紫杉醇mPEG-PLA共聚物胶束的制备及工艺优化

目的：制备紫杉醇mPEG-PLA共聚物胶束（PM-PTX），优化处方及工艺。方法：采用薄膜水化法制备PM-PTX，通过星点设计一效应面优化法设计和优化处方工艺，并评估药物的体外释放。结果：以投药量和水化体积作为自变量，包封率和载药量作为因变量，行多元线性回归及二次多项式拟合，优选最佳处方工艺并进行验证，得最优工艺条件为投药量2.71 mg，水化体积7.04 mL。按照优化处方制得的PM-PTX包封率为（83.04±1.96）%，载药量为（15.01±0.28）%，与预测值的偏差分别为1.91%和0.99%。PM-PTX溶液为无色透明，透射电镜下显示胶束呈类圆形，胶束粒径为（87.74±2.3）nm，多分散指数为0.259±0.014，Zeta电位为（-1.22±0.133）mV。在3种pH环境下，胶束的药物释放速率无明显差异（P>0.05），96 h的累积释放率分别为（92.19±3.17）%，（88.37±5.62）%和（86.04±2.16）%（pH=5.8，7.2，7.4）。结论：星点设计-效应面优化法对PM-PTX的制备工艺优化有效，所建立的模型预测性良好。

Optimized preparation of paclitaxel loaded mPEG-PLA polymer micelles

OBJECTIVE To optimize the formulation of paclitaxel loaded mPEG-PLA polymeric micelles (PM-PTX).METHODS PM-PTX were prepared by a thin film dispersion method.A central composite design/response surface methodology (CCD/RSM) was applied to optimize the preparation process.The in vitro release profile was evaluated.RESULTS The effects of independent variables (the amount of PTX and hydration volume) on response variables (the encapsulation efficiency and drug loading) were investigated using multiple linear regression and second order polynomial equation.The optimum condition was selected for predictive analysis.The best condition for optimization was the amount of PTX 2.71 mg,the hydration volume 7.04 mL.With the optimized formulation,PM-PTX were obtained.The encapsulation efficiency and drug loading were (83.04±1.96)% and (15.01±0.28)%,respectively.Bias between the experimental and predicted values was 1.91% and 0.99%,respectively.The micelles were spherical in shape and had a smooth surface as examined by transmission electron microscopy (TEM).The average size of the optimized micelles was (87.74±2.3)nm,with a polydispersity index of 0.259±0.014,a Zeta potential of (-1.22±0.133)mV.The pH value of release media did not notably affect the drug release patterns (P>0.05).After 96 h,the cumulative percentages of released PTX was (92.19±3.17)%,(88.37±5.62)% and (86.04±2.16)% under pH 5.8,7.2 and 7.4,respectively.CONCLUSION The CCD/RSM can well predict preparation technology for PM-PTX with good practicality.