Zhou Xin, Qu Yusheng, Passini Elisa, Bueno-Orovio Alfonso, Liu Yang, Vargas Hugo M, Rodriguez Blanca
Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, United Kingdom.
SPARC, Amgen Research, Amgen Inc., Thousand Oaks, CA, United States.
Front Pharmacol. 2020 Jan 30;10:1643. doi: 10.3389/fphar.2019.01643. eCollection 2019.
Torsades de Pointes (TdP) is a type of ventricular arrhythmia which could be observed as an unwanted drug-induced cardiac side effect, and it is associated with repolarization abnormalities in single cells. The pharmacological evaluations of TdP risk in previous years mainly focused on the hERG channel due to its vital role in the repolarization of cardiomyocytes. However, only considering drug effects on hERG led to false positive predictions since the drug action on other ion channels can also have crucial regulatory effects on repolarization. To address the limitation of only evaluating hERG, the Comprehensive in Vitro Proarrhythmia Assay initiative has proposed to systematically integrate drug effects on multiple ion channels into drug trial to improve TdP risk assessment. It is not clear how many ion channels are sufficient for reliable TdP risk predictions, and whether differences in IC and Hill coefficient values from independent sources can lead to divergent prediction outcomes. The rationale of this work is to investigate the above two questions using a computationally efficient population of human ventricular cells optimized to favor repolarization abnormality. Our blinded results based on two independent data sources confirm that simulations with the optimized population of human ventricular cell models enable efficient in silico drug screening, and also provide direct observation and mechanistic analysis of repolarization abnormality. Our results show that 1) the minimum set of ion channels required for reliable TdP risk predictions are Nav1.5 (peak), Cav1.2, and hERG; 2) for drugs with multiple ion channel blockage effects, moderate IC variations combined with variable Hill coefficients can affect the accuracy of predictions.
尖端扭转型室性心动过速(TdP)是一种室性心律失常,可作为药物诱发的不良心脏副作用被观察到,并且与单细胞复极化异常有关。由于hERG通道在心肌细胞复极化中起关键作用,过去几年对TdP风险的药理学评估主要集中在该通道上。然而,仅考虑药物对hERG的作用会导致假阳性预测,因为药物对其他离子通道的作用也可能对复极化产生关键的调节作用。为了解决仅评估hERG的局限性,体外全面致心律失常试验计划提议将药物对多个离子通道的作用系统地整合到药物试验中,以改善TdP风险评估。目前尚不清楚多少离子通道足以进行可靠的TdP风险预测,以及来自独立来源的IC和希尔系数值的差异是否会导致不同的预测结果。这项工作的基本原理是使用经过优化以利于复极化异常的计算高效的人类心室细胞群体来研究上述两个问题。我们基于两个独立数据源的盲法结果证实,使用优化的人类心室细胞模型群体进行模拟能够实现高效的计算机药物筛选,并且还能对复极化异常进行直接观察和机制分析。我们的结果表明:1)可靠的TdP风险预测所需的最小离子通道集是Nav1.5(峰值)、Cav1.2和hERG;2)对于具有多种离子通道阻断作用的药物,适度的IC变化与可变的希尔系数相结合会影响预测的准确性。