Application of multiple regression analysis in optimization of anastrozole-loaded PLGA nanoparticles

AbstractThe present investigation deals with development of anastrozole-loaded PLGA nanoparticles (NPs) as an alternate to conventional cancer therapy. The NPs were prepared by nanoprecipitation method and optimized using multiple regression analysis. Independent variables included drug:polymer ratio (X1), polymer concentration in organic phase (X2) and surfactant concentration in aqueous phase (X3) while dependent variables were percentage drug entrapment (PDE) and particle size (PS). Results of desirability criteria, check point analysis and normalized error were considered for selecting the formulation with highest PDE and lowest PS. Prepared NPs were characterized for zeta potential, transmission electron microscopy (TEM), differential scanning calorimetry (DSC) and in vitro drug release studies. DSC and TEM studies indicated absence of any drug–polymer interaction and spherical nature of NPs, respectively. In vitro drug release showed biphasic pattern exhibiting Fickian diffusion-based release mechan...