Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1016/j.comtox.2021.100195 |
Lizenz  |
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| Titel (primär) | Towards a qAOP framework for predictive toxicology - Linking data to decisions |
| Autor | Paini, A.; Campia, I.; Cronin, M.T.D.; Asturiol, D.; Ceriani, L.; Exner, T.E.; Gao, W.; Gomes, C.; Kruisselbrink, J.; Martens, M.; Meek, M.E.B.; Pamies, D.; Pletz, J.; Scholz, S.
; Schüttler, A.; Spînu, N.; Villeneuve, D.L.; Wittwehr, C.; Worth, A.; Luijten, M. |
| Quelle | Computational Toxicology |
| Erscheinungsjahr | 2022 |
| Department | BIOTOX |
| Band/Volume | 21 |
| Seite von | art. 100195 |
| Sprache | englisch |
| Topic | T9 Healthy Planet |
| Keywords | quantitative Adverse Outcome Pathway (qAOP); hazard assessment; weight of evidence (WoE); in vitro data; in silico data; predictive toxicology |
| Abstract | 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. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=25252 |
| Paini, A., Campia, I., Cronin, M.T.D., Asturiol, D., Ceriani, L., Exner, T.E., Gao, W., Gomes, C., Kruisselbrink, J., Martens, M., Meek, M.E.B., Pamies, D., Pletz, J., Scholz, S., Schüttler, A., Spînu, N., Villeneuve, D.L., Wittwehr, C., Worth, A., Luijten, M. (2022): Towards a qAOP framework for predictive toxicology - Linking data to decisions Comput. Toxicol. 21 , art. 100195 10.1016/j.comtox.2021.100195 |
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