Adaptive fuzzy controller design of drug dosage using optimal trajectories in a chemoimmunotherapy cancer treatment model

Abstract

Cancer is the leading cause of death in several countries, and it is necessary to investigate new methods for effectively eradicating cancerous cells with fewer side effects. Immunotherapy, which has received considerable attention in recent years, is one of the most promising methods. However, one of the most significant challenges is integrating these novel methods and controlling the doses of various drugs. In this paper, optimal and adaptive fuzzy control is conducted on a recent comprehensive chemoimmunotherapy cancer model. Furthermore, chemotherapy and two methods of immunotherapy, including CD8+ T cells and cytokine IL-2 injection, are employed. The objectives are to determine the optimal combination of these treatments, identify the optimal drug injection routes during treatment, and control drug delivery to eradicate cancer cells when the dynamics of the cancer model are unknown. It is shown that performing immunotherapy methods alone, particularly cytokine IL-2 injection, are not very effective. However, when they are combined with chemotherapy, the most effective result is obtained. For instance, in the initial number of cancer cells of approximately 109, chemotherapy alone destroys the tumor in 49 days, while its combination with immunotherapy methods achieves this in 37 days. In addition, the designed fuzzy adaptive controller can destroy cancerous tumors when the model parameters are unknown. Fuzzy functions are established using training data, and they can efficiently estimate real model functions. A minimum of 10 days and a maximum of 40 days of experimentation during a 50-day treatment period are required to collect adequate training data for the cancer model's various state variables.

Keywords

• Chemoimmunotherapy
• Adaptive control
• Fuzzy control
• Optimal control
• Cancer treatment