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Sample records for predict potential drug

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

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    Lai Luhua

    2007-09-01

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

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

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

    2014-12-01

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

  3. Potential for Drug Abuse: the Predictive Role of Parenting Styles, Stress and Type D Personality

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    mahin soheili

    2015-06-01

    Full Text Available Objective: This study was an attempt to predict potential for drug abuse on the basis of three predictors of parenting style, stress and type D personality. Method: In this descriptive-correlational study, 200 students (100 males and 100 females of Islamic Azad University of Karaj were selected by convenience sampling. For data collection, perceived parenting styles questionnaire, perceived stress scale, type D personality scale, and addiction potential scale were used. Results: The results showed that rejecting/neglecting parenting style and emotional warmth were positively and negatively correlated with addiction potential, respectively. Conclusion: The child-parent relationship and also the relationship between stress and type D personality can be considered as predictive factors in addiction potential.

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

    Science.gov (United States)

    Mead, Andy N

    2014-11-01

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

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

    Science.gov (United States)

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

    2016-05-01

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

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

    Science.gov (United States)

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

    2016-05-01

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

  7. 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.

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

    Science.gov (United States)

    Srinivas, Nuggehally R

    2016-01-20

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

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

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    Kuroda, Yukihiro; Saito, Madoka

    2010-03-01

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

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

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

    2012-05-01

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

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

    International Nuclear Information System (INIS)

    Valerio, Luis G.; Cross, Kevin P.

    2012-01-01

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

  12. 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

  13. 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

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    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.

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

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

    2017-09-01

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

  15. Oral administration of drugs with hypersensitivity potential induces germinal center hyperplasia in secondary lymphoid organ/tissue in Brown Norway rats, and this histological lesion is a promising candidate as a predictive biomarker for drug hypersensitivity occurrence in humans

    International Nuclear Information System (INIS)

    Tamura, Akitoshi; Miyawaki, Izuru; Yamada, Toru; Kimura, Juki; Funabashi, Hitoshi

    2013-01-01

    It is important to evaluate the potential of drug hypersensitivity as well as other adverse effects during the preclinical stage of the drug development process, but validated methods are not available yet. In the present study we examined whether it would be possible to develop a new predictive model of drug hypersensitivity using Brown Norway (BN) rats. As representative drugs with hypersensitivity potential in humans, phenytoin (PHT), carbamazepine (CBZ), amoxicillin (AMX), and sulfamethoxazole (SMX) were orally administered to BN rats for 28 days to investigate their effects on these animals by examinations including observation of clinical signs, hematology, determination of serum IgE levels, histology, and flow cytometric analysis. Skin rashes were not observed in any animals treated with these drugs. Increases in the number of circulating inflammatory cells and serum IgE level did not necessarily occur in the animals treated with these drugs. However, histological examination revealed that germinal center hyperplasia was commonly induced in secondary lymphoid organs/tissues in the animals treated with these drugs. In cytometric analysis, changes in proportions of lymphocyte subsets were noted in the spleen of the animals treated with PHT or CBZ during the early period of administration. The results indicated that the potential of drug hypersensitivity was identified in BN rat by performing histological examination of secondary lymphoid organs/tissues. Data obtained herein suggested that drugs with hypersensitivity potential in humans gained immune reactivity in BN rat, and the germinal center hyperplasia induced by administration of these drugs may serve as a predictive biomarker for drug hypersensitivity occurrence. - Highlights: • We tested Brown Norway rats as a candidate model for predicting drug hypersensitivity. • The allergic drugs did not induce skin rash, whereas D-penicillamine did so in the rats. • Some of allergic drugs increased

  16. Oral administration of drugs with hypersensitivity potential induces germinal center hyperplasia in secondary lymphoid organ/tissue in Brown Norway rats, and this histological lesion is a promising candidate as a predictive biomarker for drug hypersensitivity occurrence in humans

    Energy Technology Data Exchange (ETDEWEB)

    Tamura, Akitoshi, E-mail: akitoshi-tamura@ds-pharma.co.jp; Miyawaki, Izuru; Yamada, Toru; Kimura, Juki; Funabashi, Hitoshi

    2013-08-15

    It is important to evaluate the potential of drug hypersensitivity as well as other adverse effects during the preclinical stage of the drug development process, but validated methods are not available yet. In the present study we examined whether it would be possible to develop a new predictive model of drug hypersensitivity using Brown Norway (BN) rats. As representative drugs with hypersensitivity potential in humans, phenytoin (PHT), carbamazepine (CBZ), amoxicillin (AMX), and sulfamethoxazole (SMX) were orally administered to BN rats for 28 days to investigate their effects on these animals by examinations including observation of clinical signs, hematology, determination of serum IgE levels, histology, and flow cytometric analysis. Skin rashes were not observed in any animals treated with these drugs. Increases in the number of circulating inflammatory cells and serum IgE level did not necessarily occur in the animals treated with these drugs. However, histological examination revealed that germinal center hyperplasia was commonly induced in secondary lymphoid organs/tissues in the animals treated with these drugs. In cytometric analysis, changes in proportions of lymphocyte subsets were noted in the spleen of the animals treated with PHT or CBZ during the early period of administration. The results indicated that the potential of drug hypersensitivity was identified in BN rat by performing histological examination of secondary lymphoid organs/tissues. Data obtained herein suggested that drugs with hypersensitivity potential in humans gained immune reactivity in BN rat, and the germinal center hyperplasia induced by administration of these drugs may serve as a predictive biomarker for drug hypersensitivity occurrence. - Highlights: • We tested Brown Norway rats as a candidate model for predicting drug hypersensitivity. • The allergic drugs did not induce skin rash, whereas D-penicillamine did so in the rats. • Some of allergic drugs increased

  17. 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

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

    Science.gov (United States)

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

    2017-06-08

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

  19. The Brain Activity in Brodmann Area 17: A Potential Bio-Marker to Predict Patient Responses to Antiepileptic Drugs.

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    Yida Hu

    Full Text Available In this study, we aimed to predict newly diagnosed patient responses to antiepileptic drugs (AEDs using resting-state functional magnetic resonance imaging tools to explore changes in spontaneous brain activity. We recruited 21 newly diagnosed epileptic patients, 8 drug-resistant (DR patients, 11 well-healed (WH patients, and 13 healthy controls. After a 12-month follow-up, 11 newly diagnosed epileptic patients who showed a poor response to AEDs were placed into the seizures uncontrolled (SUC group, while 10 patients were enrolled in the seizure-controlled (SC group. By calculating the amplitude of fractional low-frequency fluctuations (fALFF of blood oxygen level-dependent signals to measure brain activity during rest, we found that the SUC patients showed increased activity in the bilateral occipital lobe, particularly in the cuneus and lingual gyrus compared with the SC group and healthy controls. Interestingly, DR patients also showed increased activity in the identical cuneus and lingual gyrus regions, which comprise Brodmann's area 17 (BA17, compared with the SUC patients; however, these abnormalities were not observed in SC and WH patients. The receiver operating characteristic (ROC curves indicated that the fALFF value of BA17 could differentiate SUC patients from SC patients and healthy controls with sufficient sensitivity and specificity prior to the administration of medication. Functional connectivity analysis was subsequently performed to evaluate the difference in connectivity between BA17 and other brain regions in the SUC, SC and control groups. Regions nearby the cuneus and lingual gyrus were found positive connectivity increased changes or positive connectivity changes with BA17 in the SUC patients, while remarkably negative connectivity increased changes or positive connectivity decreased changes were found in the SC patients. Additionally, default mode network (DMN regions showed negative connectivity increased changes or

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

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    Ferdousi, Reza; Safdari, Reza; Omidi, Yadollah

    2017-06-01

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

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

    OpenAIRE

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

    2017-01-01

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

  2. A Systems-Pharmacology Analysis of Herbal Medicines Used in Health Improvement Treatment: Predicting Potential New Drugs and Targets

    Directory of Open Access Journals (Sweden)

    Jianling Liu

    2013-01-01

    Full Text Available For thousands of years, tonic herbs have been successfully used all around the world to improve health, energy, and vitality. However, their underlying mechanisms of action in molecular/systems levels are still a mystery. In this work, two sets of tonic herbs, so called Qi-enriching herbs (QEH and Blood-tonifying herbs (BTH in TCM, were selected to elucidate why they can restore proper balance and harmony inside body, organ and energy system. Firstly, a pattern recognition model based on artificial neural network and discriminant analysis for assessing the molecular difference between QEH and BTH was developed. It is indicated that QEH compounds have high lipophilicity while BTH compounds possess high chemical reactivity. Secondly, a systematic investigation integrating ADME (absorption, distribution, metabolism, and excretion prediction, target fishing and network analysis was performed and validated on these herbs to obtain the compound-target associations for reconstructing the biologically-meaningful networks. The results suggest QEH enhance physical strength, immune system and normal well-being, acting as adjuvant therapy for chronic disorders while BTH stimulate hematopoiesis function in body. As an emerging approach, the systems pharmacology model might facilitate to understand the mechanisms of action of the tonic herbs, which brings about new development for complementary and alternative medicine.

  3. Assessing the proarrhythmic potential of drugs

    DEFF Research Database (Denmark)

    Thomsen, Morten Bækgaard; Matz, Jørgen; Volders, Paul G A

    2006-01-01

    Torsades de pointes (TdP) is a potentially lethal cardiac arrhythmia that can occur as an unwanted adverse effect of various pharmacological therapies. Before a drug is approved for marketing, its effects on cardiac repolarisation are examined clinically and experimentally. This paper expresses...... the opinion that effects on repolarisation duration cannot directly be translated to risk of proarrhythmia. Current safety assessments of drugs only involve repolarisation assays, however the proarrhythmic profile can only be determined in the predisposed model. The availability of these proarrhythmic animal...... surrogate parameters possessing predictive power of TdP arrhythmia are reviewed. As these parameters are not developed to finalisation, any meaningful study of the proarrhythmic potential of a new drug will include evaluation in an integrated model of TdP arrhythmia....

  4. 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...

  5. 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 ...

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

    Science.gov (United States)

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

    2017-04-07

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

  7. 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.

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

    Science.gov (United States)

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

    2012-01-01

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

  9. 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.

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

    DEFF Research Database (Denmark)

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

    2005-01-01

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

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-11-08

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

  17. 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...

  18. 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

  19. 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.

  20. 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

  1. 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.

  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.

  3. 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

  4. 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

  5. Prediction of acid generation potential

    International Nuclear Information System (INIS)

    Nalbandyan, V.B.

    1992-01-01

    This paper discusses acid rock drainage (ARD), a term used to describe leachate, seepage, or drainage that has been affected by the natural oxidation of sulfide minerals contained in rock which is exposed to air and water. The principal ingredients for ARD formation are reactive sulfide minerals, oxygen, and water. The oxidation reactions responsible for the formation of ARD are often accelerated by biological activity. These reactions yield low pH (acidic) water that has the potential to mobilize heavy metals that may be contained in the geologic materials that are contacted. ARD can cause a detrimental impact on the quality of ground or surface water to which it discharges. ARD likely has been associated with mines since mining began. ARD is not necessarily confined to mining activities, but can occur naturally wherever sulfide-bearing rock is exposed to air and water. It is important to recognize that not all operations that expose sulfide-bearing rock will result in ARD

  6. 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

  7. 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.

  8. 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

  9. 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.

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

    Science.gov (United States)

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

    2017-09-18

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

  11. 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; ...

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. 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

  19. 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.

  20. Predicted levels of HIV drug resistance

    DEFF Research Database (Denmark)

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

    2014-01-01

    -term effects. METHODS: The previously validated HIV Synthesis model was calibrated to South Africa. Resistance was modeled at the level of single mutations, transmission potential, persistence, and effect on drug activity. RESULTS: We estimate 652 000 people (90% uncertainty range: 543 000-744 000) are living...... are maintained, in 20 years' time HIV incidence is projected to have declined by 22% (95% confidence interval, CI -23 to -21%), and the number of people carrying NNRTI resistance to be 2.9-fold higher. If enhancements in diagnosis and retention in care occur, and ART is initiated at CD4 cell count less than 500......  cells/μl, HIV incidence is projected to decline by 36% (95% CI: -37 to -36%) and the number of people with NNRTI resistance to be 4.1-fold higher than currently. Prevalence of people with viral load more than 500  copies/ml carrying NRMV is not projected to differ markedly according to future ART...

  1. Predicting the Potential Market for Electric Vehicles

    DEFF Research Database (Denmark)

    Jensen, Anders Fjendbo; Cherchi, Elisabetta; Mabit, Stefan Lindhard

    2017-01-01

    diffusion models in marketing research use fairly simple demand models. In this paper we discuss the problem of predicting market shares for new products and suggest a method that combines advanced choice models with a diffusion model to take into account that new products often need time to gain......Forecasting the potential demand for electric vehicles is a challenging task. Because most studies for new technologies rely on stated preference (SP) data, market share predictions will reflect shares in the SP data and not in the real market. Moreover, typical disaggregate demand models...... are suitable to forecast demand in relatively stable markets, but show limitations in the case of innovations. When predicting the market for new products it is crucial to account for the role played by innovation and how it penetrates the new market over time through a diffusion process. However, typical...

  2. Potential Predictability and Prediction Skill for Southern Peru Summertime Rainfall

    Science.gov (United States)

    WU, S.; Notaro, M.; Vavrus, S. J.; Mortensen, E.; Block, P. J.; Montgomery, R. J.; De Pierola, J. N.; Sanchez, C.

    2016-12-01

    The central Andes receive over 50% of annual climatological rainfall during the short period of January-March. This summertime rainfall exhibits strong interannual and decadal variability, including severe drought events that incur devastating societal impacts and cause agricultural communities and mining facilities to compete for limited water resources. An improved seasonal prediction skill of summertime rainfall would aid in water resource planning and allocation across the water-limited southern Peru. While various underlying mechanisms have been proposed by past studies for the drivers of interannual variability in summertime rainfall across southern Peru, such as the El Niño-Southern Oscillation (ENSO), Madden Julian Oscillation (MJO), and extratropical forcings, operational forecasts continue to be largely based on rudimentary ENSO-based indices, such as NINO3.4, justifying further exploration of predictive skill. In order to bridge this gap between the understanding of driving mechanisms and the operational forecast, we performed systematic studies on the predictability and prediction skill of southern Peru summertime rainfall by constructing statistical forecast models using best available weather station and reanalysis datasets. At first, by assuming the first two empirical orthogonal functions (EOFs) of summertime rainfall are predictable, the potential predictability skill was evaluated for southern Peru. Then, we constructed a simple regression model, based on the time series of tropical Pacific sea-surface temperatures (SSTs), and a more advanced Linear Inverse Model (LIM), based on the EOFs of tropical ocean SSTs and large-scale atmosphere variables from reanalysis. Our results show that the LIM model consistently outperforms the more rudimentary regression models on the forecast skill of domain averaged precipitation index and individual station indices. The improvement of forecast correlation skill ranges from 10% to over 200% for different

  3. 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.

  4. 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.

  5. 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...

  6. 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

  7. 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

  8. 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

  9. 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.

  10. Cariogenic Potential of Inhaled Antiasthmatic Drugs.

    Science.gov (United States)

    Brigic, Amela; Kobaslija, Sedin; Zukanovic, Amila

    2015-08-01

    The organism of children with asthma is exposed to the effects of the disease but also the drugs for its treatment. Antiasthmatic drugs have different modes that promote the caries formation which varies according to their basic pharmacological composition. Namely, these drugs have a relatively low pH (5.5), can contain sweeteners such as lactose monohydrate in order to improve the drug taste or both. Frequent consumption of these inhalers in combination with reduced secretion of saliva increases the risk of caries. The study sample consisted of 200 patients, age from 7-14 years, divided into two groups: control group (n1 = 100) consisted of healthy children and the experimental group consisted of children suffering from asthma (n2 = 100). In both groups of respondents are determined the DMFT index, plaque index value and hygienic-dietary habits using the questionnaire. The subjects in the control group had significantly higher DMFT index than subjects in the experimental group (p = 0.004). It is determined that there are no significant differences in the values of plaque index (p>0.05). The effect of different diseases or medications from their treatment, diet and fermentable carbohydrates in the etiology of dental caries cannot be observed outside the living conditions of subjects, their social epidemiologic status, age, habits, oral hygiene, fluoride use, etc.

  11. 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.

  12. 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.

  13. 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

  14. Herbal drug patenting in India: IP potential.

    Science.gov (United States)

    Sahoo, Niharika; Manchikanti, Padmavati; Dey, Satya Hari

    2011-09-01

    Herbal drugs are gaining worldwide prominence due to their distinct advantages. Developing countries have started exploring the ethnopharmacological approach of drug discovery and have begun to file patents on herbal drugs. The expansion of R&D in Indian herbal research organizations and presence of manufacturing units at non-Indian sites is an indication of the capability to develop new products and processes. The present study attempts to identify innovations in the Indian herbal drug sector by analyzing the patenting trends in India, US and EU. Based on key word and IPC based search at the IPO, USPTO, Esp@cenet and WIPO databases, patent applications and grant in herbal drugs by Indian applicants/assignees was collected for the last ten years (from 1st January 2001 to 31st October 2010). From this collection patents related to human therapeutic use only were selected. Analysis was performed to identify filing trends, major applicants/assignees, disease area and major plant species used for various treatments. There is a gradual increase in patent filing through the years. In India, individual inventors have maximum applications and grants. CSIR, among research organizations and Hindustan Unilever, Avesthagen, Piramal Life Science, Sahajanand Biotech and Indus Biotech among the companies have the maximum granted patents in India, US and EU respectively. Diabetes, cancer and inflammatory disorders are the major areas for patenting in India and abroad. Recent patents are on new herbal formulations for treatment of AIDS, hepatitis, skin disorders and gastrointestinal disorders. A majority of the herbal patents applications and grants in India are with individual inventors. Claim analysis indicates that these patents include novel multi-herb compositions with synergistic action. Indian research organizations are more active than companies in filing for patents. CSIR has maximum numbers of applications not only in India but also in the US and EU. Patents by research

  15. 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 ...

  16. Developmental Potential for Endomorphin Opioidmimetic Drugs

    Directory of Open Access Journals (Sweden)

    Yoshio Okada

    2012-01-01

    Full Text Available Morphine, which is agonist for μ-opioid receptors, has been used as an anti-pain drug for millennia. The opiate antagonists, naloxone and naltrexone, derived from morphine, were employed for drug addiction and alcohol abuse. However, these exogenous agonists and antagonists exhibit numerous and unacceptable side effects. Of the endogenous opioid peptides, endomorphin(EM-1 and endomorphin(EM-2 with their high μ-receptor affinity and exceptionally high selectivity relative to δ- and κ-receptors in vitro and in vivo provided a sufficiently sequence-flexible entity in order to prepare opioid-based drugs. We took advantage of this unique feature of the endomorphins by exchanging the N-terminal residue Tyr1 with 2′,6′-dimethyl-L-tyrosine (Dmt to increase their stability and the spectrum of bioactivity. We systematically altered specific residues of [Dmt1]EM-1 and [Dmt1]EM-2 to produce various analogues. Of these analogues, [N-allyl-Dmt1]EM-1 (47 and [N-allyl-Dmt1]EM-2 (48 exhibited potent and selective antagonism to μ-receptors: they completely inhibited naloxone- and naltrexone-induced withdrawal from following acute morphine dependency in mice and reversed the alcohol-induced changes observed in sIPSC in hippocampal slices. Overall, we developed novel and efficacious opioid drugs without deleterious side effects that were able to resist enzymatic degradation and were readily transported intact through epithelial membranes in the gastrointestinal tract and the blood-brain-barrier.

  17. 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...

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

    Science.gov (United States)

    DeCou, J; Johnson, K

    2017-01-01

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

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. Gold - Old Drug with New Potentials.

    Science.gov (United States)

    Faa, Gavino; Gerosa, Clara; Fanni, Daniela; Lachowicz, Joanna I; Nurchi, Valeria M

    2018-01-01

    Research into gold-based drugs for a range of human diseases has seen a revival in recent years. This article reviews the most important applications of gold products in different fields of human pathology. Au(I) and Au(III) compounds have been re-introduced in clinical practice for targeting the cellular components involved in the onset and progression of viral and parasitic diseases, rheumatoid arthritis and cancer. After some brief historical notes, this article takes into account the applications of gold compounds against Mycobacterium tuberculosis, and also in tuberculosis and in rheumatoid arthritis treatment. The use of gold containing drugs in the cure of cancer are then considered, with special emphasis to the use of nanoparticles and to the photo-thermal cancer therapy. The use of colloidal gold in diagnostics, introduced in the last decade is widely discussed. As a last point a survey on the adverse effects and on the toxicity of the various gold derivatives in use in medicine is presented. In this review, we described the surprisingly broad spectrum of possible uses of gold in diagnostics and in therapeutic approaches to multiple human diseases, ranging from degenerative to infectious diseases, and to cancer. In particular, gold nanoparticles appear as attractive elements in modern clinical medicine, combining high therapeutic properties, high selectivity in targeting cancer cells and low toxicity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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...

