In vitro‐in vivo extrapolation of quantitative hepatic biotransformation data for fish. II. Modeled effects on chemical bioaccumulation

Nichols, John W.; Fitzsimmons, Patrick N.; Burkhard, Lawrence P.

doi:10.1897/06-259r.1

Cited by 73 publications

(77 citation statements)

References 69 publications

(197 reference statements)

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“…PBTK model development in ecotoxicology has thus far largely been focused on fish, e.g. (Nichols et al, 1990;Nichols and Fitzsimmons, 2004;Murphy et al, 2005Murphy et al, , 2009Watanabe et al, 2009;Li et al, 2011a;Stadnicka et al, 2012), with incorporation of biotransformation being an area of active research (Nichols et al, 2006(Nichols et al, , 2007(Nichols et al, , 2013Fay et al, 2014). The same concepts can certainly be applied to other organisms, such as invertebrates, e.g.…”

Section: Integration With Computational Modelsmentioning

confidence: 99%

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

et al. 2015

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To elucidate the effects of chemicals on populations of different species in the environment, efficient testing and modeling approaches are needed that consider multiple stressors and allow reliable extrapolation of responses across species. An adverse outcome pathway (AOP) is a concept that provides a framework for organizing knowledge about the progression of toxicity events across scales of biological organization that lead to adverse outcomes relevant for risk assessment. In this paper, we focus on exploring how the AOP concept can be used to guide research aimed at improving both our understanding of chronic toxicity, including delayed toxicity as well as epigenetic and transgenerational effects of chemicals, and our ability to predict adverse outcomes. A better understanding of the influence of subtle toxicity on individual and population fitness would support a broader integration of sublethal endpoints into risk assessment frameworks. Detailed mechanistic knowledge would facilitate the development of alternative testing methods as well as help prioritize higher tier toxicity testing. We argue that targeted development of AOPs supports both of these aspects by promoting the elucidation of molecular mechanisms and their contribution to relevant toxicity outcomes across biological scales. We further discuss information requirements and challenges in application of AOPs for chemical- and site-specific risk assessment and for extrapolation across species. We provide recommendations for potential extension of the AOP framework to incorporate information on exposure, toxicokinetics and situation-specific ecological contexts, and discuss common interfaces that can be employed to couple AOPs with computational modeling approaches and with evolutionary life history theory. The extended AOP framework can serve as a venue for integration of knowledge derived from various sources, including empirical data as well as molecular, quantitative and evolutionary-based models describing species responses to toxicants. This will allow a more efficient application of AOP knowledge for quantitative chemical- and site-specific risk assessment as well as for extrapolation across species in the future.

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Section: Integration With Computational Modelsmentioning

confidence: 99%

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

et al. 2015

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“…However, different dietary items may have different digestion characteristics. Fifth, the rate constants for growth dilution and biotransformation can play a significant role by decreasing BCFs, BAFs, BMFs, and TMFs [31,32]. These rates are most important for hydrophobic chemicals for which k R /(L C Â K OW ) and hence k V , the respiration loss rate constant, is small.…”

Section: Discussionmentioning

confidence: 99%

Mathematical relationships between metrics of chemical bioaccumulation in fish

Mackay

Arnot

Gobas

et al. 2013

Enviro Toxic and Chemistry

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Five widely used metrics of bioaccumulation in fish are defined and discussed, namely the octanol-water partition coefficient (K OW ), bioconcentration factor (BCF), bioaccumulation factor (BAF), biomagnification factor (BMF), and trophic magnification factor (TMF). Algebraic relationships between these metrics are developed and discussed using conventional expressions for chemical uptake from water and food and first-order losses by respiration, egestion, biotransformation, and growth dilution. Two BCFs may be defined, namely as an equilibrium partition coefficient K FW or as a nonequilibrium BCF K in which egestion losses are included. Bioaccumulation factors are shown to be the product of the BCF K and a novel equilibrium multiplier M containing 2 ratios, namely, the diet-to-water concentration ratio and the ratio of uptake rate constants for respiration and dietary uptake. Biomagnification factors are shown to be proportional to the lipid-normalized ratio of the predator/prey values of BCF K and the ratio of the equilibrium multipliers. Relationships with TMFs are also discussed. The effects of chemical hydrophobicity, biotransformation, and growth are evaluated by applying the relationships to a range of illustrative chemicals of varying K OW in a linear 4-trophic-level food web with typical values for uptake and loss rate constants. The roles of respiratory and dietary intakes are demonstrated, and even slow rates of biotransformation and growth can significantly affect bioaccumulation. The BCF K s and the values of M can be regarded as the fundamental determinants of bioaccumulation and biomagnification in aquatic food webs. Analyzing data from food webs can be enhanced by plotting logarithmic lipid-normalized concentrations or fugacities as a linear function of trophic level to deduce TMFs. Implications for determining bioaccumulation by laboratory tests for regulatory purposes are discussed.

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“…The ventilation rate is limited by the metabolism of enzymes in the liver and this limitation can be described by a Michaelis-Menten equation [44]: where VR (L/g/d) is the ventilation rate used as input to the model; Q Liver (99.14· 10 −3 L/g/d) is the liver blood flow, calculated from the arterial blood flow to liver compartment determined by Nichols et al [44]; and VR max (L/g/d) is the maximal ventilation rate derived from previous studies as described below.…”

Section: Methodsmentioning

confidence: 99%

Development and Validation of a Biodynamic Model for Mechanistically Predicting Metal Accumulation in Fish-Parasite Systems

Nachev

Grabner

et al. 2016

PLoS ONE

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Because of different reported effects of parasitism on the accumulation of metals in fish, it is important to consider parasites while interpreting bioaccumulation data from biomonitoring programmes. Accordingly, the first step is to take parasitism into consideration when simulating metal bioaccumulation in the fish host under laboratory conditions. In the present study, the accumulation of metals in fish-parasite systems was simulated by a one-compartment toxicokinetic model and compared to uninfected conspecifics. As such, metal accumulation in fish was assumed to result from a balance of different uptake and loss processes depending on the infection status. The uptake by parasites was considered an efflux from the fish host, similar to elimination. Physiological rate constants for the uninfected fish were parameterised based on the covalent index and the species weight while the parameterisation for the infected fish was carried out based on the reported effects of parasites on the uptake kinetics of the fish host. The model was then validated for the system of the chub Squalius cephalus and the acanthocephalan Pomphorhynchus tereticollis following 36-day exposure to waterborne Pb. The dissolved concentration of Pb in the exposure tank water fluctuated during the exposure, ranging from 40 to 120 μg/L. Generally, the present study shows that the one-compartment model can be an effective method for simulating the accumulation of metals in fish, taking into account effects of parasitism. In particular, the predicted concentrations of Cu, Fe, Zn, and Pb in the uninfected chub as well as in the infected chub and the acanthocephalans were within one order of magnitude of the measurements. The variation in the absorption efficiency and the elimination rate constant of the uninfected chub resulted in variations of about one order of magnitude in the predicted concentrations of Pb. Inclusion of further assumptions for simulating metal accumulation in the infected chub led to variations of around two orders of magnitude in the predictions. Therefore, further research is required to reduce uncertainty while characterising and parameterising the model for infected fish.

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In vitro‐in vivo extrapolation of quantitative hepatic biotransformation data for fish. II. Modeled effects on chemical bioaccumulation

Cited by 73 publications

References 69 publications

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

Mathematical relationships between metrics of chemical bioaccumulation in fish

Development and Validation of a Biodynamic Model for Mechanistically Predicting Metal Accumulation in Fish-Parasite Systems

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