Special Issues

On optimal and suboptimal treatment strategies for a mathematical model of leukemia

  • Received: 01 March 2012 Accepted: 29 June 2018 Published: 01 December 2012
  • MSC : 49J15.

  • In this work an optimization problem for a leukemia treatment modelbased on the Gompertzian law of cell growth is considered. The quantitiesof the leukemic and of the healthy cells at the end of the therapy are chosenas the criterion of the treatment quality. In the case where the number ofhealthy cells at the end of the therapy is higher than a chosen desired number,an analytical solution of the optimization problem for a wide class of therapyprocesses is given. If this is not the case, a control strategy called alternative issuggested.

    Citation: Elena Fimmel, Yury S. Semenov, Alexander S. Bratus. On optimal and suboptimal treatment strategies for a mathematical model of leukemia[J]. Mathematical Biosciences and Engineering, 2013, 10(1): 151-165. doi: 10.3934/mbe.2013.10.151

    Related Papers:

    [1] Pedro José Gutiérrez-Diez, Jose Russo . Design of personalized cancer treatments by use of optimal control problems: The case of chronic myeloid leukemia. Mathematical Biosciences and Engineering, 2020, 17(5): 4773-4800. doi: 10.3934/mbe.2020261
    [2] Urszula Ledzewicz, Heinz Schättler . The Influence of PK/PD on the Structure of Optimal Controls in Cancer Chemotherapy Models. Mathematical Biosciences and Engineering, 2005, 2(3): 561-578. doi: 10.3934/mbe.2005.2.561
    [3] Haifeng Zhang, Jinzhi Lei . Optimal treatment strategy of cancers with intratumor heterogeneity. Mathematical Biosciences and Engineering, 2022, 19(12): 13337-13373. doi: 10.3934/mbe.2022625
    [4] B. M. Adams, H. T. Banks, Hee-Dae Kwon, Hien T. Tran . Dynamic Multidrug Therapies for HIV: Optimal and STI Control Approaches. Mathematical Biosciences and Engineering, 2004, 1(2): 223-241. doi: 10.3934/mbe.2004.1.223
    [5] Hongli Yang, Jinzhi Lei . A mathematical model of chromosome recombination-induced drug resistance in cancer therapy. Mathematical Biosciences and Engineering, 2019, 16(6): 7098-7111. doi: 10.3934/mbe.2019356
    [6] Shuo Wang, Heinz Schättler . Optimal control of a mathematical model for cancer chemotherapy under tumor heterogeneity. Mathematical Biosciences and Engineering, 2016, 13(6): 1223-1240. doi: 10.3934/mbe.2016040
    [7] Rujing Zhao, Xiulan Lai . Evolutionary analysis of replicator dynamics about anti-cancer combination therapy. Mathematical Biosciences and Engineering, 2023, 20(1): 656-682. doi: 10.3934/mbe.2023030
    [8] Urszula Ledzewicz, Shuo Wang, Heinz Schättler, Nicolas André, Marie Amélie Heng, Eddy Pasquier . On drug resistance and metronomic chemotherapy: A mathematical modeling and optimal control approach. Mathematical Biosciences and Engineering, 2017, 14(1): 217-235. doi: 10.3934/mbe.2017014
    [9] Joseph Malinzi, Rachid Ouifki, Amina Eladdadi, Delfim F. M. Torres, K. A. Jane White . Enhancement of chemotherapy using oncolytic virotherapy: Mathematical and optimal control analysis. Mathematical Biosciences and Engineering, 2018, 15(6): 1435-1463. doi: 10.3934/mbe.2018066
    [10] Urszula Ledzewicz, Heinz Schättler, Mostafa Reisi Gahrooi, Siamak Mahmoudian Dehkordi . On the MTD paradigm and optimal control for multi-drug cancer chemotherapy. Mathematical Biosciences and Engineering, 2013, 10(3): 803-819. doi: 10.3934/mbe.2013.10.803
  • In this work an optimization problem for a leukemia treatment modelbased on the Gompertzian law of cell growth is considered. The quantitiesof the leukemic and of the healthy cells at the end of the therapy are chosenas the criterion of the treatment quality. In the case where the number ofhealthy cells at the end of the therapy is higher than a chosen desired number,an analytical solution of the optimization problem for a wide class of therapyprocesses is given. If this is not the case, a control strategy called alternative issuggested.


