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...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Published: |
2014
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| _version_ | 1797077081566216192 |
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| author | Mirams, G Davies, M Brough, S Bridgland−Taylor, M Cui, Y Gavaghan, D Abi−Gerges, N |
| author_facet | Mirams, G Davies, M Brough, S Bridgland−Taylor, M Cui, Y Gavaghan, D Abi−Gerges, N |
| author_sort | Mirams, G |
| collection | OXFORD |
| 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 and 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 (2006), Grandi (2010) and O'Hara (2011) human ventricular action potential models, pacing at 1Hz 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 >= 5ms was predicted with up to 79% sensitivity and 91% 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 such approaches will provide a useful tool in cardiac safety assessment. |
| first_indexed | 2024-03-07T00:12:43Z |
| format | Journal article |
| id | oxford-uuid:79c83b83-6d9b-41c2-88d3-3f71ee484e57 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T00:12:43Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | oxford-uuid:79c83b83-6d9b-41c2-88d3-3f71ee484e572022-03-26T20:39:36ZPrediction of Thorough QT study results using action potential simulations based on ion channel screensJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:79c83b83-6d9b-41c2-88d3-3f71ee484e57Department of Computer Science2014Mirams, GDavies, MBrough, SBridgland−Taylor, MCui, YGavaghan, DAbi−Gerges, NIntroduction: 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 and 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 (2006), Grandi (2010) and O'Hara (2011) human ventricular action potential models, pacing at 1Hz 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 >= 5ms was predicted with up to 79% sensitivity and 91% 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 such approaches will provide a useful tool in cardiac safety assessment. |
| spellingShingle | Mirams, G Davies, M Brough, S Bridgland−Taylor, M Cui, Y Gavaghan, D Abi−Gerges, N 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 |
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