OPTIMAL CONTROL STRATEGIES FOR ENHANCING THE EFFECTIVENESS OF CHEMOTHERAPY, RADIOTHERAPY, AND IMMUNOTHERAPY IN A CANCER MODEL - Yolandyne Bunga, Esther
Cancer is a disease caused by the uncontrolled growth of abnormal cells that can spread to surrounding areas. The HCMDI model is used to describe the dynamics of T cell interactions in combating cancer cells, consisting of compartments H (CD4 T cells), C (CD8 T cells), M (myeloid cells), D (dendritic cells), and I (IL ? 2). In this model, control inputs such as chemotherapy, radiotherapy, and immunotherapy are applied to minimize the number of cancer cells and the costs of treatment intervention. Various control strategies are implemented, including the individual use of chemotherapy, radiotherapy, and immunotherapy, as well as combinations of these three controls. The optimal control problem is solved using Pontryagin’s Minimum Principle (PMP), which provides an analytical approach to find the optimal control policy by minimizing the cost function. The results are also compared with Model Predictive Control (MPC), which uses a predictive approach to assess the effectiveness and efficiency of the applied control strategies. Numerical simulations using PMP show that the combination of chemotherapy and immunotherapy is the best strategy to minimize the number of cancer cells and treatment intervention costs. In contrast, numerical simulations using MPC indicate that the combination of chemotherapy and radiotherapy is most effective in reducing the number of cancer cells with the lowest intervention costs. In-depth analysis shows that PMP offers higher effectiveness in minimizing the number of cancer cells and treatment intervention costs, while MPC provides greater flexibility and practicality in adjusting doses and therapy schedules.