Zhihua Li
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.
Authors
Wendy W. Wu
Jiansong Sheng
Phu N. Tran
Min Wu
Aaron Ranolph
Ross H. Johnstone
Professor GARY MIRAMS GARY.MIRAMS@NOTTINGHAM.AC.UK
PROFESSOR OF MATHEMATICAL BIOLOGY
Yuri Kuryshev
James Kramer
Caiyun Wu
William J. Crumb
David G. Strauss
Abstract
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.
Citation
Li, Z., Wu, W. W., Sheng, J., Tran, P. N., Wu, M., Ranolph, A., Johnstone, R. H., Mirams, G. R., Kuryshev, Y., Kramer, J., Wu, C., Crumb, W. J., & Strauss, D. G. (2019). Assessment of an in silico mechanistic model for proarrhythmia risk prediction under the CiPA initiative. Clinical Pharmacology and Therapeutics, 105(2), 466-475. https://doi.org/10.1002/cpt.1184
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 25, 2018 |
Online Publication Date | Aug 27, 2018 |
Publication Date | 2019-02 |
Deposit Date | Jul 31, 2018 |
Publicly Available Date | Sep 11, 2018 |
Journal | Clinical Pharmacology & Therapeutics |
Print ISSN | 0009-9236 |
Electronic ISSN | 1532-6535 |
Publisher | American Society for Clinical Pharmacology and Therapeutics |
Peer Reviewed | Peer Reviewed |
Volume | 105 |
Issue | 2 |
Pages | 466-475 |
DOI | https://doi.org/10.1002/cpt.1184 |
Public URL | https://nottingham-repository.worktribe.com/output/947750 |
Publisher URL | https://ascpt.onlinelibrary.wiley.com/doi/abs/10.1002/cpt.1184?af=R |
Contract Date | Sep 11, 2018 |
Files
Assessment of an In Silico
(304 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
Ten simple rules for training scientists to make better software
(2024)
Journal Article
Geometrically-derived action potential markers for model development: a principled approach?
(2024)
Preprint / Working Paper
Optimising experimental designs for model selection of ion channel drug binding mechanisms
(2024)
Preprint / Working Paper
Evaluating the predictive accuracy of ion channel models using data from multiple experimental designs
(2024)
Preprint / Working Paper
A range of voltage-clamp protocol designs for rapid capture of hERG kinetics
(2024)
Preprint / Working Paper
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search