Predicting determinants of susceptibility to drug-induced arrhythmias

预测药物性心律失常易感性的决定因素

• 批准号: 10608557
• 负责人: ERIC A SOBIE
• 金额: $ 42.18万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-15 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608557
• 关键词: Action Potentials; Adverse event; Arrhythmia; Calcium; Cardiac; Cardiac Electrophysiologic Techniques; Characteristics; Chronic; Classification; Dangerousness; Data; Disease; Drug usage; Electrophysiology (science); Event; Fever; Heart Diseases; Hypokalemia; In Vitro; Individual; Inflammation; Machine Learning; Measures; Minority; Modeling; Muscle Cells; Patients; Pharmaceutical Preparations; Phenotype; Physiology; Population Heterogeneity; Predisposition; Probability; Research; Risk; Sex Differences; System; Testing; Ventricular Arrhythmia; Work; associated symptom; comorbidity; demographics; drug candidate; drug classification; experimental study; heart cell; innovation; machine learning classifier; machine learning model; mathematical model; medication safety; novel therapeutics; response; risk prediction; simulation; stem cells

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.

项目概要 所有新候选药物都必须测试其作为药物引起的心律失常的可能性 不良事件。近年来，在开发更敏感、更具体的方面取得了实质性进展。 预测哪些药物可能会增加心律失常的风险，我的团队一直处于努力的最前沿 采用机械模型来定量预测心脏药物安全性。尽管如此，心律失常仍 罕见事件，甚至被认为危险的药物也只会在少数患者中引起心律失常。 因此，确定哪些患者最容易发生心律失常，以及哪些情况会增加其风险， 与药品分类一样重要。这里提出的工作将通过以下方式解决这些具有挑战性的问题 创新组合：(1) 体外生理学实验，将量化药物如何影响肌细胞 动作电位和细胞内钙； (2) 机械数学模型的模拟 纳入群体之间以及同一群体内个体之间的表型差异；和（3） 机器学习来综合结果并开发预测分类系统。进行的实验 干细胞衍生的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调整。随后对异质群体的模拟将 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如何 心脏电生理学和预先存在的心脏病的存在会影响药物反应。 通过结合细胞实验、心脏细胞的机械数学模型和 通过机器学习模型，我们将量化每个因素对心律失常风险的影响程度。 2. 常见疾病相关症状如何影响潜在的促心律失常作用 用于治疗这些疾病的药物有哪些？许多疾病都与影响心脏的疾病有关 电生理学，例如发烧、低钾血症和慢性炎症。我们将开发一个模拟平台 这就是这些影响的原因。 3. 一组中哪些患者的风险特别高？除了量化差异的影响之外 在组之间，我们的机器学习分类器将允许我们预测组中的哪些患者 由于其“电生理特征”，特别容易受到药物引起的心律失常的影响。 这些研究将为定量理解和预测药物诱发的疾病提供新的范式。 心律失常不仅考虑药物之间的差异，还考虑服用这些药物的患者之间的差异 药物。