Electrophysiological responses to seizurogenic compounds dependent on E/I balance in human iPSC-derived cortical neural networks.

R Yokoi, T Shigemoto-Kuroda, N Matsuda, A Odawara, I Suzuki

Journal of pharmacological sciences 2022 Feb

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Construction of in vitro functional assay systems using human-induced pluripotent stem cells (iPSCs) as indicators for evaluating seizure liability of compounds has been anticipated. Imbalance of excitation/inhibition (E/I) inputs triggers seizure; however, the appropriate ratio of E/I neurons for evaluating seizure liability of compounds in a human iPSC-derived neural network is unknown. Here, five neural networks with varying E/I ratios (88/12, 84/16, 74/26, 58/42, and 48/52) were constructed by altering the ratios of glutamatergic (E) and GABA (I) neurons. The responsiveness of each network against six seizurogenic compounds and two GABA receptor agonists was then examined by using six representative parameters. The 52% GABA neuron network, which had the highest ratio of GABA neurons, showed the most marked response to seizurogenic compounds, however, it suggested the possibility of producing false positives. Moreover, analytical parameters were found to vary with E/I ratio and to differ for seizurogenic compounds with different mechanism of action (MoA) even at the same E/I ratio. Clustering analysis using six parameters showed the balance of 84/16, which is the closest to the biological balance, was the most suitable for detection of concentration-dependent change and classification of the MoA of seizurogenic compounds. These results suggest the importance of using a human-iPSC-derived neural network similar to the E/I balance of the living body in order to improve the prediction accuracy in the in vitro seizure liability assessment. Copyright © 2022 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Citation

R Yokoi, T Shigemoto-Kuroda, N Matsuda, A Odawara, I Suzuki. Electrophysiological responses to seizurogenic compounds dependent on E/I balance in human iPSC-derived cortical neural networks. Journal of pharmacological sciences. 2022 Feb;148(2):267-278