WSEAS Transactions on Biology and Biomedicine
Print ISSN: 1109-9518, E-ISSN: 2224-2902
Volume 18, 2021
Discrete ARMA Model Applied for Tumor Growth Inhibition Modeling and LQR-based Chemotherapy Optimization
Authors: ,
Abstract: Mathematical models for tumor growth inhibition (TGI) are an important tool in the battle against cancer allowing preclinical evaluation of potential anti-cancer drugs and treatment schedules. However, most of these models are nonlinear and their structure is based on complex hypotheses. Therefore, tumor growth mathematical models with simple structure and minimal number of parameters could be of great importance. In this article, an autoregressive moving average (ARMA) model for cancer tumor growth and equivalent its state space representation are estimated, presented and evaluated based on laboratory data of TGI in mice. The proposed model was proven capable of describing with accuracy the tumor growth under single-agent chemotherapy. At the same time, an optimal control problem was formulated to identify optimal drug dosages for the tumor eradication. The linear quadratic regulator (LQR) controller was used with success in optimizing both periodic and intermittent chemotherapy treatment schedules reducing the tumor mass while keeping dosages under acceptable toxicity
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Keywords: Cancer, tumor growth inhibition (TGI), TGI ARMA model, optimal control, linear quadratic regulator (LQR),
optimized periodic chemotherapy, intermittent chemotherapy
Pages: 141-145
DOI: 10.37394/23208.2021.18.17