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Human and Ecological Risk Assessment, 13: 1164–1191, 2007Copyright C© Taylor & Francis Group, LLCISSN: 1080-7039 print / 1549-7680 onlineDOI: 10.1080/10807030701655897

WORKSHOP REPORT

Use of In Vitro Absorption, Distribution, Metabolism,and Excretion (ADME) Data in BioaccumulationAssessments for Fish

John Nichols,1 Susan Erhardt,2 Scott Dyer,3 Margaret James,4 Margo Moore,5

Kathleen Plotzke,6 Helmut Segner,7 Irvin Schultz,8 Karluss Thomas,9

Luba Vasiluk,10 and Annie Weisbrod3

1National Health and Environmental Effects Research Laboratory, Office ofResearch and Development, U.S. Environmental Protection Agency, Duluth, MN,USA; 2Toxicology, Environmental Research and Consulting, Dow ChemicalCompany, Midland, MI, USA; 3Central Product Safety, Procter and GambleCompany, Cincinnati, OH, USA; 4 Department of Medicinal Chemistry, Universityof Florida, Gainesville, FL, USA; 5Department of Biological Sciences, Simon FraserUniversity, Burnaby, BC, Canada; 6Health and Environmental Sciences, DowCorning Corporation, Midland, MI, USA; 7Centre for Fish and Wildlife Health,University of Bern, Bern, Switzerland; 8Battelle, Pacific Northwest NationalLaboratory-Marine Research Operations, Sequim, WA, USA; 9ILSI Health andEnvironmental Sciences Institute, Washington, DC, USA; 10Department of LandResource Science, University of Guelph, Guelph, ON, Canada

ABSTRACTA scientific workshop was held in 2006 to discuss the use of in vitro Absorption,

Distribution, Metabolism, and Excretion (ADME) data in chemical bioaccumulationassessments for fish. Computer-based (in silico) modeling tools are widely used to es-timate chemical bioaccumulation. These in silico methods have inherent limitationsthat result in inaccurate estimates for many compounds. Based on a review of the sci-ence, workshop participants concluded that two factors, absorption and metabolism,represent the greatest sources of uncertainty in current bioaccumulation models.Both factors can be investigated experimentally using in vitro test systems. A variety

Received 27 March 2007; revised manuscript accepted 1 April 2007.This article has been subjected to review by the National Health and Environmental EffectsResearch Laboratory and approved for publication. Approval does not signify that the contentsreflect the views of the Agency, nor does mention of trade names or commercial productsconstitute endorsement or recommendation for use.Address correspondence to John Nichols, National Health and Environmental Effects Re-search Laboratory, Office of Research and Development, U.S. Environmental ProtectionAgency, Duluth, MN 55804, USA. E-mail: [email protected]

1164

Use of In Vitro Data in Fish Bioaccumulation Assessments

of abiotic and biotic systems have been used to predict chemical accumulation byinvertebrates, and dietary absorption of drugs and xenobiotics by mammals. Re-search is needed to determine whether these or similar methods can be used to betterpredict chemical absorption across the gills and gut of fish. Scientists studying mam-mals have developed a stepwise approach to extrapolate in vitro hepatic metabolismdata to the whole animal. A series of demonstration projects was proposed to investi-gate the utility of these in vitro–in vivo extrapolation procedures in bioaccumulationassessments for fish and delineate the applicability domain of different in vitro testsystems. Anticipating research progress on these topics, participants developed a“decision tree” to show how in vitro information for individual compounds couldbe used in a tiered approach to improve bioaccumulation assessments for fish andinform the possible need for whole-animal testing.

Key Words: fish, bioaccumulation, bioconcentration, metabolism, biotransforma-tion, absorption.

LIST OF ABBREVIATIONS

ADME Absorption, Distribution, Metabolism, ExcretionBAF Bioaccumulation FactorBCF Bioconcentration FactorCEPA Canadian Environmental Protection ActCDSL Canadian Domestic Substances ListD7.4 n-octanol/phosphate buffer distribution coefficient at a pH of 7.4GIT Gastrointestinal TractILSI-HESI International Life Sciences Institute–Health and Environmental

Sciences InstituteKow n-octanol/water partition coefficientOECD Organization for Economic Cooperation and DevelopmentPAMPA Parallel Artificial Membrane Permeability AssayPBiT Persistent, Bioaccumulative, and inherently Toxic substancePOP Persistent Organic PollutantQSAR Quantitative Structure–Activity RelationshipREACH Registration, Evaluation, and Authorization of Chemicals Program

(European Union)UNEP United Nations Environment ProgramUSEPA U.S. Environmental Protection Agency

INTRODUCTION

The accumulation of xenobiotics in fish and other aquatic biota is an issue oflong-standing concern to industry, government regulators, the academic commu-nity, and the general public. Extensive research has been conducted to understandthe chemical and biological processes that promote bioaccumulation, and detailedinformation is available for a small number of compounds, several of which arenow banned from production and use. Increasingly, however, there is a need to

Hum. Ecol. Risk Assess. Vol. 13, No. 6, 2007 1165

J. Nichols et al.

perform less intensive assessments for a much larger number of compounds. Reg-ulatory programs in Europe and North America are being revised to support the2004 enactment of the United Nations Stockholm Convention (also known as thePersistent Organic Pollutants (POPs) Protocol), which governs the assessment, use,trade, release, and replacement of all persistent (P), bioaccumulative (B), and in-herently toxic substances (iT), or PBiTs (UNEP 2006). For example, the CanadianEnvironmental Protection Act (CEPA) required the Ministers of Environment andHealth to categorize the hazard of approximately 23,000 chemicals on a DomesticSubstances List (CDSL) and, as necessary, conduct screening level assessments todetermine whether they are “. . . toxic or capable of becoming toxic to the environ-ment or human health” (Government of Canada 1999; the word “toxic” is definedin Part 5, Section 64 of the Act). Legislation in Europe (Registration, Evaluation,and Authorization of Chemicals program; REACH) could result in similar reviewsof tens of thousands of compounds (Rogers 2003).

In most cases, these reviews are conducted in the absence of measured bioaccu-mulation data. Moreover, because of ethical concerns, many government agenciesand animal welfare organizations are advocating large reductions in vertebrate test-ing, including testing with fish. These considerations suggest a need for alternativemethods to assess the potential for chemicals to accumulate in fish. One methodthat is receiving considerable attention involves the use of in vitro test systems, aloneor in combination with mathematical models.

