摘要
目的以去甲基斑蝥素为模型药物,运用正交设计及多元回归分析多指标综合优化其低相对分子质量壳聚糖纳米粒制备工艺。方法采用离子诱导法制备去甲基斑蝥素低相对分子质量壳聚糖纳米粒,以粒径、包封率为评价指标,以低相对分子质量壳聚糖(LCS)、三聚磷酸钠(TPP)浓度和温度为3个因素,选用L9(34)正交实验设计结合多元回归筛选纳米粒的制备工艺,并根据多指标权重对其线性加权和进行优化,推断优化方案。同时,应用数学模型,绘出各指标随因素变化的趋势图,预测优化结果。结果根据优化条件验证制备工艺,所得纳米粒粒径为(131±6)nm,包封率45.12%,载药量7.3%。优化处方工艺具有粒径小、包封率高特性。结论该数据处理方法用于制备工艺优化结果精确、高效,预测结果准确,具有推广应用价值。
Abstract
OBJECTIVE To select norcantharidin as module drug and synchronously optimize the preparation of low molecular weight(LMW)chitosan nanoparticles(CS-NP)through orthogonal design combined with multi-variable regression analysis.METHODS LMW CS-NP loading norcantharidin was prepared with ionic cross-linkage.Three factors,LCS concentration,TPP concentration and temperature which might affect the preparation technique of CS-NP were arranged in a L9(34)orthogonal experimental table using particle size and entrapment efficiency as integration indexes.Multi-variable regression model combined with the weight value of each indexes/factors was used to fit the design conditions.Meanwhile,two-dimensional/three-dimension curve line/response surface plots showing the index variation with the factors change was drawn,and the experiment results were predicted.RESULTS The particle size of prepared CS-NP by the optimized technique,were(131±6)nm,entrapment efficiency 45.12% and loading capacity 7.3%,and the observed data were similar with the predicted results.CONCLUSION The analytical method of orthogonal data is accurate,efficient and scientific to optimize the preparation of CS-NP.
关键词
正交设计 /
多元回归 /
壳聚糖纳米粒 /
处方优化 /
去甲基斑蝥素
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Key words
orthogonal design /
multiple regress /
chitosan nanoparticle /
formula optimization /
norcantharidin
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张玮;龚金红;张学农;刘扬;杨志强.
多元回归综合优化去甲基斑蝥素壳聚糖纳米粒的制备工艺[J]. 中国药学杂志, 2008, 43(15): 1162-1166
ZHNG Wei;GONG Jin-hong;ZHNG Xue-nong;LIU Yng;YNG Zhi-qing.
Multi-Variable Regression Analysis Applied to Synchronously Optimize Preparation of Nanoparticles [J]. Chinese Pharmaceutical Journal, 2008, 43(15): 1162-1166
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参考文献
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脚注
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基金
“十一五”国家科技支撑计划项目资助(2006BAI09B00);国家科技部科技型中小企业技术创新基金(07C26223201333);江苏省科技厅社会发展项目资助(BS200522);江苏省高新技术产业发展项目资助(JHB05-46);江苏省卫生厅招标课题(H200630)
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