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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...

  18. 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...

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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…

  4. Potential for western US seasonal snowpack prediction

    Science.gov (United States)

    Kapnick, Sarah B.; Yang, Xiaosong; Vecchi, Gabriel A.; Delworth, Thomas L.; Gudgel, Rich; Malyshev, Sergey; Milly, Paul C. D.; Shevliakova, Elena; Underwood, Seth; Margulis, Steven A.

    2018-01-01

    Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of th ecentury and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 month sin advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.)

  10. 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.

  11. 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.

  12. Clinical Drug-Drug Pharmacokinetic Interaction Potential of Sucralfate with Other Drugs: Review and Perspectives.

    Science.gov (United States)

    Sulochana, Suresh P; Syed, Muzeeb; Chandrasekar, Devaraj V; Mullangi, Ramesh; Srinivas, Nuggehally R

    2016-10-01

    Sucralfate, a complex of aluminium hydroxide with sulfated sucrose, forms a strong gastrointestinal tract (GIT) mucosal barrier with excellent anti-ulcer property. Because sucralfate does not undergo any significant oral absorption, sucralfate resides in the GIT for a considerable length of time. The unabsorbed sucralfate may alter the pharmacokinetics of the oral drugs by impeding its absorption and reducing the oral bioavailability. Because of the increased use of sucralfate, it was important to provide a reappraisal of the published clinical drug-drug interaction studies of sucralfate with scores 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 with study design, objectives and key remarks. While the loss of oral bioavailability was significant for the fluoroquinolone class, it generally varied for other classes of drugs, suggesting that impact of the co-administration of sucralfate is manageable in clinical situations. Given the technology advancement in formulation development, it may be in order feasible to develop appropriate formulation strategies to either avoid or minimize the absorption-related issues when co-administered with sucralfate. It is recommended that consideration of both in vitro and preclinical studies may be in order to gauge the level of interaction of a drug with sucralfate. Such data may aid in the development of appropriate strategies to navigate the co-administration of sucralfate with other drugs in this age of polypharmacy.

  13. 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.

  14. 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

  15. 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.

  16. 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 ...

  17. 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

  18. 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.

  19. 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

  20. 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

  1. 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.

  2. 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.

  3. 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.

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

    Science.gov (United States)

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

    2014-12-22

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

  5. 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.

  6. 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.

  7.  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.

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

    Science.gov (United States)

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

    2013-01-01

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

  9. 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.

  10. 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.

  11. 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.

  12. Potential genetic polymorphisms predicting polycystic ovary syndrome

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    Yao Chen

    2018-05-01

    Full Text Available Polycystic ovary syndrome (PCOS is a heterogenous endocrine disorder with typical symptoms of oligomenorrhoea, hyperandrogenism, hirsutism, obesity, insulin resistance and increased risk of type 2 diabetes mellitus. Extensive evidence indicates that PCOS is a genetic disease and numerous biochemical pathways have been linked with its pathogenesis. A number of genes from these pathways have been investigated, which include those involved with steroid hormone biosynthesis and metabolism, action of gonadotropin and gonadal hormones, folliculogenesis, obesity and energy regulation, insulin secretion and action and many others. In this review, we summarize the historical and recent findings in genetic polymorphisms of PCOS from the relevant publications and outline some genetic polymorphisms that are potentially associated with the risk of PCOS. This information could uncover candidate genes associating with PCOS, which will be valuable for the development of novel diagnostic and treatment platforms for PCOS patients.

  13. 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.

  14. 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

  15. 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...

  16. 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.

  17. 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.

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. Computational drug designing of fungal pigments as potential aromatase inhibitors

    Directory of Open Access Journals (Sweden)

    Nighat Fatima

    2014-12-01

    Full Text Available The existing aromatase inhibitors produced unwelcome effects impose the discovery of novel drugs with privileged selectivity, a reduced amount of toxicity and humanizing potency. In this study, we illuminate the binding mode of polyketide azaphilanoid pigments monascin, ankaflavin, monascorubrin and monascorubramine isolated from Monascus fungus to the aromatase by molecular docking. The 3-dimensional structure of aromatase enzyme (PDB: 4KQ8 was obtained from the Protein Data Bank. PatchDock docking software was used to analyze structural complexes of the aromatase with monascus pigments. Comparatively, the AutoGrid model presented the most briskly constructive binding mode of monascin to aromatase. Docked energies in kcal/mol are: monascin;-13.2; monascorubramine:-12.8, monascorubrin:-12.3; ankaflavin: -10.5. These outcomes exposed these ligands could be potential drugs to treat hormone dependent breast cancer.

  3. [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.

  4. 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.

  5. Heuristic lipophilicity potential for computer-aided rational drug design

    Science.gov (United States)

    Du, Qishi; Arteca, Gustavo A.; Mezey, Paul G.

    1997-09-01

    In this contribution we suggest a heuristic molecular lipophilicitypotential (HMLP), which is a structure-based technique requiring noempirical indices of atomic lipophilicity. The input data used in thisapproach are molecular geometries and molecular surfaces. The HMLP is amodified electrostatic potential, combined with the averaged influences fromthe molecular environment. Quantum mechanics is used to calculate theelectron density function ρ(r) and the electrostatic potential V(r), andfrom this information a lipophilicity potential L(r) is generated. The HMLPis a unified lipophilicity and hydrophilicity potential. The interactions ofdipole and multipole moments, hydrogen bonds, and charged atoms in amolecule are included in the hydrophilic interactions in this model. TheHMLP is used to study hydrogen bonds and water-octanol partitioncoefficients in several examples. The calculated results show that the HMLPgives qualitatively and quantitatively correct, as well as chemicallyreasonable, results in cases where comparisons are available. Thesecomparisons indicate that the HMLP has advantages over the empiricallipophilicity potential in many aspects. The HMLP is a three-dimensional andeasily visualizable representation of molecular lipophilicity, suggested asa potential tool in computer-aided three-dimensional drug design.

  6. 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.

  7. 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.

  8. Potential of carrageenans to protect drugs from polymorphic transformation.

    Science.gov (United States)

    Schmidt, Andrea G; Wartewig, Siegfried; Picker, Katharina M

    2003-07-01

    Carrageenans were analysed in mixture with polymorphic drugs to test their potential for minimising polymorphic or pseudopolymorphic transitions, which are induced by the tableting process. The kappa-carrageenans Gelcarin GP-812 NF and Gelcarin GP-911 NF and the iota-carrageenan Gelcarin GP-379 NF were tested in comparison to the well-known tableting excipients microcrystalline cellulose (MCC), hydroxypropyl methylcellulose (HPMC), and dicalcium phosphate dihydrate (DCPD). Amorphous indomethacin was chosen as model drug since its well-known recrystallisation behaviour can be mechanically stimulated. Further on, theophylline monohydrate was used. Its dehydration is induced by tableting. Pure materials and mixtures containing 20% (w/w) drug were compressed up to different maximum relative densities. The data obtained during tableting were analysed by three-dimensional (3D) modelling. Afterwards tablets were broken and examined by Fourier transform Raman spectroscopy in order to determine the degree of transformation inside the tablet. For quantitative interpretation, the intensities of representative bands were used. Thermal analysis was used additionally. Using 3D modelling a decrease of plastic deformation can be noticed in the order HPMC>MCC>carrageenans, whereas DCPD represents an exception because of brittle fracture. Best hindrance of polymorphic transformation showed the carrageenans, the hindrance was slightly worse for HPMC. MCC and DCPD could not hinder transformation. A complete protection of the amorphous form could not be achieved. For theophylline monohydrate, the results were similar.

  9. [Prevalence of potentially inappropriate drug prescription in the elderly].

    Science.gov (United States)

    Fajreldines, A; Insua, J; Schnitzler, E

    2016-01-01

    One of the causes of preventable adverse drug events (ADES) in older patients constitutes inappropriate prescription of drugs (PIM). The PIM is where risks exceed the clinical benefit. Several instruments can be use to measure this problem, the most used are: a) Beers criteria; b) Screening tool to Older People Potentially inappropriate Prescription (STOPP); c) Screening tool to Alert Doctors to Right Appropriate indicated Treatments (START); d) The Medication Appropriateness Index (MAI). This study aims to assess the prevalence of PIM, in a population of older adults in three clinical scopes of university hospital. cross sectional study of 300 cases from a random sample of fields: hospitalization (n=100), ambulatory (n=100) and emergency (n=100), all patients over 65 years old or more who where treated at our hospital. 1355 prescription drugs were analized, finding patients hospitalized (PIM) of 57.7%, 55%, 26%, and 80% according to Beers, in ambulatory 36%, 36.5%, 5% and 52% with the same tools and in emergency 35%, 35%, 6% y 52% with the same tools. Was found significant association the PIM with polipharmacy with Beers, STOPP and MAI. results can be compare to world literature (26-80% vs 11-73.1%). The STOPP-START used in an integrated manner would be best estimating the problem of PIM. Copyright © 2016 SECA. Publicado por Elsevier España, S.L.U. All rights reserved.

  10. Antioxidant and drug detoxification potentials of Hibiscus sabdariffa anthocyanin extract.

    Science.gov (United States)

    Ajiboye, Taofeek O; Salawu, Nasir A; Yakubu, Musa T; Oladiji, Adenike T; Akanji, Musbau A; Okogun, Joseph I

    2011-04-01

    The antioxidant and drug metabolizing potentials of Hibiscus anthocyanin extract in CCl(4)- induced oxidative damage of rat liver was investigated. Hibiscus anthocyanin extract effectively scavenge α-diphenyl-β-picrylhydrazyl (DPPH) radical, superoxide ion, and hydrogen peroxide. It produced a 92% scavenging effect of DPPH radical at a concentration of 2.0 mg/mL. Hibiscus anthocyanin extract produced a 69 and 90% scavenging effect on superoxide ion and hydrogen peroxide, respectively, at 1.0 mg/mL, which compared favorably with the synthetic antioxidant (butylated hydroanisole and α-tocopherol). A reducing power of this anthocyanin was examined using K(3)Fe(CN)(6). Hibiscus anthocyanin extract has reducing power that is approximately 2-fold that of the synthetic antioxidant, butylated hydroanisole. Hibiscus anthocyanin extract produced a significantly increase and completely attenuated the CCl(4)-mediated decrease in antioxidant enzymes (e.g., catalase, superoxide dismutase, glutathione peroxidase, and glutathione reductase). However, the level of nonenzymic antioxidant molecules (i.e., vitamins C and E) were significant preserved by Hibiscus anthocyanin extract. There was an induction of phase II drug-detoxifying enzymes: glutathione S-transferase, NAD(H):quinone oxidoreductase, and uridyl diphosphoglucuronosyl transferase by 65, 45, and 57%, respectively. In view of these properties, Hibiscus sabdariffa anthocyanin extract can act as a prophylactic by intervening as a free radical scavenger both in vitro and in vivo as well as inducing the phase II drug detoxification enzymes.

  11. 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.

  12. 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

  13. 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

  14. 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.

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

    Science.gov (United States)

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

    2015-01-26

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

  16. 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.

  17. 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

  18. 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.

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

    Science.gov (United States)

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

    2016-04-07

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

  20. 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

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. Indolealkylamines: Biotransformations and Potential Drug–Drug Interactions

    OpenAIRE

    Yu, Ai-Ming

    2008-01-01

    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 ab...

  8. Interdisciplinary researches for potential developments of drugs and natural products

    Directory of Open Access Journals (Sweden)

    Arunrat Chaveerach

    2017-04-01

    Full Text Available Developments of drugs or natural products from plants are possibly made, simple to use and lower cost than modern drugs. The development processes can be started with studying local wisdom and literature reviews to choose the plants which have long been used in diverse areas, such as foods, traditional medicine, fragrances and seasonings. Then those data will be associated with scientific researches, namely plant collection and identification, phytochemical screening by gas chromatography-mass spectrometry, pharmacological study/review for their functions, and finally safety and efficiency tests in human. For safety testing, in vitro cell toxicity by cell viability assessment and in vitro testing of DNA breaks by the comet assay in human peripheral blood mononuclear cells can be performed. When active chemicals and functions containing plants were chosen with safety and efficacy for human uses, then, the potential medicinal natural products will be produced. Based on these procedures, the producing cost will be cheaper and the products can be evaluated for their clinical properties. Thus, the best and lowest-priced medicines and natural products can be distributed worldwide.

  9. Historical Spice as a Future Drug: Therapeutic Potential of Piperlongumine.

    Science.gov (United States)

    Prasad, Sahdeo; Tyagi, Amit K

    2016-01-01

    Spice and spice-derived compounds have been identified and explored for their health benefits since centuries. One of the spice long pepper has been traditionally used to treat chronic bronchitis, asthma, constipation, gonorrhea, paralysis of the tongue, diarrhea, cholera, malaria, viral hepatitis, respiratory infections, stomach ache, diseases of the spleen, cough, and tumors. In this review, the evidences for the chemopreventive and chemotherapeutic potential of piperlongumine have been described. The active component piperlonguime has shown effective against various ailments including cancer, neurogenerative disease, arthritis, melanogenesis, lupus nephritis, and hyperlipidemic. These beneficial effects of piperlongumine is attributed to its ability to modulate several signaling molecules like reactive oxygen species, kinases, proteasome, proto-oncogenes, transcription factors, cell cycle, inflammatory molecules and cell growth and survival molecules. Piperlongumine also chemosensitizes to drugs resistant cancer cells. Overall the consumption of long peppers is therefore recommended for the prevention and treatment of various diseases including cancer, and thus piperlongumine may be a promising future candidate drug against cancer.

  10. Interdisciplinary researches for potential developments of drugs and natural products

    Institute of Scientific and Technical Information of China (English)

    Arunrat Chaveerach; Runglawan Sudmoon; Tawatchai Tanee

    2017-01-01

    Developments of drugs or natural products from plants are possibly made,simple to use and lower cost than modern drugs.The development processes can be started with studying local wisdom and literature reviews to choose the plants which have long been used in diverse areas,such as foods,traditional medicine,fragrances and seasonings.Then those data will be associated with scientific researches,namely plant collection and identification,phytochemical screening by gas chromatography-mass spectrometry,pharmacological study/review for their functions,and finally safety and efficiency tests in human.For safety testing,in vitro cell toxicity by cell viability assessment and in vitro testing of DNA breaks by the comet assay in human peripheral blood mononuclear cells can be performed.When active chemicals and functions containing plants were chosen with safety and efficacy for human uses,then,the potential medicinal natural products will be produced.Based on these procedures,the producing cost will be cheaper and the products can be evaluated for their clinical properties.Thus,the best and lowest-priced medicines and natural products can be distributed worldwide.

  11. 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

  12. 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

  13. The potential role of biomarkers in predicting gestational diabetes

    Directory of Open Access Journals (Sweden)

    Huguette S Brink

    2016-08-01

    Full Text Available Gestational diabetes (GD is a frequent complication during pregnancy and is associated with maternal and neonatal complications. It is suggested that a disturbing environment for the foetus, such as impaired glucose metabolism during intrauterine life, may result in enduring epigenetic changes leading to increased disease risk in adult life. Hence, early prediction of GD is vital. Current risk prediction models are based on maternal and clinical parameters, lacking a strong predictive value. Adipokines are mainly produced by adipocytes and suggested to be a link between obesity and its cardiovascular complications. Various adipokines, including adiponectin, leptin and TNFα, have shown to be dysregulated in GD. This review aims to outline biomarkers potentially associated with the pathophysiology of GD and discuss the role of integrating predictive biomarkers in current clinical risk prediction models, in order to enhance the identification of those at risk.

  14. [MT-45--a dangerous and potentially ototoxic internet drug].

    Science.gov (United States)

    Lindeman, Erik; Bäckberg, Matilda; Personne, Mark; Helander, Anders

    2014-09-11

    During the last years several synthetic opioids have been introduced on Internet sites selling new psychoactive substances (NPS). One of these, called MT-45, a piperazine derivative originally synthesized as a therapeutic drug candidate in the 1970s, has recently been detected in 21 deaths, according to unpublished data from the Swedish National Board of Forensic Medicine. We present clinical data from 12 analytically confirmed hospital cases of MT-45 poisoning. The cases demonstrate that MT-45, like other opioids, can induce potentially life threatening respiratory depression and loss of consciousness in users and that symptoms are usually reversed by standard doses of the opioid receptor antagonist naloxone. Significant auditory symptoms with transient tinnitus and hearing loss occurred in two cases and a pronounced sensorineural hearing loss still present at two weeks follow-up in one case. This indicates that MT-45 may be an ototoxic substance, illustrating the ubiquitous risk of unintended adverse effects NPSs pose to users.

  15. A functional perspective of nitazoxanide as a potential anticancer drug

    International Nuclear Information System (INIS)

    Di Santo, Nicola; Ehrisman, Jessie

    2014-01-01

    Highlights: • Combination anti-cancer therapies are associated with increased toxicity and cross-resistance. • Some antiparasitic compounds may have anti-cancer potential. • Nitazoxanide interferes with metabolic and pro-death signaling. • Preclinical studies are needed to confirm anticancer ability of nitazoxanide. - Abstract: Cancer is a group of diseases characterized by uncontrolled cell proliferation, evasion of cell death and the ability to invade and disrupt vital tissue function. The classic model of carcinogenesis describes successive clonal expansion driven by the accumulation of mutations that eliminate restraints on proliferation and cell survival. It has been proposed that during cancer's development, the loose-knit colonies of only partially differentiated cells display some unicellular/prokaryotic behavior reminiscent of robust ancient life forms. The seeming “regression” of cancer cells involves changes within metabolic machinery and survival strategies. This atavist change in physiology enables cancer cells to behave as selfish “neo-endo-parasites” that exploit the tumor stromal cells in order to extract nutrients from the surrounding microenvironment. In this framework, it is conceivable that anti-parasitic compounds might serve as promising anticancer drugs. Nitazoxanide (NTZ), a thiazolide compound, has shown antimicrobial properties against anaerobic bacteria, as well as against helminths and protozoa. NTZ has also been successfully used to promote Hepatitis C virus (HCV) elimination by improving interferon signaling and promoting autophagy. More compelling however are the potential anti-cancer properties that have been observed. NTZ seems to be able to interfere with crucial metabolic and pro-death signaling such as drug detoxification, unfolded protein response (UPR), autophagy, anti-cytokine activities and c-Myc inhibition. In this article, we review the ability of NTZ to interfere with integrated survival mechanisms of

  16. A functional perspective of nitazoxanide as a potential anticancer drug

    Energy Technology Data Exchange (ETDEWEB)

    Di Santo, Nicola, E-mail: nico.disanto@duke.edu; Ehrisman, Jessie, E-mail: jessie.ehrisman@duke.edu

    2014-10-15

    Highlights: • Combination anti-cancer therapies are associated with increased toxicity and cross-resistance. • Some antiparasitic compounds may have anti-cancer potential. • Nitazoxanide interferes with metabolic and pro-death signaling. • Preclinical studies are needed to confirm anticancer ability of nitazoxanide. - Abstract: Cancer is a group of diseases characterized by uncontrolled cell proliferation, evasion of cell death and the ability to invade and disrupt vital tissue function. The classic model of carcinogenesis describes successive clonal expansion driven by the accumulation of mutations that eliminate restraints on proliferation and cell survival. It has been proposed that during cancer's development, the loose-knit colonies of only partially differentiated cells display some unicellular/prokaryotic behavior reminiscent of robust ancient life forms. The seeming “regression” of cancer cells involves changes within metabolic machinery and survival strategies. This atavist change in physiology enables cancer cells to behave as selfish “neo-endo-parasites” that exploit the tumor stromal cells in order to extract nutrients from the surrounding microenvironment. In this framework, it is conceivable that anti-parasitic compounds might serve as promising anticancer drugs. Nitazoxanide (NTZ), a thiazolide compound, has shown antimicrobial properties against anaerobic bacteria, as well as against helminths and protozoa. NTZ has also been successfully used to promote Hepatitis C virus (HCV) elimination by improving interferon signaling and promoting autophagy. More compelling however are the potential anti-cancer properties that have been observed. NTZ seems to be able to interfere with crucial metabolic and pro-death signaling such as drug detoxification, unfolded protein response (UPR), autophagy, anti-cytokine activities and c-Myc inhibition. In this article, we review the ability of NTZ to interfere with integrated survival mechanisms of

