Optimization of Process Parameters of Osthole-Loaded PLGA Microparticles Prepared Using Emulsification–Solvent Extraction

Abstract

Osthole-loaded poly(d,l-lactic-co-glycolic) acid (PLGA) microparticles were prepared by oil-inwater (o/w) emulsification. The organic phase in emulsions was extracted by conventional evaporation and supercritical fluid extraction of emulsions. A Box-Behnken experimental design was used to evaluate the effects and to optimize the variables. Results indicated that the effects from two variables, that is, the emulsification stirring speed and the ratio of osthole to PLGA, had statistically significant on the encapsulation efficiency, while another variable, that is, the volume ratio of o/w, has no independent impact on the encapsulation. The interactions exist between the ratio of osthole to PLGA and the stirring speed, and between the volume ratio of o/w and the stirring speed. A second-order polynomial model was well adjusted to predict response variables, and 90.9% encapsulation efficiency could be realized at optimized conditions. The encapsulation efficiency of microparticles obtained with conventional evaporation was higher than that with supercritical fluid extraction of emulsions. The release curve of osthole from the microparticles could be nicely fitted by the Weibull equation and the release follows Fickian diffusion.

Injectable sustained‐release poly(lactic‐co‐glycolic acid) (PLGA) microspheres of exenatide prepared by supercritical fluid extraction of emulsion process based on a design of experiment approach

“…Further, lack of fit, coefficient of variance (CV, %), coefficient of determination (R 2 ), adjusted coefficient of determination (R 2 adj), predicted coefficient of determination (R 2 pred), adequate precision, and prediction sum of square (PRESS) of the models were also used to determine the adequacy and goodness of fit for each polynomial equation model. 47 , 94 , 95 , 96 Among the different polynomial models, the best‐fit mathematical model was selected based on statistical parameters. The polynomial quadratic equation for the model is given as: …”