Agent-Based Modelling Reveals the Role of the Tumor Microenvironment on the Short-Term Success of Combination Temozolomide/Immune Checkpoint Blockade to Treat Glioblastoma

Integrated pharmacokinetic-pharmacodynamic and agent-based modelling in drug development: Current status and future perspectives

In the past two decades, quantitative mathematical modelling approaches have emerged as a paradigm to improve new drug research productivity, especially the application of pharmacokinetic-pharmacodynamic and quantitative systems pharmacology modelling. These approaches have largely made use of deterministic, differential equation-based models, however there is a growing use of agent-based models. In this review, the current applications and practices of agent-based model development are reviewed via relevant case studies from the literature. Gaps in the implementation of these models are identified and a future perspective on the priorities for further agent-based model development is given.

Revisiting the classical target cell limited dynamical within-host HIV model - Basic mathematical properties and stability analysis

In this article, we reconsider the classical target cell limited dynamical within-host HIV model, solely taking into account the interaction between $ {\rm{CD}}4^{+} $ T cells and virus particles. First, we summarize some analytical results regarding the corresponding dynamical system. For that purpose, we proved some analytical results regarding the system of differential equations as our first main contribution. Specifically, we showed non-negativity and boundedness of solutions, global existence in time and global uniqueness in time and examined stability properties of two possible equilibria. In particular, we demonstrated that the virus-free equilibrium and the plateau-phase equilibrium are locally asymptotically stable using the Routh-Hurwitz criterion under appropriate conditions. As our second main contribution, we underline our theoretical findings through some numerical experiments with standard Runge-Kutta time stepping schemes. We conclude this work with a summary of our main results and a suggestion of an extension for more complex dynamical systems with regard to HIV-infection.

Systems immunology insights into brain metastasis

Brain metastasis poses formidable clinical challenges due to its intricate interactions with the brain's unique immune environment, often resulting in poor prognoses. This review delves into systems immunology's role in uncovering the dynamic interplay between metastatic cancer cells and brain immunity. Leveraging spatial and single-cell technologies, along with advanced computational modeling, systems immunology offers unprecedented insights into mechanisms of immune evasion and tumor proliferation. Recent studies highlight potential immunotherapeutic targets, suggesting strategies to boost antitumor immunity and counteract cancer cell evasion in the brain. Despite substantial progress, challenges persist, particularly in accurately simulating human conditions. This review underscores the need for interdisciplinary collaboration to harness systems immunology's full potential, aiming to dramatically improve outcomes for patients with brain metastasis.

Spatial computational modelling illuminates the role of the tumour microenvironment for treating glioblastoma with immunotherapies

Glioblastoma is the most common and deadliest brain tumour in adults, with a median survival of 15 months under the current standard of care. Immunotherapies like immune checkpoint inhibitors and oncolytic viruses have been extensively studied to improve this endpoint. However, most thus far have failed. To improve the efficacy of immunotherapies to treat glioblastoma, new single-cell imaging modalities like imaging mass cytometry can be leveraged and integrated with computational models. This enables a better understanding of the tumour microenvironment and its role in treatment success or failure in this hard-to-treat tumour. Here, we implemented an agent-based model that allows for spatial predictions of combination chemotherapy, oncolytic virus, and immune checkpoint inhibitors against glioblastoma. We initialised our model with patient imaging mass cytometry data to predict patient-specific responses and found that oncolytic viruses drive combination treatment responses determined by intratumoral cell density. We found that tumours with higher tumour cell density responded better to treatment. When fixing the number of cancer cells, treatment efficacy was shown to be a function of CD4 + T cell and, to a lesser extent, of macrophage counts. Critically, our simulations show that care must be put into the integration of spatial data and agent-based models to effectively capture intratumoral dynamics. Together, this study emphasizes the use of predictive spatial modelling to better understand cancer immunotherapy treatment dynamics, while highlighting key factors to consider during model design and implementation.