Examination of the effect of niosome preparation methods in encapsulating model antigens on the vesicle characteristics and their ability to induce immune responses

Abstract

Niosome nanoparticles can be prepared using different methods, each of which can affect the size and homogeneity of the prepared particles. The aim of this study was to establish if the method of preparation impacted on the prepared vesicles when loaded with a model protein and the type of immune responses induced to the vaccine antigen. Niosomes were prepared using both the traditional thin film hydration (TFH) technique and the microfluidic mixing (MM) technique. Influenza antigen was then entrapped in the niosomes and formulations tested for their ability to induce in vivo immune responses in immunised BALB/c mice. Niosomes prepared by MM had a mean size of 157 ± 1.8 nm and were significantly more uniform compared with the niosomes prepared using TFH (mean size 388 ± 10 nm). Niosomes play a key role as an adjuvant to help raise high antibody immune responses. This was confirmed in this study since animals treated with both types of niosomes and antigen were more responsive than unentrapped (free) antigen. Cytokine analysis showed that the TFH niosomes induced a Th1 immune response by raising IgG2a and high levels of IFN-ɣ, while the MM niosomes induced a Th2 immune response by inducing IgG1 (p < .05). These results confirmed that the method of preparation of the niosomes nanoparticles induced different immune responses and the average particle size of the niosomes differed depending on the method of manufacture. This indicates that particle size and uniformity are of importance and should be taken into consideration when designing an oral based delivery system for vaccine delivery.

Keywords:

• Thin film hydration
• microfluidic mixing
• niosomes nanoparticle
• drug delivery
• influenza vaccines

Acknowledgements

The authors would like to thank Government Pharmaceutical Organization in Thailand for funding this work. Also, the authors would like to thank Kevin O’Halloran for technical support.

Disclosure statement

No potential conflict of interest was reported by the author(s).