the influence of in vitro gill and liver metabolism of

THE INFLUENCE OF IN VITRO GILL AND LIVER METABOLISM

OF XENOBIOTICS ON FISH BIOCONCENTRATION

Cristi Frasier Gomez, B.S., M.S.

Dissertation Prepared for the Degree of

DOCTOR OF PHILOSOPHY

UNIVERSITY OF NORTH TEXAS

August 2010

APPROVED:

Duane B. Huggett, Major Professor Mark Burleson, Committee Member Edward Dzialowski, Committee Member Thomas La Point, Committee Member Barney J. Venables, Committee Member Arthur J. Goven, Chair of the Department of

Biological Sciences James D. Meernik, Acting Dean of the

Robert B. Toulouse School of Graduate Studies

Gomez, Cristi Frasier. The influence of in vitro gill and liver metabolism of

xenobiotics on fish bioconcentration. Doctor of Philosophy (Biology), August 2010, 175

pp., 11 tables, 27 illustrations, reference list, 122 titles.

This dissertation examines the ability of in vitro biotransformation assays to

provide an indication of metabolic potential. The potential for xenobiotic compounds to

bioconcentrate in aquatic organisms is expressed through the bioconcentration factor

(BCF). The metabolic loss of ibuprofen, norethindrone and propranolol was measured

using rainbow trout (Oncorhynchus mykiss) and channel catfish (Ictalurus punctatus) gill

and liver S9 fractions, microsomes and cell suspensions. Metabolic transformation

rates (kM) were extrapolated from in vitro intrinsic clearance of parent compound (CLm)

and integrated into a refined BCF model. In general, CLm of test compounds was

greater in liver S9 fractions and hepatocytes. However, the influence of hepatic

metabolism on kM and BCF was limited by hepatic blood flow (20-25%) compared to gill

blood flow (~100%). A significant difference was noted between BCF solely based on

KOW and BCF including kM. These studies indicate that the inclusion of kM in BCF

models can bring predicted bioconcentration estimates closer to in vivo values. Primary

cell suspensions are preferred over subcellular fractions as cell suspensions possess

both phase I and phase II enzyme activity. Further study was conducted on ibuprofen

biotransformation pathways. As fish do not contain the same cytochrome P450 (CYP)

2C homologs known to metabolize ibuprofen in mammals, it cannot be assumed that

piscine biotransformation is similar. Metabolite analysis found 2-hydroxy-ibuprofen as

the major metabolite in S9 and microsomal fractions. Additional assays involving the

induction and inhibition of specific CYP isozymes support CYP1A2 as an alternative

metabolic pathway.

ii

Copyright 2010

by

Cristi Frasier Gomez

iii

ACKNOWLEDGEMENTS

This work would not have been possible without the financial support of the

Pfizer Global Research and Development, whose collaboration is greatly appreciated. I

am especially indebted to Dr. Duane Huggett, my major professor, who has been a

mentor and a friend. I cannot praise Dr. Huggett enough for his graciousness, flexibility

and support.

I am grateful to everyone with whom I have had the pleasure to work with during

this project. Each member of my Dissertation Committee has provided

me with guidance that has helped shape this dissertation. I would especially like to

thank Dr. Barney Venables for opening me up to the world of analytical chemistry. I

cannot thank him enough for his patience as I muddled my way through my first six

months of sample analysis. I also thank my lab mates for their encouragement and

laughter.

No one has been more important to me in the pursuit of this project than my

family. I thank my three wonderful children for their patience with my harried schedule,

my sister and mother-in-law for always being there to babysit or lend a hand, and my

mom and dad for all their unconditional love and support in spirit and in practice. Most

importantly, I wish to thank my loving and supportive husband, Matthew, who truly made

this possible.

iv

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ............................................................................................... iii

LIST OF TABLES ........................................................................................................... vi

LIST OF FIGURES ......................................................................................................... vii

Chapter

1. INTRODUCTION ......................................................................................................... 1

Project Overview .......................................................................................................... 1

Background .................................................................................................................. 2

Review of Bioconcentration Assays and Models .......................................................... 6

Fish Physiology and Function ...................................................................................... 9

Objectives and Hypotheses ....................................................................................... 15

Experimental Design .................................................................................................. 17

References ................................................................................................................. 25

2. THE INFLUENCE OF GILL AND LIVER METABOLISM IN S9 FRACTIONS ON THE PREDICTED BIOCONCENTRATION OF PHARMACEUTICALS IN FISH ................... 38

Introduction ................................................................................................................ 38

Materials and Methods ............................................................................................... 40

Results ....................................................................................................................... 48

Discussion and Conclusions ...................................................................................... 54

References ................................................................................................................. 56

3. IBUPROFEN METABOLISM IN THE LIVER AND GILL OF RAINBOW TROUT, Oncorhynchus mykiss ................................................................................................... 60

Introduction ................................................................................................................ 60


Results and Discussion .............................................................................................. 64

References ................................................................................................................. 68

v

4. METABOLISM OF THREE PHARMACEUTICALS IN RAINBOW TROUT AND CHANNEL CATFISH LIVER AND GILL CELL SUSPENSIONS, AND THE EFFECT ON PREDICTED BIOCONCENTRATION ........................................................................... 70

Introduction ................................................................................................................ 70


Results ....................................................................................................................... 80

Discussion and Conclusions ...................................................................................... 84

References ................................................................................................................. 87

5. CONCLUSIONS ........................................................................................................ 93

Introduction ................................................................................................................ 93

Comparison of Subcellular Fractions and Cell Suspensions ...................................... 93

Objectives and Hypotheses ..................................................................................... 105

BCF Model Evaluation ............................................................................................. 111

Conclusion ............................................................................................................... 115

References ............................................................................................................... 115

APPENDIX A: PROTOCOLS ...................................................................................... 118

APPENDIX B: IN VITRO BIOTRANSFORMATION ASSAY FORM ............................ 136

APPENDIX C: ILSI/HESI BCF MODEL (FROM MICROSOFT EXCEL) ...................... 138

APPENDIX D: EXPONENTIAL PLOTS OF LOSS OF PARENT DATA………………..142

REFERENCES ............................................................................................................ 159

vi

LIST OF TABLES

Table 1. Concentrations of ibuprofen observed in water samples. ................................ 21

Table 2. Experimental log KOW values for propranolol (Johnson et al., 2007). .............. 22

Table 3. Concentrations of propranolol found in water samples. ................................... 23

Table 4. Physiological and physical characteristics used to extrapolate in vitro metabolic

transformation of subcellular fractions within the BCF model ........................................ 47

Table 5. Comparison of intrinsic clearance rate (CLm), extrapolated metabolic

transformation rate (kM), BCFM and BCF0 ..................................................................... 52

Table 6. Physiological and physical characteristic used to extrapolate in vitro metabolic

transformation of cell suspensions within the BCF model ............................................. 79

Table 7. Comparison of intrinsic clearance rate of parent (CLm), extrapolated metabolic

transformation rate (kM), BCFM and BCF0 ..................................................................... 85

Table 8. Mean coefficient of determination (R2 ) for exponential curves fit to

concentration versus time ............................................................................................. 94

Table 9. Influence of kM on predicted BCF .................................................................. 107

Table 10. Statistical analysis between predicted in vitro BCFM(total) and in vivo BCF ... 109

Table 11. Example of the influence of the fractional lipid content of whole fish body on

extrapolation of in vitro biotransformation data. ........................................................... 113

vii

LIST OF FIGURES

Fig. 1. Timeline of key events in the history of persistent, bioaccumulative and toxic

(PBT) substances. ........................................................................................................... 3

Fig. 2. Biotransformation of norethindrone in humans. .................................................. 18

