Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.
Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.
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DOI：
10.3389/fphys.2017.00668
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发表时间：
2017
影响因子：
4
通讯作者：
Rodriguez B
中科院分区：
文献类型：
--
作者：
Passini E;Britton OJ;Lu HR;Rohrbacher J;Hermans AN;Gallacher DJ;Greig RJH;Bueno-Orovio A;Rodriguez B
心脏毒性的早期预测对于药物开发至关重要。目前的动物模型提出了伦理和转化问题，并且临床风险预测的准确性有限。基于人类的计算机模型构成了一种快速、廉价且可能有效的实验分析替代方案，也促进了向人类的转化。主要挑战包括考虑药物反应的细胞间变异性以及安全药理学中计算和实验方法的整合。我们的目的是评估人类动作电位 (AP) 模型群体中的计算机药物试验基于离子通道信息预测药物诱发心律失常的临床风险的能力，并将模拟结果与药物测试常用的实验测定进行比较。构建了 1,213 个人类心室 AP 模型的对照群体，与实验记录一致。使用孔阻塞药物模型（IC50/希尔系数）对多种浓度的 62 种参考化合物进行了计算机药物试验。对药物引起的 AP 生物标志物的变化以及复极/去极异常的发生进行了量化。模拟结果用于根据尖端扭转型室速心律失常的报告预测临床风险，并通过与兔楔形制剂的心电图和人诱导多能干细胞衍生的心肌细胞 (hiPS-CM) 中的 Ca2+ 瞬时记录进行比较，进一步评估化合物的子集。药物引起的计算机变化的大小取决于群体中每个模型的特定离子谱，从而可以识别出现异常 AP 表型的较高风险的细胞亚群。复极储备低（Ca2+/晚Na+电流和Na+/Ca2+交换器增加，Na+/K+泵减少）的模型极易受到药物诱导的复极异常的影响，而内向电流密度降低（快/晚Na+和Ca2+电流）的模型则对去极化异常高度敏感。计算机复极异常可预测所有化合物的临床风险，准确度为 89%。药物引起的生物标志物变化在不同的测定中总体一致：计算机 AP 持续时间变化反映了在兔 QT 间期和 hiPS-CM Ca2+ 瞬变中观察到的变化，模拟的上冲速度捕获了兔 QRS 复合波中的变化。我们的结果表明，人体计算机药物试验构成了预测临床促心律失常心脏毒性的强大方法，可以集成到现有的药物安全评估流程中。
Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC50/Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca2+-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca2+/late Na+ currents and Na+/Ca2+-exchanger, reduced Na+/K+-pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na+ and Ca2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca2+-transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.
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影响因子：
5
作者：
Kim, Jong J.;Yang, Lei;Lin, Bo;Zhu, Xiaodong;Sun, Bin;Kaplan, Aaron D.;Bett, Glenna C. L.;Rasmusson, Randall L.;London, Barry;Salama, Guy
通讯作者：
Salama, Guy
影响因子：
3.6
作者：
Braga RC;Alves VM;Silva MF;Muratov E;Fourches D;Lião LM;Tropsha A;Andrade CH
通讯作者：
Andrade CH
DOI：
10.1016/j.vascn.2013.04.004
发表时间：
2013-07
影响因子：
1.9
作者：
Beattie KA;Luscombe C;Williams G;Munoz-Muriedas J;Gavaghan DJ;Cui Y;Mirams GR
通讯作者：
Mirams GR
影响因子：
4
作者：
Gauthier LD;Greenstein JL;Winslow RL
通讯作者：
Winslow RL
影响因子：
2.7
作者：
Hindmarsh, AC;Brown, PN;Woodward, CS
通讯作者：
Woodward, CS