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Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment
Drug-induced Torsade-de-Pointes (TdP) has been responsible for the withdrawal of many drugs from the market and is therefore of major concern to global regulatory agencies and the pharmaceutical industry. The Comprehensive in vitro Proarrhythmia Assay (CiPA) was proposed to improve prediction of TdP...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572155/ https://www.ncbi.nlm.nih.gov/pubmed/28878692 http://dx.doi.org/10.3389/fphys.2017.00616 |
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author | Dutta, Sara Chang, Kelly C. Beattie, Kylie A. Sheng, Jiansong Tran, Phu N. Wu, Wendy W. Wu, Min Strauss, David G. Colatsky, Thomas Li, Zhihua |
author_facet | Dutta, Sara Chang, Kelly C. Beattie, Kylie A. Sheng, Jiansong Tran, Phu N. Wu, Wendy W. Wu, Min Strauss, David G. Colatsky, Thomas Li, Zhihua |
author_sort | Dutta, Sara |
collection | PubMed |
description | Drug-induced Torsade-de-Pointes (TdP) has been responsible for the withdrawal of many drugs from the market and is therefore of major concern to global regulatory agencies and the pharmaceutical industry. The Comprehensive in vitro Proarrhythmia Assay (CiPA) was proposed to improve prediction of TdP risk, using in silico models and in vitro multi-channel pharmacology data as integral parts of this initiative. Previously, we reported that combining dynamic interactions between drugs and the rapid delayed rectifier potassium current (IKr) with multi-channel pharmacology is important for TdP risk classification, and we modified the original O'Hara Rudy ventricular cell mathematical model to include a Markov model of IKr to represent dynamic drug-IKr interactions (IKr-dynamic ORd model). We also developed a novel metric that could separate drugs with different TdP liabilities at high concentrations based on total electronic charge carried by the major inward ionic currents during the action potential. In this study, we further optimized the IKr-dynamic ORd model by refining model parameters using published human cardiomyocyte experimental data under control and drug block conditions. Using this optimized model and manual patch clamp data, we developed an updated version of the metric that quantifies the net electronic charge carried by major inward and outward ionic currents during the steady state action potential, which could classify the level of drug-induced TdP risk across a wide range of concentrations and pacing rates. We also established a framework to quantitatively evaluate a system's robustness against the induction of early afterdepolarizations (EADs), and demonstrated that the new metric is correlated with the cell's robustness to the pro-EAD perturbation of IKr conductance reduction. In summary, in this work we present an optimized model that is more consistent with experimental data, an improved metric that can classify drugs at concentrations both near and higher than clinical exposure, and a physiological framework to check the relationship between a metric and EAD. These findings provide a solid foundation for using in silico models for the regulatory assessment of TdP risk under the CiPA paradigm. |
format | Online Article Text |
id | pubmed-5572155 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-55721552017-09-06 Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment Dutta, Sara Chang, Kelly C. Beattie, Kylie A. Sheng, Jiansong Tran, Phu N. Wu, Wendy W. Wu, Min Strauss, David G. Colatsky, Thomas Li, Zhihua Front Physiol Physiology Drug-induced Torsade-de-Pointes (TdP) has been responsible for the withdrawal of many drugs from the market and is therefore of major concern to global regulatory agencies and the pharmaceutical industry. The Comprehensive in vitro Proarrhythmia Assay (CiPA) was proposed to improve prediction of TdP risk, using in silico models and in vitro multi-channel pharmacology data as integral parts of this initiative. Previously, we reported that combining dynamic interactions between drugs and the rapid delayed rectifier potassium current (IKr) with multi-channel pharmacology is important for TdP risk classification, and we modified the original O'Hara Rudy ventricular cell mathematical model to include a Markov model of IKr to represent dynamic drug-IKr interactions (IKr-dynamic ORd model). We also developed a novel metric that could separate drugs with different TdP liabilities at high concentrations based on total electronic charge carried by the major inward ionic currents during the action potential. In this study, we further optimized the IKr-dynamic ORd model by refining model parameters using published human cardiomyocyte experimental data under control and drug block conditions. Using this optimized model and manual patch clamp data, we developed an updated version of the metric that quantifies the net electronic charge carried by major inward and outward ionic currents during the steady state action potential, which could classify the level of drug-induced TdP risk across a wide range of concentrations and pacing rates. We also established a framework to quantitatively evaluate a system's robustness against the induction of early afterdepolarizations (EADs), and demonstrated that the new metric is correlated with the cell's robustness to the pro-EAD perturbation of IKr conductance reduction. In summary, in this work we present an optimized model that is more consistent with experimental data, an improved metric that can classify drugs at concentrations both near and higher than clinical exposure, and a physiological framework to check the relationship between a metric and EAD. These findings provide a solid foundation for using in silico models for the regulatory assessment of TdP risk under the CiPA paradigm. Frontiers Media S.A. 2017-08-23 /pmc/articles/PMC5572155/ /pubmed/28878692 http://dx.doi.org/10.3389/fphys.2017.00616 Text en Copyright © 2017 Dutta, Chang, Beattie, Sheng, Tran, Wu, Wu, Strauss, Colatsky and Li. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Dutta, Sara Chang, Kelly C. Beattie, Kylie A. Sheng, Jiansong Tran, Phu N. Wu, Wendy W. Wu, Min Strauss, David G. Colatsky, Thomas Li, Zhihua Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment |
title | Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment |
title_full | Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment |
title_fullStr | Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment |
title_full_unstemmed | Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment |
title_short | Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment |
title_sort | optimization of an in silico cardiac cell model for proarrhythmia risk assessment |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572155/ https://www.ncbi.nlm.nih.gov/pubmed/28878692 http://dx.doi.org/10.3389/fphys.2017.00616 |
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