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    基于Box-Behnken响应面法结合BP神经网络优化决明子脐贴的处方

    Optimization of the Prescription of Cassiae Semen Umbilical Patch Based on Box-Behnken Response Surface Method Combined with BP Neural Network

      摘要
    • 摘要:
      目的 优化决明子脐贴的处方。
      方法 以外观、橙黄决明素与大黄酚峰面积之和、有效成分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、载药量、薄荷醇用量，制备决明子脐贴。
      结果 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。与Box-Behnken响应面验证试验比较，BP神经网络综合评价指标更高且差异有统计学意义(P<0.01)。择优确定了BP神经网络预测结果为脐贴最优处方，即 PEG6000∶PEG4000为3∶2(g∶g)、载药量为14.8%、薄荷醇用量为4.2%。
      结论 该处方制得的决明子脐贴外观光滑平整、无气泡，具有良好的皮肤渗透性。

       

      Abstract:
      OBJECTIVE To optimize the prescription of Cassiae Semen umbilical patch.
      METHODS Taking appearance, the sum of peak areas of aurantio-obtusin and chrysophanol, and the total peak area of active ingredients as evaluation indexes. A single factor experiments, Box-Behnken response surface methodology combined with BP neural network were used to optimize the PEG6000∶PEG4000, drug loading, and menthol dosage in umbilical patch formulations, and prepare Cassiae Semen umbilical patch.
      RESULTS The test results of Box-Behnken response surface method and the prediction results of BP neural network were tested each other, and the average comprehensive evaluation index values of the validation results were 0.976 and 1.040, respectively. Compared with the Box-Behnken response surface validation test, the comprehensive evaluation index of BP neural network was higher and the different was statistically significant(P<0.01). The prediction result of BP neural network were selected as the optimal prescription for umbilical patch, that was, PEG6000∶PEG4000 ratio of 3∶2 (g∶g), drug loading of 14.8%, and menthol dosage of 4.2%.
      CONCLUSION Cassiae Semen umbilical patch prepared with this prescription has a smooth and flat appearance, no bubbles, and good skin permeability.

       

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