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In-vivo Biotransformation Rates of Organic Chemicals in Fish: Relationship with Bioconcentration and Biomagnification Factors Justin C. Lo, Daniel J. Letinski, Thomas F. Parkerton, Dave A Campbell, and Frank A.P.C. Gobas Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b03602 • Publication Date (Web): 11 Nov 2016 Downloaded from http://pubs.acs.org on November 14, 2016

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Environmental Science & Technology

In-vivo Biotransformation Rates of Organic

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Chemicals in Fish: Relationship with

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Bioconcentration and Biomagnification Factors

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Justin C. Lo† Daniel J. Letinski‼, Thomas F. Parkerton‡ Dave A. Campbell§ and

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Frank A.P.C. Gobas *,†,∥

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† Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia,

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Canada V5A 1S6

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‼ExxonMobil Biomedical Sciences, Inc., Annandale, NJ 08801, United States

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‡ ExxonMobil Biomedical Sciences, Inc., Spring, Texas 77339, United States

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§ Department of Statistics and Actuarial Science, Simon Fraser University, Surrey, British

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Columbia, Canada V5A 1S6

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∥ School of Resource and Environmental Management, Faculty of the Environment, Simon

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Fraser University, Burnaby, British Columbia, Canada V5A 1S6

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* Corresponding Author

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Corresponding Author email: [email protected] , tel: 778-782-5928

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ACS Paragon Plus Environment

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ABSTRACT

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In-vivo dietary bioaccumulation experiments for 85 hydrophobic organic substances were

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conducted to derive in-vivo gastro-intestinal biotransformation rates, somatic biotransformation

21

rates, bioconcentration factors (BCF) and biomagnification factors (BMF) for improving

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methods for bioaccumulation assessment and to develop an in-vivo biotransformation rate data

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base for QSAR development and in-vitro to in-vivo biotransformation rate extrapolation. The

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capacity of chemicals to be biotransformed in fish was found to be highly dependent on the route

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of exposure. Somatic biotransformation was the dominant pathway for most chemicals absorbed

26

via the respiratory route. Intestinal biotransformation was the dominant metabolic pathway for

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most chemicals absorbed via the diet. For substances not biotransformed or transformed

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exclusively in the body of the fish, the BCF and BMF appeared to be closely correlated. For

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substances subject to intestinal biotransformation, the same correlation did not apply. We

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conclude that intestinal biotransformation and bioavailability in water can modulate the

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relationship between the BCF and BMF. However, this study also supports a fairly simple rule of

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thumb that may be useful in the interpretation of dietary bioaccumulation tests, i.e., chemicals

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with a BMFL < 1 tend to exhibit BCFs based on either freely dissolved (BCFww,fd) or total

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concentration (BCFww,t) of the chemical in the water that are less than 5,000.

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INTRODUCTION The capacity of chemicals to bioaccumulate in biota is recognized to be an important

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property that contributes to a substances’ potential to harm wildlife. Bioaccumulation is therefore

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widely considered in international and national chemical management programs (1-5). The

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bioconcentration factor (BCF) is a common metric used in regulations to express the extent of

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chemical bioaccumulation. The chemical’s octanol-water partition coefficient (KOW;

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COctanol/CWater) is a surrogate used to predict the extent of bioaccumulation. The field-derived

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bioaccumulation factor (BAF; COrganism/CWater) may also be used. Recent guidance also includes

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the biomagnification factor (BMF; COrganism/CDiet) and the trophic magnification factor (TMF; the

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antilog of the log-linear regression slope of COrganism vs. trophic level) and recommends a weight

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of evidence approach in bioaccumulation assessments (6-8). However, to date, the BCF often

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remains the preferred metric used in regulatory evaluations. The BCF is typically measured in

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laboratory bioconcentration tests, where organisms (e.g. fish) are exposed to the chemical via

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water. The preferred method for the determination of the BCF conforms with guidelines

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developed by the OECD (9). Current OECD protocols for bioaccumulation testing provide

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options for tests involving both aqueous and dietary exposure. Bioaccumulation tests are

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typically costly; time consuming; and require substantial animal use. An alternative to such

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testing, is the use of bioaccumulation models. These models have shown to be successful at

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estimating the BCF and BAF for chemicals that are not biotransformed considerably in the

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organism (10-11) but overestimate the extent of bioaccumulation for chemicals that are

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biotransformed (12). This bias is due to the fact that the models do not a priori incorporate

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predictions of the biotransformation rates of chemicals in organisms. To develop methods for

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improving BCF estimates of the many thousands of chemicals in commerce requiring evaluation,


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several research initiatives have developed. One initiative involves the back calculation of

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biotransformation rates from BCFs using the AQUAWEB model (10) and the subsequent

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development of a quantitative structure activity relationship (QSAR) for biotransformation that is

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incorporated in the US EPA EPI Suite program for estimating BCFs (13-16). A second initiative

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uses in-vitro measurements of chemical depletion rates in liver homogenates and hepatocytes,

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which are then extrapolated to make estimates of whole organism biotransformation rates, and

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then used as input to extrapolation models to estimate BCF values (17-23). This initiative aims to

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make BCF determinations less labor intensive, cheaper, and less animal-use intensive. A third

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initiative, explored in this study, involves the development of simplified test designs (involving

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less animals, labor and costs than typical OECD bioconcentration tests) to measure in-vivo

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biotransformation rates and corresponding BCFs of chemicals. This research serves to fill an

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important data gap for biotransformable substances because it provides actual measurements of

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biotransformation rates of chemicals in whole animals. The biotransformation rate data can be

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used to test the ability of in-vitro and QSAR-based methods to reliably estimate in-vivo

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biotransformation rates and BCFs.

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Generally accepted bioassays for the measurement of in-vivo biotransformation rates do not exist

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to date. Previous work on experimentally deriving in-vivo biotransformation rates revealed that

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in-vivo biotransformation involve both hepatic and gastro-intestinal biotransformation rates and

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that the contribution of somatic and gastro-intestinal biotransformation to bioaccumulation is

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dependent on the route of chemical exposure (24). This means that BCFs and BMFs may be

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affected by biotransformation in different ways. However, in most bioaccumulation models, in-

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vivo biotransformation is viewed as depuration from the body (i.e. somatic) only (25), hence

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affecting BCF and BMFs in a similar way.


