Forecasting clinical dose-response from pre-clinical studies in tuberculosis research - translational predictions with rifampicin

Sebastian G. Wicha; Oskar Clewe; Robin J. Svensson; Stephen H. Gillespie; Yanmin Hu; Anthony R.m. Coates; Ulrika S.h. Simonsson

doi:10.1002/cpt.1102

Forecasting clinical dose-response from pre-clinical studies in tuberculosis research - translational predictions with rifampicin

Sebastian G. Wicha, Oskar Clewe, Robin J. Svensson, Stephen H. Gillespie, Yanmin Hu, Anthony R.m. Coates, Ulrika S.h. Simonsson

Research output: Contribution to journal › Article › peer-review

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 EBA_{0‐2 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.

Original language	English
Journal	Clinical Pharmacology & Therapeutics
Volume	Early View
Early online date	19 Jun 2018
DOIs	https://doi.org/10.1002/cpt.1102
Publication status	E-pub ahead of print - 19 Jun 2018

Keywords

Forward translational
Tuberculosis
Rifampicin

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1002/cpt.1102Licence: CC BY-NC

Wicha_et_al-2018-Clinical_Pharmacology_26_Therapeutics_CC
© 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.
Final published version, 638 KBLicence: CC BY-NC

IMI PREDICT TB: EU Innovative Medicines Initiative (IMI) 75% account
Gillespie, S. H. (PI)
European Commission
1/05/12 → 31/10/17
Project: Standard

Cite this

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title = "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.",

keywords = "Forward translational, Tuberculosis, Rifampicin",

author = "Wicha, {Sebastian G.} and Oskar Clewe and Svensson, {Robin J.} and Gillespie, {Stephen H.} and Yanmin Hu and Coates, {Anthony R.m.} and Simonsson, {Ulrika S.h.}",

note = "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. ",

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AU - Gillespie, Stephen H.

AU - Hu, Yanmin

AU - Coates, Anthony R.m.

AU - Simonsson, Ulrika S.h.

N1 - 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.

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N2 - 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.

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JO - Clinical Pharmacology & Therapeutics

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Forecasting clinical dose-response from pre-clinical studies in tuberculosis research - translational predictions with rifampicin

Abstract

Keywords

UN SDGs

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Projects

IMI PREDICT TB: EU Innovative Medicines Initiative (IMI) 75% account

Cite this