Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
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
_version_ 1782416787792461824
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
work_keys_str_mv AT karageorgisanastassia anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT dufortsandrine anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT sanceylucie anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT henrymaxime anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT hirsjarvisamuli anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT passiranicatherine anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT benoitjeanpierre anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT gravierjulien anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT texierisabelle anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT montigonolivier anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT benmeradmeriem anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT sirouxvalerie anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT barbieremmanuell anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT colljeanluc anmribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT karageorgisanastassia mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT dufortsandrine mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT sanceylucie mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT henrymaxime mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT hirsjarvisamuli mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT passiranicatherine mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT benoitjeanpierre mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT gravierjulien mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT texierisabelle mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT montigonolivier mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT benmeradmeriem mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT sirouxvalerie mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT barbieremmanuell mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors
AT colljeanluc mribasedclassificationschemetopredictpassiveaccessof5to50nmlargenanoparticlestotumors