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用统计学方法优化硝苯地平脂质体处方 被引量:1

Optimize the Preparation Formulation of Nifedipine Liposome by Statistical Methods
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摘要 采用乙醚注入法制备硝苯地平脂质体,在单因素实验基础上用Box-Behnken设计实验,并用统计学方法考察磷脂量、脂药比和胆固醇磷脂比对脂质体包封率的影响.结果表明:多元二次回归模型最优处方制备的硝苯地平脂质体平均包封率为86.54%;人工神经网络模型结合遗传算法最优处方制备的硝苯地平脂质体平均包封率为97.25%;与二次回归模型相比,人工神经网络模型更适于优化硝苯地平脂质体的制备.优化后得到最佳制备处方为:磷脂量612.4mg,脂药比为60.02,磷脂胆固醇比为6.401. The nifedipine liposome was prepared from an appropriate amount of lecithine, cholesterol and nifedipine by the ether injection method. On the basis of single factor experiment, we used statistical methods to investigate the effects of phospholipid amount, lipid drug ratio, cholesterol phospholipid ratio on the encapsulation efficiency of liposome. The results show that the encapsulation rate of the liposome prepared based on multivariate quadratic regression model was 86. 54%, and the encapsulation rate of the liposome prepared based on artificial neural network model was 97. 25%. Compared with the quadratic regression model, artificial neural network model combined with genetic algorithm is more suitable for optimizing the preparation technique of nifedipine liposome. After optimizing, the best preparation formulation is 612.4 mg of phospholipid with a lipid drug ratio of 60.02 and a cholesterol phospholipid ratio of 6. 401.
出处 《吉林大学学报(理学版)》 CAS CSCD 北大核心 2015年第3期572-576,共5页 Journal of Jilin University:Science Edition
关键词 硝苯地平 脂质体 乙醚注入法 Box-Behnken 人工神经网络 nifedipine liposome ether injection method Box-Behnken artificial neural network
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