目的 Box-Behnken设计-效应面法优化鞣花酸（EA）纳米结构脂质载体（EA-NLC）处方，并进行药动学研究。方法 采用乳化超声法制备EA-NLC。以包封率、载药量和粒径为考察指标，采用单因素考察和Box-Behnken设计-效应面法优化EA-NLC的处方。对最佳处方进行表征，并比较体内药动学行为。结果 最佳处方为脂-药比为13.7∶1、固-液脂质比为4.4∶1、泊洛沙姆188的用量为1.2%。EA-NLC包封率为（85.06±0.48）%，载药量为（5.53±0.15）%，粒径为（166.5±4.6）nm。体外释药具有明显的缓释特征，释药过程符合Weibull模型：lnln[1/（1－M_t/M_∞）]＝0.682 1 lnt－2.028 4， r＝0.982 7。体内药动学结果显示，EA-NLC的达峰时间（t_max）、半衰期（t_1/2）、达峰浓度（C_max）、时间-曲线下面积（AUC_0～t和AUC_0～∞）等主要参数与原料药相比均有显著性差异（P<0.05、0.01），将鞣花酸口服吸收生物利用度提高至4.67倍。结论 Box-Behnken设计-效应面法所建立的模型能较好地用于EA-NLC处方优化，准确度高，预测效果较好，且EA-NLC显著增加了EA口服吸收生物利用度。

Objective To optimize the formulation of ellagic acid (EA) nanostructured lipid carriers (EA-NLC) using Box-Behnken design-response surface method, and conduct its pharmacokinetics studies. Methods Emulsion ultrasonic method was used to prepare EA-NLC. Encapsulation rate, drug loading and particle size were taken as evaluation index, univariate investigation and Box-Behnken response surface design method were used to optimize the formulation of EA-NLC. The optimal formulation was characterized and pharmacokinetics behavior in vivo was also compared. Results The optimal formulation: lipid- drug ratio was 13.7∶1, solid-liquid lipid ratio was 4.4∶1 and surfactant concentration was 1.2%. Envelopment efficiency, drug loading and particle size of EA-NLC were (85.06 ±0.48)%, (5.53 ±0.15)%, and (166.5 ±4.6) nm, respectively. The drug release in vitro has obvious sustained-release characteristics, and the release process conformed to the Weibull model: lnln[1/(1－M_t/M_∞)]＝0.682 1 lnt－2.028 4, r = 0.982 7. The main pharmacokinetic parameters such as t_max, t_1/2, C_max, AUC_0—t and AUC_0—∞of EA-NLC had significant difference compared to ellagic acid (P < 0.05, 0.01). The oral bioavailability of ellagic acid was enhanced to 4.67 times. Conclusion The model established by the Box-Behnken design-response surface method could be used to optimize the formulation of EA-NLC with high accuracy and good prediction effect. And the oral bioavailability of ellagic acid was increased by EA-NLC effectively.

R283.6

国家重大科技专项（2017ZX0911002-001-005）；河南省重大科技专项（182102731003）