Principal Component Analysis to Distinguish Seizure Liability of Drugs in Human iPS Cell-Derived Neurons

Screening for drug discovery targeting the central nervous system requires the establishment of efficient and highly accurate toxicity test methods that can reduce costs and time while maintaining high throughput using the function of an in vitro neural network. In particular, an evaluation system using a human-derived neural network is desirable in terms of species difference. Despite the attention, the microelectrode array (MEA) is attracting among the evaluation systems that can measure in vitro neural activity, an effective analysis method for evaluation of toxicity and mechanism of action has not yet been established. Here we established analytical parameters and multivariate analysis method capable of detecting seizure liability of drugs using MEA measurement of human iPS cell-derived neurons. Using the spike time series data of all drugs, we established periodicity as a new analytical parameter. Periodicity has facilitated the detection of responses to seizurogenic drugs, previously difficult to detect with conventional analytical parameters. By constructing a multivariate analytical method that identifies a parameter set that achieves an arbitrary condition, we found that the parameter set comprising total spikes, maximum frequency (MF), inter- MF interval (IMFI), coefficient of variance of IMFI, and periodicity can uniformly detect the seizure liability of seizurogenic drugs with different mechanisms of action. Seizurogenic drugs were suggested to increase the regularity of the network burst in MEA measurements in human iPS cell-derived neurons.