×

zbMATH — the first resource for mathematics

Mathematical model of chimeric anti-gene receptor (CAR) T cell therapy with presence of cytokine. (English) Zbl 1406.92318
Summary: In this paper, we reconstruct a mathematical model of therapy by CAR T cells for acute lymphoblastic leukemia (ALL) With injection of modified T cells to body, then some signs such as fever, nausea and etc appear. These signs occur for the sake of cytokine release syndrome (CRS). This syndrome has a direct effect on result and satisfaction of therapy. So, the presence of cytokine will be played an important role in modelling process of therapy (CAR T cells). Therefore, the model will include the CAR T cells, B healthy and cancer cells, other circulating lymphocytes in blood, and cytokine. We analyse stability conditions of therapy. Without any control, the dynamic model evidences sub-clinical or clinical decay, chronic destabilization, singularity immediately after a few hours and finally, it depends on the initial conditions. Hence, we try to show by which conditions, therapy will be effective. For this aim, we apply optimal control theory. Since the therapy of CAR T cells affects on both normal and cancer cell; so the optimization dose of CAR T cells will be played an important role and added to system as one controller \( u_{1} \). On the other hand, in order to control of cytokine release syndrome which is a factor for occurrence of singularity, one other controller \( u_{2} \) as tocilizumab, an immunosuppressant drug for cytokine release syndrome is added to system. At the end, we apply method of Pontryagin’s maximum principle for optimal control theory and simulate the clinical results by Matlab (ode15s and ode45).

MSC:
92C50 Medical applications (general)
49N90 Applications of optimal control and differential games
Software:
Matlab
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] R. J. Brentjens, Adoptive Therapy of Cancer with T cells Genetically Targeted to Tumor Associated Antigens through the Introduction of Chimeric Antigen Receptors (CARs): Trafficking, Persistence, and Perseverance American society of Gene and cell therapy, 14th Annual Meeting, 2011.
[2] F. Castiglione; B. Piccoli, Cancer immunotherapy, mathematical modeling and optimal control, Journal of Theoretical Biology, 247, 723, (2007)
[3] F. H. Clarke, Optimization and Nonsmooth Analysis, Classics in Applied Mathematics, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, second edition, 1990. · Zbl 0696.49002
[4] L. G. DE Pillis; A. E. Radunskaya, A mathematical tumor model with immune resistance and drug therapy: an optimal control approach, J. Theor. Medicine., 3, 79, (2001) · Zbl 0985.92023
[5] L. G. DE Pillis; W. Gu; A. E. Radunskayab, Mixed immunotherapy and chemotherapy of tumors: modeling, applications and biological interpretations, J. Theor. Biol., 238, 841, (2006)
[6] L. G. DE Pillis and et al., Mathematical model creation for cancer chemo-immunotherapy, J. Computational and Mathematical Methods in Medicine, 10 (2009), 165-184. · Zbl 1312.92026
[7] F. R. Gantmacher, Applications of the Theory of Matrices, New York: Wiley, 2005.
[8] S. A. Grupp et al., Chimeric antigen receptor-modified T cells for acute lymphoid leukemia, New Engl. J. Med., 16 (2013), 1509-1518.
[9] UL. Heinz Schattler, Geometric Optimal Control Theory, Methods and Examples, Springer. New York, 2012. · Zbl 1276.49002
[10] M. I. Kamien and N. L. Schwartz, Dynamic Optimization: The Calulus of Variations and Optimal Control in Economics and Management, North-Holland, 1991. · Zbl 0727.90002
[11] M. Kalos, B. L. Levine, D. L. Porter and et al., T cells with chimeric antigen receptors have potent antitumor effects and can establish memory in patients with advanced leukemia, Sci. Transl. Med., 3 (2011), 95-73.
[12] JN. Kochenderfer, WH. Wilson, JE. Janik and et al., Eradication of B-lineage cells and regression of lymphoma in a patient treated with autologous T cells genetically engineered to recognize CD19, Blood, 2010.
[13] Y. Kuznetsov, Elements of Applied Bifurcation Theory, Second Edition, Springer, Berlin, 1998. · Zbl 0914.58025
[14] D. W. Lee and et al. T cells expressing CD19 chimeric antigen receptors for acute lymphoblastic leukaemia in children and young adults: a phase 1 dose-escalation trial, Lancent J., 385 (2015), 517-528.
[15] S. Lenhart and J. T. Workman, Optimal Control Applied to Biological Models, Chapman & Hall/CRC Mathematical and Computational Biology Series. Chapman & Hall/CRC, Boca Raton, FL, 2007. · Zbl 1291.92010
[16] E. Maino and et al., Modern immunotherapy of adult B-Lineage acute lymphoblastic leukemia with monoclonal antibodies and chimeric antigen receptor modified T cells, Mediterr J Hematol Infect Dis., 7 (2015).
[17] A. Marciniak-Czochra, T. Stiehl, A. Ho, W. Jager and W. Wagner, Modeling of Asymmetric Cell Division in Hematopoietic Stem Cells-Regulation of Self-Renewal Is Essential for Efficient Repopulation, Mary Ann Liebert Inc, New Rochelle, 2009.
[18] SL. Maude and et al. Chimeric antigen receptor T cells for sustained remissions in leukemia, New Engl. J. Med., 16 (2014), 1507-1517.
[19] R. Mostolizadeh, Mathematical model of Chimeric Antigene Receptor(CAR)T cell therapy, Preprint. · Zbl 1406.92318
[20] Pontryagin, The Mathematical Theory of Optimal Processes, Translated from the Russian by K. N. Trirogoff, edited by L. W. Neustadt. Interscience Publishers John Wiley & Sons, Inc. New York-London, 1962.
[21] S. P. Sethi and G. L. Thompson, Optimal Control Theory: Applications to Management Science and Economics, Kluwer Academic Publishers, 2000. · Zbl 0998.49002
[22] MP. Velders; S. T. Horst; WM. Kast, Sprospects for immunotherapy of acute lymphoblastic leukemia, Nature Leukemia, 15, 701, (2001)
[23] X. Wang, Solving optimal control problems with MATLAB: Indirect methods, Technical report, ISE Dept., NCSU, 2009.
[24] J. P. Zbilut, Unstable Singularities and Randomness: Their Importance in the Complexity of Physical, Biological and Social Sciences, Elsevier Press, 2004. · Zbl 1075.34001
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.