Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions

Drug-drug interactions (DDIs) are a common cause of adverse drug events. In this paper, we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs. We predicted an initial set of 13197 potential DDIs based on subs...

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Bibliographic Details
Published in:	PLoS computational biology Vol. 8; no. 8; p. e1002614
Main Authors:	Duke, Jon D, Han, Xu, Wang, Zhiping, Subhadarshini, Abhinita, Karnik, Shreyas D, Li, Xiaochun, Hall, Stephen D, Jin, Yan, Callaghan, J Thomas, Overhage, Marcus J, Flockhart, David A, Strother, R Matthew, Quinney, Sara K, Li, Lang
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 01-08-2012 Public Library of Science (PLoS)
Subjects:	Alprazolam - adverse effects Clinical chemistry Computer Science Databases, Factual Drug Interactions Drug metabolism Duloxetine Hydrochloride Electronic Health Records Experiments Humans Hypotheses Hypothesis testing Indoles - adverse effects Loratadine - adverse effects Mathematics Medical records Medicine Mortality Muscular Diseases - chemically induced Pharmaceutical industry Pharmacokinetics Promethazine - adverse effects Simvastatin - adverse effects Studies Thiophenes - adverse effects United States Alprazolam Drug Interactions Thiophenes Humans Promethazine Simvastatin Muscular Diseases Indoles Electronic Health Records Databases, Factual Loratadine
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Summary:	Drug-drug interactions (DDIs) are a common cause of adverse drug events. In this paper, we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs. We predicted an initial set of 13197 potential DDIs based on substrates and inhibitors of cytochrome P450 (CYP) metabolism enzymes identified from published in vitro pharmacology experiments. Using a clinical repository of over 800,000 patients, we narrowed this theoretical set of DDIs to 3670 drug pairs actually taken by patients. Finally, we sought to identify novel combinations that synergistically increased the risk of myopathy. Five pairs were identified with their p-values less than 1E-06: loratadine and simvastatin (relative risk or RR = 1.69); loratadine and alprazolam (RR = 1.86); loratadine and duloxetine (RR = 1.94); loratadine and ropinirole (RR = 3.21); and promethazine and tegaserod (RR = 3.00). When taken together, each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone. Based on additional literature data on in vitro drug metabolism and inhibition potency, loratadine and simvastatin and tegaserod and promethazine were predicted to have a strong DDI through the CYP3A4 and CYP2D6 enzymes, respectively. This new translational biomedical informatics approach supports not only detection of new clinically significant DDI signals, but also evaluation of their potential molecular mechanisms.
Bibliography:	Conceived and designed the experiments: JDD XL SDH YJ JTC MJO DAF RMS SKQ LL. Performed the experiments: JDD XH ZW AS SDK LL. Analyzed the data: XH ZW LL. Wrote the paper: JDD XL SKQ LL. Proposed the whole picture of the DDI research project: LL. Guided all the data analyses: LL. Finalized the writing of the paper: LL. Contributed to the pharmaco-epidemiology design: JDD XL RMS SKQ. Contributed to the interpretation of the DDI results: JDD RMS SKQ. Performed the DDI/myopathy association analysis: XH. Provided the mechanistic interpretation of DDIs: SDH. Curated the data: XH AS SDK. Performed the EMR data mapping, data extraction and merging, and analyzable data preparation: ZW. Performed the literature mining: AS SDK. Contributed to the initial ideas formulation: SDH YJ JTC MJO. Contributed to the analysis strategy: XL. The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1002614