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Raster plots machine learning to predict the seizure liability of drugs and to identify drugs

In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to predict seizure and toxicity liability as well as dru...

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Autores principales: Matsuda, N., Odawara, A., Kinoshita, K., Okamura, A., Shirakawa, T., Suzuki, I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831568/
https://www.ncbi.nlm.nih.gov/pubmed/35145132
http://dx.doi.org/10.1038/s41598-022-05697-8
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author Matsuda, N.
Odawara, A.
Kinoshita, K.
Okamura, A.
Shirakawa, T.
Suzuki, I.
author_facet Matsuda, N.
Odawara, A.
Kinoshita, K.
Okamura, A.
Shirakawa, T.
Suzuki, I.
author_sort Matsuda, N.
collection PubMed
description In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to predict seizure and toxicity liability as well as drug mechanisms of action. In the present study, we developed an artificial intelligence (AI) capable of predicting the seizure liability of drugs and identifying drugs using deep learning based on raster plots of neural network activity. The seizure liability prediction AI had a prediction accuracy of 98.4% for the drugs used to train it, classifying them correctly based on their responses as either seizure-causing compounds or seizure-free compounds. The AI also made concentration-dependent judgments of the seizure liability of drugs that it was not trained on. In addition, the drug identification AI implemented using the leave-one-sample-out scheme could distinguish among 13 seizure-causing compounds as well as seizure-free compound responses, with a mean accuracy of 99.9 ± 0.1% for all drugs. These AI prediction models are able to identify seizure liability concentration-dependence, rank the level of seizure liability based on the seizure liability probability, and identify the mechanism of the action of compounds. This holds promise for the future of in vitro MEA assessment as a powerful, high-accuracy new seizure liability prediction method.
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spelling pubmed-88315682022-02-14 Raster plots machine learning to predict the seizure liability of drugs and to identify drugs Matsuda, N. Odawara, A. Kinoshita, K. Okamura, A. Shirakawa, T. Suzuki, I. Sci Rep Article In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to predict seizure and toxicity liability as well as drug mechanisms of action. In the present study, we developed an artificial intelligence (AI) capable of predicting the seizure liability of drugs and identifying drugs using deep learning based on raster plots of neural network activity. The seizure liability prediction AI had a prediction accuracy of 98.4% for the drugs used to train it, classifying them correctly based on their responses as either seizure-causing compounds or seizure-free compounds. The AI also made concentration-dependent judgments of the seizure liability of drugs that it was not trained on. In addition, the drug identification AI implemented using the leave-one-sample-out scheme could distinguish among 13 seizure-causing compounds as well as seizure-free compound responses, with a mean accuracy of 99.9 ± 0.1% for all drugs. These AI prediction models are able to identify seizure liability concentration-dependence, rank the level of seizure liability based on the seizure liability probability, and identify the mechanism of the action of compounds. This holds promise for the future of in vitro MEA assessment as a powerful, high-accuracy new seizure liability prediction method. Nature Publishing Group UK 2022-02-10 /pmc/articles/PMC8831568/ /pubmed/35145132 http://dx.doi.org/10.1038/s41598-022-05697-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Matsuda, N.
Odawara, A.
Kinoshita, K.
Okamura, A.
Shirakawa, T.
Suzuki, I.
Raster plots machine learning to predict the seizure liability of drugs and to identify drugs
title Raster plots machine learning to predict the seizure liability of drugs and to identify drugs
title_full Raster plots machine learning to predict the seizure liability of drugs and to identify drugs
title_fullStr Raster plots machine learning to predict the seizure liability of drugs and to identify drugs
title_full_unstemmed Raster plots machine learning to predict the seizure liability of drugs and to identify drugs
title_short Raster plots machine learning to predict the seizure liability of drugs and to identify drugs
title_sort raster plots machine learning to predict the seizure liability of drugs and to identify drugs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831568/
https://www.ncbi.nlm.nih.gov/pubmed/35145132
http://dx.doi.org/10.1038/s41598-022-05697-8
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