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Integrated bioinformatic and pharmacokinetic models of high-dimensional drug interactions

高维药物相互作用的综合生物信息学和药代动力学模型

基本信息

批准号：
9008147
负责人：
Sara K Quinney
金额：
$ 36.73万
依托单位：
INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-01-01 至 2018-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9008147
关键词：
Adverse drug event Adverse event Adverse reactions Algorithms American Benefits and Risks Binding Bioinformatics Biological Assay Caring Cause of Death Clinical Complex Computational algorithm Computer Simulation Computerized Medical Record Cytochrome P450 Data Data Set Databases Detection Drug Combinations Drug Interactions Drug Kinetics Drug Prescriptions Elderly Enzymes Feedback Goals Healthcare Systems In Vitro Indiana Individual Lead Medical Metabolism Methods Mining Modeling Molecular Morbidity - disease rate Ontology Patient Care Patients Performance Pharmaceutical Preparations Pharmacology Polypharmacy Process Reaction Regimen Reporting Research Risk Role Signal Transduction Specific qualifier value Speed Statistical Algorithm Statistical Models Structure System Time United States clinically relevant clinically significant data mining data modeling drug metabolism economic cost health record inhibitor/antagonist innovation insight novel pharmacokinetic model simulation

项目摘要

Project Summary Adverse drug events are the fifth leading cause of death in the United States, lead to increased morbidity, and are responsible for a large economic cost on the healthcare system. Polypharmacy is associated with increased risk of adverse events. We hypothesize that individuals taking multiple medications are at an increased risk of drug-drug interactions, leading to clinically relevant adverse events. Through a combination of computational data mining algorithms, statistical inference, and mechanistic pharmacology models, we seek to identify and evaluate clinically significant high dimensional drug interactions (HD-DDIs). We propose a novel frequent close itemset mining algorithm to identify candidate HD-DDIs with adverse reactions from large health record data sets. These HD-DDIs identified by the computational algorithm will be subjected to an innovative empirical Bayes statistical inference to determine this false positive, hence its statistical significance in its potential relevance of each interaction. As a large number of drug interactions are potentiated through the cytochrome P450 (CYP450) system, the mechanistic potential of interactions among multidrug regimens will be evaluated using in vitro metabolism assays. This innovative approach, combining graphical, statistical inference and mechanistic pharmacology models will provide insight into the role of polypharmacy in adverse drug events.

项目总结