FACTORIAL DESIGN AS THE METHOD IN THE OPTIMIZATION OF TIMOLOL MALEATE-LOADED NANOPARTICLE PREPARED BY IONIC GELATION TECHNIQUE
Objective: This study aims to optimize the timolol maleate (TM) nanoparticle prepared by ionic gelation method using the factors of pectin (PC), calcium chloride (CC), and chitosan (CS) concentrations with the responses of entrapment efficiency, particle size, and polydispersity index using 23 factorial design.
Methods: TM nanoparticle suspensions were obtained by mixing of PC (0,4-0,6% (w/v)), CC (0,2-0,4% (w/v)), and CS (0,01-0,02% (w/v)) with TM concentration of 0,02% w/v. Each mixture was then tested for entrapment efficiency, particle size, and polydispersity index. The test results were analyzed with 23 factorial design using Design-Expert software in order to determine the optimum formula.
Results: The optimization study showed that all of the factors influenced the responses significantly (p<0.05) based on the analysis of variance (ANOVA) of the suggested models. The R2value and the adequate precision value of the three models were more than 0.7 and 4, respectively. The difference between Adjusted R-Squared and Predicted R-Squared value were less than 0.200. The optimum condition of TM nanoparticle was suggested at the desirability value of 0.839 with the concentration of PC, CC, and CS of 0,4% (w/v), 0,2% (w/v), and 0,01% (w/v), respectively. The entrapment efficiency, particle size, and polydispersity index of the optimum condition were 24.791±2.84%, 274.867±14.45 nm, and 0.634±0.066, respectively.
Conclusion: The 23factorial design has been proved as the suitable method to determine the optimum condition that yields the good results of the entrapment efficiency, particle size, and polydispersity index of the TM-loaded nanoparticle prepared by ionic gelation method.
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