Analysis and Modeling to evaluate influenza vaccine responses

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

Andreas Handel

2023-09-15

Influenza Population Biology

US influenza Incidence. Source: 538.

Influenza Population Biology

US influenza deaths. Nelson & Holmes, Nat Rev Gen 2007

Influenza Population Biology

Influenza in Vietnam. H1N1:orange, H3N2:red, B:blue. Servadio et al, Plos Comp Bio 2023

Influenza Ecology

Figure 1: Long et al, 2019, Nat Rev Mic

Influenza Evolution

Figure 2: H3N2 phylogeny. Source: Trevor Bedford

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

See here for more: Collaborative Influenza Vaccine Innovation Centers

Universal flu vaccine challenges

  • Many
    • How to assess 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 mangitude/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 expected biophysical properties.
  • Phenotypic: Antigenic cartography based on HAI assays.

Strain distance measures

Strain distance measures

Quantifying vaccine responses

Comparing vaccine responses

Testing our method - the data

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

Testing our method - the data

  • We sampled strains to mimic different labs performing experiments

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.

Conclusions

  • 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 fully deal with the censored data yet.

Future work

  • We need to update our methods to properly deal with the censored values. Then we can do another comparison of our method and the current approach.

Some other projects

R packages to make modeling easier I

For teaching (stable):

R packages to make modeling easier II

For research (WIP):

Online modeling/analysis courses

Keep going?

Image by Aline Dassel/Pixabay

Pre-vaccination titer and vaccine response

Introduction

  • More pre-vaccination titer leads to more post-vaccination titer, but the boost is reduced.
  • Called response blunting, antibody ceiling effect, negative interference.
  • For a nice summary, see Oidtman et al. 2021 Trends Mic

Schematic

Pre-vaccination titer and vaccine response

Left: H1N1 vaccine, Right: H5N1 vaccine. Zarnitsyna et al 2015 Phil Trans B.

Pre-vaccination titer and vaccine response

Ranjeva et al 2019 Nat Com

Pre-vaccination titer and vaccine response

Left: H1N1, Right: H3N2. U of Rochester cohort. Moritzky et al 2023 JID.

UGAFluVac cohort

Epitope-masking can explain some of those patterns

Zarnitsyna et al 2015 Phil Trans B, 2016 Plos Path.

Modeling pre-vaccination titer and heterologous vaccine response

Extension of the model can be used to explore predictions for heterologous response.

\[ \begin{aligned} \textrm{free antigen (type 1)} \qquad \dot{H_f} & = - k_1 A_1 H_f - k_2 A_2 H_f - d_f H_f \\ \textrm{bound antigen (type 1)} \qquad \dot{H_b} & = k_1 A_1 H_f + k_2 A_2 H_f - d_b H_b \\ \textrm{B-cell type 1} \qquad \dot{B_1} & = \frac{s_1 B_1 H_f}{p_1 + H_f} \\ \textrm{antibody type 1} \qquad \dot{A_1} & = g_1 B_1 -k_1 A_1 H_f - d_1 A_1\\ \textrm{B-cell type 2} \qquad \dot{B_2} & = \frac{s_2 B_2 H_f}{p_2 + H_f} \\ \textrm{antibody type 2} \qquad \dot{A_2} & = g_2 B_2 -k_2 A_2 H_f - d_2 A_2 \\ \end{aligned} \]

With \(k_2 = k_1(1-AD)\), \(s_2 = s_1(1-AD)\)

Modeling pre-vaccination titer and heterologous vaccine response

Extension of the model can be used to explore predictions for heterologous response for different antigenic distance (AD).

Heterologous vaccine response patterns - H1N1

Heterologous vaccine response patterns - H1N1

Heterologous vaccine response patterns - H1N1

Conclusions

We need to dig deeper.

Image by Alexa/Pixabay

Acknowledgements

  • Zane Billings - for doing most of the work and creating most of the figures (with 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