Optimization of polylactic-co-glycolic acid nanoparticles containing itraconazole using 23 factorial design

Abstract

This study investigated the utility of a 23 factorial design and optimization process for polylactic-co-glycolic acid (PLGA) nanoparticles containing itraconazole with 5 replicates at the center of the design. Nanoparticles were prepared by solvent displacement technique with PLGA X1 (10, 100 mg/mL), benzyl benzoate X2 (5, 20 μg/mL), and itraconazole X3 (200, 1800 μg/mL). Particle size (Y1), the amount of itraconazole entrapped in the nanoparticles (Y2), and encapsulation efficiency (Y3) were used as responses. A validated statistical model having significant coefficient figures (P < .001) for the particle size (Y1), the amount of itraconazole entrapped in the nanoparticles (Y2), and encapsulation efficiency (Y3) as function of the PLGA (X1), benzyl benzoate (X2), and itraconazole (X3) were developed: Y1 = 373.75 + 66.54X1+ 52.09X2 + 105.06X3 - 4.73X1X2 + 46.30X1X3; Y2 = 472.93 + 73.45X1 + 169.06X2 + 333.03X3 + 62.40X1X3 + 141.49X2X3; Y3 = 57.36 + 6.53X1 + 15.52X2 - 12.59X3 + 1.01X1X3 + 1.73X2X3. X1, X2, and X3 had a significant effect (P < .001) on Y1, Y2, and Y3. The particle size, the amount of itraconazole entrapped in the nanoparticles, and the encapsulation efficiency of the 4 formulas were in agreement with the predictions obtained from the models (P