Abstract
Introduction: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a major global health concern. It spreads through airborne droplets and has a high mortality rate, particularly without treatment. Drug resistance is rising, with treatments against multidrug-resistant TB (MDR-TB) showing poor treatment success rates. The thick, lipid-rich wall of Mtb and its slow growth reduce antibiotic effectiveness, requiring long treatment courses of 4–6 months. Current therapies often fail against drug-resistant strains, highlighting the urgent need for new, short-course treatment, affordable, and combination-friendly drugs. Areas covered: Within this perspective, the authors review and comment on the following topics regarding Mtb resistance emergence and treatment strategies: i) Existing treatment ii) Resistance evolution in Mtb; iii) Key challenges in drug discovery targeting Mtb; iv) emerging strategies and recent advances in Mtb drug discovery, and v) Next-generation approaches. Literature was identified through a search of PubMed, google scholar, and web of science, from January 2010 to March 2025. Expert opinion: AI is accelerating the discovery of bioavailable and safe preclinical drug candidates for TB, though data limitations and biological complexity remain challenging. Future progress requires multi-modal models, open-access datasets, and interdisciplinary collaboration.
| Original language | English |
|---|---|
| Pages (from-to) | 1115-1130 |
| Number of pages | 16 |
| Journal | Expert Opinion on Drug Discovery |
| Volume | 20 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 1 Jan 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- AI-driven drug discovery
- Anti-microbial resistance (AMR)
- anti-evolution drugs
- drug discovery
- drug-resistance mechanism
- mycobacterium tuberculosis (mtb)
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