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Updated: May 21, 2026
Establishing a Physiologic Human Vascularized Micro-Tumor Model for Cancer Research
Published on: September 15, 2023
Optimization of vascular-targeting drugs in a computational model of tumor growth
1Department of Mathematics and Statistics, The College of New Jersey, Ewing, New Jersey 08628, USA. gevertz@tcnj.edu
Abstract:
A biophysical tool is introduced that seeks to provide a theoretical basis for helping drug design teams assess the most promising drug targets and design optimal treatment strategies. The tool is grounded in a previously validated computational model of the feedback that occurs between a growing tumor and the evolving vasculature. In this paper, the model is particularly used to explore the therapeutic effectiveness of two drugs that target the tumor vasculature: angiogenesis inhibitors (AIs) and vascular disrupting agents (VDAs). Using sensitivity analyses, the impact of VDA dosing parameters is explored, as is the effects of administering a VDA with an AI. Further, a stochastic optimization scheme is utilized to identify an optimal dosing schedule for treatment with an AI and a chemotherapeutic. The treatment regimen identified can successfully halt simulated tumor growth, even after the cessation of therapy.
Insights
This study introduces a computational tool to optimize cancer drug design. It simulates tumor-vasculature interactions to identify effective treatment strategies, halting tumor growth even after therapy ends.
Area of Science:
- Biophysics
- Computational Biology
- Pharmacology
Background:
- Tumor growth is intricately linked with the development of tumor vasculature.
- Targeting tumor vasculature is a key strategy in cancer therapy.
- Optimizing drug design and treatment requires robust theoretical frameworks.
Purpose of the Study:
- Introduce a biophysical tool for drug design and treatment strategy optimization.
- Explore the therapeutic efficacy of angiogenesis inhibitors (AIs) and vascular disrupting agents (VDAs).
- Investigate combination therapies involving AIs, VDAs, and chemotherapy.
Main Methods:
- Utilized a validated computational model of tumor-vasculature feedback.
- Performed sensitivity analyses on vascular disrupting agent (VDA) dosing parameters.
- Employed a stochastic optimization scheme to determine optimal dosing schedules.
Main Results:
- Sensitivity analyses revealed key parameters influencing VDA efficacy.
- Simulations demonstrated the potential of combined AI and VDA therapy.
- An optimized regimen of an AI and a chemotherapeutic halted simulated tumor growth post-treatment.
Conclusions:
- The biophysical tool provides a theoretical basis for rational drug design.
- Computational modeling can guide the development of effective anti-angiogenic and anti-vasculature cancer treatments.
- Optimized treatment schedules can achieve sustained tumor growth inhibition.
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