Predicting determinants of susceptibility to drug-induced arrhythmias

项目总结 所有新的候选药物都必须进行测试，以确定它们是否有可能导致药物引起的心律失常 不良事件。近年来，在制定更敏感和更具体的 预测哪些药物可能会增加心律失常的风险，我的团队一直站在努力的前沿 使用机械模型对心脏药物安全性进行定量预测。尽管如此，心律失常 罕见的事件，甚至被认为危险的药物只会在少数患者中导致心律失常。 因此，确定哪些患者最有可能发生心律失常，哪些情况会增加他们的风险， 和毒品分类一样重要。这里提出的工作将解决这些具有挑战性的问题，通过 一种创新的组合：(1)将量化药物如何影响心肌细胞的体外生理学实验 动作电位和细胞内钙；(2)用机械数学模型模拟 纳入群体之间和同一群体内个体之间的表型差异；以及(3) 机器学习来综合结果并开发预测性分类系统。进行的实验 干细胞来源的心肌细胞将测量细胞对多种药物的反应，这些数据将 允许对数学模型进行严格的调整。随后对异质种群的模拟将 解决具有挑战性的未解决问题，例如： 1.患者特征如何影响心律失常风险？模拟将解决性别差异 在心脏电生理学中，既往心脏病的存在会影响药物反应。 通过细胞实验，心脏细胞的机械数学建模，以及 机器学习模型中，我们将量化每个因素对心律失常风险的影响程度。 2.与常见疾病相关的症状如何影响潜在的促心律失常作用 用来治疗这些疾病的药物吗？许多疾病都与影响心脏的疾病有关。 电生理学，如发烧、低钾血症和慢性炎症。我们将开发一个仿真平台 这就解释了这些影响。 3.同一组中哪些患者的风险特别高？除了量化差异的影响 在小组之间，我们的机器学习分类器将允许我们预测小组中的哪些患者 根据他们的“电生理特征”，特别容易发生药物引起的心律失常。 这些研究将为定量理解和预测药物引起的疾病提供一个新的范式。 心律失常，不仅考虑药物之间的差异，也考虑服用这些药物的患者之间的差异 毒品。

PROJECT SUMMARY All new drug candidates must be tested for their potential to cause arrhythmia as a drug-induced adverse event. Recent years have seen substantial progress in developing more sensitive and specific predictions of which drugs may increase arrhythmia risk, and my group has been at the forefront of efforts to employ mechanistic modeling for quantitative predictions of cardiac drug safety. Nonetheless, arrhythmias are rare events, and even drugs that are considered dangerous only induce arrhythmias in a minority of patients. Therefore, identifying which patients are most at risk of arrhythmia, and which conditions increase their risk, is as important as classifying drugs. The work proposed here will address these challenging questions through an innovative combination of: (1) in vitro physiology experiments that will quantify how drugs influence myocyte action potentials and intracellular calcium; (2) simulations with mechanistic mathematical models that incorporate phenotypic differences between groups and between individuals within the same group; and (3) machine learning to synthesize results and develop predictive classification systems. Experiments performed in stem cell-derived myocytes will measure cellular responses to a wide range of drugs, and these data will allow for rigorous tuning of mathematical models. Subsequent simulations of heterogeneous populations will address challenging unresolved questions, such as: 1. How do patient characteristics influence arrhythmia risk? Simulations will address how sex differences in cardiac electrophysiology and the presence of pre-existing cardiac disease influence drug responses. Through a combination of cellular experiments, mechanistic mathematical modeling of heart cells, and machine learning models, we will quantify how much each factor influences arrhythmia risk. 2. How do symptoms associated with common diseases influence the potential pro-arrhythmic effects of drugs used to treat those diseases? Many diseases are associated with conditions that influence cardiac electrophysiology, such as fever, hypokalemia, and chronic inflammation. We will develop a simulation platform that accounts for these effects. 3. Which patients within a group are especially at risk? Besides quantifying the effects of differences between groups, our machine learning classifiers will allow us to predict which patients within a group are especially susceptible to drug-induced arrhythmia on the basis of their “electrophysiological signatures.” Together these studies will offer a new paradigm for quantitative understanding and prediction of drug-induced arrhythmia that considers not only differences between drugs, but also between the patients that take these drugs.