脑源性神经营养因子PLGA纳米粒的处方工艺研究

目的制备脑源性神经营养因子聚乳酸-羟基乙酸共聚物(PLGA)纳米粒,使用星点设计-效应面法对处方工艺进行优化筛选。方法以生物降解型聚乳酸-羟基乙酸共聚物为载体材料,采用复乳化溶剂挥干法制备脑源性神经营养因子PL-GA纳米粒。观察纳米粒的形态、大小和粒径分布。以PLGA的用量及形成复乳时PVA含量为考察因素,体外释放为评价指标,用线性方程和二次及三次多项式描述体外释放和两个影响因素之间的数学关系,根据最佳数学模型描绘效应面,选择最佳处方,并进行预测分析。结果各指标的三项式拟合方程均优于多元线形回归方程,建立的数学模型的预测值与实际值符合较好。结论用星点设计-效应面法优化处方工艺预测性良好。

Optimized Formulation of Brain-derived Neurotrophic Factor-loaded PLGA Nanoparticles

Aim To prepare the brain-derived neurotrophic factor(BDNF)-loaded PLGA nanoparticles and to optimize the formulations of nanoparticles by a central composite design/response surface methodology.Methods BDNF-loaded PLGA nanoparticles were prepared dry-emulsion solvent.The shape,size and size distribution of nanoparticles were observed.Independent variables were PLGA content and PVA content of the first emulsion,and in vitro release was dependent variable.Linear or nonlinear mathematic models were used to estimate the relationship between independent and dependent variables.Response surfaces were delineated according to best-fit mathematic models,and optimum formulations were selected.A prediction was made by comparing the observed and predicted values.Results The trinomial fitting equations of indexes were all superior to their multi-linear regression equations,and the predictive values in the established mathematical model were in good conformity with the experimental values.Conclusion The central composite design/response surface method shows a good predictability in optimizing the formula technology of BDNF-loaded PLGA nanoparticles