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The objective of the present study is to develop and apply a method for simultaneously

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determining in-vivo gastro-intestinal biotransformation rates, somatic biotransformation rates,

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BCFs and BMFs. Such a test methodology does currently not exist. A second goal is to provide

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measurements of in-vivo biotransformation rates for a number of structurally diverse chemicals

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to allow the testing and further development of quantitative structure activity relationships for

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predicting biotransformation rates and the testing of extrapolation methods for estimating in-vivo

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rates from in-vitro biotransformation rate data. Such a biotransformation data base is also not

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available to date. A third objective is to investigate the relationship between the BCFs and BMFs

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for substances subject to somatic and gastrointestinal biotransformation. This information is also

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not available. The main purpose of the study is to make bioaccumulation determinations for

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substances more accurate, efficient, and less costly, while reducing animal use.

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THEORY Bioaccumulation Model for in-vivo biotransformation studies

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To describe the contribution of biotransformation of chemicals in the soma (i.e. somatic

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biotransformation including hepatic metabolism) and in the gut of the fish (i.e. gastro-intestinal

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biotransformation in the lumen of the intestines due to intestinal microflora and gastric

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enzymes), the fish is divided into two compartments, i.e., the body (B) and the gastrointestinal

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content or digesta (G). The following mass balance for the body of the fish describes this process

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(24):

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dCB/dt = kB1·CWD + (kGB /(kGB + kGE + kGM))·(GI / WB)·CD – (kB2 + kBG·((kGE + kGM)/(kGB + kGE +

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kGM)) + kGD + kBM)·CB

(1)

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where CB is the concentration of the chemical in the body of the fish (mol/kg fish); GI is the

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food ingestion rate (kg food·d-1), CWD is the freely dissolved concentration of the chemical in the

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water (mol chemical·L-1); CD is the concentration of the chemical in ingested diet (mol

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chemical·kg food-1); WB is the weight of the fish (kg) on a wet weight (ww) basis; kB1 is the

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uptake clearance rate for respiratory uptake (L water·kg ww fish-1·d-1); kB2, kGB, kBG, kGD, kBM,

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kGE, and kGM are the rate constants (d-1) for respiratory elimination, chemical transfer from the

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gastrointestinal content to the fish body; chemical transfer from the fish body to the

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gastrointestinal content, growth dilution, biotransformation of the chemical in the body of the

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fish (i.e. somatic biotransformation), fecal egestion of the gastrointestinal content, and

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biotransformation of the chemical in the gastrointestinal content, respectively; and t is time (d).

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This equation can be simplified by recognizing that kGB / (kGB + kGE + kGM) in Equation 1 is the

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dietary uptake efficiency for a substance that is biotransformed in the gastro-intestinal tract

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(ED,M) and that (GI/WB) is the proportional feeding rate FD expressed as the fraction of the fish’s

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body weight consumed in food per day:

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dCB/dt = kB1·CWD + ED,M ·FD·CD – (kB2 + kBG·(1 – ED,M) + kGD + kBM)·CB (2)

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A detailed derivation of the model can be found in Lo et al. (24). Figure 1 shows that intestinal

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biotransformation includes both (i) chemical transformation upon ingestion and (ii) chemical

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transformation upon chemical elimination from the body of the fish into the lumen. Likewise, the

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chemical flux biotransformed in the soma also consists of dual contributions, i.e. (i) chemical

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transformation upon respiratory uptake and (ii) chemical transformation upon chemical transport

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from the lumen into the fish body. The total chemical flux biotransformed due to gastrointestinal


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biotransformation (i.e. kGM·MG) or somatic biotransformation (i.e. kBM·MB) is therefore

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dependent on the route of chemical intake. The application of the mass balance approach to

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determine biotransformation rates is a frequently used strategy in biotransformation research. It

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is based on the assumption that loss of mass of the test chemicals relative to non-

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biotransformable reference chemicals is due to biotransformation. This research strategy can

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derive overall biotransformation rates of chemicals, but lacks the capacity to detect individual

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biotransformation products. It complements research focused on detection of specific

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metabolites.

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Somatic biotransformation rate constant (kBM)

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Under conditions of first order kinetics of biotransformation and transport kinetics, the somatic

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biotransformation rate constant (kBM) can be determined from measurements of the depuration

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rate constants when using non-biotransformable reference chemicals (24) since, for

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biotransformed chemicals, the total elimination rate constant in the body (kBT) is:

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kBT = kB2 + kBG·(1 – ED,M) + kGD + kBM

(3)

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while for the non-biotransformed reference chemicals, the total depuration rate constant of the

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chemical from the body of the fish (kBT,R) is

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kBT,R = kB2 + kBG·(1 – ED,N) + kGD

(4)

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where ED,N is the dietary uptake efficiency for a non-biotransformed substance, i.e. ED,M but with

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a kBM of 0. The somatic biotransformation rate constant in the body of the fish can therefore be

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determined as:

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kBM = kBT,R – kBT

(5)

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where kBT,R is depuration rate constant of a reference chemical with the same KOW as that of the

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test chemical. For substances with a log Kow > 3, the following linear regression model (26) can

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be used to determine kBT,R:

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kBT,R = (1/ω)·(1/KOW) + β

(6)

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Where 1/ω and β are regression coefficients in units of d-1. The intercept β represents the kBT,R

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for a substance with an infinite KOW and hence can be approximated with kGD. As described in

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Gobas and Lo (26), 1/ω represents the increase in resistance to chemical transport from the fish

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to the water with increasing KOW, and is a function of the lipid content fish body ΦBL and the

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body weight of the fish (27). To derive a relationship between kBT,R and 1/KOW that can account

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for the differences in growth rates, lipid contents, and body weights between the multiple

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bioaccumulation tests of the present study, Equation 6 was rewritten as:

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kBT,R = (α·WBb/ ΦBL)·(1/KOW) + kGD

(7)

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where α and b are allometric coefficients, describing the fish’s body weight dependence of the

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water phase transport parameter. It should be stressed that when following this method for

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deriving biotransformation using structurally different test and reference chemicals, it is

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inherently assumed that KOW is the most important chemical-specific factor controlling the non-

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biotransformation related depuration kinetics of non-ionic hydrophobic substances.

