Leveraging pre-vaccination antibody titres across multiple influenza H3N2 variants to forecast the post-vaccination response.

Journal: EBioMedicine
Published Date: May 26, 2025

Abstract

BACKGROUND: Despite decades of research on the influenza virus, we still lack a predictive understanding of how vaccination reshapes each person's antibody response, which impedes efforts to design better vaccines. Models using pre-vaccination antibody haemagglutination inhibition (HAI) titres against the vaccine strain alone poorly predict post-vaccination responses.

Authors

  • Hannah Stacey
    Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, USA.
  • Michael A Carlock
    Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA; Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA; Florida Research and Innovation Center, Cleveland Clinic, Port Saint Lucie, FL 34987, USA.
  • James D Allen
    Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA; Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA; Florida Research and Innovation Center, Cleveland Clinic, Port Saint Lucie, FL 34987, USA.
  • Hannah B Hanley
    Center for Vaccines and Immunology, University of Georgia, Athens, GA 30602, USA; Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA.
  • Shane Crotty
    Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, USA; Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA.
  • Ted M Ross
    Florida Research and Innovation Center, Port Saint, Cleveland Clinic Florida, Port St. Lucie, FL, USA; Center for Vaccines and Immunology, College of Veterinary Medicine, University of Georgia, Athens, GA, United States.
  • Tal Einav
    Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, USA; Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA. Electronic address: tal.einav@lji.org.

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