JOURNAL ARTICLE

Mathematical modeling of drug resistance in heterogeneous cancer cell populations.

  • Published In: International Journal of Biomathematics, 2025, v. 18, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bao, Kangbo; Liang, Guizhen; TIAN, TIANHAI; Zhang, Xingan 3 of 3

Abstract

Drug resistance is one of the most intractable issues associated with cancer treatment in clinical practice. Mathematical models provide an analytic framework for facilitating the understanding of resistance evolution dynamics and the design of cancer clinical trial. In this paper, we develop an elementary, compartmental mathematical model for absolute drug resistance, focusing on the effects of point mutations in genetic drivers of malignancy. A set of ordinary differential equations (ODEs) is used to describe the dynamics of competing heterogeneous cancer cell populations while taking account of pharmacokinetics. All possible equilibria and their local geometric properties are analyzed, with the result suggests that the system exhibits bistable dynamics. The existence of optimal treatment time is discussed. To identify the critical parameters which influence cellular dynamics, we also perform parameter sensitivity analysis. Finally, numerical simulations are presented to verify the feasibilities of our analytical results and to find that the pre-existence of resistant cell phenotypes contributes more than resistant mutants generated during the treatment phase. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Biomathematics. 2025/02, Vol. 18, Issue 2, p1
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2025
  • ISSN:1793-5245
  • DOI:10.1142/S1793524523501012
  • Accession Number:182370889
  • Copyright Statement:Copyright of International Journal of Biomathematics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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