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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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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. |
format | Online Article Text |
id | pubmed-7261727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
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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