    [1] in "Computational Medicine, Public Health, and Biotechnology Part I. World Scientific" New Jersey, (1995), pp. 397.
    [2] in "Mathematical Models in Medical and Health Sciences" (eds. M. A. Horn, G. Simonett and G. F. Webb), Vanderbilt University. Nashville, (1998), 1-8.
    [3] Comm. Theor. Biol., 8 (2003), 225-253.
    [4] J. Can. Det. Prev.,, 20 (1996), 171-179.
    [5] Math. Biosci., 138 (1996), 79-100.
    [6] Zh. Vychisl. Mat. Mat. Fiz., 49 (2009), 1907-1919
    [7] Nonlinear Analysis: Real World Applications, 13 (2012), 1044-1059.
    [8] Cancer, 30 (1972), 1572-1582.
    [9] Cell Tissue Kinet., 18 (1985), 307-319.
    [10] Mathematical Biosciences, 229 (2011), 123-134.
    [11] Springer, 1988.
    [12] SIAM Journal on Applied Mathematics, 63 (2003), 1954-1971.
    [13] SIAM Journal on Applied Mathematics, 60 (2000), 1059-1072.
    [14] SIAM J. Appl. Math., 46 (1986), 614-624.
    [15] J. Theor. Biol., 225 (2003), 147-151.
    [16] World Scientific. Vol. 9, 2008.
    [17] Clin. Pharmacokin, 6 (1981), 429-453.
    [18] Prentice-Hall, 1970
    [19] Mathematical Biosciences, 222 (2009), 13-26.
    [20] Mathematical Biosciences, 206 (2007), 320-342.
    [21] IMA J. Math. Appl. Med. Biol., 18 (2001), 25-40.
    [22] Nature Clinical Practice, 3 Nr. 8, (2006).
    [23] Cancer Treat Rep., 61(1977) Oct, 1307-1317. PubMed PMID: 589597.
    [24] Mathematical Biosciences, 146 (1997), 89-113.
    [25] Biophys. J., 16 (1976), 897-910.
    [26] Cancer Chemo. Rep., 25 (1964), 1-111.
    [27] Clin. Pharmacology & Therapeutics, 71 (2002), pp.304.
    [28] Bull. Math. Biol., 39 (1977), 317-337.
    [29] Russian Journal of Numerical Analysis and Mathematical Modelling, 26 (2011), 589-604.
  • This article has been cited by:

    1. A.S. Bratus, E. Fimmel, S.Yu. Kovalenko, On assessing quality of therapy in non-linear distributed mathematical models for brain tumor growth dynamics, 2014, 248, 00255564, 88, 10.1016/j.mbs.2013.12.007
    2. Chahrazed Benosman, Bedr’Eddine Aïnseba, Arnaud Ducrot, Optimization of Cytostatic Leukemia Therapy in an Advection–Reaction–Diffusion Model, 2015, 167, 0022-3239, 296, 10.1007/s10957-014-0667-7
    3. Ekaterina Guzev, Suchita Suryakant Jadhav, Eleonora Ela Hezkiy, Michael Y. Sherman, Michael A. Firer, Svetlana Bunimovich-Mendrazitsky, Validation of a Mathematical Model Describing the Dynamics of Chemotherapy for Chronic Lymphocytic Leukemia In Vivo, 2022, 11, 2073-4409, 2325, 10.3390/cells11152325
    4. N. L. Grigorenko, E. N. Khailov, E. V. Grigorieva, A. D. Klimenkova, Optimal Strategies in the Treatment of Cancers in the Lotka–Volterra Mathematical Model of Competition, 2021, 313, 0081-5438, S100, 10.1134/S0081543821030111
    5. N. L. Grigorenko, E. N. Khailov, E. V. Grigorieva, A. D. Klimenkova, Lotka–Volterra Competition Model with a Nonmonotone Therapy Function for Finding Optimal Strategies in the Treatment of Blood Cancers, 2022, 317, 0081-5438, S71, 10.1134/S0081543822030063
  • Reader Comments
  • © 2013 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)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2936) PDF downloads(540) Cited by(5)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog