Formulation optimization of paclitaxel carried by PEGylated emulsions based on artificial neural network

Purpose. To develop paclitaxel carried by injectable PEGylated emulsions, an artificial neural network (ANN) was used to optimize the formulation-which has a small particle size, high entrapment efficiency, and good stability-and to investigate the role of each ingredient in the emulsion. Methods. Paclitaxel emulsions were prepared by a modified ethanol injection method. A computer optimization technique based on a spherical experimental design for three-level, three factors [ soybean oil (X1), PEG-DSPE (X2) and polysorbate 80 (X3)] were used to optimize the formulation. The entrapment efficiency of paclitaxel (Y1) was quantified by HPLC; the particle size of the emulsions (Y2) was measured by dynamic laser light scattering and the stability of paclitaxel emulsions was monitored by the changes in drug concentration (Y3) and particle size (Y4) after storage at 4degreesC. Results. The entrapment efficiency, particle size and stability of paclitaxel emulsions were influenced by PEG-DSPE, polysorbate 80, and soybean oil. Paclitaxel emulsions of small size (262 nm), high entrapment efficiency (96.7%), and good stability were obtained by the optimization. Conclusions. A novel formulation for paclitaxel emulsions was optimized with ANN and prepared. The contribution indices of each component suggested that PEG-DSPE mainly contributes to the entrapment efficiency and particle size of paclitaxel emulsions, while polysorbate 80 contributes to stability.