In Vitro–In Vivo Extrapolation of Hepatic Biotransformation Data for Fish. III. An In-depth Case Study with Pyrene

Abstract

Computational models that predict chemical bioaccumulation in fish generally account for biotransformation using an apparent first‐order whole‐body rate constant (kB; d–1). The use of such models requires, therefore, that methods exist for estimating kB, ideally without the need to expose live animals. One promising approach for estimating kB involves the extrapolation of measured in vitro intrinsic clearance (CLIN VITRO,INT) to the whole animal (in vitro–in vivo extrapolation, [IVIVE]). To date, however, the accuracy of such predictions has been difficult to assess due to uncertainties associated with one or more extrapolation factors and/or a mismatch between fish used to generate in vitro data and those used to conduct in vivo exposures. In the present study we employed a combined in vitro and in vivo experimental approach to evaluate the IVIVE procedure using pyrene (PYR) as a model chemical. To the extent possible, measured rates of CLIN VITRO,INT were extrapolated to estimates of kB using extrapolation factors based on measured values. In vitro material (liver S9 fraction) was obtained from fish exposed to PYR in a controlled bioconcentration study protocol. Fish from the same study were then used to estimate in vivo kB values from an analysis of chemical depuration data. Averaged across four study groups, kB values estimated by IVIVE underestimated those determined from in vivo data by 2.6‐fold. This difference corresponds to a 4.1‐fold underestimation of true in vivo intrinsic clearance, assuming the liver is the only site of biotransformation. These findings are consistent with previous work performed using mammals and have important implications for use of measured CLIN VITRO,INT values in bioaccumulation assessments with fish. Environ Toxicol Chem 2023;42:1501–1515. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

A Comparison of In Vitro Metabolic Clearance of Various Regulatory Fish Species Using Hepatic S9 Fractions

“…The goal of the present study was to perform fish interspecies comparisons and elucidate the between-species variability. This approach would help to decrease the uncertainty in predicting the bioaccumulative potential of different chemicals in risk assessment because current predictions suffer from uncertainties related to mismatches in studied species (Nichols et al, 2023). In the present study, the BCF was only assessed using Nichols et al's (2013) IVIVE model (BAT) as the available trout model because there are no IVIVE models available for the other species.…”

“…The in silico approach to organism reduction uses computational modeling programs, such as EPI Suite, which uses the bioconcentration factor (BCF)/bioaccumulation factor (BAF) quantitative structure-activity relationships (QSAR) model to predict physiochemical properties of novel chemicals, and ECOSAR toxicity prediction software, which utilizes a suite of QSAR models. These models rely primarily on physiochemical properties such as structures and octanol-water partitioning coefficients (K OW ), while leveraging multiple experimentally derived databases Arnot et al, 2009;Krause & Goss, 2020;Nichols et al, 2023) to predict bioaccumulation potential.…”

“…These estimates of bioaccumulation potential are significantly improved by the incorporation of metabolism in fish, as demonstrated by the Bioaccumulation Assessment Tool (BAT) and Bioaccumulation Estimation Tool (BET) models Kramer et al, 2009;Nichols et al, 2023).…”

“…The in silico approach to organism reduction uses computational modeling programs, such as EPI Suite, which uses the bioconcentration factor (BCF)/bioaccumulation factor (BAF) quantitative structure–activity relationships (QSAR) model to predict physiochemical properties of novel chemicals, and ECOSAR toxicity prediction software, which utilizes a suite of QSAR models. These models rely primarily on physiochemical properties such as structures and octanol–water partitioning coefficients ( K OW ), while leveraging multiple experimentally derived databases (Arnot, Mackay, & Bonnell, 2008; Arnot, Mackay, et al, 2008; Arnot et al, 2009; Krause & Goss, 2020; Nichols et al, 2023) to predict bioaccumulation potential. These estimates of bioaccumulation potential are significantly improved by the incorporation of metabolism in fish, as demonstrated by the Bioaccumulation Assessment Tool (BAT) and Bioaccumulation Estimation Tool (BET) models (Arnot, Mackay, & Bonnell, 2008; Kramer et al, 2009; Nichols et al, 2023).…”

“…These models rely primarily on physiochemical properties such as structures and octanol–water partitioning coefficients ( K OW ), while leveraging multiple experimentally derived databases (Arnot, Mackay, & Bonnell, 2008; Arnot, Mackay, et al, 2008; Arnot et al, 2009; Krause & Goss, 2020; Nichols et al, 2023) to predict bioaccumulation potential. These estimates of bioaccumulation potential are significantly improved by the incorporation of metabolism in fish, as demonstrated by the Bioaccumulation Assessment Tool (BAT) and Bioaccumulation Estimation Tool (BET) models (Arnot, Mackay, & Bonnell, 2008; Kramer et al, 2009; Nichols et al, 2023).…”