Jon A. Arnot

A food web bioaccumulation model for organic chemicals in aquatic ecosystems

The present study examines a new bioaccumulation model for hydrophobic organic chemicals in aquatic food webs. The purpose of the model is to provide site-specific estimates of chemical concentrations and associated bioconcentration factors, bioaccumulation factors, and biota-sediment accumulation factors in organisms of aquatic food webs using a limited number of chemical, organism, and site-specific data inputs. The model is a modification of a previous model and incorporates new insights regarding the mechanism of bioaccumulation derived from laboratory experiments and field studies as well as improvements in model parameterization. The new elements of the model include: A model for the partitioning of chemicals into organisms; kinetic models for predicting chemical concentrations in algae, phytoplankton, and zooplankton; new allometric relationships for predicting gill ventilation rates in a wide range of aquatic species; and a mechanistic model for predicting gastrointestinal magnification of organic chemicals in a range of species. Model performance is evaluated using empirical data from three different freshwater ecosystems involving 1,019 observations for 35 species and 64 chemicals. The effects of each modification on the models performance are illustrated. The new model is able to provide better estimates of bioaccumulation factors in comparison to the previous food web bioaccumulation model while the model input requirements remain largely unchanged.

Hexabromocyclododecane: Current Understanding of Chemistry, Environmental Fate and Toxicology and Implications for Global Management

Hexabromocyclododecane (HBCD) is a globally produced brominated flame retardant (BFR) used primarily as an additive FR in polystyrene and textile products and has been the subject of intensified research, monitoring and regulatory interest over the past decade. HBCD is currently being evaluated under the Stockholm Convention on Persistent Organic Pollutants. HBCD is hydrophobic (i.e., has low water solubility) and thus partitions to organic phases in the aquatic environment (e.g., lipids, suspended solids). It is ubiquitous in the global environment with monitoring data generally exhibiting the expected relationship between proximity to known sources and levels; however, temporal trends are not consistent. Estimated degradation half-lives, together with data in abiotic compartments and long-range transport potential indicate HBCD may be sufficiently persistent and distributed to be of global concern. The detection of HBCD in biota in the Arctic and in source regions and available bioaccumulation data also support the case for regulatory scrutiny. Toxicity testing has detected reproductive, developmental and behavioral effects in animals where exposures are sufficient. Recent toxicological advances include a better mechanistic understanding of how HBCD can interfere with the hypothalamic-pituitary-thyroid axis, affect normal development, and impact the central nervous system; however, levels in biota in remote locations are below known effects thresholds. For many regulatory criteria, there are substantial uncertainties that reduce confidence in evaluations and thereby confound management decision-making based on currently available information.

High-throughput models for exposure-based chemical prioritization in the ExpoCast project.

The United States Environmental Protection Agency (U.S. EPA) must characterize potential risks to human health and the environment associated with manufacture and use of thousands of chemicals. High-throughput screening (HTS) for biological activity allows the ToxCast research program to prioritize chemical inventories for potential hazard. Similar capabilities for estimating exposure potential would support rapid risk-based prioritization for chemicals with limited information; here, we propose a framework for high-throughput exposure assessment. To demonstrate application, an analysis was conducted that predicts human exposure potential for chemicals and estimates uncertainty in these predictions by comparison to biomonitoring data. We evaluated 1936 chemicals using far-field mass balance human exposure models (USEtox and RAIDAR) and an indicator for indoor and/or consumer use. These predictions were compared to exposures inferred by Bayesian analysis from urine concentrations for 82 chemicals reported in the National Health and Nutrition Examination Survey (NHANES). Joint regression on all factors provided a calibrated consensus prediction, the variance of which serves as an empirical determination of uncertainty for prioritization on absolute exposure potential. Information on use was found to be most predictive; generally, chemicals above the limit of detection in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure HTS can place risk earlier in decision processes. High-priority chemicals become targets for further data collection.

A quantitative structure-activity relationship for predicting metabolic biotransformation rates for organic chemicals in fish.

