Personalized drug administration for cancer treatment using Model Reference Adaptive Control

A new Model Reference Adaptive Control (MRAC) approach is proposed for the nonlinear regulation problem of cancer treatment via chemotherapy. We suggest an approach for determining an optimal anticancer drug delivery scenario for cancer patients without prior knowledge of nonlinear model structure and parameters by compounding State Dependent Riccati Equation (SDRE) and MRAC which will lead to personalized drug administration. Several approaches have been proposed for eradicating cancerous cells in nonlinear tumor growth model. The main difficulty in these approaches is the requirement of nonlinear model parameters, which are unknown to physicians in reality. To cope with this shortage, we first determine the drug delivery scenario for a reference patient with known mathematical model and parameters via SDRE technique, and by using the proposed approach we adapt the drug administration scenario for another cancer patient despite unknown nonlinear model structure and model parameters. We propose an efficient approach to determine drug administration which will help physicians for prescribing a chemotherapy protocol for a cancer patient by regulating the drug delivery scenario of the reference patient. Stabilizing the tumor growth nonlinear model has been achieved via full state feedback techniques and yields a near optimal solution to cancer treatment problem. Numerical simulations show the effectiveness of the proposed algorithm for eradicating tumor lumps with different sizes in different patients. (C) 2015 Elsevier Ltd. All rights reserved.