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A Computational Pipeline to Predict Cardiotoxicity: From the Atom to the Rhythm.
Circulation Research ( IF 20.1 ) Pub Date : 2020-02-24 , DOI: 10.1161/circresaha.119.316404
Pei-Chi Yang 1 , Kevin R DeMarco 1 , Parya Aghasafari 1 , Mao-Tsuen Jeng 1 , John R D Dawson 1, 2 , Slava Bekker 3 , Sergei Y Noskov 4 , Vladimir Yarov-Yarovoy 1 , Igor Vorobyov 1, 5 , Colleen E Clancy 1, 5
Affiliation  

Rationale: Drug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery. Objective: To predict the impact of a drug from the drug chemistry on the cardiac rhythm. Methods and Results: In a new linkage, we connected atomistic scale information to protein, cell and tissue scales by predicting drug binding affinities and rates from simulation of ion channel and drug structure interactions and then used these values to model drug effects on the hERG channel. Model components were integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model framework validation and showed excellent agreement, demonstrating feasibility of a new approach for cardiotoxicity prediction. Conclusions: We present a multiscale model framework to predict electro-toxicity in the heart from the atom to the rhythm. Novel mechanistic insights emerged at all scales of the system, from the specific nature of proarrhythmic drug interaction with the hERG channel, to the fundamental cellular and tissue level arrhythmia mechanisms. Applications of machine learning indicate necessary and sufficient parameters that predict arrhythmia vulnerability. We expect that the model framework may be expanded to make an impact in drug discovery, drug safety screening for a variety of compounds and targets, and in a variety of regulatory processes.

中文翻译:

预测心脏毒性的计算管道:从原子到节奏。

基本原理:药物诱发的心律失常与 QT 间期延长密切相关,因此 QT 延长是公认的心律失常替代标志物。但是QT间期作为一个标志物过于敏感,没有选择性,导致很多有用的药物在药物发现中被淘汰。目的:从药物化学预测药物对心律的影响。方法和结果:在一个新的联系中,我们通过模拟离子通道和药物结构相互作用预测药物结合亲和力和速率,将原子尺度信息与蛋白质、细胞和组织尺度联系起来,然后使用这些值来模拟药物对 hERG 通道的影响. 模型组件被集成到细胞和组织尺度的预测模型中,以揭示基本的心律失常脆弱性机制和紧急行为背后的复杂相互作用。人体临床数据用于模型框架验证并显示出极好的一致性,证明了一种新的心脏毒性预测方法的可行性。结论:我们提出了一个多尺度模型框架来预测心脏从原子到节律的电毒性。从促心律失常药物与 hERG 通道相互作用的特殊性质,到基本的细胞和组织水平心律失常机制,系统的所有规模都出现了新的机制见解。机器学习的应用表明了预测心律失常易感性的必要和充分的参数。
更新日期:2020-04-09
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