A database of fish biotransformation rates for organic chemicals

Arnot, Jon A.; Mackay, Don; Parkerton, Thomas F.; Bonnell, Mark

doi:10.1897/08-058.1

Cited by 118 publications

(134 citation statements)

References 34 publications

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“…Arnot and colleagues compiled a database [55] of wholebody biotransformation rate constants (k M , 1/d) for organic chemicals in fish and subsequently developed a screening level quantitative structure-activity relationship (QSAR) model to predict k M from chemical structure [56]. In that QSAR, K OW is one of the parameters included in the prediction of k M .…”

Section: Biotransformationmentioning

confidence: 99%

“…Although deriving regression-based equations to describe the relationship between log BCF and hydrophobicity can be successful, it is also apparent that a substantial portion of the variability in empirical BCFs cannot be explained without introducing additional parameters, most importantly to represent susceptibility to biotransformation [55,56]. For example, empirical BCFs for acids with log K OW,N ¼ 6 span approximately three orders of magnitude.…”

Section: Relationship Between Empirical Bcf and Hydrophobicitymentioning

confidence: 99%

“…6.0), no empirical k M data are available for these compounds so estimated values were used in all cases. Carboxylic acids are not well represented in the training and validation sets for the k M -QSAR [55,56]; therefore, these k M predictions may have substantial errors.…”

Section: Evaluation and Comparison Of Model Performance: Model Test Setmentioning

confidence: 99%

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Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish

Armitage

Arnot

Wania

et al. 2012

Enviro Toxic and Chemistry

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155

241

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Abstract-A mechanistic mass balance bioconcentration model is developed and parameterized for ionogenic organic chemicals (IOCs) in fish and evaluated against a compilation of empirical bioconcentration factors (BCFs). The model is subsequently applied to a set of perfluoroalkyl acids. Key aspects of model development include revised methods to estimate the chemical absorption efficiency of IOCs at the respiratory surface (E W ) and the use of distribution ratios to characterize the overall sorption capacity of the organism. Membranewater distribution ratios (D MW ) are used to characterize sorption to phospholipids instead of only considering the octanol-water distribution ratio (D OW ). Modeled BCFs are well correlated with the observations (e.g., r 2 ¼ 0.68 and 0.75 for organic acids and bases, respectively) and accurate to within a factor of three on average. Model prediction errors appear to be largely the result of uncertainties in the biotransformation rate constant (k M ) estimates and the generic approaches for estimating sorption capacity (e.g., D MW ). Model performance for the set of perfluoroalkyl acids considered is highly dependent on the input parameters describing hydrophobicity (i.e., log K OW of the neutral form). The model applications broadly support the hypothesis that phospholipids contribute substantially to the sorption capacity of fish, particularly for compounds that exhibit a high degree of ionization at biologically relevant pH. Additional empirical data on biotransformation and sorption to phospholipids and subsequent incorporation into property estimation approaches (e.g., k M , D MW ) are priorities with respect to improving model performance. Environ. Toxicol. Chem. 2013;32:115-128. # 2012 SETAC

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Section: Biotransformationmentioning

confidence: 99%

Section: Relationship Between Empirical Bcf and Hydrophobicitymentioning

confidence: 99%

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Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish

Armitage

Arnot

Wania

et al. 2012

Enviro Toxic and Chemistry

Self Cite

155

241

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“…Nonetheless, as molecular and biochemical methods have advanced, there is growing evidence of both Phase I and II enzyme activity in fish [21,27,28] and recent studies have addressed how dietary and trophic variables may affect enzyme activity in fish [29]. There are also a growing number of studies on the metabolism of pharmaceuticals in fish [30][31][32][33][34][35][36][37][38][39][40] and to a far lesser extent invertebrates [41]. Veterinary pharmaceuticals have also been studied from a comparative metabolism perspective [42,43].…”

Section: Introductionmentioning

confidence: 99%

Comparative metabolism as a key driver of wildlife species sensitivity to human and veterinary pharmaceuticals

Hutchinson

Madden

Naidoo

et al. 2014

Phil. Trans. R. Soc. B

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Human and veterinary drug development addresses absorption, distribution, metabolism, elimination and toxicology (ADMET) of the Active Pharmaceutical Ingredient (API) in the target species. Metabolism is an important factor in controlling circulating plasma and target tissue API concentrations and in generating metabolites which are more easily eliminated in bile, faeces and urine. The essential purpose of xenobiotic metabolism is to convert lipid-soluble, non-polar and non-excretable chemicals into water soluble, polar molecules that are readily excreted. Xenobiotic metabolism is classified into Phase I enzymatic reactions (which add or expose reactive functional groups on xenobiotic molecules), Phase II reactions (resulting in xenobiotic conjugation with large water-soluble, polar molecules) and Phase III cellular efflux transport processes. The human–fish plasma model provides a useful approach to understanding the pharmacokinetics of APIs (e.g. diclofenac, ibuprofen and propranolol) in freshwater fish, where gill and liver metabolism of APIs have been shown to be of importance. By contrast, wildlife species with low metabolic competency may exhibit zero-order metabolic (pharmacokinetic) profiles and thus high API toxicity, as in the case of diclofenac and the dramatic decline of vulture populations across the Indian subcontinent. A similar threat looms for African Cape Griffon vultures exposed to ketoprofen and meloxicam, recent studies indicating toxicity relates to zero-order metabolism (suggesting P450 Phase I enzyme system or Phase II glucuronidation deficiencies). While all aspects of ADMET are important in toxicity evaluations, these observations demonstrate the importance of methods for predicting API comparative metabolism as a central part of environmental risk assessment.

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“…In the NRC vision, PBPK modeling would provide dosimetry extrapolations to correlate in vitro exposure conditions with tissue exposures expected in exposed human populations. PBPK model development would be based on estimates of partitioning, metabolism, and interactions among chemicals derived from in vitro measurements or from structure-activity relationships (SAR) for these key parameters (7,8,10 …”

Section: Physiologically Based Pharmacokinetic Modelingmentioning

confidence: 99%

New Directions in Toxicity Testing

Krewski

Westphal

Al-Zoughool

et al. 2011

Annu. Rev. Public Health

100

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Keywordstoxicity pathways, pathway perturbations, high-throughput in vitro screens, computational toxicology, regulatory risk assessment AbstractIn 2007, the U.S. National Research Council (NRC) published a groundbreaking report entitled Toxicity Testing in the 21st Century: A Vision and a Strategy. The purpose of this report was to develop a longrange strategic plan to update and advance the way environmental agents are tested for toxicity. The vision focused on the identification of critical perturbations of toxicity pathways that lead to adverse human health outcomes using modern scientific tools and technologies. This review describes how emerging scientific methods will move the NRC vision forward and improve the manner in which the potential health risks associated with exposure to environmental agents are assessed. The new paradigm for toxicity testing is compatible with the widely used fourstage risk assessment framework originally proposed by the NRC in 1983 in the so-called Red Book. The Nrf2 antioxidant pathway provides a detailed example of how relevant pathway perturbations will be identified within the context of the new NRC vision for the future of toxicity testing. The implications of the NRC vision for toxicity testing for regulatory risk assessment are also discussed. 161

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A database of fish biotransformation rates for organic chemicals

Cited by 118 publications

References 34 publications

Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish

Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish

Comparative metabolism as a key driver of wildlife species sensitivity to human and veterinary pharmaceuticals

New Directions in Toxicity Testing

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