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Respiratory uptake and elimination rate constants and the Bioconcentration Factor (BCF)

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As detailed in the SI, the wet weight based BCF based on the freely dissolved concentration of

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the chemical in the water (BCFWW,fd) can also be derived as:

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BCFWW,fd = kB1/kBT = (α·WBb/dL)/kBT

(8)

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BCFs calculated in this fashion are kinetic BCFs at steady-state based on freely dissolved

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concentrations of the chemical. BCFs based on the total concentration of the chemical in the

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water (BCFWW,t) measured in OECD 305 style aqueous exposure tests and considered in most

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regulations are based on a total chemical concentration in the water and are lower than those

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calculated here, especially for very hydrophobic chemicals due to their high binding affinity to

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organic matter in the water. The BCFWW,fd based on freely dissolved concentrations of the

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chemical in the water can be converted into the BCFWW,t following equations by Burkhard (28)

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or Arnot and Gobas (10) based on equilibrium partitioning of the chemical between the water

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and dissolved organic matter:

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BCFww,t = BCFww,fd.(1 + χOC.KOC)-1

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(9)

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where χOC is the concentration of organic carbon in the water (kg/L), respectively. KOC is the

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equilibrium partition coefficient of the chemical between organic carbon and water. The BCFww,t

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and BCFww,fd can be expressed on a lipid normalized basis as BCFL,t, i.e. BCFL,t =BCFWW,t / ΦBL

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and BCFL,fd =BCFWW,fd / ΦBL respectively, or expressed as a BCF for fish with a lipid content of

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5%, i.e. BCF5%,t = 0.05·BCFL,t and BCF5%,fd = 0.05·BCFL,fd respectively, if the BCF follows a

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linear relationship with the lipid content of the fish (e.g. lipophilic chemicals).

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Gastrointestinal biotransformation (kGM)

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Under conditions of first order kinetics of biotransformation and transport kinetics, the intestinal

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biotransformation rate constant (kGM) can be determined from measurements of the dietary

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uptake efficiencies for biotransformable test chemicals (ED,M) and non-biotransformable

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reference (ED,N) chemicals (24) as:

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-1

-1

kGM = (ED,M – ED,N )·(ED,N / (1-ED,N))·(GGE / WG)

(10)

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where GGE (kg digesta·d-1) is the fecal egestion rate and WG (kg) is the steady state amount of

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digesta in the gastrointestinal tract. As described in Lo et al. (24), GGE can be estimated from the

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dietary ingestion rate GI, i.e. the product of the proportional feeding rate FD and the weight of the

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body of the fish WB, and the food assimilation efficiency γGI as γGI·GI. WG can be estimated as


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the ratio GI/δ, where δ is the digesta evacuation rate constant (d-1), which can be approximated

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by the 95% digesta evacuation time (tE,95) as 3/tE,95, as explained in the SI.

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Biomagnification factors (BMF)

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BMFs can be determined from the dietary uptake efficiency and the depuration rate constant as:

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BMFWW = kBD / kBT = ED,M·FD / kBT

(11)

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The BMFWW can be expressed on a lipid normalized basis as BMFL, i.e. BMFL = BMFWW·(ΦDL /

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ΦBL). The BMFL expresses true chemical magnification, i.e. an increase in chemical potential (or

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activity) that occurs as a result dietary bioaccumulation. The BCFL can also be expressed in a

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form that approximates the maximum theoretical bioconcentration at equilibrium by dividing

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BCFL by KOW, for substances for which octanol adequately represents the partitioning properties

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of the chemical in the lipids of the fish (6).

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EXPERIMENTAL

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General:

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To measure somatic and intestinal biotransformation rates, dietary uptake efficiencies, BCFs and

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BMFs of a range of neutral hydrophobic organic chemicals in rainbow trout, ten dietary

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bioaccumulation tests (i.e. 9 studies performed at Exxon Mobil Biomedical Sciences. Inc.

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(EMBSI) and one study (24), conducted at Simon Fraser University (SFU)) following a similar

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methodology were carried out. The SFU study complements the EMBSI study by providing


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reference chemicals that cover the range of log KOW of the test chemicals in the EMBSI study.

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Details of the study at SFU can be found in (24). The tests performed at EMBSI are described

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below. Methods for chemical and lipid content analyses are in the SI.

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Fish:

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Juvenile rainbow trout (Oncorhynchus mykiss) were obtained from Thomas Fish Company. Fish

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were kept in 31 L flow-through aquaria, with a flow through rate of approximately 5-7

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replacement volumes per day. An average of 53 (SE 5) test fish were used in each test. Water

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temperatures were kept at 13.6 (SD 0.3) ºC, approximately the same to the 12.6 ºC in Lo et al.

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(24). Water contained a mean dissolved oxygen content of 9.0 (SD 0.2) mg/L, and the pH was

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7.7 (SD 0.2). The mean fish weight from all experiments was 1.5 (SD 0.5, range 0.9 – 2.3) g, and

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the mean fish lipid content was 3.6 (SD 0.8, range 2.4 – 5.6) % wet weight (Table S1). Fish were

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fed Finfish Starter, #1 crumble (Zeigler Bros., Inc., Gardners, PA, USA) an average of 3 (SD 1) %

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bodyweight·d-1. The dietary lipid content in the studies ranged between 15 and 15.6 %, and was

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slightly lower than the value of 18.6% used in Lo et al. (24). In each feeding study, a control fish

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group was present to monitor for effects and to account for potential background concentrations

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of the test substances in fish tissues. Effects monitored in both test and control fish groups

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included mortality, growth rate constants, changes in feeding behavior (any deviations from

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rapid feeding), and other adverse effects including physical attributes (e.g. pigmentation, etc.),

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lethargy, and swimming behavior.

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Chemicals:


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Test chemicals included parent and alkylated aromatic hydrocarbons, cycloalkanes, and linear

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and branched aliphatic hydrocarbons, musk xylene and methoxychlor. The log KOW of the test

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chemicals were obtained from EpiSuite 4.11 and varied between 3.3 and 8.9 (Table S2). All 9

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dietary bioaccumulation tests included the reference chemical hexachlorobenzene which was

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assumed to undergo no or negligible biotransformation. The test chemical trans-decalin was also

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considered a reference chemical because previous work found trans-decalin to resist somatic

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biotransformation in rainbow trout (24). In each of 9 tests, 5 to 14 test and reference chemicals

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(Table S2) were dissolved in corn oil and added to the feed. Individual chemicals in the test

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mixture were selected to provide diverse hydrocarbon structures of varying hydrophobicity and

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facilitate use of a common analytical method while avoiding toxicity to exposed fish.

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Motivations for investigating multiple test compound exposures rather than individual chemicals

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were to reduce vertebrate animal use, testing costs and time required for in-vivo data collection.

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The content of corn oil spiked to diet was 0.5%. The chemical concentrations in the diet were

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measured in triplicate at the beginning and end of the uptake period to confirm the stability of the

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chemical in the food, as described in the Supporting Information where the mean and standard

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deviation of diet exposure concentrations are reported in Table S2.

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Dietary bioaccumulation studies:

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Fish were fed a contaminated diet for 10 to 14 days, followed by a 3-24 day depuration phase

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with no chemical exposure. Diets contained an average of 11 (range 5-14) chemicals per test

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(Table S2). Fish were sampled throughout the uptake and depuration phase, with 3-10 fish

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sampled for each time point. The whole fish were homogenized and used for chemical

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extraction.