Fig. 3. Biotransformation of (S)-(+)-ibuprofen. ............................................................... 20

Fig. 4. Propranolol. ........................................................................................................ 22

Fig. 5. Extrapolation of in vitro S9 metabolism assay to the metabolic transformation

rate (kM) ........................................................................................................................ 46

Fig. 6. Percent increase in resorufin over 60 minute time course. ................................. 49

Fig. 7. Mean loss of parent compound over 90 minute study time period. .................... 50

Fig. 8. Mean CLm of S9 fractions ................................................................................... 51

Fig. 9. Relative loss of parent compound over 90 minute study time period ................. 64

Fig. 10. (A) Mean CLm of uninduced S9 fractions. (B) Mean CLm of BNF-induced and

uninduced trout liver microsomes (TLM) and trout gill microsomes (TGM) ................... 65

Fig. 11. Relative frequencies of ibuprofen and its metabolites ...................................... 66

Fig. 12. Extrapolation of in vitro primary cell suspension metabolism assay to metabolic

transformation rate (kM) ................................................................................................. 78

Fig. 13. Mean loss of parent compound in deactivated control cells. ............................ 80

Fig. 14. Mean loss of parent compound over 120 minute study time period. ............... 81

Fig. 15. (A) Mean intrinsic clearance rate of parent (CLm) and (B) mean extrapolated

metabolic transformation rate (kM) of cell suspensions.................................................. 82

Fig. 16. Intrinsic clearance of parent compound (CLm) between cell suspension lots. .. 84

Fig. 17. Example of first order kinetics in catfish hepatocyte assay results .................. 94

viii

Fig. 18. Mean percent loss of norethindrone across liver and gill S9, microsomes and

cell suspensions. ........................................................................................................... 96

Fig. 19. Comparison of mean intrinsic clearance rate (CLm) of norethindrone in liver and

gill subcellular fractions and primary cell suspensions. ................................................. 97

Fig. 20. Comparison of mean metabolic transformation rate (kM) of norethindrone in liver

and gill subcellular fractions and primary cell suspensions ........................................... 98

Fig. 21. Mean percent loss of propranolol across liver and gill S9, microsomes and cell

suspensions. ............................................................................................................... 100

Fig. 22. Comparison of mean intrinsic clearance rate (CLm) of propranolol in liver and gill

subcellular fractions and primary cell suspensions ..................................................... 100

Fig. 23. Comparison of mean metabolic transformation rate (kM) of propranolol in liver

and gill subcellular fractions and primary cell suspensions ......................................... 101

Fig. 25. Comparison of mean intrinsic clearance rate (CLm) of ibuprofen in liver and gill

subcellular fractions and primary cell suspensions ..................................................... 104

Fig. 26. Comparison of mean metabolic transformation rate (kM) of ibuprofen in liver and

gill subcellular fractions and primary cell suspensions ................................................ 104

Fig. 27. Comparison of BCF ....................................................................................... 110

1

CHAPTER 1

INTRODUCTION

1.1 Project Overview

The potential for a chemical to bioconcentrate is often expressed through the

calculation of a bioconcentration factor (BCF). The ability of a substance to

bioconcentrate is an important chemical characteristic as it indicates the potential for a

substance to partition into and remain in the organism, increasing the potential for

chronic toxic effects. BCF is the proportionality constant of the chemical concentration

in an aquatic animal at steady-state equilibrium (CB) to the chemical concentration in

ambient water (CW) (Veith and DeFoe, 1979).

(BCF = CB / CW) (1)

BCF may also be calculated from a one-compartment model, where k1 is the

uptake rate constant and k2 is the elimination rate constant (Barron, 1990).

(BCF = k1 / k2) (2)

Through a joint effort with the International Life Sciences Institute – Human and

Environmental Sciences Institute (ILSI/HESI), a multi-compartment model based on the

principles of absorption, distribution, metabolism and elimination (ADME) has been

proposed. This model in includes: k1 (uptake via gills), kU (dietary ingestion), k2

(elimination across gills), kG (growth), kM (metabolism) and kE (egestion) (Gobas, 1993).

BCF = (k1 + kU) / (k2 + kG +kM +kE) (3)

2

This dissertation focuses on the determination and applicability of the metabolic

transformation rate constant (kM). Moreover, the following work centers on metabolic

clearance of the gills and liver. A series of in vitro biotransformation assays were

conducted in order to develop a simple and cost effective test method for estimating the

intrinsic clearance rate of parent compound (CLm) and extrapolating it to kM. kM was

then incorporated into a multi-compartmental BCF model. Rainbow trout

(Oncorhynchus mykiss) and channel catfish (Ictalurus punctatus) served as model

organisms to evaluate species differences in metabolism.

1.2. Background

1.2.1. Historical Review

Bioconcentration has traditionally been defined as the accumulation of chemical

substances from aquatic environments through non-dietary routes (Barron, 1990).

Bioconcentration often represents a surrogate measure for “B” in PBT, a regulatory

classification for persistent, bioaccumulative and toxic substances that has taken on

greater importance in the past few years with the implementation of the European Union

regulation on the Registration, Evaluation and Authorisation of Chemicals (REACH) and

the Canadian assessment of chemicals on the Domestic Substances List (DSL).

However, bioaccumulative substances should not be solely classified as

emerging contaminants. They have long played a leading role in the history of

environmental toxicology (Fig. 1). Since the publication of Silent Spring by Rachel

Carson in 1962, the fate of chemicals in the environment has received growing

attention, particularly those chemicals with PBT characteristics. Some place the

synthesis of benzene hexachloride by Michael Faraday in 1825 as a pivotal moment in

3

time that lead to the synthesis halogenated organics in large, industrial quantities

(Lipnick and Muir, 2000).

Fig. 1. Timeline of key events in the history of persistent, bioaccumulative and toxic

(PBT) substances.

In the 1930s and 1940s, a new generation of pesticides became commercially

available, which have since been termed organochlorines. These pesticides had lower

acute toxicity than their precursors, such as arsenic and hydrogen cyanide (Kovach et

al., 1992). However, their indirect and chronic effects did not become cause for concern

until many years later (Harner et al., 1999). One of the key indirect effects was

bioaccumulation and biomagnification through the food chain. For example, DDT was

linked to the population decline of large raptors, not through direct exposure to DDT, but

through the highly lipophilic compound’s magnification up the food chain. A strong

inverse correlation between eggshell thickness and DDE, the primary metabolite of

4

DDT, was found in the eggs of peregrine falcons (Falco peregrines) and California

condors (Gymnogypus californianus) (van Emden and Peakall, 1999). The inability to

effectively reproduce decimated the two bird populations. Thus, the need to evaluate a

chemical’s indirect routes of toxicity became apparent. This prompted the creation of

several regulatory restrictions on PBT substances.

1.2.2. Regulatory Overview

Currently, Canada, the European Union, the United States and the United

Nations have independent definitions for what constitutes a PBT. In Canada, a

substance deemed inherently toxic may be restricted or banned from trade if it meets

the criteria for persistence or bioaccumulation. Inherent toxicity is defined as chronic no

observable effect concentration (NOEC) < 0.1mg L-1 or acute LC50 (EC50) < 1 mg L-1

(Environment Canada, 2003). The term “inherently toxic” is used to distinguish from

other regulatory definitions of “toxic.” A chemical is considered bioaccumulative if it has

a BCF or bioaccumulation factor (BAF) > 5,000 or a log KOW > 5 (Environment Canada,

2003). BAF includes all exposure routes, including dietary routes, and are often

considered a more complete quotient than BCF. BAF can be calculated from BCF by

including a food chain multiplier (FCM) (NYDEC, 1998).

