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



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.



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.



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.
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.
Xu
X
Han
ChienWei
C
Chiang
Charles E
CE
Leonard
Warren B
WB
Bilker
Colleen M
CM
Brensinger
Lang
L
Li
Sean
S
Hennessy
D019540:Area Under Curve; D002219:Carbamates / Q000009:adverse effects; D003510:Cyclohexanes / Q000009:adverse effects; D016208:Databases, Factual; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy*; D004347:Drug Interactions; D005913:Glipizide / Q000009:adverse effects; D005905:Glyburide / Q000009:adverse effects; D006801:Humans; D007003:Hypoglycemia / Q000139:chemically induced*; D007004:Hypoglycemic Agents / Q000009:adverse effects*; D008490:Medical Informatics; D000077715:Nateglinide; D017891:Pharmacoepidemiology; D010649:Phenylalanine / Q000009:adverse effects / Q000031:analogs & derivatives; D010880:Piperidines / Q000009:adverse effects; D013453:Sulfonylurea Compounds / Q000009:adverse effects
D002219:Carbamates; D003510:Cyclohexanes; D007004:Hypoglycemic Agents; D010880:Piperidines; D013453:Sulfonylurea Compounds; D000077715:Nateglinide; D010649:Phenylalanine; C072379:repaglinide; C057619:glimepiride; D005905:Glyburide; D005913:Glipizide