Objective: To optimize the prescription dose of Mahuang decoction in a multi-target manner, in order to provide reference for the quantitative optimization of the prescription dose of the traditional Chinese medicine compound.
Method: The number of diaphoretic spots in rats, the tracheal antispasmodic rate in guinea pigs and the writhing times by acetic acid in mice were taken as the indexes for evaluating the diaphoretic, antispasmodic and analgesic effects. According to the experimental results of the 16 orthogonal combination prescriptions, a mathematical dose-effect model was built by support vector regression (SVR) and quadratic response surface regression (RSR) respectively. The multi-target optimization was achieved by elitist non-dominated sorting genetic algorithm (NSGA-II) and entropy weight TOPSIS method.
Result: The optimal dose of Mahuang decoction after being optimized by SVR modeling contained 17.71 g of Ephedrae Herba, 9.57 g of Cinnamomi Ramulus, 11.75 g of Armeniacae Semen Amarum and 4.39 g of Glycyrrhizae Radix et Rhizoma Praeparata Cum Melle. The optimized result by RSR modeling contained 13.37 g of Ephedrae Herba, 11.61 g of Cinnamomi Ramulus, 11.98 g of Armeniacae Semen Amarum and 5.67 g of Glycyrrhizae Radix et Rhizoma Praeparate Cum Melle. SVR was superior to RSR in both of the forecast capacity and optimization results.
Conclusion: SVR-NSGA-II-TOPSIS method could be adopted for the multi-target optimization for the dose of Mahuang decoction and other traditional Chinese medicine compounds. It is proved to be the optimal prescription with the best efficacy, and could provide scientific quantitative basis for determining the dose of traditional Chinese medicine compound prescriptions and developing new traditional Chinese medicines.