Connecting Subcellular Electrophysiology to Patient Arrhythmia: A Case Study of Amiodarone

项目总结 心律失常或心律不齐，影响超过2%的人，并导致 大多数病例为心源性猝死。了解人类心脏电生理学对我们的 预防和治疗心律失常的能力。要做到这一点的一个关键障碍是，心率既涉及空间和空间，也涉及 心肌细胞离子通道活动的时间协调性。然而，虽然健壮的时间 单细胞水平的表征是可行的，将其与器官水平的紧急空间表现联系起来。 心律失常的诊断要困难得多。尽管疾病负担很大，但目前的抗心律失常药物 治疗方法往往缺乏治疗效果，并受到显著副作用的阻碍。胺碘酮是一种常见的 抗心律失常处方药，具有多种临床适应症。虽然有效，但胺碘酮表现出显著的 心外毒性，影响多个电流和离子通道，使其难以表征。进一步 限制这一努力的是健康的人类心脏细胞和组织的临床样本有限 实验，有必要使用不太理想的动物模型替代品。在这里，我提议一个高- 人源性诱导多能干细胞单细胞和组织分析的吞吐量集成平台 心肌细胞(HiPSC-CMS)，以强有力地表征电生理，钙处理，和 抗心律失常分子处理的人来源细胞的生物力学。我将使用一个计算模型 HiPSC-CMS链接这些数据。然后，我将在分离的患者心室肌细胞中完成同样的分析， 创新的方法。然后，我将使用这些数据来训练计算的心室肌细胞模型，并将这些 通过既定的方法。在这里，我采取自下而上的方法来理解成功和失败的机制 最常用的抗心律失常药物之一，胺碘酮。我的目标是建立一个框架来获得洞察力 进入安全有效的下一代疗法的合理设计。

PROJECT SUMMARY Abnormalities in cardiac rhythm, or arrhythmia, affect more than 2% of individuals and are responsible for the majority of cases of sudden cardiac death. Understanding human cardiac electrophysiology is crucial to our ability to prevent and treat arrhythmias. A critical obstacle to this is that cardiac rhythm involves both spatial and temporal coordination of ion channel activity in heart muscle cells. However, while robust temporal characterization at the single-cell level is feasible, linking this with emergent organ-level spatial manifestations of cardiac arrhythmia is far more difficult. Despite a significant disease burden, current antiarrhythmic drug therapies often lack therapeutic efficacy and are hampered by significant side effects. Amiodarone is a commonly prescribed antiarrhythmic drug, with multiple clinical indications. While effective, amiodarone exhibits significant extracardiac toxicity and affects multiple currents and ion channels, making it difficult to characterize. Further constraining this effort is the limited availability of clinical samples of healthy human cardiac cells and tissues for experimentation, necessitating the use of suboptimal animal model surrogates. Here, I propose a high- throughput integrated platform of single-cell and tissue analyses in human-derived induced pluripotent stem cell cardiomyocytes (hiPSC-CMs) to robustly characterize the electrophysiology, calcium handling, and biomechanics of human-derived cells treated with antiarrhythmic molecules. I will use a computational model of hiPSC-CMs to link these data. I will then complete the same analyses in isolated patient ventricular myocytes, an innovative approach. I will then use this data to train a computational ventricular myocyte model and link these via established methods. Here, I take a bottom-up approach to understand mechanisms of success and failure of the most commonly prescribed antiarrhythmic drug, amiodarone. My goal is to build a framework to gain insight into rational design of the next generation of therapeutics that are safe and effective.