AUTHOR=Leonardelli Lorena , Lofano Giuseppe , Selvaggio Gianluca , Parolo Silvia , Giampiccolo Stefano , Tomasoni Danilo , Domenici Enrico , Priami Corrado , Song Haifeng , Medini Duccio , Marchetti Luca , Siena Emilio TITLE=Literature Mining and Mechanistic Graphical Modelling to Improve mRNA Vaccine Platforms JOURNAL=Frontiers in Immunology VOLUME=12 YEAR=2021 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.738388 DOI=10.3389/fimmu.2021.738388 ISSN=1664-3224 ABSTRACT=

RNA vaccines represent a milestone in the history of vaccinology. They provide several advantages over more traditional approaches to vaccine development, showing strong immunogenicity and an overall favorable safety profile. While preclinical testing has provided some key insights on how RNA vaccines interact with the innate immune system, their mechanism of action appears to be fragmented amid the literature, making it difficult to formulate new hypotheses to be tested in clinical settings and ultimately improve this technology platform. Here, we propose a systems biology approach, based on the combination of literature mining and mechanistic graphical modeling, to consolidate existing knowledge around mRNA vaccines mode of action and enhance the translatability of preclinical hypotheses into clinical evidence. A Natural Language Processing (NLP) pipeline for automated knowledge extraction retrieved key biological evidences that were joined into an interactive mechanistic graphical model representing the chain of immune events induced by mRNA vaccines administration. The achieved mechanistic graphical model will help the design of future experiments, foster the generation of new hypotheses and set the basis for the development of mathematical models capable of simulating and predicting the immune response to mRNA vaccines.