BAF = (FCM) * (BCF) (4)

The European Union has two regulatory classifications: PBT and very persistent,

very bioaccumulative (vPvB). PBTs must meet the criteria for persistence,

bioaccumulation and toxicity, in which the criteria for bioaccumulation is a BCF > 2,000

or log KOW > 4.5. vPvBs must only meet the criteria for persistence and

5

bioaccumulation, in which the criteria for bioaccumulation is raised to a BCF > 5,000 or

log KOW > 5, mirroring Canada’s criteria. Persistence classification is based on the half-

life in either water or sediment. The criteria differ for marine and freshwater

environments. The EU criteria for toxic requires a chronic NOEC < 0.01 mg/L,

classification as a carcinogen, mutagen or reproductive toxin (CMR) or evidence

showing endocrine disrupting effects (European Chemicals Bureau, 2003).

Classification as a PBT or vPvB leads to the authorization of chemical use under

REACH (European Union, 2006).

The United States Environmental Protection Agency (USEPA) established PBT

criteria in which all three characteristics must be met. Substances with a BCF or BAF >

1,000 must undergo further testing. Substances with a BCF or BAF > 5,000 may be

banned from commerce (USEPA, 1999).

Furthermore, the United Nations Environmental Programme, through the

Stockholm Convention on Persistent Organic Pollutants (POPs). POPs addresses a

select group of chemicals that present PBT characteristics, but also are transported

through air, water and migratory species across international boundaries and deposited

far from their place of release. The first twelve compounds subject to the Convention

are primarily pesticides, but also include other halogenated organics: aldrin, chlordane,

dieldrin, endrin, heptachlor, hexachlorobenzene (HCB), mirex, toxaphene, PCBs, DDT,

polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs).

The Convention established a bioaccumulation criteria at a BCF > 5,000 (United

Nations, 2001).

6

1.3. Review of Bioconcentration Assays and Models

1.3.1. OECD 305: Bioconcentration: Flow-Through Fish Test

OECD 305: Bioconcentration: Flow-Through Fish Test is an experimental in vivo

determination of the BCF in fish (OECD, 1996). The test is conducted in two phases:

an exposure phase and a depuration phase. In the exposure phase, fish are exposed

to two sublethal concentrations of the test chemical. Both fish and water are sampled at

regular intervals, and the concentration of the original test compound is measured.

After 28-days of toxicant exposure, or when a steady-state is reached in the fish, the

fish are transferred to clean water and the depuration phase commences for an

additional 14 days.

The BCF may be expressed as either a function of the total wet weight of fish or

the total lipid weight of fish. Thus, OECD 305 is a one compartment model, in which the

single compartment represents either a homogenate of the fish or the homogenized lipid

portion of the fish. Additionally, as described in Eqs. (1) and (2) above, two

mathematical models may be used to determine the BCF from OECD 305. The first

calculates BCF as the concentration of a chemical in fish (CB) divided by the

concentration in water (CW) under steady-state conditions. The second calculates BCF

using first-order kinetic data, where k1 represents the rate constant for uptake and k2

represents the rate constant for elimination. European ring tests found variations

between the two methods to be less than the interlaboratory variation (Kristensen and

Tyle, 1991).

7

1.3.2. Hydrophobicity Model

Several studies have shown a relationship between BCF and the octanol-water

partition coefficient (KOW) (Barron, 1990). The hydrophobicity model considers

bioconcentration as the partitioning of a chemical between the ambient water and the

lipid portion of an aquatic organism (Barron, 1990). This one compartment model

assumes first order kinetics in which uptake is limited only by diffusion and metabolism

is negligible. Additionally, the BCF value is independent of exposure concentration

(Barron, 1990). The hydrophobicity model may be expressed as a regression equation,

where a and b are empirical constants calculated from the regression analysis of BCF-

KOW data sets.

log BCF = a log KOW + b (5)

This regression equation was first proposed for predicting the BCF of nonionic

organic chemicals by Neely et al. (1974). In the following decades, several other

regression equations have been proposed, which were based on different BCF-KOW

data sets (Veith and DeFoe, 1979; Schurmann and Klein, 1988; Bintein et al., 1993).

DeWolf et al. (1992) concluded that the slope of the regression equation should be

close to one and the intercept should be approximately zero.

Hydrophobicity models most likely oversimplify the multifaceted characteristic of

bioconcentration. More complex multi-compartment models are expected to portray a

more accurate picture of a chemical’s bioconcentration potential. For instance,

regression equations apply best to nonionic, organic chemicals with a small molecular

weights (<1000 g mol-1) that are metabolized very slowly or not at all (Dimitrov et al.,

8

2005). Thus, they underestimate the potential for metabolic elimination. Moreover,

new, emerging environmental contaminants, such as ionizable pharmaceuticals, cannot

be assumed to behave the same as the historical neutral, lipophilic contaminants.

Assumptions must be rethought with regard to their uptake, distribution, metabolism,

elimination and potential accumulation in the aquatic environment.

1.3.3. Other Bioconcentration Models

Of the thousands of chemicals currently in commerce, most lack published data

on their bioaccumulative potential. For example, of the 11,300 organic chemicals under

review on the Canadian DSL, only 4% have publicly published BAC or BCF values

(Arnot and Gobas, 2006). Thus, bioaccumulation/ bioconcentration assessments are

often extrapolated across chemical classes. Chemical classes may be determined by

grouping substances with similar chemical structures or similar physical and/or chemical

properties. Assumptions are then made for the entire chemical class based on

experimental data collected on a few individual compounds within that chemical class.

Quantitative structure activity relationships (QSARs) and other computer models

are valuable tools in assessing BAF/BCF potential of a single chemical or a class of

chemicals. Most in silico models were developed using bioaccumulation data for 60-

700 chemicals (Dimitrov et al., 2005). Models then use knowledge rules to predict the

BAF/BCF of a compound with similar chemical structure or similar physical and/or

chemical properties. Generally, the KOW of a substance is one of the principal

characteristics considered. Popular BCF models include the BCFWIN QSAR model,

the Mekenyan-Dimitrov POPs model and the Gobas-Arnot BCF model (Meylan et al.,

1999; Arnot and Gobas, 2003; Mekenyan et al., 2005).

9

1.4. Fish Physiology and Function

1.4.1. Gill Structure

Teleost fish typically have four gill arches consisting of holobranchs and one arch

comprising of a vascularized psuedobranch (Laurent, 1984). A psuedobranch is found

in rainbow trout, but not channel catfish. Water flows through the branchial cavity,

which houses the gills arches, and leaves through the operculum. Each gill arch is lined

with two rows of gill filaments that project posteriolaterally (Wilson and Laurent, 2002).

Gill filaments consist of folded lamellae. The shape and size of lamellae vary between

fish species; however, the surface area provided by the lamella is critical to the

exchange of gases and other materials between water and blood. Lamellae typically

are curved and consist of the respiratory epithelium (Laurent, 1984). The respiratory

epithelium is composed of two epithelial layers separated by a series of modified

endothelial cells.

The ultrastructure of the gill contains various epithelial cells types, including

pavement cells, chloride cells and mucus cells. The pavement cell is a modified

endothelial cell, lining the blood spaces within the lamellae. Pavement cells are

squamous or cuboidal cells unique to the fish gill and represent the major protein source

in the gill epithelium (Kültz and Somero, 1996). They contain a nucleus and typical

intracellular organelles, such as rough and smooth endoplasmic reticulum, Golgi

apparatus and lysosomes. However, pavement cells have low mitochondrial densities.

