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Updated: Sep 28, 2026

Utilizing Murine Inducible Telomerase Alleles in the Studies of Tissue Degeneration/Regeneration and Cancer
08:34

Utilizing Murine Inducible Telomerase Alleles in the Studies of Tissue Degeneration/Regeneration and Cancer

Published on: April 13, 2015

Cancer treatment by telomerase inhibitors: predictions by a kinetic model

Igor A Sidorov1, Ken S Hirsch, Calvin B Harley

  • 1National Cancer Institute, NCI-Frederick, NIH, PO Box B, Frederick, MD 21702-1201, USA. sidorovi@ncifcrf.gov

Mathematical Biosciences
|November 26, 2002
PubMed

Insights

Telomerase inhibitors suppress cell growth by shortening telomeres. Tumor growth dynamics depend on telomere length distribution and cell doubling times, impacting treatment efficacy delay.

Area of Science:

  • Oncology
  • Molecular Biology
  • Biophysics

Background:

  • Telomere shortening inhibits cell proliferation when lengths fall below a critical threshold (1-2 kb).
  • Telomerase inhibitors aim to suppress cancer cell growth by accelerating telomere attrition.
  • Understanding the time to reach this threshold (efficacy delay) is crucial for therapeutic success.

Purpose of the Study:

  • To model and analyze the efficacy delay of telomerase inhibitors.
  • To investigate the impact of telomere length distribution on tumor growth dynamics under telomerase inhibition.
  • To determine key factors influencing the effectiveness of telomerase-targeted therapies.

Main Methods:

  • Developed a mathematical model solving a system of differential equations.
  • Analyzed the relationship between telomere size distribution and efficacy delay.
  • Simulated tumor growth dynamics based on telomere length and doubling times.

Main Results:

  • Efficacy delay is highly sensitive to the initial size distribution of telomeres.
  • Increased telomere length heterogeneity shortens the efficacy delay.
  • Telomerase inhibitors more significantly impact long-term tumor dynamics in homogeneous telomere populations.

Conclusions:

  • Telomere size distribution and tumor doubling times are critical determinants of tumor growth dynamics with telomerase inhibitors.
  • Heterogeneity in telomere length influences the onset and progression of treatment effects.
  • Mathematical modeling provides insights into optimizing telomerase inhibitor efficacy.

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