Bioinformatics approach to prioritize known drugs towards repurposing for tuberculosis. (June 2017)

• Record Type: Journal Article
• Authors: Brindha, Sridharan; Vincent, Savariar; Velmurugan, Devadasan; Ananthakrishnan, Dhanabalan; Sundaramurthi, Jagadish Chandrabose; Gnanadoss, John Joel
• Is Part Of: Medical hypotheses. Volume 103(2017)
• Page Start: 39
• Page End: 45
• Publication Date: 2017-06
• Subjects: Tuberculosis -- Drugs -- Docking -- pknB -- Repurposing -- Bioinformatics; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Medicine; Periodicals; 610
• DOI: 10.1016/j.mehy.2017.04.005
• Languages: English
• ISSNs: 0306-9877

Abstract

Abstract: New drugs are urgently needed to cure tuberculosis (TB) in a short period of time without causing any adverse effects since currently used drugs for the treatment of multi drug-resistant TB cause several adverse effects with poor success rate. Therefore, we aimed to prioritize known drugs towards repurposing for TB by employing bioinformatics approach in the present study. A total of 1554 FDA approved drugs were obtained from DrugBank. Serine/threonine-protein kinase, pknB (Rv0014c) of Mycobacterium tuberculosis ( Mtb ) was selected as the drug target since it involves in several vital functions of the Mtb . All of the 1554 drugs were subjected to molecular docking with pknB . Glide and AutoDock Vina were employed using rigid docking followed by induced fit docking protocol for prioritization of drugs. Out of 14 drugs prioritized, six are suggested as high-confident drugs towards repurposing for TB as they were consistently found within top 10 ranks of both methods, and strongly binding in the active site of the pknB . We also found atorvastatin as one of the high-confident drugs, which has already been demonstrated to be active against Mtb under in vitro conditions by other researchers. Therefore, we propose that the prioritized six high-confident drugs as potential candidates for repurposing for TB and suggest for further experimental studies. We also suggest that the bioinformatics procedure we have employed in this study could be effectively applied for prioritization of drugs for other diseases.