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Kinetic analysis:

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The methodology for the kinetic analysis of the data is included in the SI.

290 291 292

RESULTS AND DISCUSSION

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Diet:

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Measured concentrations of the various test chemicals in the diet ranged from 370 to 1171µg/g

296

(Table S2). Concentrations of the test and reference chemicals in the diet did not appear to

297

change significantly throughout the exposure period, as evidenced by the low standard deviations

298

of the dietary concentration measured throughout the exposure period and the associated low

299

coefficient of variation for concentrations in fish foods from the beginning and end of the

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exposure period.

301 302

Fish:

303

No fish mortalities, changes in feeding behavior, or other apparent adverse effects were observed

304

in the exposure and control groups of all experiments. Also, there was no evidence of a

305

difference in growth rates (kGD) between control and test groups (Table S1). Mean fish body

306

weights increased over time. The growth rates, calculated as the slope of the natural logarithm of

307

the fish weight vs. time for each of the 9 experiments varied from 0.027 to 0.047 d-1 with a mean

308

of 0.04 (SD 0.01) d-1. Starting fish weights among 9 experiments ranged between 0.88 and 2.3 g

309

with a mean value of 1.5 (SD 0.5) g (Table S1). The lipid content of the fish body among the


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tests varied between 2.4 and 5.6%. The mean lipid content of the fish’s diet was 15.5% (range

311

15-15.6%) (Table S1).

312 313

Concentrations of chemicals in the fish body:

314

Concentrations of the test and reference chemicals in the control fish groups were below their

315

limits of quantitation. In all cases, the mean concentration of the chemicals in the test fish body

316

increased throughout the dietary exposure phase, with certain chemicals approaching an apparent

317

steady state before the end of the exposure period. During the depuration phase, mean

318

concentrations in the fish body decreased in an apparent log-linear fashion, with concentrations

319

of certain chemicals decreasing below detectable levels before the end of the depuration period

320

(Figure S1).

321 322

Depuration rate constants

323

Total depuration rate constants from the fish body for the reference (kBT,R) and test (kBT)

324

chemicals (Table S2) decreased with increasing KOW (Figure 2A). This is due in part to the

325

decrease in respiratory elimination with increasing hydrophobicity. A weighted multiple linear

326

regression (regression weights = SE-1) of the depuration rate constants of the reference chemicals

327

(kBT,R) in the study by Lo et al. (24) (n = 8) and in the present study (n = 10) using Equation 7 for

328

the juvenile rainbow trout produced the following relationship:

329 330

kBT,R = [291(SE 34)WB[-0.19(SE 0.02)] / ΦBL]·(1/KOW) + kGD, n=18, RMSE = 0.15

(12)

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where fish body lipid content (ΦBL), fish body weight (WB), and the growth rate (kGD) from

333

populations of test fish were specific to each experiment (Table S1). Empirical kBT,R values for

334

each test chemical are reported in Table S2. Figure S3 shows that all test chemicals exhibited

335

depuration rate constants that were equal to or greater than the kBT,R, with the exception of

336

naphthalene, 1,3,5-trimethylbenzene, and cis-bicyclo(4,3,0)nonane, which exhibit some of the

337

lowest reported KOW values of the chemicals in the present study. These chemicals may illustrate

338

the limits of the current study design for determining somatic biotransformation rates. For these

339

low Kow chemicals, respiratory elimination rate constants are high, making it difficult to obtain

340

reliable values of relatively low biotransformation rate constants, which are derived as the

341

difference between the high depuration rates of both test and reference chemicals.

342 343

Somatic biotransformation rate constants

344

The somatic biotransformation rate constant (kBM) estimates for the test chemicals are listed in

345

Table S2. Somatic biotransformation rate constants of 6 of the test chemicals in the EMBSI

346

studies were in good agreement with those in Lo et al. (24) after the somatic biotransformation

347

rate constants are normalized to the same size fish following Arnot et al. (14) (Figure S4). A

348

comparison of empirical kBM and body-weight normalized kM values estimated by the BCFBAF

349

QSAR (EpiSuite 4.11) illustrates some agreement between kBM and kM values (Figure 3). A

350

regression analysis of the empirical log kBM data and BCFBAF QSAR log kM estimates indicates

351

a correlation coefficient (r2) of 0.23 and that kM estimates are approximately within 2 orders of

352

magnitude of the empirical kBM data in 95% of cases. It should be stressed that because fish were

353

exposed to multiple chemicals at a single concentration for each chemical, there is the potential

354

that both competitive inhibition and enzyme saturation effects affect the measured


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biotransformation rates. The in-vivo biotransformation rate constants reported in this study may

356

therefore be more conservative (i.e. lower) than in single compound experiments where dietary

357

chemical concentrations are lower than those used in this study and/or competing substrates are

358

absent.

359 360

Dietary Uptake Efficiency

361

The following relationship between ED,N and KOW (Figure S5) observed for the reference

362

chemicals from Lo et al. (24) (n = 7) was used to determine intestinal biotransformation rate

363

constants:

364 365

E, = 5.6 × 10-9 (SE 1.8 × 10-9) × KOW + 1.9 (SE 0.1)

(13)

366 367

In all tests, the dietary assimilation efficiencies of test chemicals were either equal to or less than

368

the dietary assimilation efficiencies of the corresponding reference chemicals (Figure 2C). This

369

indicates that the ED,N-KOW relationship serves as a reasonable reference point for deriving

370

intestinal biotransformation rates in fish species.

371 372

Gastro-intestinal biotransformation rate constants

373

Estimates of kGM are listed in Table S4. For the majority of chemicals tested in this study, there

374

are no gastro-intestinal biotransformation rate data that can be used for comparison. However,

375

benzo[a]pyrene and related polycyclic aromatic hydrocarbons (PAHs) have shown low dietary

376

uptake efficiencies in rainbow trout (30-33) and are significantly biotransformed into water

377

soluble metabolites in the intestines of fish, with 50% (for un-induced animals) and 90% (for


17


Page 18 of 41

378

induced animals) recovery of benzo[a]pyrene from the portal vein in the form of metabolites

379

(34). These findings are in agreement with the findings from this study and further indicate the

380

importance of gastrointestinal biotransformation on bioaccumulation of many substances in fish.