  17. Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy

    Science.gov (United States)

    2013-03-01

    Neratinib Versus Lapatinib Plus Capecitabine For ErbB2 Positive Advanced Breast Cancer Active, not recruiting No Results Available YES neratinib -9...Drug: Neratinib |Drug: Lapatinib|Drug: Capecitabine Efficacy and Safety of BMS-690514 in Combination With Letrozole to Treat Metastatic Breast Cancer

  18. 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

  19. Pharmacokinetics, efficacy prediction indexes and residue depletion of antibacterial drugs.

    Directory of Open Access Journals (Sweden)

    Arturo Anadón

    2016-06-01

    Full Text Available Pharmacokinetics behaviour of the antibacterial in food producing animals, provides information on the rates of absorption and elimination, half-life in plasma and tissue, elimination pathways and metabolism. The dose and the dosing interval of the antimicrobial can be justified by considering the pharmacokinetic/pharmacodynamic (PK/PD relationship, if established, as well as the severity of the disease, whereas the number of administrations should be in line with the nature of the disease. The target population for therapy should be well defined and possible to identify under field conditions. Based on in vitro susceptibility data, and target animal PK data, an analysis for the PK/PD relationship may be used to support dose regimen selection and interpretation criteria for a clinical breakpoint. Therefore, for all antibacterials with systemic activity, the MIC data collected should be compared with the concentration of the compound at the relevant biophase following administration at the assumed therapeutic dose as recorded in the pharmacokinetic studies. Currently, the most frequently used parameters to express the PK/PD relationship are Cmax/MIC (maximum serum concentration/MIC, %T > MIC (fraction of time in which concentration exceeds MIC and AUC/MIC (area under the inhibitory concentration– time curve/MIC. Furthermore, the pharmacokinetic parameters provide the first indication of the potential for persistent residues and the tissues in which they may occur. The information on residue depletion in food-producing animals, provides the data on which MRL recommendations will be based. A critical factor in the antibacterial medication of all food-producing animals is the mandatory withdrawal period, defined as the time during which drug must not be administered prior to the slaughter of the animal for consumption. The withdrawal period is an integral part of the regulatory authorities’ approval process and is designed to ensure that no

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Experimental approaches to predict allergenic potential of novel food

    DEFF Research Database (Denmark)

    Madsen, Charlotte Bernhard; Kroghsbo, Stine; Bøgh, Katrine Lindholm

    2013-01-01

    ’t know under what circumstances oral tolerance develops. With all these unanswered questions, it is a big challenge to designan animal model that, with relatively few animals, is able to predict if a food protein is a potential allergen. An even larger challenge is to predict its potency, a prerequisite...... for risk evaluation.Attempts have been made to rank proteins according to their allergenic potency based on the magnitude of the IgE response in experimental animals. This ranking has not included abundance as a parameter. We may be able to predict potential allergenicity i.e. hazard but our lack......There are many unanswered questions relating to food allergy sensitization in humans. We don’t know under what circumstances sensitization takes place i.e. route (oral, dermal, respiratory), age, dose, frequencyof exposure, infection or by-stander effect of other allergens. In addition we don...

  6. Relativistic predictive quantum potential: the N-body case

    International Nuclear Information System (INIS)

    Garuccio, A.; Kyprianidis, A.; Vigier, J.P.

    1984-01-01

    It is generalized to a system of N scalar particles the casual description with action at a distance already given for two-particle systems in EPR type of experiments. The many body quantum potential is shown to satisfy the predictivity constraints established by Droz-Vincent for relativistic mechanics

  7. Anticoagulant Medicine: Potential for Drug-Food Interactions

    Science.gov (United States)

    ... Medications Anticoagulants and Drug-Food Interactions Anticoagulants and Drug-Food Interactions Make an Appointment Ask a Question Refer Patient ... Jewish Health wants you to be aware these drug-food interactions when taking anticoagulant medicine. Ask your health care ...

  8. Metabolomics has the potential to improve drug therapy

    DEFF Research Database (Denmark)

    Stage, Claus; Jürgens, Gesche; Dalhoff, Kim Peder

    2014-01-01

    Until now drug therapy has primarily been controlled by dose titration on the basis of effects and side effects. However, a lot of people being treated with a drug experience too little effect or too many side effects. Therefore it will be advantageous to improve drug therapy and make it even more...

  9. Reimbursed Price of Orphan Drugs: Current Strategies and Potential Improvements.

    Science.gov (United States)

    Mincarone, Pierpaolo; Leo, Carlo Giacomo; Sabina, Saverio; Sarriá-Santamera, Antonio; Taruscio, Domenica; Serrano-Aguilar, Pedro Guillermo; Kanavos, Panos

    2017-01-01

    The pricing and reimbursement policies for pharmaceuticals are relevant to balance timely and equitable access for all patients, financial sustainability, and reward for valuable innovation. The proliferation of high-cost specialty medicines is particularly true in rare diseases (RDs) where the pricing mechanism is characterised by a lack of transparency. This work provides an overall picture of current strategies for the definition of the reimbursed prices of orphan drugs (ODs) and highlights some potential improvements. Current strategies and suggestions are presented along 4 dimensions: (1) comprehensive value assessment, (2) early dialogs among relevant stakeholders, (3) innovative reimbursement approaches, and (4) societal participation in producing ODs. Comprehensive value assessment could be achieved by clarifying the approach of distributive justice to adopt, ensuring a representative participation of stakeholders, and with a broad consideration of value-bearing factors. With respect to early dialogs, cross-border cooperation can be determinant to companies and agencies. The cost-benefit ratio of early dialogs needs to be demonstrated and the "regulatory capture" effect should be monitored. Innovative reimbursement approaches were developed to balance the need for evidence-based decisions with the timely access to innovative drugs. The societal participation in producing ODs needs to be recognised in a collaborating framework where adaptive agreements can be developed with mutual satisfaction. Such agreements could also impact on coverage and reimbursement decisions as additional elements for the determination of a comprehensive societal value of ODs. Further research is needed to investigate the highlighted open challenges so that RDs will not remain, in practical terms, orphan diseases. © 2017 S. Karger AG, Basel.

  10. 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.

  11. An algorithm to discover gene signatures with predictive potential

    Directory of Open Access Journals (Sweden)

    Hallett Robin M

    2010-09-01

    Full Text Available Abstract Background The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment. Results Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients. Conclusions We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.

  12. 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…

  13. 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...

  14. 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.

  15. Pharmacogenetic approaches to the prediction of drug response

    International Nuclear Information System (INIS)

    Vesell, E.S.

    1986-01-01

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

  16. 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.

  17. 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

  18. 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

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

    Science.gov (United States)

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

    2017-11-20

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

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

  1. Maxent modelling for predicting the potential distribution of Thai Palms

    DEFF Research Database (Denmark)

    Tovaranonte, Jantrararuk; Barfod, Anders S.; Overgaard, Anne Blach

    2011-01-01

    on presence data. The aim was to identify potential hot spot areas, assess the determinants of palm distribution ranges, and provide a firmer knowledge base for future conservation actions. We focused on a relatively small number of climatic, environmental and spatial variables in order to avoid...... overprediction of species distribution ranges. The models with the best predictive power were found by calculating the area under the curve (AUC) of receiver-operating characteristic (ROC). Here, we provide examples of contrasting predicted species distribution ranges as well as a map of modeled palm diversity...

  2. 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.

  3. Pharmacogenetics and Predictive Testing of Drug Hypersensitivity Reactions.

    Science.gov (United States)

    Böhm, Ruwen; Cascorbi, Ingolf

    2016-01-01

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

  4. 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.

  5. 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.

  6. Predicting the potential of energy from agricultural wastes in Malaysia

    International Nuclear Information System (INIS)

    Arifah Bahar; Ahmad Mahir Razali; Kamaruzzaman Sopian

    2000-01-01

    This paper presents the prediction of the potential of energy supply from agricultural wastes in Malaysia until the year 2005. The exponential smoothing method is used to predict the supply of energy from these resources. The prediction is based on four scenarios namely (a) business as usual, (b) increase in the plantation area by 1 % (c) increase in productivity by 1 % with no increase in plantation area and (d) decrease in plantation area of 1%. The agricultural wastes considered are from rubber, oil palm ,cocoa, paddy, coconut and pineapple resources. In Peninsular Malaysia, these resources include groundnut, sugar cane, and tapioca. Assuming an energy conversion of 30%, only three agricultural wastes can contribute as an energy supply i.e. oil palm, paddy and sugar cane wastes. The contribution of these resources to the demand of energy for Malaysia is 21% in the year 2000 and 17% in the year 2005. (Author)

  7. Predictive mapping of the acidifying potential for acid sulfate soils

    DEFF Research Database (Denmark)

    Boman, A; Beucher, Amélie; Mattbäck, S

    Developing methods for the predictive mapping of the potential environmental impact from acid sulfate soils is important because recent studies (e.g. Mattbäck et al., under revision) have shown that the environmental hazards (e.g. leaching of acidity) related to acid sulfate soils vary depending...... on their texture (clay, silt, sand etc.). Moreover, acidity correlates, not only with the sulfur content, but also with the electrical conductivity (EC) measured after incubation. Electromagnetic induction (EMI) data collected from an EM38 proximal sensor also enabled the detailed mapping of acid sulfate soils...... over a field (Huang et al., 2014).This study aims at assessing the use of EMI data for the predictive mapping of the acidifying potential in an acid sulfate soil area in western Finland. Different supervised classification modelling techniques, such as Artificial Neural Networks (Beucher et al., 2015...

  8. Scoring radiologic characteristics to predict proliferative potential in meningiomas

    International Nuclear Information System (INIS)

    Hashiba, Tetsuo; Hashimoto, Naoya; Maruno, Motohiko; Izumoto, Shuichi; Suzuki, Tsuyoshi; Kagawa, Naoki; Yoshimine, Toshiki

    2006-01-01

    We investigated the feasibility of using radiologic characteristics to predict the proliferative potential in meningiomas. Our statistical analysis revealed that the presence of peritumoral edema, an ambiguous brain-tumor border, and irregular tumor shape were significantly correlated with a higher MIB-1 staining index (SI) value. We developed the following scoring system for specific features in each tumor: peritumoral edema (tumor with edema=1, tumor without edema=0); brain-tumor border (tumor with any ambiguous border=1, tumor circumscribed by a distinct rim=0); and tumor shape (tumor with irregular shape=1, tumor with smooth shape=0). Using Spearman's correlation coefficient analysis, we found a significant correlation (P<0.005) between total score calculated for each patient and SI value. Our findings suggest that the proliferative potential of meningiomas can be predicted using a less invasive preoperative examination focusing on the presence of peritumoral edema, ambiguous brain-tumor border, and irregular tumor shape. (author)

  9. 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.

  10. Substandard drugs: a potential crisis for public health

    Science.gov (United States)

    Johnston, Atholl; Holt, David W

    2014-01-01

    Poor-quality medicines present a serious public health problem, particularly in emerging economies and developing countries, and may have a significant impact on the national clinical and economic burden. Attention has largely focused on the increasing availability of deliberately falsified drugs, but substandard medicines are also reaching patients because of poor manufacturing and quality-control practices in the production of genuine drugs (either branded or generic). Substandard medicines are widespread and represent a threat to health because they can inadvertently lead to healthcare failures, such as antibiotic resistance and the spread of disease within a community, as well as death or additional illness in individuals. This article reviews the different aspects of substandard drug formulation that can occur (for example, pharmacological variability between drug batches or between generic and originator drugs, incorrect drug quantity and presence of impurities). The possible means of addressing substandard manufacturing practices are also discussed. A concerted effort is required on the part of governments, drug manufacturers, charities and healthcare providers to ensure that only drugs of acceptable quality reach the patient. PMID:24286459

  11. Characteristics of potential drug-related problems among oncology patients

    NARCIS (Netherlands)

    Bulsink, Arjan; Imholz, Alex L. T.; Brouwers, Jacobus R. B. J.; Jansman, Frank G. A.

    Background Oncology patients are more at risk for drug related problems because of treatment with (combinations of) anticancer drugs, as they have a higher risk for organ failure or altered metabolism with progression of their disease. Objective The aim of this study was to characterize and to

  12. Radiotracer technique to predict irritation potential of soap

    International Nuclear Information System (INIS)

    Castaneda, S.S.; Garcia, T.Y.; Santos, F.L.

    1990-01-01

    The application of a radiotracer technique using tritiated water to predict the irritation potentials of some soap products is demonstrated. Collagen films are treated with 0.5% and 1.0% soap solutions and tritiated water then incubated at 50 degrees centigrade for 24 hours. After incubation, the uptake of tritiated water by the collagen films was measured by liquid scintillation counting. (Auth.). 6 refs., 2 tabs

  13. Pharmacogenomics and its potential impact on drug and formulation development.

    Science.gov (United States)

    Regnstrom, Karin; Burgess, Diane J

    2005-01-01

    Recent advances in genomic research have provided the basis for new insights into the importance of genetic and genomic markers during the different stages of drug development. A new field of research, pharmacogenomics, which studies the relationship between drug effects and the genome, has emerged. Structural pharmacogenomics maps the complete DNA sequences of whole genomes (genotypes) including individual variations, and functional pharmacogenomics assesses the expression levels of thousands of genes in one single experiment. Together, these two areas of pharmacogenomics have generated massive databases, which have become a challenge for the research field of informatics and have fostered a new branch of research, bioinformatics. If skillfully used, the databases generated by pharmacogenomics together with data mining on the Web promise to improve the drug development process in a variety of areas: identification of drug targets, evaluation of toxicity, classification of diseases, evaluation of formulations, assessment of drug response and treatment, post-marketing applications, and development of personalized medicines.

  14. 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.

  15. 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.

  16. Prediction of Addiction Potential in Youth According to Attachment Styles

    Directory of Open Access Journals (Sweden)

    Mahdieh Adroom

    2014-05-01

    Full Text Available Background: The present study aim is to predict the psychological inclination to drug use in youths by studying their attachment styles. Materials and Methods: The research sample includes male and female students of Zahedan Medical Science University with the average age of 19-24. The proportional cluster random sampling was used for selection of participant. The hypotheses were analyzed, using Pearson correlation method, regression analysis, one way variance analysis and t-test for two independent groups4T. Results: The results indicated positive relationships among addiction aptitude and insecure-avoidant attachment style and negative relationship between addiction aptitude and secure attachment style4T. Conclusion: It is necessary to focus training intervention and prevention on all students4T.

  17. 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.

  18. Potential drug-drug interactions in a Brazilian teaching hospital: age-related differences?

    Directory of Open Access Journals (Sweden)

    Daniela Oliveira Melo

    2016-07-01

    Full Text Available This study proposes to measure frequency and to characterize the profile of potential drug interactions (pDDI in a general medicine ward of a teaching hospital. Data about identification and clinical status of patients were extracted from medical records between March to August 2006. The occurrence of pDDI was analyzed using the database monographs Micromedex® DrugReax® System. From 5,336 prescriptions with two or more drugs, 3,097 (58.0% contained pDDI. The frequency of major and well document pDDI was 26.5%. Among 647 patients, 432 (66.8% were exposed to at least one pDDI and 283 (43.7% to major pDDI. The multivariate analysis identified that factors related to higher rates of major pDDI were the same age (p< 0.0001, length of stay (p< 0.0001, prevalence of hypertension [OR=3.42 (p< 0.0001] and diabetes mellitus [OR=2.1 (p< 0.0001], cardiovascular diseases (p< 0.0001 and the number of prescribed drugs (Spearman’s correlation=0.640622, p< 0.0001. Between major pDDI, the main risk was hemorrhage (50.3%, the most frequent major pDDI involved combination of anticoagulants and antiplatelet drugs. Among moderate pDDI, 3,866 (90.8% involved medicines for the treatment of chronic non-communicable diseases, mainly hypertension. In HU-USP, the profile of pDDI was similar among adults and elderly (the most frequent pDDI and major pDDI were same, the difference was only the frequency in either group. The efforts of the clinical pharmacists should be directed to elderly patients with cardiovascular compromise, mainly in use of anticoagulants and antiplatelet drugs. Furthermore, hospital managers should increase the integration between levels of health care to promote safety patient after discharge.Keywords: Drug interactions. Aged. Internal Medicine. Hospitals, University. RESUMOInterações medicamentosas potenciais em um hospital escolar brasileiro: diferenças relacionadas à idade?O estudo tem por objetivo descrever o perfil de intera

  19. 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

  20. 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 ...

  1. Indian marine bivalves: Potential source of antiviral drugs

    Digital Repository Service at National Institute of Oceanography (India)

    Chatterji, A.; Ansari, Z.A.; Ingole, B.S.; Bichurina, M.A.; Sovetova, M.; Boikov, Y.A.

    in large quantities by traditional methods and sold live in the market for human consumption. The economically important sp e cies of marine bivalves are green mussel ( Perna viridis ), e s tuarine oyster ( Crassostrea madrasensis ), giant oyster... in developing an effecti ve drug has been the unique characteristics of antigenic variation of virus resulting in the emergence of new variant virus strains 14 . There are a number of antiviral drugs introduced in the market such as tricyclic sy m- metric...

  2. 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.

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

    Science.gov (United States)

    Kunimoto, Ryo; Bajorath, Jürgen

    2018-02-01

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

  4. 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

  5. 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.

  6. 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.

  7. Drug nanocrystals for the formulation of poorly soluble drugs and its application as a potential drug delivery system

    International Nuclear Information System (INIS)

    Gao Lei; Zhang Dianrui; Chen Minghui

    2008-01-01

    Formulation of poorly soluble drugs is a general intractable problem in pharmaceutical field, especially those compounds poorly soluble in both aqueous and organic media. It is difficult to resolve this problem using conventional formulation approaches, so many drugs are abandoned early in discovery. Nanocrystals, a new carrier-free colloidal drug delivery system with a particle size ranging from 100 to 1000 nm, is thought as a viable drug delivery strategy to develop the poorly soluble drugs, because of their simplicity in preparation and general applicability. In this article, the product techniques of the nanocrystals were reviewed and compared, the special features of drug nanocrystals were discussed. The researches on the application of the drug nanocrystals to various administration routes were described in detail. In addition, as introduced later, the nanocrystals could be easily scaled up, which was the prerequisite to the development of a delivery system as a market product

  8. PREDICTIVE POTENTIAL FIELD-BASED COLLISION AVOIDANCE FOR MULTICOPTERS

    Directory of Open Access Journals (Sweden)

    M. Nieuwenhuisen

    2013-08-01

    Full Text Available Reliable obstacle avoidance is a key to navigating with UAVs in the close vicinity of static and dynamic obstacles. Wheel-based mobile robots are often equipped with 2D or 3D laser range finders that cover the 2D workspace sufficiently accurate and at a high rate. Micro UAV platforms operate in a 3D environment, but the restricted payload prohibits the use of fast state-of-the-art 3D sensors. Thus, perception of small obstacles is often only possible in the vicinity of the UAV and a fast collision avoidance system is necessary. We propose a reactive collision avoidance system based on artificial potential fields, that takes the special dynamics of UAVs into account by predicting the influence of obstacles on the estimated trajectory in the near future using a learned motion model. Experimental evaluation shows that the prediction leads to smoother trajectories and allows to navigate collision-free through passageways.

  9. Potential Drug-Drug Interactions among Patients prescriptions collected from Medicine Out-patient Setting.

    Science.gov (United States)

    Farooqui, Riffat; Hoor, Talea; Karim, Nasim; Muneer, Mehtab

    2018-01-01

    To identify and evaluate the frequency, severity, mechanism and common pairs of drug-drug interactions (DDIs) in prescriptions by consultants in medicine outpatient department. This cross sectional descriptive study was done by Pharmacology department of Bahria University Medical & Dental College (BUMDC) in medicine outpatient department (OPD) of a private hospital in Karachi from December 2015 to January 2016. A total of 220 prescriptions written by consultants were collected. Medications given with patient's diagnosis were recorded. Drugs were analyzed for interactions by utilizing Medscape drug interaction checker, drugs.com checker and stockley`s drug interactions index. Two hundred eleven prescriptions were selected while remaining were excluded from the study because of unavailability of the prescribed drugs in the drug interaction checkers. In 211 prescriptions, two common diagnoses were diabetes mellitus (28.43%) and hypertension (27.96%). A total of 978 medications were given. Mean number of medications per prescription was 4.6. A total of 369 drug-drug interactions were identified in 211 prescriptions (175%). They were serious 4.33%, significant 66.12% and minor 29.53%. Pharmacokinetic and pharmacodynamic interactions were 37.94% and 51.21% respectively while 10.84% had unknown mechanism. Number wise common pairs of DDIs were Omeprazole-Losartan (S), Gabapentine- Acetaminophen (M), Losartan-Diclofenac (S). The frequency of DDIs is found to be too high in prescriptions of consultants from medicine OPD of a private hospital in Karachi. Significant drug-drug interactions were more and mostly caused by Pharmacodynamic mechanism. Number wise evaluation showed three common pairs of drugs involved in interactions.