This report describes the results of a workshop held March 3–4, 2006, in SanDiego, California, USA. Workshop participants were asked to review the state-of-the-science regarding the incorporation of in vitro Absorption, Distribution, Metabolism,and Excretion (ADME) information into bioaccumulation assessments for fish, andidentify research needed to expand the utility and applications of this approach.An important outcome of this workshop was a proposal to conduct research onhepatic biotransformation in fish, with the goal of relating in vitro metabolic rate,in vivo metabolic rate, and measured levels of accumulation for a set of strategi-cally selected compounds. Participants also discussed how in vitro data could beused in a tiered approach for bioaccumulation assessments. Based on this discus-sion, a “decision tree” was proposed to identify information required at each tierin the assessment process and provide guidance on the need for whole animaltesting.

The scope of the workshop was limited to consideration of in vitro methods thatcould be used to predict chemical accumulation in fish under standardized labora-tory conditions. Participants recognized that a large number of factors may compli-cate efforts to predict accumulation in a natural setting, including the distributionof chemicals among environmental compartments, food web structure and func-tion, and seasonal movements of animals. The extrapolation of bioaccumulationpredictions from the lab to the field, and among different environmental settings,was identified as an important topic for future scientific workshops.

Defining Bioconcentration and Bioaccumulation

As applied to fish, the term bioconcentration refers to chemical accumulation thatoccurs in a waterborne exposure due to uptake across the gills and skin, whereas

1166 Hum. Ecol. Risk Assess. Vol. 13, No. 6, 2007


the term bioaccumulation refers to chemical accumulation resulting from all pos-sible routes of exposure, including dietary uptake. Bioconcentration is generallymeasured in controlled laboratory exposures, whereas bioaccumulation is typicallycharacterized by measuring chemical concentrations in field-collected animals. Theextent of bioconcentration may be expressed by calculating a bioconcentration fac-tor (BCF; L/kg), which is the total chemical concentration in the animal (mg/kg)divided by that in water (mg/L). A bioaccumulation factor (BAF) with the same unitscan be developed to describe the extent of bioaccumulation. Alternatively, bioaccu-mulation may be referenced to the chemical concentration in sediment, resultingin a biota-sediment accumulation factor (BSAF). Unless otherwise indicated, BCFs,BAFs, and BSAFs represent the extent of accumulation that would be expected in along-term exposure (i.e ., under steady-state conditions). These ratios are often nor-malized to the lipid content of fish and the freely dissolved chemical concentrationin water (or, in the case of the BSAF, the lipid content of fish and the organic carboncontent of sediment). The goal of this normalization is to account for differences infactors that control the uptake and accumulation of hydrophobic organic chemicalssuch as binding to organic material in water or sediment, and partitioning to tissuelipid. Bioaccumulation is the condition that results from a natural exposure, particu-larly for hydrophobic substances; however, BAFs and BSAFs are difficult to measureexperimentally. As a result, regulators often use measured or modeled BCFs to es-timate the potential for a compound to bioaccumulate, and legislated criteria for“bioaccumulation” are generally expressed as BCF values (Arnot and Gobas 2006).

The major processes that determine the extent of chemical accumulation in fishare illustrated in Figure 1. Uptake processes include chemical absorption acrossthe gills, skin, and gut. Loss processes include chemical efflux across the gills, skin,and gut; urinary and biliary elimination; and biotransformation. Growth affects themeasured concentration of a chemical by increasing the tissue mass into which itis diluted. Additional processes are responsible for the internal distribution of achemical. Among these are blood flow rates to individual tissues and tissue-specificdifferences in lipid content.

Current Methods to Estimate Bioaccumulation in Fish

Three techniques are currently employed to assess the potential for a chemical toaccumulate in fish: controlled exposures of fish to test chemicals in the water or diet,measurement of chemical residues in field-collected animals, and computationalmodeling.

Measured in vivo accumulation data for fish are relatively scarce. For example,Arnot and Gobas (2006) reported that measured BCFs and BAFs are available for<4% of organic chemicals on the CDSL. Currently, there is a single internationalguideline, OECD 305 (OECD 1996), which is used to measure in vivo bioconcen-tration in fish following an aqueous exposure to a test chemical. The test takes 3–6months to conduct and costs an average of $125,000 U.S., per chemical (Woodburnand Springer 2004). Costs associated with measuring environmental contaminantsin field-caught animals vary but may be substantial, particularly when they includethe cost of animal collection.


J. Nichols et al.

Figure 1. Processes responsible for the uptake and accumulation of chemicalsby fish. Symbols and abbreviations: k1—branchial uptake rate constant(L/kg/d); k2—branchial efflux rate constant (1/d); kd—dietary up-take rate constant (kg/kg/d); ke—fecal elimination rate constant (1/d);kmet—metabolism rate constant (1/d); kg—growth rate constant (1/d);Cwater—freely dissolved concentration of chemical in water (g/L); Cfish—total concentration of chemical in fish (g/kg); Cprey-total concentrationof chemical in prey (g/kg).

Bioaccumulation assessments for many compounds are conducted using computer-based modeling tools. Quantitative Structure Activity Relationships (QSPRs) arewidely used to predict the BCF for a compound based on its n-octanol/water par-tition coefficient, Kow (Veith et al. 1979). Compartmental models have been usedto predict BCFs from measured or estimated uptake and elimination rate constants(Branson et al. 1975). Additional models have been developed to predict bioaccu-mulation in aquatic food webs occurring within a natural environment (Thomannet al. 1992; Gobas 1993). These approaches are described briefly in another ILSI-HESI workshop publication (Weisbrod et al. 2007). Generally, models of chemicalaccumulation in fish have been developed and evaluated using data for chemicalsthat passively diffuse across biological membranes, partition non-specifically to tissuelipid, and undergo little or no metabolism. These models may provide inaccurateestimates of accumulation for compounds that exhibit more complex behaviors.

RESEARCH TO SUPPORT THE INCORPORATION OF IN VITRO DATA INTOBIOACCUMULATION MODELS

Workshop participants concluded that two factors, absorption and metabolism,represent the greatest sources of uncertainty in current models of chemical accumu-lation by fish. Although empirical relationships exist to predict branchial (McKimet al. 1985) and dietary uptake (Gobas et al. 1988) of chemicals as functions of Kow,there is considerable scatter in the data used to develop these relationships, andlimited knowledge of uptake mechanisms other than simple diffusion.