381 382

Respiratory uptake and elimination rate constants and BCF

383

The bodyweight-scaled respiratory uptake rate constant (kB1) and elimination rate constant (kB2)

384

were derived as α·WBb/dL and (α·WBb/ΦBL)·(1/KOW) respectively, based on the regression

385

coefficients α of 291 (SE 34), b of -0.19 (SE 0.02) (Equation 12), the density of lipid dL of 0.9

386

and the lipid content of the fish (ΦBL) in each experiment and listed in Table S3. BCFWW,fd

387

derived as kB1/kBT, and BCFww,t and corresponding values that are adjusted to a 5% lipid content

388

(BCF5%,t t) are listed in Table S3. The mean BCFww,fd and a BCFww,t of all test chemicals were

389

less than 5,000. The mean BCF5%,t was also less than the regulatory criterion of 5,000 for all

390

chemicals except hexadecahydropyrene. No test chemical was found to exhibit a BCFww,fd ,

391

BCFww,t or BCF5%,t significantly (p=0.05) greater than 5,000. There appears little correlation

392

between the BCF5%,t and KOW for the test chemicals (Figure 2B), caused in large part by the fact

393

that the majority of test chemicals are biotransformed at rates that exceed respiratory elimination

394

rates. The lack of correlation between BCF5%,t and KOW suggests caution in the derivation of the

395

BCF5%,t from BCFww,t as linearity between the BCFww,t and the fish’s lipid content may not exist

396

for biotransforming chemicals.

397 398

Dietary uptake and excretion rate constants and BMF

399

Rate constants for gastro-intestinal exchange (kGB, kBG) and fecal egestion (kGE), derived from the

400

experimental observations according to Lo et al. (24), are listed in Table S5. The BMFs, derived


18

Page 19 of 41


401

as (FD·ED,M)/kBT are reported in Table S4 for all test chemicals and are also expressed on a lipid

402

normalized basis (as BMFL in units of L dietary lipid/L fish body lipids) in Figure 2D. The

403

BMFL of all test chemicals were less than 1. Only the reference chemical hexachlorobenzene

404

exhibited a BMFL greater than 1. There appeared to be no relationship between the BMFL and

405

KOW, likely as a result of the high biotransformation rates of the test chemicals in both the soma

406

and gastro-intestinal contents of the fish.

407 408

Internal distribution

409

The ability of the test design to derive the various rate constants (Tables S1-S5) in the

410

bioaccumulation model (Figure 1) from the empirical data allows for evaluation of the internal

411

distribution of the test and reference chemicals in fish and to determine the contribution of

412

transport and transformation processes to the bioaccumulation behavior of the chemical under

413

various exposure scenarios. Figure 4, which shows chemical transport and transformation fluxes

414

as a fraction of the total intake flux from either a dietary or aqueous exposure, illustrates that

415

benzo[a]pyrene is biotransformed in both the digesta and in the body (including the liver) of the

416

fish. When exposed via the diet, the great majority of benzo[a]pyrene (i.e. 98.1%) is

417

biotransformed in the intestinal tract. Vetter et al. (35) also demonstrated rapid benzo[a]pyrene

418

metabolism in the intestines of fish. Bock et al. (36) demonstrated that benzo[a]pyrene is

419

extensively metabolized during the passage through the gastrointestinal tract of the rat. Figure 4

420

also shows that when fish are exposed via the respiratory route, the great majority of

421

benzo[a]pyrene (i.e. 98.5%) is biotransformed in the body of the fish. For benzo[a]pyrene, both

422

somatic and intestinal biotransformation appear to play an important role in chemical

423

bioaccumulation and the relative contribution of the soma and intestines as a site for


19


Page 20 of 41

424

biotransformation is largely controlled by the relative concentrations of benzo[a]pyrene in the

425

diet and water of the fish. Figure 4 shows that this is not the case for triphenylene. Triphenylene

426

appears to be virtually recalcitrant in the intestinal tract while it is quickly biotransformed in the

427

fish body. Upon ingestion, approximately half the ingested dose of triphenylene is egested in an

428

untransformed state in fecal matter. The other half of the ingested dose is absorbed into the body

429

of the fish and then almost fully biotransformed in fish body. Upon respiratory uptake via water

430

exposure, virtually all triphenylene is biotransformed in the fish body.

431 432

Figure 5A illustrates the relative contribution of somatic and gastro-intestinal biotransformation

433

for all the test chemicals when exposed only through the diet. It shows that for the majority of

434

the ingested test chemicals, gastro-intestinal biotransformation contributes the majority of a

435

substance’s biotransformation. Figure 5B shows that test chemicals exposed via the respiratory

436

route are in most cases primarily biotransformed in the fish body. In real world exposure

437

scenarios, where exposure occurs via both respiratory and dietary routes, the relative contribution

438

of somatic and intestinal biotransformation and hence the rate of biotransformation will depend

439

on the relative concentrations of the chemical in the water and the diet. Figure S6 shows that for

440

the chemicals tested there is not a general relationship between the somatic and gastro-intestinal

441

biotransformation rate constants. This suggests that biotransformation pathways and associated

442

metabolic stability of a chemical in the liver and the intestinal tract may differ substantially.

443

These findings suggest that while biotransformation rate determinations in hepatocytes, liver

444

tissues or liver homogenates such as S9 and liver microsomes are useful measures of somatic

445

biotransformation rates, they do not fully characterize the ability of biotransformation processes

446

in the fish to mitigate bioaccumulation of chemicals. Extra-hepatic biotransformation in the


20

Page 21 of 41


447

intestinal tract due to digestive and intestinal mucosal enzymes and resident bacteria is

448

recognized for many food components and chemicals (37). The development of in-vitro

449

bioassays for gastro-intestinal biotransformation may be a useful contribution to on-going

450

hepatic in-vitro to in-vivo extrapolation methods for bioaccumulation assessments. Further

451

research is needed to understand the role of fish enzymes and micro-flora on biotransformation

452

in the intestinal tract and to characterize biotransformation pathways in the gut. Further analysis

453

of dietary in-vivo bioaccumulation test data in relation to chemical structure may support the

454

development of quantitative structure-activity relationships for both somatic and gastrointestinal

455

biotransformation that are needed to advance in-silico methods for improving bioaccumulation

456

model predictions.

457 458

Relationship between BCF and BMF

459

Because the testing methodology explored in this study produces both BCF and BMF estimates,

460

the relationship between the BCF and BMF can be explored. The BCF-BMF relationship is

461

useful in interpreting data from dietary bioaccumulation tests in terms of the BCF required by

462

regulations. Figure 6 illustrates that, within a single test, the freely dissolved wet weight BCF in

463

rainbow trout, (BCFWW,fd) and the lipid-normalized BMF (BMFL) of non-biotransformable

464

substances, represented by the reference chemicals, are closely related:

465 466

log BCFWW,fd = 1.20(SE 0.11)·log BMFL + 3.72(SE 0.06), n=16, r2=0.90, p < 0.001

(14)

467 468

The close relationship between the BCFWW,fd and BMFL is due to the fact that both the BCF and

469

the BMF are a function of the same depuration rate constant (kBT). The theoretical basis for the


21


Page 22 of 41

470

relationship between the BCF and BMF has been discussed in more detail in Mackay et al. (25).