An evaluated database of whole body in vivo biotransformation rate estimates in fish was used to develop a model for predicting the primary biotransformation half-lives of organic chemicals. The estimated biotransformation rates were converted to half-lives and divided into a model development set (n=421) and an external validation set (n=211) to test the model. The model uses molecular substructures similar to those of other biodegradation models. The biotransformation half-life predictions were calculated based on multiple linear regressions of development set data against counts of 57 molecular substructures, the octanol-water partition coefficient, and molar mass. The coefficient of determination (r2) for the development set was 0.82, the cross-validation (leave-one-out coefficient of determination, q2) was 0.75, and the mean absolute error (MAE) was 0.38 log units (factor of 2.4). Results for the external validation of the model using an independent test set were r2 = 0.73 and MAE = 0.45 log units (factor of 2.8). For the development set, 68 and 95% of the predicted values were within a factor of 3 and a factor of 10 of the expected values, respectively. For the test (or validation) set, 63 and 90% of the predicted values were within a factor of 3 and a factor of 10 of the expected values, respectively. Reasons for discrepancies between model predictions and expected values are discussed and recommendations are made for improving the model. This model can predict biotransformation rate constants from chemical structure for screening level bioaccumulation hazard assessments, exposure and risk assessments, comparisons with other in vivo and in vitro estimates, and as a contribution to testing strategies that reduce animal usage.

Estimating metabolic biotransformation rates in fish from laboratory data

A method is proposed for estimating metabolic biotransformation rate constants for nonionic organic chemicals from measured laboratory bioconcentration and dietary bioaccumulation data in fish. Data have been selected based on a quality review to reduce uncertainty in the measured values. A kinetic mass balance model is used to estimate rates of chemical uptake and elimination. Biotransformation rate constants are essentially calculated as the difference between two quantities, a measured bioconcentration factor or elimination rate constant, and a model-derived bioconcentration factor or elimination rate constant estimated assuming no biotransformation. Model parameterization exploits key empirical data when they are available and assumes default values when study specific data are unavailable. Uncertainty analyses provide screening level assessments for confidence in the biotransformation rate constant estimates. The uncertainty analyses include the range for 95% of the predicted values and 95% confidence intervals for the calculated biotransformation values. Case studies are provided to illustrate the calculation and uncertainty methods. Biotransformation rate constants calculated by the proposed method are compared with other published estimates for 31 chemicals that range in octanol-water partition coefficients from approximately 10(1) to 10(8) and represent over four orders of magnitude in biotransformation potential. The comparison of previously published values with those calculated by the proposed method shows general agreement with 82% of the estimated values falling within a factor of three.

A database of fish biotransformation rates for organic chemicals

Biotransformation is a key process that can mitigate the bioaccumulation potential of organic substances and is an important parameter for exposure assessments. A recently published method for estimating whole-body in vivo metabolic biotransformation rate constants (kM) is applied to a database of measured laboratory bioconcentration factors and total elimination rate constants for fish. The method uses a kinetic mass balance model to estimate rates of chemical uptake and elimination when measured values are not reported. More than 5400 measurements for more than 1000 organic chemicals were critically reviewed to compile a database of 1535 kM estimates for 702 organic chemicals. Biotransformation rates range over six orders of magnitude across a diverse domain of chemical classes and structures. Screening-level uncertainty analyses provide guidance for the selection and interpretation of kM values. In general, variation in kM estimates from different routes of exposure (water vs diet) and between fish species is approximately equal to the calculation uncertainty in kM values. Examples are presented of structure-biotransformation relationships. Biotransformation rate estimates in the database are compared with estimates of biodegradation rates from existing quantitative structure-activity relationship models. Modest correlations are found, suggesting some consistency in biotransformation capabilities between fish and microorganisms. Additional analyses to further explore possible quantitative structure-biotransformation relationships for estimating kM from chemical structure are encouraged, and recommendations for improving the database are provided.

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

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. Membrane-water 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)  = 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.

