Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time

Area of Science:

• Infectious Diseases
• Microbiology
• Pharmacology

Background:

• Current tuberculosis (TB) drug regimens are long and difficult, leading to poor patient adherence, drug resistance, and disease relapse.
• Developing shorter, more effective TB treatments is crucial to combatting the global health crisis.
• Previous research identified promising experimental regimens using an in vitro model and an optimization platform.

Purpose of the Study:

• To optimize in vivo drug doses for two promising experimental TB regimens in a mouse model.
• To evaluate the efficacy of these optimized regimens compared to the standard TB treatment.
• To assess the potential of these regimens for treating multidrug-resistant and extensively drug-resistant TB.

Main Methods:

• Utilized an output-driven optimization platform to identify potential drug-dose combinations.
• Tested optimized experimental regimens in a mouse model of pulmonary tuberculosis.
• Compared the efficacy of experimental regimens against the current standard TB treatment in terms of bacterial killing and relapse-free cure.

Main Results:

• Experimental regimens demonstrated significantly more rapid killing of Mycobacterium tuberculosis compared to the standard regimen.
• Treatment time to relapse-free cure was reduced by 75% with the experimental regimens.
• The optimized regimens omit key drugs like isoniazid, rifampicin, fluoroquinolones, and injectable aminoglycosides.

Conclusions:

• Optimized experimental drug regimens show potential for a markedly shorter TB treatment course in humans.
• These regimens are suitable for treating multidrug-resistant and extensively drug-resistant tuberculosis.
• Further clinical evaluation is warranted to translate these findings into improved patient outcomes.