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

ABSTRACT: 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 judgm

SUBMITTER: Matsuda N 

PROVIDER: S-EPMC8831568 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature