Dynamics of a Model of Tumor–Immune Cell Interactions Under Chemotherapy

This paper analyzes a mathematical model to investigate the complex interactions between tumor cells, immune cells (natural killer (NK) cells and CD8+ cytotoxic T lymphocytes (CTLs)) and chemotherapy. The primary objectives are to analyze tumor–immune interactions without and under treatment, identify critical thresholds for tumor eradication, and evaluate how chemotherapy parameters influence therapeutic outcomes. The model integrates NK cells and CTLs as effector cells, combining their dynamics linearly for simplicity. Tumor growth follows a logistic function, while immune–tumor interactions are modeled using a Hill function for fractional cell death. Stability and bifurcation analysis are employed to identify equilibria (tumor-free, high-tumor, and a novel middle steady state), bistability regimes, and critical parameter thresholds. Numerical simulations use experimentally validated parameter values from the literature. This mathematical analysis provides a framework for assessing the efficacy of chemotherapy by examining the dynamic interplay between tumor biology and treatment parameters. Our findings reveal that treatment outcomes are sensitive to the balance between the immune system’s biological parameters and chemotherapy-specific factors. The model highlights scenarios where chemotherapy may fail due to bistability and identifies critical thresholds for successful tumor eradication. These insights can guide clinical decision making in dosing strategies and suggest combination therapies such as immunotherapy–chemotherapy synergies to shift the system toward favorable equilibria.

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Publication Details

Journal
Mathematics
Published
2025-07-05
DOI
https://doi.org/10.3390/math13132200
Citations
1
Primary Topic
Mathematical Biology Tumor Growth
Type
article
Field-Weighted Citation Impact
1.37

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article

Dynamics of a Model of Tumor–Immune Cell Interactions Under Chemotherapy

Abdelhamid Ajbar, Rubayyi T. Alqahtani, Eman Hamed Aljebli
1 citations
Mathematics
Mathematical Biology Tumor Growth
1.37
article

Dynamics of a Model of Tumor–Immune Cell Interactions Under Chemotherapy

Abdelhamid Ajbar, Rubayyi T. Alqahtani, Eman Hamed Aljebli
article en
1 citations

Abstract

This paper analyzes a mathematical model to investigate the complex interactions between tumor cells, immune cells (natural killer (NK) cells and CD8+ cytotoxic T lymphocytes (CTLs)) and chemotherapy. The primary objectives are to analyze tumor–immune interactions without and under treatment, identify critical thresholds for tumor eradication, and evaluate how chemotherapy parameters influence therapeutic outcomes. The model integrates NK cells and CTLs as effector cells, combining their dynamics linearly for simplicity. Tumor growth follows a logistic function, while immune–tumor interactions are modeled using a Hill function for fractional cell death. Stability and bifurcation analysis are employed to identify equilibria (tumor-free, high-tumor, and a novel middle steady state), bistability regimes, and critical parameter thresholds. Numerical simulations use experimentally validated parameter values from the literature. This mathematical analysis provides a framework for assessing the efficacy of chemotherapy by examining the dynamic interplay between tumor biology and treatment parameters. Our findings reveal that treatment outcomes are sensitive to the balance between the immune system’s biological parameters and chemotherapy-specific factors. The model highlights scenarios where chemotherapy may fail due to bistability and identifies critical thresholds for successful tumor eradication. These insights can guide clinical decision making in dosing strategies and suggest combination therapies such as immunotherapy–chemotherapy synergies to shift the system toward favorable equilibria.

MathematicsVol. 13(13)
Imam Mohammad ibn Saud Islamic University (SA), King Saud University (SA)
Al-Imam Muhammad Ibn Saud Islamic University
Openalex Percentile: Top 19%
Mathematical Biology Tumor Growth
1.37
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