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

Abstract

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

Graphical Abstract

Keywords:

• isoniazid
• multidrug resistance
• Mycobacterium tuberculosis
• pharmacophore
• virtual drug screening

Acknowledgement

We are thankful to all members of Avinash Sonawane’s laboratory for valuable discussions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by Department of Biotechnology, Government of India (BT/PR23317/MED/29/1186/2017) to Dr Avinash Sonawane. Manaswini Jagadeb was supported by DST-INSPIRE (IF150835), Department of Science and Technology, Govternment of India.