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

基本信息

批准号：
10470389
负责人：
Alexander P Clark
金额：
$ 2.03万
依托单位：
CORNELL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-21 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10470389
关键词：
Action Potentials Address Adverse effects Affect Algorithms Animals Arrhythmia Behavior Benchmarking Binding Cardiac 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 induced pluripotent stem cell derived cardiomyocytes insight machine learning algorithm novel patch clamp pre-clinical predictive modeling screening sudden cardiac death voltage voltage clamp

项目摘要

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.

项目概要药物开发和批准是一个成本高昂的过程，每种批准的药物花费近 20 亿美元。造成这种高成本的最重要原因之一是开发未能通过试验的药物的费用临床试验——开始临床试验的药物中只有 15% 被批准用于人体，这是一个常见的原因。未达到 FDA 批准标准的原因是该药物被归类为心脏毒性药物，不能在药物开发的早期阶段发现的药物导致心脏毒性的一种方式是改变。离子通道的电活动负责激发泵血的心脏组织了解哪些离子通道以及这些离子通道受到影响的程度是非常重要的。确定药物心脏毒性的核心是心脏毒性的早期预测基于动物。人类心脏行为模型较差的研究或错误的单细胞电生理学研究基于动作电位变化假设潜在的病理生理学。诱导多能干细胞来源的心肌细胞（hiPSC-CM）为研究药物作用提供了机会在这项研究中，我们采用了计算 iPSC-CM 来拟合人类细胞。药物应用前后采集的电压钳 (VC) 数据模型提供了一种量化方法未知的药物对特定心脏离子通道的影响我们将通过以下内容来解决这一假设。具体目标：1）使用机器学习设计一种新颖的 VC 协议，以提高数据质量 hiPSC-CM 模型拟合 2) 量化药物前后 hiPSC-CM 离子通道电导的变化。我们将使用机器学习来开发一种新颖的电压钳协议，以改进从我们的 hiPSC-CM 获取的电生理学数据该数据经过优化以改进对结果的预测。在心脏动作电位期间激活的所有离子通道的电导我们可以应用该电压。在药物应用之前和之后在体外环境中钳制协议，然后将我们的计算模型拟合到每个数据集。通过模型拟合预测的离子通道电导的变化可作为估计值。该提案的贡献将是重大的，因为它将是药物的通道特异性作用。第一项使用人类心肌细胞对通道特异性药物靶标进行定量测量的研究这可能会导致致命的心律失常。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Leak current, even with gigaohm seals, can cause misinterpretation of stem cell-derived cardiomyocyte action potential recordings.

即使采用千兆欧姆密封，漏电流也会导致干细胞衍生的心肌细胞动作电位记录的误解。

DOI：
发表时间：
2023-08-02
期刊：
影响因子：
0
作者：
Clark, Alexander P;Clerx, Michael;Wei, Siyu;Lei, Chon Lok;de Boer, Teun P;Mirams, Gary R;Christini, David J;Krogh
通讯作者：
Krogh

An in silico-in vitro pipeline for drug cardiotoxicity screening identifies ionic pro-arrhythmia mechanisms.

用于药物心脏毒性筛选的计算机体外管道确定了离子促心律失常机制。