Understanding the key challenges in tuberculosis drug discovery: what does the future hold?
Rima Zein-Eddine, Masoud Ramuz, Guislaine Refrégier, Johannes F. Lutzeyer, Alexey Igorevich Alexandrov, Hannu Myllykallio
Expert Opinion on Drug Discovery · 2025-07
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.
MeSH terms
- Drug discovery
- Key (lock)
- Tuberculosis
- Drug
- Data science
- Medicine
- Computational biology
- Computer science