Prediction of the Partition Coefficient between Adipose Tissue and Blood for Environmental Chemicals: From Single QSAR Models to an Integrated Approach.

Autor: Cappelli CI; Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France.; Currently at S-IN Soluzioni Informatiche S.r.l., Vicenza, Italy., Manganelli S; Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France.; Currently at Chemical Food Safety Group, Nestlé Research, Lausanne, Switzerland., Toma C; Laboratory of Environmental Chemistry and Toxicology, Department Environmental Health Sciences, IRCCS - Istituto di Ricerche Farmacologiche Mario, Negri, Milan, Italy., Benfenati E; Laboratory of Environmental Chemistry and Toxicology, Department Environmental Health Sciences, IRCCS - Istituto di Ricerche Farmacologiche Mario, Negri, Milan, Italy., Mombelli E; Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France.
Jazyk: angličtina
Zdroj: Molecular informatics [Mol Inform] 2021 Mar; Vol. 40 (3), pp. e2000072. Date of Electronic Publication: 2020 Nov 02.
DOI: 10.1002/minf.202000072
Abstrakt: The adipose tissue:blood partition coefficient is a key-endpoint to predict the pharmacokinetics of chemicals in humans and animals, since other organ:blood affinities can be estimated as a function of this parameter. We performed a search in the literature to select all the available rat in vivo data. This approach resulted into two improvements to existing models: a homogeneous definition of the endpoint and an expanded data collection. The resulting dataset was used to develop QSAR models as a function of linear and non-linear algorithms. Several applicability domain definitions were assessed and the definition corresponding to a good balance between performance and coverage was retained. We assessed the pertinence of combining single models into integrated approaches to increase the accuracy in predictions. The best integrated model outperformed the single models and it was characterized by an external mean absolute error (MAE) equal to 0.26, while preserving an adequate coverage (84 %). This performance is comparable to experimental variability and it highlights the pertinence of the integrated model.
(© 2020 Wiley-VCH GmbH.)
Databáze: MEDLINE
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