Chloride cells, on the other hand, are mitochondria-rich cells concentrated in the

afferent edge of the filament epithelium (Laurent, 1984). Multiple gill cells types have

been found to contribute to xenobiotic metabolism, including pavement cells and pillar

10

cells (Miller et al., 1988; Miller et al., 1989; Leguen et al., 2000; Leguen et al., 2001).

Most in vitro cultured gill epithelia contain a mixture of pavement and chloride cells

(Galvez et al., 2008).

The gill is a highly vascularized tissue. In most teleosts, gill vascular resistance

is between 25% and 35% of systemic resistance (Olsen, 2002). Three blood vessels

support the gill filaments: (1) the afferent artery, moving blood into the filament, (2) the

efferent artery, moving blood away from the filament and (3) the central vein, returning

blood to systemic circulation (Payan et al., 1984). Blood flows between the pavement

cells lining the interior of the respiratory epithelium in the direction opposite to that of

water flowing over the gills (Laurent, 1984). This efferant/ afferent flow facilitates gas

and material exchange between water and blood, and is commonly referred to as

counter-current exchange.

1.4.2. Liver Structure

The teleost liver is similar to the mammalian liver in that it plays a prominent role

in fish physiology. The liver has both anabolic (protein, lipid, carbohydrate) and

catabolic (glycogenolysis, metabolic transformation) functions (Datta Munshi and Dutta,

1996). Additionally, the fish liver plays an important role in vitellogenesis.

The fish liver resides ventrally in the cranial region of the general cavity. It may

be divided into three lobes in some teleost species. However, rainbow trout and

channel catfish both have single lobe livers (Robertson and Wexler, 1960; Datta Munshi

and Dutta, 1996). The liver is associated with three primary blood vessels: two afferent

blood vessels, the hepatic artery and portal vein, and an efferent vessel, the hepatic

vein.

11

Several cell types are found in the fish liver. Hepatocytes comprise the major cell

type, occupying 80-85% of liver volume (Hampton et al., 1989). Biliary epithelial cells,

exocrine pancreatic cells, Ito cells, macrophages and fibroblasts are also found in fish

liver. Studies have observed cytochrome P450 (CYP) 1A1 presence not only in

hepatocytes, but also in biliary epithelial cells and sinusoidal endothelial cells (Lester et

al., 1993).

1.4.3. Chemical Absorption and Uptake

Two common assumptions of fish bioconcentration models are (1) chemical

uptake across the gill dominates all other routes of uptake and (2) chemical uptake

occurs through passive diffusion (Barron, 1990; Randall et al., 1991; Barber, 2003).

Xenobiotics traditionally enter the body via passive diffusion through semi-permeable

membranes of the gill, the lining of the mouth or the gastrointestinal tract (Spacie and

Hamelink, 1985).

However, often individual characteristics of the fish or the compound itself

determine whether these statements correctly predict a specific uptake situation. For

example, absorption across cutaneous surfaces may contribute to chemical uptake in

smaller fish (e.g., fathead minnow, Pimephales promelas) due to their larger surface

area to volume ratio and the relatively short diffusion distance across the skin compared

to larger fish (Lien and McKim, 1993). Additionally, it has been hypothesized that

dietary uptake, rather than uptake across the gill, is the primary route by which fish

accumulate very hydrophobic compounds (KOW > 5) that are not soluble in water

(Bruggeman et al., 1984).

12

Several physiological factors affect absorption across the gill and subsequent

uptake into the fish. The gill contains a porous capillary network that facilitates

absorption of xenobiotics into systemic circulation (Olsen, 2002). The functional surface

area of the lamellae also affects absorption. The rate of chemical uptake across the

gills is dependent on several factors. Multiple studies have determined that chemical

uptake is positively correlated with water flow across the gill (Norstrom et al., 1976;

McKim and Goeden, 1982). Additionally, it has been shown that diffusion from water to

blood is a rate-limiting step for many chemicals. Sijm et al. (1993) developed a gill

uptake rate constant (k, liter kg-1 day-1) based on in vivo data from the literature, where

W is weight in grams (Sijm et al., 1993).

k = (550 ± 42) * W-0.27 ± 0.13 (6)

This model appears to fit best with regards to compounds with a log KOW > 3

(Sijm et al., 1995), thus its use in predicting the uptake of ionic compounds must be

evaluated.

1.4.4. Metabolic Transformation

Biotransformation and the subsequent elimination of metabolites are key factors

in the toxicity and bioaccumulation of a chemical. A chemical that is rapidly

biotransformed into a more polar compound is more likely to be eliminated quickly and

thus less likely to accumulate in the organism. Biotransformation typically occurs in two

phases: phase I biotransformation (or metabolism) and phase II conjugation. Phase I

metabolism often involves oxidation, reduction or hydrolysis of a compound into a

slightly more polar intermediate compound (Goeptar et al., 1995).

13

Cytochrome P450 isozymes catalyze a large portion of phase I reactions by

inserting a single oxygen atom into an organic substrate while reducing the other

oxygen to water. P450s are a superfamily of heme-thiolate enzymes responsible for the

metabolism of endogenous and xenobiotic compounds. P450s are thought to have

originated over 3.5 million years ago and are found in most forms of life, including

bacteria, fungi, plants and animals (Chang and Kam, 1999). In teleosts, P450s typically

reside in the membrane of endoplasmic reticulum, but have also been found in

mitochondria (Buhler and Wang-Buhler, 1998).

Phase II metabolism involves the conjugation of the intermediate metabolite with

a polar functional group to increase water solubility. Conjugation typically reduces any

bioactivity of the intermediate metabolite. Phase II enzymes are specific for a

functional group. For example, UDP-glucuronosyltransferase (UGT) catalyzes phase II

biotransformation through conjugation with glucuronic acid (Ritter, 2000; Buckley and

Klaassen, 2007). Phase II reactions also include sulfonation, methylation, acetylation,

conjugation with glutathione, and conjugation with certain amino acids, such as glycine,

glutamic acid and taurine (Parkinson and Ogilvie, 2007).

1.4.4.1. Gill Metabolism

The gill is a metabolically active tissue in the fish and has been shown to contain

both phase I and II enzymes (Buhler and Wang-Buhler, 1998; Walter et al., 2001;

Klaren et al., 2007). CYP1A1 was first isolated in rainbow trout liver microsomes in

1982 (Williams and Buhler, 1984). It has been found in the pavement cells of the gill

along with several other monooxygenases. (Buhler and Wang-Buhler, 1998).

Mammalian species have been hypothesized to have over 60 distinguishably different

14

types of P450 enzymes (Coon et al., 1996). The diversity of P450s in rainbow trout is

expected to be as varied as that of mammalian species (Lee et al., 1998).

Limited research has suggested that the metabolic activity in the gill is less than

that of the liver. Arachidonic acid metabolism in scrup (Stenotomus chrysops) gill

microsomes was found to be 10- to 30-fold less than that of scrup liver microsomes

(Schlezinger et al., 1998). Metabolic activity in gill epithelial cells was observed at

between 7- and 60-fold less than the activity level in isolated hepatocytes, depending on

the substrate (7-ethoxycoumarin, aniline and testosterone) (Leguen et al., 2000).

Others have reported similar metabolic differences between the gill and liver (Lindstrom-

Seppa et al., 1981; Pesonen and Andersson, 1991).

1.4.4.2. Liver Metabolism

Enzymes capable of xenobiotic biotransformation are found in a most tissues. In

vertebrates, the liver is considered to be the primary site of biotransformation

(Parkinson and Ogilvie, 2007). The liver has dual blood sources from the hepatic and

vein and hepatic artery. The hepatic vein supplies blood from the gastrointestinal tract.

