Navigating the Polypharmacy Landscape: A Bibliometric Analysis of Computational Approaches for Predicting Adverse Drug Reactions

Abstract

Polypharmacy, the concurrent use of multiple drugs in a patient due to complex diseases or multiple morbidities, poses potential hazards through adverse drug reactions (ADRs). Conventional in vivo and in vitro ADR identification methods are challenging, making computational alternatives vital for minimizing patient risk. This study evaluates the scientific outputs of computational approaches to predict ADRs associated with polypharmacy through bibliometric analysis. A comprehensive literature search was conducted on Web of Science, Scopus and PubMed, which yielded 258 selected publications. Quantitative variable analysis was performed, and VosViewer was used to visualise networks and co-occurrences. The United States and China lead in publications, with ‘drug-drug interaction’ being the most frequent keyword. The Journal of Biomedical Informatics was ranked top, followed by BMC Bioinformatics and Briefings in Bioinformatics. The results indicate a growing global interest in computational methods for predicting adverse drug reactions associated with polypharmacy, primarily focusing on drug-drug interactions.