Rapid and accurate assessment of seizure liability of drugs by using an optimal support vector machine method

Drug-induced seizures are a serious adverse effect and assessment of seizure risk usually takes place at the late stage of drug discovery process, which does not allow sufficient time to reduce the risk by chemical modification. Thus early identification of chemicals with seizure liability using rapid and cheaper approaches would be preferable. In this study, an optimal support vector machine (SVM) modeling method has been employed to develop a prediction model of seizure liability of chemicals. A set of 680 compounds were used to train the SVM model. The established SVM model was then validated by an independent test set comprising 175 compounds, which gave a prediction accuracy of 86.9%. Further, the SVM-based prediction model of seizure liability was compared with various preclinical seizure assays, including in vitro rat hippocampal brain slice, in vivo zebrafish larvae assay, mouse spontaneous seizure model, and mouse EEG model. In terms of predictability, the SVM model was ranked just behind the mouse EEG model, but better than the rat brain slice and zebrafish models. Nevertheless, the SVM model has considerable advantages compared with the preclinical seizure assays in speed and cost. In summary, the SVM-based prediction model of seizure liability established here offers potential as a cheaper, rapid and accurate assessment of seizure liability of drugs, which could be used in the seizure risk assessment at the early stage of drug discovery. The prediction model is freely available online at http://www.sklb.scu.edu.cn/lab/yangsy/download/ADMET/seizure_pred.tar.