(296c) A Mathematical Model for Simulating T-Cell Induced Vaccines

Vaccines are one way to take advantage of the fascinating nature of the adaptive immune system. However, the vaccine development process is hindered by time and resource limitations, which makes it challenging to develop vaccines for complicated pathogens causing diseases such as HIV, Ebola, and most recently COVID-19. T-cell induced vaccines are one of the most promising vaccine types. T-cells provide a unique and robust response to infections that can provide long-term immunity. This work contributes to the vaccine development process by creating a mathematical model that can simulate the immune response to a certain vaccine dose and injection method.

The attached figure shows the life cycle of a T-cell which starts as a naïve cell that had previously matured in the thymus. When the cell encounters an antigen-presenting cell (arising from the vaccine), it is activated. This marks the beginning of the expansion phase where cells proliferate exponentially. Every cell that proliferates creates two new cells of a new generation. This proliferation stops after a specific period and the contraction phase starts. By the end of the contraction phase, the number of T-cells left in the body (memory T-cells) is only 5-10 % of the total cells generated, which provides robust long-term immunity against future infections. A major factor affecting the T-cell response is cell migration and recirculation through the body. A good understanding and representation of cell migration can help estimate an accurate memory T-cell population, which is an excellent advantage for the drug development process. There is a gap in the literature that was highlighted in a review by Brown et al. (2022), for stochastic agent-based models that simulate the different phases of the immune response [1]. Current models in the literature are deterministic. This is problematic because it does not reflect the true stochastic nature of mammalian immune systems. Stochastic models are more powerful as they can be used to predict the possible discrete outcomes in an induvial as well as probability distributions of whole populations.

This work is a continuation of the previously developed STochastic Omentum REsponse (STORE) model, which is an agent-based model that showed great potential in modeling the immune response in the omentum during the expansion phase by simulating cell counts for the different cell generations [2]. We have extended this model to a network of interconnected immune-relevant tissues and the blood. This multi-tissue model simulates cell migration and recirculation patterns to predict the dynamic immune response in the different body tissues. The model is created by combining many individual system elements based on First-Principles. This approach minimizes the number of tuning parameters. Most parameters are determined based on biological knowledge of the individual tissues. Then the system as a whole is validated through additional experimental data collection.

The model is useful for rapid vaccine development since it allows us to connect measurable properties such as T-cell counts in the blood with immeasurable properties such as T-cell counts in various tissues (without invasive tissue sampling). It can also be used to help predict future vaccine effectiveness in a patient by measuring blood samples shortly after vaccination. In addition, it could be useful in individualized medicine because it is able to account for differences in the sizes of various tissues which can affect T-cell dynamics.

References:

1. Brown, L. V., Coles, M. C., McConnell, M., Ratushny, A. V., & Gaffney, E. A. (2022). Analysis of cellular kinetic models suggest that physiologically based model parameters may be inherently, practically unidentifiable. Journal of Pharmacokinetics and Pharmacodynamics, 49(5), 539–556. https://doi.org/10.1007/s10928-022-09819-7

2. Christian, D. A., Adams, T. A., 2nd, Shallberg, L. A., Phan, A. T., Smith, T. E., Abraha, M., Perry, J., Ruthel, G., Clark, J. T., Pritchard, G. H., Aronson, L. R., Gossa, S., McGavern, D. B., Kedl, R. M., & Hunter, C. A. (2022). cDC1 coordinate innate and adaptive responses in the omentum required for T cell priming and memory. Science immunology, 7(75), eabq7432. https://doi.org/10.1126/sciimmunol.abq7432