Artificial neural network for optimizing the formulation of curcumin-loaded liposomes from statistically designed experiments.

Abstract

Curcumin is a primary polyphenol of the rhizomatous perennial plant called Curcuma Longa. Curcumin interferes favorably with the cellular events that take place in the inflammatory and proliferative stages of wound healing, hence its importance in skin regeneration and wound healing. Curcumin is however lipophilic, and this must be considered in the choice of its drug delivery system. Liposomes are spherical vesicles with bi-lipid layers. Liposomes can encapsulate both lipophilic and hydrophilic drugs, hence their suitability as an ideal drug delivery system for curcumin. There is, nevertheless, a tendency for liposomes to be unstable and have low encapsulation efficiency if it is not formulated properly. Formulation optimization of curcumin-loaded liposomes was studied by the application of artificial neural network (ANN) to improve encapsulation efficiency and flux of the liposomes. The input factors selected for optimization of the formulation were sonication time, hydration volume, and lipid/curcumin ratio. The response variables were encapsulation efficiency and flux. The maximum encapsulation efficiency and flux were obtained using lipid/curcumin ratio of 4.35, sonicator time of 15 min, and hydration volume of 25 mL. The maximum encapsulation efficiency and flux predicted were 100% and 51.23 µg/cm²/h, respectively. The experimental values were 99.934% and 51.229 µg/cm²/h, respectively. Curcumin-loaded liposome formulation is a promising drug delivery system in the pharmaceutical industry when formulated using optimized parameters derived from ANN statistically designed models.