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Quantitative adverse outcome pathway (qAOP) models for toxicity prediction

The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development...

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Autores principales: Spinu, Nicoleta, Cronin, Mark T. D., Enoch, Steven J., Madden, Judith C., Worth, Andrew P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7261727/
https://www.ncbi.nlm.nih.gov/pubmed/32424443
http://dx.doi.org/10.1007/s00204-020-02774-7
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author Spinu, Nicoleta
Cronin, Mark T. D.
Enoch, Steven J.
Madden, Judith C.
Worth, Andrew P.
author_facet Spinu, Nicoleta
Cronin, Mark T. D.
Enoch, Steven J.
Madden, Judith C.
Worth, Andrew P.
author_sort Spinu, Nicoleta
collection PubMed
description The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP models derived from the published definitions, and (iii) identify and assess the existing published qAOP models and associated software tools. As a result, five probabilistic qAOPs and ten mechanistic qAOPs were evaluated against the common features. The review offers an overview of how the qAOP concept has advanced and how it can aid toxicity assessment in the future. Further efforts are required to achieve validation, harmonisation and regulatory acceptance of qAOP models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00204-020-02774-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-72617272020-06-10 Quantitative adverse outcome pathway (qAOP) models for toxicity prediction Spinu, Nicoleta Cronin, Mark T. D. Enoch, Steven J. Madden, Judith C. Worth, Andrew P. Arch Toxicol Review Article The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP models derived from the published definitions, and (iii) identify and assess the existing published qAOP models and associated software tools. As a result, five probabilistic qAOPs and ten mechanistic qAOPs were evaluated against the common features. The review offers an overview of how the qAOP concept has advanced and how it can aid toxicity assessment in the future. Further efforts are required to achieve validation, harmonisation and regulatory acceptance of qAOP models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00204-020-02774-7) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-05-18 2020 /pmc/articles/PMC7261727/ /pubmed/32424443 http://dx.doi.org/10.1007/s00204-020-02774-7 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Review Article
Spinu, Nicoleta
Cronin, Mark T. D.
Enoch, Steven J.
Madden, Judith C.
Worth, Andrew P.
Quantitative adverse outcome pathway (qAOP) models for toxicity prediction
title Quantitative adverse outcome pathway (qAOP) models for toxicity prediction
title_full Quantitative adverse outcome pathway (qAOP) models for toxicity prediction
title_fullStr Quantitative adverse outcome pathway (qAOP) models for toxicity prediction
title_full_unstemmed Quantitative adverse outcome pathway (qAOP) models for toxicity prediction
title_short Quantitative adverse outcome pathway (qAOP) models for toxicity prediction
title_sort quantitative adverse outcome pathway (qaop) models for toxicity prediction
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7261727/
https://www.ncbi.nlm.nih.gov/pubmed/32424443
http://dx.doi.org/10.1007/s00204-020-02774-7
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