471

Equation 14 shows that if a non-biotransformable substance exhibits a BCFWW,fd equal to

472

regulatory criterion of 5,000, then the mean predicted BMFL can be expected to be

473

approximately 1.0 with lower and upper 95% confidence intervals of 0.8 and 1.3 respectively.

474

This confirms that the regulatory criterion for the BCF of 5,000 is a reasonable threshold for

475

identifying non-biotransformable chemicals that have significant biomagnification potential.

476

Non-biotransformable substances with BCFWW,fd less than the regulatory criterion of 5,000 do

477

not show a significant biomagnification potential (Figure 6). Substances that are biotransformed

478

exclusively in the body of the fish also adhere to the same BCFWW,fd-BMFL relationship

479

applicable to non-biotransforming substances because somatic biotransformation contributes to

480

the whole body depuration rate that controls both the biomagnification and bioconcentration

481

factors. However, Figure 6 shows that a loss of the BCFWW,fd -BMFL relationship occurs for

482

substances that are biotransformed in the intestines of the fish. In all cases, intestinal

483

biotransformation produces BMFL that are less than expected from the BCFWW,fd -BMFL

484

relationship described by Equation 14. Gastro-intestinal biotransformation lowers the effective

485

concentration of the chemical in the intestinal tract and reduces the chemical’s dietary uptake

486

efficiency. For substances subject to intestinal biotransformation, the BCF has a tendency to

487

overestimate the biomagnification potential of substances. Substances that are significantly

488

transformed in the intestinal tract do not have a biomagnification potential and may be of less

489

concern for bioaccumulation than chemicals that biomagnify (i.e. BMF >1). Bioaccumulation

490

tests using aqueous exposure can only identify the bioconcentration behavior of the test chemical

491

because it is insensitive to the intestinal biotransformation rate. Dietary bioaccumulation tests,

492

which are often less costly, time-involved and labor intensive than bioconcentration tests are


22

Page 23 of 41


493

more insightful than standard bioconcentration tests because of their ability to provide

494

information on somatic and intestinal biotransformation rates as well as the BMF and BCF.

495

Empirical correlations between the BCF and BMF (38), which are attractive because of their

496

ability to express data from dietary bioaccumulation tests in terms of the regulatory required

497

BCF pose considerable limitations since the correlation is highly sensitive to the inclusion of

498

chemicals subject to high rates of intestinal biotransformation and low bioavailability in water.

499

As this study shows many hydrophobic organic chemicals are subject to intestinal

500

biotransformation and exhibit a reduced bioavailability in water due to their high sorption

501

potential to organic matter in the water phase. While this study demonstrates that intestinal

502

biotransformation and bioavailability in water can modulate the relationship between the BCF

503

and BMF, this study also supports a fairly simple rule of thumb that may be useful in the

504

interpretation of dietary bioaccumulation tests, namely that chemicals with a BMFL < 1 tend to

505

exhibit BCFs based on either freely dissolved (BCFww,fd) or total concentration (BCFww,t) of the

506

chemical in the water that are less than 5,000.

507 508 509 510 511 512

ACKNOWLEDGEMENT The authors acknowledge the Natural Science & Research Council of Canada for support of this study.


23


513 514 515 516 517 518 519 520 521

Page 24 of 41

SUPPORTING INFORMATION The supporting information includes (i) the derivation of kBG (chemical transfer from the body to the GI content rate constant) and δ (digesta evacuation rate constant) and (ii) details on the measurement of chemical concentrations, lipid content and details on the estimation of kBT and kBM, BCFWW,fd, ED,M and kGM. This material is available free of charge via the Internet at http://pubs.acs.org. REFERENCES

522

(1) United Nations Environmental Programme (2001), Final Act of the Conference of

523

Pleniopotentiaries on The Stockholm Convention on Persistent Organic Pollutants.

524

Secretariat of the Stockholm Convention, Geneva, Switzerland. [cited 2016, May 6].

525

Available from:

526

http://www.pops.int/documents/meetingsdipcon/25june2001/conf4_finalact/en/FINALA

527

CT-English.pdf

528 529 530 531 532

(2) Government of Canada (1999), Canadian Environmental Protection Act, 1999. Canada Gazette, Part III, Vol 22. Public Works and Government Services, Ottawa, ON, Canada. (3) US Congress (1976), Toxic Substances Control Act, Pub. L. No. 94-469 (October 11, 1976). Washington, DC. (4) European Commission (2006), Regulation (EC) 1907/2006 of the European Parliament

533

and of Council of 18 December 2006 concerning the registration, evaluation,

534

authorization and restriction of chemicals (REACH), establishing a European Chemicals

535

Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) 79393

536

and Commission Regulation (EC) No. 1488/94 as well as Council Directive 76/769/EEC

537

and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC.

538

Official J Eur Union L396:374-375.


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(5) Japanese Ministry of the Environment (2001), Japanese Chemical Substances Control

540

Law. Ministry of Economy, Trade and Industry (METI), Labor and Welfare (MHLW),

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Ministry of the Environment (MOE), Tokyo, Japan.

542

(6) Burkhard, L.P., J.A. Arnot, , M. R. Embry, K. J. Farley|, R. A. Hoke, M. Kitano, H. A.

543

Leslie, G. R. Lotufo, T. F. Parkerton, K. G. Sappington, G. T. Tomy (2012), Comparing

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Laboratory and Field Measured Bioaccumulation Endpoints, Integr Environ Assess

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Manag, 8: 17–31.

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(7) ECHA. Guidance on Information Requirements and Chemical Safety Assessment.

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Chapter r.7c: Endpoint Specific Guidance. Version 2.0; European Chemicals Agency:

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Helsinki, November 2014.

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(8) ECHA. Guidance on Information Requirements and Chemical Safety Assessment.

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Chapter r.11: PBT/vPvB Assessment. Version 2.0; European Chemicals Agency:

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Helsinki, November 2014.

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(9) OECD. OECD Guidelines for Testing Chemicals. Test No. 305: Bioaccumulation in Fish:

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Aqueous and Dietary Exposure; Organization for Economic Co-operation and

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Development: Paris, 2012.

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Arnot, J.A.; Gobas, F. A. P. C. A food web bioaccumulation model for organic

chemicals in aquatic ecosystems. Environ. Toxicol. Chem. 2004, 23 (10), 2343-2355. (11)

Gobas, F. A. P. C.; Arnot, J. A. Food web bioaccumulation model for

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polychlorinated biphenyls in San Francisco Bay, California, USA. Environ. Toxicol.

