Global Analysis of r3LCMV Cancer Immunotherapy Model

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DOI: https://doi.org/10.28924/2291-8639-23-2025-147
Published: Jun 16, 2025
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Afnan D. Al Agha, Global Analysis of r3LCMV Cancer Immunotherapy Model, Int. J. Anal. Appl., 23 (2025), 147.

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Afnan D. Al Agha

Abstract

An attenuated lymphocytic choriomeningitis virus (r3LCMV) has shown safety and efficacy in treating cancer. This paper develops a within-host r3LCMV cancer immunotherapy model. The model considers the interconnection between nutrient, normal cells, tumor cells, infected tumor cells, viral vector, and virus-specific CTLs. The nonnegativity and boundedness of the solutions are verified. The equilibrium points with the biological acceptance conditions are calculated. The global stability of each point is demonstrated. Numerical simulations are implemented to ratify the theoretical results. It is found that the equilibria exhibit four main states: a healthy individual who does not have cancer, a cancer patient who does not receive any treatments, a cancer patient who receives r3LCMV cancer therapy with inactive immunity, and a cancer patient who receives r3LCMV therapy with active virus-specific CTLs. The parameters that control the transition between these states need to be carefully chosen. Increasing the stimulation rate of CTLs induced by r3LCMV viral vector reduces the concentration of infected tumor cells. The attenuation rate of the viral vector affects its ability to eliminate tumor cells from the body. Therefore, these rates need to be cautiously selected and tested.

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References

  1. American Cancer Society, American Cancer Society Releases Latest Global Cancer Statistics; Cancer Cases Expected to Rise to 35 Million Worldwide by 2050, The ASCO Post, 2024.
  2. Y.R. Chung, B. Awakoaiye, T. Dangi, N. Irani, S. Fourati, P. Penaloza-MacMaster, An Attenuated Lymphocytic Choriomeningitis Virus Vector Enhances Tumor Control in Mice Partly Via IFN-I, J. Clin. Invest. 134 (2024), e178945. https://doi.org/10.1172/jci178945.
  3. A.D. Al Agha, Analysis of a Reaction-diffusion Oncolytic Sars-cov-2 Model, Model. Earth Syst. Environ. 10 (2024), 4641–4662. https://doi.org/10.1007/s40808-024-02009-z.
  4. A.A. Agha, H.A. Garalleh, Oncolysis by Sars-cov-2: Modeling and Analysis, AIMS Math. 9 (2024), 7212–7252. https://doi.org/10.3934/math.2024351.
  5. A. Friedman, X. Lai, Combination Therapy for Cancer with Oncolytic Virus and Checkpoint Inhibitor: A Mathematical Model, PLOS ONE 13 (2018), e0192449. https://doi.org/10.1371/journal.pone.0192449.
  6. M. Purde, J. Cupovic, Y.A. Palmowski, et al. A Replicating Lcmv-based Vaccine for the Treatment of Solid Tumors, Mol. Therapy 32 (2024), 426–439. https://doi.org/10.1016/j.ymthe.2023.11.026.
  7. C. Gomar, C.A. Di Trani, A. Bella, et al. Efficacy of Lcmv-based Cancer Immunotherapies Is Unleashed by Intratumoral Injections of Polyi:c, J. ImmunoTher. Cancer 12 (2024), e008287. https://doi.org/10.1136/jitc-2023-008287.
  8. Z. Wang, Z. Guo, H. Peng, A Mathematical Model Verifying Potent Oncolytic Efficacy of M1 Virus, Math. Biosci. 276 (2016), 19-27. https://doi.org/10.1016/j.mbs.2016.03.001.
  9. A. Elaiw, A. Hobiny, A. Al Agha, Global Dynamics of Reaction-diffusion Oncolytic M1 Virotherapy with Immune Response, Appl. Math. Comput. 367 (2020), 124758. https://doi.org/10.1016/j.amc.2019.124758.
  10. J. Malinzi, R. Ouifki, A. Eladdadi, et al. Enhancement of Chemotherapy Using Oncolytic Virotherapy: Mathematical and Optimal Control Analysis, Math. Biosci. Eng. 15 (2018), 1435–1463. https://doi.org/10.3934/mbe.2018066.
  11. T. Alzahrani, R. Eftimie, D. Trucu, Multiscale Modelling of Cancer Response to Oncolytic Viral Therapy, Math. Biosci. 310 (2019), 76-95. https://doi.org/10.1016/j.mbs.2018.12.018.
  12. E. Guo, H.M. Dobrovolny, Mathematical Modeling of Oncolytic Virus Therapy Reveals Role of the Immune Response, Viruses 15 (2023), 1812. https://doi.org/10.3390/v15091812.
  13. Z. Abernathy, K. Abernathy, J. Stevens, et al. A Mathematical Model for Tumor Growth and Treatment Using Virotherapy, AIMS Math. 5 (2020), 4136–4150. https://doi.org/10.3934/math.2020265.
  14. J. Malinzi, A Mathematical Model for Oncolytic Virus Spread Using the Telegraph Equation, Commun. Nonlinear Sci. Numer. Simul. 102 (2021), 105944. https://doi.org/10.1016/j.cnsns.2021.105944.
  15. S. Wang, S. Wang, X. Song, Hopf Bifurcation Analysis in a Delayed Oncolytic Virus Dynamics with Continuous Control, Nonlinear Dyn. 67 (2011), 629-640. https://doi.org/10.1007/s11071-011-0015-5.
  16. H.K. Khalil, Nonlinear Systems, Prentice-Hall, (1996).
  17. C. Li, J. Xu, J. Liu, et al. The Within-Host Viral Kinetics of SARS-CoV-2, Math. Biosci. Eng. 17 (2020), 2853–2861. https://doi.org/10.3934/mbe.2020159.
  18. A. KOROBEINIKOV, Global Properties of Basic Virus Dynamics Models, Bull. Math. Biol. 66 (2004), 879-883. https://doi.org/10.1016/j.bulm.2004.02.001.