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

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.

Figure

Heatmap showing semi-Bayes and metformin adjusted rate ratios (RR_SBM). Precipitants are shown alphabetically. (a) RR_SBM of precipitants from acetaminophen to dexamethasone. (b) RR_SBM of precipitants from diazepam to levodopa. (c) RR_SBM of precipitants from levofloxacin to pregabalin. (d) RR_SBM of precipitants from primidone to zolpidem. RRSBM and 95% CI greater than 1: RRSBM and 95% CI less than 1: RRSBM and 95% CI spanning 1:

Figure

Heatmap showing semi-Bayes and metformin adjusted rate ratios (RR_SBM). Precipitants are shown alphabetically. (a) RR_SBM of precipitants from acetaminophen to dexamethasone. (b) RR_SBM of precipitants from diazepam to levodopa. (c) RR_SBM of precipitants from levofloxacin to pregabalin. (d) RR_SBM of precipitants from primidone to zolpidem. RRSBM and 95% CI greater than 1: RRSBM and 95% CI less than 1: RRSBM and 95% CI spanning 1:

Figure

Heatmap showing semi-Bayes and metformin adjusted rate ratios (RR_SBM). Precipitants are shown alphabetically. (a) RR_SBM of precipitants from acetaminophen to dexamethasone. (b) RR_SBM of precipitants from diazepam to levodopa. (c) RR_SBM of precipitants from levofloxacin to pregabalin. (d) RR_SBM of precipitants from primidone to zolpidem. RRSBM and 95% CI greater than 1: RRSBM and 95% CI less than 1: RRSBM and 95% CI spanning 1:

Figure

Heatmap showing semi-Bayes and metformin adjusted rate ratios (RR_SBM). Precipitants are shown alphabetically. (a) RR_SBM of precipitants from acetaminophen to dexamethasone. (b) RR_SBM of precipitants from diazepam to levodopa. (c) RR_SBM of precipitants from levofloxacin to pregabalin. (d) RR_SBM of precipitants from primidone to zolpidem. RRSBM and 95% CI greater than 1: RRSBM and 95% CI less than 1: RRSBM and 95% CI spanning 1: