A Novel Method for Screening the Effective Compatibility Fractions of Naomaitong for the Treatment of Cerebral Ischemic Stroke Using Support Vector Machine Model

Abstract

Background: Screening the fractions of traditional Chinese medicine (TCM) medications effective against a disease without affecting their compatibility is a major challenge. Therefore, the goal of the present study was to establish a novel methodology for screening the bioactive fractions of Naomaitong (NMT) utilizing the Support Vector Machine (SVM) model. Methods: The extract of NMT was separated into 21 fractions using macroporous resin. The different combinations of elution fractions were selected using the Plackett-Burman method and used as an input value for SVM. Infarct size in the rat model of cerebral ischemia-reperfusion injury and the content of selected compounds measured by HPLC were used as output vectors of the SVM training set. Based on the SVM target model, the optimal combination of elution fractions was predicted and verified in the rat model of ischemia-reperfusion injury. Results: The effective compatibility of NMT was screened by SVM simulation: all elution fractions of rhubarb, 30%, 60%, and 95% ethanol elution fractions of ginseng, and 30% and 60% ethanol elution fractions of puerarin and chuanxiong. The optimization of compatibility fractions of NMT was found to have the same protective efficacy in cerebral ischemia-reperfusion injury in rats. Conclusions: The performed analysis established a novel SVM-based method for screening the compatibility fractions of NMT effective against cerebral ischemic stroke injury. The results provide a new methodology for the optimization of the compatibility of TCM compounds and the discovery of effective components.