Assessment of an in silico mechanistic model for proarrhythmia risk prediction under the CiPA initiative
International Council on Harmonization S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are proarrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi-ion channel pharmacology data in vitro into a hu...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
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American Society for Clinical Pharmacology and Therapeutics
2018
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| Online Access: | https://eprints.nottingham.ac.uk/53210/ |
| _version_ | 1848798902404251648 |
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| author | Li, Zhihua Wu, Wendy W. Sheng, Jiansong Tran, Phu N. Wu, Min Ranolph, Aaron Johnstone, Ross H. Mirams, Gary R. Kuryshev, Yuri Kramer, James Wu, Caiyun Crumb, William J. Strauss, David G. |
| author_facet | Li, Zhihua Wu, Wendy W. Sheng, Jiansong Tran, Phu N. Wu, Min Ranolph, Aaron Johnstone, Ross H. Mirams, Gary R. Kuryshev, Yuri Kramer, James Wu, Caiyun Crumb, William J. Strauss, David G. |
| author_sort | Li, Zhihua |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | International Council on Harmonization S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are proarrhythmic. 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 is fit for regulatory use, and standard experimental protocols and quality control criteria could increase the model prediction accuracy even further. |
| first_indexed | 2025-11-14T20:27:09Z |
| format | Article |
| id | nottingham-53210 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:27:09Z |
| publishDate | 2018 |
| publisher | American Society for Clinical Pharmacology and Therapeutics |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-532102020-05-04T19:47:17Z https://eprints.nottingham.ac.uk/53210/ Assessment of an in silico mechanistic model for proarrhythmia risk prediction under the CiPA initiative Li, Zhihua Wu, Wendy W. Sheng, Jiansong Tran, Phu N. Wu, Min Ranolph, Aaron Johnstone, Ross H. Mirams, Gary R. Kuryshev, Yuri Kramer, James Wu, Caiyun Crumb, William J. Strauss, David G. International Council on Harmonization S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are proarrhythmic. 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 is fit for regulatory use, and standard experimental protocols and quality control criteria could increase the model prediction accuracy even further. American Society for Clinical Pharmacology and Therapeutics 2018-07-25 Article PeerReviewed Li, Zhihua, Wu, Wendy W., Sheng, Jiansong, Tran, Phu N., Wu, Min, Ranolph, Aaron, Johnstone, Ross H., Mirams, Gary R., Kuryshev, Yuri, Kramer, James, Wu, Caiyun, Crumb, William J. and Strauss, David G. (2018) Assessment of an in silico mechanistic model for proarrhythmia risk prediction under the CiPA initiative. Clinical Pharmacology & Therapeutics . ISSN 0009-9236 (In Press) |
| spellingShingle | Li, Zhihua Wu, Wendy W. Sheng, Jiansong Tran, Phu N. Wu, Min Ranolph, 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 |
| url | https://eprints.nottingham.ac.uk/53210/ |