  10. EEG potentials predict upcoming emergency brakings during simulated driving

    Science.gov (United States)

    Haufe, Stefan; Treder, Matthias S.; Gugler, Manfred F.; Sagebaum, Max; Curio, Gabriel; Blankertz, Benjamin

    2011-10-01

    Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automatically performs emergency braking. Here, we present the results of a driving simulator study indicating that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly than electromyography (EMG), and using EMG, which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses. At 100 km h-1 driving speed, this amounts to reducing the braking distance by 3.66 m. This result motivates a neuroergonomic approach to driving assistance. Our EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, we conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance we observed.

  11. 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

  12. FAMILY HEALTH PROGRAM: CHALLENGES AND POTENTIALITIES REGARDING DRUGS USE

    Directory of Open Access Journals (Sweden)

    Marcelle Aparecida de Barros

    2006-04-01

    Full Text Available ABSTRACT: Epidemiological studies on drugs use point towards this phenomenon as a public health problem. Nowadays, the Family Health Program (FHP is presented by the Health Ministry as a model to restructure primary health care and aims to offer family-centered care, permeated by integrality, problem solving and community bonds. This article aims to discuss action possibilities of Family Health Care professionals involving drugs patients. It is evident that, as opposed to other actions already developed by FHP professionals in other health care areas, which has appeared clearly and objectively. This fact is observed in the daily activities of FHP professionals, which give little attention to drugs-related problems. However, research emphasizes that there exists a broad range of action possibilities for FHP professionals. Although other studies evidence this team’s fragilities in terms of care for drugs users, these can be overcome by investing in the training and valuation of these professionals. KEY WORDS: Family Health Program; Street drugs; Health Knowledge, Attitudes, Practice.

  13. Transdermal Delivery of Drugs with Microneedles—Potential and Challenges

    Directory of Open Access Journals (Sweden)

    Kevin Ita

    2015-06-01

    Full Text Available Transdermal drug delivery offers a number of advantages including improved patient compliance, sustained release, avoidance of gastric irritation, as well as elimination of pre-systemic first-pass effect. However, only few medications can be delivered through the transdermal route in therapeutic amounts. Microneedles can be used to enhance transdermal drug delivery. In this review, different types of microneedles are described and their methods of fabrication highlighted. Microneedles can be fabricated in different forms: hollow, solid, and dissolving. There are also hydrogel-forming microneedles. A special attention is paid to hydrogel-forming microneedles. These are innovative microneedles which do not contain drugs but imbibe interstitial fluid to form continuous conduits between dermal microcirculation and an attached patch-type reservoir. Several microneedles approved by regulatory authorities for clinical use are also examined. The last part of this review discusses concerns and challenges regarding microneedle use.

  14. In vitro inhibitory effects of major bioactive constituents of Andrographis paniculata, Curcuma longa and Silybum marianum on human liver microsomal morphine glucuronidation: A prediction of potential herb-drug interactions arising from andrographolide, curcumin and silybin inhibition in humans.

    Science.gov (United States)

    Uchaipichat, Verawan

    2018-02-01

    This study aimed to investigate the liver microsomal inhibitory effects of silybin, silychristin, andrographolide, and curcumin by using morphine as an in vitro UGT2B7 probe substrate, and predict the magnitude of the herb-drug interaction arising from these herbal constituents' inhibition in vivo. Studies were performed in the incubation with and without bovine serum albumin (BSA). Andrographolide and curcumin showed a marked inhibition on morphine 3- and 6-glucuronidation with IC 50 of 50&87 and 96&111 μM, respectively. In the presence of 2%BSA, andrographolide also showed a strong inhibition on morphine 3- and 6-glucuronidation (IC 50 4.4&21.6 μM) whereas curcumin showed moderate inhibition (IC 50 338&333 μM). In the absence and presence of 2%BSA, morphine 3- and 6-glucuronidation was moderately inhibited by silybin (IC 50 583&862 and 1252&1421 μM, respectively), however was weakly inhibited by silychristin (IC 50 3527&3504 and 1124&1530 μM, respectively). The K i of andrographolide, curcumin and silybin on morphine 3- and 6-glucuronidation were 7.1&9.5, 72.7&65.2, and 224.5&159.7 μM, respectively, while the respective values generated from the system containing 2%BSA were 2.4&3.1, 96.4&108.8, and 366.3&394.5 μM. Using the in vitro and in vivo extrapolation approach, andrographolide was herbal component that may have had a potential interaction in vivo when it was co-administered with morphine. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

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

    CSIR Research Space (South Africa)

    Brandt, P

    2014-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2005-07-26

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

  1. Selenium nanoparticles: potential in cancer gene and drug delivery.

    Science.gov (United States)

    Maiyo, Fiona; Singh, Moganavelli

    2017-05-01

    In recent decades, colloidal selenium nanoparticles have emerged as exceptional selenium species with reported chemopreventative and therapeutic properties. This has sparked widespread interest in their use as a carrier of therapeutic agents with results displaying synergistic effects of selenium with its therapeutic cargo and improved anticancer activity. Functionalization remains a critical step in selenium nanoparticles' development for application in gene or drug delivery. In this review, we highlight recent developments in the synthesis and functionalization strategies of selenium nanoparticles used in cancer drug and gene delivery systems. We also provide an update of recent preclinical studies utilizing selenium nanoparticles in cancer therapeutics.

  2. Tartrazine: a potentially hazardous dye in Canadian drugs.

    Science.gov (United States)

    MacCara, M. E.

    1982-01-01

    The literature was reviewed to determine the incidence of idiosyncratic reactions to tartrazine. From 4% to 14% of individuals with asthma or allergies or both and from 7% to 20% of persons who are sensitive to acetylsalicylic acid may react to this dye. The mechanism of such reactions is unknown. Pharmaceutical manufacturers and distributors were surveyed and a list was prepared of approximately 450 Canadian pharmaceuticals that contain tartrazine. The 53 pharmaceutical and manufacturers and distributors whose drug products do not contain this dye were also listed. It is recommended that information concerning the tartrazine content of drugs be included on package labels. PMID:7074487

  3. 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.

  4. Potential Predictability of ZPD of Children’s Cognitive Development

    Directory of Open Access Journals (Sweden)

    Parviz Birjandi

    2011-05-01

    Full Text Available Obtaining information on whether the child has the potential for growth is not an easy task. Research shows that using different matrix like Raven or different batteries in a static way cannot
    be indicative of children further development. This study attempts to probe the potential predictability of children’s performance during Dynamic Assessment of their Future development.
    41 children between ages 3 to 6 years old participated in this study. The data in pretest, ZPD, and posttest were converted into Rasch Measure. The results of different analysis indicate that relying on children’s actual performance cannot be an indicative factor of their development in the future.

  5. 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.

  6. 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.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  8. 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.

  9. 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.

  10. 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.

  11. Predicting Energy Consumption for Potential Effective Use in Hybrid Vehicle Powertrain Management Using Driver Prediction

    Science.gov (United States)

    Magnuson, Brian

    A proof-of-concept software-in-the-loop study is performed to assess the accuracy of predicted net and charge-gaining energy consumption for potential effective use in optimizing powertrain management of hybrid vehicles. With promising results of improving fuel efficiency of a thermostatic control strategy for a series, plug-ing, hybrid-electric vehicle by 8.24%, the route and speed prediction machine learning algorithms are redesigned and implemented for real- world testing in a stand-alone C++ code-base to ingest map data, learn and predict driver habits, and store driver data for fast startup and shutdown of the controller or computer used to execute the compiled algorithm. Speed prediction is performed using a multi-layer, multi-input, multi- output neural network using feed-forward prediction and gradient descent through back- propagation training. Route prediction utilizes a Hidden Markov Model with a recurrent forward algorithm for prediction and multi-dimensional hash maps to store state and state distribution constraining associations between atomic road segments and end destinations. Predicted energy is calculated using the predicted time-series speed and elevation profile over the predicted route and the road-load equation. Testing of the code-base is performed over a known road network spanning 24x35 blocks on the south hill of Spokane, Washington. A large set of training routes are traversed once to add randomness to the route prediction algorithm, and a subset of the training routes, testing routes, are traversed to assess the accuracy of the net and charge-gaining predicted energy consumption. Each test route is traveled a random number of times with varying speed conditions from traffic and pedestrians to add randomness to speed prediction. Prediction data is stored and analyzed in a post process Matlab script. The aggregated results and analysis of all traversals of all test routes reflect the performance of the Driver Prediction algorithm. The

  12. 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

  13. Methotrexate and epirubicin conjugates as potential antitumor drugs

    Directory of Open Access Journals (Sweden)

    Szymon Wojciech Kmiecik

    2017-07-01

    Full Text Available Introduction: The use of hybrid molecules has become one of the most significant approaches in new cytotoxic drug design. This study describes synthesis and characterization of conjugates consisting of two well-known and characterized chemotherapeutic agents: methotrexate (MTX and epirubicin (EPR. The synthesized conjugates combine two significant anticancer strategies: combinatory therapy and targeted therapy. These two drugs were chosen because they have different mechanisms of action, which can increase the anticancer effect of the obtained conjugates. MTX, which is a folic acid analog, has high cytotoxic properties and can serve as a targeting moiety that can reach folate receptors (FRs overexpresing tumor cells. Combination of nonselective drugs such as EPR with MTX can increase the selectivity of the obtained conjugates, while maintaining the high cytotoxic properties.Materials and methods: Conjugates were purified by RP-HPLC and the structure was investigated by MS and MS/MS methods. The effect of the conjugates on proliferation of LoVo, LoVo/Dx, MCF-7 and MV-4-11 human cancer cell lines was determined by SRB or MTT assay.Results: The conjugation reaction results in the formation of monosubstituted (α, γ and disubstituted MTX derivatives. In vitro proliferation data demonstrate that the conjugates synthesized in our study show lower cytotoxic properties than both chemotherapeutics used alone.Discussion: Epirubicin cytotoxicity was not observed in obtained conjugates. Effective drugs release after internalization needs further investigation.

  14. Evaluation of Herbs as Potential Drugs/Medicines | Odhiambo ...

    African Journals Online (AJOL)

    Herbal drugs have been used since ancient times as medicines for the treatment of a wide range of diseases, for both human and livestock. A study conducted in the Lake Victoria Basin Kenya revealed vast knowledge and reliance on traditional medicine as a source of healthcare. The study documented 34 medicinal plant ...

  15. Potential for treating tuberculosis with nano drug delivery system

    CSIR Research Space (South Africa)

    Swai, H

    2006-11-01

    Full Text Available www.csir.co.za • Performed in guinea pigs • Similar pathophysiology to humans • Drugs administered orally • Analyzed in lung, spleen and liver • ATD loaded PLGA detectable up to 11 days • Free ATD cleared in 1-2 days • No toxic side effects...

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. Probabilistic empirical prediction of seasonal climate: evaluation and potential applications

    Science.gov (United States)

    Dieppois, B.; Eden, J.; van Oldenborgh, G. J.

    2017-12-01

    Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a new evaluation of an established empirical system used to predict seasonal climate across the globe. Forecasts for surface air temperature, precipitation and sea level pressure are produced by the KNMI Probabilistic Empirical Prediction (K-PREP) system every month and disseminated via the KNMI Climate Explorer (climexp.knmi.nl). K-PREP is based on multiple linear regression and built on physical principles to the fullest extent with predictive information taken from the global CO2-equivalent concentration, large-scale modes of variability in the climate system and regional-scale information. K-PREP seasonal forecasts for the period 1981-2016 will be compared with corresponding dynamically generated forecasts produced by operational forecast systems. While there are many regions of the world where empirical forecast skill is extremely limited, several areas are identified where K-PREP offers comparable skill to dynamical systems. We discuss two key points in the future development and application of the K-PREP system: (a) the potential for K-PREP to provide a more useful basis for reference forecasts than those based on persistence or climatology, and (b) the added value of including K-PREP forecast information in multi-model forecast products, at least for known regions of good skill. We also discuss the potential development of

  1. Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

    Science.gov (United States)

    Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

    2016-07-05

    Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

  2. 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)

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Predicting relapse of Graves' disease following treatment with antithyroid drugs

    Science.gov (United States)

    LIU, LIN; LU, HONGWEN; LIU, YANG; LIU, CHANGSHAN; XUN, CHU

    2016-01-01

    The aim of the present study was to monitor long term antithyroid drug treatments and to identify prognostic factors for Graves' disease (GD). A total of 306 patients with GD who were referred to the Endocrinology Clinic at Weifang People's Hospital (Weifang, China) between August 2005 and June 2009 and treated with methimazole were included in the present study. Following treatment, patients were divided into non-remission, including recurrence and constant treatment subgroups, and remission groups. Various prognosis factors were analyzed and compared, including: Patient age, gender, size of thyroid prior to and following treatment, thyroid hormone levels, disease relapse, hypothyroidism and drug side-effects, and states of thyrotropin suppression were observed at 3, 6 and 12 months post-treatment. Sixty-five patients (21.2%) were male, and 241 patients (78.8%) were female. The mean age was 42±11 years, and the follow-up was 31.5±6.8 months. Following long-term treatment, 141 patients (46%) demonstrated remission of hyperthyroidism with a mean duration of 18.7±1.9 months. The average age at diagnosis was 45.6±10.3 years in the remission group, as compared with 36.4±8.8 years in the non-remission group (t=3.152; P=0.002). Free thyroxine (FT)3 levels were demonstrated to be 25.2±8.9 and 18.7±9.4 pmol/l in the non-remission and remission groups, respectively (t=3.326, P=0.001). The FT3/FT4 ratio and thyrotrophin receptor antibody (TRAb) levels were both significantly higher in the non-remission group (t=3.331, 3.389, P=0.001), as compared with the remission group. Logistic regression analysis demonstrated that elevated thyroid size, FT3/FT4 ratio and TRAb at diagnosis were associated with poor outcomes. The ratio of continued thyrotropin suppression in the recurrent subgroup was significantly increased, as compared with the remission group (P=0.001), as thyroid function reached euthyroid state at 3, 6 and 12 months post-treatment. Patients with GD exhibiting

  9. 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.

  10. [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.

  11. 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

  12. Predicting hydrocarbon potential of an earth formation underlying water

    International Nuclear Information System (INIS)

    Damaison, G.J.; Kaplan, I.R.

    1981-01-01

    A method for the on-site collection and examination of small concentrations of a carbonaceous gas, e.g. methane, dissolved in a body of water overlying an earth formation to predict hydrocarbon potential of the earth formation under the body of water, the formation being a source of carbonaceous gas, comprises at a known geographic location sampling the water at a selected flow rate and at a selected depth; continuously vacuum separating the water into liquid and gas phases; separating a selected carbonaceous gas from interfering gas species in the presence of an air carrier vented to atmosphere at a known flow rate; and quantitatively oxidizing the selected gas and then cryogenically trapping an oxidant thereof in the presence of said air carrier to provide for an accurate isotopic examination. (author)

  13. The potential predictability of fire danger provided by ECMWF forecast

    Science.gov (United States)

    Di Giuseppe, Francesca

    2017-04-01

    The European Forest Fire Information System (EFFIS), is currently being developed in the framework of the Copernicus Emergency Management Services to monitor and forecast fire danger in Europe. The system provides timely information to civil protection authorities in 38 nations across Europe and mostly concentrates on flagging regions which might be at high danger of spontaneous ignition due to persistent drought. The daily predictions of fire danger conditions are based on the US Forest Service National Fire Danger Rating System (NFDRS), the Canadian forest service Fire Weather Index Rating System (FWI) and the Australian McArthur (MARK-5) rating systems. Weather forcings are provided in real time by the European Centre for Medium range Weather Forecasts (ECMWF) forecasting system. The global system's potential predictability is assessed using re-analysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 years of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire and low values correspond to non observed events. A more quantitative skill evaluation was performed using the Extremal Dependency Index which is a skill score specifically designed for rare events. It revealed that the three indices were more skilful on a global scale than the random forecast to detect large fires. The performance peaks in the boreal forests, in the Mediterranean, the Amazon rain-forests and southeast Asia. The skill-scores were then aggregated at country level to reveal which nations could potentiallty benefit from the system information in aid of decision making and fire control support. Overall we found that fire danger modelling based on weather forecasts, can provide reasonable predictability over large parts of the global landmass.

  14. Do resting brain dynamics predict oddball evoked-potential?

    Directory of Open Access Journals (Sweden)

    Lee Tien-Wen

    2011-11-01

    Full Text Available Abstract Background The oddball paradigm is widely applied to the investigation of cognitive function in neuroscience and in neuropsychiatry. Whether cortical oscillation in the resting state can predict the elicited oddball event-related potential (ERP is still not clear. This study explored the relationship between resting electroencephalography (EEG and oddball ERPs. The regional powers of 18 electrodes across delta, theta, alpha and beta frequencies were correlated with the amplitude and latency of N1, P2, N2 and P3 components of oddball ERPs. A multivariate analysis based on partial least squares (PLS was applied to further examine the spatial pattern revealed by multiple correlations. Results Higher synchronization in the resting state, especially at the alpha spectrum, is associated with higher neural responsiveness and faster neural propagation, as indicated by the higher amplitude change of N1/N2 and shorter latency of P2. None of the resting quantitative EEG indices predict P3 latency and amplitude. The PLS analysis confirms that the resting cortical dynamics which explains N1/N2 amplitude and P2 latency does not show regional specificity, indicating a global property of the brain. Conclusions This study differs from previous approaches by relating dynamics in the resting state to neural responsiveness in the activation state. Our analyses suggest that the neural characteristics carried by resting brain dynamics modulate the earlier/automatic stage of target detection.

  15. 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.

  16. 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

  17. TCGA bladder cancer study reveals potential drug targets

    Science.gov (United States)

    Investigators with TCGA have identified new potential therapeutic targets for a major form of bladder cancer, including important genes and pathways that are disrupted in the disease. They also discovered that, at the molecular level, some subtypes of bla

  18. 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.

  19. 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.

  20. The potential drug-drug interaction between proton pump inhibitors and warfarin

    DEFF Research Database (Denmark)

    Henriksen, Daniel Pilsgaard; Stage, Tore Bjerregaard; Hansen, Morten Rix

    2015-01-01

    BACKGROUND: Proton pump inhibitors (PPIs) have been suggested to increase the effect of warfarin, and clinical guidelines recommend careful monitoring of international normalized ratio (INR) when initiating PPI among warfarin users. However, this drug-drug interaction is sparsely investigated...... in a clinical setting. The aim was to assess whether initiation of PPI treatment among users of warfarin leads to increased INR values. METHODS: The study was an observational self-controlled study from 1998 to 2012 leveraging data on INR measurements on patients treated with warfarin from primary care...... and outpatient clinics and their use of prescription drugs. Data were analyzed in 2015. We assessed INR, warfarin dose, and dose/INR ratio before and after initiating PPI treatment using the paired student's t-test. RESULTS: We identified 305 warfarin users initiating treatment with PPIs. The median age was 71...

  1. 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....

  2. The Potential of Tropospheric Gradients for Regional Precipitation Prediction

    Science.gov (United States)

    Boisits, Janina; Möller, Gregor; Wittmann, Christoph; Weber, Robert

    2017-04-01

    Changes of temperature and humidity in the neutral atmosphere cause variations in tropospheric path delays and tropospheric gradients. By estimating zenith wet delays (ZWD) and gradients using a GNSS reference station network the obtained time series provide information about spatial and temporal variations of water vapour in the atmosphere. Thus, GNSS-based tropospheric parameters can contribute to the forecast of regional precipitation events. In a recently finalized master thesis at TU Wien the potential of tropospheric gradients for weather prediction was investigated. Therefore, ZWD and gradient time series at selected GNSS reference stations were compared to precipitation data over a period of six months (April to September 2014). The selected GNSS stations form two test areas within Austria. All required meteorological data was provided by the Central Institution for Meteorology and Geodynamics (ZAMG). Two characteristics in ZWD and gradient time series can be anticipated in case of an approaching weather front. First, an induced asymmetry in tropospheric delays results in both, an increased magnitude of the gradient and in gradients pointing towards the weather front. Second, an increase in ZWD reflects the increased water vapour concentration right before a precipitation event. To investigate these characteristics exemplary test events were processed. On the one hand, the sequence of the anticipated increase in ZWD at each GNSS station obtained by cross correlation of the time series indicates the direction of the approaching weather front. On the other hand, the corresponding peak in gradient time series allows the deduction of the direction of movement as well. To verify the results precipitation data from ZAMG was used. It can be deduced, that tropospheric gradients show high potential for predicting precipitation events. While ZWD time series rather indicate the orientation of the air mass boundary, gradients rather indicate the direction of movement

  3. 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.

