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Optimal adaptive cancer therapy based on evolutionary game theory.

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  • Author(s): Li Z;Li Z; Tan X; Tan X; Yu Y; Yu Y
  • Source:
    PloS one [PLoS One] 2025 Apr 23; Vol. 20 (4), pp. e0320677. Date of Electronic Publication: 2025 Apr 23 (Print Publication: 2025).
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Game Theory* ; Neoplasms*/drug therapy ; Neoplasms*/pathology ; Neoplasms*/therapy ; Antineoplastic Agents*/therapeutic use ; Antineoplastic Agents*/pharmacokinetics; Humans ; Drug Resistance, Neoplasm ; Computer Simulation ; Models, Biological
    • Abstract:
      Competing Interests: The authors have declared that no competing interests exist.
      Cancer development is a dynamic and continuously evolving process, with the emergence of drug-resistant cancer cells being one of the primary reasons for the failure of traditional treatments. Adaptive therapy, as an emerging cancer treatment strategy, is increasingly being applied in oncology. In this study, we incorporate pharmacokinetics into a cancer evolutionary game theory model and propose an optimal control problem constrained by maximum drug concentration and maximum tumor burden. Firstly, we demonstrate the existence of an optimal control for this problem. Secondly, using Pontryagin's minimum principle, we formulated the structure of the optimal control to design an optimal adaptive therapy strategy. Finally, through numerical simulations, we compare the optimal adaptive therapy strategy with other adaptive therapies and traditional treatments, and further develop personalized treatment plans for different patient groups. The results demonstrate that the optimized adaptive treatment strategy effectively preserves a high survival rate of healthy cells during treatment. By maintaining drug-sensitive and drug-resistant cell populations in a state of low-level competition, this approach prevents the proliferation of drug-resistant cells, reduces the tumor burden on patients, and extends overall survival.
      (Copyright: © 2025 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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    • Accession Number:
      0 (Antineoplastic Agents)
    • Publication Date:
      Date Created: 20250423 Date Completed: 20250423 Latest Revision: 20250425
    • Publication Date:
      20250425
    • Accession Number:
      PMC12017572
    • Accession Number:
      10.1371/journal.pone.0320677
    • Accession Number:
      40267171