Sex-Specific Classification of Drug-Induced Torsade de Pointes Susceptibility Using Cardiac Simulations and Machine Learning.
Sex-Specific Classification of Drug-Induced Torsade de Pointes Susceptibility Using Cardiac Simulations and Machine Learning.
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DOI：
10.1002/cpt.2240
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发表时间：
2021-08
影响因子：
6.7
通讯作者：
Grandi E
中科院分区：
文献类型：
--
作者：
Fogli Iseppe A;Ni H;Zhu S;Zhang X;Coppini R;Yang PC;Srivatsa U;Clancy CE;Edwards AG;Morotti S;Grandi E
尖端扭转型室性心动过速（TdP）是一种罕见但致命的室性心律失常，是许多药物的毒副作用。为了评估TdP风险，安全性监管指南要求对所有新治疗化合物的体外hERG通道阻滞和体内QT间期延长进行定量。不幸的是，这些已被证明是扭转型室性心动过速风险的不良预测因子，并可能阻止安全的化合物进入临床阶段。虽然这激发了许多努力来定义心脏安全性的新范例，但最近开发的策略中没有一个考虑到患者的情况。特别是，尽管是TdP的一个公认的独立风险因素，但女性在基础研究和临床研究中的代表性都很低，因此目前的TdP指标可能偏向于男性。在这里，我们将统计学习应用于通过模拟药物对捕获男性和女性电生理学的心肌细胞模型的影响而生成的合成数据，以开发新的TdP风险的性别特异性分类框架。我们表明：（1）TdP分类器在女性和男性中需要不同的特征;（2）基于男性的分类器在应用于女性数据时表现更差;（3）基于女性的分类器性能在很大程度上不受激素急性效应的影响（即，在月经周期的各个阶段）。值得注意的是，在预测女性模拟数据的中间药物的TdP风险时，男性偏见的预测模型始终低估了女性的TdP风险。因此，我们得出结论，临床前心脏毒性风险评估的管道应考虑性别作为一个关键变量，以避免对女性人群造成潜在的危及生命的后果。
Torsade de Pointes (TdP), a rare but lethal ventricular arrhythmia, is a toxic side effect of many drugs. To assess TdP risk, safety regulatory guidelines require quantification of hERG channel block in vitro and QT interval prolongation in vivo for all new therapeutic compounds. Unfortunately, these have proven to be poor predictors of torsadogenic risk, and are likely to have prevented safe compounds from reaching clinical phases. While this has stimulated numerous efforts to define new paradigms for cardiac safety, none of the recently developed strategies accounts for patient conditions. In particular, despite being a well-established independent risk factor for TdP, female sex is vastly underrepresented in both basic research and clinical studies, and thus current TdP metrics are likely biased toward the male sex. Here, we apply statistical learning to synthetic data, generated by simulating drug effects on cardiac myocyte models capturing male and female electrophysiology, to develop new sex-specific classification frameworks for TdP risk. We show that (1) TdP classifiers require different features in females vs. males; (2) male-based classifiers perform more poorly when applied to female data; (3) female-based classifier performance is largely unaffected by acute effects of hormones (i.e., during various phases of the menstrual cycle). Notably, when predicting TdP risk of intermediate drugs on female simulated data, male-biased predictive models consistently underestimate TdP risk in women. Therefore, we conclude that pipelines for preclinical cardiotoxicity risk assessment should consider sex as a key variable to avoid potentially life-threatening consequences for the female population.
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影响因子：
6.7
作者：
Li Z;Ridder BJ;Han X;Wu WW;Sheng J;Tran PN;Wu M;Randolph A;Johnstone RH;Mirams GR;Kuryshev Y;Kramer J;Wu C;Crumb WJ Jr;Strauss DG
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