Combining Systems Pharmacology Modeling With Machine Learning To Identify Sub-Populations At Risk Of Drug-Induced Torsades de Pointes

将系统药理学建模与机器学习相结合，识别面临药物诱发尖端扭转型室速风险的亚群

• 批准号: 10082298
• 负责人: Meera Varshneya
• 金额: $ 3.4万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2021-09-26
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10082298
• 关键词: Action Potentials; Adverse event; Algorithms; Antibiotics; Antihistamines; Antipsychotic Agents; Arrhythmia; Biological Factors; Cardiac; Cells; Characteristics; Classification; Clinical; Clinical Trials; Computational Technique; Consumption; Disease; Echocardiography; Electrocardiogram; Exposure to; Goals; Hand; Incidence; Individual; Ion Channel; Knowledge; Lead; Life; Machine Learning; Measures; Modeling; Molecular; Muscle Cells; Patient risk; Patients; Pattern; Persons; Pharmaceutical Preparations; Pharmacology; Physiological; Physiology; Population; Populations at Risk; Predisposition; Reporting; Research Project Grants; Resources; Risk; Statistical Data Interpretation; System; Therapeutic; Time; Tissues; Torsades de Pointes; Translating; Ventricular; Ventricular Arrhythmia; Ventricular Fibrillation; Ventricular Tachycardia; base; complex data; delayed rectifier potassium channel; drug market; efficacious treatment; experimental study; heart cell; high risk; high risk population; improved; insight; patient subsets; precision medicine; prevent; responders and non-responders; risk prediction; risk stratification; side effect; simulation; sudden cardiac death; supervised learning; trait

Project Summary Torsades de Pointes, a lethal ventricular arrhythmia, is a side effect of several commonly used antiarrhythmics, antibiotics, antipsychotics, antihistamines and other ‘non-cardiovascular’ therapies. Though this adverse event is rare, it can lead to ventricular fibrillation and sudden cardiac death. The ignorance about the underlying differences between those at high risk versus low risk of forming this drug-induced arrhythmia halts any considerable progress in preventing it. Rather than simply removing these drugs from the market, a closer examination of the physiological and clinical traits of patients who benefited from the treatment and those who formed the arrhythmia needs to be performed. This highlights the idea of precision medicine and the importance of identifying relevant sub-groups of patients likely to benefit from a treatment versus those who are highly susceptible to a drug-induced adverse event. The current standards for predicting risk, a lengthened action potential (AP) duration of cells and a prolonged QT interval on an echocardiogram (ECG) have proven ineffective. Thus, there is a need to extract pertinent information from the cellular and tissue levels before administration of the therapeutic to detect patterns only apparent in the high-risk population. To analyze this concept, I plan to (1) explain at a mechanistic level the differences between the healthy and at-risk patients, (2) identify important AP and ECG signatures that can predict risk early on, and (3) connect the physiological and clinical findings to improve the profile and description of the high-risk population. I will combine two complementary computational techniques: (1) simulations with mechanistic quantitative systems pharmacology models of heart cells and tissues; and (2) advanced machine learning approaches that can identify hidden patterns. Thus, this project aims to develop an algorithm which will improve risk prediction and upgrade the current imperfect and unreliable standards for prescribing proarrhythmic therapies.

项目概要 尖端扭转型室性心动过速（Torsades de Pointes）是一种致命性室性心律失常，是几种常用药物的副作用 抗心律失常药、抗生素、抗精神病药、抗组胺药和其他“非心血管”疗法。 这种不良事件很少见，它可能导致心室颤动和心源性猝死。 形成这种药物性心律失常的高风险人群与低风险人群之间的根本差异 阻止在预防方面取得任何重大进展，而不是简单地将这些药物从市场上撤下。 更仔细地检查受益于治疗的患者的生理和临床特征 那些形成了心律失常的人需要进行治疗，这凸显了精准医学的理念和理念。 确定可能从治疗中受益的相关患者亚组与那些可能受益于治疗的患者的重要性 非常容易受到药物引起的不良事件的影响 目前预测风险的标准延长了。 已证明细胞动作电位 (AP) 持续时间和超声心动图 (ECG) 上的 QT 间期延长 因此，需要先从细胞和组织水平提取相关信息。 进行治疗以检测仅在高危人群中出现的模式来分析这一点。 概念，我计划（1）从机械层面解释健康患者和高危患者之间的差异，（2） 识别可以及早预测风险的重要 AP 和 ECG 特征，以及 (3) 将生理和心电图联系起来 为了完善对高危人群的概况和描述，我将结合两个临床发现。 补充计算技术：（1）机械定量系统药理学模拟 心脏细胞和组织模型；（2）先进的机器学习方法，可以识别隐藏的 因此，该项目旨在开发一种算法来改进风险预测并升级风险预测。 目前抗心律失常治疗的处方标准不完善且不可靠。