Improving treatment outcome assessment in a mouse tuberculosis model

Area of Science:

• Preclinical research
• Tuberculosis (TB) drug development
• Animal models in infectious disease

Background:

• Current preclinical tuberculosis (TB) studies primarily use mouse models to assess treatment relapse rates.
• Existing designs lack data on the correlation between treatment duration and outcomes, hindering the estimation of treatment-shortening potential for TB regimens.
• A new approach is needed to accurately evaluate TB treatment efficacy and optimize drug regimens.

Purpose of the Study:

• To develop and validate a new methodology for preclinical tuberculosis (TB) treatment outcome evaluation in mice.
• To establish a mathematical model for assessing the relationship between treatment length and treatment outcome.
• To compare the efficacy of different TB drug regimens, including R_pZHE, RZME, and RZMH, in a mouse model.

Main Methods:

• BALB/c mice were infected with Mycobacterium tuberculosis (Beijing genotype).
• Mice received one of three treatment regimens (R_pZHE, RZME, RZMH) for 9 different durations (2-6 months).
• Treatment outcomes were analyzed using a sigmoidal E_max mathematical model to fit observational data and compare regimens.

Main Results:

• The sigmoidal E_max model provided the best fit for the observational data, outperforming linear and conventional E_max models.
• Regimen-specific parameters indicated significantly higher curative potential for RZME and R_pZHE compared to RZMH.
• The study successfully established a quantitative relationship between treatment length and predicted cure rates for TB regimens.

Conclusions:

• A novel mouse model design for TB treatment evaluation has been developed, offering accurate tools to assess treatment length-cure relationships.
• This approach facilitates efficient comparison of different TB drug regimens' efficacy.
• The methodology aligns with the principles of reduction and refinement in laboratory animal use for TB research.