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

项目摘要 尖端扭矩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。尽管 这种不良事件很少见，可导致室颤和心源性猝死。对……的无知 形成这种药物引起的心律失常的高风险和低风险人群之间的潜在差异 阻止在防止它方面取得任何重大进展。与其简单地将这些药物从市场上撤下，不如 更仔细地检查从治疗和治疗中受益的患者的生理和临床特征 那些形成心律失常的人需要进行手术。这突出了精准医学的理念和 确定可能从治疗中受益的相关患者亚群与那些 对药物引起的不良事件非常敏感。目前预测风险的标准，延长了 细胞的动作电位(AP)持续时间和超声心动图(ECG)上QT间期的延长已被证明 效果不佳。因此，在此之前需要从细胞和组织水平提取相关信息 给予治疗药物，以检测仅在高危人群中才明显的模式。要分析这一点 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区别，(2) 识别可早期预测风险的重要AP和心电信号，以及(3)将生理学和 临床表现，以改善对高危人群的概况和描述。我会把两个组合在一起 互补计算技术：(1)机械定量系统药理学模拟 心脏细胞和组织的模型；(2)先进的机器学习方法，可以识别隐藏的 模式。因此，该项目旨在开发一种算法，该算法将改进风险预测并升级 目前的心律失常药物处方标准不完善、不可靠。

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.