Biomedical Informatics Approaches to Identifying Drug–Drug Interactions

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 co-prescribed 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.

Comparative risk of serious hypoglycemia with oral antidiabetic monotherapy: A retrospective cohort study

“…The outcome of interest was serious hypoglycemia, operationally defined by one of the following ICD‐9‐CM discharge diagnosis codes in any position on an ED claim or the principal position on an inpatient claim: (1) 251.0 (hypoglycemic coma); (2) 251.1 (other specific hypoglycemia); (3) 251.2 (hypoglycemia, unspecified); or (4) 250.8X (diabetes with other specified manifestations), as long as not co‐occurring with ≥1 exclusionary diagnosis suggesting manifestations other than hypoglycemia (Table S3). This algorithm, which we have used previously, has a positive predictive value of 89% for the ED component and 78% for the inpatient component . These performance measures were derived from validation studies within Emergency Medicine Network data and Centers for Medicare and Medicaid Services data, respectively, using medical records as the gold standard.…”

Serious Hypoglycemia and Use of Warfarin in Combination With Sulfonylureas or Metformin

“…Sulfonylureas and warfarin are among the drugs that are most frequently associated with adverse drug events . Han and colleagues conducted a series of high‐throughput screening studies to identify potential drug–drug interactions involving sulfonylureas that may lead to serious hypoglycemia . That article suggested that warfarin may be associated with an increased risk of serious hypoglycemia among persons taking glimepiride or glipizide.…”

“…[1][2][3][4][5][6] Han and colleagues conducted a series of high-throughput screening studies to identify potential drug-drug interactions involving sulfonylureas that may lead to serious hypoglycemia. 7 That article suggested that warfarin may be associated with an increased risk of serious hypoglycemia among persons taking glimepiride or glipizide. Furthermore, because cytochrome P450 (CYP) 2C9 is involved in the metabolism of both sulfonylureas [8][9][10][11][12][13] and warfarin, [13][14][15] there is a potential pharmacokinetic mechanism for such an interaction.…”

Clopidogrel Drug Interactions and Serious Bleeding: Generating Real‐World Evidence via Automated High‐Throughput Pharmacoepidemiologic Screening

“…Our study has notable strengths. First, it utilized a self‐controlled case series design, ideal for DDI screening, to minimize confounding. Second, we used a bidirectional implementation of the design to minimize exposure trend bias .…”

“…Although the “case series” phrase within self‐controlled case series may seem to imply the absence of a comparator, the approach is a rigorous, controlled epidemiologic study design that is the cohort analogue of the case‐crossover design . The self‐controlled case series design has the following advantages that make it ideal for DDI screening: (i) it is highly computationally‐efficient, since it includes only persons who experienced the outcome of interest; (ii) the causal contrast is made within individual and thus inherently controls for confounding by both measured and unmeasured factors that remain constant within an individual over the observation period (e.g., sex, genetics, chronic diseases, frailty, socioeconomic status); (iii) the underlying statistical model can accommodate time‐varying factors; and (iv) a high‐throughput approach has been developed and used previously …”