Metabolism may substantially reduce the extent to which a compound accumu-lates in fish, and was recognized as the single most important source of modeling un-certainty. This conclusion was based on known variation in metabolism rates among



species and chemicals, as well as the sensitivity of models to changes in the value ofthis parameter (Clark et al. 1990; Dimitrov et al. 2005). In vitro methods have been de-veloped to predict absorption and metabolism in a variety of biological systems. Thefollowing sections describe these methods, emphasizing their current or potentialapplication to bioaccumulation assessments for fish.

Absorption

Chemical absorption by fish may be viewed as a process that depends on bothchemical speciation and membrane permeatic. Most compounds cross biologicalmembranes by passive diffusion. In general, ionized and bound chemical speciesexhibit low rates of diffusion. Neutral, freely dissolved forms may exhibit high ratesof diffusion, depending on other physicochemical properties (see later). Membranetransport proteins may facilitate the absorption of some compounds, resulting inuptake rates much greater than those expected from simple diffusion. Alternatively,efflux transporters may promote the elimination of a compound, reducing the extentto which it accumulates. Both types of transporters are active in the gastrointestinaltract (GIT) of fish. Biotransformation at the site of absorption tends to limit the rateof uptake by consuming chemical that diffuses into the tissue.

Physicochemical properties that may limit membrane diffusion

Physicochemical properties of a compound may determine the extent of passivediffusion across biological membranes. For example, Lipinski et al. (1997) foundthat four physicochemical parameter ranges were associated with 90% of all orallyadministered drugs. Drugs that were considered bioavailable had a molecular weight≤500, a log Kowvalue ≤5, ≤5 hydrogen (H)-bond donors, and ≤10 H-bond acceptors.Wenlock et al. (2003) reviewed data for several hundred additional oral drugs andfound that 90% had a molecular weight <473, log D7.4 value <4.3 (partitioningbetween n-octanol and phosphate buffer at pH 7.4), <4 H-bond donors, and <7H-bond acceptors. These and similar relationships are widely used to screen potentialdrug candidates, with the goal of identifying compounds that are likely to be poorlyabsorbed across the GIT.

Previous work has shown that organochlorines with a molecular weight >600 ormolecular volume >3 nm3 are poorly absorbed across the GIT of rainbow trout(Niimi and Oliver 1988). Other researchers have suggested that molecular shapeand flexibility are important determinants of chemical uptake and accumulation byfish (Opperhuizen et al. 1985; Dimitrov et al. 2002). After reviewing these and otherstudies, de Wolf et al. (2007) recommended several guidelines based on molecularweight, length, cross-sectional diameter, and lipophilicity to screen chemicals fortheir potential to bioconcentrate in fish. The value of this approach is that it may bepossible to identify compounds which, based on their physicochemical properties,can be eliminated from further consideration in bioaccumulation assessment efforts.The authors cautioned, however, that there were no clear cut-off values for chemicalabsorption by fish.


J. Nichols et al.

General considerations for assay selection

The standard test for chemical absorption is to measure the extent of uptake byexperimental animals in a controlled laboratory exposure. Such tests are impracti-cal, however, for screening a large number of compounds. Alternatively, it may bepossible to conduct screening-level assessments of absorption using one or more invitro assays.

A variety of in vitro test systems have been used to predict in vivo absorption.These are listed in Table 1, along with their advantages and limitations, and the typeof data that they generate. For a particular test system and compound, bioavailability(defined here as the amount of compound present as a neutral, diffusing form) ormembrane transport may constitute the principal rate limitation on membrane flux.The selection of an appropriate in vitro test system should therefore reflect know-ledge of the predominant route of exposure, chemical speciation, and predictedmodes of uptake.

Because chemical speciation has a large impact on absorption, experimental fac-tors that influence speciation should be controlled and measured. These include pH,alkalinity, hardness, temperature, dissolved organic carbon, and particulate organiccarbon. Membrane bilayer structure also can influence the rate of passive diffusion(Frezard and Garnier-Suillerot 1998). In fish and other poikilotherms, membranebilayer composition may change with season due to changes in water temperature.These changes have been shown to influence the absorption of lipophilic chemicals(Elskus et al. 2005).

Degradation of a compound in the external medium (water, gut contents) as wellas metabolism within tissues of the gill or gut may modify uptake (Van Veld et al.1988; Barron et al. 1989). In some cases it may be difficult to determine whether therate of absorption is controlled by the rate of diffusion across the membrane or therate of loss of parent chemical. Hence, appropriate controls must be included toaccount for the potential contribution of metabolism.

In vitro systems to predict xenobiotic absorption

Various partitioning systems (e .g ., n-octanol and water) have been used to char-acterize the relative hydrophobicity of a compound, which is equivalent in thesesystems to lipophilicity. Because biological membranes consist mostly of lipid, mea-sures of hydrophobicity often correlate with the rate of membrane diffusion. Ingeneral, diffusion rates are greatest for moderately hydrophobic chemicals and de-cline substantially for chemicals that exhibit extreme hydrophilic or hydrophobiccharacter.

Using both liquid and solid phase partitioning devices, researchers have charac-terized the bioavailability of hydrophobic compounds in water and sediment, andpredicted chemical accumulation in invertebrates (Sabaliunas and Sodergren 1996;Mayer et al. 2000; Wilcockson and Gobas 2001; Cornelissen et al. 2001). In this ap-plication, the liquid or solid phase may be viewed as a surrogate for tissue lipid. Inprinciple, biomimetic partitioning devices could be used to predict chemical accu-mulation in fish under a particular set of environmental conditions, but this usageremains largely unexplored.


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1171

J. Nichols et al.

Artificial lipid bilayers have been used extensively to predict the membrane trans-port of drugs (Parallel Artificial Membrane Permeability Assay (PAMPA); Kansy et al.1998). For many drugs, values obtained using PAMPA exhibit good agreement withthose obtained using cell-based systems such as Caco-2 cells (Bermejo et al. 2004).Kwon et al. (2006) used PAMPA to estimate passive uptake and elimination ratesfor 23 simple aromatic compounds in small fish. In general, the experimentallydetermined values exhibited good correspondence with literature values; however,there were some exceptions for which the in vitro system did not accurately predictmeasured in vivo rates. The principal drawback of physical systems such as artifi-cial membranes and partitioning devices is that they do not account for complexmembrane processes such as active transport and metabolism.

Chemical uptake from the diet represents a special case because conditions withinthe GIT (low pH, gut microflora, and the presence of digestive enzymes) may rad-ically alter speciation and bioavailability. “Physiologically based” in vitro extractionsystems that duplicate these conditions have been developed to predict dietary up-take of chemicals by mammals (Hack and Selenka 1996; Van de Wiele et al. 2004;Ruby 2004). No such systems have been developed for fish.

