Forecasting clinical dose-response from pre-clinical studies in tuberculosis research - translational predictions with rifampicin
Abstract
A crucial step for accelerating tuberculosis drug development is bridging the gap between pre‐clinical and clinical trials. In this study, we developed a pre‐clinical model‐informed translational approach to predict drug effects across pre‐clinical 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 (i) a hollow‐fiber infection model, (ii) a murine study to determine PK/PD indices, and (iii) 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.
Citation
Wicha , S G , Clewe , O , Svensson , R J , Gillespie , S H , Hu , Y , Coates , A R M & Simonsson , U S H 2018 , ' Forecasting clinical dose-response from pre-clinical studies in tuberculosis research - translational predictions with rifampicin ' , Clinical Pharmacology & Therapeutics , vol. Early View . https://doi.org/10.1002/cpt.1102
Publication
Clinical Pharmacology & Therapeutics
Status
Peer reviewed
ISSN
0009-9236Type
Journal article
Rights
© 2018 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Description
This work was funded by: The Swedish Research Council (grant number 521‐2011‐3442) and the Innovative Medicines Initiative Joint Undertaking (www.imi.europe.eu) under grant agreement 115337, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007‐2013) and EFPIA companies' in kind contribution.Collections
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