AUTHOR=Jeong Eugene , Malin Bradley , Nelson Scott D. , Su Yu , Li Lang , Chen You 

TITLE=Revealing the dynamic landscape of drug-drug interactions through network analysis

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1211491

DOI=10.3389/fphar.2023.1211491

ISSN=1663-9812

ABSTRACT=The landscape of drug-drug interactions (DDIs) has evolved significantly over the past sixty years, necessitating a retrospective analysis to identify research trends and under-explored areas. This groundbreaking investigation employs natural language processing (NLP) to extract pharmacokinetic (PK) and pharmacodynamic (PD) DDI evidence from PubMed articles and reveals key trends and patterns through an innovative network analysis approach. Our compelling results shed light on the scale-free nature of PK and PD networks, where a select few drugs serve as central hubs, engaging in numerous interactions with a multitude of other drugs. Interestingly, the networks conform to a densification power law, illustrating that the number of DDIs grows exponentially as new drugs are added to the DDI network. Notably, we discovered that drugs connected in PK and PD networks predominantly belong to the same categories defined by the Anatomical Therapeutic Chemical (ATC) classification system, with fewer interactions observed between drugs from different categories. This finding suggests that PK and PD DDIs between drugs from different ATC categories have not been studied as extensively as those between drugs within the same categories. By unearthing these hidden patterns, our study paves the way for a deeper understanding of the DDI landscape, providing valuable information for future DDI research, clinical practice, and drug development focus areas.