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引用本文:赵莹莹,李强,赵学玲,王柳婷,李鑫.Box-Behnken响应面法优化姜黄素氧化锌纳米粒的制备工艺[J].中国现代应用药学,2020,37(19):2349-2355.
ZHAO Yingying,LI Qiang,ZHAO Xueling,WANG Liuting,LI Xin.Optimization of Preparation Process of ZnO-Curcumin Nanoparticles by Box-Behnken Response Surface Methodology[J].Chin J Mod Appl Pharm(中国现代应用药学),2020,37(19):2349-2355.
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Box-Behnken响应面法优化姜黄素氧化锌纳米粒的制备工艺
赵莹莹1, 李强2, 赵学玲2, 王柳婷1, 李鑫1
1.哈尔滨商业大学药学院, 哈尔滨 150076;2.黑龙江大学化学化工与材料学院, 哈尔滨 150080
摘要:
目的 优化姜黄素氧化锌纳米粒(ZnO-Curcumin nanoparticles,ZnO-Cur NPs)的制备工艺。方法 利用姜黄素分子上的基团可以与氧化锌纳米粒(ZnO nanoparticles,ZnO NPs)表面的羟基形成氢键及配位键,来制备ZnO-Cur NPs,通过单因素考察后,以载药量为指标,以制备温度、pH、空白ZnO NPs与姜黄素的投料比为响应因子,采用Box-Behnken响应面法优化ZnO-Cur NPs的制备工艺。结果 ZnO-Cur NPs的优化制备工艺为温度90.3℃,pH=8.93,ZnO NPs与姜黄素的投料比为1∶2。在此工艺条件下制备的ZnO NPs载药量可达29.33%,与预测值30.15%的相对误差为0.60%。结论 优化所得ZnO-Cur NPs制备工艺稳定、可行,适用于该制剂的制备,同时也说明建立的回归模型合理。
关键词:  姜黄素  氧化锌纳米粒  制备工艺  Box-Behnken响应面法
DOI:10.13748/j.cnki.issn1007-7693.2020.19.009
分类号:R283.6
基金项目:
Optimization of Preparation Process of ZnO-Curcumin Nanoparticles by Box-Behnken Response Surface Methodology
ZHAO Yingying1, LI Qiang2, ZHAO Xueling2, WANG Liuting1, LI Xin1
1.School of Pharmaceutical, Harbin University of Commerce, Harbin 150076, China;2.School of Chemistry and Materials Science, Heilongjiang University, Harbin 150080, China
Abstract:
OBJECTIVE To optimize the preparation process of ZnO-Curcumin nanoparticles(ZnO-Cur NPs). METHODS The groups on the curcumin molecule could form hydrogen bonds and coordination bonds with the hydroxyl groups on the surface of zinc oxide nanoparticles, which be used to prepare ZnO-Cur NPs. Based on the results of the single-factor tests, the preparation process of ZnO-Cur NPs was optimized by Box-Behnken response surface methodology with drug loading efficiency as the indicator, and temperature, pH and ratio of blank ZnO NPs to curcumin as the response factors. RESULTS The optimized preparation process of ZnO-Cur NPs included preparation temperature of 90.3℃, pH=8.93, and ratio of ZnO NPs to curcumin was 1:2. Under this process condition, the drug loading efficiency of ZnO-Cur NPs was 29.33%, and the relative error with the predicted value(30.15%) was 0.60%. CONCLUSION The optimized preparation process of ZnO-Cur NPs is stable and feasible, and it is suitable for the preparation of ZnO-Cur NPs. Meanwhile, the established regression model is reasonable.
Key words:  curcumin  ZnO nanoparticles  preparation process  Box-Behnken response surface methodology
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