Tuberculosis (TB) continues to be a major global health burden, with high incidence and mortality rates, compounded by the emergence and spread of drug-resistant strains. The limitations of current TB medications and the urgent need for new drugs targeting drug-resistant strains, particularly multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB, underscore the pressing demand for innovative anti-TB drugs that can shorten treatment duration. This has led to a focus on targeting energy metabolism of Mycobacterium tuberculosis (Mtb) as a promising approach for drug discovery. This study focused on repurposing drugs against the crucial mycobacterial protein, electron transfer flavoprotein oxidoreductase (EtfD), integral to utilizing fatty acids and cholesterol as a carbon source during infection. The research adopted an integrative approach, starting with virtual screening of approved drugs from the ZINC20 database against EtfD, followed by molecular docking, and concluding with molecular dynamics (MD) simulations. Diacerein, levonadifloxacin, and gatifloxacin were identified as promising candidates for repurposing against TB based on their strong binding affinity, stability, and interactions with EtfD. ADMET analysis and anti-TB sensitivity predictions assessed their pharmacokinetic and therapeutic potential. Diacerein and levonadifloxacin, previously unexplored in anti-tuberculous therapy, along with gatifloxacin, known for its efficacy in drug-resistant TB, have broad-spectrum antimicrobial properties and favorable pharmacokinetic profiles, suggesting potential as alternatives to current TB treatments, especially against resistant strains. This study underscores the efficacy of computational drug repurposing, highlighting bacterial energy metabolism and lipid catabolism as fruitful targets. Further research is necessary to validate the clinical suitability and efficacy of diacerein, levonadifloxacin, and gatifloxacin, potentially enhancing the arsenal against global TB.
Copyright: © 2024 Arshad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.