Analysis and Modeling to evaluate influenza vaccine responses

Slides: https://www.andreashandel.com/presentations/

Andreas Handel

2023-10-11

Financial disclosures

  • NIH support

Learning Objectives

  • Identify problems and possible solutions for comparing future universal influenza vaccine candidates.
  • Describe possible problems related to repeat influenza vaccinations.

Part 1 - Assessing influenza vaccine candidates

Current Influenza vaccines

  • Need to be reformulated almost every year because of virus evolution
  • Need to be taken annually, due to virus evolution and vaccine waning
  • Are not very good, even if the vaccine and circulating strains match

Future Influenza vaccines

  • Should protect for a long time (lifelong?)
  • Should have high efficacy
  • Should protect against a wide range of strains

Universal flu vaccine challenges

  • Many
    • How to assess/compare vaccine candidates

How do we define a vaccine response?

Quantifying vaccine responses

Quantifying vaccine responses

Quantifying vaccine responses

  • Magnitude: \(\frac{1}{N}\sum_{n} log(\textrm{TI}_{n,j=1})\)
  • Overall strength: \(\frac{1}{N*J}\sum_{n} \sum_{j} log(\textrm{TI}_{n,j})\)
  • Breadth: \(\frac{1}{N*J}\sum_{n} \sum_{j} \textrm{SC}_{n,j}\)

SC = Seroconversion, TI = Titer Increase (D28/D0), n = individuals, j = Strains.

Comparing vaccine responses

A new method to quantify/compare vaccine responses

  • Organize strains by antigenic distance
  • Fit a model to more robustly estimate magnitude/breadth/strength

Strain distance

Strain distance measures

  • Time: absolute difference in years of strain isolation.
  • Sequence: Some measure based on sequence difference.
  • Biophysical: Measures based on computed or measured biophysical properties.
  • Phenotypic: Antigenic cartography based on HAI assays.

“Our” data

  • Data from UGAFluVac study
  • Individuals received vaccine, response to multiple strains was tested

Strain distance measures

Strain distance measures

Quantifying vaccine responses

Comparing vaccine responses

Testing our method

We sampled from the panel of heterologous strains from UGAFluVac to mimic different labs

Testing our method - the results

The table shows the coefficient of variation for each outcome.

Current method Proposed method
Magnitude 0.088 0.103
Breadth 0.059 0.431
Overall strength 0.083 0.081

Our new method is worse (more variable)!

Testing our method with simulations

  • Create a universe of 50 possible heterologous strains with varying antigenic distances.

  • Create 10 lab panels by randomly sampling 9 strains and adding the homologous strain (distance of 0).

  • For each lab, generate 100 random individuals by simulating flu vaccine response titers from a model that shows linearly reduced response with increasing antigenic distance.

Simulation results

Current method Proposed method
Magnitude 0.025 0.008
Breadth 0.199 0.020
Overall strength 0.155 0.007

Now our new method is better. Hm…

The culprit

Simulation results with 30% censored data

Current method Proposed method
Magnitude 0.028 0.033
Breadth 0.290 0.316
Overall strength 0.137 0.071

With censored data, the current method looks artificially good.

Part 1 Summary

  • Our proposed new method seems to be generally more robust.
  • If a good amount of censored data are present, the current method falsely under-estimates the uncertainty.
  • Our method also doesn’t properly handle the censored data (yet).
  • We need to update our method to properly deal with the censored values. Then we can do another comparison of our method and the current approach.

Part 2 - Repeat influenza vaccinations

The role of prior vaccinations

There is some evidence that repeat vaccinations leads to reduced vaccine effectiveness (VE).

Bi et al, 2023 medRxiv

The role of prior vaccinations

  • Is the reduced VE mediated by antibody titers?
  • Repeat vaccinators mount a less strong vaccine response. Is that just because they start at higher pre-vaccination titers?

Repeat vaccinations

No impact other than pre-vaccine antibodies?

Solid: recent vaccination. Open: no vaccination last 3 years. Linderman et al 2020 CSH

Repeat vaccinations - pre-vac HAI

Data from the UGA vaccine cohort described earlier.

Repeat vaccinations - HAI increase

Repeat vaccinations - post-vac HAI

UoR - pre-vac IgG

Data from a U of Rochester vaccine cohort, courtesy of Andrea Sant. See also Moritzky et al 2023 JID.

UoR - IgG increase

UoR - post-vac IgG

UoR - pre-vac CD4

UoR - CD4 increase

UoR - post-vac CD4

Part 2 Summary

  • Prior vaccination seems to induce a less robust response independent of prior antibody levels.
  • Post-vaccination titers show no clear pattern between repeat vaccinators and non-vaccinators.
  • We don’t quite understand yet how exactly prior vaccination might lead to reduced protection.
  • We also want to explore those patterns for heterologous responses.

Acknowledgements

  • Zane Billings - for doing most of the work and creating most of the figures (with R and Biorender)
  • Ted Ross & Andrea Sant - for data sharing
  • Many other colleagues and collaborators
  • NIH

Questions?

https://phdcomics.com/

  • Slides: https://www.andreashandel.com/presentations/
  • Contact: https://www.andreashandel.com

Extra Slides

Repeat vaccinations - H1N1 pre-vac HAI

Data from the UGA vaccine cohort described earlier.

Repeat vaccinations - H1N1 HAI increase

Repeat vaccinations - post-vac H1N1 HAI

UoR - pre-vac HAI

Data from a U of Rochester vaccine cohort, courtesy of Andrea Sant. See also Moritzky et al 2023 JID.

UoR - HAI increase

UoR - post-vac HAI