Estimating Metabolic Biotransformation Rates in Fish from Laboratory Data

Arnot, Jon A.; Mackay, Donald; Bonnell, Mark R.

doi:10.1897/07-310

Cited by 51 publications

(114 citation statements)

References 30 publications

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“…The underestimation of observed total chemical elimination rate constants is generally explained by metabolic transformation in the organism [16,42]. Hendriks et al [16] report possible metabolic transformation for polycyclic heteroaromatic hydrocarbons, phosphorbiocides, phenols, nitrogenbiocides, halobiphenyls, halobiocides, haloaliphatic hydrocarbons, chlorodibenzo-p-furans, chlorodibenzo-p-dioxins, and alcohol ethoxylates.…”

Section: Parameterizationmentioning

confidence: 99%

“…The uncertainty ranges for nonpersistent substances could therefore be different than assessed in the present study without metabolic transformation. We investigated this effect by including metabolic transformation rates for benzo [a]anthracene in fish and invertebrates as reported by Moermond et al [47], taking uncertainty factors from Arnot et al [42] for the case of the measured total elimination rate constants (Supplemental Data, Table S14). The uncertainty in estimated BAFs decreases with a factor of 5 when the uncertainty in metabolic transformation is included, whereas the estimated BAFs decrease by about a factor of 185.…”

Section: Uncertainty Rangesmentioning

confidence: 99%

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Parameter uncertainty in modeling bioaccumulation factors of fish

Hauck

Hendriks

Huijbregts

et al. 2011

Enviro Toxic and Chemistry

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We quantified the uncertainty due to biota-related parameters in estimated bioaccumulation factors (BAFs) of persistent organic pollutants for fish through Monte Carlo simulations. For this purpose, the bioaccumulation model OMEGA (Optimal Modeling for EcotoxicoloGical Applications) was parameterized based on data from the existing literature, analysis of allometric data, and maximum likelihood estimation. Lipid contents, fractions of food assimilated, the allometric rate exponent, normalized food intakes, respiration and growth dilution rates, and partial mass transfer resistances in water and lipid layers were included as uncertain parameters. The uncertainty in partial resistances was particularly important in the estimation of the rate constants for chemical intake from water by fish. Uncertainties in the fractions of food assimilated and partial water layer resistances from and to food were particularly important in the estimation of the rate constants of chemical intake from food. The uncertainty in the model outcomes for the bioaccumulation factors for fish was a factor of 10 (ratio of 95th and fifth percentile estimates), which was mainly caused by the uncertainty in the lipid fraction. For chemicals with a K(OW) of 10(3) to 10(6), the uncertainty in the lipid contents of fish accounted for more than 50% of the uncertainty in the estimated bioaccumulation factor. For chemicals with a high K(OW) (10(7) and higher), the fractions of food assimilated and partial resistances also contributed to uncertainty in the estimated bioaccumulation factor (up to 60%). A case study showed that uncertainty in estimated BAF for nonpersistent substances can be dominated by uncertainty in the rate constants for metabolic transformation.

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Section: Parameterizationmentioning

confidence: 99%

Section: Uncertainty Rangesmentioning

confidence: 99%

Parameter uncertainty in modeling bioaccumulation factors of fish

Hauck

Hendriks

Huijbregts

et al. 2011

Enviro Toxic and Chemistry

View full text Add to dashboard Cite

show abstract

“…It is important to obtain and include reliable estimates for biotransformation half-lives for assessments of exposure, hazard, and risk. The availability of biotransformation data is limited, but research in this area is advancing (25). Notably, DSL(I) and DSL(O) categorization decisions based on existing methods and criteria generally assumed negligible rates of biotransformation, and thus the holistic methods are consistent with existing methods for comparisons in this analysis.…”

Section: Raidar Assessment Factors For Dsl Chemicals and Popsmentioning

confidence: 99%

Policies for Chemical Hazard and Risk Priority Setting: Can Persistence, Bioaccumulation, Toxicity, and Quantity Information Be Combined?

Arnot

Mackay

2008

Environ. Sci. Technol.

138

132

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Existing methods used to screen chemical inventories for hazardous substances that may pose risks to humans and the environment are evaluated with a holistic mass balance modeling approach. The model integrates persistence (P), bioaccumulation (B), toxicity (T), and quantity (Q) information for a specific substance to assess chemical exposure, hazard, and risk. P and B are combined in an exposure assessment factor (EAF), P, B, and T in a hazard assessment factor (HAF), and P, B, T, and Q in a risk assessment factor (RAF) providing single values for transparent comparisons of exposure, hazard, and risk for priority setting. This holistic approach is illustrated using 200 Canadian Domestic Substances List (DSL) chemicals and 12 United Nations listed Persistent Organic Pollutants (POPs). Priority setting results are evaluated with those of multiple category-based screening methods employed by Environment Canada and applied elsewhere that use cutoff criteria in multiple categories (P, B, and T) to identify hazardous chemicals for more comprehensive evaluations. Existing methods have categorized the DSL chemicals as either higher priority (requiring further assessment; screened in) or lower priority (requiring no further action at this time; screened out). The priority setting results of the cutoff-based categorization are largely inconsistent with the proposed integrated method, and reasons for these discrepancies are discussed. Many chemicals screened out using existing methods have equivalent or greater risk potential than chemicals screened in. Decisions for screening assessments using binary classification on the basis of cutoff criteria can be flawed, and complementary holistic methods for priority setting evaluations such as the one proposed should be considered.

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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%

“…If fish are fed a maintenance diet of known chemical concentration in a defined food matrix, one may estimate k D, the dietary assimilation efficiency, and k T . These test data are also particularly valuable for obtaining estimates of k M [31]. A BAF and BMF can thus be estimated from experimental data.…”

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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Estimating Metabolic Biotransformation Rates in Fish from Laboratory Data

Cited by 51 publications

References 30 publications

Parameter uncertainty in modeling bioaccumulation factors of fish

Parameter uncertainty in modeling bioaccumulation factors of fish

Policies for Chemical Hazard and Risk Priority Setting: Can Persistence, Bioaccumulation, Toxicity, and Quantity Information Be Combined?

Mathematical relationships between metrics of chemical bioaccumulation in fish

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