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 journalArticle

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 (US)
JournalClinical and Translational Science
DOIs
StateAccepted/In press - 2017

Fingerprint

Tuberculosis
Confidence Intervals
Anthralin
Rifampin
Pharmacokinetics
Erythrasma
Forearm
Adaptive Immunity
Drug Interactions
Mycobacterium tuberculosis
Protein Binding
Clinical Trials
Pathology
Recurrence
Drug Therapy
Bis(4-Methyl-1-Homopiperazinylthiocarbonyl)disulfide
Database Management Systems
Blood Stains
Drug interactions
Pharmacodynamics

ASJC Scopus subject areas

  • Neuroscience(all)
  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

Cite this

Bartelink, I. H., Zhang, N., Keizer, R. J., Strydom, N., Converse, P. J., Dooley, K. E., ... Savic, R. M. (2017). New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response. Clinical and Translational Science. DOI: 10.1111/cts.12472

New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response. / Bartelink, I. H.; Zhang, N.; Keizer, R. J.; Strydom, N.; Converse, P. J.; Dooley, K. E.; Nuermberger, E. L.; Savic, R. M.

In: Clinical and Translational Science, 2017.

Research output: Contribution to journalArticle

Bartelink, I. H.; Zhang, N.; Keizer, R. J.; Strydom, N.; Converse, P. J.; Dooley, K. E.; Nuermberger, E. L.; Savic, R. M. / New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response.

In: Clinical and Translational Science, 2017.

Research output: Contribution to journalArticle

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