Caco-2 cells, alone or in combination with physiologically based extraction meth-ods, have been used to predict the dietary uptake of drugs (Hidalgo 2001; vanBreemen and Li 2005) and environmental contaminants (Oomen et al. 2001; Buesenet al. 2003; Vasiluk et al. 2005; Minhas et al. 2006) by mammals. Caco-2 cells exhibitmorphological characteristics of small intestinal cells and express most of the en-zymes and transporters that exist in vivo. In vitro assays are performed using a cellmonolayer that can be scaled to predict in vivo permeability. It is not known whetherCaco-2 cells or a cell type derived from fish tissue could be used to predict chemicaluptake across the gills or gut of fish. Enterocytes from fish have been used to ex-amine ion transport and uptake of fatty acids (Schoenmakers et al. 1993; Perez et al.1999), whereas cultured gill epithelial cells have been used to study ion regulationand branchial excretion of ammonia (Wood et al. 2002). To date, however, neitherof these cell-based systems has been used to study the absorption of environmentalcontaminants.

Research is needed to develop in vitro assays that can be used to better predictchemical absorption by fish. Partitioning-based approaches provide useful informa-tion related to bioavailability and passive diffusion but are unlikely to deliver dataneeded to predict the accumulation of chemicals that undergo metabolism in thegills or gut. The successful use of cell-based systems to predict dietary uptake ofchemicals in mammals provides an important example for future research with fish.This work should be conducted using piscine cell lines because taxonomic differ-ences in membrane transporters, enzyme complement, and fatty acid compositioncould influence the absorption process.

Metabolism

For many xenobiotics, metabolism information is required to accurately predictin vivo bioaccumulation. The major questions that must be addressed are whethera compound is metabolized at all, and if so at what rate. At an early stage of theassessment process, a yes/no answer concerning the likelihood for metabolism



may be sufficient. Methods to derive this information include QSAR in silico mod-els (Borodina et al. 2003) and knowledge-based expert systems such as METEOR(Balmat et al. 2005), OASIS (Mekenyan et al. 2006), and META (Klopman et al. 1994;Talafous et al. 1994).

If the information required for a particular assessment goes beyond a yes/nodecision, then quantitative estimates of biotransformation may be needed. Methodsused in studies with mammals to extrapolate in vitro hepatic clearance data to thewhole animal are well described and widely accepted (Rane et al. 1977; Wilkinson1987; Houston 1994; Houston and Carlile 1997; Iwatsubo et al. 1997). It has beenproposed that these or similar methods could be used to extrapolate in vitro hepaticmetabolism data for fish, and that clearance constants calculated from this informa-tion could serve as inputs to bioconcentration and bioaccumulation models (Nicholset al. 2006). Each of the major in vitro hepatic metabolism assays has strengths andlimitations. The selection of a “preferred” assay system relates in part to the questionsthat are being asked, ease of preparation, and metabolic pathways of concern. Thecharacteristics of each in vitro hepatic metabolism assay as well as their advantagesand limitations are summarized in Table 2.

Purified enzymes and recombinant enzyme systems

Purified enzymes and recombinant enzyme systems provide information for a spe-cific pathway of interest (Dierickx 1985; Buhler and Wang-Buhler 1998; Leaver andGeorge 1998; Tong and James 2000; Sugahara et al. 2003; Chung et al. 2004). Fromthe perspective of predicting effects of metabolism on bioaccumulation, this focuson a single pathway may be disadvantageous if other (uncharacterized) pathwayscontribute substantially to elimination. Additional disadvantages include the techni-cal challenges of preparing these systems and difficulty relating biotransformationdata to the whole animal in the absence of information concerning tissue enzymecontent.

Subcellular preparations

Most of the in vitro metabolism data for fish have been collected using subcellularS9 or microsomal fractions (Fitzsimmons et al. 2007). Subcellular preparations areamenable to cryopreservation and can be frozen for up to 1 year at –80◦C while main-taining initial levels of enzymatic activity (Hodson et al. 1991). The appropriatenessof a particular preparation depends on whether it contains the enzymes required tometabolize the chemical of interest. Co-factors required for Phase I and II metabolicactivity must be supplied as appropriate to a given system and reaction pathway.

Isolated and cultured primary liver cells

Isolated hepatocytes are obtained by enzymatic treatment of liver tissue, followedby mechanical dissociation of cells (Mommsen et al. 1994; Segner 1998a). In com-parison to subcellular fractions, intact hepatocytes provide greater biological realismbecause membrane transport mechanisms and intracellular compartments are main-tained. In some cases, diffusion across the hepatocyte membrane may limit the rateat which a chemical is metabolized (Lu et al. 2006), resulting in a lower level of activitythan that observed in subcellular fractions. For other compounds, transport proteins


Tab

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Invi

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1174


located within the membrane (e .g ., p-GP, OATp) may contribute to high rates ofhepatic clearance (Sturm and Segner 2005). In studies with mammals, hepatic clear-ance rates calculated from freshly isolated hepatocytes tend to correlate with ratesobtained using liver microsomes, but often provide more accurate predictions ofin vivo clearance (Houston 1994; Houston and Carlile 1997; Jones and Houston2004).

Cryopreserved hepatocytes can be purchased from commercial sources for severalmammalian species, but are not yet available for fish. The use of primary hepatocytestherefore requires access to live animals. A significant problem when using primaryhepatocytes from fish is the variability among preparations with respect to cell viabil-ity and metabolic capability. This variability can be accounted for, however, by usingbenchmark compounds with known metabolic characteristics to characterize eachcell preparation (Segner 1998a).

Immortalized cell lines from fish liver tissue

Immortalized fish cell lines derived from liver tissue have been used extensivelyto study cytotoxicity and induction of metabolic enzymes. The primary advantagesof these systems are their availability and the fact that they do not require the useof animals. The principal disadvantage is that metabolic pathways and rates maynot reflect the biotransformation capabilities of tissues from which these cell linesoriginated (Nehls and Segner 2001; Castano et al. 2003). In general, metabolismrates in fish cell lines are lower than those in primary liver cells (Segner 1998b; Dyeret al. 2003; Dyer and Bernhard 2004).

Precision-cut liver slices

A liver slice represents the complete cellular environment for hepatic biotrans-formation. Procedures used to obtain precision-cut liver slices from fish have beendescribed (Kane and Thohan 1996; Gilroy et al. 1996; Singh et al. 1996), but theuse of slices in metabolism studies with fish has been highly limited. A potentialdisadvantage of liver slices is that diffusion limitations on chemical flux can createinternal concentration gradients, reducing the rate of metabolism and complicatingin vitro–in vivo extrapolation efforts (Worboys et al. 1996a,b).

