Mixed therapy in cancer treatment for personalized drug administration using model reference adaptive control

Babaei N., SALAMCİ M. U.

EUROPEAN JOURNAL OF CONTROL, cilt.50, ss.117-137, 2019 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.ejcon.2019.03.001
  • Sayfa Sayıları: ss.117-137


The paper presents Model Reference Adaptive Control (MRAC) design strategy to determine personalized drug delivery protocol for mixed therapy with chemotherapy and immunotherapy in cancer treatment. We consider a nonlinear mathematical ODE set for cancer dynamics that includes tumor, natural killers, circulating lymphocytes and cytotoxic T-cells population together with the interaction of chemotherapy and immunotherapy. For researchers and physicians, the main challenge in mathematical models is the determination of the exact model parameters. In order to have a drug administration policy for a patient with unknown parameter set, we develop State Dependent Riccati Equations (SDRE) based MRAC design approach to determine the personalized drug delivery protocol for patients with unknown model parameters. First of all, we determine the optimal drug delivery scenario for a reference patient with known dynamics parameters using SDRE approach. Then for any patient with unknown parameters, the personalized mixed therapy protocol is determined based on the treatment regimen of the reference patient. In the proposed methodology, unknown patients are considered as a black-box simulator in the design and the mathematical model parameters of the patient are not essential for the design of drug administration protocol. In addition, the Bang-Bang and continuous drug delivery regimens could be obtained using proper adaptation gains in the presented MRAC methodology. The simulation results demonstrate the effectiveness of the proposed MRAC approach for prescribing a treatment regimen of chemo-immunotherapy. (C) 2019 European Control Association. Published by Elsevier Ltd. All rights reserved.