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An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors
Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinic...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4759815/ https://www.ncbi.nlm.nih.gov/pubmed/26892874 http://dx.doi.org/10.1038/srep21417 |
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author | Karageorgis, Anastassia Dufort, Sandrine Sancey, Lucie Henry, Maxime Hirsjärvi, Samuli Passirani, Catherine Benoit, Jean-Pierre Gravier, Julien Texier, Isabelle Montigon, Olivier Benmerad, Mériem Siroux, Valérie Barbier, Emmanuel L. Coll, Jean-Luc |
author_facet | Karageorgis, Anastassia Dufort, Sandrine Sancey, Lucie Henry, Maxime Hirsjärvi, Samuli Passirani, Catherine Benoit, Jean-Pierre Gravier, Julien Texier, Isabelle Montigon, Olivier Benmerad, Mériem Siroux, Valérie Barbier, Emmanuel L. Coll, Jean-Luc |
author_sort | Karageorgis, Anastassia |
collection | PubMed |
description | Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines. |
format | Online Article Text |
id | pubmed-4759815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-47598152016-02-29 An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors Karageorgis, Anastassia Dufort, Sandrine Sancey, Lucie Henry, Maxime Hirsjärvi, Samuli Passirani, Catherine Benoit, Jean-Pierre Gravier, Julien Texier, Isabelle Montigon, Olivier Benmerad, Mériem Siroux, Valérie Barbier, Emmanuel L. Coll, Jean-Luc Sci Rep Article Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines. Nature Publishing Group 2016-02-19 /pmc/articles/PMC4759815/ /pubmed/26892874 http://dx.doi.org/10.1038/srep21417 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Karageorgis, Anastassia Dufort, Sandrine Sancey, Lucie Henry, Maxime Hirsjärvi, Samuli Passirani, Catherine Benoit, Jean-Pierre Gravier, Julien Texier, Isabelle Montigon, Olivier Benmerad, Mériem Siroux, Valérie Barbier, Emmanuel L. Coll, Jean-Luc An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors |
title | An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors |
title_full | An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors |
title_fullStr | An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors |
title_full_unstemmed | An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors |
title_short | An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors |
title_sort | mri-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4759815/ https://www.ncbi.nlm.nih.gov/pubmed/26892874 http://dx.doi.org/10.1038/srep21417 |
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