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Author Notes:

Julia E. Babensee Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University Georgia Institute of Technology 313 Ferst Dr. Atlanta, GA 30332-0535 Tel: 404-385-0130 Fax:404-894-2291 julia.babensee@bme.gatech.edu.

The authors thank Dr. Melissa L. Kemp of Georgia Institute of Technology for helpful discussion on multivariate analysis and critical review of the manuscript; Dr. Manu O. Platt of Georgia Institute of Technology for access to the Simca P+ software; and Dr. Rana Sodhi for performing the XPS measurements and analysis.

Subject:

Research Funding:

This research was supported by the National Institutes of Health grants EB004633 (JEB); and the 96-well pMA testbed was developed under NIH grant EB001046 (RESBIO) at NJ Center for Biomaterials, Rutgers University.

Keywords:

  • Science & Technology
  • Technology
  • Engineering, Biomedical
  • Materials Science, Biomaterials
  • Engineering
  • Materials Science
  • Dendritic cells
  • Polymethacrylate
  • Combinatorial library
  • Material properties
  • Principal component analysis
  • Partial linear squares regression
  • HOST TISSUE RESPONSES
  • T-CELLS
  • POLY(LACTIC-CO-GLYCOLIC ACID)
  • COMBINATORIAL LIBRARIES
  • POLYMERIC BIOMATERIALS
  • FIBRINOGEN ADSORPTION
  • ADSORBED PROTEINS
  • IMMUNE-RESPONSES
  • IN-VITRO
  • SURFACES

Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates

Journal Title:

Biomaterials

Volume:

Volume 33, Number 6

Publisher:

, Pages 1699-1713

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response toward the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-modulatory biomaterials. In this study, DC response to a set of 12 polymethacrylates (pMAs) was assessed in terms of surface marker expression and cytokine profile. Principal component analysis (PCA) determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an immature DC phenotype. Partial square linear regression, a multivariate modeling approach, was implemented and successfully predicted biomaterial-induced DC phenotype in terms of surface marker expression from biomaterial properties with R prediction2 = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with Rprediction2 = 0.80. These results demonstrated that immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection.

Copyright information:

© 2011 Elsevier Ltd. All rights reserved

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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