Dynamics analysis in a tumor-immune system with chemotherapy*

Liu, Haiying; Yang, Hai; Yang, Liangui

doi:10.1088/1674-1056/abcf49

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…Many mathematical models of tumor-immune dynamics have been developed (Eladdadi et al (2014); Sardar et al (2024); Cherraf et al (2023); Song et al (2021); Liu et al (2021); Yin et al (2019); Nikolopoulou et al (2018); Lai and Friedman (2017); de Pillis et al (2005); De Pillis and Radunskaya (2003)), including a few that focus specifically on GBM (Anderson et al (2023); Santurio and Barros (2022); Khajanchi (2021); Storey et al (2020)). Here, we extend the ODE model of Anderson et al (2023), which incorporates tumor cells, T cells, and MDSCs, to include treatment with anti-PD-1 and a CCR2 antagonist and subsequently apply optimal control theory.…”

Section: Introductionmentioning

confidence: 99%

Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model

Anderson,

Takacs,

Harrison

et al. 2024

Preprint

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The immune checkpoint inhibitor anti-PD-1, commonly used in cancer immunotherapy, has not been successful as a monotherapy for the highly aggressive brain cancer glioblastoma. However, when used in conjunction with a CC-chemokine receptor-2 (CCR2) antagonist, anti-PD-1 has shown efficacy in preclinical studies. In this paper, we aim to optimize treatment regimens for this combination immunotherapy using optimal control theory. We extend a treatment-free glioblastoma-immune dynamics ODE model to include interventions with anti-PD-1 and the CCR2 antagonist. An optimized regimen increases the survival of an average mouse from 32 days post-tumor implantation without treatment to 111 days with treatment. We scale this approach to a virtual murine cohort to evaluate mortality and quality of life concerns during treatment, and predict survival, tumor recurrence, or death after treatment. A parameter identifiability analysis identifies five parameters suitable for personalizing treatment within the virtual cohort. Sampling from these five practically identifiable parameters for the virtual murine cohort reveals that personalized, optimized regimens enhance survival: 84% of the virtual mice survive to day 100, compared to 60% survival in a previously-studied experimental regimen. Subjects with high tumor growth rates and low T cell kill rates are identified as more likely to die during and after treatment due to their compromised immune systems and more aggressive tumors. Notably, the MDSC death rate emerges as a long-term predictor of either disease-free survival or death.

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