All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.

中文翻译：

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使用机器学习根据致心律失常风险对药物进行分类


所有药物都有副作用。其中最严重的是心律失常。目前的药物安全性评估范式成本高昂、耗时长、保守，并且阻碍了有效的药物开发。在这里，我们结合多尺度实验和模拟、高性能计算和机器学习来创建风险估计器，根据新药和现有药物的致心律失常潜力对它们进行分层。我们利用机器学习的最新发展，整合空间和时间上 10 个数量级的信息，以提供药物单独或与其他药物结合的效果的整体情况。我们通过实验和计算表明，药物引起的心律失常主要是由两种具有相反作用的电流之间的相互作用决定的：快速延迟整流钾电流和 L 型钙电流。使用高斯过程分类，我们创建了一个分类器，针对这两种电流的任意组合将药物分为安全域和心律失常域。我们证明，我们的分类器仅根据 50% 当前块的浓度正确识别 22 种常见药物的风险类别。我们的新风险评估工具解释了在哪些条件下阻断 L 型钙电流可以延迟甚至完全抑制致心律失常事件。在药物安全性评价中使用机器学习可以对新药的致心律失常潜力提供更准确、更全面的机制评估。我们的研究为建立基于科学的标准铺平了道路，以加速药物开发、设计更安全的药物和减少心律失常。