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Tuberculosis (TB) remains a global public health threat. Understanding the dynamics of host-pathogen interactions within TB granulomas will assist in identifying what leads to the successful elimination of infection. In vitro TB models provide a controllable environment to study these granuloma dynamics. Previously we developed a biomimetic 3D spheroid granuloma model that controls bacteria better than a traditional monolayer culture counterpart. We used agent-based simulations to predict the mechanistic reason for this difference. Our calibrated simulations were able to predict heterogeneous bacterial dynamics that are consistent with experimental data. In one group of simulations, spheroids are found to have higher macrophage activation than their traditional counterparts, leading to better bacterial control. This higher macrophage activation in the spheroids was not due to higher counts of activated T cells, instead fewer activated T cells were able to activate more macrophages due to the proximity of these cells to each other within the spheroid. In a second group of simulations, spheroids again have more macrophage activation but also more T cell activation, specifically CD8+ T cells. This higher level of CD8+ T cell activation is predicted to be due to the proximity of these cells to the cells that activate them. Multiple mechanisms of control were predicted. Simulations removing individual mechanisms show that one group of simulations has a CD4+ T cell dominant response, while the other has a mixed/CD8+ T cell dominant response. Lastly, we demonstrated that in spheroids the initial structure and movement rules work synergistically to reduce bacterial load. These findings provide valuable insights into how the structural complexity of in vitro models impacts immune responses. Moreover, our study has implications for engineering more physiologically relevant in vitro models and advancing our understanding of TB pathogenesis and potential therapeutic interventions. Author summary: Tuberculosis (TB) is a respiratory infection that is associated with granulomas, organized clusters of immune cells that form around the bacteria. Recent work has focused on making in vitro models that better approximate these granulomas, such as our spheroid granuloma model. We found that this spheroid was better able to control bacteria than its traditional culture counterpart, so, in this work, we use agent-based modeling to explore the causes of this differential control. The simulated spheroids have increased macrophage activation. Surprisingly, this is not due to larger numbers of activated T cells, which activate these macrophages. Rather, the increase in activated macrophages is due to their proximity to fewer activated T cells. We also see a subset of simulations where the spheroids have increased cytotoxic CD8+ T cell activation. The simulation is further used to simulate changes in the structure of the in vitro models to explore its impact on immune response and bacterial control. These findings reveal the importance of structural complexity in in vitro models and offer insights into TB pathogenesis. Ultimately, our work paves the way for more realistic in vitro models and a deeper understanding of how our bodies combat TB. [ABSTRACT FROM AUTHOR]

The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led to the development of personalized neoantigen cancer vaccines that are tailored to the unique variants found in a patient's cancer. These vaccines can provide significant clinical benefit by leveraging the patient's immune response to eliminate malignant cells. However, determining the optimal vaccine dose for each patient is a challenge due to the heterogeneity of tumors. To address this challenge, we formulate a mathematical dose optimization problem based on a previous mathematical model that encompasses the immune response cascade produced by the vaccine in a patient. We propose an optimization approach to identify the optimal personalized vaccine doses, considering a fixed vaccination schedule, while simultaneously minimizing the overall number of tumor and activated T cells. To validate our approach, we perform in silico experiments on six real-world clinical trial patients with advanced melanoma. We compare the results of applying an optimal vaccine dose to those of a suboptimal dose (the dose used in the clinical trial and its deviations). Our simulations reveal that an optimal vaccine regimen of higher initial doses and lower final doses may lead to a reduction in tumor size for certain patients. Our mathematical dose optimization offers a promising approach to determining an optimal vaccine dose for each patient and improving clinical outcomes. Author summary: The development of neoantigen cancer vaccines have rapidly increased over the past decade with the advancement of next-generation sequencing technologies to determine immunogenic peptides from patient's somatic mutations. However, traditional methods to determine the cancer vaccine dose often produce suboptimal clinical outcomes. This work use our previous mathematical model to represent the immunological cascade at the cellular and subcellular levels elicited by the vaccine dose, and focuses on developing a mathematical optimization approach to identify the optimal vaccine dose to minimize two objective functionals, (i) minimize the amount of peptide dose and tumor size, and (ii) minimize the number of activated T cells in addition to the objective functional i. The optimization approach allows the identification of optimal vaccine doses among a set of tested doses for a higher clinical benefit in tumor reduction. To demonstrate and validate our optimization approach, we perform in silico experiments on six patients with melanoma from a clinical trial study. The results show that the predicted optimal vaccine doses can provide higher clinical benefit in tumor reduction when compared to the clinical trial doses for some patients. [ABSTRACT FROM AUTHOR]