Authors:

Sanjana Prashanth

Date Created:

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About Paper:

Vaccine design remains largely empirical with trial-and-error approaches, leading to inefficient development of effective administration protocols. Computational modeling has shown promise in elucidating immune dynamics, but current models lack validation with preclinical data. Improved predictive computational models can validate experimental data and inform variations in vaccine administration strategies, thereby reducing the number of iterations required to develop effective vaccine protocols. This study employs a mathematical modeling framework to simulate immunological responses to a localized, polymeric collagen vaccine delivery mechanism. By formulating differential equations representing key immune processes, initializing parameters based on antigen and adjuvant concentrations, and analyzing the resulting dynamics through graphs, the model's predictions are calibrated to experimental data to assess its ability to reproduce in-vivo vaccine responses. Previous preclinical rodent studies revealed that the collagen delivery vehicle localized antigen availability over an extended period, leading to enhanced germinal center (GC) activity and prolonged B cell maturation. The model captures the kinetics of antigen retention, GC formation, and antibody production, showing qualitative agreement with the in-vivo experimental data. By accurately reflecting experimental observations, the model provides a valuable tool for optimizing vaccine administration strategies and guiding rational design, ultimately reducing reliance on repetitive preclinical testing. Keywords: Computational Modeling; Vaccine Delivery; Differential Equations; Germinal Center Enhancement

Source:

Purdue University / 2025

Topics:

No topics listed

Co-authors:

Sanjana Prashanth