Screening druggable targets and predicting therapeutic drugs for COVID-19 via integrated bioinformatics analysis

Abstract

Background: Since the outbreak of coronavirus disease 2019 (COVID-19) in China, numerous research institutions have invested in the development of anti-COVID-19 vaccines and screening for efficacious drugs to manage the virus. Objective: To explore the potential targets and therapeutic drugs for the prevention and treatment of COVID-19 through data mining and bioinformatics. Methods: We integrated and profoundly analyzed 10 drugs previously assessed to have promising therapeutic potential in COVID-19 management, and have been recommended for clinical trials. To explore the mechanisms by which these drugs may be involved in the treatment of COVID-19, gene-drug interactions were identified using the DGIdb database after which functional enrichment analysis, protein–protein interaction (PPI) network, and miRNA-gene network construction were performed. We adopted the DGIdb database to explore the candidate drugs for COVID-19. Results: A total of 43 genes associated with the 10 potential COVID-19 drugs were identified. Function enrichment analysis revealed that these genes were mainly enriched in response to other invasions, toll-like receptor pathways, and they play positive roles in the production of cytokines such as IL-6, IL-8, and INF-β. TNF, TLR3, TLR7, TLR9, and CXCL10 were identified as crucial genes in COVID-19. Through the DGIdb database, we predicted 87 molecules as promising druggable molecules for managing COVID-19. Conclusions: Findings from this work may provide new insights into COVID-19 mechanisms and treatments. Further, the already identified candidate drugs may improve the efficiency of pharmaceutical treatment in this rapidly evolving global situation.