학술논문
Literature Mining and Mechanistic Graphical Modelling to Improve mRNA Vaccine Platforms.
Document Type
Academic Journal
Author
Leonardelli L; Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.; Lofano G; Preclinical, GSK, Rockville, MD, United States.; Selvaggio G; Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.; Parolo S; Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.; Giampiccolo S; Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.; Tomasoni D; Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.; Domenici E; Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.; Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Povo, Italy.; Priami C; Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.; Department of Computer Science, University of Pisa, Pisa, Italy.; Song H; Preclinical, GSK, Rockville, MD, United States.; Medini D; Toscana Life Sciences Foundation, Siena, Italy.; Marchetti L; Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.; Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Povo, Italy.; Siena E; Data Science and Computational Vaccinology, GSK, Siena, Italy.
Source
Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101560960 Publication Model: eCollection Cited Medium: Internet ISSN: 1664-3224 (Electronic) Linking ISSN: 16643224 NLM ISO Abbreviation: Front Immunol Subsets: MEDLINE
Subject
Language
English
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.
Competing Interests: GL, HS, DM, and ES were all employees of the GSK group of companies at the time of the study. The “Fondazione The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI)” institute received financial remuneration for conducting the activates described in this study. The authors declare that this study received funding from GlaxoSmithKline Biologicals SA. The funder had the following involvement in the study: study design, interpretation of data, the writing of this article and the decision to submit it for publication. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2021 Leonardelli, Lofano, Selvaggio, Parolo, Giampiccolo, Tomasoni, Domenici, Priami, Song, Medini, Marchetti and Siena.)
Competing Interests: GL, HS, DM, and ES were all employees of the GSK group of companies at the time of the study. The “Fondazione The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI)” institute received financial remuneration for conducting the activates described in this study. The authors declare that this study received funding from GlaxoSmithKline Biologicals SA. The funder had the following involvement in the study: study design, interpretation of data, the writing of this article and the decision to submit it for publication. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2021 Leonardelli, Lofano, Selvaggio, Parolo, Giampiccolo, Tomasoni, Domenici, Priami, Song, Medini, Marchetti and Siena.)