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Seasonal Influenza H3N2 virus poses a great threat to public health, but its vaccine efficacy remains suboptimal. One critical step in influenza vaccine production is the viral passage in embryonated eggs. Recently, the strength of egg passage adaptation was found to be rapidly increasing with time driven by convergent evolution at a set of functionally important codons in the hemagglutinin (HA1). In this study, we aim to take advantage of the negative correlation between egg passage adaptation and vaccine effectiveness (VE) and develop a computational tool for selecting the best candidate vaccine virus (CVV) for vaccine production. Using a probabilistic approach known as mutational mapping, we characterized the pattern of sequence evolution driven by egg passage adaptation and developed a new metric known as the adaptive distance (AD) which measures the overall strength of egg passage adaptation. We found that AD is negatively correlated with the influenza H3N2 vaccine effectiveness (VE) and ~75% of the variability in VE can be explained by AD. Based on these findings, we developed a computational package that can Measure the Adaptive Distance and predict vaccine Effectiveness (MADE). MADE provides a powerful tool for the community to calibrate the effect of egg passage adaptation and select more reliable strains with minimum egg-passaged changes as the seasonal A/H3N2 influenza vaccine.

MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs