Vaccine models predict rules for updating vaccines against evolving pathogens such as SARS-CoV-2 and influenza in the context of pre-existing immunity

• Type of Material: Article
• Authors: Rajat Desikan, GlaxoSmithKline GSK; Susanne L Linderman, Emory University; Carl Davis, Emory University; Veronika Zarnitsyna, Emory University; Hasan Ahmed, Emory University; Rustom Antia, Emory University
• Language: English
• Date: 2022-10-03
• Publisher: FRONTIERS MEDIA SA
• Publication Version: Final Published Version
• Copyright Statement: © 2022 Desikan, Linderman, Davis, Zarnitsyna, Ahmed and Antia
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3389/fimmu.2022.985478
• Title of Journal or Parent Work: FRONTIERS IN IMMUNOLOGY
• Volume: 13
• Start Page: 985478
• End Page: 985478
• Grant/Funding Information: We acknowledge funding from the National Institutes of Health (NIH) grants U01 AI150747, U01 HL139483, and U01 AI144616.
• Keywords: SELECTION; simulations; Science & Technology; INFECTION; COVID-19; VARIANTS; ANTIBODIES; omicron; B-CELL RESPONSES; GERMINAL-CENTERS; Life Sciences & Biomedicine; modeling; Vaccine; variants; Immunology; SARS-CoV-2
• Research Categories: Health Sciences, Immunology; Health Sciences, Pharmacology; Biology, Microbiology

Abstract

Currently, vaccines for SARS-CoV-2 and influenza viruses are updated if the new vaccine induces higher antibody-titers to circulating variants than current vaccines. This approach does not account for complex dynamics of how prior immunity skews recall responses to the updated vaccine. We: (i) use computational models to mechanistically dissect how prior immunity influences recall responses; (ii) explore how this affects the rules for evaluating and deploying updated vaccines; and (iii) apply this to SARS-CoV-2. Our analysis of existing data suggests that there is a strong benefit to updating the current SARS-CoV-2 vaccines to match the currently circulating variants. We propose a general two-dose strategy for determining if vaccines need updating as well as for vaccinating high-risk individuals. Finally, we directly validate our model by reanalysis of earlier human H5N1 influenza vaccine studies.