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Towards a qAOP framework for predictive toxicology - Linking data to decisions

The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increa...

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Autores principales: Paini, Alicia, Campia, Ivana, Cronin, Mark T.D., Asturiol, David, Ceriani, Lidia, Exner, Thomas E., Gao, Wang, Gomes, Caroline, Kruisselbrink, Johannes, Martens, Marvin, Meek, M.E. Bette, Pamies, David, Pletz, Julia, Scholz, Stefan, Schüttler, Andreas, Spînu, Nicoleta, Villeneuve, Daniel L., Wittwehr, Clemens, Worth, Andrew, Luijten, Mirjam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850654/
https://www.ncbi.nlm.nih.gov/pubmed/35211660
http://dx.doi.org/10.1016/j.comtox.2021.100195
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author Paini, Alicia
Campia, Ivana
Cronin, Mark T.D.
Asturiol, David
Ceriani, Lidia
Exner, Thomas E.
Gao, Wang
Gomes, Caroline
Kruisselbrink, Johannes
Martens, Marvin
Meek, M.E. Bette
Pamies, David
Pletz, Julia
Scholz, Stefan
Schüttler, Andreas
Spînu, Nicoleta
Villeneuve, Daniel L.
Wittwehr, Clemens
Worth, Andrew
Luijten, Mirjam
author_facet Paini, Alicia
Campia, Ivana
Cronin, Mark T.D.
Asturiol, David
Ceriani, Lidia
Exner, Thomas E.
Gao, Wang
Gomes, Caroline
Kruisselbrink, Johannes
Martens, Marvin
Meek, M.E. Bette
Pamies, David
Pletz, Julia
Scholz, Stefan
Schüttler, Andreas
Spînu, Nicoleta
Villeneuve, Daniel L.
Wittwehr, Clemens
Worth, Andrew
Luijten, Mirjam
author_sort Paini, Alicia
collection PubMed
description The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.
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spelling pubmed-88506542022-02-22 Towards a qAOP framework for predictive toxicology - Linking data to decisions Paini, Alicia Campia, Ivana Cronin, Mark T.D. Asturiol, David Ceriani, Lidia Exner, Thomas E. Gao, Wang Gomes, Caroline Kruisselbrink, Johannes Martens, Marvin Meek, M.E. Bette Pamies, David Pletz, Julia Scholz, Stefan Schüttler, Andreas Spînu, Nicoleta Villeneuve, Daniel L. Wittwehr, Clemens Worth, Andrew Luijten, Mirjam Comput Toxicol Article The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area. Elsevier B.V 2022-02 /pmc/articles/PMC8850654/ /pubmed/35211660 http://dx.doi.org/10.1016/j.comtox.2021.100195 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Paini, Alicia
Campia, Ivana
Cronin, Mark T.D.
Asturiol, David
Ceriani, Lidia
Exner, Thomas E.
Gao, Wang
Gomes, Caroline
Kruisselbrink, Johannes
Martens, Marvin
Meek, M.E. Bette
Pamies, David
Pletz, Julia
Scholz, Stefan
Schüttler, Andreas
Spînu, Nicoleta
Villeneuve, Daniel L.
Wittwehr, Clemens
Worth, Andrew
Luijten, Mirjam
Towards a qAOP framework for predictive toxicology - Linking data to decisions
title Towards a qAOP framework for predictive toxicology - Linking data to decisions
title_full Towards a qAOP framework for predictive toxicology - Linking data to decisions
title_fullStr Towards a qAOP framework for predictive toxicology - Linking data to decisions
title_full_unstemmed Towards a qAOP framework for predictive toxicology - Linking data to decisions
title_short Towards a qAOP framework for predictive toxicology - Linking data to decisions
title_sort towards a qaop framework for predictive toxicology - linking data to decisions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850654/
https://www.ncbi.nlm.nih.gov/pubmed/35211660
http://dx.doi.org/10.1016/j.comtox.2021.100195
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