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Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative
The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro‐arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi‐ion channel pharmacology data in vitr...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492074/ https://www.ncbi.nlm.nih.gov/pubmed/30151907 http://dx.doi.org/10.1002/cpt.1184 |
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author | Li, Zhihua Ridder, Bradley J. Han, Xiaomei Wu, Wendy W. Sheng, Jiansong Tran, Phu N. Wu, Min Randolph, Aaron Johnstone, Ross H. Mirams, Gary R. Kuryshev, Yuri Kramer, James Wu, Caiyun Crumb, William J. Strauss, David G. |
author_facet | Li, Zhihua Ridder, Bradley J. Han, Xiaomei Wu, Wendy W. Sheng, Jiansong Tran, Phu N. Wu, Min Randolph, Aaron Johnstone, Ross H. Mirams, Gary R. Kuryshev, Yuri Kramer, James Wu, Caiyun Crumb, William J. Strauss, David G. |
author_sort | Li, Zhihua |
collection | PubMed |
description | The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro‐arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi‐ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on CiPA training drugs. In this study, we report the application of the prespecified model and metric to independent CiPA validation drugs. Over two validation datasets, the CiPA model performance meets all pre‐specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current CiPA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further. |
format | Online Article Text |
id | pubmed-6492074 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64920742019-05-06 Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative Li, Zhihua Ridder, Bradley J. Han, Xiaomei Wu, Wendy W. Sheng, Jiansong Tran, Phu N. Wu, Min Randolph, Aaron Johnstone, Ross H. Mirams, Gary R. Kuryshev, Yuri Kramer, James Wu, Caiyun Crumb, William J. Strauss, David G. Clin Pharmacol Ther Research The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro‐arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi‐ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on CiPA training drugs. In this study, we report the application of the prespecified model and metric to independent CiPA validation drugs. Over two validation datasets, the CiPA model performance meets all pre‐specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current CiPA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further. John Wiley and Sons Inc. 2018-08-27 2019-02 /pmc/articles/PMC6492074/ /pubmed/30151907 http://dx.doi.org/10.1002/cpt.1184 Text en © 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 http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Li, Zhihua Ridder, Bradley J. Han, Xiaomei Wu, Wendy W. Sheng, Jiansong Tran, Phu N. Wu, Min Randolph, Aaron Johnstone, Ross H. Mirams, Gary R. Kuryshev, Yuri Kramer, James Wu, Caiyun Crumb, William J. Strauss, David G. Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative |
title | Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative |
title_full | Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative |
title_fullStr | Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative |
title_full_unstemmed | Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative |
title_short | Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative |
title_sort | assessment of an in silico mechanistic model for proarrhythmia risk prediction under the cipa initiative |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492074/ https://www.ncbi.nlm.nih.gov/pubmed/30151907 http://dx.doi.org/10.1002/cpt.1184 |
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