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Publikationstyp
Wissenschaftlicher Artikel
Erscheinungsjahr
2020
Zebrafish AC50 modelling: (Q)SAR models to predict developmental toxicity in zebrafish embryo
Zebrafish AC50 modelling: (Q)SAR models to predict developmental toxicity in zebrafish embryo
Autor:innen
Herausgeber
Quelle
Ecotoxicology and Environmental Safety
202 (2020)
202 (2020)
Schlagwörter
Klassifikation, Teratogenität
Zitation
LAVADO, Giovanna J., Jürgen ARNING, Domenico GADALETA und Cosimo TOMA, 2020. Zebrafish AC50 modelling: (Q)SAR models to predict developmental toxicity in zebrafish embryo. Ecotoxicology and Environmental Safety [online]. 2020. Bd. 202 (2020). DOI 10.60810/openumwelt-1242. Verfügbar unter: https://openumwelt.de/handle/123456789/4226
Zusammenfassung englisch
Developmental toxicity refers to the occurrence of adverse effects on a developing organism as a consequence of exposure to hazardous chemicals. The assessment of developmental toxicity has become relevant to the safety assessment process of chemicals. The zebrafish embryo developmental toxicology assay is an emerging test used to screen the teratogenic potential of chemicals and it is proposed as a promising test to replace teratogenic assays with animals. Supported by the increased availability of data from this test, the developmental toxicity assay with zebrafish has become an interesting endpoint for the in silico modelling. The purpose of this study was to build up quantitative structure-activity relationship (QSAR) models. In this work, new in silico models for the evaluation of developmental toxicity were built using a well-defined set of data from the ToxCastTM Phase I chemical library on the zebrafish embryo. Categorical and continuous QSAR models were built by gradient boosting machine learning and the Monte Carlo technique respectively, in accordance with Organization for Economic Co-operation and Development principles and their statistical quality was satisfactory. The classification model reached balanced accuracy 0.89 and Matthews correlation coefficient 0.77 on the test set. The regression model reached correlation coefficient R2 0.70 in external validation and leave-one-out cross-validated Q2 0.73 in internal validation. © 2020 Elsevier Inc.