  4. Nonstructural Proteins of Alphavirus—Potential Targets for Drug Development

    Directory of Open Access Journals (Sweden)

    Farhana Abu Bakar

    2018-02-01

    Full Text Available Alphaviruses are enveloped, positive single-stranded RNA viruses, typically transmitted by arthropods. They often cause arthralgia or encephalitic diseases in infected humans and there is currently no targeted antiviral treatment available. The re-emergence of alphaviruses in Asia, Europe, and the Americas over the last decade, including chikungunya and o’nyong’nyong viruses, have intensified the search for selective inhibitors. In this review, we highlight key molecular determinants within the alphavirus replication complex that have been identified as viral targets, focusing on their structure and functionality in viral dissemination. We also summarize recent structural data of these viral targets and discuss how these could serve as templates to facilitate structure-based drug design and development of small molecule inhibitors.

  5. Potentiation of Anticancer Drugs: Effects of Pentoxifylline on Neoplastic Cells

    Directory of Open Access Journals (Sweden)

    Miroslav Barancik

    2011-12-01

    Full Text Available The drug efflux activity of P-glycoprotein (P-gp, a product of the mdr1 gene, ABCB1 member of ABC transporter family represents a mechanism by which tumor cells escape death induced by chemotherapeutics. In this study, we investigated the mechanisms involved in the effects of pentoxifylline (PTX on P-gp-mediated multidrug resistance (MDR in mouse leukemia L1210/VCR cells. Parental sensitive mouse leukemia cells L1210, and multidrug-resistant cells, L1210/VCR, which are characterized by the overexpression of P-gp, were used as experimental models. The cells were exposed to 100 μmol/L PTX in the presence or absence of 1.2 μmol/L vincristine (VCR. Western blot analysis indicated a downregulation of P-gp protein expression when multidrug-resistant L1210/VCR cells were exposed to PTX. The effects of PTX on the sensitization of L1210/VCR cells to VCR correlate with the stimulation of apoptosis detected by Annexin V/propidium iodide apoptosis necrosis kit and proteolytic activation of both caspase-3 and caspase-9 monitored by Western blot analysis. Higher release of matrix metalloproteinases (MMPs, especially MMP-2, which could be attenuated by PTX, was found in L1210/VCR than in L1210 cells by gelatin zymography in electrophoretic gel. Exposure of resistant cells to PTX increased the content of phosphorylated Akt kinase. In contrast, the presence of VCR eliminated the effects of PTX on Akt kinase phosphorylation. Taken together, we conclude that PTX induces the sensitization of multidrug-resistant cells to VCR via downregulation of P-gp, stimulation of apoptosis and reduction of MMPs released from drug-resistant L1210/VCR cells. These facts bring new insights into the mechanisms of PTX action on cancer cells.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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...

  12. 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.

  13. 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.

  14. 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

  15. Strontium doped injectable bone cement for potential drug delivery applications.

    Science.gov (United States)

    Taha, Ali; Akram, Muhammad; Jawad, Zaidoon; Alshemary, Ammar Z; Hussain, Rafaqat

    2017-11-01

    Microwave assisted wet precipitation method was used to synthesize calcium deficient strontium doped β-tricalcium phosphate (Sr-βTCP) with a chemical formula of Ca 2.96-x Sr x (PO 4 ) 2 . Sr-βTCP was reacted with monocalcium phosphate monohydrate [Ca(H 2 PO 4 ) 2 .H 2 O, MCPM] in presence of water to furnish corresponding Sr containing brushite cement (Sr-Brc). The samples were characterized by using X-ray diffractometry (XRD), Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FESEM). Strontium content in the prepared samples was determined by using inductively coupled plasma optical emission spectrometry (ICP-OES). The effect of Sr 2+ ions on the structural, mechanical, setting properties and drug release of the cement is reported. Incorporation of Sr 2+ ions improved the injectability, setting time and mechanical properties of the Brc. The release profiles of antibiotics incorporated in Brc and Sr-Brc confirmed that the Sr incorporation into the Brc results in the efficient release of the antibiotics from the cement. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. 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.

  17. 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.

  18. Prediction of the acid generating potential of coal mining spoils

    International Nuclear Information System (INIS)

    Monterroso, C.; Macias, F.

    1998-01-01

    The sulfide oxidation impact on mined land reclamation makes it necessary for mine spoils to be classified according to their acidifying potential. In this paper predictions were made of the acid generating potential of sulfide-containing spoils from the Puentes lignite mine (Galicia, NW Spain), and the limits of sulfur contents allowable for their storage in aerobic conditions, were established. Using samples of fresh spoils, analyses were made of the content and speciation of sulfur, pH was measured after oxidation of the sample with H 2 O 2 (pH of oxidation = pH OX ), and titration of the oxidation extract with 0.1N NaOH to pH = 7 was carried out (Net Acid Production = NAP). The total sulfur content (S T ) varied between 3%, with pyritic-S being the most common form (> 80%). pH OX varied between 1.6 and 6.4 and NAP between 1.2 and 85.0 Kg-CaCO 3 t -1 . A high correlation was found between the NAP and the S T (r-0.98, p T > 0.15% cause high risks of mine-soil acidification, and create the need for large doses of CaCO 3 to be used on final surface of the mine dump. Use of fly ash, produced from the combustion of lignite, as an alternative to commercial lime is more effective in the control of acidity generated by spoils with high S T . 20 refs., 5 figs., 1 tab

  19. Electrochemical Oxidation by Square-Wave Potential Pulses in the Imitation of Oxidative Drug Metabolism

    NARCIS (Netherlands)

    Nouri-Nigjeh, Eslam; Permentier, Hjalmar P.; Bischoff, Rainer; Bruins, Andries P.

    2011-01-01

    Electrochemistry combined with mass spectrometry (EC-MS) is an emerging analytical technique in the imitation of oxidative drug metabolism at the early stages of new drug development. Here, we present the benefits of electrochemical oxidation by square-wave potential pulses for the oxidation of

  20. 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.

  1. Fumigation in Ayurveda: potential strategy for drug discovery and drug delivery.

    Science.gov (United States)

    Vishnuprasad, Chethala N; Pradeep, Nediyamparambu Sukumaran; Cho, Yong Woo; Gangadharan, Geethalayam Gopinathan; Han, Sung Soo

    2013-09-16

    Ayurveda has its unique perceptions and resultant methodologies for defining and treating human diseases. Fumigation therapy is one of the several treatment methods described in Ayurveda whereby fumes produced from defined drug formulations are inhaled by patients. This therapeutic procedure offers promising research opportunities from phytochemical and ethnopharmacological viewpoints, however, it remains under-noticed. Considering these facts, this review is primarily aimed at introducing said Ayurvedic fumigation therapy and discussing its scientific gaps and future challenges. A search of multiple bibliographical databases and traditional Ayurvedic text books was conducted and the articles analyzed under various key themes, e.g., Ayurvedic fumigation, fumigation therapy, medicinal fumigation, inhalation of drugs and aerosol therapy. Ayurveda recommends fumigation as a method of sterilization and therapeutic procedure for various human diseases including microbial infections and psychological disorders. However, it has not gained much attention as a prospective field with multiple research opportunities. It is necessary to have a more detailed and systematic investigation of the phytochemical and pharmacodynamic properties of Ayurvedic fumigation therapy in order to facilitate the identification of novel bioactive compounds and more effective drug administration methods. © 2013 Elsevier Ireland Ltd. All rights reserved.

  2. Detection of Potential Drug-Drug Interactions for Outpatients across Hospitals

    Directory of Open Access Journals (Sweden)

    Yu-Ting Yeh

    2014-01-01

    Full Text Available The National Health Insurance Administration (NHIA has adopted smart cards (or NHI-IC cards as health cards to carry patients’ medication histories across hospitals in Taiwan. The aims of this study are to enhance a computerized physician order entry system to support drug-drug interaction (DDI checking based on a patient’s medication history stored in his/her NHI-IC card. For performance evaluation, we developed a transaction tracking log to keep track of every operation on NHI-IC cards. Based on analysis of the transaction tracking log from 1 August to 31 October 2007, physicians read patients’ NHI-IC cards in 71.01% (8,246 of patient visits; 33.02% (2,723 of the card reads showed at least one medicine currently being taken by the patient, 82.94% of which were prescribed during the last visit. Among 10,036 issued prescriptions, seven prescriptions (0.09% contained at least one drug item that might interact with the currently-taken medicines stored in NHI-IC cards and triggered pop-up alerts. This study showed that the capacity of an NHI-IC card is adequate to support DDI checking across hospitals. Thus, the enhanced computerized physician order entry (CPOE system can support better DDI checking when physicians are making prescriptions and provide safer medication care, particularly for patients who receive medication care from different hospitals.

  3. Detection of potential drug-drug interactions for outpatients across hospitals.

    Science.gov (United States)

    Yeh, Yu-Ting; Hsu, Min-Hui; Chen, Chien-Yuan; Lo, Yu-Sheng; Liu, Chien-Tsai

    2014-01-27

    The National Health Insurance Administration (NHIA) has adopted smart cards (or NHI-IC cards) as health cards to carry patients' medication histories across hospitals in Taiwan. The aims of this study are to enhance a computerized physician order entry system to support drug-drug interaction (DDI) checking based on a patient's medication history stored in his/her NHI-IC card. For performance evaluation, we developed a transaction tracking log to keep track of every operation on NHI-IC cards. Based on analysis of the transaction tracking log from 1 August to 31 October 2007, physicians read patients' NHI-IC cards in 71.01% (8,246) of patient visits; 33.02% (2,723) of the card reads showed at least one medicine currently being taken by the patient, 82.94% of which were prescribed during the last visit. Among 10,036 issued prescriptions, seven prescriptions (0.09%) contained at least one drug item that might interact with the currently-taken medicines stored in NHI-IC cards and triggered pop-up alerts. This study showed that the capacity of an NHI-IC card is adequate to support DDI checking across hospitals. Thus, the enhanced computerized physician order entry (CPOE) system can support better DDI checking when physicians are making prescriptions and provide safer medication care, particularly for patients who receive medication care from different hospitals.

  4. Computerized techniques pave the way for drug-drug interaction prediction and interpretation

    Directory of Open Access Journals (Sweden)

    Reza Safdari

    2016-06-01

    Results: Computerized data-mining in pharmaceutical sciences and related databases provide new key transformative paradigms that can revolutionize the treatment of diseases and hence medical care. Given that various aspects of drug discovery and pharmacotherapy are closely related to the clinical and molecular/biological information, the scientifically sound databases (e.g., DDIs, ADRs can be of importance for the success of pharmacotherapy modalities. Conclusion: A better understanding of DDIs not only provides a robust means for designing more effective medicines but also grantees patient safety.

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

    Science.gov (United States)

    Cami, Aurel; Reis, Ben Y

    2014-08-22

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

  6. Characterization of Different Functionalized Lipidic Nanocapsules as Potential Drug Carriers

    Directory of Open Access Journals (Sweden)

    José Manuel Peula-García

    2012-02-01

    Full Text Available Lipid nanocapsules (LNC based on a core-shell structure consisting of an oil-filled core with a surrounding polymer layer are known to be promising vehicles for the delivery of hydrophobic drugs in the new therapeutic strategies in anti-cancer treatments. The present work has been designed as basic research about different LNC systems. We have synthesized—and physico-chemically characterized—three different LNC systems in which the core was constituted by olive oil and the shell by different phospholipids (phosphatidyl-serine or lecithin and other biocompatible molecules such as Pluronic® F68 or chitosan. It is notable that the olive-oil-phosphatidyl-serine LCN is a novel formulation presented in this work and was designed to generate an enriched carboxylic surface. This carboxylic layer is meant to link specific antibodies, which could facilitate the specific nanocapsule uptake by cancer cells. This is why nanoparticles with phosphatidyl-serine in their shell have also been used in this work to form immuno-nanocapsules containing a polyclonal IgG against a model antigen (C-reactive protein covalently bounded by means of a simple and reproducible carbodiimide method. An immunological study was made to verify that these IgG-LNC complexes showed the expected specific immune response. Finally, a preliminary in vitro study was performed by culturing a breast-carcinoma cell line (MCF-7 with Nile-Red-loaded LNC. We found that these cancer cells take up the fluorescent Nile-Red molecule in a process dependent on the surface properties of the nanocarriers.

  7. 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.

  8. Predictive tests to evaluate oxidative potential of engineered nanomaterials

    Science.gov (United States)

    Ghiazza, Mara; Carella, Emanuele; Oliaro-Bosso, Simonetta; Corazzari, Ingrid; Viola, Franca; Fenoglio, Ivana

    2013-04-01

    Oxidative stress constitutes one of the principal injury mechanisms through which particulate toxicants (asbestos, crystalline silica, hard metals) and engineered nanomaterials can induce adverse health effects. ROS may be generated indirectly by activated cells and/or directly at the surface of the material. The occurrence of these processes depends upon the type of material. Many authors have recently demonstrated that metal oxides and carbon-based nanoparticles may influence (increasing or decreasing) the generation of oxygen radicals in a cell environment. Metal oxide, such as iron oxides, crystalline silica, and titanium dioxide are able to generate free radicals via different mechanisms causing an imbalance within oxidant species. The increase of ROS species may lead to inflammatory responses and in some cases to the development of cancer. On the other hand carbon-based nanomaterials, such as fullerene, carbon nanotubes, carbon black as well as cerium dioxide are able to scavenge the free radicals generated acting as antioxidant. The high numbers of new-engineered nanomaterials, which are introduced in the market, are exponentially increasing. Therefore the definition of toxicological strategies is urgently needed. The development of acellular screening tests will make possible the reduction of the number of in vitro and in vivo tests to be performed. An integrated protocol that may be used to predict the oxidant/antioxidant potential of engineered nanoparticles will be here presented.

  9. Predictive tests to evaluate oxidative potential of engineered nanomaterials

    International Nuclear Information System (INIS)

    Ghiazza, Mara; Carella, Emanuele; Corazzari, Ingrid; Fenoglio, Ivana; Oliaro-Bosso, Simonetta; Viola, Franca

    2013-01-01

    Oxidative stress constitutes one of the principal injury mechanisms through which particulate toxicants (asbestos, crystalline silica, hard metals) and engineered nanomaterials can induce adverse health effects. ROS may be generated indirectly by activated cells and/or directly at the surface of the material. The occurrence of these processes depends upon the type of material. Many authors have recently demonstrated that metal oxides and carbon-based nanoparticles may influence (increasing or decreasing) the generation of oxygen radicals in a cell environment. Metal oxide, such as iron oxides, crystalline silica, and titanium dioxide are able to generate free radicals via different mechanisms causing an imbalance within oxidant species. The increase of ROS species may lead to inflammatory responses and in some cases to the development of cancer. On the other hand carbon-based nanomaterials, such as fullerene, carbon nanotubes, carbon black as well as cerium dioxide are able to scavenge the free radicals generated acting as antioxidant. The high numbers of new-engineered nanomaterials, which are introduced in the market, are exponentially increasing. Therefore the definition of toxicological strategies is urgently needed. The development of acellular screening tests will make possible the reduction of the number of in vitro and in vivo tests to be performed. An integrated protocol that may be used to predict the oxidant/antioxidant potential of engineered nanoparticles will be here presented.

  10. Predicting pharmacy syringe sales to people who inject drugs: Policy, practice and perceptions.

    Science.gov (United States)

    Meyerson, Beth E; Davis, Alissa; Agley, Jon D; Shannon, David J; Lawrence, Carrie A; Ryder, Priscilla T; Ritchie, Karleen; Gassman, Ruth

    2018-06-01

    Pharmacies have much to contribute to the health of people who inject drugs (PWID) and to community efforts in HIV and hepatitis C (HCV) prevention through syringe access. However, little is known about what predicts pharmacy syringe sales without a prescription. To identify factors predicting pharmacy syringes sales to PWID. A hybrid staggered online survey of 298 Indiana community pharmacists occurred from July-September 2016 measuring pharmacy policy, practice, and pharmacist perceptions about syringe sales to PWID. Separate bivariate logistical regressions were followed by multivariable logistic regression to predict pharmacy syringe sales and pharmacist comfort dispensing syringes to PWID. Half (50.5%) of Indiana pharmacies sold syringes without a prescription to PWID. Pharmacy syringe sales was strongly associated with pharmacist supportive beliefs about syringe access by PWID and their comfort level selling syringes to PWID. Notably, pharmacies located in communities with high rates of opioid overdose mortality were 56% less likely to sell syringes without a prescription than those in communities with lower rates. Pharmacist comfort dispensing syringes was associated with being male, working at a pharmacy that sold syringes to PWID and one that stocked naloxone, having been asked about syringe access by medical providers, and agreement that PWID should be able to buy syringes without a prescription. As communities with high rates of opioid overdose mortality were less likely to have pharmacies that dispensed syringes to PWID, a concerted effort with these communities and their pharmacies should be made to understand opportunities to increase syringe access. Future studies should explore nuances between theoretical support for syringe access by PWID without a prescription and actual dispensing behaviors. Addressing potential policy conflicts and offering continuing education on non-prescription syringe distribution for pharmacists may improve comfort

  11. BTS 72664-- a novel CNS drug with potential anticonvulsant, neuroprotective, and antimigraine properties.

    Science.gov (United States)

    Smith, S L; Thompson, K S; Sargent, B J; Heal, D J

    2001-01-01

    BTS 72664, (R)-7-[1-(4-chlorophenoxy)]ethyl]-1,2,4-triazolo(1,5-alpha)pyrimidine, was identified as a drug development candidate from a research program designed to discover novel, broad-spectrum, non-sedative anticonvulsant drugs. BTS 72664 antagonized bicuculline (BIC)- and maximal electroshock (MES)-induced convulsions with ED(50) values of 1.9 and 47.5 mg/kg p.o., respectively. In rodents, it has a wide spectrum of activity preventing seizures induced by picrotoxin, pentylenetetrazol, i.c.v. 4-aminopyridine or NMDA, and audiogenic seizures in DBA-2 mice and GEPR-9 rats. BTS 72664 was also effective in preventing convulsions in amygdala-kindled rats The lack of sedative potential was predicted on the basis of wide separation between ED(50) in anticonvulsant models and TD(50) for motor impairment in mice in rotating rod and inverted horizontal grid tests. BTS 72664 is likely to produce its anticonvulsant effect by enhancing chloride currents through picrotoxin-sensitive chloride channels, and by weak inhibition of Na(+) and NMDA channels. It does not act, however, at the benzodiazepine binding site. In addition to its potential use in the treatment of epilepsy BTS 72664 may be useful in the treatment of stroke. At 50 mg/kg p.o. x 4, given to rats at 12 hourly intervals, starting at 15 min after permanent occlusion of middle cerebral artery (MCA), it reduced cerebral infarct size by 31% (measured at 2 days after insult) and accelerated recovery in a functional behavioral model. BTS 72664 prevented increases in extraneuronal concentrations of glutamate, glycine and serine brain levels induced by a cortical insult to rats (cf. cortical spreading depression). It may, therefore, have also antimigraine activity.

  12. 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...

