Clinical Data Repositories: Primary Sources of Drug-to-Drug Interaction Detection and Risk Assessment

Abstract

Background: Polypharmacy can be a source of adverse drug events including those caused by drug to drug interaction (DDI) exposures. Web-based DDI databases are available to researchers for the identification of potential DDI exposures. Rather than relying on potentially incomplete DDI databases, large clinical data repositories (CDR) which are integrated data sources fed with millions of heterogeneous electronic health records (EHRs) containing real-world data should be leveraged for data driven DDI identification. Objective: To explore and validate the viability of clinical data repositories as data driven resources for clinically important adverse drug events detection and surveillance. Methods: This work leverages a minimum clinical data set from the University of Minnesota's CDR to identify drugs that have statin to drug interaction (SDI) potential and compares the findings with results of web based DDI databases. Using an SDI identification matrix, we identified several potential novel SDI drugs that were not mentioned in the web-based sources but explored through our study as drugs with SDI potential. Results: Drugs flagged by our SDI identification matrix but not mentioned in the web-based sources include Lysine, Ketotifen, Latanoprost, Methylcellulose, Oxazepam, Linseed Oil, and others. Conclusion: Our findings identified potential gaps regarding the completeness, currency, and overall reliability of open source and commercial DDI databases. CDRs can be a primary source for identifying drug to drug interactions. Keywords: clinical data repository, drug to drug interaction databases, drug to drug interaction, statin to drug interaction, polypharmacy, statin to drug interaction identification matrix, adverse drug event, statin.