著者

小林 憲司 坂本 嵩 佐藤 元重 佐藤 由宇 杉本 翔哉 中田 康史 野山 大樹 山下 愛博

出版者

日本神経回路学会

雑誌

日本神経回路学会誌 (ISSN:1340766X)

巻号頁・発行日

vol.28, no.2, pp.103-108, 2021

著者

高 夢璇 佐藤 元重 池谷 裕二

出版者

公益社団法人 日本薬学会

雑誌

YAKUGAKU ZASSHI (ISSN:00316903)

巻号頁・発行日

vol.138, no.6, pp.809-813, 2018-06-01 (Released:2018-06-01)

参考文献数

7

被引用文献数

2

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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.