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How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic un...

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Autores principales: Wittwehr, Clemens, Aladjov, Hristo, Ankley, Gerald, Byrne, Hugh J., de Knecht, Joop, Heinzle, Elmar, Klambauer, Günter, Landesmann, Brigitte, Luijten, Mirjam, MacKay, Cameron, Maxwell, Gavin, Meek, M. E. (Bette), Paini, Alicia, Perkins, Edward, Sobanski, Tomasz, Villeneuve, Dan, Waters, Katrina M., Whelan, Maurice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5340205/
https://www.ncbi.nlm.nih.gov/pubmed/27994170
http://dx.doi.org/10.1093/toxsci/kfw207
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author Wittwehr, Clemens
Aladjov, Hristo
Ankley, Gerald
Byrne, Hugh J.
de Knecht, Joop
Heinzle, Elmar
Klambauer, Günter
Landesmann, Brigitte
Luijten, Mirjam
MacKay, Cameron
Maxwell, Gavin
Meek, M. E. (Bette)
Paini, Alicia
Perkins, Edward
Sobanski, Tomasz
Villeneuve, Dan
Waters, Katrina M.
Whelan, Maurice
author_facet Wittwehr, Clemens
Aladjov, Hristo
Ankley, Gerald
Byrne, Hugh J.
de Knecht, Joop
Heinzle, Elmar
Klambauer, Günter
Landesmann, Brigitte
Luijten, Mirjam
MacKay, Cameron
Maxwell, Gavin
Meek, M. E. (Bette)
Paini, Alicia
Perkins, Edward
Sobanski, Tomasz
Villeneuve, Dan
Waters, Katrina M.
Whelan, Maurice
author_sort Wittwehr, Clemens
collection PubMed
description Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24–25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.
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spelling pubmed-53402052017-03-13 How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology Wittwehr, Clemens Aladjov, Hristo Ankley, Gerald Byrne, Hugh J. de Knecht, Joop Heinzle, Elmar Klambauer, Günter Landesmann, Brigitte Luijten, Mirjam MacKay, Cameron Maxwell, Gavin Meek, M. E. (Bette) Paini, Alicia Perkins, Edward Sobanski, Tomasz Villeneuve, Dan Waters, Katrina M. Whelan, Maurice Toxicol Sci Forum: AOPs, Computational Models, and Regulatory Toxicity Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24–25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment. Oxford University Press 2017-02 2016-12-19 /pmc/articles/PMC5340205/ /pubmed/27994170 http://dx.doi.org/10.1093/toxsci/kfw207 Text en © The Author 2016. Published by Oxford University Press on behalf of the Society of Toxicology. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Forum: AOPs, Computational Models, and Regulatory Toxicity
Wittwehr, Clemens
Aladjov, Hristo
Ankley, Gerald
Byrne, Hugh J.
de Knecht, Joop
Heinzle, Elmar
Klambauer, Günter
Landesmann, Brigitte
Luijten, Mirjam
MacKay, Cameron
Maxwell, Gavin
Meek, M. E. (Bette)
Paini, Alicia
Perkins, Edward
Sobanski, Tomasz
Villeneuve, Dan
Waters, Katrina M.
Whelan, Maurice
How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology
title How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology
title_full How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology
title_fullStr How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology
title_full_unstemmed How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology
title_short How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology
title_sort how adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology
topic Forum: AOPs, Computational Models, and Regulatory Toxicity
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5340205/
https://www.ncbi.nlm.nih.gov/pubmed/27994170
http://dx.doi.org/10.1093/toxsci/kfw207
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