IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v391y2021ics0096300320305117.html
   My bibliography  Save this article

Phase-specific cancer-immune model considering acquired resistance to therapeutic agents

Author

Listed:
  • Byun, Jong Hyuk
  • Jung, Il Hyo

Abstract

We formulated a mechanistic model for the cancer-immune system associated with therapy. In this model, cancer is divided into two types: cancer that is sensitive to treatment (CST) and cancer that gradually acquires resistance to therapeutic agents (CRT). Cancer activates various mechanisms to evade the actions of therapeutic agents, including chemotherapy or targeted therapy. A positive response is observed at the early stage of treatment when cancer therapy is administered through subcutaneous or intravenous injection. However, over time, cancer acquires resistance against the treatment and begins to show rapid growth. Previous models have suggested strategies that can effectively suppress cancer by determining an appropriate dosing regimen but are limited in that cancer inhibition depends only on the dose amount and regimen. In contrast to a model in which there is a steady decline in cancer due to continuous-infusion therapy, the proposed model incorporates the fact that cancer cells may grow despite successive therapy administration, owing to the transition from CST to CRT. This consideration indicates that cancer suppression can be determined by the delay of therapy delivery to the site of action and the transition time. The delay of therapy and the transition time thus determine the period of cancer growth and the increase or decrease in cancer cell growth, respectively. This model was then used to the ratio of CST to CRT and to explore the therapy infusion rate under constant and periodic conditions in association with a pharmacokinetic model.

Suggested Citation

  • Byun, Jong Hyuk & Jung, Il Hyo, 2021. "Phase-specific cancer-immune model considering acquired resistance to therapeutic agents," Applied Mathematics and Computation, Elsevier, vol. 391(C).
  • Handle: RePEc:eee:apmaco:v:391:y:2021:i:c:s0096300320305117
    DOI: 10.1016/j.amc.2020.125555
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300320305117
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2020.125555?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Xiangdong & Li, Qingze & Pan, Jianxin, 2018. "A deterministic and stochastic model for the system dynamics of tumor–immune responses to chemotherapy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 162-176.
    2. Angstmann, C.N. & Henry, B.I. & McGann, A.V., 2016. "A fractional-order infectivity SIR model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 86-93.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Angstmann, C.N. & Henry, B.I. & Jacobs, B.A. & McGann, A.V., 2017. "A time-fractional generalised advection equation from a stochastic process," Chaos, Solitons & Fractals, Elsevier, vol. 102(C), pages 175-183.
    2. DAŞBAŞI, Bahatdin, 2020. "Stability analysis of the hiv model through incommensurate fractional-order nonlinear system," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    3. Duan, Wei-Long & Fang, Hui & Zeng, Chunhua, 2019. "The stability analysis of tumor-immune responses to chemotherapy system with gaussian white noises," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 96-102.
    4. Duan, Wei-Long & Lin, Ling, 2021. "Noise and delay enhanced stability in tumor-immune responses to chemotherapy system," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    5. Duan, Wei-Long, 2020. "The stability analysis of tumor-immune responses to chemotherapy system driven by Gaussian colored noises," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    6. Han, Ping & Xu, Wei & Wang, Liang & Zhang, Hongxia & Ma, Shichao, 2020. "Most probable dynamics of the tumor growth model with immune surveillance under cross-correlated noises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    7. Sk, Tahajuddin & Biswas, Santosh & Sardar, Tridip, 2022. "The impact of a power law-induced memory effect on the SARS-CoV-2 transmission," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    8. Sene, Ndolane, 2020. "SIR epidemic model with Mittag–Leffler fractional derivative," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    9. Tirumalasetty Chiranjeevi & Raj Kumar Biswas, 2017. "Discrete-Time Fractional Optimal Control," Mathematics, MDPI, vol. 5(2), pages 1-12, April.
    10. Babaei, A. & Ahmadi, M. & Jafari, H. & Liya, A., 2021. "A mathematical model to examine the effect of quarantine on the spread of coronavirus," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    11. Akinlar, M.A. & Inc, Mustafa & Gómez-Aguilar, J.F. & Boutarfa, B., 2020. "Solutions of a disease model with fractional white noise," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    12. Bashkirtseva, I. & Ryashko, L., 2020. "Analysis of noise-induced phenomena in the nonlinear tumor–immune system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    13. Wenjia Liu & Jian Wang & Yanfeng Ouyang, 2022. "Rumor Transmission in Online Social Networks Under Nash Equilibrium of a Psychological Decision Game," Networks and Spatial Economics, Springer, vol. 22(4), pages 831-854, December.
    14. Fathalla A. Rihan & Chinnathambi Rajivganthi, 2021. "Dynamics of Tumor-Immune System with Random Noise," Mathematics, MDPI, vol. 9(21), pages 1-14, October.
    15. Nan Xu & Yaoqun Xu, 2022. "Research on Tacit Knowledge Dissemination of Automobile Consumers’ Low-Carbon Purchase Intention," Sustainability, MDPI, vol. 14(16), pages 1-26, August.
    16. Min, Seungsik & Shin, Ki-Hong & Baek, Woonhak & Kim, Kyungsik & You, Cheol-Hwan & Lee, Dong-In & Yum, Seong Soo & Kim, Wonheung & Chang, Ki-Ho, 2020. "Dynamical behavior of combined detrended cross-correlation analysis methods in random walks and Lévy flights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:391:y:2021:i:c:s0096300320305117. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.