs that cannot translocate through the cell plasma membrane have been successfully delivered into cells when grafted to a CPP. Various cargoes have been linked to CPP, such as oligonucleotides, pharmacologically active drugs, contrast agents for imaging, or nanoparticles as platforms for multigrafting purposes… This review illustrates the fabulous potential of CPP and the diversity of their use, but their most interesting application appears their future clinical use for the treatment of various pathological conditions. © 2011 médecine/sciences - Inserm / SRMS.

Inter-decadal change in potential predictability of the East Asian summer monsoon

Science.gov (United States)

Li, Jiao; Ding, Ruiqiang; Wu, Zhiwei; Zhong, Quanjia; Li, Baosheng; Li, Jianping

2018-05-01

The significant inter-decadal change in potential predictability of the East Asian summer monsoon (EASM) has been investigated using the signal-to-noise ratio method. The relatively low potential predictability appears from the early 1950s through the late 1970s and during the early 2000s, whereas the potential predictability is relatively high from the early 1980s through the late 1990s. The inter-decadal change in potential predictability of the EASM can be attributed mainly to variations in the external signal of the EASM. The latter is mostly caused by the El Niño-Southern Oscillation (ENSO) inter-decadal variability. As a major external signal of the EASM, the ENSO inter-decadal variability experiences phase transitions from negative to positive phases in the late 1970s, and to negative phases in the late 1990s. Additionally, ENSO is generally strong (weak) during a positive (negative) phase of the ENSO inter-decadal variability. The strong ENSO is expected to have a greater influence on the EASM, and vice versa. As a result, the potential predictability of the EASM tends to be high (low) during a positive (negative) phase of the ENSO inter-decadal variability. Furthermore, a suite of Pacific Pacemaker experiments suggests that the ENSO inter-decadal variability may be a key pacemaker of the inter-decadal change in potential predictability of the EASM.

Opportunities for Web-based Drug Repositioning: Searching for Potential Antihypertensive Agents with Hypotension Adverse Events.

Science.gov (United States)

Wang, Kejian; Wan, Mei; Wang, Rui-Sheng; Weng, Zuquan

2016-04-01

Drug repositioning refers to the process of developing new indications for existing drugs. As a phenotypic indicator of drug response in humans, clinical side effects may provide straightforward signals and unique opportunities for drug repositioning. We aimed to identify drugs frequently associated with hypotension adverse reactions (ie, the opposite condition of hypertension), which could be potential candidates as antihypertensive agents. We systematically searched the electronic records of the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) through the openFDA platform to assess the association between hypotension incidence and antihypertensive therapeutic effect regarding a list of 683 drugs. Statistical analysis of FAERS data demonstrated that those drugs frequently co-occurring with hypotension events were more likely to have antihypertensive activity. Ranked by the statistical significance of frequent hypotension reporting, the well-known antihypertensive drugs were effectively distinguished from others (with an area under the receiver operating characteristic curve > 0.80 and a normalized discounted cumulative gain of 0.77). In addition, we found a series of antihypertensive agents (particularly drugs originally developed for treating nervous system diseases) among the drugs with top significant reporting, suggesting the good potential of Web-based and data-driven drug repositioning. We found several candidate agents among the hypotension-related drugs on our list that may be redirected for lowering blood pressure. More important, we showed that a pharmacovigilance system could alternatively be used to identify antihypertensive agents and sustainably create opportunities for drug repositioning.

Polymer adhesion predictions for oral dosage forms to enhance drug administration safety. Part 2: In vitro approach using mechanical force methods.

Science.gov (United States)

Drumond, Nélio; Stegemann, Sven

2018-06-01

Predicting the potential for unintended adhesion of solid oral dosage forms (SODF) to mucosal tissue is an important aspect that should be considered during drug product development. Previous investigations into low strength mucoadhesion based on particle interactions methods provided evidence that rheological measurements could be used to obtain valid predictions for the development of SODF coatings that can be safely swallowed. The aim of this second work was to estimate the low mucoadhesive strength properties of different polymers using in vitro methods based on mechanical forces and to identify which methods are more precise when measuring reduced mucoadhesion. Another aim was to compare the obtained results to the ones achieved with in vitro particle interaction methods in order to evaluate which methodology can provide stronger predictions. The combined results correlate between particle interaction methods and mechanical force measurements. The polyethylene glycol grades (PEG) and carnauba wax showed the lowest adhesive potential and are predicted to support safe swallowing. Hydroxypropyl methylcellulose (HPMC) along with high molecular grades of polyvinylpyrrolidone (PVP) and polyvinyl alcohol (PVA) exhibited strong in vitro mucoadhesive strength. The combination of rheological and force tensiometer measurements should be considered when assessing the reduced mucoadhesion of polymer coatings to support safe swallowing of SODF. Copyright © 2018 Elsevier B.V. All rights reserved.

Potential intravenous drug incompatibilities in a pediatric unit.

Science.gov (United States)

Leal, Karla Dalliane Batista; Leopoldino, Ramon Weyler Duarte; Martins, Rand Randall; Veríssimo, Lourena Mafra

2016-01-01

To investigate potential intravenous drug incompatibilities and related risk factors in a pediatric unit. A cross-sectional analytical study conducted in the pediatric unit of a university hospital in Brazil. Data on prescriptions given to children aged 0-15 years from June to October 2014 were collected. Prescriptions that did not include intravenous drugs and prescriptions with incomplete dosage regimen or written in poor handwriting were excluded. Associations between variables and the risk of potential incompatibility were investigated using the Student's t test and ANOVA; the level of significance was set at 5% (ppenicilina G e ceftriaxona. Quase 85% das crianças apresentaram ao menos uma potencial incompatibilidade, razão de 1,2 incompatibilidades/paciente. Os tipos de incompatibilidades mais comuns foram: não testada (93,4%), precipitação (5,5%), turbidez (0,7%) e decomposição química (0,4%). Os fatores associados a potenciais incompatibilidades foram: número de medicamentos e a prescrição dos medicamentos diazepam, fenitoína, fenobarbital e metronidazol. A maioria das prescrições pediátricas apresentou potenciais incompatibilidades e a incompatibilidade não testada foi o tipo mais comum. O número de medicamentos e a prescrição dos medicamentos diazepam, fenobarbital, fenitoína e metronidazol foram fatores de risco para potenciais incompatibilidades.

Perioperative drug management. Reduction of potential drug-related problems in patients undergoing orthopaedic surgery by perioperative participation of a hospital pharmacist

NARCIS (Netherlands)

Duyvendak, M.; Bosman, J.; Klopotowska, J.; Kuiper-Herder, A.J.; Van Roon, E.N.; Brouwers, J.R.B.J.

2007-01-01

Objective: Drug management in the perioperative period is complex. Only little is known about the effects of clinical pharmaceutical care in this setting. The aim of this study was to determine the effect of a clinical pharmacy-based intervention on the number of potential drug-related problems in

Integrating genomics and proteomics data to predict drug effects using binary linear programming.

Science.gov (United States)

Ji, Zhiwei; Su, Jing; Liu, Chenglin; Wang, Hongyan; Huang, Deshuang; Zhou, Xiaobo

2014-01-01

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be

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)…