基于Box-Behnken响应面法结合BP神经网络优化决明子脐贴的制备

Optimization of Preparation of Cassia Seed Umbilical Patch Based on Box-Behnken Response Surface Methodology and BP Neural Network

关键词:  ∶决明子脐贴  单因素试验  体外经皮渗透试验  Box-Behnken响应面  BP神经网络

Key words:  Cassia seed umbilical patch  Single factor test  In vitro percutaneous penetration test  Box-Behnken response surface  BP neural network

摘要:

优化决明子脐贴(简称脐贴)的处方及制备工艺。以外观、橙黄决明素与大黄酚(Aurantio-obtusin and Chrysophanol,简称AC)峰面积之和、有效成分(Active ingredient,简称AI)总峰面积为评价指标,采用单因素试验、Box-Behnken响应面结合BP神经网络优化脐贴处方中的PEG6000∶PEG4000用量、载药量、薄荷醇用量。将Box-Behnken响应面法试验结果与BP神经网络预测结果相互检验,验证结果综合评价指标平均值分别为0.976、1.040,择优确定了BP神经网络预测结果为脐贴最优制备工艺,制备工艺为PEG6000∶PEG 4000用量为60%∶40%、载药量为14.8%、薄荷醇用量为4.2%,该工艺制备的脐贴光滑平整,无气泡,具有良好的皮肤渗透性。

Abstract:

Optimize the prescription and preparation process of Cassia seed umbilical patch (referred to as umbilical patch). Using appearance, the sum of peak areas of Aurantio-obtusin and Chrysophanol (AC), and the total peak area of active ingredient (AI) as evaluation indicators, single factor experiments, Box-Behnken response surface combined with BP neural network were used to optimize the dosage of PEG6000∶PEG4000, drug loading, and menthol in the umbilical patch prescription. The Box-Behnken response surface method test results were compared with the BP neural network prediction results, and the average comprehensive evaluation index values of the validation results were 0.976 and 1.040, respectively. The optimal BP neural network prediction result was determined as the optimal preparation process for the umbilical cord patch. The preparation process was PEG6000∶PEG4000 with a dosage of 60%: 40%, drug loading of 14.8%, and menthol dosage of 4.2%. The umbilical cord patch prepared by this process was smooth and flat, without bubbles, Has good skin permeability.