PMID- 25665717
OWN - NLM
STAT- MEDLINE
DCOM- 20151207
LR  - 20150323
IS  - 1095-8541 (Electronic)
IS  - 0022-5193 (Linking)
VI  - 371
DP  - 2015 Apr 21
TI  - Personalized drug administration for cancer treatment using Model Reference 
      Adaptive Control.
PG  - 24-44
LID - S0022-5193(15)00055-7 [pii]
LID - 10.1016/j.jtbi.2015.01.038 [doi]
AB  - 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.
CI  - Copyright (c) 2015 Elsevier Ltd. All rights reserved.
FAU - Babaei, Naser
AU  - Babaei N
AD  - Department of Mechanical Engineering, Faculty of Engineering, Gazi University, 
      06570 Maltepe/Ankara, Turkey. Electronic address: naser.babaei@gazi.edu.tr.
FAU - Salamci, Metin U
AU  - Salamci MU
AD  - Department of Mechanical Engineering, Faculty of Engineering, Gazi University, 
      06570 Maltepe/Ankara, Turkey. Electronic address: msalamci@gazi.edu.tr.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20150207
PL  - England
TA  - J Theor Biol
JT  - Journal of theoretical biology
JID - 0376342
RN  - 0 (Antineoplastic Agents)
SB  - IM
MH  - Algorithms
MH  - Antineoplastic Agents/administration & dosage/*therapeutic use
MH  - Cell Count
MH  - Dose-Response Relationship, Drug
MH  - Drug Delivery Systems
MH  - Humans
MH  - Models, Theoretical
MH  - Neoplasms/*drug therapy/pathology
MH  - Nonlinear Dynamics
MH  - Numerical Analysis, Computer-Assisted
MH  - Physicians
MH  - *Precision Medicine
MH  - *Self Administration
OTO - NOTNLM
OT  - Chemotherapy
OT  - MRAC
OT  - Optimal drug delivery
OT  - SDRE control
EDAT- 2015/02/11 06:00
MHDA- 2015/12/15 06:00
CRDT- 2015/02/11 06:00
PHST- 2014/09/04 00:00 [received]
PHST- 2014/12/29 00:00 [revised]
PHST- 2015/01/27 00:00 [accepted]
PHST- 2015/02/11 06:00 [entrez]
PHST- 2015/02/11 06:00 [pubmed]
PHST- 2015/12/15 06:00 [medline]
AID - S0022-5193(15)00055-7 [pii]
AID - 10.1016/j.jtbi.2015.01.038 [doi]
PST - ppublish
SO  - J Theor Biol. 2015 Apr 21;371:24-44. doi: 10.1016/j.jtbi.2015.01.038. Epub 2015 
      Feb 7.