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Prediction of Thorough QT study results using action potential simulations based on ion channel screens

INTRODUCTION: Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predi...

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Autores principales: Mirams, Gary R., Davies, Mark R., Brough, Stephen J., Bridgland-Taylor, Matthew H., Cui, Yi, Gavaghan, David J., Abi-Gerges, Najah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4266452/
https://www.ncbi.nlm.nih.gov/pubmed/25087753
http://dx.doi.org/10.1016/j.vascn.2014.07.002
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author Mirams, Gary R.
Davies, Mark R.
Brough, Stephen J.
Bridgland-Taylor, Matthew H.
Cui, Yi
Gavaghan, David J.
Abi-Gerges, Najah
author_facet Mirams, Gary R.
Davies, Mark R.
Brough, Stephen J.
Bridgland-Taylor, Matthew H.
Cui, Yi
Gavaghan, David J.
Abi-Gerges, Najah
author_sort Mirams, Gary R.
collection PubMed
description INTRODUCTION: Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. METHODS: Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms — IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration–effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1 Hz and running to steady state, for a range of concentrations. RESULTS: We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥ 5 ms was predicted with up to 79% sensitivity and 100% specificity. DISCUSSION: This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety assessment.
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spelling pubmed-42664522014-12-16 Prediction of Thorough QT study results using action potential simulations based on ion channel screens Mirams, Gary R. Davies, Mark R. Brough, Stephen J. Bridgland-Taylor, Matthew H. Cui, Yi Gavaghan, David J. Abi-Gerges, Najah J Pharmacol Toxicol Methods Original Article INTRODUCTION: Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. METHODS: Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms — IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration–effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1 Hz and running to steady state, for a range of concentrations. RESULTS: We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥ 5 ms was predicted with up to 79% sensitivity and 100% specificity. DISCUSSION: This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety assessment. Elsevier 2014-11 /pmc/articles/PMC4266452/ /pubmed/25087753 http://dx.doi.org/10.1016/j.vascn.2014.07.002 Text en © 2014 The Authors https://creativecommons.org/licenses/by/3.0/This work is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/) .
spellingShingle Original Article
Mirams, Gary R.
Davies, Mark R.
Brough, Stephen J.
Bridgland-Taylor, Matthew H.
Cui, Yi
Gavaghan, David J.
Abi-Gerges, Najah
Prediction of Thorough QT study results using action potential simulations based on ion channel screens
title Prediction of Thorough QT study results using action potential simulations based on ion channel screens
title_full Prediction of Thorough QT study results using action potential simulations based on ion channel screens
title_fullStr Prediction of Thorough QT study results using action potential simulations based on ion channel screens
title_full_unstemmed Prediction of Thorough QT study results using action potential simulations based on ion channel screens
title_short Prediction of Thorough QT study results using action potential simulations based on ion channel screens
title_sort prediction of thorough qt study results using action potential simulations based on ion channel screens
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4266452/
https://www.ncbi.nlm.nih.gov/pubmed/25087753
http://dx.doi.org/10.1016/j.vascn.2014.07.002
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