Assay conditions

A number of factors may complicate attempts to use in vitro metabolism data inbioaccumulation assessments for fish. Included among these are factors that relateto the collection of in vitro data, as well as the application of this data to diversechemicals and species living within a complex natural environment. Here we singleout two factors for special consideration: in vitro bioavailability and temperature.

Chemicals that tend to accumulate in fish often exhibit physicochemical charac-teristics (e .g ., high log Kow values) that make them difficult to work with. Chemicaladsorption to the reaction vessel and/or binding to macromolecules within the as-say system may substantially reduce the free concentration of a chemical in solution.The interpretation of in vitro test data may be improved by expressing the resultsin relation to the freely dissolved concentration of the test compound. Establishedmethods for measuring in vitro binding include equilibrium dialysis (Obach 1997),


J. Nichols et al.

ultracentrifugation (Tang et al. 2002), and solid phase microextraction (Heringaet al. 2004). Empirical models have been developed to estimate free concentrationsin vitro (Austin et al. 2002; Gulden et al. 2005), but these must be re-evaluated whenusing biological material from a new source.

The maximum in vitro activity (Vmax) of metabolic enzymes from fish acclimatedto a particular temperature generally doubles with a 10◦C increase in temperature(Fitzsimmons et al. 2007). Changes in acclimation temperature may be accompanied,however, by ideal (or near ideal) temperature compensation. The result of thiscompensation is that activities of metabolic enzymes measured at the temperatureto which individuals are acclimated tend to be similar. To facilitate extrapolationof in vitro biotransformation data to the whole animal, it is recommended that allmetabolism studies be performed at or near the acclimation temperature of theanimal.

In vitro–in vivo extrapolation of hepatic metabolism

Scientists using mammals have developed a stepwise approach to predictin vivo hepatic clearance from measurements of in vitro hepatic metabolism (Raneet al. 1977; Wilkinson 1987; Houston 1994; Houston and Carlile 1997; Iwatsubo et al.1997). Nichols et al. (2006) reviewed this procedure and suggested ways that it couldbe adapted to studies with fish. Briefly the steps in this procedure are: (1) calculatein vitro intrinsic clearance (CLin vitro, int; µL/min/mg microsomal protein) from theratio Vmax/Km,or the rate of decline of parent chemical concentration in a substratedepletion experiment; (2) employ extrapolation factors to calculate in vivo intrin-sic clearance normalized to fish body weight (CLin vivo,int; L/d/kg); and (3) convertCLin vivo,int to in vivo hepatic clearance (CLh; L/d/kg) using a liver model that inter-relates biotransformation, liver blood flow rate, and the effect of binding to proteinsand other macromolecules.

The simplest liver model is termed the venous equilibration model, which can beexpressed as (Rowland et al. 1973; Wilkinson and Shand 1975):

CLh = Q liver × fu × CL in vivo,int

Q liver + (fu × CL in vivo,int)

where Q liver(L/d/kg) is the liver blood flow rate and fu (unitless) is a term thatcorrects for the binding of a chemical in blood and in the in vitro system used toassess metabolism.

Bioaccumulation models for fish are generally referenced to the whole-body con-centration of a chemical. The incorporation of CLh into this type of model requiresthat CLh be divided by an apparent volume of distribution (Vd,blood; L/kg), refer-enced to the total chemical concentration in blood. The result of these calculationsis a whole-animal clearance rate constant with units of inverse time (kmet; 1/d).

An examination of this procedure suggests the need for several types of informa-tion specific to the fish species tested. First, one or more extrapolation factors arerequired to convert CLin vitro,int to CLin vivo,int, depending on the type of in vitro systemused to characterize enzyme activity. Calculation of CLh also requires knowledgeof liver blood flow. Extrapolation factors and liver blood flow estimates have beenpublished for trout (Nichols et al. 2006). These terms are not yet available for most



other fish species. Empirical relationships based in part on measured binding ofchemicals in trout plasma may be used to estimate fu (Nichols et al. 2006); however,the role that binding plays in limiting the metabolism of hydrophobic environmentalcontaminants is poorly known (see later).

DEMONSTRATION PROJECT PROPOSAL FOR METABOLISMEXTRAPOLATIONS

A key finding of this workshop was the need to evaluate the relationship betweenin vitro hepatic metabolism rate, in vivo metabolic rate, and measured bioconcentra-tion/bioaccumulation. The goal of this effort is to determine the practical limits ofin vitro–in vivo extrapolation procedures across a range of chemicals and metabolicpathways. To this end, workshop participants developed specific recommendationsfor a proposed set of demonstration projects.

In Vitro–In Vivo Experimental Design

Procedures used to extrapolate metabolism information should be evaluated us-ing a linked in vitro–in vivo experimental design. In each case, it is necessary to usethe same chemical and fish species for both in vitro and in vivo experiments. Becausemetabolism within a species can vary with gender, season, and life-stage, these fac-tors should be controlled. Most of the existing BCF data has been obtained usingrainbow trout and carp (Arnot and Gobas 2006). These species provide sufficientliver tissue for the preparation of subcellular fractions and isolated hepatocytes, andare therefore recommended for initial testing efforts.

Important experimental issues include the selection of an in vitro assay, selectionof a higher order system (in vivo or in situ) to evaluate the accuracy of extrapolatedin vitro values, and the need to collect appropriate supporting data. A subcellularfraction is recommended for most applications due to ease of preparation. An S9or microsomal system is appropriate if the rate of hepatic clearance is limited byPhase I metabolic activity. An S9 fraction is preferred if clearance is limited by PhaseII activity. Isolated hepatocytes would constitute the system of choice if chemicaldiffusion across the hepatocyte membrane limited the rate metabolic clearance.

In Situ and In Vivo Experimental Approaches

Two general approaches exist to evaluate the accuracy of extrapolated in vitrometabolism values. The first is to measure hepatic clearance directly using an isolatedperfused liver or one of several in vivo experimental approaches. The second is toestimate in vivo whole-animal clearance based on either the disappearance of parentchemical or appearance of metabolic products.

Isolated perfused liver

Liver perfusion studies integrate the two major factors that determine hepaticclearance in vivo: enzyme activity and perfusion limitations on the rate of chemicaldelivery to the site of metabolism (Forlin and Andersson 1981; Andersson et al. 1983).The main advantage of this technique is that measured concentrations of parentchemical in the perfusion buffer provide a direct estimate of hepatic clearance.


