Computational Drug Repositioning in Cardiorenal Disease: Opportunities, Challenges, and Approaches

The article explores computational drug repositioning for cardiorenal diseases, emphasizing its potential to find new uses for existing drugs through various computational methods like network-based, pathway-based, and knowledge graph-based approaches. These methods leverage data from drug targets, molecular pathways, and real-world sources like electronic health records to identify new drug-disease connections. The document highlights successful repositioning examples, such as GLP1 agonists and metformin, and discusses challenges like patentability and regulatory hurdles. It concludes that drug repositioning is a promising strategy for rapidly developing new treatments, particularly for rare diseases, despite challenges in funding and trial phases.