Literature DB >> 19615383

A pharmacologically based multiscale mathematical model of angiogenesis and its use in investigating the efficacy of a new cancer treatment strategy.

Frédérique Billy1, Benjamin Ribba, Olivier Saut, Hélène Morre-Trouilhet, Thierry Colin, Didier Bresch, Jean-Pierre Boissel, Emmanuel Grenier, Jean-Pierre Flandrois.   

Abstract

Tumor angiogenesis is the process by which new blood vessels are formed and enhance the oxygenation and growth of tumors. As angiogenesis is recognized as being a critical event in cancer development, considerable efforts have been made to identify inhibitors of this process. Cytostatic treatments that target the molecular events of the angiogenesis process have been developed, and have met with some success. However, it is usually difficult to preclinically assess the effectiveness of targeted therapies, and apparently promising compounds sometimes fail in clinical trials. We have developed a multiscale mathematical model of angiogenesis and tumor growth. At the molecular level, the model focuses on molecular competition between pro- and anti-angiogenic substances modeled on the basis of pharmacological laws. At the tissue scale, the model uses partial differential equations to describe the spatio-temporal changes in cancer cells during three stages of the cell cycle, as well as those of the endothelial cells that constitute the blood vessel walls. This model is used to qualitatively assess how efficient endostatin gene therapy is. Endostatin is an anti-angiogenic endogenous substance. The gene therapy entails overexpressing endostatin in the tumor and in the surrounding tissue. Simulations show that there is a critical treatment dose below which increasing the duration of treatment leads to a loss of efficacy. This theoretical model may be useful to evaluate the efficacy of therapies targeting angiogenesis, and could therefore contribute to designing prospective clinical trials.

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Year:  2009        PMID: 19615383     DOI: 10.1016/j.jtbi.2009.06.026

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  23 in total

Review 1.  Systems biology of the microvasculature.

Authors:  Lindsay E Clegg; Feilim Mac Gabhann
Journal:  Integr Biol (Camb)       Date:  2015-04-02       Impact factor: 2.192

Review 2.  Harnessing systems biology approaches to engineer functional microvascular networks.

Authors:  Lauren S Sefcik; Jennifer L Wilson; Jason A Papin; Edward A Botchwey
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3.  Computational modelling of anti-angiogenic therapies based on multiparametric molecular imaging data.

Authors:  Benjamin Titz; Kevin R Kozak; Robert Jeraj
Journal:  Phys Med Biol       Date:  2012-09-13       Impact factor: 3.609

4.  Mesoscopic and continuum modelling of angiogenesis.

Authors:  F Spill; P Guerrero; T Alarcon; P K Maini; H M Byrne
Journal:  J Math Biol       Date:  2014-03-11       Impact factor: 2.259

5.  Computational models of VEGF-associated angiogenic processes in cancer.

Authors:  Marianne O Stefanini; Amina A Qutub; Feilim Mac Gabhann; Aleksander S Popel
Journal:  Math Med Biol       Date:  2011-01-25       Impact factor: 1.854

Review 6.  Gene therapy from the perspective of systems biology.

Authors:  Feilim Mac Gabhann; Brian H Annex; Aleksander S Popel
Journal:  Curr Opin Mol Ther       Date:  2010-10

Review 7.  Computational systems biology approaches to anti-angiogenic cancer therapeutics.

Authors:  Stacey D Finley; Liang-Hui Chu; Aleksander S Popel
Journal:  Drug Discov Today       Date:  2014-10-05       Impact factor: 7.851

8.  A mathematical model of angiogenesis and tumor growth: analysis and application in anti-angiogenesis therapy.

Authors:  Xiaoming Zheng; Mohye Sweidan
Journal:  J Math Biol       Date:  2018-07-17       Impact factor: 2.259

9.  Mathematical modeling predicts synergistic antitumor effects of combining a macrophage-based, hypoxia-targeted gene therapy with chemotherapy.

Authors:  Markus R Owen; I Johanna Stamper; Munitta Muthana; Giles W Richardson; Jon Dobson; Claire E Lewis; Helen M Byrne
Journal:  Cancer Res       Date:  2011-03-01       Impact factor: 12.701

Review 10.  Cancer systems biology: a network modeling perspective.

Authors:  Pamela K Kreeger; Douglas A Lauffenburger
Journal:  Carcinogenesis       Date:  2009-10-27       Impact factor: 4.944

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