Optimal Chemotherapy Control and Dynamics of Tumor–Immune Interactions

Vollständiger Abstract

Worum geht es in dieser Arbeit?

This study develops and analyzes a nonlinear tumor–immune–chemotherapy model to investigate how chemotherapy influences tumor suppression in the presence of heterogeneous tumor subpopulations and immune responses. The model incorporates two tumor cell populations with different immunogenic characteristics, innate and adaptive immune responses, and a dynamic chemotherapy effect. We analytically identify the equilibrium points and assess the conditions for their stability in the presence of chemotherapy. A bifurcation analysis with respect to the chemotherapy tumor killing rate of the less immunogenic tumor reveals Hopf bifurcations, indicating transitions to oscillatory behavior and complex tumor–immune dynamics under varying treatment intensities. The effects of chemotherapy dosing and drug potency are investigated through six-month numerical treatment simulations to evaluate tumor suppression and long-term system behavior under different treatment scenarios. An optimal control problem is then formulated to determine chemotherapy schedules that reduce tumor burden while limiting drug exposure. Numerical results indicate that the optimal chemotherapy protocol produces faster and more efficient tumor reduction than a constant-dose treatment strategy. Overall, this study provides a treatment-focused dynamical framework for understanding how chemotherapy interacts with tumor heterogeneity and immune response, providing theoretical insight into treatment-dependent tumor–immune dynamics and chemotherapy scheduling.