[关键词]
[摘要]
目的 采用Box-Behnken效应面法筛选吴茱萸次碱脂质液晶纳米粒(rutaecarpine lipid liquid crystalline nanoparticles,Rut-LLCN)的最优处方。方法 采用前体注入联合高压均质法制备Rut-LLCN,以甘油单油酸酯(GMO)的用量、泊洛沙姆407(F127)与GMO的质量分数、吴茱萸次碱(Rut)的用量为考察对象,以包封率、载药量、粒径、多分散指数(PDI)为考察指标,利用3因素3水平Box-Behnken效应面设计法筛选Rut-LLCN的最优处方。结果 Rut-LLCN的最优处方为GMO的用量为450 mg,F127-GMO的质量分数为12%,Rut的用量为20 mg,优化处方各指标和目标值接近。按最优处方制备的Rut-LLCN的包封率为(84.02±7.99)%,载药量为(3.24±0.30)%,平均粒径为(186.90±13.50)nm,PDI为 0.313±0.020。结论 采用Box-Behnke效应面法优化了Rut-LLCN的处方,以包封率、载药量、平均粒径、PDI为指标评价该模型,表明该模型预测性良好。
[Key word]
[Abstract]
Objective To optimize the formulation of rutaecarpine lipid liquid crystalline nanoparticles (Rut-LLCN) by Box-Behnken design-response surface methodology. Methods Rut-LLCN were prepared by precursor injection-high pressure homogenization method. A three factor and three-level Box-Behnken design was employed with the glyceryl monoolein quality, percentage of poloxamer in glyceryl monoolein and the rutaecarpine quality as independent variables, the entrapment efficiency, drug loading, mean particle size and polydispersity index as the dependent variables to sereen the optimal formaula. Results Optimized prescription was GMO 450 mg, F127-GMO 12%, and Rut 20 mg. All items of optimized prescription were similar to target values. According to the optimized prescription, the entrapment efficiency, drug loading, average particle size, and PDI of Rut-LLCN were (84.02 ±7.99)%, (3.24 ±0.30)%, (186.90 ±13.50) nm, and 0.313 ±0.020, respectively. Conclusion The prescription optimization model of Rut-LLCN was optimized by Box-Behnken designs-response surface methodology, and entrapment efficiency, drug loading, mean particle size, and PDI of Rut-LLCN are measured to investigate the model.
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[基金项目]
国家重点研发项目(2017YFC1702900)