Through first pass elimination, the liver processes xenobiotics absorbed from dietary

sources. The liver also receives blood flow from the hepatic artery, which allows for

biotransformation of xenobiotics systemically available in the bloodstream (Parkinson

and Ogilvie, 2007).

In most vertebrates, the highest concentration of P450 enzymes involved in

xenobiotic metabolism is found in the endoplasmic reticulum of hepatocytes (Parkinson

and Ogilvie, 2007). The liver is also a site for phase II biotransformation. Studies have

15

shown the presence of sulfotransferase and UGT in the livers of most teleost fish

(Klaren et al., 2007).

1.4.5. Elimination

The potential for a substance to bioconcentrate greatly depends on the

organism’s ability to eliminate the substance. Compounds which are easily eliminated

have less potential to remain in the body and accumulate. Substances may be

eliminated via biliary excretion, urinary excretion or passive diffusion across the gill

(Streit, 1998). In general, xenobiotics are biotransformed into more polar compounds

prior to elimination; thus, an organism’s ability to metabolize a compound plays a critical

role in elimination. Additionally, metabolism at the site of elimination (e.g., the gill)

reduces residency time in the organism and possible toxicity and bioaccumulation

(Black et al., 1995).

The gill is considered a key site for elimination. Many of the properties that

facilitate uptake across the gill, such as large surface area, short diffusion distance and

counter-current blood flow allow for elimination across the gill (Streit, 1998). In addition,

the large proportion of cardiac output to the gill further facilitates elimination across the

gill.

1.5. Objectives and Hypotheses

1.5.1. Objective 1

Determine the in vitro intrinsic clearance rate of parent compound (CLm) and the

metabolic rate constant (kM) for the test compounds in gill S9 fractions.

16

Hypothesis 1: HO: Loss of parent material will not be significantly different

from starting concentrations. α = 0.05

Hypothesis 2: HO: In vitro gill kM will not significantly decrease model BCF

values. α = 0.05

1.5.2. Objective 2


metabolic rate constant (kM) for the test compounds in liver S9 fractions.



Hypothesis 4: HO: In vitro liver kM will not significantly decrease model BCF

values. α = 0.05

1.5.3. Objective 3


metabolic rate constant (kM) for the test compounds in primary gill cell suspensions.




values. α = 0.05

1.5.4. Objective 4


metabolic rate constant (kM) for the test compounds in primary hepatocyte suspensions.

17




values. α = 0.05

1.5.5. Objective 5

Compare in vitro metabolism data to in vivo whole fish bioconcentration data.

Hypothesis 9: HO: Calculated BCF including in vitro kM values will not

significantly differ from in vivo BCF. α = 0.05

1.6. Experimental Design

1.6.1. Test Materials

1.6.1.1. Norethindrone

Norethindrone (19-norpregn-4-en-20-yn-3-one,17-hydroxy-,[17α-]-; C20H26O2)

is a synthetic progestin commonly found in oral contraceptives. In humans,

norethindrone is metabolized by CYP2C19 and CYP3A4 (Fig. 2) (Korhonen et al.,

2008). In human liver microsomes, hydroxylation accounts for ~98% of norethindrone

metabolites (Korhonen et al., 2008).

18

Fig. 2. Biotransformation of norethindrone in humans as adapted from Korhonen et al

(2008).

Additionally, norethindrone binds extensively to plasma albumin and sex

hormone binding globulin (SHBG) (Orme, 1982). This supports the systemic circulation

of norethindrone. Norethindrone has reported log KOW of 2.97 and 2.99, indicating only

moderate hydrophobicity (Saha et al., 2000; Lee et al., 2007).

The presence of norethindrone in the environment is attributed to its presence in

wastewater. A study of Canadian municipal sewage detected norethindrone in

wastewater influent samples at levels ranging from 26 – 224ng L-1. The study found

norethindrone in only one wastewater effluent sample (159ng L-1) (Fernandez et al.,

2007).

19

1.6.1.2. Ibuprofen

Ibuprofen is a nonsteroidal anti-inflammatory drug (NSAID) found in over-the-

counter pain relievers and several prescription pharmaceuticals (Fig. 3). In the human

and rat liver, CYP2C9 and CYP2C8 metabolize ibuprofen (Davies, 1998; Jacqz-Aigrain

and Anderson, 2006). Ibuprofen also undergoes direct glucuronidation (Jacqz-Aigrain

and Anderson, 2006). UGT 1A9, UGT1A3, UGT2B1 and UGT2B7 are known to

conjugate ibuprofen (Ritter, 2000).

Several differing log KOW values have been reported in the literature for

ibuprofen, depending on the pH of solubilizing matrix (Jones et al., 2002; Weigel et al.,

2004. However, the majority of the literature places the log KOW at approximately 1.0

(Cleuvers, 2003) .

20

Fig. 3. Biotransformation of (S)-(+)-ibuprofen as adapted from Chang et al. (2008).

Ibuprofen has been identified in the environment at various concentrations.

Table 1 provides a summary from the literature. The literature review resulted in a

maximum concentration of 4239ng L-1 ibuprofen observed in wastewater effluent and

2370ng L-1 ibuprofen in surface water (Roberts and Thomas, 2006).

21

Table 1

Concentrations of ibuprofen observed in water samples.

Study Matrix Concentration (ng L-1)

Ternes (2001)

Wastewater effluent 370 (median)

Surface water 70 (median)

Farre et al. (2001)

Wastewater effluent 868

Surface water Not detected to 1500

Ollers et al. (2001)

Wastewater effluent 5 to 1500

Surface water Not detected to 80

Andreozzi et al. (2003) Wastewater effluent 20 to 1820

Roberts and Thomas (2006)

Wastewater influent 7741 to 33,764


Surface water 144 to 2370

Nebot et al. (2007) Wastewater effluent 405

1.6.1.3. Propranolol

Propranolol is a non-selective beta-blocker used to treat a variety of ailments,

including hypertension and migraine (Fig. 4). CYP2D6 is thought to catalyze a large

portion of propranolol biotransformation in humans (Masubuchi et al., 1994).

22

Fig. 4. Propranolol.

The log KOW of propranolol has been reported extensively in the literature. The

log KOW value differs depending on pH (Table 2). For the purpose of this dissertation,

the log KOW of 0.78 at the physiological pH 7 was used.

Table 2

Experimental log KOW values for propranolol (Johnson et al., 2007).

log KOW pH

0.78 7

1.9 8

3.5 9.5

Propranolol has been identified in the environment at various concentrations.

Table 3 provides a summary from the literature. The literature review resulted in a

maximum concentration of 1900ng L-1 in wastewater effluent and 107ng L-1 in surface

water (Huggett et al., 2003; Roberts and Thomas, 2006).

23

Table 3

Concentrations of propranolol found in water samples.

Study Matrix Concentration (ng L-1)

Ternes (2001)

Wastewater effluent 170 (median)

Surface water 12 (median)

Andreozzi et al. (2003) Wastewater effluent 10 to 90

Huggett et al. (2003) Wastewater effluent 26 to 1900

Roberts and Thomas (2006)

Wastewater influent 60 to 119


Surface water 35 to 107

1.6.2. Model Organisms

Fish are commonly used as model organisms in environmental toxicology (Bols

et al., 2001). Fish are the most diverse group of vertebrates with approximately 20,000

different species (Bols et al., 2005). Fish occupy a broad range of habitats and can be

found in most aquatic niches. They also are found at a variety of trophic levels, an

important attribute when studying bioaccumulation and biomagnification. Fish function

well as laboratory test subjects as many species can live out their entire life cycle in a

laboratory environment. Furthermore, fish provide not only an indicator of aquatic

health, but also of human health. Contaminants released to the aquatic environment

(e.g., wastewater discharge, storm water runoff, aerial deposition) ultimately lead to

human exposure either through direct contact with a water body via recreational use or

24

through indirect consumption of aquatic resources (Bols et al., 2005). Lastly, fish also

are of economic importance. The health of the aquatic environment directly impacts the

strength of fisheries and aquaculture facilities (Costanza et al., 1997).

