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Comparison of a simple and a complex model for BCF prediction using in vitro biotransformation data.

Authors
  • Krause, Sophia1
  • Goss, Kai-Uwe2
  • 1 Helmholtz Centre for Environmental Research, Department of Analytical Environmental Chemistry, Permoserstr. 15, 04318, Leipzig, Germany. Electronic address: [email protected] , (Germany)
  • 2 Helmholtz Centre for Environmental Research, Department of Analytical Environmental Chemistry, Permoserstr. 15, 04318, Leipzig, Germany; University of Halle-Wittenberg, Institute of Chemistry, Kurt-Mothes-Str. 2, 06120, Halle, Germany. , (Germany)
Type
Published Article
Journal
Chemosphere
Publication Date
Oct 01, 2020
Volume
256
Pages
127048–127048
Identifiers
DOI: 10.1016/j.chemosphere.2020.127048
PMID: 32446001
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

A promising approach for bioaccumulation assessment with reduced animal use is the prediction of bioconcentration factors (BCFs) using in vitro biotransformation data. However, it has been recognized that the BCFs predicted using current models often are in poor agreement with experimental BCFs. Furthermore, extrahepatic biotransformation (e.g. in gill or GIT) is usually not accounted for. Here, we compare two BCF prediction models: a simple one-compartment and a more advanced multi-compartment model. Both models are implemented in a two-in-one calculation tool for the prediction of BCFs using in vitro data. Furthermore, both models were set up in a way that in vitro data for extrahepatic biotransformation can be easily considered, if desired. The models differ in their complexity: the one-compartment model is attractive because its simplicity, while the multi-compartment model is characterized by its refined closeness to reality. A comparison of the results shows that both models yield almost identical results for the presently evaluated cases with plausible physiological data. For regulatory purposes, there is thus no reason not to use the simple one-compartment model. However, if it is desired to represent special in vivo characteristics, e.g. first-pass effects or the direct GIT-to-liver blood flow, the multi-compartment model should be used. Copyright © 2020 Elsevier Ltd. All rights reserved.

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