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Research Article

Optimizing cardio, hepato and phospholipidosis toxicity of the Bedaquiline by chemoinformatics and molecular modelling approach

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Pages 215-235 | Received 25 Dec 2021, Accepted 03 Feb 2022, Published online: 28 Feb 2022
 

ABSTRACT

The FDA granted expedited approval for Johnson and Johnson’s Bedaquiline to treat pulmonary multidrug resistant tuberculosis on 28 December 2012 which is more common in China, Russian Federation and India. Bedaquiline is the first anti-tubercular drug approved by the FDA in the last 40 years, and it has become a cynosure in the circles of synthetic chemists researching new anti-tubercular drugs. Bedaquiline’s highly lipophilic nature raises major concerns like suppression of the hERG gene, hepatotoxicity, and phospholipidosis despite its potential antitubercular profile. To address these toxicity concerns, in the present work, we have employed the structural optimization of Bedaquiline using the ADMETopt web server, which optimizes lead with scaffold hopping and ADMET screening. The ADMETopt web server yielded the 476 structures through optimization of three sites in Bedaquiline. Further, we have validated the optimized structures for their activity by performing molecular docking and molecular dynamics (MD) simulations against the mycobacterial ATP synthase enzyme and density functional theory (DFT) study further provides insight into the reactivity of the compounds. After screening and analysis, compound #449 was observed to be the most promising mycobacterial ATP synthase inhibitor with minimal cardiotoxicity, hepatotoxicity and phospholipidosis.

Acknowledgements

One of the author (Rukaiyya Girase) would like to thank “National Fellowship for Pearson with Disabilities (NFPwD)-UGC” (Grant No. NFPWD-2018-20-MAH-6427) for funding the project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental data

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2022.2041724.

Additional information

Funding

This work was supported by the National Fellowship for Pearson with Disabilities (NFPwD)-UGC [NFPWD-2018-20-MAH-6427].

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