Predicting Drug Cardiotoxicity Targets Using iPSC-Derived Cardiomyocytes and Machine Learning

使用 iPSC 衍生的心肌细胞和机器学习预测药物心脏毒性目标

• 批准号: 10433823
• 负责人: Alexander P Clark
• 金额: $ 3.96万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-21 至 2023-08-20
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10433823
• 关键词: Action Potentials; Address; Adverse effects; Affect; Algorithms; Animals; Arrhythmia; Behavior; Benchmarking; Binding; Cardiac; Cardiac Myocytes; Cardiotoxicity; Cavia; Cells; Clinical Trials; Closure by clamp; Complex; Computer Models; Computing Methodologies; Data; Data Set; Detection; Development; Disease; Drug Approval; Drug Costs; Drug Targeting; Early Diagnosis; Electrophysiology (science); Equation; Failure; Functional disorder; Goals; Heart; Human; In Vitro; Ion Channel; Kinetics; Knowledge; Lead; Learning; Machine Learning; Manuals; Measurement; Measures; Methods; Modeling; Morphology; Pharmaceutical Preparations; Population; Process; Protocols documentation; Publishing; Recovery; Research; Risk; Risk Assessment; Sensitivity and Specificity; Standard Model; Testing; Tissues; base; blood pump; cost; design; drug development; experimental study; heart cell; human data; human model; human stem cells; human subject; improved; induced pluripotent stem cell; insight; machine learning algorithm; novel; patch clamp; pre-clinical; predictive modeling; screening; sudden cardiac death; voltage; voltage clamp

PROJECT SUMMARY Drug development and approval is a costly process with nearly $2 billion spent for each drug that is approved. One of the most significant contributors to this high cost is the expense of developing drugs that fail to pass clinical trials – only 15% of drugs that begin clinical trials are approved for use on humans. A common reason for not reaching the FDA’s criteria for approval is that the drug is classified as cardiotoxic, which cannot be detected during early stages of drug development. One way that drugs can lead to cardiotoxicity is by altering the electrical activity of ion channels that are responsible for the excitation of the heart tissue that pumps blood to the body. Understanding which ion channels, and the extent to which these ion channels are affected is central to determining the cardiotoxicity of a drug. Early-stage predictions of cardiotoxicity are based on animal studies that are poor models of human heart behavior or single-cell electrophysiological studies that falsely assume underlying pathophysiology based on action potential changes. The recent development of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) offers an opportunity to study drug effects on human cells in a preclinical setting. In this study, we hypothesize that fitting a computational iPSC-CM model to voltage-clamp (VC) data acquired before and after drug application provides a means of quantifying unknown drug effects on specific cardiac ion channels. We will address this hypothesis through the following Specific Aims: 1) Use machine learning to design a novel VC protocol that improves the quality of data for hiPSC-CM model fitting. 2) Quantify the change in hiPSC-CM ion channel conductances before and after drug application. We will use machine learning to develop a novel voltage clamp protocol that improves the electrophysiology data acquired from our hiPSC-CMs. The data is optimized to improve predictions of the conductances for all ion channels activated during the cardiac action potential. We can apply this voltage clamp protocol in an in vitro setting before and after drug application, then fit our computational model to each dataset. The change in ion channel conductances, predicted by the model fit, serves as an estimate of the channel-specific effects of the drug. The contributions of this proposal will be significant because it will be the first study to use human cardiac cells to produce quantitative measurements of channel-specific drug targets that may lead to lethal cardiac arrhythmias.

项目摘要 药物开发和批准是一个昂贵的过程，每种批准的药物花费了近20亿美元。 高成本的最重要的贡献者之一是开发无法通过的药物的费用 临床试验 - 批准开始临床试验的药物中只有15％用于人类。一个常见的原因 由于未达到FDA的批准标准是该药物被归类为心脏毒性，这是不可能的 在药物开发的早期阶段检测到。药物可以导致心脏毒性的一种方法是改变 离子通道的电活动，导致泵血的心脏组织兴奋 到身体。了解哪些离子通道以及这些离子通道受到影响的程度是 确定药物的心脏毒性的中心。心脏毒性的早期预测是基于动物的 研究是人类心脏行为或单细胞电生理研究的贫困模型的研究 基于动作电位变化，假设潜在的病理生理学。人类的最新发展 诱导多能干细胞衍生的心肌细胞（HIPSC-CMS）提供了研究药物作用的机会 在临床前的人类细胞上。在这项研究中，我们假设适合计算IPSC-CM 在申请药物之前和之后获取的电压钳（VC）数据的模型提供了一种量化的方法 未知药物对特定心脏离子通道的影响。我们将通过以下来解决这一假设 具体目的：1）使用机器学习设计新颖的VC协议，以提高数据质量 HIPSC-CM模型拟合。 2）量化药物之前和之后的H​​IPSC-CM离子通道电导的变化 应用。我们将使用机器学习来开发一种新颖的电压夹协议，以改善 从我们的HIPSC-CMS获得的电生理数据。优化数据以改善对 在心脏作用电位期间激活的所有离子通道的电导。我们可以应用此电压 在药物施用之前和之后，在体外环境中的夹紧协议，然后将我们的计算模型适合每个 数据集。模型拟合预测的离子通道电导的变化是对 该药物的渠道特异性作用。该提案的贡献将是重要的，因为它将是 首先使用人类心脏细胞生成通道特异性药物靶标的定量测量的研究 这可能导致致命的心律不齐。