Application of the artificial neural network to optimize the formulation of self-nanoemulsifying drug delivery system containing rosuvastatin

Abstract

The objectives of this study were to optimize the formula of the self-nanoemulsifying drug delivery system (SNEDDS) containing rosuvastatin and to evaluate its physicochemical characteristics. The solubility and compatibility of rosuvastatin in surfactants, cosurfactants, and oil excipients were evaluated. The D-optimal experimental design, created by JMP 15 software, was used for analyzing the effects of excipients on the physicochemical characteristics of SNEDDS to optimize the rosuvastatin SNEDDS formula. The generated nanoemulsions from Ros SNEDDS were characterized for droplet size, polydispersity index, and entrapment efficiency. As a result, Cremophor RH40, Capryol 90, and PEG 400 were selected to develop the pseudoternary phase diagram to identify the area capable of selfforming nanoemulsion. As the percentage of rosuvastatin calcium increased from 8% to 12%, the area for optimizing the formula of Ros SNEDDS decreased. The Ros SNEDDS prepared according to predicted formulas possessed selfemulsification to form nanoemulsion with average droplet size less than 100 nm, polydispersity index less than 0.3, and rosuvastatin entrapment higher than 90%.