Neural-Network-Based Immune Optimization Regulation Using Adaptive Dynamic Programming

Area of Science:

• Computational Biology and Bioinformatics
• Control Theory and Systems Engineering
• Immunology and Cancer Research

Background:

• Cancer treatment faces challenges in balancing tumor suppression with preserving immune function.
• Chemotherapy and immunotherapy can have significant negative side effects.
• Optimal control strategies are needed to minimize drug toxicity while maximizing therapeutic efficacy.

Purpose of the Study:

• To develop an optimal regulation scheme for tumor-immune cell dynamics.
• To inhibit tumor growth to a tolerable level while simultaneously maximizing immune cell populations.
• To minimize the required doses of chemotherapeutic and immunotherapeutic drugs.

Main Methods:

• Utilized the adaptive dynamic programming (ADP) approach for controller design.
• Employed a nonlinear dynamical mathematical model of tumor cell interactions.
• Implemented a closed-loop control strategy for drug administration.

Main Results:

• Derived a reliable controller using ADP to achieve desired cell population states.
• Demonstrated that the control strategy can weaken negative effects of chemotherapy and immunotherapy.
• Proved system states and critic weight errors are ultimately uniformly bounded.

Conclusions:

• The adaptive dynamic programming approach provides an effective cybernetic methodology for cancer treatment optimization.
• This strategy enables precise regulation of tumor and immune cells, leading to potentially improved therapeutic outcomes.
• Simulation results validate the effectiveness of the proposed control strategy in managing cancer dynamics.