Workgroup report: review of fish bioaccumulation databases used to identify persistent, bioaccumulative, toxic substances.

Chemical management programs strive to protect human health and the environment by accurately identifying persistent, bioaccumulative, toxic substances and restricting their use in commerce. The advance of these programs is challenged by the reality that few empirical data are available for the tens of thousands of commercial substances that require evaluation. Therefore, most preliminary assessments rely on model predictions and data extrapolation. In November 2005, a workshop was held for experts from governments, industry, and academia to examine the availability and quality of in vivo fish bioconcentration and bioaccumulation data, and to propose steps to improve its prediction. The workshop focused on fish data because regulatory assessments predominantly focus on the bioconcentration of substances from water into fish, as measured using in vivo tests or predicted using computer models. In this article we review of the quantity, features, and public availability of bioconcentration, bioaccumulation, and biota–sediment accumulation data. The workshop revealed that there is significant overlap in the data contained within the various fish bioaccumulation data sources reviewed, and further, that no database contained all of the available fish bioaccumulation data. We believe that a majority of the available bioaccumulation data have been used in the development and testing of quantitative structure–activity relationships and computer models currently in use. Workshop recommendations included the publication of guidance on bioconcentration study quality, the combination of data from various sources to permit better access for modelers and assessors, and the review of chemical domains of existing models to identify areas for expansion.

Toward improved models for predicting bioconcentration of well‐metabolized compounds by rainbow trout using measured rates of in vitro intrinsic clearance

Models were developed to predict the bioconcentration of well-metabolized chemicals by rainbow trout. The models employ intrinsic clearance data from in vitro studies with liver S9 fractions or isolated hepatocytes to estimate a liver clearance rate, which is extrapolated to a whole-body biotransformation rate constant (kMET ). Estimated kMET values are then used as inputs to a mass-balance bioconcentration prediction model. An updated algorithm based on measured binding values in trout is used to predict unbound chemical fractions in blood, while other model parameters are designed to be representative of small fish typically used in whole-animal bioconcentration testing efforts. Overall model behavior was shown to be strongly dependent on the relative hydrophobicity of the test compound and assumed rate of in vitro activity. The results of a restricted sensitivity analysis highlight critical research needs and provide guidance on the use of in vitro biotransformation data in a tiered approach to bioaccumulation assessment.

Comparing laboratory and field measured bioaccumulation endpoints

An approach for comparing laboratory and field measures of bioaccumulation is presented to facilitate the interpretation of different sources of bioaccumulation data. Differences in numerical scales and units are eliminated by converting the data to dimensionless fugacity (or concentration-normalized) ratios. The approach expresses bioaccumulation metrics in terms of the equilibrium status of the chemical, with respect to a reference phase. When the fugacity ratios of the bioaccumulation metrics are plotted, the degree of variability within and across metrics is easily visualized for a given chemical because their numerical scales are the same for all endpoints. Fugacity ratios greater than 1 indicate an increase in chemical thermodynamic activity in organisms with respect to a reference phase (e.g., biomagnification). Fugacity ratios less than 1 indicate a decrease in chemical thermodynamic activity in organisms with respect to a reference phase (e.g., biodilution). This method provides a holistic, weight-of-evidence approach for assessing the biomagnification potential of individual chemicals because bioconcentration factors, bioaccumulation factors, biota-sediment accumulation factors, biomagnification factors, biota-suspended solids accumulation factors, and trophic magnification factors can be included in the evaluation. The approach is illustrated using a total 2393 measured data points from 171 reports, for 15 nonionic organic chemicals that were selected based on data availability, a range of physicochemical partitioning properties, and biotransformation rates. Laboratory and field fugacity ratios derived from the various bioaccumulation metrics were generally consistent in categorizing substances with respect to either an increased or decreased thermodynamic status in biota, i.e., biomagnification or biodilution, respectively. The proposed comparative bioaccumulation endpoint assessment method could therefore be considered for decision making in a chemicals management context.