Biomedical informatics approaches to identifying drug-drug interactions application to insulin secretagogues

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

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)459-468
Number of pages10
JournalEpidemiology
Volume28
Issue number3
DOIs
StatePublished - May 1 2017

ASJC Scopus subject areas

  • Epidemiology

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    Han, X., Chiang, C. W., Leonard, C. E., Bilker, W. B., Brensinger, C. M., Li, L., & Hennessy, S. (2017). Biomedical informatics approaches to identifying drug-drug interactions application to insulin secretagogues. Epidemiology, 28(3), 459-468. https://doi.org/10.1097/EDE.0000000000000638