Carmine Schiavone1,2, Joseph Cave2,3, Zhihui Wang2,3, Vittorio Cristini2,3,4, Sergio Caserta1,5, Prashant Dogra2,3
1Department of Chemical, Materials and Production Engineering, University of Naples Federico II, 2Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, 3Weill Cornell Medicine, 4Department of Imaging Physics, University of Texas MD Anderson Cancer Center, 5CEINGE Advanced Biotechnologies
Objectives: Vaccines are pivotal in combating infectious diseases, saving millions of lives worldwide and vastly improving public health. Despite their success, vaccine effectiveness varies among individuals due to factors such as previous viral exposure, age, sex, genetic predisposition, and co-morbidities (1,2). This variability presents a significant challenge in achieving optimal community-wide immunity. To address this challenge, we developed a mechanistic model that integrates the key phases of the immune response following vaccination. Our objective is to use this model to gain a deeper understanding of individual immune trajectories and to identify sub-categories of subjects with similar immune response patterns. This, in turn, will help rationalize vaccine distribution and improve immune protection in special populations. Methods: Building on our previous work (3), we constructed a system of ordinary differential equations to describe the temporal evolution of key immunological variables, including CD4? T follicular helper cells, germinal center B cells, memory B cells, plasma cells, and IgG antibodies. Antigen kinetics were modeled analytically and used to drive both adaptive and innate responses via integrated exposure windows (t_CD4, t_B). The model was calibrated using longitudinal antibody measurements from over 5,000 individuals vaccinated against COVID-19, spanning 360 days. Parameters were estimated at the individual level using a Markov Chain Monte Carlo (MCMC) algorithm. Model performance was assessed via Pearson correlation (r > 0.9, log–log scale) and root mean squared error (RMSE). A global sensitivity analysis (GSA) was conducted to identify parameters with the greatest impact on antibody dynamics. Results: The model accurately reproduced antibody trajectories across the cohort, with most of the fits having RMSE < 0.2 AU/mL and strong correlation with observed data. Sensitivity analysis identified four parameters—antigen stimulation rate (K_ant), antibody degradation rate (d_ab), antibody production rate (P_ab), and plasma cell generation rate (P_pc)—as key drivers of variability. These parameters exhibited unimodal distributions with standard deviations in log_10-space ranging from 0.365 to 0.643, reflecting diverse underlying immune kinetics. From the fitted trajectories, we extracted summary features such as initial slope (µ = 0.208, s = 0.112), decay time (t_decay; µ = 2.024, s = 0.264), time to peak (T_max; µ = 2.037, s = 0.346), and peak antibody level (µ = 4.117, s = 0.691). These metrics captured both the magnitude and temporal dynamics of the immune response, and highlighted wide heterogeneity across individuals—peak titers alone spanned more than two orders of magnitude. Stratification by age, sex, and comorbidity status did not reveal significant trends, suggesting that conventional clinical covariates may be insufficient to explain immune variability. This underscores the need for deeper mechanistic or systems-level markers. Conclusions: Our study demonstrates that a mechanistic modeling approach can effectively characterize inter-individual variability in vaccine responses. By leveraging high-resolution clinical data and global sensitivity analysis, we quantified the key determinants of antibody dynamics and identified immunological features that vary most strongly across individuals. This integrative framework enables not only the functional classification of immune trajectories, but also supports hypothesis generation regarding latent drivers of poor vaccine response. Such insights are critical for informing next-generation vaccination strategies, particularly in the context of emerging variants, booster schedules, or vulnerable populations with atypical immunity.
[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 33 (2025) Abstr 11672 [www.page-meeting.org/?abstract=11672]
Poster: Drug/Disease Modelling - Other Topics