Rainbow trout (Oncorhynchus mykiss) and channel catfish (Ictalurus punctatus)

served as model organisms in this dissertation. They represent cold and warm water

freshwater teleosts. Rainbow trout inhabit cold water environments while channel

catfish reside in warm water habitats. From a regulatory prospective, fish are the most

commonly used model organism (i.e., OECD 305 as discussed above) (OECD, 1996).

Rainbow trout has a long history as a model species in toxicological research (McKim et

al., 1987; Bailey et al., 1996; Buhler and Wang-Buhler, 1998). Furthermore, a large

body of literature exists studying phase I biotransformation and overall metabolism in

rainbow trout. It is speculated that P450 enzymes of rainbow trout are the most

intensively studied among all aquatic species (Buhler and Wang-Buhler, 1998).

1.6.3. In Vitro Design

Several important issues propel the movement towards in vitro environmental

toxicology assessments. The increased regulatory push for BCF values for all

chemicals in commerce places cost as a key factor. Weisbrod et al. (2007) estimates

the cost for an in vivo OECD 305 study conducted under good laboratory practices

(GLP) at approximately $125,000. When that cost is multiplied by the tens of thousands

of chemicals in commerce without bioconcentration or bioaccumulation data, the cost

become fiscally prohibitive. In vitro studies, such as the ones evaluated in this

dissertation, have the advantage of lower costs. Additionally, in vitro study results are

25

obtained more quickly, allow for more control over dosing and are often more

reproducible (Bols et al., 2005).

In vitro studies also satisfy the societal desire to reduce the use of animals in

toxicological testing (Bols et al., 2005; de Wolf et al., 2007; Weisbrod et al., 2007).

Russell and Burch (1959) defined the principles of the 3Rs (replace, reduce and refine)

to provide a framework for the development of more humane experimental design.

Replacement involves the substitution of insentient test materials for conscious, higher

order animals. Reduction regards the reduction in the number of animals used in a

study. Refinement aims to decrease the incidence and/or severity of inhumane

techniques applied during the course of a study (de Wolf et al., 2007). The studies

conducted in this dissertation fall into the realm of reduction. Through the use of

composited tissue batches, several compounds could be evaluated with the sacrifice of

less than 5% of the fish required to evaluate a single compound with OECD 305.

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P450 1A inducers. Arch. Biochem. Biophys. 353, 265-275.

Schurmann, G., Klein, W., 1988. Advances in bioconcentration prediction.

Chemosphere 17, 1551-1574.

Sijm, D., Part, P., Opperhuizen, A., 1993. The influence of temperature on the uptake

rate constants of hydrophobic compounds determined by the isolated perfused

gills of rainbow trout (Oncorhynchus mykiss). Aquat. Toxicol. 25, 1-14.

Sijm, D., Verberne, M., deJonge, W., Part, P., Opperhuizen, A., 1995. Allometry in the

uptake of hydrophobic chemicals determined in vivo and in isolated perfused

gills. Toxicol. Appl. Pharmacol. 131, 130-135.

36

Spacie, A., & Hamelink, J. (1985). Bioaccumulation. in Rand, G., Petrocelli, S. (Eds.),

Fundamentals of Aquatic Toxicology: Methods and Applications. Hemisphere

Publishing Corp., New York, pp. 495-525.

Streit, B., 1998. Bioaccumulation of contaminants in fish. in Braunbeck, T., Hinton, D.,

Streit, B. (Eds.), Fish Ecotoxicology. Birkhauser Verlag Basel, Switzerland, pp.

353-387.

Ternes, T., 2001. Analytical methods for the determination of pharmaceuticals in

aqueous environmental samples. Trends Anal. Chem. 20, :419-434.

United Nations, 2001. Stockholm Convention on Persistent Organic Pollutants.

<http://chm.pops.int/Portals/0/Repository/convention_text/UNEP-POPS-COP-

CONVTEXT-FULL.English.PDF>.

USEPA (United States Environmental Protection Agency), 1999. Category for

Persistent, Bioaccumulative, and Toxic New Chemicals. 2009, from Federal

Register: November 4, 1999 (Volume 64, Number 213).

<http://www.epa.gov/fedrgstr/EPA-TOX/1999/November/Day-04/t28888.htm>.

van Emden, H.F., Peakall, D.B., 1999. Beyond Silent Spring: Integrated Pest

Management and Chemical Safety. Integrated Pest Mgmt. Rev. 4, 269-270.

Veith, G., DeFoe, D., 1979. Measuring and estimating the bioconcentration factor of

chemicals in fish. Fish Res. Board Can. 36, 1040-48.

Walter, R., Sung, H., Intano, G., Walter, C., 2001. Characterization of O(6)-

methylguanine-DNA-methyltransferase (O(6)-MGMT) activity in Xiphophorus

fishes. Mutat. Res. 493, 11-22.

37

Weigel, S., Kallenborn, R., Hühnerfuss, H., 2004. Simultaneous solid-phase extraction

of acidic, neutral and basic pharmaceuticals from aqueous samples at ambient

(neutral) pH and their determination by gas chromatography–mass spectrometry.

J. Chromatogr. A 1023, 183-195.

Weisbrod, A., Burkhard, L., Arnot, J., Mekenyan, O., Howard, P.H., Russom, C.,

Boethling, R., Sakuratani, Y., Traas, T., Bridges, T., Lutz, C., Bonnell, M.,

Woodburn, K., Parkerton, T., 2007. Workgroup report: review of fish

bioaccumulation databases used to identify persistent, bioaccumulative, toxic

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Biochem. Pharmacol. 43, 4752-4756.

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213.

38

CHAPTER 2

THE INFLUENCE OF GILL AND LIVER METABOLISM IN S9 FRACTIONS ON THE

PREDICTED BIOCONCENTRATION OF PHARMACEUTICALS IN FISH

2.1. Introduction

The bioconcentration factor (BCF) is a quantitative expression of a compound’s

ability to partition into and remain in an organism following aqueous exposure,

increasing the potential for chronic toxic effects and/or biomagnification in higher trophic

levels. Bioconcentration often represents a surrogate measure for “B” in PBT, a

regulatory classification for persistent, bioaccumulative and toxic substances that has

taken on greater importance with the implementation of the European Union regulation

on the Registration, Evaluation and Authorisation of Chemicals (REACH) and the

Canadian assessment of chemicals on the Domestic Substances List (DSL). Of the

thousands of chemicals currently in commerce, most lack published data on their

bioaccumulation potential. For example, of the 11,300 organic chemicals under review

on the Canadian DSL, only 4% have publicly published BAF or BCF values (Arnot and

Gobas, 2006). Thus, a method for accurately determining large quantities of BCF

values is necessary. Multi-compartment BCF models provide such an alternative

method.

Historically, models utilize KOW as the primary determinant of BCF. Recent BCF

modeling efforts have proposed a multi-compartment model based on the principles of

absorption, distribution, metabolism and elimination (ADME) (Arnot and Gobas, 2004;

Cowan-Ellsberry et al., 2008; Arnot et al., 2009). The model includes: uptake via gills

39

(k1), dietary ingestion (kU), elimination across gills (k2 ), growth (kG), metabolism (kM)

and egestion (kE) (Gobas, 1993).

