[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].
[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].
复制标题
[基于机器学习的药物不良反应引起癫痫发作的预测]。
DOI：
10.1248/yakushi.17-00213-1
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Yuji Ikegaya
中科院分区：
文献类型：
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作者：
Mengxuan Gao;Motoshige Sato;Yuji Ikegaya
在药物开发的临床前研究阶段，动物试验被广泛用于帮助筛选药物的危险副作用。然而，预测中枢神经系统内的副作用仍然很困难。在这里，我们介绍了一种基于机器学习的体外系统，旨在在临床试验前检测诱发癫痫的副作用。我们记录了急性小鼠新皮质海马切片中 CA1 肺泡的局部场电位，这些切片用 14 种不同药物中的每一种进行浴灌注，每种药物有 5 种不同的浓度。对于每一个实验条件，我们收集了类似癫痫发作的神经元活动，并将它们的波形合并为一个图形图像，然后使用 Caffe（一种深度学习的开放框架）将其进一步转换为特征向量。在前两个主要组成部分的空间中，支持向量机完全分离了诱发癫痫样事件的向量（即单个药物的剂量），并将苯海拉明、依诺沙星、士的宁和茶碱识别为“诱发癫痫”药物，这些药物确实有报道在临床情况下诱发癫痫发作。因此，这种基于人工智能的分类可能提供一个新的平台来临床前检测药物引起癫痫发作的副作用。
During the preclinical research period of drug development, animal testing is widely used to help screen out a drug's dangerous side effects. However, it remains difficult to predict side effects within the central nervous system. Here, we introduce a machine learning-based in vitro system designed to detect seizure-inducing side effects before clinical trial. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices that were bath-perfused with each of 14 different drugs, and at 5 different concentrations of each drug. For each of these experimental conditions, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs) that induced seizure-like events, and identified diphenhydramine, enoxacin, strychnine and theophylline as "seizure-inducing" drugs, which have indeed been reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to pre-clinically detect seizure-inducing side effects of drugs.