Optimal CAR T-cell Immunotherapy Strategies for a Leukemia Treatment Model
CAR T-cell immunotherapy is a new development in the treatment of leukemia, promising a new era in oncology. Although so far, this procedure only helps 50–90% of patients and, like other cancer treatments, has serious side effects. In this work, we have proposed a controlled model for leukemia treat...
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| Format: | Article |
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MDPI AG
2020-11-01
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| Series: | Games |
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| Online Access: | https://www.mdpi.com/2073-4336/11/4/53 |
| _version_ | 1797547571579715584 |
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| author | Evgenii Khailov Ellina Grigorieva Anna Klimenkova |
| author_facet | Evgenii Khailov Ellina Grigorieva Anna Klimenkova |
| author_sort | Evgenii Khailov |
| collection | DOAJ |
| description | CAR T-cell immunotherapy is a new development in the treatment of leukemia, promising a new era in oncology. Although so far, this procedure only helps 50–90% of patients and, like other cancer treatments, has serious side effects. In this work, we have proposed a controlled model for leukemia treatment to explore possible ways to improve immunotherapy methodology. Our model is described by four nonlinear differential equations with two bounded controls, which are responsible for the rate of injection of chimeric cells, as well as for the dosage of the drug that suppresses the so-called “cytokine storm”. The optimal control problem of minimizing the cancer cells and the activity of the cytokine is stated and solved using the Pontryagin maximum principle. The five possible optimal control scenarios are predicted analytically using investigation of the behavior of the switching functions. The optimal solutions, obtained numerically using BOCOP-2.2.0, confirmed our analytical findings. Interesting results, explaining, why therapies with rest intervals (for example, stopping injections in the middle of the treatment interval) are more effective (within the model), rather than with continuous injections, are presented. Possible improvements to the mathematical model and method of immunotherapy are discussed. |
| first_indexed | 2024-03-10T14:45:58Z |
| format | Article |
| id | doaj.art-91ccf0595145472aa7b39030b4dc5388 |
| institution | Directory Open Access Journal |
| issn | 2073-4336 |
| language | English |
| last_indexed | 2024-03-10T14:45:58Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Games |
| spelling | doaj.art-91ccf0595145472aa7b39030b4dc53882023-11-20T21:22:15ZengMDPI AGGames2073-43362020-11-011145310.3390/g11040053Optimal CAR T-cell Immunotherapy Strategies for a Leukemia Treatment ModelEvgenii Khailov0Ellina Grigorieva1Anna Klimenkova2Department of Optimal Control, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 119992 Moscow, RussiaDepartment of Mathematics and Computer Science, Texas Woman’s University, Denton, TX 76204, USADepartment of Optimal Control, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 119992 Moscow, RussiaCAR T-cell immunotherapy is a new development in the treatment of leukemia, promising a new era in oncology. Although so far, this procedure only helps 50–90% of patients and, like other cancer treatments, has serious side effects. In this work, we have proposed a controlled model for leukemia treatment to explore possible ways to improve immunotherapy methodology. Our model is described by four nonlinear differential equations with two bounded controls, which are responsible for the rate of injection of chimeric cells, as well as for the dosage of the drug that suppresses the so-called “cytokine storm”. The optimal control problem of minimizing the cancer cells and the activity of the cytokine is stated and solved using the Pontryagin maximum principle. The five possible optimal control scenarios are predicted analytically using investigation of the behavior of the switching functions. The optimal solutions, obtained numerically using BOCOP-2.2.0, confirmed our analytical findings. Interesting results, explaining, why therapies with rest intervals (for example, stopping injections in the middle of the treatment interval) are more effective (within the model), rather than with continuous injections, are presented. Possible improvements to the mathematical model and method of immunotherapy are discussed.https://www.mdpi.com/2073-4336/11/4/53leukemianonlinear control systemoptimal controlPontryagin maximum principleswitching functionGeneralized Rolle’s Theorem |
| spellingShingle | Evgenii Khailov Ellina Grigorieva Anna Klimenkova Optimal CAR T-cell Immunotherapy Strategies for a Leukemia Treatment Model Games leukemia nonlinear control system optimal control Pontryagin maximum principle switching function Generalized Rolle’s Theorem |
| title | Optimal CAR T-cell Immunotherapy Strategies for a Leukemia Treatment Model |
| title_full | Optimal CAR T-cell Immunotherapy Strategies for a Leukemia Treatment Model |
| title_fullStr | Optimal CAR T-cell Immunotherapy Strategies for a Leukemia Treatment Model |
| title_full_unstemmed | Optimal CAR T-cell Immunotherapy Strategies for a Leukemia Treatment Model |
| title_short | Optimal CAR T-cell Immunotherapy Strategies for a Leukemia Treatment Model |
| title_sort | optimal car t cell immunotherapy strategies for a leukemia treatment model |
| topic | leukemia nonlinear control system optimal control Pontryagin maximum principle switching function Generalized Rolle’s Theorem |
| url | https://www.mdpi.com/2073-4336/11/4/53 |
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