A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide‐resistant Mycobacterium tuberculosis

• Published in: Journal of cellular and molecular medicine Vol. 28; no. 8; pp. e18279 - n/a
• Main Authors: Shahab, Muhammad, Farias Morais, Gabriel Christian, Akash, Shopnil, Fulco, Umberto Laino, Oliveira, Jonas Ivan Nobre, Zheng, Guojun, Akter, Shahina
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.04.2024; John Wiley and Sons Inc
• Subjects: ADMET; Antitubercular Agents - chemistry; Antitubercular Agents - metabolism; Antitubercular Agents - pharmacology; Computer applications; docking; Drug development; Drug resistance; Humans; Microbial Sensitivity Tests; molecular dynamic; molecular modelling; Mutation; Mycobacterium tuberculosis; Mycobacterium tuberculosis - genetics; Original; pharmacoinformatic; pharmacophore‐based virtual screening; Pyrazinamide; Pyrazinamide - chemistry; Pyrazinamide - metabolism; Pyrazinamide - pharmacology; pyrazinamide resistance; quantum chemical calculations; Research methodology; Ribosomal protein S1; Tuberculosis; Tuberculosis - microbiology; Tuberculosis, Multidrug-Resistant; Pakistan; docking; Mycobacterium tuberculosis; molecular modelling; pharmacoinformatic; quantum chemical calculations; pharmacophore‐based virtual screening; pyrazinamide resistance; molecular dynamic; ADMET
• ISSN: 1582-1838; 1582-4934; 1582-4934
• DOI: 10.1111/jcmm.18279

The rise of pyrazinamide (PZA)‐resistant strains of Mycobacterium tuberculosis (MTB) poses a major challenge to conventional tuberculosis (TB) treatments. PZA, a cornerstone of TB therapy, must be activated by the mycobacterial enzyme pyrazinamidase (PZase) to convert its active form, pyrazinoic acid, which targets the ribosomal protein S1. Resistance, often associated with mutations in the RpsA protein, complicates treatment and highlights a critical gap in the understanding of structural dynamics and mechanisms of resistance, particularly in the context of the G97D mutation. This study utilizes a novel integration of computational techniques, including multiscale biomolecular and molecular dynamics simulations, physicochemical and medicinal chemistry predictions, quantum computations and virtual screening from the ZINC and Chembridge databases, to elucidate the resistance mechanism and identify lead compounds that have the potential to improve treatment outcomes for PZA‐resistant MTB, namely ZINC15913786, ZINC20735155, Chem10269711, Chem10279789 and Chem10295790. These computational methods offer a cost‐effective, rapid alternative to traditional drug trials by bypassing the need for organic subjects while providing highly accurate insight into the binding sites and efficacy of new drug candidates. The need for rapid and appropriate drug development emphasizes the need for robust computational analysis to justify further validation through in vitro and in vivo experiments.