A population-based in silico platform for arrhythmia prediction

项目总结 室性心律失常是心脏性猝死的主要原因，每年约有300,000人死亡 仅在美国一年。然而，目前还没有出现类似的药物或生物疗法。 植入型心律转复除颤器的疗效。主要障碍是：1)在个人层面上，心律失常 有多方面、多尺度的原因和机制。药物在分子水平上针对实体，但 心律失常基本上是组织规模的现象，由于复杂，不存在简单的一对一关系 多尺度非线性相互作用。抗心律失常药物可能抑制一种特殊的心律失常机制 但强化了另一种机制，意外地增加而不是降低死亡率，如 临床试验；以及2)在人群规模上，一种药物可能对一个个体具有抗心律失常作用，但具有促心律失常作用 另一个原因是个体间的可变性/多样性和复杂的环境差异，这也可能 这是目前抗心律失常药物治疗失败的原因。因此，对于抗心律失常药物的发现，一 必须评估分子干预或药物不仅对单一心律失常机制的影响，而且 所有可能的心律失常机制。此外，还必须考虑到个体间的可变性和 复杂的环境压力。另一个同样重要和关键的问题是有效的心律失常风险 (心脏毒性)药物安全性筛查。过去，从市场上下架的药品中约有30%是由于 他们的心律失常风险。由于问题的极端复杂性，计算机建模和仿真将 被要求评估一种药物的抗心律失常和促心律失常效果。最近，心脏安全性研究 联合会和FDA建议将计算机模拟作为治疗心律失常的补充方法 药物筛查。然而，传统的建模方法是有限的和基于总体的建模 方法是必需的。此外，模型群体需要准确地考虑个体之间的关系 准确预测药物抗心律失常和致心律失常的变异性和心律失常机制 效果。本项目提出了开发一种新型的包含正常和疾病多尺度的硅胶平台 模拟人类种群个体间变异性的模型种群。模范人群将是 根据正常和疾病条件下的临床数据进行筛选和验证。“虚拟临床试验”将是 然后进行抗心律失常药物发现和药物安全性筛选。具体目标是：1)发展 并验证整合了模型种群的电子平台，该模型种群模拟了 在正常和疾病条件下的人类种群；2)利用电子人类模型种群作为 抗心律失常新药研发和心脏毒性筛选平台。这是一种数据驱动的硅胶技术 集成计算建模、实验人体心脏数据和临床数据以进行转换的方法 临床医学的计算机建模。

PROJECT SUMMARY Ventricular arrhythmias are the leading cause of sudden cardiac death accounting for ~300,000 deaths per year in the US alone. However, no pharmacological or biological therapy has yet emerged with comparable efficacy to the implantable cardioverter-defibrillator. The major hurdles are: 1) at the individual scale, arrhythmias have multiple and multiscale causes and mechanisms. Drugs target entities at the molecular scale but arrhythmias are fundamentally tissue-scale phenomena, with no simple one-to-one relationships due to complex multiscale nonlinear interactions. An antiarrhythmic drug may suppress one particular arrhythmia mechanism but potentiate another mechanism, unexpectedly increasing rather than decreasing mortality as shown in large clinical trials; and 2) at the population scale, a drug may be antiarrhythmic for one individual but proarrhythmic for another due to inter-individual variability/diversity and complex environmental differences, which may also account for the failure of current antiarrhythmic drug therapies. Therefore, for antiarrhythmic drug discovery, one must evaluate the effects of a molecular intervention or a drug on not just a single arrhythmia mechanism, but all possible arrhythmia mechanisms. Additionally, one must take into account inter-individual variability and complex environmental stresses. An equally important and crucial problem is effective proarrhythmia risk (cardiotoxicity) screening for drug safety. In the past, ~30% of the drugs removed from the market were due to their proarrhythmia risk. Owing to the extreme complexity of the problem, computer modeling and simulation will be required to evaluate a drug's antiarrhythmic and proarrhythmic effects. Recently, the Cardiac Safety Research Consortium and FDA have recommended computer simulation as a complementary approach for proarrhythmia drug screening. However, traditional modeling approaches are limited and population-based modeling approaches are required. Moreover, the model populations need to accurately account for the inter-individual variability and arrhythmia mechanisms for accurate prediction of a drug's antiarrhythmic and proarrhythmic effects. This project proposes to develop a novel in silico platform which includes multiscale normal and diseased model populations emulating the inter-individual variability of human populations. The model populations will be filtered and validated against clinical data under normal and diseased conditions. “Virtual clinical trials” will be then performed for antiarrhythmic drug discovery and drug safety screening. The specific aims are: 1) to develop and validate an in silico platform incorporating model populations that emulate the inter-individual variability of human populations under normal and disease conditions; 2) to utilize the in silico human model populations as a platform for novel antiarrhythmic drug discovery and cardiotoxicity screening. This is a data-driven in silico approach which integrates computational modeling, experimental human heart data, and clinical data to translate computational modeling to clinical medicine.