Improving treatment outcome assessment in a mouse tuberculosis model

Preclinical treatment outcome evaluation of tuberculosis (TB) occurs primarily in mice. Current designs compare relapse rates of different regimens at selected time points, but lack information about the correlation between treatment length and treatment outcome, which is required to efficiently estimate a regimens’ treatment-shortening potential. Therefore we developed a new approach. BALB/c mice were infected with a Mycobacterium tuberculosis Beijing genotype strain and were treated with rifapentine-pyrazinamide-isoniazid-ethambutol (R(p)ZHE), rifampicin-pyrazinamide-moxifloxacin-ethambutol (RZME) or rifampicin-pyrazinamide-moxifloxacin-isoniazid (RZMH). Treatment outcome was assessed in n = 3 mice after 9 different treatment lengths between 2–6 months. Next, we created a mathematical model that best fitted the observational data and used this for inter-regimen comparison. The observed data were best described by a sigmoidal E(max) model in favor over linear or conventional E(max) models. Estimating regimen-specific parameters showed significantly higher curative potentials for RZME and R(p)ZHE compared to RZMH. In conclusion, we provide a new design for treatment outcome evaluation in a mouse TB model, which (i) provides accurate tools for assessment of the relationship between treatment length and predicted cure, (ii) allows for efficient comparison between regimens and (iii) adheres to the reduction and refinement principles of laboratory animal use.