Carmine Schiavone(1,2), Joseph Cave (2,3), Zhihui Wang (2,3), Sergio Caserta (1,4), Vittorio Cristini (2,3,5) and Prashant Dogra (2,3)
: (1) Department of Chemical, Materials and Production Engineering, University of Naples Federico II, P.le V. Tecchio 80, Napoli 80125, Italy (2) Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, Texas 77030, USA (3) Weill Cornell Medicine, New York, NY 10065, USA. (4) CEINGE Advanced Biotechnologies, via Gaetano Salvatore 486, Napoli 80145, Italy (5) Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Objectives: : Vaccines are pivotal in combating infectious diseases, saving millions of
lives worldwide and vastly improving public health. Despite their success, the effectiveness of
vaccines varies across individuals, posing a significant challenge in achieving optimal community-wide immunity. Several host factors such as virus pre-exposure, age, sex, genetics, and co-morbidities can influence the strength of the vaccine-induced immune response(1,2). This variability
thus affects the population-scale efficacy of vaccines and underlines the need for an approach to optimize the use of vaccines to improve outcomes. To tackle this challenge, we developed a
mechanistic model that incorporates the key phases of the immunogenic response. Our aim is to elucidate the influence of various demographic factors on vaccine efficacy by rigorously quantifying the immunological parameters across diverse cohorts. This approach is intended to unravel the complex interplay between individual demographic characteristics and their collective impact on immunological response to vaccination
Methods: Based on our previous work (3), we developed a model based on a system of ordinary differential equations to define the temporal evolution of the key immunological variables, including CD4+ T-cells, antibody-secreting plasmablasts, neutralizing antibodies, and cytokines (IL-2 and IFN gamma). A closed form solution of the kinetics of the injected antigen governs the activation of CD4+ T-cells, which initiate the downstream immunological processes leading to antibody-induced immunity. We explored extensive clinical data from literature involving immune response to COVID 19 vaccines in healthy adults (N = 1,593). Despite being healthy, these individuals exhibited significant variability in antibody response. We fitted our model to individual-scale immune response kinetics data (over 230 days) to estimate unknown model parameters. This was followed by global sensitivity analysis (GSA) to identify the key parameters influencing antibody response, an indicator of the immune signature shaping vaccine immunogenicity.
Results: The model fits were in good agreement with the data (Pearson correlation R > 0.9); through GSA we identified production rate of antibodies per cell (𝑃𝐴𝑏) and activation rate of plasma cells (𝑇𝑃) as the top two parameters governing antibody response. Statistically significant differences were observed in the values of these two parameters between high and low responders, which were defined as subjects exhibiting high versus low area under the antibody kinetics curve, respectively. We thus infer that the difference in humoral response between individuals can be attributed to differences in their 𝑃𝐴𝑏 and 𝑇𝑃 values, suggesting that these parameters may constitute the broader immune signature shaping vaccine immunogenicity. These preliminary findings warrant further investigation, emphasizing the need for a more nuanced and quantitative characterization of immune response
Conclusions: Our study presents a quantitative model that elucidates the underlying variability in vaccine-induced immune responses. By identifying distinct immunogenic profiles within the population, we offer a novel lens through which vaccine efficacy can be optimized on a demographic basis. This approach not only enhances our understanding of immune response dynamics but also holds the promise of refining vaccine distribution strategies to achieve more uniform community-wide immunity. Furthermore, the insights garnered from this research could be instrumental in streamlining the design of clinical trials, particularly in the rapid development of vaccines during pandemics when resource allocation and participant recruitment are constrained. Ultimately, our findings lay the groundwork for a more nuanced and effective deployment of vaccines, tailored to meet the specific needs of diverse population segments
References:
[1] Klein SL, Flanagan KL. Sex differences in immune responses. Nature Reviews Immunology.
2016/10/01 2016;16(10):626-638. doi:10.1038/nri.2016.90
[2] Lynn DJ, Benson SC, Lynn MA, Pulendran B. Modulation of immune responses to vaccination by the microbiota: implications and potential mechanisms. Nature Reviews Immunology. 2022/01/01 2022;22(1):33-46. doi:10.1038/s41577-021-00554-7
[3]Dogra P, Schiavone C, Wang Z, et al. A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection. JCI Insight. 05/25/2023;doi:10.1172/jci.insight.169860
Reference: PAGE 32 (2024) Abstr 11140 [www.page-meeting.org/?abstract=11140]
Poster: Drug/Disease Modelling - Infection