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

被引:22
|
作者
Paini, Alicia [1 ]
Campia, Ivana [1 ]
Cronin, Mark T. D. [2 ]
Asturiol, David [1 ]
Ceriani, Lidia [3 ]
Exner, Thomas E. [4 ,14 ]
Gao, Wang [5 ]
Gomes, Caroline [6 ]
Kruisselbrink, Johannes [7 ]
Martens, Marvin [8 ]
Meek, M. E. Bette [9 ]
Pamies, David [10 ]
Pletz, Julia [2 ]
Scholz, Stefan [11 ]
Schuettler, Andreas [11 ]
Spinu, Nicoleta [2 ]
Villeneuve, Daniel L. [12 ]
Wittwehr, Clemens [1 ]
Worth, Andrew [1 ]
Luijten, Mirjam [13 ]
机构
[1] European Commiss, Joint Res Ctr JRC, Ispra, Italy
[2] Liverpool John Moores Univ, Liverpool, England
[3] Humane Soc Int, Brussels, Belgium
[4] Technol Pk Basel, Edelweiss Connect GmbH, Basel, Switzerland
[5] Inst Natl Environm Ind & Risques INERIS, Verneuil En Halatte, France
[6] BASF, Ludwigshafen, Germany
[7] Wageningen Univ & Res, Wageningen, Netherlands
[8] Maastricht Univ, Maastricht, Netherlands
[9] Univ Ottawa, Ottawa, ON, Canada
[10] Univ Lausanne, Lausanne & Swiss Ctr Appl Human Toxicol SCAHT, Dept Physiol, Lausanne, Switzerland
[11] Helmholtz Ctr Environm Res GmbH UFZ, Leipzig, Germany
[12] US EPA, Great Lakes Toxicol & Ecol Div, Duluth, MN USA
[13] Natl Inst Publ Hlth & Environm RIVM, Bilthoven, Netherlands
[14] Seven Nine Doo, Cerknica, Slovenia
关键词
quantitative Adverse Outcome Pathway; (qAOP); Hazard assessment; Weight of evidence (WoE); In vitro data; In silico data; Predictive toxicology; ORGANOPHOSPHATES BINDING; TETRAHYMENA-PYRIFORMIS; CLUSTERING OBJECTS; QSAR MODELS; PHENOLS; IDENTIFICATION; SIMILARITY; SUBSETS;
D O I
10.1016/j.comtox.2021.100195
中图分类号
R99 [毒物学(毒理学)];
学科分类号
100405 ;
摘要
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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页数:14
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