Identification of Repurpose Drugs by Computational Analysis of Disease–Gene–Drug Associations

Abstract

Repurposing of marketed drugs to find new indications has become an alternative to circumvent the risk of traditional drug development by its productivity quality. Despite many approaches, computational analysis has great potential to fuel the development of all-rounder drugs to find new classes of medicine for neglected and rare disease. The genes that can explain variations in drug response associated to disease are more important and significant in drug therapeutics necessitate elucidating the relationships of a gene, drug, and disease. The proposed computational analysis facilitates the discovery of knowledge on both target and disease-based relationships from large sources of biomedical literature spread over different platforms. It uses the utility of text mining for automatic extraction of valuable aggregated biomedical entities (disease, gene, and drug) from PubMed to serves as an input to the analysis of association prediction. The top-ranked associations considered for identification of repurposing drugs and also the hidden associations identified using concurrence principle to extrapolate the new relationships. Such findings are reported as novel and contribute to the knowledge base for pharmacogenomics, would immensely support the discovery and progress of novel therapeutic pathways and patient segment biomarkers.