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Translational Model‐Informed Approach for Selection of Tuberculosis Drug Combination Regimens in Early Clinical Development
Author(s) -
Susanto Budi O.,
Wicha Sebastian G.,
Hu Yanmin,
Coates Anthony R.M.,
Simonsson Ulrika S.H.
Publication year - 2020
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1002/cpt.1814
Subject(s) - medicine , pharmacodynamics , drug development , rifampicin , tuberculosis , clinical trial , drug , isoniazid , pharmacology , selection (genetic algorithm) , translational research , intensive care medicine , computational biology , pharmacokinetics , computer science , biology , machine learning , pathology
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 EBA 0–2 days , EBA 0–5 days , and EBA 0–14 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.