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Computational and Mathematical Methods in Medicine
Article . 2011 . Peer-reviewed
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Article . 2011
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Article . 2011
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Article . 2011
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Computational Modeling of Tumor Response to Vascular‐Targeting Therapies—Part I: Validation

Computational modeling of tumor response to vascular-targeting therapies. I: Validation
Authors: Jana L. Gevertz;

Computational Modeling of Tumor Response to Vascular‐Targeting Therapies—Part I: Validation

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.

Related Organizations
Keywords

Angiogenesis Inhibitors, Antineoplastic Agents, Antibodies, Monoclonal, Humanized, Models, Biological, Medical applications (general), Neoplasms, Antineoplastic Combined Chemotherapy Protocols, Stilbenes, Temozolomide, Animals, Humans, Computer Simulation, Computational methods for problems pertaining to biology, Antineoplastic Agents, Alkylating, Cytotoxins, Antibodies, Monoclonal, Antineoplastic Agents, Phytogenic, Bevacizumab, Dacarbazine, Blood Vessels, Glioblastoma, Algorithms, Research Article

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
35
Top 10%
Top 10%
Top 10%
Green
gold
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Cancer Research