  13. Advances in Development of Antimicrobial Peptidomimetics as Potential Drugs.

    Science.gov (United States)

    Molchanova, Natalia; Hansen, Paul R; Franzyk, Henrik

    2017-08-29

    The rapid emergence of multidrug-resistant pathogens has evolved into a global health problem as current treatment options are failing for infections caused by pan-resistant bacteria. Hence, novel antibiotics are in high demand, and for this reason antimicrobial peptides (AMPs) have attracted considerable interest, since they often show broad-spectrum activity, fast killing and high cell selectivity. However, the therapeutic potential of natural AMPs is limited by their short plasma half-life. Antimicrobial peptidomimetics mimic the structure and biological activity of AMPs, but display extended stability in the presence of biological matrices. In the present review, focus is on the developments reported in the last decade with respect to their design, synthesis, antimicrobial activity, cytotoxic side effects as well as their potential applications as anti-infective agents. Specifically, only peptidomimetics with a modular structure of residues connected via amide linkages will be discussed. These comprise the classes of α-peptoids ( N -alkylated glycine oligomers), β-peptoids ( N -alkylated β-alanine oligomers), β³-peptides, α/β³-peptides, α-peptide/β-peptoid hybrids, α/γ N -acylated N -aminoethylpeptides (AApeptides), and oligoacyllysines (OAKs). Such peptidomimetics are of particular interest due to their potent antimicrobial activity, versatile design, and convenient optimization via assembly by standard solid-phase procedures.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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...

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. Natural Compounds from Mexican Medicinal Plants as Potential Drug Leads for Anti-Tuberculosis Drugs

    Directory of Open Access Journals (Sweden)

    ROCIO GÓMEZ-CANSINO

    Full Text Available ABSTRACT In Mexican Traditional Medicine 187 plant species are used in the treatment of respiratory conditions that may be associated with tuberculosis. In this contribution, we review the ethnobotany, chemistry and pharmacology of 63 species whose extracts have been assayed for antimycobacterial activity in vitro. Among these, the most potent is Aristolochia brevipes (MIC= 12.5 µg/mL, followed by Aristolochia taliscana, Citrus sinensis, Chrysactinia mexicana, Persea americana, and Olea europaea (MIC 95%, 50 µg/mL include: Amphipterygium adstringens, Larrea divaricata, and Phoradendron robinsoni. Several active compounds have been identified, the most potent are: Licarin A (isolated from A. taliscana, and 9-amino-9-methoxy-3,4-dihydro-2H-benzo[h]-chromen-2-one (transformation product of 9-methoxytariacuripyrone isolated from Aristolochia brevipes, both with MIC= 3.125 µg/mL, that is 8-fold less potent than the reference drug Rifampicin (MIC= 0.5 µg/mL. Any of the compounds or extracts here reviewed has been studied in clinical trials or with animal models; however, these should be accomplished since several are active against strains resistant to common drugs.

  4. Drug-Carrying Magnetic Nanocomposite Particles for Potential Drug Delivery Systems

    Directory of Open Access Journals (Sweden)

    R. Asmatulu

    2009-01-01

    nanoparticles and poly (D,L-lactide-co-glycolide (PLGA for the purpose of magnetic targeted drug delivery. Magnetic nanoparticles (∼13 nm on average of magnetite were prepared by a chemical coprecipitation of ferric and ferrous chloride salts in the presence of a strong basic solution (ammonium hydroxide. An oil-in-oil emulsion/solvent evaporation technique was conducted at 7000 rpm and 1.5–2 hours agitation for the synthesis of nanocomposite spheres. Specifically, PLGA and drug were first dissolved in acetonitrile (oily phase I and combined with magnetic nanoparticles, then added dropwise into viscous paraffin oil combined with Span 80 (oily phase II. With different contents (0%, 10%, 20%, and 25% of magnetite, the nanocomposite spheres were evaluated in terms of particle size, morphology, and magnetic properties by using dynamic laser light scattering (DLLS, scanning electron microscopy (SEM, transmission electron microscopy (TEM, and a superconducting quantum interference device (SQUID. The results indicate that nanocomposite spheres (200 nm to 1.1 μm in diameter are superparamagnetic above the blocking temperature near 40 K and their magnetization saturates above 5 000 Oe at room temperature.

  5. Zirconium Phosphate Nanoplatelet Potential for Anticancer Drug Delivery Applications.

    Science.gov (United States)

    González, Millie L; Ortiz, Mayra; Hernández, Carmen; Cabán, Jennifer; Rodríguez, Axel; Colón, Jorge L; Báez, Adriana

    2016-01-01

    Zirconium phosphate (ZrP) nanoplatelets can intercalate anticancer agents via an ion exchange reaction creating an inorganic delivery system with potential for cancer treatment. ZrP delivery of anticancer agents inside tumor cells was explored in vitro. Internalization and cytotoxicity of ZrP nanoplatelets were studied in MCF-7 and MCF-10A cells. DOX-loaded ZrP nanoplatelets (DOX@ZrP) uptake was assessed by confocal (CLSM) and transmission electron microscopy (TEM). Cytotoxicity to MCF-7 and MCF-10A cells was determined by the MTT assay. Reactive Oxy- gen Species (ROS) production was analyzed by fluorometric assay, and cell cycle alterations and induction of apoptosis were analyzed by flow cytometry. ZrP nanoplatelets were localized in the endosomes of MCF-7 cells. DOX and ZrP nanoplatelets were co-internalized into MCF-7 cells as detected by CLSM. While ZrP showed limited toxicity to MCF-7 cells, DOX@ZrP was cytotoxic at an IC₅₀ similar to that of free DOX. Meanwhile, DOX lC₅₀ was significantly lower than the equivalent concentration of DOX@ZrP in MCF-10A cells. ZrP did not induce apoptosis in both cell lines. DOX and DOX@ZrP induced significant oxidative stress in both cell models. Results suggest that ZrP nanoplatelets are promising as carriers of anticancer agents into cancer cells.

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

    Science.gov (United States)

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

    2009-12-01

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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

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

    Science.gov (United States)

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

    2018-01-15

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

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. Photopatternable Magnetic Hollowbots by Nd-Fe-B Nanocomposite for Potential Targeted Drug Delivery Applications

    Directory of Open Access Journals (Sweden)

    Hui Li

    2018-04-01

    Full Text Available In contrast to traditional drug administration, targeted drug delivery can prolong, localize, target and have a protected drug interaction with the diseased tissue. Drug delivery carriers, such as polymeric micelles, liposomes, dendrimers, nanotubes, and so on, are hard to scale-up, costly, and have short shelf life. Here we show the novel fabrication and characterization of photopatternable magnetic hollow microrobots that can potentially be utilized in microfluidics and drug delivery applications. These magnetic hollowbots can be fabricated using standard ultraviolet (UV lithography with low cost and easily accessible equipment, which results in them being easy to scale up, and inexpensive to fabricate. Contact-free actuation of freestanding magnetic hollowbots were demonstrated by using an applied 900 G external magnetic field to achieve the movement control in an aqueous environment. According to the movement clip, the average speed of the magnetic hollowbots was estimated to be 1.9 mm/s.

  19. Pharmacokinetic drug-drug interaction between erlotinib and paracetamol: A potential risk for clinical practice.

    Science.gov (United States)

    Karbownik, Agnieszka; Szałek, Edyta; Sobańska, Katarzyna; Grabowski, Tomasz; Wolc, Anna; Grześkowiak, Edmund

    2017-05-01

    Erlotinib is a tyrosine kinase inhibitor available for the treatment of non-small cell lung cancer. Paracetamol is an analgesic agent, commonly used in cancer patients. Because these drugs are often co-administered, there is an increasing issue of interaction between them. The aim of the study was to investigate the effect of paracetamol on the pharmacokinetic parameters of erlotinib, as well as the influence of erlotinib on the pharmacokinetics of paracetamol. The rabbits were divided into three groups: the rabbits receiving erlotinib (I ER ), the group receiving paracetamol (II PR ), and the rabbits receiving erlotinib+paracetamol (III ER+PR ). A single dose of erlotinib was administered orally (25mg) and was administered intravenously (35mg/kg). Plasma concentrations of erlotinib, its metabolite (OSI420), paracetamol and its metabolites - glucuronide and sulphate were measured with the validated method. During paracetamol co-administration we observed increased erlotinib maximum concentration (C max ) and area under the plasma concentration-time curve from time zero to infinity (AUC 0-∞ ) by 87.7% and 31.1%, respectively. In turn, erlotinib lead to decreased paracetamol AUC 0-∞ by 35.5% and C max by 18.9%. The mean values of paracetamol glucuronide/paracetamol ratios for C max were 32.2% higher, whereas paracetamol sulphate/paracetamol ratios for C max and AUC 0-∞ were 37.1% and 57.1% lower in the II PR group, when compared to the III ER+PR group. Paracetamol had significant effect on the enhanced plasma exposure of erlotinib. Additionally, erlotinib contributed to the lower concentrations of paracetamol. Decreased glucuronidation and increased sulphation of paracetamol after co-administration of erlotinib were also observed. Copyright © 2017. Published by Elsevier B.V.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. Molecular mechanisms and theranostic potential of miRNAs in drug resistance of gastric cancer.

    Science.gov (United States)

    Yang, Wanli; Ma, Jiaojiao; Zhou, Wei; Cao, Bo; Zhou, Xin; Yang, Zhiping; Zhang, Hongwei; Zhao, Qingchuan; Fan, Daiming; Hong, Liu

    2017-11-01

    Systemic chemotherapy is a curative approach to inhibit gastric cancer cells proliferation. Despite the great progress in anti-cancer treatment achieved during the last decades, drug resistance and treatment refractoriness still extensively persists. Recently, accumulating studies have highlighted the role of miRNAs in drug resistance of gastric cancers by modulating some drug resistance-related proteins and genes expression. Pre-clinical reports indicate that miRNAs might serve as ideal biomarkers and potential targets, thus holding great promise for developing targeted therapy and personalized treatment for the patients with gastric cancer. Areas covered: This review provide a comprehensive overview of the current advances of miRNAs and molecular mechanisms underlying miRNA-mediated drug resistance in gastric cancer. We particularly focus on the potential values of drug resistance-related miRNAs as biomarkers and novel targets in gastric cancer therapy and envisage the future research developments of these miRNAs and challenges in translating the new findings into clinical applications. Expert opinion: Although the concrete mechanisms of miRNAs in drug resistance of gastric cancer have not been fully clarified, miRNA may be a promising theranostic approach. Further studies are still needed to facilitate the clinical applications of miRNAs in drug resistant gastric cancer.

  7. Highly lipophilic pluronics-conjugated polyamidoamine dendrimer nanocarriers as potential delivery system for hydrophobic drugs

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, Thi Tram Chau [Institute of Research and Development, Duy Tan University, Da Nang City 550000 (Viet Nam); Department of Chemical Engineering, Industrial University of HCMC, HCMC 70000 (Viet Nam); Nguyen, Cuu Khoa, E-mail: nckhoavnn@yahoo.com [Department of Materials and Pharmaceutical Chemistry, Vietnam Academy of Science and Technology, HCMC 70000 (Viet Nam); Nguyen, Thi Hiep [Biomedical Engineering Department, International University, National Universities in HCMC, HCMC 70000 (Viet Nam); Tran, Ngoc Quyen, E-mail: tnquyen@iams.vast.vn [Institute of Research and Development, Duy Tan University, Da Nang City 550000 (Viet Nam); Department of Materials and Pharmaceutical Chemistry, Vietnam Academy of Science and Technology, HCMC 70000 (Viet Nam)

    2017-01-01

    In the study, four kinds of pluronics (P123, F68, F127 and F108) with varying hydrophilic-lipophilic balance (HLB) values were modified and conjugated on 4th generation of polyamidoamine dendrimer (PAMAM). The obtained results from FT-IR, {sup 1}H NMR and GPC showed that the pluronics effectively conjugated on the dendrimer. The molecular weight of four PAMAM G4.0-Pluronics and its morphologies are in range of 200.15–377.14 kDa and around 60–180 nm in diameter by TEM, respectively. Loading efficiency and release of hydrophobic fluorouracil (5-FU) anticancer drug were evaluated by HPLC; Interesting that the dendrimer nanocarrier was conjugated with the highly lipophilic pluronic P123 (G4.0-P123) exhibiting a higher drug loading efficiency (up to 76.25%) in comparison with another pluronics. Live/dead fibroblast cell staining assay mentioned that all conjugated nanocarriers are highly biocompatible. The drug-loaded nanocarriers also indicated a highly anti-proliferative activity against MCF-7 breast cancer cell. The obtained results demonstrated a great potential of the highly lipophilic pluronics-conjugated nanocarriers in hydrophobic drugs delivery for biomedical applications. - Highlights: • Biocompatible pluronic-conjugated polyamidoamine dendrimers were prepared at nanoscale for drug delivery. • The dendrimer nanocarrier was decorated with a lipophilic pluronic exhibiting a higher drug loading efficiency. • The pluronic-functionalized nanocarriers demonstrated a great potential for delivering hydrophobic drugs.

  8. Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point.

    Science.gov (United States)

    Rodrigues, Tiago

    2017-11-15

    Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

  9. 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.

  10. 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.

  11. 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

  12. Drug-drug Interactions of Statins Potentially Leading to Muscle-Related Side Effects in Hospitalized Patients.

    Science.gov (United States)

    Bucsa, Camelia; Farcas, Andreea; Leucuta, D; Mogosan, Cristina; Bojita, M; Dumitrascu, D L

    2015-01-01

    The associations of drugs that may interact with the statins resulting in elevated serum concentration of the statins are an important risk factor for statin induced muscle disorders. We aimed to determine the prevalence of these associations in all hospitalized patients that had been prescribed statins before/during hospitalization and to find out how often they are associated with muscle-related side effects. This prospective, non-interventional study performed in two internal medicine departments included patients with statin therapy before/during hospitalization. Data on each patient demographic characteristics, co-morbidities and treatment was collected from medical charts and interviews. We evaluated patients' therapy for the targeted associations using Thomson Micromedex Drug Interactions checker and we ranked the identified drug-drug interactions (DDIs) accordingly. Each patient with statin treatment before admission was additionally interviewed in order to identify muscular symptoms. In 109 patients on statin treatment we found 35 potential (p) DDIs of statins in 30 (27.5%) patients, most of which were in the therapy before admission (27 pDDIs). The pDDIs were moderate (20 pDDIs) and major (15 pDDIs). Of the total number of pDDIs, 24 were targeting the muscular system. The drugs most frequently involved in the statins' pDDIs were amiodarone and fenofibrate. Two of the patients with pDDIs reported muscle pain, both having additional risk factors for statin induced muscular effects. The prevalence of statins' pDDIs was high in our study, mostly in the therapy before admission, with only a small number of pDDIs resulting in clinical outcome.

  13. Predicting Customer Potential Value: an application in the insurance industry

    NARCIS (Netherlands)

    P.C. Verhoef (Peter); A.C.D. Donkers (Bas)

    2001-01-01

    textabstractFor effective Customer Relationship Management (CRM), it is essential to have information on the potential value of customers. Based on the interplay between potential value and realized value, managers can devise customer specific strategies. In this article we introduce a model for

  14. Potential predictive factors of positive prostate biopsy in the Chinese ...

    African Journals Online (AJOL)

    Yomi

    2012-01-16

    Jan 16, 2012 ... Therefore, it might be inappropriate that we apply these western models to the. Chinese population that has a lower incidence of PCa. Therefore, this retrospective study aimed to determine predictive factors for a positive prostate biopsy in Chinese men. Our ultimate goal is to develop a simple model for ...

  15. Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules.

    Directory of Open Access Journals (Sweden)

    Konda Leela Sarath Kumar

    Full Text Available Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage.The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with 'High' reliability scoring, DEREK (accuracy = 72.73% and CCR = 71.44% and TOPKAT (accuracy = 60.00% and CCR = 61.67%. Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%, the coverage was very low (only 10 out of 77 molecules were predicted reliably.Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing.

  16. 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.

  17. 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.

  18. 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

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

    OpenAIRE

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

    2010-01-01

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

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Hugo Geerts

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

  2. Effective Drug Delivery in Diffuse Intrinsic Pontine Glioma: A Theoretical Model to Identify Potential Candidates

    Directory of Open Access Journals (Sweden)

    Fatma E. El-Khouly

    2017-10-01

    Full Text Available Despite decades of clinical trials for diffuse intrinsic pontine glioma (DIPG, patient survival does not exceed 10% at two years post-diagnosis. Lack of benefit from systemic chemotherapy may be attributed to an intact bloodbrain barrier (BBB. We aim to develop a theoretical model including relevant physicochemical properties in order to review whether applied chemotherapeutics are suitable for passive diffusion through an intact BBB or whether local administration via convection-enhanced delivery (CED may increase their therapeutic potential. Physicochemical properties (lipophilicity, molecular weight, and charge in physiological environment of anticancer drugs historically and currently administered to DIPG patients, that affect passive diffusion over the BBB, were included in the model. Subsequently, the likelihood of BBB passage of these drugs was ascertained, as well as their potential for intratumoral administration via CED. As only non-molecularly charged, lipophilic, and relatively small sized drugs are likely to passively diffuse through the BBB, out of 51 drugs modeled, only 8 (15%—carmustine, lomustine, erlotinib, vismodegib, lenalomide, thalidomide, vorinostat, and mebendazole—are theoretically qualified for systemic administration in DIPG. Local administration via CED might create more therapeutic options, excluding only positively charged drugs and drugs that are either prodrugs and/or only available as oral formulation. A wide variety of drugs have been administered systemically to DIPG patients. Our model shows that only few are likely to penetrate the BBB via passive diffusion, which may partly explain the lack of efficacy. Drug distribution via CED is less dependent on physicochemical properties and may increase the therapeutic options for DIPG.

  3. 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.

  4. Predicting Potential C Mineralization of Tundra Soils Using Spectroscopy Techniques

    Science.gov (United States)

    The large amounts of organic matter stored in permafrost-region soils are preserved in a relatively undecomposed state by the cold and wet environmental conditions limiting decomposer activity. With pending climate changes and the potential for warming of Arctic soils, there is a need to better unde...

  5. Predicting the potential distribution of invasive silver carp ...

    African Journals Online (AJOL)

    The potential range of silver carp in South Africa was identified based on ecological niche modelling (ENM) using the maximum entropy method. Models were constructed using occurrence records and a defined background, and calibrated using a k-fold method. The area under the receiver operating characteristics curve ...

  6. Prediction of scale potential in ethylene glycol containing solutions

    Energy Technology Data Exchange (ETDEWEB)

    Sandengen, Kristian; Oestvold, Terje

    2006-03-15

    This work presents a method for scale prediction in MEG (Mono Ethylene Glycol / 1,2-ethane-diol) containing solutions. It is based on an existing PVT scale model using a Pitzer ion interaction model for the aqueous phase. The model is well suited for scale prediction in saline solutions, where the PVT part is necessary for calculating CO{sub 2} phase equilibria being critical for carbonate scale. MEG influences the equilibria contained in the model, and its effect has been added empirically. Thus the accuracy of the model is limited by the amount of available experimental data. The model is applicable in the range 0-99wt% MEG and includes a wide variety of salts. In addition to the aspects of scale modelling in MEG+water solutions, this work presents new experimental data on CaSO4 solubility (0-95wt% MEG and 22-80 deg.C). CaSO4 solubility is greatly reduced by MEG to an extent that ''Salting-out'' is possible. (author) (tk)

  7. Predicting Tropical Cyclone Destructive Potential by Integrated Kinetic Energy According to the Powell/Reinhold Scale

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A method of predicting the destructive capacity of a tropical cyclone based on a new Wind Destructive Potential (WDP) and Storm Surge Destructive Potential (SDP)...

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

    Science.gov (United States)

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

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

  9. 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.

  10. 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.

  11. Occupational Exposure to Antineoplastic Drugs: Identification of Job Categories Potentially Exposed throughout the Hospital Medication System

    Directory of Open Access Journals (Sweden)

    Chun-Yip Hon

    2011-09-01

    Conclusion: We found drug contamination on select surfaces at every stage of the medication system, which indicates the existence of an exposure potential throughout the facility. Our results suggest that a broader range of workers are potentially exposed than has been previously examined. These results will allow us to develop a more inclusive exposure assessment encompassing all healthcare workers that are at risk throughout the hospital medication system.

  12. [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.

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

    African Journals Online (AJOL)

    The zeta potential (ZP) of colloidal systems and nano-medicines, as well as their particle size exert a major effect on the various properties of nano-drug delivery systems. Not only the stability of dosage forms and their release rate are affected but also their circulation in the blood stream and absorption into body membranes ...

