Identifying Determinants of Vaccine Efficacy: A Mechanistic Modeling Approach
Year: 2024
Type: conference-paper
Abstract: 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 protection. 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. In this study we developed a mechanistic model to analyze how these factors influence the immune response to vaccination, aiming to enhance vaccine efficacy across populations. 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 plasma cells, 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. Analyzing immune responses to COVID-19 vaccines in approximately 3700 healthy adults, we fitted our model to population-scale data and performed a global sensitivity analysis to identify critical parameters affecting antibody production. Results: The model fits were in good agreement with the data (Pearson correlation R > 0.9) Global Sensitivity Analysis (GSA) revealed that the duplication rate of plasma cells (P_PB), antigen potency (K_Ant), death rate of plasma blasts (D_PB), and the production rate of antibodies per cell (P_Ab) were the four key parameters driving antibody responses. Subsequently, we characterized the distribution of these 4 parameters for various cohorts representing a demographically diverse population. Significant differences were observed between the parameter distributions of cohorts, thereby quantifying the effect of demographic covariates on vaccine-induced immunity. This allowed us to simulate vaccine clinical trials and identify strategies, such as injection timing optimization, to enhance the immune response of weakly responsive cohorts. Conclusion: Our findings provide insights into the variability of vaccine-induced immune response and propose a framework for optimizing vaccine efficacy tailored to demographic characteristics. This approach can improve vaccine distribution strategies and aid in the design of clinical trials, particularly crucial during pandemics.
DOI: 10.70534/dmxi8078
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4407699644