BCF = (k1 + kU) / (k2 + kG +kM +kE) (7)

By far, a majority of efforts have focused on the role of metabolism on the

process of bioconcentration (Arnot et al., 2008; Cowan-Ellsberry et al., 2008; Dyer et al.,

2009). Building on concepts developed within the human pharmaceutical sector, in vitro

fish metabolism information may be able to predict whole fish clearance, and thus,

contribute to a more realistic calculated BCF (Obach, 1999; Mohutsky et al., 2006;

Cowan-Ellsberry et al., 2008).

While human science has focused on ionizable pharmaceuticals, most

environmental work has focused on neutral, lipophilic chemicals. This study evaluates

the ability of in vitro fish metabolism data to determine whole fish intrinsic clearance,

using ibuprofen (a weak acid), norethindrone (a neutral compound) and propranolol (a

weak base) as the test compounds. Test compounds were chosen as they are readily

metabolized in human and mammalian liver and have been found in the aquatic

environment at trace levels. In the human and rat liver, cytochrome P450 (CYP) 2C9

and 2C8 contribute to the majority of phase I metabolism of ibuprofen (Jacqz-Aigrain

and Anderson, 2006). Ibuprofen also undergoes glucuronidation by multiple UDP-

glucuronosyltransferase (UGT) isoforms (Ritter, 2000). In human liver, norethindrone is

primarily metabolized by CYP3A4 (Korhonen et al., 2008). CYP2D6 is thought to

catalyze a large portion of propranolol biotransformation in humans (Masubuchi et al.,

1994). As fish do not contain the same CYP isoforms as mammals, and are specifically

40

known not to have CYP2C or 2D homologs (Buhler and Wang-Buhler, 1998), it cannot

be assumed that fish biotransformation of these pharmaceuticals will be equivalent to

that of mammals. Therefore, this study assessed biotransformation through the

measurement of loss of parent compound.

2.2. Materials and Methods

2.2.1. Materials

All chemicals used in incubation assays were purchased from Sigma-Aldrich

Corp. (St. Louis, MO). Homogenization buffer (50mM potassium phosphate, 0.15M

potassium chloride, 0.2M sucrose) materials were obtained from Fisher Chemical

(Fairlawn, NJ). The Bradford Protein Assay was procured from Bio-Rad Laboratories

(Hercules, CA). Uninduced, male Sprague-Dawley rat liver S9 and uninduced, male

CD-1 mouse liver S9 were purchased from Moltox (Boone, NC). (±)-Ibuprofen, d3-

ibuprofen, norethindrone and d6-norethindrone were sourced from Toronto Research

Chemicals (North York, ON, Canada). (±)-Propranolol was supplied by TOCRIS

Bioscience (Ellisville, MO). d7-propranolol was purchased from CDN Isotopes Inc.

(Pointe-Claire, Quebec, Canada).

2.2.2. Fish Cultures

Gill and liver tissue were harvested from anesthetized (e.g. MS-222) mature

rainbow trout (Oncorhynchus mykiss) and channel catfish (Ictalurus punctatus). Fish

were procured from Greers Ferry National Fish Hatchery (Heber Springs, AR). Fish

were maintained in dechlorinated tap water at the appropriate temperature (16°C for

rainbow trout and room temperature (~25°C) for channel catfish) in Frigid Units Living

41

Streams under fluorescent lights with a 16 hour light/8 hour dark photoperiod. Fish

were fed ground Purina Trout Chow. All studies were conducted in accordance with

University of North Texas animal use protocols.

2.2.3. S9 Fractions

Tissues were rinsed thoroughly with a homogenization buffer and immediately

placed on ice (Kelly et al., 2000). Gill, liver and whole fish wet weights were collected

prior to further processing. Gill tissue was then composited, minced and mixed with a

homogenization buffer in a 2:1 buffer to tissue ratio (v:w). Liver tissues were

homogenized with a 4:1 buffer to tissue ratio. Tissue from five to ten fish were

homogenized together to reduce interfish variability. Additionally, multiple tissue

homogenate batches were prepared to evaluate homogenate variability. Four to ten

tissue homogenates were utilized to evaluate loss of parent (i.e., n = 4-10) within each

tissue type. Tissues were homogenized on ice using an electric homogenizer. The

homogenate was placed in chilled centrifuge tubes and centrifuged at 9,000g for 20

minutes at 4°C. The S9 supernatant was carefully separated from the pellet via pipette

and stored at -80°C.

2.2.4. Incubation of S9 fractions

S9 fractions were diluted to 2mg protein mL-1 with 0.01M phosphate buffer (pH

7.4). The Bradford Protein Assay was used to determine the protein content of each

sample. A NADPH regeneration system containing 7mM isocitrate and isocitrate

dehydrogenase (0.5 units of activity mL-1) was then added to 600μL S9 in

microcentrifuge tubes. Test compounds were dissolved in ethanol to create 100X stock

42

solutions. To reduce any reaction interference from the ethanol, only 6.5μL was added

to the S9 samples to bring the reaction vessel concentration to 10μM test compound.

Samples were run in duplicate or triplicate. S9 matrix controls and solvent controls

were run with each assay to assure that loss of parent was not due to binding effects.

Samples were placed in a temperature controlled shaker (15°C for trout, 25°C for catfish

and 37°C for mammalian) to equilibrate for 10 minutes. To initiate the reactions, 6.5μL

of 50mM NADPH in phosphate buffer was added to each sample. S9 and solvent

controls did not receive NADPH. Samples were returned to the shaker and incubated

for 90 to 120 minutes. Aliquots of 100μL were removed at the appropriate time points

for each test material. Aliquots were placed in 100μL cold methanol and vortexed to

stop biological activity. Deuterated internal standard was then added. Samples were

centrifuged at 2500g for 10 minutes to pellet the denatured protein. The supernatant

was carefully removed via pipette and stored at 4°C until GC/MS or LC-MS/MS

analysis.

2.2.5. Instrumental Analysis

2.2.5.1. GC/MS

Ibuprofen samples were dried under a gentle stream of nitrogen. The

derivatizing agent boron trifluoride (BF3)-methanol and acetonitrile were added.

Samples were incubated at 85°C for two hours. Approximately 200µL of milli-Q water

(MQ) was added and the derivatized ibuprofen was back extracted into 1:1 hexane:ethyl

acetate. Samples were then dried again under nitrogen gas and reconstituted to 100µL

with hexane. Samples were analyzed using an Agilent 6890N Network GC System

coupled with an Agilent 5973 inert Mass Selective Detector and an Agilent autosampler

43

(Agilent Technologies, Inc.). Samples were injected at 260°C in pulsed splitless mode

onto an Econo-Cap™ EC™-5 (30m x 0.25mm x 0.25μm) column. Injection pressure

was held constant at 9.5psi and with a flow of 18.5mL min-1. Initial temperature of 40°C

was held for three minutes, increased 10°C min-1 until 200°C, further increased 15°C

min-1 until 300°C and then held for five minutes. Analytes were detected and quantified

in selective ion monitoring (SIM) mode with the quantitative ions 161 m/z for ibuprofen

and 164 m/z for d6-ibuprofen. A seven-point calibration curve was used for

quantification.

2.2.5.2. LC-MS/MS Analysis

Norethindrone and propranolol samples were dried under nitrogen and

reconstituted in 100µL 1:1 methanol:MQ. LC-MS/MS was carried out using a Waters

2695 Separations Module and autosampler (Waters). Chromatography was performed

on a Sunfire C18 (2.1mm x 50mm, 3.5µm) column (Waters). Analyte identification was

performed using a Micromass Quattro Ultima mass spectrometry system with an

electrospray interface run in positive mode (Micromass). The flow rate for all

applications was 0.2mL min-1 with an injection volume of 10µL.