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Chem. 2010, 29 (6), 1385-1395.


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Kuo D.T.; Di Toro D.M. A reductionist mechanistic model for bioconcentration

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of neutral and weakly polar organic compounds in fish. Environ. Toxicol. Chem. 2013.

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32(9), 1385-1395.

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U.S. Environmental Protection Agency Estimation Program Interface for

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Windows (EPI Suite v. 4.11) [Computer software] Syracuse, NY: EPA's Office of

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Arnot, J. A.; Meylan, W.; Tunkel, J.; Howard, P. H.; Mackay, D.; Bonnell, M.;

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Boethling, R. S. A quantitative structure-activity relationship for predicting metabolic

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biotransformation rates for organic chemicals in fish. Environ. Toxicol. Chem. 2009, 28

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(6), 1168-1177.

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Brown, T. N.; Arnot, J. A.; Wania, F. Iterative fragment selection: A group

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contribution approach to predicting fish biotransformation half-lives. Environ. Sci.

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Technol. 2012, 46 (15), 8253-8260.

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Papa, E.; van der Wal, L.; Arnot, J. A.; Gramatica, P. Metabolic

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biotransformation half-lives in fish: QSAR modelling and consensus analysis. Sci. Total

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Environ. 2014, 470-471, 1040-1046.

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Fay, K. A.; Mingoia, R. T.; Goeritz, I.; Nabb, D. L.; Hoffman, A, D.; Ferrell, B,

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D.; Peterson, H, M.; Nichols, J. W.; Segner, H.; Han, X. Intra- and interlaboratory

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reliability of a cryopreserved trout hepatocyte assay for the prediction of chemical

579

bioaccumulation potential. Environ. Sci. Technol. 2014, 48 (14), 8170-8178.

580 581

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Han, X.; Nabb, D. L.; Yang, C.-H.; Snajdr, S. I.; Mingoia, R. T. Liver

microsomes and S9 from Rainbow Trout (Oncorhynchus mykiss): comparison of basal-


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level enzyme activities with rat and determination of xenobiotic intrinsic clearance in

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support of bioaccumulation assessment. Environ. Toxicol. Chem. 2009, 28 (3), 481-488.

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Lee, Y-S.; Lee, D. H. Y.; Delafoulhouze, M.; Otton, S. V.; Moore, M. M.;

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Kennedy, C. J.; Gobas, F. A. P. C. In-vitro biotransformation rates in fish liver S9: effect

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of dosing techniques. Environ. Toxicol. Chem. 2014, 33 (8), 1885-1893.

587

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Lo, J. C.; Allard, G. N.; Otton, S. V.; Campbell, D. A.; Gobas, F. A. P. C.

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Concentration dependence of biotransformation in fish liver S9: optimizing substrate

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concentrations to estimate hepatic clearance for bioaccumulation assessment. Environ.

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Toxicol. Chem. 2015, 34 (12), 2782-2790.

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Nichols, J. W.; Schultz, I. R.; Fitzsimmons, P, N. In-vitro-in-vivo extrapolation of

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quantitative hepatic biotransformation data for fish: I. a review of methods and strategies

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for incorporating intrinsic clearance estimates into chemical kinetic models. Aquat

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Toxicol. 2006, 78 (1),74-90.

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Cowan-Ellsberry, C. E.; Dyer, S. D.; Erhardt, S.; Bernhard, M. J.; Roe, A. L.;

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Dowty, M. E.; Weisbrod, A. V. Approach for extrapolating in-vitro metabolism data to

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refine bioconcentration factor estimates. Chemosphere. 2008, 70, 1804-1817.

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Nichols, J.W.; Huggett, D. B.; Arnot, J. A.; Fitzsimmons, P. N.; Cowan-Ellsberry,

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C. E. Towards improved models for predicting bioconcentration of well-metabolized

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compounds by rainbow trout using measured rates of in-vitro intrinsic clearance.

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Environ. Toxicol. Chem. 2013, 32 (7):1611-1622.

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Lo, J. C.; Campbell, D. A.; Kennedy, C. J.; Gobas, F. A. P. C. Somatic and

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gastro-intestinal in-vivo biotransformation rates of hydrophobic chemicals in fish.

604

Environ. Toxicol. Chem. 2015, 34 (10), 2282-2294.


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Mackay, D.; Arnot, J. A.; Gobas, F. A. P. C.; Powell, D. E. Mathematical

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relationships between metrics of chemical bioaccumulation in fish. Environ. Toxicol.

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Chem. 2013, 32 (7), 1459-1466.

608

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Gobas, F. A. P. C.; Lo, J. C. Deriving bioconcentration factors and somatic

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biotransformation rates from dietary bioaccumulation and depuration tests. Environ.

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Toxicol. Chem. 2016, DOI: 10.1002/etc.3481

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Gobas, F. A. P. C.; Mackay, D. Dynamics of hydrophobic organic chemical

bioconcentration in fish. Environ. Toxicol. Chem. 1987, 6, 495-504. (28)

Burkhard, L.P. Estimating dissolved organic carbon partition coefficients for

nonionic organic chemicals. Environ. Sci. Technol. 2000, 34 (22), 4663-4667 (29)

McGrath, J. A. & Di Toro, D. M. Validation of the target lipid model for toxicity

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assessment of residual petroleum constituents: Monocyclic and polycyclic aromatic

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hydrocarbons. Environ. Toxicol. Chem. 2009, 28(6): 1130-1148.

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Niimi, A. J.; Palazzo, V. Biological half-lives of eight polycyclic aromatic

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hydrocarbons (PAHs) in rainbow trout (Salmo gairdneri). Wat. Res. 1986, 20 (4), 503-

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Sandvik, M.; Horsberg, T. E.; Skaare, J. U.; Ingebrigtsen, K. Comparison of

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dietary and waterborne exposure to benzo[a]pyrene: bioavailability, tissue disposition and

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CYP1A1 induction in rainbow trout (Oncorhynchus mykiss). Biomarkers 1998, 3 (6),

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399-410.

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Niimi, A. J.; Dookhran, G. P. Dietary absorption efficiencies and elimination rates

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of polycyclic aromatic hydrocarbons (PAHs) in rainbow trout (Salmo gairdneri).

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Environ. Toxicol. Chem. 1989, 8 (8), 719-722.


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629


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Kennedy, C. J.; Law, F. C. P. Toxicokinetics of selected polycyclic aromatic

630

hydrocarbons in rainbow trout following different routes of exposure. Environ. Toxicol.

631

Chem. 1990, 9 (2), 133-139.

