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Research Article | Open Access

A class of constrained optimal control problems arising in an immunotherapy cancer remission process

Yineng Ouyang1Zhaotao Liang1Zhihui Ma1Lei Wang2Zhaohua Gong3( )Jun Xie4Kuikui Gao5
Department of Mathematics, School of Science, Shihezi University, Shihezi 832000, China
School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China
School of Mathematics and Information Science, Shandong Technology and Business University, Yantai 264005, China
Department of Basics, PLA Dalian Naval Academy, Dalian 116018, China
Ayata Inc., 2700 Post Oak Blvd, 21st Floor Houston, TX 77056, USA
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Abstract

By considering both the single drug dose and the total drug input during the treatment period, we propose a new optimal control problem by maximizing the immune cell levels and minimizing the tumor cell count, as well as the negative effects of the total drug quantity over time. To solve this problem, the control parameterization technique is employed to approximate the control function by a piecewise constant function, which gives rise to a sequence of mathematical programming problems. Then, we derive gradients of the cost function and/or the constraints in the resulting problems. On the basis of this gradient information, we develop a numerical approach to seek the optimal control strategy for a discrete drug administration. Finally, numerical simulations are conducted to assess the impact of the total drug input on the tumor treatment and to evaluate the rationality of the treatment strategy within the anti-cancer cycle. These results provide a theoretical framework that can guide clinical trials in immunotherapy.

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Electronic Research Archive
Pages 5868-5888

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Cite this article:
Ouyang Y, Liang Z, Ma Z, et al. A class of constrained optimal control problems arising in an immunotherapy cancer remission process. Electronic Research Archive, 2024, 32(10): 5868-5888. https://doi.org/10.3934/era.2024271

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Received: 22 August 2024
Revised: 03 October 2024
Accepted: 12 October 2024
Published: 15 October 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)