(T-002) Representation of antibodies is key to generate a predictive vaccine quantitative systems pharmacology (QSP) model

Ruth Abrams, PhD – Associate Director, Daiichi Sankyo; Ryoko Sawamura, PhD – Associate Director, Daiichi Sankyo Co., Ltd.; Nao Jonai, PhD – Senior Director, Daiichi Sankyo Co., Ltd.; Takashi Ito, PhD – Senior Director, Daiichi Sankyo Co., Ltd.; Ryuta Mukasa, PhD – Associate Director, Daiichi Sankyo Co., Ltd.; Giorgio Senaldi, MD, PhD – Executive Director, Daiichi Sankyo; Shinji Shimizu, PhD – Senior Manager, Daiichi Sankyo Co., Ltd.; Daisuke Sugiyama, PhD – Senior Director, Daiichi Sankyo Co.; Tarek Leil, PhD – Executive Director, Daiichi Sankyo

Vaccine efficacy is often assessed by levels of neutralizing antibodies, which reflect clonal diversity, amount, and affinity of the antibodies induced. These properties impact vaccine efficacy, determining protection against current and future strains of pathogenic infectious agents like the COVID-19 virus. Prior QSP models of the humoral immune response and vaccines have used a similar generic set of polyclonal antibody affinities. Our work examines: 1) how can we represent the spectrum of clonal diversity of the IgG antibodies induced by a COVID-19 vaccine in a QSP model; and 2) what effect will clonal diversity have on vaccine efficacy?

We have constructed a QSP model of the humoral immune response to a COVID-19 vaccine in Matlab 2024a Simbiology. We have used internal data and literature publications to inform the design of the vaccine model. The representation of the immune response in the lymph node was adapted from a publication of anti-drug antibody production[1]. The basis for the number and affinity of IgG clones produced by vaccinated patients was taken from publications on approved COVID-19 vaccines[2]. The model predicts the amount of each IgG clone produced over time, which is used to calculate the average antigen binding affinity. A sensitivity analysis was performed to understand the importance of polyclonal antibody affinity on predicting both IgG level and average antigen binding affinity.

We have used our model to test a range of IgG affinities based on measurements from COVID-19-vaccinated patients. Our analysis shows that IgG levels and average antigen binding affinity are sensitive to both the affinities and number of antibody clones defined in the model. We also show the way in which IgG affinity will impact the predicted IgG level and average affinity is complex. The vaccine model is designed so that the affinity of antibodies and resultant antibody/antigen complex level has multiple effects including the efficiency of the antibody in binding to antigen, the clearance of antibody (free or in complex), the rate of B-cell activation, and tolerance induction. We show how this complex representation impacts model predictions, and how these predictions compare to what is known about COVID-19 vaccine-induced antibody levels.

While previous QSP vaccine models have used a generic set of antibodies, our analysis shows that the choice of polyclonal antibodies is important and needs to be reexamined for each infectious disease. By demonstrating the complex response predicted by this model, we suggest that assumptions about how antibody affinity and antigen-antibody complex levels effect the immune process may need to be reexamined to ensure that known vaccine properties are captured. There is currently an underutilization of modeling in the vaccinology field. QSP models have the potential to be valuable tools in furthering the understanding of vaccination if we ensure that these models are validated and capture established biology.

Citations: [1] Chen X, et. al. CPT Pharmacometrics Syst Pharmacol. 3(9):e133(2014)
[2] Macdonald, P., et. al. Communications Medicine 2:109 (2022)