J. Nichols et al.

These studies are technically demanding, however, and are best suited to collectingdetailed information for a small number of animals and compounds. The need touse relatively large fish also limits this technique to laboratories with adequate fishholding facilities.

In vivo measurement of hepatic clearance

Researchers working with mammals have developed methods to estimate hepaticclearance by kinetic analysis of plasma data from bolus i.v. injection, continuous i.v.

infusion (Iwatsubo et al. 1997), or intraportal infusion (Carlile et al. 1997) studies.The applicability of these methods to studies with fish is largely unexplored.

In vivo measurement of whole-body clearance

The protocol most commonly used to estimate in vivo metabolism rates in fishinvolves dosing animals with a chemical for a period of time and then allowing themto depurate in clean water. The rate of metabolism is calculated as the differencebetween the observed rate of elimination of parent chemical and the rate of elimina-tion expected due to non-metabolic pathways (Sijm and Opperhuizen 1988; de Wolfet al. 1993; Fisk et al. 1998). Accurate determinations of metabolism rate are difficultto make if the total rate of elimination is very slow, or the rate of metabolism is slowrelative to that of non-metabolic elimination pathways. Exposures can be conductedby exposing fish to chemicals in water, although the dietary route may be bettersuited for compounds that possess low water solubility (Fisk et al. 1998; Wong et al.2002).

Whole-animal metabolism rates also can be estimated by analysis of measuredwhole-body chemical concentrations resulting from defined laboratory or field expo-sures (Arnot and Gobas 2003; Dimitrov et al. 2005). Using an appropriate mathemat-ical model, the apparent rate of metabolism is determined by adjusting a metabolismrate constant until model simulations predict the observed level of accumulation.This approach requires high-quality BCF (or BAF) data, and is highly dependenton the accuracy of other model parameters (in particular, the rate of eliminationthat would be expected in the absence of metabolism). The main advantage of thisapproach is that metabolism rates may be estimated using existing test data.

In a small number of studies, investigators have estimated whole-animal rates ofmetabolism by measuring the appearance of metabolic products in fish and/or theexposure water (Karara and Hayton 1984; Barron et al. 1990; Bradbury et al. 1993).This approach provides a direct measure of metabolism but is technically demand-ing. The utility of metabolite data also depends on whether all major metabolic path-ways are accounted for. Otherwise, an incomplete accounting of metabolic productscould result in underestimation of the true rate of parent chemical metabolism.

Supporting Data

Supporting data are required to maximize the utility of in vitro data in metabolismextrapolations for fish. As indicated earlier, the model used to translate CLin vitro,int toan estimate of CLh (Eq. [1]) contains a term (fu) that accounts for binding of chemi-cals in blood and in the in vitro system used to assess metabolism. The role that bind-ing plays in limiting hepatic metabolism of hydrophobic compounds (log Kow > 4) is



Table 3. Factors used to select test chemicals for the metabolismdemonstration project.

Estimated PredictedChemical bioconcentration Measured metabolicclass (from Kow)a bioconcentrationb Predicted kc

met pathway(s)d

A High <Kow-only High Phase I and IIB High ∼Kow-only Low NAC Low <Kow-only High Phase I and IID Low ∼Kow-only Low NA

aExtent of bioconcentration predicted from chemical Kow in the absence ofmetabolism.bExtent of bioconcentration measured in a reliable test:< Kow-only—bioaccumulationis less than that predicted from chemical Kow (column 2); ∼ Kow-only—bioaccumu-lation is approximately equal to that predicted from chemical Kow (column 2).ckmet is the rate of whole-fish metabolism estimated from in vitro and/or in vivo testresults, as described in the text.dMetabolic pathways are determined by best professional judgment based on existingdata for fish and/or mammals. Where data are lacking, likely metabolic pathways maybe based on QSARs and/or knowledge-based expert systems as described in the text.

poorly understood. Although theoretical approaches suggest that these compoundsare unlikely to be metabolized due to low bioavailability in plasma, experimentaldata and measured chemical concentrations in field-collected animals suggest thatthis is not always the case. As a class, the PAHs represent a good example of thisapparent discrepancy. Although they possess relatively high log Kow values, PAHs arereadily metabolized by fish (Varanasi et al. 1989). Research is needed to determinethe effects of binding on in vitro and in vivo metabolism of hydrophobic chemicalsby fish.

Extrapolation parameters required to translate CLin vitro,int into an estimate of CLh

vary with the in vitro system used to characterize this activity, but may include liversize, liver blood flow rate, S9 protein content (mg/g liver, total), microsomal proteincontent (mg/g liver, total), and hepatocellularity (106 hepatocytes/g liver). Methodsused to estimate these parameters were reviewed by Nichols et al. (2006), along withexisting data for rainbow trout. Additional work is required to characterize theseparameters for other species.

Strategic Selection of Chemicals

Issues that pertain to the selection of appropriate test chemicals are summarizedin Table 3. Chemicals representing four classes (A–D) can be segregated based onpredicted or measured BCF (or BAF) values, and predicted degree of metabolic clear-ance. Within the two chemical classes (A and C) that undergo significant metabolism,there is a need to test compounds that are substrates for each of the major Phase Iand II metabolic pathways. Because the endpoint of concern is chemical accumu-lation in fish, demonstration projects should be performed using compounds forwhich measured BCFs (or BAFs) already exist. Additional priority should be givento chemicals for which established analytical methods are available.


J. Nichols et al.

Modeling studies suggest that even high rates of metabolism are unlikely to re-duce the accumulation of chemicals with log Kow values less than 3 (Nichols et al.2007). In contrast, relatively low rates of metabolism may have a large influenceon the accumulation of very hydrophobic compounds (log Kow > 6). However,these chemicals are often difficult to work with. Adsorption to the reaction vesseland/or binding to macromolecules can substantially reduce the amount of chem-ical available to interact with an in vitro test system. Hydrophobic chemicals alsoexhibit low aqueous solubility, complicating efforts to conduct in vivo waterborneexposures. Based on these considerations, it is recommended that initial valida-tion studies be carried out on compounds with log Kow values ranging from 3 toabout 6.

Methods Standardization and Cross-Laboratory Comparisons

In vitro assay methods must be standardized if the data they generate are going tobe used for environmental and public health priority-setting and decision-making.This includes standardization of the assay protocols themselves, as well as proceduresused to obtain biological material. The acceptance of a “standardized” method maybe facilitated by demonstrating its reproducibility in a set of cross-laboratory com-parisons using the same chemicals and source material.

