New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response

Area of Science:

• Pharmacometrics
• Translational Medicine
• Tuberculosis Research

Background:

• Recent tuberculosis chemotherapy trials yielded disappointing results, indicating suboptimal use of preclinical data.
• Effective preclinical to clinical translation is crucial for improving tuberculosis treatment outcomes.

Purpose of the Study:

• To develop and validate a mouse-to-human translational pharmacokinetics-pharmacodynamics (PKs-PDs) model for predicting tuberculosis chemotherapy trial outcomes.
• To assess the model's ability to inform tuberculosis regimen optimization and predict clinical trial results.

Main Methods:

• Constructed a comprehensive PKs-PDs model incorporating Mycobacterium tuberculosis growth, immune response, drug concentration-effect relationships (moxifloxacin, rifapentine, rifampin), clinical PK data, protein binding, drug-drug interactions, and patient pathology.
• Simulated outcomes of recent 4-month and 6-month tuberculosis treatment trials using the developed model.

Main Results:

• The model predicted 65% relapse-free patients for 4-month regimens in REMox-TB (vs. 80% observed) and 79% for Rifaquin (vs. 82% observed).
• For 6-month regimens, the model predicted 97% relapse-free rates for control arms (vs. 92-95% observed) and 100% for a specific rifampin arm (vs. 100% observed).

Conclusions:

• The developed translational PKs-PDs model demonstrates potential for improving the prediction of tuberculosis clinical trial outcomes.
• This model can serve as a valuable tool for optimizing tuberculosis drug regimens and guiding future clinical trial design.