Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment.
Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment.
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在脑膜心律失常风险评估中优化硅心细胞模型。
DOI：
10.3389/fphys.2017.00616
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发表时间：
2017
影响因子：
4
通讯作者：
Li Z
中科院分区：
文献类型：
--
作者：
Dutta S;Chang KC;Beattie KA;Sheng J;Tran PN;Wu WW;Wu M;Strauss DG;Colatsky T;Li Z
药物引起的扭曲症(TDP)是导致许多药物从市场上撤出的原因，因此引起全球监管机构和制药业的主要关注。综合体外心律失常分析(CIPA)是为了提高对TDP风险的预测，在电子计算机模型和体外多通道药理学数据中作为这一倡议的组成部分。以前，我们报道了将药物与快速延迟整流钾电流(IKR)之间的动态相互作用与多通道药理学结合起来对TDP风险分类具有重要意义，我们修改了原始的O‘Hara Rudy脑细胞数学模型，将IKR的马尔可夫模型(IKR-Dynamic Ord Model)包含进来，以描述药物与IKR的动态相互作用。我们还开发了一种新的测量方法，可以根据主要内向离子电流在动作电位期间携带的总电子电荷，在高浓度下分离具有不同TDP敏感性的药物。在这项研究中，我们使用公开的人类心肌细胞在对照和药物阻断条件下的实验数据，通过优化模型参数进一步优化了IKR-Dynamic Ord模型。使用这个优化的模型和手动膜片钳数据，我们开发了一个更新版本的度量标准，它量化了稳态动作电位期间主要内向和外向离子电流携带的净电子电荷，可以在广泛的浓度和起搏速率范围内对药物诱导的TDP风险水平进行分类。我们还建立了一个框架来定量评估系统对早期后去极化(EAD)诱导的稳健性，并证明了新的度量与细胞对早期后去极化(EAD)扰动的稳健性相关。总之，在这项工作中，我们提出了一个与实验数据更一致的优化模型，一个改进的指标，可以对接近和高于临床暴露浓度的药物进行分类，以及一个检查指标和EAD之间关系的生理框架。这些发现为在CIPA范式下使用电子计算机模型对TDP风险进行监管评估提供了坚实的基础。
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.
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影响因子：
4.3
作者：
Cummins MA;Dalal PJ;Bugana M;Severi S;Sobie EA
通讯作者：
Sobie EA
影响因子：
20.1
作者：
Armoundas, AA;Hobai, IA;O'Rourke, B
通讯作者：
O'Rourke, B
影响因子：
5.5
作者：
Gray, Richard A.;Huelsing, Deilah J.
通讯作者：
Huelsing, Deilah J.
影响因子：
3.4
作者：
Kurata, Yasutaka;Matsuda, Hiroyuki;Shibamoto, Toshishige
通讯作者：
Shibamoto, Toshishige
DOI：
10.1016/j.vascn.2016.03.009
发表时间：
2016-09-01
影响因子：
1.9
作者：
Crumb, William J., Jr.;Vicente, Jose;Strauss, David G.
通讯作者：
Strauss, David G.