Uncovering Drugs with Anti-Tubercular Activity: A Computer-Aided Drug Discovery Approach
Abdulmajeed Yunusa, Peter James, Albashir Tahir · Pharmacology and Toxicology of Natural Medicines (ISSN 2756-6838) · 2025
Background and Purpose: Tuberculosis (TB) remains a significant global health threat, with millions of new cases and high mortality rates reported each year and exacerbated by the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis. Current treatment options face limitations in efficacy, particularly against dormant bacterial cells, and are further challenged by the rise of drug-resistant TB. The present study investigates the potential of drug repurposing—a strategy that identifies new therapeutic applications for existing drugs—to discover compounds with effective antitubercular activity. Methods: A computer-aided drug discovery approach was employed, targeting two essential M. tuberculosis proteins: catalase peroxidase (KatG) and enoyl-acyl transferase. Potential anti-tubercular agents were selected based on structural similarity to known anti-tubercular drugs (isoniazid and ethionamide) and virtual screening was conducted to assess the binding affinities of the candidate drugs, followed by pharmacophore modeling to analyze critical features for protein-ligand interactions. Results: Ten drugs demonstrated strong binding affinities for the target proteins, with micafungin, pafolacianine, piperacillin, bisacodyl, and flucloxacillin showing the most promising interactions. Pharmacophore analysis revealed essential structural features, including hydrogen bond acceptors and donors, that could enhance drug efficacy and bioavailability, suggesting that these compounds may have favorable pharmacokinetic properties for TB treatment. Conclusion: The findings indicate that drugs like micafungin and flucloxacillin could be viable candidates for further study in TB treatment, offering a potential pathway to address the urgent need for novel antitubercular therapies. This study underscores the utility of computational drug discovery in identifying promising agents for repurposing, potentially expediting the development of effective treatments for TB.