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

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    This study uses adaptive dynamic programming to optimize cancer treatment, balancing tumor cell reduction with immune cell enhancement for minimal drug dosage and improved patient outcomes.

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    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.