星点设计-效应面法优化姜黄素正负离子纳米结构脂质载体处方

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)m V。结论 采用CCD-RSM优化的Cur-CNLC,包封率高,稳定性好,方法可靠。

Optimization of preparation of curcumin-catanionic nanoparticles lipid carriers by central composite design-response surface methodology

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.