(192r) Box-Behnken Design of Self-Emulsifying Emulsions for Application as Vaccine Adjuvants

Burakova, Y. - Presenter, Kansas State University
Shi, J. N., Kansas State University
Schlup, J. R., Kansas State University
Emulsions produced by low-energy methods are dependent on many formulation parameters. The detailed understanding of the relationship between these parameters and their effect on the response are very important in the design of optimal formulations. The influence of several formulation variables and the interactions between them on the formation and stability of emulsions were studied using response surface methodology. A four-factor, three-level Box-Behnken design was used to determine the optimal design space and which parameters have the largest effect on physical characteristics of emulsions for application as vaccine components for animals. The self-emulsifying mixtures were comprised of oil (mixtures of light mineral oil and MCT oil), two surfactants (sucrose palmitate and Tween 60), and glycerol. After gentle agitation with saline solution as the water phase, an emulsion with nanoscale droplet size formed. Four critical parameters for emulsion formation were identified: the ratio of sucrose palmitate to glycerol, the ratio of Tween 60 to oil phase, the ratio of glycerol to oil, and, finally, the ratio of gel to water phase. The effect of these factors on the droplet size, polydispersity, zeta potential, and stability of the emulsions were described quantitatively with polynomial equations. The Tween 60 to oil ratio followed by the oil to glycerol ratio were the most significant parameters in determining the droplet size and polydispersity of emulsions. Remarkably, low by the magnitude zeta potential values correlated with emulsions having higher stability; less stable emulsions were formed as the zeta potential became more negative. The response surface methodology permitted the important interconnections between formulation variables to be investigated, the optimal design space to be defined, and the parameters to be optimized for the desired outcome.