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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.
Abstract:
There is increasing interest in identifying therapeutic regimens capable of maintaining tumor burden within well-defined size boundaries with constraints on the amount and frequency of drugs on a patient-specific basis. We have developed coupled systems of ordinary differential equations (ODEs) capturing the temporal dynamics of tumor burden, treatment effects due to cytotoxic drugs, and neurotoxicity. The models account for tumor cell proliferation and phenotypic heterogeneity, drug availability due to continuous or impulsive drug delivery, drug-induced apoptosis and microglia activation. We utilize nullclines of the system to derive effective dose ranges that stabilize tumor burden and mitigate neurotoxicity. Our results generate bounded treatment regimens that can be validated in the experimental setting. We found that for tumors with a proliferation saturation index (i.e., pre-treatment volume to carrying capacity ratio) between 0.10 and 0.30, containing the tumor in the sense of RECIST can yield up to a 51.8% reduction in drug concentration when compared with regimens designed for tumor eradication. In silico experiments using data from a breast cancer study demonstrate that the nullcline-derived treatments maintained stable disease in the tumors with neurotoxicity maintained below the desired threshold. The methodology developed in this study provides a theoretical formalism to potentially explain several preclinical and clinical observations indicating that low dose therapy can stabilize tumor growth and result in an enhanced quality of life. Importantly, our model identified quantitative biologic indices that can offer practical guidance to the design of personalized regimens that balance treatment efficacy and toxicity.
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.
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