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目的:制备依托贝特缓释胶囊,并采用星点设计—效应面法对依托贝特缓释胶囊的处方进行优化。方法:采用离心造粒法制备依托贝特母丸,PEG4000与依托贝特的丙酮溶液包增溶层,以丙烯酸树脂水分散体溶液(Eudragit RL30D)包缓释层。考察因素为增溶层与丸心质量比(X1)、增溶层中PEG4000用量(X2)、缓释层增重百分率(X3),考察指标为4,8和24 h的累积释放度,采用Design-Expert软件建立模型描述自变量及响应值之间的关系,根据模型绘制效应面图和等高线图,通过重叠等高线图确定优化处方并通过实验对拟合结果进行验证。结果:确定依托贝特的制备处方优化参数:增溶层与丸心质量比为0.7～1.2;PEG4000占增溶层比例为3.5%～12.8%;缓释层包衣增重8.0%～13.5%,与多元线性模型相比,二次多项式模型具较高的置信度;二次多项式模型方程表明考察因素和相对应的考察指标之间存在可信的定量关系;优化处方各考察指标的预测值和实际值非常接近。结论:星点设计—效应面法可用于依托贝特缓释胶囊的处方优化,所建模型具有良好的预测能力。
OBJECTIVE: To prepare sustained-release capsules based on epigallocatechin and optimize the prescription of sustained-release capsules based on ephedrine by using the star point design-response surface method. Methods: Centrifugal granulation method was used to prepare the solubilization layer of acetone solution based on Beit Pills, PEG4000 and Eudragit, and the Eudragit RL30D solution was used to pack the sustained-release layer. The factors investigated were the mass ratio of solubilized layer to the pellet heart (X1), the amount of PEG4000 in the solubilized layer (X2), the percentage of weight gain of the sustained-release layer (X3), and the cumulative release of the indicators for 4, 8 and 24 h. The Design-Expert software builds a model that describes the relationship between the independent variables and response values, draws the renderings and contour plots from the model, optimizes the prescriptions by overlapping contour plots, and validates the fit through experiments. Results: The optimum parameters of preparation of rebebefilm were determined: the mass ratio of solubilized layer to pellet was 0.7-1.2; the proportion of PEG4000 in solubilized layer was 3.5% -12.8%; the weight of sustained-release layer was 8.0% -13.5% , The quadratic polynomial model has a higher confidence than the multivariate linear model; the quadratic polynomial model equation shows that there is a credible quantitative relationship between the investigating factor and the corresponding survey index; and the prediction value of each test index And the actual value is very close. Conclusion: Asterisk design-response surface method can be used to optimize the prescription of sustained-release capsules based on Beit, the model has good predictive ability.