Towards the use of in vitro and in silico T-cell response prediction to guide the development of vaccines, using mRNA-based rabies vaccine as a proof-of-concept.

The traditional process of designing and developing vaccines has been challenged dramatically by the COVID-19 pandemic, with the adoption of mRNA-based vaccines and a reduction of lengthy development pipelines from 10-15 years to 1-1.5 years. This creates a push for further innovation in vaccine development, in particular for diseases with a high unmet need. As an example, mortality due to rabies (a lyssavirus) remains unacceptably high. Although safe, effective vaccines are available for human and animal use, human vaccines are too expensive and generally inaccessible for widespread use in regions where the risk of bites from rabid animals is highest. mRNA approaches offer an opportunity to provide affordable vaccines with the possibility of manufacturing in low and middle income countries, with optimised design affording broader protection. The aim of such an approach would be to drive down cost and broaden supply and equity of access. Novel in silico approaches such as those analysing the T cell receptor response may permit insights into the immune response elicited by rabies vaccines, aid understanding of the mode of action and guide future use. The objective of the current project is to investigate if detailed analysis of the T-cell receptor response can be valuable to inform vaccine design and improve the vaccine development process, using a rabies mRNA vaccine as a proof-of-concept. We will combine in vitro (UAntwerp) and in silico (ImmuneWatch) techniques to gain insights into the T cell response against a range of experimental rabies mRNA vaccine constructs (Quantoom). The project is a public-private partnership aiming to explore novel approaches in vaccine development and to prepare future collaborations between the project partners.

Keywords:VACCINES, T-CELLS, IMMUNO INFORMATICS, MRNA

Disciplines:Adaptive immunology, Virology, Computational biomodelling and machine learning, Vaccinology