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Biomedical Informatics Approaches to Identifying Drug–Drug Interactions: Application to Insulin Secretagogues

Han, Xu; Chiang, ChienWei; Leonard, Charles E.; Bilker, Warren B.; Brensinger, Colleen M.; Li, Lang; Hennessy, Sean

doi: 10.1097/EDE.0000000000000638

Background: Drug–drug interactions with insulin secretagogues are associated with increased risk of serious hypoglycemia in patients with type 2 diabetes. We aimed to systematically screen for drugs that interact with the five most commonly used secretagogues—glipizide, glyburide, glimepiride, repaglinide, and nateglinide—to cause serious hypoglycemia.

Methods: We screened 400 drugs frequently coprescribed with the secretagogues as candidate interacting precipitants. We first predicted the drug–drug interaction potential based on the pharmacokinetics of each secretagogue–precipitant pair. We then performed pharmacoepidemiologic screening for each secretagogue of interest, and for metformin as a negative control, using an administrative claims database and the self-controlled case series design. The overall rate ratios (RRs) and those for four predefined risk periods were estimated using Poisson regression. The RRs were adjusted for multiple estimation using semi-Bayes method, and then adjusted for metformin results to distinguish native effects of the precipitant from a drug–drug interaction.

Results: We predicted 34 pharmacokinetic drug–drug interactions with the secretagogues, nine moderate and 25 weak. There were 140 and 61 secretagogue–precipitant pairs associated with increased rates of serious hypoglycemia before and after the metformin adjustment, respectively. The results from pharmacokinetic prediction correlated poorly with those from pharmacoepidemiologic screening.

Conclusions: The self-controlled case series design has the potential to be widely applicable to screening for drug–drug interactions that lead to adverse outcomes identifiable in healthcare databases. Coupling pharmacokinetic prediction with pharmacoepidemiologic screening did not notably improve the ability to identify drug–drug interactions in this case.

Supplemental Digital Content is available in the text.

From the aCenter for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA; bCenter for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA; cCenter for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN; dDepartment of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA; eDepartment of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN; fIndiana Institute of Personalized Medicine, School of Medicine, Indiana University, Indianapolis, IN; and gDepartment of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.

Submitted 25 November 2015; accepted 31 January 2017.

This project was supported by R01AG025152 from the National Institute on Aging, R01DK102694 from the National Institute of Diabetes and Digestive and Kidney Diseases, GM10448301 from the National Institute of General Medical Science, and LM011945 from the US National Library of Science.

The authors report no conflicts of interest.

The programming code can be found in eAppendix ( No additional data are available because sharing the databases is prohibited according to the agreements with the data distributors.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (

Correspondence: Sean Hennessy, 423 Guardian Drive, 803 Blockley Hall, Philadelphia, PA 19104. E-mail:

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