Kelly Chang
Uncertainty quantification reveals the importance of data variability and experimental design considerations for in silico proarrhythmia risk assessment
Chang, Kelly; Dutta, Sara; Mirams, Gary R.; Beattie, Kylie; Sheng, Jiansong; Tran, Phu N.; Wu, Min; Wu, Wendy W.; Colatsky, Thomas; Strauss, David G.; Li, Zhihua
Authors
Sara Dutta
Prof. GARY MIRAMS GARY.MIRAMS@NOTTINGHAM.AC.UK
Professor of Mathematical Biology
Kylie Beattie
Jiansong Sheng
Phu N. Tran
Min Wu
Wendy W. Wu
Thomas Colatsky
David G. Strauss
Zhihua Li
Abstract
The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a global initiative intended to improve drug proarrhythmia risk assessment using a new paradigm of mechanistic assays. Under the CiPA paradigm, the relative risk of drug-induced Torsade de Pointes (TdP) is assessed using an in silico model of the human ventricular action potential (AP) that integrates in vitro pharmacology data from multiple ion channels. Thus, modeling predictions of cardiac risk liability will depend critically on the variability in pharmacology data, and uncertainty quantification (UQ) must comprise an essential component of the in silico assay. This study explores UQ methods that may be incorporated into the CiPA framework. Recently, we proposed a promising in silico TdP risk metric (qNet), which is derived from AP simulations and allows separation of a set of CiPA training compounds into Low, Intermediate, and High TdP risk categories. The purpose of this study was to use UQ to evaluate the robustness of TdP risk separation by qNet. Uncertainty in the model parameters used to describe drug binding and ionic current block was estimated using the non-parametric bootstrap method and a Bayesian inference approach. Uncertainty was then propagated through AP simulations to quantify uncertainty in qNet for each drug. UQ revealed lower uncertainty and more accurate TdP risk stratification by qNet when simulations were run at concentrations below 5× the maximum therapeutic exposure (Cmax). However, when drug effects were extrapolated above 10× Cmax, UQ showed that qNet could no longer clearly separate drugs by TdP risk. This was because for most of the pharmacology data, the amount of current block measured was
Citation
Chang, K., Dutta, S., Mirams, G. R., Beattie, K., Sheng, J., Tran, P. N., …Li, Z. (2017). Uncertainty quantification reveals the importance of data variability and experimental design considerations for in silico proarrhythmia risk assessment. Frontiers in Physiology, 8, Article 917. https://doi.org/10.3389/fphys.2017.00917
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 30, 2017 |
Publication Date | Nov 21, 2017 |
Deposit Date | Nov 22, 2017 |
Publicly Available Date | Nov 22, 2017 |
Journal | Frontiers in Physiology |
Electronic ISSN | 1664-042X |
Publisher | Frontiers Media |
Peer Reviewed | Peer Reviewed |
Volume | 8 |
Article Number | 917 |
DOI | https://doi.org/10.3389/fphys.2017.00917 |
Keywords | Uncertainty quantification; Experimental variability; Cardiac electrophysiology; Action potential; Torsade de Pointes; Ion channel; Pharmacology; Computational modeling |
Public URL | https://nottingham-repository.worktribe.com/output/896352 |
Publisher URL | https://www.frontiersin.org/articles/10.3389/fphys.2017.00917/full |
Related Public URLs | https://github.com/FDA/CiPA |
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
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