Potential Errors Due to Extrahepatic Metabolism

The demonstration project proposal described earlier is focused on hepaticmetabolism because the liver is thought to be the primary organ of xenobioticmetabolism in fish. Workshop participants recognized, however, that extrahepaticmetabolism may substantially impact the extent to which chemicals accumulate.This is particularly true when presystemic metabolism in tissues of the gills or gutlimits the rate of chemical uptake at these exchange surfaces (Van Veld et al. 1988;Barron et al. 1989). Under these circumstances, the extent of accumulation pre-dicted using in vitro hepatic metabolism data could substantially overestimate ob-served values.

A need exists to better characterize the circumstances (species, chemical, routeof exposure) under which extrahepatic metabolism contributes to reduced uptakeand increased whole-body clearance. In vitro subcellular systems have been adaptedto investigate both branchial (Barron et al. 1999) and intestinal (James et al. 1997;Kleinow et al. 1998) metabolism. Metabolism by tissues of the intestinal tract also hasbeen studied using an in situ perfused intestinal preparation (Van Veld 1988; Jameset al. 1996; Kleinow et al. 1998).

DECISION TREE FOR REGULATORY APPLICATION OF IN VITRO DATA

Requirement for a Decision Tree

To ensure its credibility and defensibility, the process for deciding which chem-icals should undergo animal testing and which could be evaluated using modelsand/or in vitro data should be standardized according to a consensus of the scien-tific community. From a global perspective, this standardization would encourage



the use of experimental results obtained from different international stakeholdersand minimize duplication of testing. In this section we describe how in vitro datacould be used within the context of a “decision tree” to better assess chemical bioac-cumulation in fish and prioritize testing needs.

The decision tree consists of five tiers. Early tiers rely on QSARs, computer-basedfate and transport models, and a review of physicochemical properties to evaluatethe potential for a compound to accumulate in fish. As the process moves throughthe tiers, data from in vitro systems are used to refine modeled bioconcentrationpredictions. Whole animal testing becomes necessary if, at the end of this process,predicted bioconcentration exceeds a pre-determined criterion value. A summaryof the tiers is presented in Figures 2 and 3, and each is explained briefly in whatfollows.

Tier 1—Initial Screen for Bioaccumulation

The first step of this process is to conduct a search for BCFs or BAFs for the parentcompound and/or structural analogs that have been measured using acceptable testmethods (Weisbrod et al. 2007). If these data are lacking or thought to be of limitedvalue due to methodological concerns, a simple regression model (QSAR) can beused to estimate the BCF for a chemical based on its measured or estimated logKow value (Veith et al. 1979). If, based on this approach, the chemical is determinedto have a measured or predicted BCF greater than 500 (the Globally HarmonizedSystem value recommended by UNEP [2006]), it would be passed on for furtherevaluation.

Tier 2—Environmental Modeling

The BCF models used in Tier 1 have been developed using data for compoundswith log Kow values of 7 or lower and generally predict bioconcentration as a functionof the total concentration of chemical in water. These models do not account forprocesses such as volatilization, sediment binding, and binding to dissolved organiccarbon that may reduce the freely dissolved concentration of the chemical in water.The second step in this evaluation process, therefore, is to determine how the chem-ical of interest is likely to partition within the environment and whether it would beavailable for uptake by fish.

Fugacity-based models (Level III, Mackay and Paterson 1991) provide a usefulmeans of predicting the distribution of a compound within the environment and maybe formulated to include abiotic or biotic degradation rates. Additional empiricalmodels can be used to predict the free concentration of chemical in water as afunction of Kow, DOC, and POC (Arnot and Gobas 2004). The results from thesemodels can serve in turn as inputs to food web bioaccumulation models to obtaininitial predictions of chemical concentrations in biota occupying the lowest levels ofan aquatic food web (Thomann et al. 1992; Gobas 1993). If a chemical is determinedto be available for uptake by fish, either as freely dissolved chemical in the watercolumn or as a contaminant in lower trophic level organisms, it would be passed toTier 3 of the assessment process.


J. Nichols et al.

Figure 2. Bioaccumulation decision tree, Tiers 1–3.

Tier 3—Potential for Absorption

In vitro systems may offer a means of predicting gill and gut uptake for specificcompounds of interest. Here we propose a three-pronged approach using physico-chemical information in combination with in vitro data to estimate the potential fora chemical to be absorbed by fish.

Physicochemical parameters

The success of “Lipinski’s rule of five” for predicting the oral bioavailabilityof potential drug candidates demonstrates the value of physicochemical proper-ties (Lipinski et al. 1997). At this stage in the assessment process, physicochemical



Figure 3. Bioaccumulation decision tree, Tiers 4 and 5.

parameters for the compound of interest would be compared to guidelines for up-take and accumulation in fish (de Wolf et al. 2007). A chemical that possessed physi-cochemical parameters greatly exceeding one or more of these guidelines would beviewed as having a low probability for bioaccumulation.


J. Nichols et al.

Bioaccumulation prediction based on simple phase partitioning

Biomimetic sampling devices such as those listed in Table 1 have been used to con-duct site-specific assessments of chemical bioavailability under natural conditions.These or similar systems could be used to predict the extent of chemical accumula-tion in fish that would be expected from simple phase partitioning (water and tissuelipid). Unlike the BCF prediction from Tier 1 of this process, this experimentallydetermined partitioning estimate would account for factors (e .g ., pH, DOC) thatalter the freely dissolved concentration of the chemical in water.

Experimental assessment of membrane transport

Although biomimetic systems reflect the physicochemical process of phase parti-tioning, they lack properties such as membrane transport and metabolism that maybe important determinants of chemical uptake. The Caco-2 cell line has been usedextensively to predict the intestinal absorption of drugs and environmental contam-inants in mammals. The applicability of this or a similar approach to fish is largelyunexplored. If a cell-based system were available to predict chemical absorption forthe suspected route(s) of exposure, data from that system would be used at this time.

Tier 4—Metabolism Assessment

If it appeared that a compound would be classified as “bioaccumulative” basedon physicochemical characteristics, environmental modeling, and its potential forabsorption, then it would be necessary to characterize its potential for biotransfor-mation using a stepwise approach (Figure 3). The first step in this process is to usea QSAR (Borodina et al. 2003) and/or a knowledge-based expert system (Klopmanet al. 1994; Talafous et al. 1994; Balmat et al. 2005; Mekenyan et al. 2006) to predictlikely metabolic pathways and products. This information would be used to select anappropriate in vitro test system, as the applicability domain of these systems becomesbetter known. If the results of this analysis suggest that the compound is unlikely tobe metabolized, it may be appropriate to skip to Tier 5 in the process.

