Understanding the key challenges in tuberculosis drug discovery: what does the future hold?

Area of Science:

• Microbiology
• Infectious Diseases
• Drug Discovery

Background:

• Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a significant global health issue with high mortality.
• Rising drug resistance, particularly multidrug-resistant TB (MDR-TB), leads to poor treatment outcomes.
• Mtb's resilient cell wall and slow growth necessitate lengthy treatment regimens, often ineffective against resistant strains.

Purpose of the Study:

• To review current TB treatment strategies and resistance evolution.
• To identify challenges and emerging approaches in Mtb drug discovery.
• To explore next-generation strategies, including AI-driven drug development.

Main Methods:

• Comprehensive literature review of Mtb resistance and treatment strategies.
• Analysis of emerging trends in drug discovery targeting Mtb.
• Evaluation of AI's role in accelerating preclinical drug candidate identification.

Main Results:

• Existing TB treatments face challenges due to drug resistance and Mtb's biological characteristics.
• AI is showing potential in discovering safe and bioavailable preclinical TB drug candidates.
• Data limitations and biological complexity pose challenges to AI-driven TB drug discovery.

Conclusions:

• There is an urgent need for novel, short-course, affordable, and combination-friendly drugs for TB treatment.
• AI offers a promising avenue for accelerating TB drug discovery.
• Future advancements require multi-modal AI models, open data, and interdisciplinary collaboration.