  14. Physical stability, biocompatibility and potential use of hybrid iron oxide-gold nanoparticles as drug carriers

    Energy Technology Data Exchange (ETDEWEB)

    Barnett, Christopher M. [School of Pharmacy, Keele University (United Kingdom); Gueorguieva, Mariana [Institute of Medical Science and Technology, University of Dundee (United Kingdom); Lees, Martin R. [University of Warwick, Physics Department (United Kingdom); McGarvey, David J. [School of Physical and Geographical Sciences, Keele University, Lennard-Jones Laboratories (United Kingdom); Hoskins, Clare, E-mail: c.hoskins@keele.ac.uk [Institute for Science and Technology in Medicine, Keele University (United Kingdom)

    2013-06-15

    Hybrid nanoparticles (HNPs) such as iron oxide-gold nanoparticles are currently being exploited for their potential application in image-guided therapies. However, little investigation has been carried out into their physical or chemical stability and potential cytotoxicity in biological systems. Here, we determine the HNPs physical stability over 6 months and chemical stability in physiological conditions, and estimate the biological activity of uncoated and poly(ethylene glycol) coated nanoparticles on human pancreatic adenocarcinoma (BxPC-3) and differentiated human monocyte cells (U937). The potential of these HNPs to act as drug carrier vehicles was determined using the model drug 6-Thioguanine (6-TG). The data showed that the HNPs maintained their structural integrity both physically and chemically throughout the duration of the studies. In addition, negligible cytotoxicity or free radical production was observed in the cell lines tested. The 6-TG was successfully conjugated; with a ratio of 3:1:10 Fe:Au:6-TG (wt:wt:wt). After incubation with BxPC-3 cells, enhanced cellular uptake was reported with the 6-TG-conjugated HNPs compared with free drug along with a 10-fold decrease in IC{sub 50}. This exciting data highlights the potential of HNPs for use in image-guided drug delivery.

  15. Accuracy of professional sports drafts in predicting career potential.

    Science.gov (United States)

    Koz, D; Fraser-Thomas, J; Baker, J

    2012-08-01

    The forecasting of talented players is a crucial aspect of building a successful sports franchise and professional sports invest significant resources in making player choices in sport drafts. The current study examined the relationship between career performance (i.e. games played) and draft round for the National Football League, National Hockey League, National Basketball League, and Major League Baseball for players drafted from 1980 to 1989 (n = 4874) against the assumption of a linear relationship between performance and draft round (i.e. that players with the most potential will be selected before players of lower potential). A two-step analysis revealed significant differences in games played across draft rounds (step 1) and a significant negative relationship between draft round and games played (step 2); however, the amount of variance accounted for was relatively low (less than 17%). Results highlight the challenges of accurately evaluating amateur talent. © 2011 John Wiley & Sons A/S.

  16. Prediction of potential failures in hydraulic gear pumps

    Directory of Open Access Journals (Sweden)

    E. Lisowski

    2010-07-01

    Full Text Available Hydraulic gear pumps are used in many machines and devices. In hydraulic systems of machines gear pumps are main component ofsupply unit or perform auxiliary function. Gear pumps opposite to vane pumps are less complicated. They consists of such components as:housing, gear wheels, bearings, shaft, seal for rotation motion which are not very sensitive for damage and that is why they are using veryoften. However, gear pumps are break down from time to time. Usually damage of pump cause shutting down of machines and devices.One of the way for identifying potential failures and foreseeing their effects is a quality method. On the basis of these methods apreventing action might be undertaken before failure appear. In this paper potential failures and damages of a gear pump were presented bythe usage of matrix FMEA analysis.

  17. Potential ecological risk assessment and predicting zinc accumulation in soils

    OpenAIRE

    Baran, Agnieszka; Wieczorek, Jerzy; Mazurek, Ryszard; Urbański, Krzysztof; Klimkowicz-Pawlas, Agnieszka

    2017-01-01

    The aims of this study were to investigate zinc content in the studied soils; evaluate the efficiency of geostatistics in presenting spatial variability of zinc in the soils; assess bioavailable forms of zinc in the soils and to assess soil–zinc binding ability; and to estimate the potential ecological risk of zinc in soils. The study was conducted in southern Poland, in the Malopolska Province. This area is characterized by a great diversity of geological structures and types of land use and...

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

    Science.gov (United States)

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

    2018-03-01

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

  19. 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.

  20. Prediction of potential compressive strength of Portland clinker from its mineralogy

    DEFF Research Database (Denmark)

    Svinning, K.; Høskuldsson, Agnar; Justnes, H.

    2010-01-01

    Based on a statistical model first applied for prediction of compressive strength up to 28 d from the microstructure of Portland cement, potential compressive strength of clinker has been predicted from its mineralogy. The prediction model was evaluated by partial least squares regression...

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

    Science.gov (United States)

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

    2015-01-01

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

  2. [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.

  3. 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.

  4. Modified local diatomite as potential functional drug carrier--A model study for diclofenac sodium.

    Science.gov (United States)

    Janićijević, Jelena; Krajišnik, Danina; Čalija, Bojan; Vasiljević, Bojana Nedić; Dobričić, Vladimir; Daković, Aleksandra; Antonijević, Milan D; Milić, Jela

    2015-12-30

    Diatomite makes a promising candidate for a drug carrier because of its high porosity, large surface area, modifiable surface chemistry and biocompatibility. Herein, refined diatomite from Kolubara coal basin, which complied with the pharmacopoeial requirements for heavy metals content and microbiological quality, was used as a starting material. Inorganic modification of the starting material was performed through a simple, one-step procedure. Significant increase in adsorbent loading with diclofenac sodium (DS) was achieved after the modification process (∼373mg/g) which enabled the preparation of comprimates containing therapeutic dose of the adsorbed drug. Adsorption of DS onto modified diatomite resulted in the alteration of the drug's XRD pattern and FTIR spectrum. In vitro drug release studies in phosphate buffer pH 7.5 demonstrated prolonged DS release over 8h from comprimates containing DS adsorbed on modified diatomite (up to 37% after 8h) and those containing physical mixture of the same composition (up to 45% after 8h). The results of in vivo toxicity testing on mice pointed on potential safety of both unmodified (starting) and modified diatomite. All these findings favor the application of diatomite as a potential functional drug carrier. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. Open Innovation Drug Discovery (OIDD): a potential path to novel therapeutic chemical space.

    Science.gov (United States)

    Alvim-Gaston, Maria; Grese, Timothy; Mahoui, Abdelaziz; Palkowitz, Alan D; Pineiro-Nunez, Marta; Watson, Ian

    2014-01-01

    The continued development of computational and synthetic methods has enabled the enumeration or preparation of a nearly endless universe of chemical structures. Nevertheless, the ability of this chemical universe to deliver small molecules that can both modulate biological targets and have drug-like physicochemical properties continues to be a topic of interest to the pharmaceutical industry and academic researchers alike. The chemical space described by public, commercial, in-house and virtual compound collections has been interrogated by multiple approaches including biochemical, cellular and virtual screening, diversity analysis, and in-silico profiling. However, current drugs and known chemical probes derived from these efforts are contained within a remarkably small volume of the predicted chemical space. Access to more diverse classes of chemical scaffolds that maintain the properties relevant for drug discovery is certainly needed to meet the increasing demands for pharmaceutical innovation. The Lilly Open Innovation Drug Discovery platform (OIDD) was designed to tackle barriers to innovation through the identification of novel molecules active in relevant disease biology models. In this article we will discuss several computational approaches towards describing novel, biologically active, drug-like chemical space and illustrate how the OIDD program may facilitate access to previously untapped molecules that may aid in the search for innovative pharmaceuticals.

  6. Sodium dependent multivitamin transporter (SMVT): a potential target for drug delivery.

    Science.gov (United States)

    Vadlapudi, Aswani Dutt; Vadlapatla, Ramya Krishna; Mitra, Ashim K

    2012-06-01

    Sodium dependent multivitamin transporter (SMVT; product of the SLC5A6 gene) is an important transmembrane protein responsible for translocation of vitamins and other essential cofactors such as biotin, pantothenic acid and lipoic acid. Hydropathy plot (Kyte-Dolittle algorithm) revealed that human SMVT protein consists of 635 amino acids and 12 transmembrane domains with both amino and carboxyl termini oriented towards the cytoplasm. SMVT is expressed in various tissues such as placenta, intestine, brain, liver, lung, kidney, cornea, retina and heart. This transporter displays broad substrate specificity and excellent capacity for utilization in drug delivery. Drug absorption is often limited by the presence of physiological (epithelial tight junctions), biochemical (efflux transporters and enzymatic degradation) and chemical (size, lipophilicity, molecular weight, charge etc.) barriers. These barriers may cause many potential therapeutics to be dropped from the preliminary screening portfolio and subsequent entry into the market. Transporter targeted delivery has become a powerful approach to deliver drugs to target tissues because of the ability of the transporter to translocate the drug to intracellular organelles at a higher rate. This review highlights studies employing SMVT transporter as a target for drug delivery to improve bioavailability and investigate the feasibility of developing SMVT targeted drug delivery systems.

  7. 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

  8. Grouping nanomaterials to predict their potential to induce pulmonary inflammation

    Energy Technology Data Exchange (ETDEWEB)

    Braakhuis, Hedwig M., E-mail: hedwig.braakhuis@rivm.nl [National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven (Netherlands); Department of Toxicogenomics, Maastricht University, PO Box 616, 6200 MD Maastricht (Netherlands); Oomen, Agnes G. [National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven (Netherlands); Cassee, Flemming R. [National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven (Netherlands); Institute of Risk Assessment Sciences, Utrecht University, PO Box 80.163, 3508 TD Utrecht (Netherlands)

    2016-05-15

    The rapidly expanding manufacturing, production and use of nanomaterials have raised concerns for both worker and consumer safety. Various studies have been published in which induction of pulmonary inflammation after inhalation exposure to nanomaterials has been described. Nanomaterials can vary in aspects such as size, shape, charge, crystallinity, chemical composition, and dissolution rate. Currently, efforts are made to increase the knowledge on the characteristics of nanomaterials that can be used to categorise them into hazard groups according to these characteristics. Grouping helps to gather information on nanomaterials in an efficient way with the aim to aid risk assessment. Here, we discuss different ways of grouping nanomaterials for their risk assessment after inhalation. Since the relation between single intrinsic particle characteristics and the severity of pulmonary inflammation is unknown, grouping of nanomaterials by their intrinsic characteristics alone is not sufficient to predict their risk after inhalation. The biokinetics of nanomaterials should be taken into account as that affects the dose present at a target site over time. The parameters determining the kinetic behaviour are not the same as the hazard-determining parameters. Furthermore, characteristics of nanomaterials change in the life-cycle, resulting in human exposure to different forms and doses of these nanomaterials. As information on the biokinetics and in situ characteristics of nanomaterials is essential but often lacking, efforts should be made to include these in testing strategies. Grouping nanomaterials will probably be of the most value to risk assessors when information on intrinsic characteristics, life-cycle, biokinetics and effects are all combined. - Highlights: • Grouping of nanomaterials helps to gather information in an efficient way with the aim to aid risk assessment. • Different ways of grouping nanomaterials for their risk assessment after inhalation are

  9. Grouping nanomaterials to predict their potential to induce pulmonary inflammation

    International Nuclear Information System (INIS)

    Braakhuis, Hedwig M.; Oomen, Agnes G.; Cassee, Flemming R.

    2016-01-01

    The rapidly expanding manufacturing, production and use of nanomaterials have raised concerns for both worker and consumer safety. Various studies have been published in which induction of pulmonary inflammation after inhalation exposure to nanomaterials has been described. Nanomaterials can vary in aspects such as size, shape, charge, crystallinity, chemical composition, and dissolution rate. Currently, efforts are made to increase the knowledge on the characteristics of nanomaterials that can be used to categorise them into hazard groups according to these characteristics. Grouping helps to gather information on nanomaterials in an efficient way with the aim to aid risk assessment. Here, we discuss different ways of grouping nanomaterials for their risk assessment after inhalation. Since the relation between single intrinsic particle characteristics and the severity of pulmonary inflammation is unknown, grouping of nanomaterials by their intrinsic characteristics alone is not sufficient to predict their risk after inhalation. The biokinetics of nanomaterials should be taken into account as that affects the dose present at a target site over time. The parameters determining the kinetic behaviour are not the same as the hazard-determining parameters. Furthermore, characteristics of nanomaterials change in the life-cycle, resulting in human exposure to different forms and doses of these nanomaterials. As information on the biokinetics and in situ characteristics of nanomaterials is essential but often lacking, efforts should be made to include these in testing strategies. Grouping nanomaterials will probably be of the most value to risk assessors when information on intrinsic characteristics, life-cycle, biokinetics and effects are all combined. - Highlights: • Grouping of nanomaterials helps to gather information in an efficient way with the aim to aid risk assessment. • Different ways of grouping nanomaterials for their risk assessment after inhalation are

  10. A rat retinal damage model predicts for potential clinical visual disturbances induced by Hsp90 inhibitors

    International Nuclear Information System (INIS)

    Zhou, Dan; Liu, Yuan; Ye, Josephine; Ying, Weiwen; Ogawa, Luisa Shin; Inoue, Takayo; Tatsuta, Noriaki; Wada, Yumiko; Koya, Keizo; Huang, Qin; Bates, Richard C.; Sonderfan, Andrew J.

    2013-01-01

    In human trials certain heat shock protein 90 (Hsp90) inhibitors, including 17-DMAG and NVP-AUY922, have caused visual disorders indicative of retinal dysfunction; others such as 17-AAG and ganetespib have not. To understand these safety profile differences we evaluated histopathological changes and exposure profiles of four Hsp90 inhibitors, with or without clinical reports of adverse ocular effects, using a rat retinal model. Retinal morphology, Hsp70 expression (a surrogate marker of Hsp90 inhibition), apoptotic induction and pharmacokinetic drug exposure analysis were examined in rats treated with the ansamycins 17-DMAG and 17-AAG, or with the second-generation compounds NVP-AUY922 and ganetespib. Both 17-DMAG and NVP-AUY922 induced strong yet restricted retinal Hsp70 up-regulation and promoted marked photoreceptor cell death 24 h after the final dose. In contrast, neither 17-AAG nor ganetespib elicited photoreceptor injury. When the relationship between drug distribution and photoreceptor degeneration was examined, 17-DMAG and NVP-AUY922 showed substantial retinal accumulation, with high retina/plasma (R/P) ratios and slow elimination rates, such that 51% of 17-DMAG and 65% of NVP-AUY922 present at 30 min post-injection were retained in the retina 6 h post-dose. For 17-AAG and ganetespib, retinal elimination was rapid (90% and 70% of drugs eliminated from the retina at 6 h, respectively) which correlated with lower R/P ratios. These findings indicate that prolonged inhibition of Hsp90 activity in the eye results in photoreceptor cell death. Moreover, the results suggest that the retina/plasma exposure ratio and retinal elimination rate profiles of Hsp90 inhibitors, irrespective of their chemical class, may predict for ocular toxicity potential. - Highlights: • In human trials some Hsp90 inhibitors cause visual disorders, others do not. • Prolonged inhibition of Hsp90 in the rat eye results in photoreceptor cell death. • Retina/plasma ratio and retinal

  11. 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.

  12. 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.

  13. 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.

  14. Potential ecological risk assessment and predicting zinc accumulation in soils.

    Science.gov (United States)

    Baran, Agnieszka; Wieczorek, Jerzy; Mazurek, Ryszard; Urbański, Krzysztof; Klimkowicz-Pawlas, Agnieszka

    2018-02-01

    The aims of this study were to investigate zinc content in the studied soils; evaluate the efficiency of geostatistics in presenting spatial variability of zinc in the soils; assess bioavailable forms of zinc in the soils and to assess soil-zinc binding ability; and to estimate the potential ecological risk of zinc in soils. The study was conducted in southern Poland, in the Malopolska Province. This area is characterized by a great diversity of geological structures and types of land use and intensity of industrial development. The zinc content was affected by soil factors, and the type of land use (arable lands, grasslands, forests, wastelands). A total of 320 soil samples were characterized in terms of physicochemical properties (texture, pH, organic C content, total and available Zn content). Based on the obtained data, assessment of the ecological risk of zinc was conducted using two methods: potential ecological risk index and hazard quotient. Total Zn content in the soils ranged from 8.27 to 7221 mg kg -1 d.m. Based on the surface semivariograms, the highest variability of zinc in the soils was observed from northwest to southeast. The point sources of Zn contamination were located in the northwestern part of the area, near the mining-metallurgical activity involving processing of zinc and lead ores. These findings were confirmed by the arrangement of semivariogram surfaces and bivariate Moran's correlation coefficients. The content of bioavailable forms of zinc was between 0.05 and 46.19 mg kg -1 d.m. (0.01 mol dm -3 CaCl 2 ), and between 0.03 and 71.54 mg kg -1 d.m. (1 mol dm -3 NH 4 NO 3 ). Forest soils had the highest zinc solubility, followed by arable land, grassland and wasteland. PCA showed that organic C was the key factor to control bioavailability of zinc in the soils. The extreme, very high and medium zinc accumulation was found in 69% of studied soils. There is no ecological risk of zinc to living organisms in the study area, and in 90

  15. Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours.

    Science.gov (United States)

    Sharifi, N; Ozgoli, S; Ramezani, A

    2017-06-01

    Mixed immunotherapy and chemotherapy of tumours is one of the most efficient ways to improve cancer treatment strategies. However, it is important to 'design' an effective treatment programme which can optimize the ways of combining immunotherapy and chemotherapy to diminish their imminent side effects. Control engineering techniques could be used for this. The method of multiple model predictive controller (MMPC) is applied to the modified Stepanova model to induce the best combination of drugs scheduling under a better health criteria profile. The proposed MMPC is a feedback scheme that can perform global optimization for both tumour volume and immune competent cell density by performing multiple constraints. Although current studies usually assume that immunotherapy has no side effect, this paper presents a new method of mixed drug administration by employing MMPC, which implements several constraints for chemotherapy and immunotherapy by considering both drug toxicity and autoimmune. With designed controller we need maximum 57% and 28% of full dosage of drugs for chemotherapy and immunotherapy in some instances, respectively. Therefore, through the proposed controller less dosage of drugs are needed, which contribute to suitable results with a perceptible reduction in medicine side effects. It is observed that in the presence of MMPC, the amount of required drugs is minimized, while the tumour volume is reduced. The efficiency of the presented method has been illustrated through simulations, as the system from an initial condition in the malignant region of the state space (macroscopic tumour volume) transfers into the benign region (microscopic tumour volume) in which the immune system can control tumour growth. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    Byun, Jong Hyuk; Jung, Il Hyo

    2018-04-14

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

  17. 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.

  18. 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

  19. Prognostic and predictive potential molecular biomarkers in colon cancer.

    Science.gov (United States)

    Nastase, A; Pâslaru, L; Niculescu, A M; Ionescu, M; Dumitraşcu, T; Herlea, V; Dima, S; Gheorghe, C; Lazar, V; Popescu, I

    2011-01-01

    An important objective in nowadays research is the discovery of new biomarkers that can detect colon tumours in early stages and indicate with accuracy the status of the disease. The aim of our study was to identify potential biomarkers for colon cancer onset and progression. We assessed gene expression profiles of a list of 10 candidate genes (MMP-1, MMP-3, MMP-7, DEFA 1, DEFA-5, DEFA-6, IL-8, CXCL-1, SPP-1, CTHRC-1) by quantitative real time PCR in triplets of colonic mucosa (normal, adenoma, tumoral tissue) collected from the same patient during surgery for a group of 20 patients. Additionally we performed immunohistochemistry for DEFA1-3 and SPP1. We remarked that DEFA5 and DEFA6 are key factors in adenoma formation (p<0.05). MMP7 is important in the transition from a benign to a malignant status (p <0.01) and further in metastasis being a prognostic indicator for tumor transformation and for the metastatic potential of cancer cells. IL8, irrespective of tumor stage, has a high mRNA level in adenocarcinoma (p< 0.05). The level of expression for SPP1 is correlated with tumor level. We suggest that high levels of DEFAS, DEFA6 (key elements in adenoma formation), MMP7 (marker of colon cancer onset and progression to metastasis), SPP1 (marker of progression) and IL8 could be used to diagnose an early stage colon cancer and to evaluate the prognostic of progression for colon tumors. Further, if DEFA5 and DEFA6 level of expression are low but MMP7, SPP1 and IL8 level are high we could point out that the transition from adenoma to adenocarcinoma had already occurred. Thus, DEFA5, DEFA6, MMP7, IL8 and SPP1 consist in a valuable panel of biomarkers, whose detection can be used in early detection and progressive disease and also in prognostic of colon cancer.