Assuming that there is some potential for the compound to undergo metabolism,the first experimental step would be to screen for metabolic activity using a sub-cellular liver fraction. Due to its ease of preparation and the presence of both ofPhase I and II activities, the S9 fraction is well suited for this purpose. Selection ofa test species may be based on regulatory requirements or geographic region. Inthe absence of specific guidance, it is recommended that the experiment be con-ducted using rainbow trout or common carp. The assay would be performed usinga substrate-depletion approach, intended to quickly determine if fish can metab-olize the compound at a relatively high rate as compared to benchmark materialswith known in vivo rates of metabolism. If the rate of metabolism is not sufficientto employ a substrate depletion approach, it may be advisable to run a traditionalenzyme kinetics study utilizing multiple substrate concentrations. The results of afish hepatocyte assay also could be used at this time as a check on the results ofthe S9 assay, particularly if there was reason to believe that the two assays wouldprovide different results due to membrane diffusion limitations on metabolic rate,or because the compound is a potential substrate for membrane transport proteins.The results from these assays would be used to calculate in vitro intrinsic hepatic



clearance (CLin vitro,int).CLin vitro,int would then be extrapolated to estimate in vivo hep-atic clearance and incorporated into an established kinetic model to re-estimate theBCF. If the revised BCF is close to the criterion value, consideration should be givento repeating the analysis using metabolism information from other fish species.

Tier 5—In Vivo Determination of BCF or Risk Assessment

If, after completing Tiers 1–4, the predicted BCF is still greater than the estab-lished criterion, then one may assume that the chemical will bioaccumulate to anunacceptable level or conduct whole-animal exposures to test this possibility. Thepurpose of this test would be to provide a high-quality estimate of bioconcentrationand, by extension, provide a reliable prediction of bioaccumulation within a naturalexposure setting. Whole-animal tests of this type are generally performed using anaccepted protocol such as the OECD 305 test (OECD 1996).

SUMMARY AND RECOMMENDATIONS

This report summarizes the results of a workshop on the use of in vitro ADME datain bioaccumulation assessments for fish. Workshop participants agreed that absorp-tion and metabolism represent the two greatest sources of uncertainty in currentbioaccumulation assessments, and that both can be investigated using establishedin vitro methods. To a considerable degree, the proposal to use in vitro data in bioac-cumulation assessments builds on the successful experience of the pharmaceuticalindustry, which routinely uses in vitro data during the drug development process.Although the physicochemical characteristics of chemicals that accumulate in fishoften differ from those of drugs (in general, possessing much higher log Kow values),the problem faced in both enterprises is the same: rapid identification of compoundsthat clearly do or do not meet established criteria for acceptance (e .g ., the BCF cri-terion or, in the case potential drug candidates, criteria for oral bioavailability andelimination half-life). This allows testing resources to be focused on a much smallernumber of chemicals for which high-confidence predictions cannot be obtained bysimpler means.

Although in vitro methods exist to investigate the processes of absorption andmetabolism, very few studies have been performed to compare in vitro data within vivo outcomes for fish. The available database is too small to support conclusionson the quality of in vitro–in vivo metabolism extrapolations in piscine systems, whichin vitro system is most suitable for in vivo prediction, or which in vitro system is ap-plicable for which purpose. Established in vitro methods exist to estimate dietaryabsorption of drugs by mammals (e .g ., Caco-2 cells, PAMPA). Whether these modelscan be adapted to predict dietary uptake of xenobiotics by fish remains to be inves-tigated. There is an urgent need to conduct research to fill these data gaps; this willrequire new sources of research funding.

Because bioaccumulation assessments must be performed for a large numberof compounds, the methods used to perform these assessments must be amenableto high-throughput. One issue in this regard is the possible existence of a trade-off between the accuracy of an in vitro assay (defined as whether it predicts thesame process in vivo) and its ease of use. One system may be easy to use, but yields


J. Nichols et al.

inaccurate predictions for some compounds. A second may be more technicallydemanding, but provides data of greater accuracy. Under these circumstances, thefirst system may be more appropriate for initial screening and prioritization of largechemical inventories, whereas the second is better suited to collecting definitiveinformation for a smaller number of compounds.

An issue of critical concern for the future use of in vitro data in bioaccumulationassessments is that of methods standardization. Much of the variability noted in com-parisons of in vitro metabolism data among species may be related to methodologicaldifferences among studies. Standard methods for each of the major in vitro assaysshould be developed with the stated intent of supporting in vitro-in vivo extrapola-tions. We recommend that a future workshop be held to address this issue.

A “decision tree” approach was used to describe how in vitro data might be usedin future bioaccumulation assessments. Some reluctance on the part of scientistsand regulators to accept in vitro data in bioaccumulation assessments can be antic-ipated for the simple reason that it has not been done before. We recognize thatexisting in vitro methods may be insufficient to generate highly accurate predictionsof in vivo bioaccumulation for all chemicals. It may be possible, however, to use thisinformation in a “binning” approach (e .g ., low, medium, or high metabolism) toplace individual compounds into categories that would lead to more scientificallydefensible assessments and reduce the need for animal testing. This would representa major improvement over current assessment methods.

ACKNOWLEDGMENTS

This workshop was co-sponsored by the International Life Sciences Institute—Health and Environmental Sciences Institute (ILSI-HESI) and the Society of Envi-ronmental Toxicology and Chemistry (SETAC), and was organized by an ILSI-HESIEmerging Issues Bioaccumulation Workgroup. Financial support for the workshopwas provided by ILSI-HESI. We thank the individuals who participated in the work-shop, most of whom did so at their own expense. We also thank Dr. Keith Sappingtonand Dr. Larry Burkhard for constructive reviews of this report. The information inthis document has been funded in part by the U.S. Environmental Protection Agency.

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Andersson T, Forlin L, and Hansson T. 1983. Biotransformation of 7-ethoxycoumarin in iso-lated perfused rainbow trout liver. Drug Metab Disposit 11:494–8

Arnot JA and Gobas FAPC. 2003. A generic QSAR for assessing the bioaccumulation potentialof organic chemicals in aquatic food webs. QSAR Comb Sci 22:337–45

Arnot JA and Gobas FAPC. 2004. A food web bioaccumulation model for organic chemicalsin aquatic ecosystems. Environ Toxicol Chem 23:2343–55

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