响应面法优化PLA-α-细辛脑纳米粒的制备工艺

Response Surface Methods for Formulation Optimization of PLA-α-Asarone Nanoparticles

关键词:  α-细辛脑  纳米粒  Plackett-Burman实验设计  中心复合实验设计  工艺优化

Key words:  α-asarone  nanoparticle  Plackett-Burman  central composite design  formulation optimization

摘要:

目的 使用响应面法优化聚乳酸（polylactic acid，PLA）-α-细辛脑纳米粒的工艺条件。方法 利用Plackett-Burman实验设计对纳米粒制备过程中的各种自变量进行预筛选。筛选出的自变量通过中心复合实验设计法进一步优化工艺，以期得到具有理想粒径、载药量及包封率的纳米粒。结果 最佳工艺处方如下：PLA浓度14.76 mg·mL^-1，α-细辛脑药物浓度4.89 mg·mL^-1，PVA浓度1%，油水比1∶2。制备的PLA-α-细辛脑纳米粒圆整、粒径均一，平均粒径265.4 nm，多分散系数为0.038，载药量12.40%，包封率55.86%，与预测值相对误差较小。结论 响应面法能够有效地优化PLA-α-细辛脑纳米粒的制备，工艺稳定可行。

Abstract:

OBJECTIVE To optimize formulation of PLA-α-asarone nanoparticles(NP) by response surface methodology (RSM). METHODS Plackett-Burman design for independent variables was firstly conducted to prescreen various formulations and process variables during the development of NP. Selected primary variables were further optimized by central composite design. This process lead to an optimum formulation with desired particle size, drug loading and encapsulation efficiency. RESULTS Optimum formulation was as follows:PLA concentration 14.76 mg·mL^-1, α-asarone concentration 4.89 mg·mL^-1, PVA concentration 1% and volume ratio of oil-water 1:2. These prepared PLA-α-asarone nanoparticles had round surface and uniform size with average particle size of 265.4 nm, PDI index of 0.038, drug loading efficiency of 12.40% and encapsulation efficiency of 55.86%, relative errors of these parameters were small by comparing with the bias values. CONCLUSION The study demonstrates the feasibility that response surface methodology can be used to optimize the formulation and process variables to achieve favorable responses for PLA-α-asarone nanoparticles.