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Van Veld, P. A.; Patton, J. S.; Lee, R. F. Effect of preexposure to dietary

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benzo[a]pyrene (BP) on the first-pass metabolism of BP by the intestine of toadfish

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(Opsanus tau): in-vivo studies using portal vein-catheterized fish. Toxicol. Appl.

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Pharmacol. 1988, 92 (2), 255-265.

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Vetter, R. D.; Carey, M. C.; Patton, J. S. Coassimilation of dietary fat and

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benzo(a)pyrene in the small intestine: an absorption model using killifish. J. Lipid Res.

638

1985, 26 (4), 428-434.

639

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Bock, K. W.; von Clausbruch, U. C.; Winne, D. Absorption and metabolism of

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naphthalene and benzo[a]pyrene in the rat jejunum in situ. Med. Biol. 1979, 57 (5), 262-

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264.

642 643 644

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Rowland, I.R. Interactions of the gut microflora and the host in toxicology.

Toxicol. Pathol. 1988, 16(2), 147-153. (38)

Inoue, Y.; Hashizume, N.; Yoshida, T.; Murakami, H.;, Suzuki, Y.;, Koga, Y.;,

645

Takeshige, R.;, Kikushima, E.;, Yakata, N.;, Otsuka, M.; Comparison of bioconcentration

646

and biomagnification factors for poorly water-soluble chemicals using common carp

647

(Cyprinus carpio L.). Arch Environ Contam Toxicol. 2012 Aug;63(2):241-8. doi:

648

10.1007/s00244-012-9761-8.

649 650 651


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Page 30 of 41

Water kB2WBCB

kB1WBCW

Fish body W C W C

kBMWBCB

kGDWBCB W C

W C

kGBWGCG

kBGWBCB

Digesta W C

GC

kGMWGCG

W C

GC

GICD

GGECG

Diet

Figure 1. Detailed schematic diagram of the chemical fluxes in a 2 compartment model separating the fish body from the contents of the digestive tract illustrating the role biotransformation (represented by the arrow) in the body (kBM) and the gastrointestinal tract (kGM) of the fish.


30

Page 31 of 41


A

B 5000 BCF5%,t

kBT (d-1)

1

0.1

0.01

500

50 2.5

4.5

6.5

8.5

2.5

4.5

log KOW

8.5

log KOW

D

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

10 1 BMF(L)

Dietary uptake efficiency

C

6.5

0.1 0.01 0.001

2.5

4.5

6.5

8.5

2.5

log KOW

4.5

6.5

8.5

log KOW


31


Page 32 of 41

Figure 2. (A) The total depuration rate constant from the fish body, kBT. (B) The bioavailability-corrected bioconcentration factor normalized to 5% lipid, BCF5%,t. (C) The dietary uptake efficiency (ED,M for test chemicals, ED,N for reference chemicals) normalized to hexachlorobenzene in each test. Solid and dashed lines represent predicted 95% confidence intervals of the predicted ED,N (Equation 13). (D) The lipid normalized biomagnification factor BMFL (bottom right) versus log KOW. Solid lines in Figures 2A, 2C, and 2D are parameterized to fish with mean experimental values of WB = 1.5 g, ΦBL = 5%, kGD = 3.7%·d-1, ΦDL = 15% and FD = 3.5% ·kg.kg-1.d-1. Error bars represent the standard error of the mean values. The red × represent the reference chemicals in each test.


32

Page 33 of 41


kBM, present study (d-1)

10

1

0.1

0.01

0.001 0.001

0.01

0.1 1 -1 kBM, BCFBAF (d )

10

Figure 3 Observed somatic biotransformation rate constants (kBM) from the present study as a function of BCFBAF QSAR (EPI Suite v. 4.11) predicted biotransformation rate constants (kBM), normalized to the same fish weights as those corresponding to the observed values. Solid line represents the mean regression fit, dashed lines represent the 95% confidence intervals of the predicted mean and the dotted lines represent the 95% prediction interval of individual chemicals.


33


Page 34 of 41


34

Page 35 of 41


A

B 0.0%

0.4%

0%

100%

0.0%

0.0%

98.5%

0.0% 1.0%

0.0%

0.0%

98.5%

0.0%

0.0%

0.0% 0.0%

1.0%

1.0%

0.0% 0.0%

0.0%

1.0%

0.0% 98.1%

0.0%

1.0%

0.0%

0.0%

0.0%

98.1%

0.0%

0.9% 100%

1.0%

0.0%

1.0%

1.0%

0.1%

0.4%

0.9%

0%

0.0%


35


C

Page 36 of 41

D 0.4%

0.7%

0%

100%

0.0%

0.0%

98.8%

0.0% 51.9%

0.4%

0.2%

99.0%

0.0%

0.0%

0.2% 0.2%

52.6%

0.5%

0.2% 0.2%

0.1%

0.0%

0.0% 0.6%

0.1%

0.0%

0.0%

0.0%

0.0%

0.6%

0.0%

47.0% 100%

0.5%

0.2%

51.9%

52.5%

0.0%

0.7%

47.1%

0%

0.2%

Figure 4. Detailed schematic diagram of the internal distribution dynamics, expressed in terms of the fraction of the administered chemical intake rate in units of grams per day for benzo[a]pyrene from test #3 (A,B) and triphenylene test #7 (C,D) in a dietary-only exposure environment (A,C) and in an aqueous-only exposure environment (B,D). Biotransformation (represented by the arrows) occurs in the body (kBM) and the gastrointestinal tract (kGM) of the fish. Red boxes indicate routes of elimination in the fish model.


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Gastrointestinal

Soma

Proportion of Mass Biotransformed

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

(dietary exposure only)


37


Gastrointestinal

Page 38 of 41

Soma

Proportion of Mass Biotransformed

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

(aqueous exposure only) Figure 5. Contribution of somatic (white) and gastro-intestinal (black) biotransformation to the overall mass of chemical biotransformed in continuous dietary exposure environments (top) and in continuous water exposure environments (bottom).


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50000

BCFWW,fd

5000

500

50 0.001

0.01

0.1

1

10

100

BMFL Figure 6. BCFWW,fd vs BMFL for test chemicals (blue and red circles) and reference chemicals (green and yellow circles) from present study (green, blue) and Lo et al. 2015 (yellow, red). The solid black line represents the predicted log BMFL – log BCFWW,fd linear regression of the reference chemicals. Dotted lines represent the 95% prediction interval. The grey lines represent the regulatory criteria for BCF of 5000 and a BMFL of 1 indicating chemical biomagnification.


39


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40

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109x109mm (96 x 96 DPI)


In Vivo Biotransformation Rates of Organic ... - ACS Publications

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