In silico discovery of potential drug molecules to improve the treatment of isoniazid-resistant Mycobacterium tuberculosis.

The emergence of multidrug-resistant Mycobacterium tuberculosis (M.tb) has become one of the major hurdles in the treatment of tuberculosis (TB). Drug-resistant M.tb has evolved with various strategies to avoid killing by the anti-tubercular drugs. Thus, there is a rising need to develop effective anti-TB drugs to improve the treatment of these strains. Traditional drug design approach has earned little success due to time and the cost involved in the process of development of anti-infective drugs. Numerous reports have demonstrated that several mutations in the drug target sites cause emergence of drug-resistant M.tb strains. In this study, we performed computational mutational analysis of M.tb inhA, fabD, and ahpC genes, which are the primary targets for first-line isoniazid (INH) drug. In silico virtual drug screening was performed to identify the potent drugs from a ChEMBL compound library to improve the treatment of INH-resistant M.tb. Further, these compounds were analyzed for their binding efficiency against active drug binding cavity of M.tb wild-type and mutant InhA, FabD and AhpC proteins. The drug efficacy of predicted lead compounds was verified by molecular docking using M.tb wild-type and mutant InhA, FabD and AhpC protein template models. Different in silico and pharmacophore analysis predicted three potent lead compounds with better drug-like properties against both M.tb wild-type and mutant InhA, FabD, and AhpC proteins as compared to INH drug, and thus may be considered as effective drugs for the treatment of INH-resistant M.tb strains. We hypothesize that this work may accelerate drug discovery process for the treatment of drug-resistant TB. Communicated by Ramaswamy H. Sarma.