首页|基于Box-Behnken响应面法结合BP神经网络多指标优化汉桃叶微丸的制备工艺

基于Box-Behnken响应面法结合BP神经网络多指标优化汉桃叶微丸的制备工艺

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目的 采用Box-Behnken响应面法结合BP(back-propagation)神经网络多指标优化汉桃叶微丸的制备工艺.方法 利用挤出滚圆法制备汉桃叶微丸,在单因素试验的基础上,以辅料微粉硅胶与微晶纤维素(microcrystalline cellulose,MCC)的比例、润湿剂、载药量、滚圆频率和滚圆时间为考察因素,以收率(%)、圆整度、豪斯纳比(hausner ratio,HR)和脆碎度(%)为评价指标,基于G1-熵权法对各评价指标进行组合赋权并计算综合评价结果,从而优化处方组成及其制备工艺;建立BP神经网络模型,选取合理数据进行学习和验证并预测汉桃叶微丸的最佳制备工艺.结果 采用Box-Behnken响应面法及BP神经网络预测的汉桃叶微丸的最佳制备工艺为微粉硅胶与MCC的比例为1∶3(g∶g)、载药量为25%、滚圆频率为22 Hz.验证试验的结果表明,Box-Behnken响应面法的综合评价结果均值与响应面优化理论值的绝对误差为0.007 7,相对误差为0.79%.结论 基于Box-Behnken响应面法结合BP神经网络多指标优化的汉桃叶微丸的制备工艺稳定可行、较为合理.
Multi-index optimization of Hantaoye micro-pills based on Box-Behnken response surface method combined with BP neural network
Objective:The Box-Behnken response surface methodology combined with BP neural network was used to optimize the preparation process of Hantaoye micropills.Methods:preparation of micropills of Hantaoye by extrusion rounding method was utilized,based on the one-way test,the ratio of micronized silica gel to MCC,wetting agent,loading capacity,rounding frequency and rounding time of excipients were examined as factors,and yield(%),roundness,HR(hausner ratio)and brittleness(%)are used as evaluation indexs,based on G1-entropy weighting method,the evaluation indexs are combined and the comprehensive evaluation results are calculate-d,so as to optimize the prescription composition and its preparetion process,and a BP neural netw-ork model was established to select reasonable date for learning and validating and predicting theoptimal preparation process of Hantaoye micropills.Results:The optimum process for the preparation of Hantaoye micropills predicted by Box-Behnken response surface methododology and BP neural network was 1∶3(g∶g)ratio of micronized silica gel to MCC,25%drug loading,and 22 round-ing frequency.The results of the validation test show that the absolute error of the mean value of the comprehensive evaluation results of the Box-Behnken response surface method with the theoretical value of the response surface optimization is 0.007 7,and the relative error is 0.79%.nse surface method combined with BP neural network multi-indicator optimization is stable,feasi-ble and more reasonable.Conclusion:The preparation process of Hantao leaf micro-pills based on Box-Behnken response.

Schefflera arboricola HayataG1-entropy weighting methodBox-Behnken response surface methodBP neural networkmulti indexs optimization

木永祥、邹纯才、鄢海燕

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皖南医学院药学院,安徽 芜湖 241002

汉桃叶 G1-熵权法 Box-Behnken响应面法 BP神经网络 多指标优化

安徽高校省级自然科学研究重大项目安徽省高等学校省级质量工程一流教材建设项目皖南医学院药剂学一流本科课程安徽省省级质量工程项目药剂学

KJ2016SD602020yljc1292019ylkc0172019kfkc084

2024

10.3969/j.issn.2097-0005.2024.07.001

山东第一医科大学(山东省医学科学院)学报
泰山医学院

山东第一医科大学(山东省医学科学院)学报

影响因子：0.6
ISSN：2097-0005
年,卷(期)：2024.45(7)