From the Department of Physiology and Membrane Biology (P.-C.Y., K.R.D., P.A., M.-T.J., J.R.D.D., V.Y.-Y., I.V., C.E.C.), University of California Davis.
Biophysics Graduate Group (J.R.D.D.), University of California Davis.
Circ Res. 2020 Apr 10;126(8):947-964. doi: 10.1161/CIRCRESAHA.119.316404. Epub 2020 Feb 24.
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.
To predict the impact of a drug from the drug chemistry on the cardiac rhythm.
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.
We present a multiscale model framework to predict electrotoxicity 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.
药物引起的致心律失常与 QT 间期延长密切相关，因此 QT 间期延长是心律失常的公认替代标志物。但是 QT 间期是一个过于敏感的标志物，而且不具有选择性，导致许多有用的药物在药物发现过程中被淘汰。
预测药物对心脏节律的影响。
在一个新的关联中，我们通过模拟离子通道和药物结构相互作用来预测药物结合亲和力和速率，从而将原子尺度的信息与蛋白质、细胞和组织尺度连接起来，然后使用这些值来模拟药物对 hERG 通道的影响。模型组件被整合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潜在的复杂相互作用。使用人类临床数据对模型框架进行验证，结果显示出极好的一致性，证明了从原子到节律预测心脏毒性的新方法的可行性。
我们提出了一种从原子到心脏电毒性的多尺度模型框架。从致心律失常药物与 hERG 通道的特定相互作用，到基本的细胞和组织水平的心律失常机制，在系统的所有尺度上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所需的充分参数。我们预计该模型框架可以扩展到药物发现、各种化合物和靶点的药物安全性筛选以及各种监管过程中产生影响。
