AUTHOR=Buckley Paul R. , Alden Kieran , Coccia Margherita , Chalon Aurélie , Collignon Catherine , Temmerman Stéphane T. , Didierlaurent Arnaud M. , van der Most Robbert , Timmis Jon , Andersen Claus A. , Coles Mark C. TITLE=Application of Modeling Approaches to Explore Vaccine Adjuvant Mode-of-Action JOURNAL=Frontiers in Immunology VOLUME=10 YEAR=2019 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2019.02150 DOI=10.3389/fimmu.2019.02150 ISSN=1664-3224 ABSTRACT=

Novel adjuvant technologies have a key role in the development of next-generation vaccines, due to their capacity to modulate the duration, strength and quality of the immune response. The AS01 adjuvant is used in the malaria vaccine RTS,S/AS01 and in the licensed herpes-zoster vaccine (Shingrix) where the vaccine has proven its ability to generate protective responses with both robust humoral and T-cell responses. For many years, animal models have provided insights into adjuvant mode-of-action (MoA), generally through investigating individual genes or proteins. Furthermore, modeling and simulation techniques can be utilized to integrate a variety of different data types; ranging from serum biomarkers to large scale “omics” datasets. In this perspective we present a framework to create a holistic integration of pre-clinical datasets and immunological literature in order to develop an evidence-based hypothesis of AS01 adjuvant MoA, creating a unified view of multiple experiments. Furthermore, we highlight how holistic systems-knowledge can serve as a basis for the construction of models and simulations supporting exploration of key questions surrounding adjuvant MoA. Using the Systems-Biology-Graphical-Notation, a tool for graphical representation of biological processes, we have captured high-level cellular behaviors and interactions, and cytokine dynamics during the early immune response, which are substantiated by a series of diagrams detailing cellular dynamics. Through explicitly describing AS01 MoA we have built a consensus of understanding across multiple experiments, and so we present a framework to integrate modeling approaches into exploring adjuvant MoA, in order to guide experimental design, interpret results and inform rational design of vaccines.