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载达卡巴嗪立方液晶体制剂的研究-第一部分:制剂变量的影响。

Formulation of dacarbazine-loaded cubosomes-part I: influence of formulation variables.

机构信息

Division of Pharmaceutical Sciences, University of Missouri-Kansas City, School of Pharmacy, Kansas City, Missouri 64110-2499, USA.

出版信息

AAPS PharmSciTech. 2009;10(3):1032-9. doi: 10.1208/s12249-009-9293-3. Epub 2009 Aug 11.

Abstract

The purpose of this study was to investigate the combined influence of three-level, three-factor variables on the formulation of dacarbazine (a water-soluble drug) loaded cubosomes. Box-Behnken design was used to obtain a second-order polynomial equation with interaction terms to predict response values. In this study, the selected and coded variables X(1), X(2), and X(3) representing the amount of monoolein, polymer, and drug as the independent variables, respectively. Fifteen runs of experiments were conducted, and the particle size (Y(1)) and encapsulation efficiency (Y(2)) were evaluated as dependent variables. We performed multiple regression to establish a full-model second-order polynomial equation relating independent and dependent variables. A second-order polynomial regression model was constructed for Y(1) and confirmed by performing checkpoint analysis. The optimization process and Pareto charts were obtained automatically, and they predicted the levels of independent coded variables X(1), X(2), and X(3) (-1, 0.53485, and -1, respectively) and minimized Y(1) while maximizing Y(2). These corresponded to a cubosome formulation made from 100 mg of monoolein, 107 mg of polymer, and 2 mg with average diameter of 104.7 nm and an encapsulation efficiency of 6.9%. The Box-Behnken design proved to be a useful tool to optimize the particle size of these drug-loaded cubosomes. For encapsulation efficiency (Y(2)), further studies are needed to identify appropriate regression model.

摘要

本研究旨在探讨三水平三因素变量对水溶性药物达卡巴嗪载入立方脂质体的配方的联合影响。采用 Box-Behnken 设计,获取包含交互项的二阶多项式方程,以预测响应值。在本研究中,选择和编码变量 X(1)、X(2)和 X(3)分别代表单油酸甘油酯、聚合物和药物的量,作为自变量。进行了 15 次实验运行,评估了粒径(Y(1))和包封效率(Y(2))作为因变量。我们进行了多元回归分析,建立了一个与独立和因变量相关的全模型二阶多项式方程。建立了 Y(1)的二阶多项式回归模型,并通过进行检查点分析进行了验证。自动获得了优化过程和 Pareto 图,并预测了独立编码变量 X(1)、X(2)和 X(3)的水平(分别为-1、0.53485 和-1),同时最小化 Y(1),最大化 Y(2)。这对应于由 100mg 单油酸甘油酯、107mg 聚合物和 2mg 药物制成的立方脂质体配方,平均粒径为 104.7nm,包封效率为 6.9%。Box-Behnken 设计被证明是优化这些载药立方脂质体粒径的有用工具。对于包封效率(Y(2)),需要进一步研究来确定合适的回归模型。

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