Preparation and optimization of doxorubicin-loaded albumin nanoparticles using response surface methodology.

BACKGROUND

The objective of this work was to optimize the preparation of doxorubicin-loaded albumin nanoparticles (Dox-A-Nps) through desolvation procedures using response surface methodology (RSM). A central composite design (CCD) for four factors at five levels was used in this study.

METHOD

Albumin nanoparticles were prepared through a desolvation method and were optimized in the aid of CCD. Albumin concentration, amount of doxorubicin, pH values, and percentage of glutaraldehyde were selected as independent variables, particle size, zeta potential, drug loading, encapsulation efficiency, and nanoparticles yield were chosen as response variables. RSM and multiple response optimizations utilizing a quadratic polynomial equation were used to obtain an optimal formulation.

RESULTS

The optimal formulation for Dox-A-Nps was composed of albumin concentration of 17 mg/ml, amount of doxorubicin of 2 mg/ml, pH value is 9 and percentage of glutaraldehyde of 125% of the theoretic amount, under which the optimized conditions gave rise to the actual average value of mean particle size (151 ± 0.43 nm), zeta potential (-18.8 ± 0.21 mV), drug loading efficiency (21.4 ± 0.70%), drug entrapment efficiency (76.9 ± 0.21%) and nanoparticles yield (82.0 ± 0.34%). The storage stability experiments proved that Dox-A-Nps stable in 4°C over the period of 4 months. The in vitro experiments showed a burst release at the initial stage and followed by a prolonged release of Dox from albumin nanoparticles up to 60 h.

CONCLUSIONS

This study showed that the RSM-CCD method could efficiently be applied for the modeling of nanoparticles, which laid the foundation of the further research of immuno nanoparticles.