Regulation of dendrimer/dextran material performance by altered tissue microenvironment in inflammation and neoplasia
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Author(s)
Carcole Solanes, Maria
Beckerman, Margarita
Seliktar, Sivan
Hayward, Alison
Stanley, James R. L.
Artzi, Natalie
Oliva, Nuria
Parry, Nicola Maria Anne
Edelman, Elazer R
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.
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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.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Division of Comparative Medicine
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DOI of Published Version
https://doi.org/10.1126/scitranslmed.aaa1616
