Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles

Paul Retif,1–3 Aurélie Reinhard,2,3 Héna Paquot,2,3 Valérie Jouan-Hureaux,2,3 Alicia Chateau,2,3 Lucie Sancey,4 Muriel Barberi-Heyob,2,3 Sophie Pinel,2,3 Thierry Bastogne2,3,5 1Unité de Physique Médicale, CHR Metz-Thionville, Ars-Laqu...

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Autores principales:	Retif P, Reinhard A, Paquot H, Jouan-Hureaux V, Chateau A, Sancey L, Barberi-Heyob M, Pinel S, Bastogne T
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2016
Materias:	Biomedical applications of radiations Computer simulation Nanomedicine Virtual screening Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/6fe13d5e703b46689bd27149770d10ba
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spelling	oai:doaj.org-article:6fe13d5e703b46689bd27149770d10ba2021-12-02T00:31:16ZMonte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles1178-2013https://doaj.org/article/6fe13d5e703b46689bd27149770d10ba2016-11-01T00:00:00Zhttps://www.dovepress.com/monte-carlo-simulations-guided-by-imaging-to-predict-the-in-vitro-rank-peer-reviewed-article-IJNhttps://doaj.org/toc/1178-2013Paul Retif,1–3 Aurélie Reinhard,2,3 Héna Paquot,2,3 Valérie Jouan-Hureaux,2,3 Alicia Chateau,2,3 Lucie Sancey,4 Muriel Barberi-Heyob,2,3 Sophie Pinel,2,3 Thierry Bastogne2,3,5 1Unité de Physique Médicale, CHR Metz-Thionville, Ars-Laquenexy, 2Université de Lorraine, 3CRAN, UMR 7039, CNRS, Vandoeuvre-lès-Nancy, 4Institut Lumière Matière, UMR 5306, CNRS, Villeurbanne, 5INRIA-BIGS & CRAN, Université de Lorraine, Vandoeuvre-lès-Nancy Cedex, France Abstract: This article addresses the in silico–in vitro prediction issue of organometallic nanoparticles (NPs)-based radiosensitization enhancement. The goal was to carry out computational experiments to quickly identify efficient nanostructures and then to preferentially select the most promising ones for the subsequent in vivo studies. To this aim, this interdisciplinary article introduces a new theoretical Monte Carlo computational ranking method and tests it using 3 different organometallic NPs in terms of size and composition. While the ranking predicted in a classical theoretical scenario did not fit the reference results at all, in contrast, we showed for the first time how our accelerated in silico virtual screening method, based on basic in vitro experimental data (which takes into account the NPs cell biodistribution), was able to predict a relevant ranking in accordance with in vitro clonogenic efficiency. This corroborates the pertinence of such a prior ranking method that could speed up the preclinical development of NPs in radiation therapy. Keywords: biomedical applications of radiations, computer simulation, nanomedicine, virtual screeningRetif PReinhard APaquot HJouan-Hureaux VChateau ASancey LBarberi-Heyob MPinel SBastogne TDove Medical PressarticleBiomedical applications of radiationsComputer simulationNanomedicineVirtual screeningMedicine (General)R5-920ENInternational Journal of Nanomedicine, Vol Volume 11, Pp 6169-6179 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biomedical applications of radiations Computer simulation Nanomedicine Virtual screening Medicine (General) R5-920
spellingShingle	Biomedical applications of radiations Computer simulation Nanomedicine Virtual screening Medicine (General) R5-920 Retif P Reinhard A Paquot H Jouan-Hureaux V Chateau A Sancey L Barberi-Heyob M Pinel S Bastogne T Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles
description	Paul Retif,1–3 Aurélie Reinhard,2,3 Héna Paquot,2,3 Valérie Jouan-Hureaux,2,3 Alicia Chateau,2,3 Lucie Sancey,4 Muriel Barberi-Heyob,2,3 Sophie Pinel,2,3 Thierry Bastogne2,3,5 1Unité de Physique Médicale, CHR Metz-Thionville, Ars-Laquenexy, 2Université de Lorraine, 3CRAN, UMR 7039, CNRS, Vandoeuvre-lès-Nancy, 4Institut Lumière Matière, UMR 5306, CNRS, Villeurbanne, 5INRIA-BIGS & CRAN, Université de Lorraine, Vandoeuvre-lès-Nancy Cedex, France Abstract: This article addresses the in silico–in vitro prediction issue of organometallic nanoparticles (NPs)-based radiosensitization enhancement. The goal was to carry out computational experiments to quickly identify efficient nanostructures and then to preferentially select the most promising ones for the subsequent in vivo studies. To this aim, this interdisciplinary article introduces a new theoretical Monte Carlo computational ranking method and tests it using 3 different organometallic NPs in terms of size and composition. While the ranking predicted in a classical theoretical scenario did not fit the reference results at all, in contrast, we showed for the first time how our accelerated in silico virtual screening method, based on basic in vitro experimental data (which takes into account the NPs cell biodistribution), was able to predict a relevant ranking in accordance with in vitro clonogenic efficiency. This corroborates the pertinence of such a prior ranking method that could speed up the preclinical development of NPs in radiation therapy. Keywords: biomedical applications of radiations, computer simulation, nanomedicine, virtual screening
format	article
author	Retif P Reinhard A Paquot H Jouan-Hureaux V Chateau A Sancey L Barberi-Heyob M Pinel S Bastogne T
author_facet	Retif P Reinhard A Paquot H Jouan-Hureaux V Chateau A Sancey L Barberi-Heyob M Pinel S Bastogne T
author_sort	Retif P
title	Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles
title_short	Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles
title_full	Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles
title_fullStr	Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles
title_full_unstemmed	Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles
title_sort	monte carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles
publisher	Dove Medical Press
publishDate	2016
url	https://doaj.org/article/6fe13d5e703b46689bd27149770d10ba
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Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles

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