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Influenza vaccine strain selection with an AI-based evolutionary and antigenicity model.

Wenxian Shi1, Jeremy Wohlwend2, Menghua Wu2

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VaxSeer is a new computational method that predicts vaccine-virus matches for better influenza vaccine effectiveness. This approach improves upon current methods by forecasting future viral dominance and antigenic profiles for enhanced vaccine selection.

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Area of Science:

  • Virology
  • Immunology
  • Computational Biology
  • Vaccinology

Background:

  • Current vaccines offer limited protection against rapidly mutating viruses like influenza.
  • Influenza vaccine effectiveness in the US has averaged below 40% from 2012-2021.
  • Assessing vaccine clinical outcomes is typically a retrospective process.

Purpose of the Study:

  • To propose an in silico method, VaxSeer, for predicting vaccine candidate antigenic match with future circulating viruses.
  • To evaluate VaxSeer's performance in selecting strains with superior antigenic matches compared to traditional methods.

Main Methods:

  • Developed VaxSeer, an in silico framework utilizing sequencing and antigenicity data.
  • Evaluated VaxSeer over 10 years of retrospective data.
  • Assessed the correlation between VaxSeer's predicted antigenic match and actual influenza vaccine effectiveness.

Main Results:

  • VaxSeer consistently identified strains with better empirical antigenic matches to circulating viruses than annual recommendations.
  • The predicted antigenic match by VaxSeer showed a strong correlation with influenza vaccine effectiveness.
  • VaxSeer's predictions also correlated with reductions in disease burden.

Conclusions:

  • VaxSeer offers a promising computational approach to enhance vaccine strain selection.
  • This framework has the potential to improve future vaccine efficacy and reduce disease impact.
  • In silico prediction of antigenic match can proactively guide vaccine development.