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Compartment Model Predicts VEGF Secretion and Investigates the Effects of VEGF Trap in Tumor-Bearing Mice

Overview of attention for article published in Frontiers in oncology, January 2013
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Title
Compartment Model Predicts VEGF Secretion and Investigates the Effects of VEGF Trap in Tumor-Bearing Mice
Published in
Frontiers in oncology, January 2013
DOI 10.3389/fonc.2013.00196
Pubmed ID
Authors

Stacey D. Finley, Manjima Dhar, Aleksander S. Popel

Abstract

Angiogenesis, the formation of new blood vessels from existing vasculature, is important in tumor growth and metastasis. A key regulator of angiogenesis is vascular endothelial growth factor (VEGF), which has been targeted in numerous anti-angiogenic therapies aimed at inhibiting tumor angiogenesis. Systems biology approaches, including computational modeling, are useful for understanding this complex biological process and can aid in the development of novel and effective therapeutics that target the VEGF family of proteins and receptors. We have developed a computational model of VEGF transport and kinetics in the tumor-bearing mouse, which includes three-compartments: normal tissue, blood, and tumor. The model simulates human tumor xenografts and includes human (VEGF121 and VEGF165) and mouse (VEGF120 and VEGF164) isoforms. The model incorporates molecular interactions between these VEGF isoforms and receptors (VEGFR1 and VEGFR2), as well as co-receptors (NRP1 and NRP2). We also include important soluble factors: soluble VEGFR1 (sFlt-1) and α-2-macroglobulin. The model accounts for transport via macromolecular transendothelial permeability, lymphatic flow, and plasma clearance. We have fit the model to available in vivo experimental data on the plasma concentration of free VEGF Trap and VEGF Trap bound to mouse and human VEGF in order to estimate the rates at which parenchymal cells (myocytes and tumor cells) and endothelial cells secrete VEGF. Interestingly, the predicted tumor VEGF secretion rates are significantly lower (0.007-0.023 molecules/cell/s, depending on the tumor microenvironment) than most reported in vitro measurements (0.03-2.65 molecules/cell/s). The optimized model is used to investigate the interstitial and plasma VEGF concentrations and the effect of the VEGF-neutralizing agent, VEGF Trap (aflibercept). This work complements experimental studies performed in mice and provides a framework with which to examine the effects of anti-VEGF agents, aiding in the optimization of such anti-angiogenic therapeutics as well as analysis of clinical data. The model predictions also have implications for biomarker discovery with anti-angiogenic therapies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Switzerland 1 2%
Unknown 53 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 29%
Student > Bachelor 6 11%
Researcher 6 11%
Student > Postgraduate 6 11%
Student > Doctoral Student 4 7%
Other 10 18%
Unknown 7 13%
Readers by discipline Count As %
Engineering 15 27%
Biochemistry, Genetics and Molecular Biology 9 16%
Medicine and Dentistry 7 13%
Mathematics 4 7%
Agricultural and Biological Sciences 4 7%
Other 7 13%
Unknown 9 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 July 2013.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from Frontiers in oncology
#15,917
of 22,416 outputs
Outputs of similar age
#258,406
of 288,991 outputs
Outputs of similar age from Frontiers in oncology
#194
of 328 outputs
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So far Altmetric has tracked 22,416 research outputs from this source. They receive a mean Attention Score of 3.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 328 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.