OBJECTIVES: Evaluating drug-drug interactions (DDIs) in a real-world setting is key to accurately assess the clinical and economic outcomes of drugs. This literature review aimed to characterize current applications of real-world data (RWD) in evaluating and mitigating the risks of DDIs, and to identify gaps to be addressed.

METHODS: A targeted literature search was performed in Pubmed using a combination of key words including "drug interactions" and "real-world". The search was supplemented with gray literature and manual searches. Identified publications were reviewed and key findings related to the use of real-world evidence (RWE) to inform DDI assessment were summarized.

RESULTS: A total of 132 studies were included in the review. Of these, 58 studies (44%) described the incidence/prevalence of potential DDIs based on prescription patterns, 52 (39%) evaluated both DDI potential and clinical significance, and 22 (17%) assessed solely the clinical significance of DDIs. RWD were predominantly from electronic health records (67%) and claims databases (36%). Among studies evaluating the clinical impact of DDIs, primary outcomes included safety (74%), effectiveness (15%), and healthcare resource use (7%). A notable proportion of studies described applications of the generated RWE for regulatory purposes, as well as strategic and modelling frameworks based on RWD to predict DDI risks and support decision making.

CONCLUSIONS: This review highlights how RWE has been used to complement and support DDI research and surveillance. In addition to the significant use of RWD to identify potential DDIs and understand the clinical impact of DDIs in a real-world setting, this study also underlines the increasing interest of regulatory agencies in RWD to support decision making, hence warranting "regulatory grade" RWE on DDIs. Developing robust methodological frameworks is key to ensure the quality, relevance, and appropriate use of RWD for DDI assessments to support drug development, market access, and clinical decision-making.