An agent-based model for investigating the effect of myeloid-derived suppressor cells and its depletion on tumor immune surveillance

Abstract

Background:To predict the behavior of biological systems, mathematical models of biological systems have been shown to be useful. In particular, mathematical models of tumor-immune system interactions have demonstrated promising results in prediction of different behaviors of tumor against the immune system.Methods:This study aimed at the introduction of a new model of tumor-immune system interaction, which includes tumor and immune cells as well as myeloid-derived suppressor cells (MDSCs). MDSCs are immune suppressor cells that help the tumor cells to escape the immune system. The structure of this model is agent-based which makes possible to investigate each component as a separate agent. Moreover, in this model, the effect of low dose 5-fluorouracil (5-FU) on MDSCs depletion was considered.Results:Based on the findings of this study, MDSCs had suppressive effect on increment of immune cell number which consequently result in tumor cells escape the immune cells. It has also been demonstrated that low-dose 5-FU could help immune system eliminate the tumor cells through MDSCs depletion.Conclusion:Using this new agent-based model, multiple injection of low-dose 5-FU could eliminate MDSCs and therefore might have the potential to be considered in treatment of cancers.

Potential of Immunotherapies in Treating Hematological Cancer-Infection Comorbidities—A Mathematical Modelling Approach

“…This corresponds to 0.5 × 10 −3 -2 × 10 −2 per day. Thus, we choose a x = 1.25 × 10 −3 per day, which is slow compared to most other cancers (for which a x lies between 0.01-1.5 per day [29,34,55,59,[62][63][64][65][66][67][68]).…”

“…Across various cancers, the values of the product r x • p x varies ranging from 5 × 10 −11 -0.5 per day [34,[62][63][64][65][66]68]. Kuznetsov [28,29] took a value for p x near to but smaller than 1, e.g., 0.998, for BCL1 lymphoma in non-chimeric mice based on data from [72].…”

“…Natural death rates for active cytotoxic T-cells are reported to be in the range 4.12 × 10 −2 -0.12 per day [62,[64][65][66]73]. We chose d x = 0.4 per day.…”

A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor–Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy

“…In ABM, individual agents sense their local environment, interact with neighboring agents based on locally defined rules and produce global emergent behavior. In the field of immuno-oncology, studies have been performed with cancer cells and immune cells, represented as interacting cellular agents, to investigate the formation of spatial patterns of the cells and their response to checkpoint inhibition [42][43][44]. These models provide insight into the effectiveness of certain immune checkpoint inhibitors with tumor heterogeneity accounted for; nevertheless, due to the lack of rigorously formulated pharmacokinetic/pharmacodynamic (PK/PD) modules as part of the overall whole-patient model, it is difficult to obtain quantitative predictions of patient response to the therapies.…”

In Silico Assessment of 5-FU Therapy via A Mathematical Model with Fuzzy Uncertain Parameters

“…Therefore, deterministic models that can simulate this amount of biological detail will have very complex relationships and many parameters that are constrained by the lack of enough precise experimental data. On the other hand, stochastic models with assigning specific probability density functions (pdf) for different behaviors of cells and by probabilistic rules for simulating cell-cell interactions can simulate the behavioral uncertainty and inherent noise in tumor-immune system [29] [30][31] [32]. Often, stochastic models versus deterministic models require fewer kinetic parameters to predict dynamics, but they are computationally cost.…”