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采用三因素三水平Box-Behnken设计法对吡罗昔康前体脂质体进行处方设计与优化。

Formulation and optimization of piroxicam proniosomes by 3-factor, 3-level Box-Behnken design.

作者信息

Solanki Ajay B, Parikh Jolly R, Parikh Rajesh H

机构信息

Department of Pharmaceutics and Pharmaceutical Technology, A. R. College of Pharmacy & G. H. Patel Institute of Pharmacy, Vallabh Vidyanagar 388 120, Gujarat, India.

出版信息

AAPS PharmSciTech. 2007 Oct 19;8(4):E86. doi: 10.1208/pt0804086.

Abstract

The aim of this study was to investigate the combined influence of 3 independent variables in the preparation of piroxicam proniosomes by the slurry method. A 3-factor, 3-level Box-Behnken design was used to derive a second-order polynomial equation and construct contour plots to predict responses. The independent variables selected were molar ratio of Span 60:cholesterol (X(1)), surfactant loading (X(2)), and amount of drug (X(3)). Fifteen batches were prepared by the slurry method and evaluated for percentage drug entrapment (PDE) and vesicle size. The transformed values of the independent variables and the PDE (dependent variable) were subjected to multiple regression to establish a full-model second-order polynomial equation. F was calculated to confirm the omission of insignificant terms from the full-model equation to derive a reduced-model polynomial equation to predict the PDE of proniosome-derived niosomes. Contour plots were constructed to show the effects of X(1), X(2) and X(3) on the PDE. A model was validated for accurate prediction of the PDE by performing checkpoint analysis. The computer optimization process and contour plots predicted the levels of independent variables X(1), X(2), and X(3) (0, -0.158 and -0.158 respectively), for maximized response of PDE with constraints on vesicle size. The Box-Behnken design demonstrated the role of the derived equation and contour plots in predicting the values of dependent variables for the preparation and optimization of piroxicam proniosomes.

摘要

本研究的目的是通过淤浆法研究3个自变量对吡罗昔康前体脂质体制备的综合影响。采用三因素三水平的Box-Behnken设计来推导二阶多项式方程并构建等高线图以预测响应。选择的自变量为司盘60与胆固醇的摩尔比(X(1))、表面活性剂负载量(X(2))和药物用量(X(3))。通过淤浆法制备了15批样品,并对药物包封率(PDE)和囊泡大小进行了评估。对自变量和PDE(因变量)的转换值进行多元回归,以建立全模型二阶多项式方程。计算F值以确认从全模型方程中省略无显著意义的项,从而推导出简化模型多项式方程来预测前体脂质体衍生的脂质体的PDE。构建等高线图以显示X(1)、X(2)和X(3)对PDE的影响。通过进行检查点分析对模型进行验证,以准确预测PDE。计算机优化过程和等高线图预测了自变量X(1)、X(2)和X(3)的水平(分别为0、-0.158和-0.158),以在囊泡大小受限的情况下使PDE响应最大化。Box-Behnken设计证明了所推导方程和等高线图在预测吡罗昔康前体脂质体制备和优化过程中因变量值方面的作用。

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