Controller design for personalized drug administration in cancer therapy: Successive approximation approach

Controller design for personalized drug administration in cancer therapy: Successive... A novel controller design approach is developed to determine personalized chemotherapy drug delivery protocols for cancer treatment. The methodology combines successive approximation approach for optimal control and model reference adaptive control to realize the proposed drug administration scenario for patients without prior knowledge of model parameters in the nonlinear cancer dynamics. Although many approaches have been proposed to determine the optimal drug delivery protocol for eradicating tumor in the nonlinear cancer model, the main shortcoming of these approaches is the requirement of nonlinear model dynamics, which is unknown to physicians in reality. To overcome this deficiency, we first determine the optimal drug delivery protocol for a reference patient with known mathematical model and parameters via successive approximation approach technique. Then, using the proposed approach, we adapt the reference patient's drug administration scenario to unknown cancer patients by suggesting a new adaptation mechanism for the unknown nonlinear plant dynamics. An efficient and robust approach is proposed here for the physicians to prescribe a personalized chemotherapy protocol for a cancer patient by regulating the drug delivery protocol of a reference patient. The efficacy of the proposed algorithm in eradicating the tumor lumps with different sizes in several patients is verified using numerical simulations in which the unknown parameters are randomly selected in the Monte Carlo approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Optimal Control Applications and Methods Wiley

Controller design for personalized drug administration in cancer therapy: Successive approximation approach

Loading next page...
 
/lp/wiley/controller-design-for-personalized-drug-administration-in-cancer-cTF8QDbdi0
Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
0143-2087
eISSN
1099-1514
D.O.I.
10.1002/oca.2372
Publisher site
See Article on Publisher Site

Abstract

A novel controller design approach is developed to determine personalized chemotherapy drug delivery protocols for cancer treatment. The methodology combines successive approximation approach for optimal control and model reference adaptive control to realize the proposed drug administration scenario for patients without prior knowledge of model parameters in the nonlinear cancer dynamics. Although many approaches have been proposed to determine the optimal drug delivery protocol for eradicating tumor in the nonlinear cancer model, the main shortcoming of these approaches is the requirement of nonlinear model dynamics, which is unknown to physicians in reality. To overcome this deficiency, we first determine the optimal drug delivery protocol for a reference patient with known mathematical model and parameters via successive approximation approach technique. Then, using the proposed approach, we adapt the reference patient's drug administration scenario to unknown cancer patients by suggesting a new adaptation mechanism for the unknown nonlinear plant dynamics. An efficient and robust approach is proposed here for the physicians to prescribe a personalized chemotherapy protocol for a cancer patient by regulating the drug delivery protocol of a reference patient. The efficacy of the proposed algorithm in eradicating the tumor lumps with different sizes in several patients is verified using numerical simulations in which the unknown parameters are randomly selected in the Monte Carlo approach.

Journal

Optimal Control Applications and MethodsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ; ; ; ;

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial