Regulation of dendrimer/dextran material performance by altered tissue microenvironment in inflammation and neoplasia
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Edelman_Dendrimer.pdf
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Author(s)
Oliva Jorge, Nuria
Carcole Solanes, Maria
Beckerman, Margarita
Seliktar, Sivan
Hayward, Alison
Stanley, Butch
Parry, Nicola
Edelman, Elazer R.
Artzi, Natalie
Date Issued
January 2015
Journal
Science Translational Medicine
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Oliva, N., M. Carcole, M. Beckerman, S. Seliktar, A. Hayward, J. Stanley, N. M. A. Parry, E. R. Edelman, and N. Artzi. “Regulation of Dendrimer/dextran Material Performance by Altered Tissue Microenvironment in Inflammation and Neoplasia.” Science Translational Medicine 7, no. 272 (January 28, 2015): 272ra11–272ra11.
Version
Author's final manuscript
Abstract
A “one material fits all” mindset ignores profound differences in target tissues that affect their responses and reactivity. Yet little attention has been paid to the role of diseased tissue on material performance, biocompatibility, and healing capacity. We assessed material-tissue interactions with a prototypical adhesive material based on dendrimer/dextran and colon as a model tissue platform. Adhesive materials have high sensitivity to changes in their environment and can be exploited to probe and quantify the influence of even subtle modifications in tissue architecture and biology. We studied inflammatory colitis and colon cancer and found not only a difference in adhesion related to surface chemical interactions but also the existence of a complex interplay that determined the overall dendrimer/dextran biomaterial compatibility. Compatibility was contextual, not simply a constitutive property of the material, and was related to the extent and nature of immune cells in the diseased environment present before material implantation. We then showed how to use information about local alterations of the tissue microenvironment to assess disease severity. This in turn guided us to an optimal dendrimer/dextran formulation choice using a predictive model based on clinically relevant conditions.
Description
available in PMC 2015 October 30
MIT Department
Institute for Medical Engineering and Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1126/scitranslmed.aaa1616
