Data-driven strategies for drug repurposing: insights, recommendations, and case studies

The study investigates drug repurposing as a cost-effective alternative to traditional drug discovery by analyzing data from ChEMBL, BindingDB, and GtoPdb. It identifies 37,651 unique drug-target pairs and classifies targets into 12 functional families and indications into 28 disease categories. The research highlights ChEMBL's extensive target coverage and its potential for facilitating drug repurposing strategies. By examining drug overlap across therapeutic groups, particularly in cancer, infections, and respiratory diseases, the study identifies top repurposed drugs like dexamethasone and prednisone. The data-driven framework leverages pathway-based pipelines and public datasets to identify repurposing candidates, emphasizing the potential of biologically validated networks while acknowledging limitations such as reliance on KEGG pathways. This approach aims to streamline drug discovery and enhance translational applications.