目的 采用星点设计-效应面法（CCD-RSM）筛选姜黄素（Cur）正负离子纳米结构脂质载体（Cur-CNLC）最佳处方。方法 采用薄膜分散-超声乳化法制备Cur-CNLC，分别以固体脂质质量（X₁）、液体脂质质量（X₂）、卵磷脂质量（X₃）和混合表面活性剂用量（X₄）为考察对象，以包封率（Y₁）和脂质载药量（Y₂）为考察指标，根据CCD原理和多元线性回归及二项式拟合建立指标与因素之间的数学关系，经RSM预测最优处方。结果 按最优处方制备的Cur-CNLC包封率为（94.38±2.67）%，与预测值的偏差为1.23%；脂质载药量为（6.93±0.39）%，与预测值的偏差为2.62%；平均粒径为（235.9±9.6）nm，多分散指数（PDI）为0.272±0.017，Zeta电位为（-28.40±0.35）mV。结论 采用CCD-RSM优化的Cur-CNLC，包封率高，稳定性好，方法可靠。

Objective To optimize the formulation of curcumin-catanionic nanoparticles lipid carriers (Cur-CNLC) by central composite design-response surface methodology (CCD-RSM). Methods Cur-CNLC were prepared by film dispersion-ultrasonic emulsifying method. A four factor, five-level central composite design was employed, with the solid lipid quality (X₁), liquid lipid quality (X₂), lecithin quality (X₃), and mixed surfactant concentration (X₄) as the independent variables. The dependent variables were the entrapment efficiency (Y₁) and drug loading (Y₂). The data were simulated using multi-linear equation and second-order polynomial equation, the possibly optimal formulation was predicted by response surface method. Results The entrapment efficiency, drug loading, average particle size, polydispersity, and Zeta potential of the Cur-CNLCs prepared under the optimized conditions were (94.38 ±2.67)%, (6.93 ±0.39)%, (235.9 ±9.6) nm, 0.272 ±0.017, and (-28.40 ±0.35) mV, respectively. The bias between the measured values and the predicted ones is less than 5%. Conclusion The CCD-RSM is effective and suitable for optimizing the formulation of Cur-CNLC.

重庆市科委资助项目（cstc2015jcyjBX0027）