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Forecasting clinical dose-response from pre-clinical studies in tuberculosis research - translational predictions with rifampicin
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dc.contributor.author | Wicha, Sebastian G. | |
dc.contributor.author | Clewe, Oskar | |
dc.contributor.author | Svensson, Robin J. | |
dc.contributor.author | Gillespie, Stephen H. | |
dc.contributor.author | Hu, Yanmin | |
dc.contributor.author | Coates, Anthony R.m. | |
dc.contributor.author | Simonsson, Ulrika S.h. | |
dc.date.accessioned | 2018-06-26T11:30:06Z | |
dc.date.available | 2018-06-26T11:30:06Z | |
dc.date.issued | 2018-06-19 | |
dc.identifier.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 | en |
dc.identifier.issn | 0009-9236 | |
dc.identifier.other | PURE: 252974692 | |
dc.identifier.other | PURE UUID: d6b8c6f4-d40e-4fc9-811d-d94b3118b714 | |
dc.identifier.other | crossref: 10.1002/cpt.1102 | |
dc.identifier.other | Scopus: 85049597221 | |
dc.identifier.other | ORCID: /0000-0001-6537-7712/work/46152091 | |
dc.identifier.other | WOS: 000449645100021 | |
dc.identifier.uri | https://hdl.handle.net/10023/14604 | |
dc.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. | en |
dc.description.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. | |
dc.language.iso | eng | |
dc.relation.ispartof | Clinical Pharmacology & Therapeutics | en |
dc.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. | en |
dc.subject | Forward translational | en |
dc.subject | Tuberculosis | en |
dc.subject | Rifampicin | en |
dc.subject | RA0421 Public health. Hygiene. Preventive Medicine | en |
dc.subject | RM Therapeutics. Pharmacology | en |
dc.subject | NDAS | en |
dc.subject | SDG 3 - Good Health and Well-being | en |
dc.subject.lcc | RA0421 | en |
dc.subject.lcc | RM | en |
dc.title | Forecasting clinical dose-response from pre-clinical studies in tuberculosis research - translational predictions with rifampicin | en |
dc.type | Journal article | en |
dc.contributor.sponsor | European Commission | en |
dc.description.version | Publisher PDF | en |
dc.contributor.institution | University of St Andrews. School of Medicine | en |
dc.contributor.institution | University of St Andrews. Infection and Global Health Division | en |
dc.contributor.institution | University of St Andrews. Global Health Implementation Group | en |
dc.contributor.institution | University of St Andrews. Gillespie Group | en |
dc.contributor.institution | University of St Andrews. Biomedical Sciences Research Complex | en |
dc.contributor.institution | University of St Andrews. Infection Group | en |
dc.identifier.doi | https://doi.org/10.1002/cpt.1102 | |
dc.description.status | Peer reviewed | en |
dc.identifier.grantnumber | en |
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