Forecasting Clinical Dose–Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin

Abstract

A crucial step for accelerating tuberculosis drug development is bridging the gap between preclinical and clinical trials. In this study, we developed a preclinical model‐informed translational approach to predict drug effects across preclinical systems and early clinical trials using the in vitro‐based Multistate Tuberculosis Pharmacometric (MTP) model using rifampicin as an example. The MTP model predicted rifampicin biomarker response observed in 1) a hollow‐fiber infection model, 2) a murine study to determine pharmacokinetic/pharmacodynamic indices, and 3) several clinical phase IIa early bactericidal activity (EBA) studies. In addition, we predicted rifampicin biomarker response at high doses of up to 50 mg/kg, leading to an increased median EBA0‐2 days (90% prediction interval) of 0.513 log CFU/mL/day (0.310; 0.701) compared to the standard dose of 10 mg/kg of 0.181 log/CFU/mL/day (0.076; 0.483). These results suggest that the translational approach could assist in the selection of drugs and doses in early‐phase clinical tuberculosis trials.

Difference in persistent tuberculosis bacteria between in vitro and sputum from patients: implications for translational predictions

“…Despite the difference in levels of persisters between in vitro and humans, the persisters will remain the major sub-population both in vitro and in humans. The identified difference in phenotypic resistance can be accounted for in a model-based translational framework 11 . To show this we used samples from sputum smear positive patients that was collected prior to onset of chemotherapy, followed by mycobacterial quantification using CFU, and MPN counts treated with RPFs 17 .…”

“…The framework has been applied to describe in vitro natural growth and drug effect data 5 , 6 , in vivo data 7 and also on clinical trial data 8 , 9 bridging exposure from a population pharmacokinetic model as for example rifampicin 10 and biomarker data. As the model has ability to describe data from both pre-clinical and clinical phase of drug development, it has a role in translational efforts which previously have been demonstrated in a study that predicted rifampicin early bactericidal activity (EBA) efficacy data based on in vitro information and translational factors 11 . It has also been used as a show case example of important quantitative pharmacology work that can accelerate drug development 12 .…”

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

“…It has been successfully applied to describe in vitro [121], mouse [122], and clinical data [123]. In addition, the MTP model has been successfully used in an MID3 approach, to predict observations from early clinical studies using clinical dose-response forecasting from preclinical in vitro studies of rifampicin and in combination with isoniazid [15,16]. This model has been selected by The Impact and Influence Initiative of the Quantitative Pharmacology (QP) Network of the American society of Clinical Pharmacology and Therapeutics (ASCPT) to highlight the most impactful examples of QP applications where the role of quantitative translational pharmacology has bridged science and practice to make better, faster, and more efficient decisions in drug discovery and development [25].…”

“…Based on the exposure-response relationship in animals, and/or pure in vitro predictions, the first in-human (FIH) and early bactericidal activity (EBA) trials can be designed. These steps all require a mathematical translational approach, taking into account the PK-PD and translational factors to account for differences between preclinical species and patients [15,16].…”

Comparisons of Analysis Methods for Assessment of Pharmacodynamic Interactions Including Design Recommendations

“…The Multistate Tuberculosis Pharmacometric (MTP) model, which predicts the change in bacterial number for fast-(F), slow-(S), and non-multiplying (N) bacteria, with and without drug effects, is a semi-mechanistic PK-PD model for studying exposure-response relationships for anti-tubercular drugs and was first developed using in vitro data (5). The MTP model has successfully been implemented by Chen et al to estimate drug efficacy in a murine model (6,7), to quantify human early bacterial activity with clinical trial simulations (8) and for predicting early bacterial activity in humans using only in vitro information (9). Chunli Chen and Sebastian G. Wicha contributed equally to this work.…”