  20. Grouping nanomaterials to predict their potential to induce pulmonary inflammation.

    Science.gov (United States)

    Braakhuis, Hedwig M; Oomen, Agnes G; Cassee, Flemming R

    2016-05-15

    The rapidly expanding manufacturing, production and use of nanomaterials have raised concerns for both worker and consumer safety. Various studies have been published in which induction of pulmonary inflammation after inhalation exposure to nanomaterials has been described. Nanomaterials can vary in aspects such as size, shape, charge, crystallinity, chemical composition, and dissolution rate. Currently, efforts are made to increase the knowledge on the characteristics of nanomaterials that can be used to categorise them into hazard groups according to these characteristics. Grouping helps to gather information on nanomaterials in an efficient way with the aim to aid risk assessment. Here, we discuss different ways of grouping nanomaterials for their risk assessment after inhalation. Since the relation between single intrinsic particle characteristics and the severity of pulmonary inflammation is unknown, grouping of nanomaterials by their intrinsic characteristics alone is not sufficient to predict their risk after inhalation. The biokinetics of nanomaterials should be taken into account as that affects the dose present at a target site over time. The parameters determining the kinetic behaviour are not the same as the hazard-determining parameters. Furthermore, characteristics of nanomaterials change in the life-cycle, resulting in human exposure to different forms and doses of these nanomaterials. As information on the biokinetics and in situ characteristics of nanomaterials is essential but often lacking, efforts should be made to include these in testing strategies. Grouping nanomaterials will probably be of the most value to risk assessors when information on intrinsic characteristics, life-cycle, biokinetics and effects are all combined. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. 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.

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

    Science.gov (United States)

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

    2016-01-01

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

  3. 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.

  4. [The original nootropic and neuroprotective drug noopept potentiates the anticonvulsant activity of valproate in mice].

    Science.gov (United States)

    Kravchenko, E V; Ponteleeva, I V; Trofimov, S S; Lapa, V I; Ostrovskaia, R U; Voronina, T A

    2009-01-01

    The influence of the original dipeptide drug noopept, known to possess nootrope, neuroprotector, and anxiolytic properties, on the anticonvulsant activity of the antiepileptic drug valproate has been studied on the model of corazole-induced convulsions in mice. Neither a single administration of noopept (0.5 mg/kg, i.p.) nor its repeated introduction in 10 or 35 days enhanced the convulsant effect of corazole, which is evidence that noopept alone does not possess anticonvulsant properties. Prolonged (five weeks) preliminary administration of noopept enhanced the anticonvulsant activity of valproate. This result justifies the joint chronic administration of noopept in combination with valproate in order to potentiate the anticonvulsant effect of the latter drug. In addition, the administration of noopept favorably influences the cognitive functions and suppresses the development of neurodegenerative processes.

  5. An albumin-oligonucleotide assembly for potential combinatorial drug delivery and half-life extension applications

    DEFF Research Database (Denmark)

    Kuhlmann, Matthias; Hamming, Jonas Bohn Refslund; Voldum, Anders

    2017-01-01

    The long blood circulatory property of human serum albumin, due to engagement with the cellular recycling neonatal Fc receptor (FcRn), is an attractive drug half-life extension enabling technology. This work describes a novel site-specific albumin double-stranded (ds) DNA assembly approach, in wh...... technology platform that offers potential combinatorial drug delivery and half-life extension applications.......The long blood circulatory property of human serum albumin, due to engagement with the cellular recycling neonatal Fc receptor (FcRn), is an attractive drug half-life extension enabling technology. This work describes a novel site-specific albumin double-stranded (ds) DNA assembly approach......, in which the 3' or 5' end maleimide-derivatized oligodeoxynucleotides are conjugated to albumin cysteine at position 34 (cys34) and annealed with complementary strands to allow single site-specific protein modification with functionalized ds oligodeoxynucleotides. Electrophoretic gel shift assays...

  6. The Potential Impact of Up-Front Drug Sensitivity Testing on India's Epidemic of Multi-Drug Resistant Tuberculosis.

    Directory of Open Access Journals (Sweden)

    Kuldeep Singh Sachdeva

    Full Text Available In India as elsewhere, multi-drug resistance (MDR poses a serious challenge in the control of tuberculosis (TB. The End TB strategy, recently approved by the world health assembly, aims to reduce TB deaths by 95% and new cases by 90% between 2015 and 2035. A key pillar of this approach is early diagnosis of tuberculosis, including use of higher-sensitivity diagnostic testing and universal rapid drug susceptibility testing (DST. Despite limitations of current laboratory assays, universal access to rapid DST could become more feasible with the advent of new and emerging technologies. Here we use a mathematical model of TB transmission, calibrated to the TB epidemic in India, to explore the potential impact of a major national scale-up of rapid DST. To inform key parameters in a clinical setting, we take GeneXpert as an example of a technology that could enable such scale-up. We draw from a recent multi-centric demonstration study conducted in India that involved upfront Xpert MTB/RIF testing of all TB suspects.We find that widespread, public-sector deployment of high-sensitivity diagnostic testing and universal DST appropriately linked with treatment could substantially impact MDR-TB in India. Achieving 75% access over 3 years amongst all cases being diagnosed for TB in the public sector alone could avert over 180,000 cases of MDR-TB (95% CI 44187 - 317077 cases between 2015 and 2025. Sufficiently wide deployment of Xpert could, moreover, turn an increasing MDR epidemic into a diminishing one. Synergistic effects were observed with assumptions of simultaneously improving MDR-TB treatment outcomes. Our results illustrate the potential impact of new and emerging technologies that enable widespread, timely DST, and the important effect that universal rapid DST in the public sector can have on the MDR-TB epidemic in India.

  7. 75 FR 32952 - Draft Guidance for Industry and Food and Drug Administration Staff; “‘Harmful and Potentially...

    Science.gov (United States)

    2010-06-10

    ... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2010-D-0281] Draft Guidance for Industry and Food and Drug Administration Staff; ```Harmful and Potentially Harmful... Food, Drug, and Cosmetic Act.'' This draft guidance provides written guidance to industry and FDA staff...

  8. 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.

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

    Science.gov (United States)

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

    2017-11-01

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

  10. 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

  11. 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

  12. The dopamine hypothesis of drug addiction and its potential therapeutic value.

    Directory of Open Access Journals (Sweden)

    Marco eDiana

    2011-11-01

    Full Text Available Dopamine (DA transmission is deeply affected by drugs of abuse, and alterations in DA function are involved in various phases of drug addiction and potentially exploitable therapeutically. In particular, basic studies have documented a reduction in the electrophysiological activity of DA neurons in alcohol, opiate, cannabinoid and other drug-dependent rats. Further, DA release in the Nacc is decreased in virtually all drug-dependent rodents. In parallel, these studies are supported by increments in intracranial self stimulation (ICSS thresholds during withdrawal from alcohol, nicotine, opiates, and other drugs of abuse, thereby suggesting a hypofunction of the neural substrate of ICSS. Accordingly, morphological evaluations fed into realistic computational analysis of the Medium Spiny Neuron (MSN of the Nucleus accumbens (Nacc, post-synaptic counterpart of DA terminals, show profound changes in structure and function of the entire mesolimbic system. In line with these findings, human imaging studies have shown a reduction of dopamine receptors accompanied by a lesser release of endogenous DA in the ventral striatum of cocaine, heroin and alcohol-dependent subjects, thereby offering visual proof of the ‘dopamine-impoverished’ addicted human brain.The reduction in physiological activity of the DA system leads to the idea that an increment in its activity, to restore pre-drug levels, may yield significant clinical improvements (reduction of craving, relapse and drug-seeking/taking. In theory, it may be achieved pharmacologically and/or with novel interventions such as Transcranial Magnetic Stimulation (TMS. Its anatomo-physiological rationale as a possible therapeutic aid in alcoholics and other addicts will be described and proposed as a theoretical framework to be subjected to experimental testing in human addicts.

  13. 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

  14. Prediction of phenotypic susceptibility to antiretroviral drugs using physiochemical properties of the primary enzymatic structure combined with artificial neural networks

    DEFF Research Database (Denmark)

    Kjaer, J; Høj, L; Fox, Z

    2008-01-01

    OBJECTIVES: Genotypic interpretation systems extrapolate observed associations in datasets to predict viral susceptibility to antiretroviral drugs (ARVs) for given isolates. We aimed to develop and validate an approach using artificial neural networks (ANNs) that employ descriptors...

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  16. The Potential Return on Public Investment in Detecting Adverse Drug Effects.

    Science.gov (United States)

    Huybrechts, Krista F; Desai, Rishi J; Park, Moa; Gagne, Joshua J; Najafzadeh, Mehdi; Avorn, Jerry

    2017-06-01

    Many countries lack fully functional pharmacovigilance programs, and public budgets allocated to pharmacovigilance in industrialized countries remain low due to resource constraints and competing priorities. Using 3 case examples, we sought to estimate the public health and economic benefits resulting from public investment in active pharmacovigilance programs to detect adverse drug effects. We assessed 3 examples in which early signals of safety hazards were not adequately recognized, resulting in continued exposure of a large number of patients to these drugs when safer and effective alternative treatments were available. The drug examples studied were rofecoxib, cerivastatin, and troglitazone. Using an individual patient simulation model and the health care system perspective, we estimated the potential costs that could have been averted by early systematic detection of safety hazards through the implementation of active surveillance programs. We found that earlier drug withdrawal made possible by active safety surveillance would most likely have resulted in savings in direct medical costs of $773-$884 million for rofecoxib, $3-$10 million for cerivastatin, and $38-$63 million for troglitazone in the United States through the prevention of adverse events. By contrast, the yearly public investment in Food and Drug Administration initiated population-based pharmacovigilance activities in the United States is about $42.5 million at present. These examples illustrate a critical and economically justifiable role for active adverse effect surveillance in protecting the health of the public.

  17. 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.

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

    Science.gov (United States)

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

    1990-10-01

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

  19. Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.

    Science.gov (United States)

    Daberdaku, Sebastian; Ferrari, Carlo

    2018-02-06

    The correct determination of protein-protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein-Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction

  20. 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.

  1. Potentiality Prediction of Electric Power Replacement Based on Power Market Development Strategy

    Science.gov (United States)

    Miao, Bo; Yang, Shuo; Liu, Qiang; Lin, Jingyi; Zhao, Le; Liu, Chang; Li, Bin

    2017-05-01

    The application of electric power replacement plays an important role in promoting the development of energy conservation and emission reduction in our country. To exploit the potentiality of regional electric power replacement, the regional GDP (gross domestic product) and energy consumption are taken as potentiality evaluation indicators. The principal component factors are extracted with PCA (principal component analysis), and the integral potentiality analysis is made to the potentiality of electric power replacement in the national various regions; a region is taken as a research object, and the potentiality of electric power replacement is defined and quantified. The analytical model for the potentiality of multi-scenario electric power replacement is developed, and prediction is made to the energy consumption with the grey prediction model. The relevant theoretical research is utilized to realize prediction analysis on the potentiality amount of multi-scenario electric power replacement.

  2. Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

    Science.gov (United States)

    Chen, Lei; Zeng, Wei-Ming; Cai, Yu-Dong; Feng, Kai-Yan; Chou, Kuo-Chen

    2012-01-01

    The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels. PMID:22514724

  3. 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

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

    NARCIS (Netherlands)

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

    2013-01-01

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

  5. The Prevalence of Potential Drug Interactions Among Critically Ill Elderly Patients in the Intensive Care Unit (ICU

    Directory of Open Access Journals (Sweden)

    Hossein Rafiei

    2012-01-01

    Full Text Available Objectives: The aim of the research was to determine prevalence of potential drug interactions among elderly patients in the Shahid Bahonar ICU in Kerman. Methods & Materials: In this cross sectional study, data about all elderly patients who were admitted in the intensive care unit from 1/4/2009 to 1/4/2010 were retrieved from medical records and evaluated with regard to the number and type of drug interactions, the number of drugs administered, age, sex, length of stay in the ICU, and the number of doctors prescribing medications of medications administered. The extent and number of drug interactions were investigated based on the reference textbook Drug Interaction Facts and in order to analyze the data collected, using SPSS 18 and according to study goals, a descriptive test, Pierson's correlation test, an independent T-test and a one-way ANOVA were used. Results: In total, 77 types of drugs and 394 drugs were prescribed with a mean of 5.6(SD=1.5 drugs per patient. A total of 108 potential drug interactions were found related to drugs prescribed during the first twenty-four hours. In terms of the type of drug interactions, delayed, moderate and possible types comprised the highest proportion of drug interactions. The four major interactions were between cimetidine and methadone, furosemide and amikacine, phenytoin and dopamine, and heparin and aspirin. The results of Pierson's correlation test were inicative of a positive correlation between the number of potential drug interactions and that of the drugs prescribed (r=0.563, P<0.05. Results of a one-way ANOVA showed that the mean number of potential drug interaction were significantly higher in those who died than in other patients (P<0.05. Conclusion: Elderly patients who are admitted to the intensive care unit are at a high risk of developing drug interactions and better care must be taken by medical team members.

  6. 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.

  7. Cyclohexane, a potential drug of abuse with pernicious effects for the brain

    Directory of Open Access Journals (Sweden)

    Oscar eGonzalez-Perez

    2016-01-01

    Full Text Available Cyclohexane is a volatile solvent used as a harmless substitute for dangerous organic solvents in several products, such as paint thinners, gasoline and adhesives. Many of these products are used as drugs of abuse and can severely damage neural tissue and impair neurological functions. However, there is very little information on the effects of cyclohexane on the brain. In humans, cyclohexane produces headaches, sleepiness, dizziness, limb weakness, motor changes and verbal memory impairment. Recent studies in mice have demonstrated behavioral alterations, reactive gliosis, microglial reactivity and oxidative stress in the brains of cyclohexane-exposed animals. This indicates that cyclohexane may represent a potential problem for public health. Therefore, studies are needed to clarify the neurobiological effects of this volatile compound, including the cellular and molecular mechanisms of neurotoxicity, and to minimize the human health risk posed by the intentional or accidental inhalation of this potential drug of abuse.

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    Rafael San-Miguel Carrasco

    2016-03-01

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

  10. Potential herb-drug interactions found in a community pharmacy patients

    OpenAIRE

    C. Batista; C. Pinho; M. Castel-Branco; M. Caramona; I. Figueiredo

    2015-01-01

    Phytotherapy has always played a leading role in therapeutics. However, a strong knowledge of the risk-benefit relationship of herbal products by patients and health professionals is necessary. The goals of this study were to characterize the consumption pattern of medicinal plants in patients in a community pharmacy, identify potential herb-drug interactions, and establish a list of recommendations for health professionals and/or patients in order to prevent/minimize negative outcomes arisin...

  11. Development, Characterization and Evaluation of Solid Lipid Nanoparticles as a potential Anticancer Drug Delivery System

    Science.gov (United States)

    Patel, Meghavi

    Solid lipid nanoparticles (SLNs) consist of spherical solid lipid particles in the nanometer size range, which are dispersed in water or in an aqueous surfactant solution. SLN technology represents a promising new approach to deliver hydrophilic as well as lipophilic drugs. The commercialization of SLN technology remains limited despite numerous efforts from researchers. The purpose of this research was to advance SLN preparation methodology by investigating the feasibility of preparing glyceryl monostearate (GMS) nanoparticles by using three preparation methods namely microemulsion technique, magnetic stirring technique and temperature modulated solidification technique of which the latter two were developed in our laboratory. An anticancer drug 5-fluorouracil was incorporated in the SLNs prepared via the temperature modulated solidification process. Optimization of the magnetic stirring process was performed to evaluate how the physicochemical properties of the SLN was influenced by systematically varying process parameters including concentration of the lipid, concentration of the surfactant, type of surfactant, time of stirring and temperature of storage. The results demonstrated 1:2 GMS to tween 80 ratio, 150 ml dispersion medium and 45 min stirring at 4000 RPM speed provided an optimum formulation via the temperature modulated solidification process. SLN dispersions were lyophilized to stabilize the solid lipid nanoparticles and the lyophilizates exhibited good redispersibility. The SLNs were characterized by particle size analysis via dynamic light scattering (DLS), zeta potential, transmission electron microscopy (TEM), differential scanning calorimetry (DSC), drug encapsulation efficiency and in vitro drug release studies. Particle size of SLN dispersion prepared via the three preparation techniques was approximately 66 nm and that of redispersed lyophilizates was below 500 nm. TEM images showed spherical to oval particles that were less dense in the core

  12. 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

  13. 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...

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

    Directory of Open Access Journals (Sweden)

    Yauheniya Cherkas

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

  15. MONITORING POTENTIAL DRUG INTERACTIONS AND REACTIONS VIA NETWORK ANALYSIS OF INSTAGRAM USER TIMELINES.

    Science.gov (United States)

    Correia, Rion Brattig; Li, Lang; Rocha, Luis M

    2016-01-01

    Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products-including cannabis-which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015.We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that Instagram

  16. MONITORING POTENTIAL DRUG INTERACTIONS AND REACTIONS VIA NETWORK ANALYSIS OF INSTAGRAM USER TIMELINES

    Science.gov (United States)

    CORREIA, RION BRATTIG; LI, LANG; ROCHA, LUIS M.

    2015-01-01

    Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this “Bibliome”, the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products—including cannabis—which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015. We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that

  17. Therapeutic potential of the SARMs: revisiting the androgen receptor for drug discovery.

    Science.gov (United States)

    Segal, Scott; Narayanan, Ramesh; Dalton, James T

    2006-04-01

    Selective androgen receptor modulators (SARMS) bind to the androgen receptor and demonstrate anabolic activity in a variety of tissues; however, unlike testosterone and other anabolic steroids, these nonsteroidal agents are able to induce bone and muscle growth, as well as shrinking the prostate. The potential of SARMS is to maximise the positive attributes of steroidal androgens as well as minimising negative effects, thus providing therapeutic opportunities in a variety of diseases, including muscle wasting associated with burns, cancer, end-stage renal disease, osteoporosis, frailty and hypogonadism. This review summarises androgen physiology, the current status of the R&D of SARMS and potential therapeutic indications for this emerging class of drugs.

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

    Science.gov (United States)

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

    2011-12-01

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

  19. Glucosylceramide and Lysophosphatidylcholines as Potential Blood Biomarkers for Drug-Induced Hepatic Phospholipidosis

    Science.gov (United States)

    Saito, Kosuke; Maekawa, Keiko; Ishikawa, Masaki; Senoo, Yuya; Urata, Masayo; Murayama, Mayumi; Nakatsu, Noriyuki; Yamada, Hiroshi; Saito, Yoshiro

    2014-01-01

    Drug-induced phospholipidosis is one of the major concerns in drug development and clinical treatment. The present study involved the use of a nontargeting lipidomic analysis with liquid chromatography-mass spectrometry to explore noninvasive blood biomarkers for hepatic phospholipidosis from rat plasma. We used three tricyclic antidepressants (clomipramine [CPM], imipramine [IMI], and amitriptyline [AMT]) for the model of phospholipidosis in hepatocytes and ketoconazole (KC) for the model of phospholipidosis in cholangiocytes and administered treatment for 3 and 28 days each. Total plasma lipids were extracted and measured. Lipid molecules contributing to the separation of control and drug-treated rat plasma in a multivariate orthogonal partial least squares discriminant analysis were identified. Four lysophosphatidylcholines (LPCs) (16:1, 18:1, 18:2, and 20:4) and 42:1 hexosylceramide (HexCer) were identified as molecules separating control and drug-treated rats in all models of phospholipidosis in hepatocytes. In addition, 16:1, 18:2, and 20:4 LPCs and 42:1 HexCer were identified in a model of hepatic phospholipidosis in cholangiocytes, although LPCs were identified only in the case of 3-day treatment with KC. The levels of LPCs were decreased by drug-induced phospholipidosis, whereas those of 42:1 HexCer were increased. The increase in 42:1 HexCer was much higher in the case of IMI and AMT than in the case of CPM; moreover, the increase induced by IMI was dose-dependent. Structural characterization determining long-chain base and hexose delineated that 42:1 HexCer was d18:1/24:0 glucosylceramide (GluCer). In summary, our study demonstrated that d18:1/24:0 GluCer and LPCs are potential novel biomarkers for drug-induced hepatic phospholipidosis. PMID:24980264

  20. Evaluation of the physicochemical properties of liposomes as potential carriers of anticancer drugs: spectroscopic study

    International Nuclear Information System (INIS)

    Pentak, Danuta

    2016-01-01

    Vesicle size and composition are a critical parameter for determining the circulation half-life of liposomes. Size influences the degree of drug encapsulation in liposomes. The geometry, size, and properties of liposomes in an aqueous environment have to be described to enable potential applications of liposome systems as drug carriers. The characteristics of multiple thermotropic phase transitions are also an important consideration in liposomes used for analytical and bioanalytical purposes. The aim of this study was to evaluate the physicochemical properties of liposomes which accommodate hydrophilic and amphiphilic drugs used in cancer therapy. The