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

I. H. Bartelink, N. Zhang, R. J. Keizer, N. Strydom, P. J. Converse, K. E. Dooley, E. L. Nuermberger, R. M. Savic

Research output: Contribution to journalArticleAcademicpeer-review

28 Citations (Scopus)

Abstract

Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse-to-human translational pharmacokinetics (PKs) – pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species-specific protein binding, drug-drug interactions, and patient-specific pathology. Simulations of recent trials testing 4-month regimens predicted 65% (95% confidence interval [CI], 55–74) relapse-free patients vs. 80% observed in the REMox-TB trial, and 79% (95% CI, 72–87) vs. 82% observed in the Rifaquin trial. Simulation of 6-month regimens predicted 97% (95% CI, 93–99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials.

Original languageEnglish
Pages (from-to)366-379
Number of pages14
JournalClinical and Translational Science
Volume10
Issue number5
DOIs
Publication statusPublished - Sept 2017

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