A Dose-Aware Model for Revealing Dose-Risk Relationship of Drug-Drug Interaction.

Abstract

Drug-drug interaction (DDI) is a common cause of adverse drug events (ADEs). Despite real-world data-based studies have developed knowledge on DDI, the precise relationships between doses of two-drug combinations exposure and the risks of ADEs remain largely unknown. The estimation of the dose-risk relationship (DRR) under commonly used regression models could be subject to model misspecification or overspecification. We developed a dose-aware model (DAM) for revealing DRR. DAM could improve the DRR estimation by identifying the optimal model from a large number of meaningful models of doses of two-drug combinations exposure and risks of ADE. We compared DAM with commonly used models (e.g., exposed-versus-unexposed model, dose-response model, and saturated model), in which DAM had higher performance metrics on model fitting in real-world data analyses and DRR estimation in a simulation study. In conclusion, DAM is a powerful tool for estimating DRR for potential adverse two-drug combinations, which could be used to mitigate DDI-induced harm.