Employing nullclines to balance treatment efficacy and neurotoxicity for sustained tumor control

Lois C Okereke1, Ernesto A B F Lima2, Anna G Sorace3

  • 1Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA.

Mathematical Biosciences
|September 12, 2025
PubMed

Insights

This study developed mathematical models to maintain tumor size within boundaries using personalized, low-dose drug regimens, significantly reducing drug concentration and toxicity while stabilizing disease.

Area of Science:

  • Mathematical Oncology
  • Systems Biology
  • Pharmacodynamics

Background:

  • Increasing interest in personalized therapeutic regimens for cancer treatment.
  • Need for strategies to maintain tumor burden within specific limits while minimizing drug toxicity.
  • Tumor heterogeneity and drug delivery dynamics pose challenges to treatment optimization.

Purpose of the Study:

  • To develop and analyze mathematical models of tumor growth, cytotoxic drug effects, and neurotoxicity.
  • To identify effective dose ranges for stabilizing tumor burden and mitigating neurotoxicity.
  • To provide a theoretical framework for personalized, low-dose cancer therapy.

Main Methods:

  • Coupled systems of ordinary differential equations (ODEs) modeling tumor burden, drug effects, and neurotoxicity.
  • Analysis of nullclines to determine effective dose ranges for tumor stabilization.
  • In silico experiments using breast cancer data to validate treatment regimens.

Main Results:

  • Nullcline analysis yielded bounded treatment regimens for stabilizing tumor burden.
  • Tumor containment (RECIST) reduced drug concentration by up to 51.8% compared to eradication regimens.
  • In silico trials demonstrated stable disease maintenance with controlled neurotoxicity.

Conclusions:

  • Mathematical modeling can guide the design of personalized cancer therapies balancing efficacy and toxicity.
  • Low-dose, stabilized treatment regimens can be effective in managing tumor growth and improving quality of life.
  • Quantitative biologic indices can inform personalized treatment strategies for cancer patients.