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.
Nearly 40% of elderly Americans take 5 or more prescription medications daily. Complex drug regimens increase the likelihood of adverse events due to drug interactions. Using the FDA Adverse Event Reporting System (FAERS) and the Indiana Network for Patient Care (INPC) databases, we will develop computational and statistical models to identify multidrug interactions leading to adverse events reported and examine potential mechanisms of the interactions through in vitro pharmacology models.
| Chiang, Chien-Wei; Zhang, Pengyue; Wang, Xueying et al. (2018) Translational High-Dimensional Drug Interaction Discovery and Validation Using Health Record Databases and Pharmacokinetics Models. Clin Pharmacol Ther 103:287-295 |
| Wang, Xueying; Zhang, Pengyue; Chiang, Chien-Wei et al. (2018) Mixture drug-count response model for the high-dimensional drug combinatory effect on myopathy. Stat Med 37:673-686 |
| Zhang, Pengyue; Wu, Heng-Yi; Chiang, Chien-Wei et al. (2018) Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research. CPT Pharmacometrics Syst Pharmacol 7:90-102 |
