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Updated: Jun 3, 2026
Establishing a Physiologic Human Vascularized Micro-Tumor Model for Cancer Research
Published on: September 15, 2023
Computational modeling of tumor response to vascular-targeting therapies--part I: validation
1Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ 08628-0718, USA. gevertz@tcnj.edu
Abstract:
Mathematical modeling techniques have been widely employed to understand how cancer grows, and, more recently, such approaches have been used to understand how cancer can be controlled. In this manuscript, a previously validated hybrid cellular automaton model of tumor growth in a vascularized environment is used to study the antitumor activity of several vascular-targeting compounds of known efficacy. In particular, this model is used to test the antitumor activity of a clinically used angiogenesis inhibitor (both in isolation, and with a cytotoxic chemotherapeutic) and a vascular disrupting agent currently undergoing clinical trial testing. I demonstrate that the mathematical model can make predictions in agreement with preclinical/clinical data and can also be used to gain more insight into these treatment protocols. The results presented herein suggest that vascular-targeting agents, as currently administered, cannot lead to cancer eradication, although a highly efficacious agent may lead to long-term cancer control.
Insights
Mathematical models simulate cancer growth and control. This study uses a hybrid cellular automaton model to test vascular-targeting drugs, showing they may control but not eradicate cancer.
Area of Science:
- Computational biology
- Mathematical oncology
- Cancer research
Background:
- Mathematical modeling is crucial for understanding tumor growth and developing cancer control strategies.
- Hybrid cellular automaton models offer a validated approach to simulate tumor dynamics in vascularized environments.
Purpose of the Study:
- To utilize a validated hybrid cellular automaton model to investigate the antitumor efficacy of vascular-targeting compounds.
- To assess the model's predictive capabilities against preclinical and clinical data for angiogenesis inhibitors and vascular disrupting agents.
- To gain insights into current cancer treatment protocols involving vascular-targeting agents.
Main Methods:
- Employing a previously validated hybrid cellular automaton model for tumor growth simulation.
- Testing the antitumor activity of a clinically used angiogenesis inhibitor, both alone and combined with chemotherapy.
- Evaluating a vascular disrupting agent currently in clinical trials.
Main Results:
- The mathematical model demonstrated predictions consistent with existing preclinical and clinical data.
- Simulations provided deeper insights into the mechanisms and outcomes of the tested treatment protocols.
- Vascular-targeting agents, as administered, were found insufficient for complete cancer eradication.
Conclusions:
- Mathematical modeling serves as a valuable tool for predicting and understanding cancer treatment responses.
- Current administration of vascular-targeting agents may achieve long-term cancer control but not eradication.
- Further development of highly efficacious vascular-targeting agents could improve long-term cancer management outcomes.
