Translational Model-Informed Approach for Selection of Tuberculosis Drug Combination Regimens in Early Clinical Development

Clin Pharmacol Ther. 2020 Aug;108(2):274-286. doi: 10.1002/cpt.1814. Epub 2020 Apr 2.

Abstract

The development of optimal treatment regimens in tuberculosis (TB) remains challenging due to the need of combination therapy and possibility of pharmacodynamic (PD) interactions. Preclinical information about PD interactions needs to be used more optimally when designing early bactericidal activity (EBA) studies. In this work, we developed a translational approach which can allow for forward translation to predict efficacy of drug combination in EBA studies using the Multistate Tuberculosis Pharmacometric (MTP) and the General Pharmacodynamic Interaction (GPDI) models informed by in vitro static time-kill data. These models were linked with translational factors to account for differences between the in vitro system and humans. Our translational MTP-GPDI model approach was able to predict the EBA0-2 days , EBA0-5 days , and EBA0-14 days from different EBA studies of rifampicin and isoniazid in monotherapy and combination. Our translational model approach can contribute to an optimal dose selection of drug combinations in early TB clinical trials.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antibiotics, Antitubercular / administration & dosage*
  • Antibiotics, Antitubercular / pharmacokinetics
  • Antitubercular Agents / administration & dosage*
  • Antitubercular Agents / pharmacokinetics
  • Bacterial Load
  • Clinical Trials, Phase II as Topic
  • Drug Development*
  • Drug Dosage Calculations
  • Drug Therapy, Combination
  • Humans
  • Isoniazid / administration & dosage*
  • Isoniazid / pharmacokinetics
  • Microbial Sensitivity Tests
  • Models, Biological
  • Mycobacterium tuberculosis / drug effects*
  • Mycobacterium tuberculosis / growth & development
  • Rifampin / administration & dosage*
  • Rifampin / pharmacokinetics
  • Translational Research, Biomedical*
  • Tuberculosis / drug therapy*
  • Tuberculosis / microbiology

Substances

  • Antibiotics, Antitubercular
  • Antitubercular Agents
  • Isoniazid
  • Rifampin