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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...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V
2022
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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. |
format | Online Article Text |
id | pubmed-8850654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier B.V |
record_format | MEDLINE/PubMed |
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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