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

Giang Thi Thu Vu, Nghia Thi Phan, Huyen Thi Nguyen, Hung Canh Nguyen, Yen Thi Hai Tran, Tung Bao Pham, Linh Tran Nguyen, Hoa Dang Nguyen.


The objectives of this study were to optimize the formula of the self-nano emulsifying drug delivery system (SNEDDS) containing rosuvastatin and evaluate its physicochemical characteristics. The solubility and compatibility of rosuvastatin in surfactants, co-surfactants 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 self-forming nanoemulsion. At the percentage of rosuvastatin calcium from 8 to 12 %, the area for optimizing the formula of Ros SNEDDS increased inversely proportional to the percentage of Ros. The Ros SNEDDS prepared according to predicted formulas possessed self-emulsification to form nanoemulsion with average droplet size less than 100 nm, polydispersity index less than 0.3 and rosuvastatin entrapment higher than 90 %.

Key words: rosuvastatin; self-nanoemulsifying drug delivery system, nanoemulsion, droplet size, polydispersity index (PDI), artificial neural network.

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