Identifying Drug–Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects

Abstract

Translational approaches can benefit post‐marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug–drug interaction (DDI) signals and differentiating between individual drug and drug combination signals. This framework is coupled with a systems pharmacology approach for automated biological plausibility assessment. Integrating statistical and biological evidence, our method achieves a 16.5% improvement (AUC: from 0.620 to 0.722) with drug‐target‐adverse event associations, 16.0% (AUC: from 0.580 to 0.673) with drug enzyme, and 15.0% (AUC: from 0.568 to 0.653) with drug transporter information. Applying this approach to detect potential DDI signals of QT prolongation and rhabdomyolysis within the FDA Adverse Event Reporting System (FAERS), we emphasize the significance of systems pharmacology in enhancing statistical signal detection in pharmacovigilance. Our study showcases the promise of data‐driven biological plausibility assessment in the context of challenging post‐marketing DDI surveillance.