Computational Drug Discovery in Chemotherapy-induced Alopecia via Text Mining and Biomedical Databases

Abstract

PurposeChemotherapy-induced alopecia (CIA) is a common and often stressful adverse effect associated with chemotherapy. CIA can cause more psychosocial pressure in patients, including effects on sexuality, self-esteem, and social relationships. We analyzed publicly available data to identify drugs formulated for topical use targeting the relevant CIA molecular pathways by using computational tools. MethodsThe genes associated with CIA were determined by text mining, and the gene ontology of the gene set was studied using the Functional Enrichment analysis tool. Protein–protein interaction network analysis was performed using the String database. Enriched gene sets belonging to the identified pathways were queried against the Drug–Gene Interaction database to find drug candidates for topical use in CIA. FindingsOur analysis identified 427 genes common to CIA text-mining concepts. Gene enrichment analysis and protein–protein interaction analysis yielded 19 genes potentially targetable by a total of 29 drugs that could possibly be formulated for topical application. ImplicationsThe findings from the present analysis would give a new thought to help discover more effective agents, and present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.