Prediction of GABA receptor antagonist-induced convulsion in cynomolgus monkeys by combining machine learning and heart rate variability analysis

Abstract

Drug-induced convulsion is a severe adverse event; however, no useful biomarkers for it have been discovered. We propose a new method for predicting drug-induced convulsions in monkeys based on heart rate variability (HRV) and a machine learning technique. Because autonomic nervous activities are altered around the time of a convulsion and such alterations affect HRV, they may be predicted by monitoring HRV. In the proposed method, anomalous changes in multiple HRV parameters are monitored by means of a convulsion prediction model, and convulsion alarms are issued when abnormal changes in HRV are detected. The convulsion prediction model is constructed based on multivariate statistical process control (MSPC), a well-known anomaly detection algorithm in machine learning. In this study, HRV data were collected from four cynomolgus monkeys administered with multiple doses of pentylenetetrazol (PTZ) and picrotoxin (PTX), which are GABA receptor antagonists, as convulsant agents. In addition, low doses of pilocarpine (PILO) were administered as a negative control. Twelve HRV parameters in three hours after drug administration were monitored by means of the prediction model. The number and duration of convulsion alarms from HRV increased at medium and high doses of PTZ and PTX (1/3 or 1/4 of convulsion dose). On the other hand, the frequency of convulsion alarms did not increase with PILO. Although vomiting was observed in all drugs, convulsion alarms were not associated with the vomiting. Thus, convulsion alarms can be used as a biomarker for convulsions induced by GABA receptor antagonists.