The mobile phase for norethindrone analysis consisted of MQ with 0.1% formic

acid (solvent A) and methanol with 0.1% formic acid (Solvent B1). Initial mobile phase

conditions of 70% solvent A:30% solvent B1 were held for one minute and then solvent

B1 was increased to 70% over three minutes. Solvent B1 was further increased to 95%

over 30 seconds and then decreased to 70% over two minutes. Initial mobile phase

conditions were returned over 30 seconds and held for three minutes. For propranolol,

the mobile phase comprised of solvent A and 2:1 acetonitrile:methanol with 0.1% formic

44

acid (solvent B2). Initial mobile phase of 90% solvent A:10% solvent B2 was held for 30

seconds. Solvent B2 linearly increased to 90% over 9.5 minutes. Initial conditions were

then linearly resumed over the course of two minutes.

Individual tune files were created for each analyte. Norethindrone settings

consisted of 4.0kV capillary voltage, 60V cone voltage and 30eV collision energy. For

propranolol, capillary voltage was 4.0kV with cone voltage at 45V and collision energy

at 17eV. Protonated ions were dissociated with argon for norethindrone (m/z

298.5>108.2), d6-norethindrone (m/z 304.1>113.8), propranolol (m/z 260.1>183.4) and

d7-propranolol (m/z 267.1>190.1) with detection in multiple reaction monitoring (MRM)

mode. Six-point calibration curves of analyte standards were used for quantification.

2.2.6. Ethoxyresorufin-O-deethylase (EROD) activity

To ensure S9 fractions were metabolically active, the dealkylation of

ethoxyresorufin to resorufin was measured. S9 fractions were diluted to 2mg protein

mL-1 with 0.01M phosphate buffer (pH 7.4). Ethoxyresorufin (5µM) was added.

NADPH (10µM) was added to initiate the reaction. Samples were covered in aluminum

foil and placed in a shaker within a dark incubator (15°C for rainbow trout fractions,

25°C for channel catfish fractions). S9 matrix controls and blanks did not receive

NADPH. Aliquots were removed at 0, 20, 40 and 60 minute time points and placed in

four volumes of cold methanol in black 96-well plates to stop the reaction. Samples

were immediately read for fluorescence (excitation 530nm, emission 590nm) on a

Synergy 2 Multi-Mode Microplate Reader (BioTek). A five-point resorufin standard

curve (10-50µg mL-1) was used for quantification. All work was conducted in low light

conditions.

45

2.2.7. In vitro metabolic transformation rates of the gill and liver

In vitro metabolic transformation rates of the gill and liver were determined using

the bioconcentration model described by Cowan-Ellsberry et al. (2008) (Fig. 5). The

model extrapolates in vitro test data to determine a whole body metabolic

transformation rate using key fish physiological information (Table 4). This extrapolation

is based on concepts commonly applied in mammalian pharmacokinetics (Obach,

1999). Intrinsic clearance rate of parent compound (CLm) was determined from in vitro

S9 data per mg protein in the assay vessel. Multiplication of tissue weight and total

protein content determined intrinsic clearance in tissue (CLi), which was extrapolated to

tissue clearance (CLt) by including cardiac output, fraction of blood flow through tissue

and a free fraction correction term. Division by the volume of distribution then produced

the metabolic transformation rate (kM). BCF was then calculated, incorporating kM

(BCFM) and with kM equal to zero (BCF0). BCF calculations also included predicted k1,

k2, kE and kG values based upon KOW, total fish weight and fish lipid content.

Parameters, such as body composition and blood composition obtained from Cowan-

Ellsberry et al (2008), were not altered across species as they affected BCF calculations

and eliminated the ability to directly compare the impact of the metabolic transformation

rate.

46

Fig. 5. Extrapolation of in vitro S9 metabolism assay to the metabolic transformation

rate (kM) based upon Cowan-Ellsberry et al. (2008). [CLm, intrinsic clearance rate of

parent compound (mL hr-1 (mg protein)-1) ; CLi, intrinsic clearance in tissue (L d-1 kg-1);

TW, tissue weight (g tissue (kg body weight)-1); PL, protein per g tissue (g g-1); CLt,

tissue clearance (L d-1 kg-1); CO, cardiac output (mL min-1 kg-1); TF, fraction of blood

flow through tissue; fu, free fraction correction term; Vd, volume of distribution (L kg-1)].

47

Table 4

Physiological and physical characteristics used to extrapolate in vitro metabolic

transformation within the BCF model. Mean values are reported. Tissue batch specific

values were used in the calculation of individual kM and BCFM. Values were determined

during the course of the study or in other unpublished laboratory studies. *Information

to calculate characteristic provided by supplier (Moltox). †Information collected from

literature (Davies and Morris, 1993; Gearhart et al., 1994; Houston, 1994; Janssen et

al., 2002).

Trout Gill

Trout Liver

Catfish Gill

Catfish Liver

Rat Liver

Mouse Liver

Body weight (kg) 0.204 0.204 0.157 0.157 0.289* 0.028*

Tissue weight (g (g bw)-1) 12.2 15.1 6.9 19.4 37.8* 37.9*

Protein level (g protein (g tissue)-1)

0.042 0.078 0.024 0.094 0.045† 0.045†

Cardiac output (mL min-1 kg-1) 16.9 16.9 26.2 26.2 263† 484†

Fraction of blood flow through tissue

1.0 0.20 1.0 0.20 0.20† 0.25†

Body composition

lipid 0.10 0.10 0.10 0.10 0.1 0.1

non-lipid organic 0.20 0.20 0.20 0.20 0.2 0.2

water 0.70 0.70 0.70 0.70 0.7 0.7

Blood composition

blood lipid 0.01 0.01 0.01 0.01 0.01 0.01

non-lipid organic 0.15 0.15 0.15 0.15 0.15 0.15

water 0.84 0.84 0.84 0.84 0.84 0.84

Temperature of environment 15°C 15°C 25°C 25°C 37°C 37°C

48

2.2.8. Statistical Analysis

Statistical analysis was conducted using GraphPad Prism 5 (GraphPad Software,

Inc.). Loss of parent data was found to be homoscedastic within each S9 fraction type

(i.e., catfish liver S9, etc.). Two-way repeated measures ANOVA confirmed statistical

significance of loss of parent compound over time while Bonferroni post tests were used

to evaluate the differences between tissue homogenate batches at each sample time

point. CLm and BCF values were analyzed either via an unpaired t-test or one-way

ANOVA with Bonferroni's multiple comparison post test, depending upon the number of

items compared. Mean and standard deviation are shown in all figures.

2.3. Results

2.3.1. EROD Activity

EROD assays confirmed metabolic activity of the multiple tissue fractions.

Resorufin concentration significantly increased over time in each test article, signifying

CYP1A2 activity (Fig. 6). Resorufin was observed for trout liver and gill S9 at

376.4±23.4 and 363.7±17.9pmol mg protein-1 after 60 minutes, which equated to mean

32.7% and 13.3% increases in resorufin. For catfish liver and gill S9, resorufin

concentrations were 269.5±37.3 and 268.2±15.8pmol mg protein-1 after 60 minutes,

which equated to mean 16.3% and 27.7% increases.

49

Fig. 6. Percent increase in resorufin over 60 minute time course.

2.3.2. Intrinsic clearance rate of parent compound

Loss of parent compound was observed in all trials, indicating metabolic activity

in the S9 fractions. However, loss of parent compound occurred at varying rates among

species and tissue type (Fig. 7). CLm was calculated from loss of parent using

analytically confirmed initial parent concentrations (Fig. 8). In general, liver fractions

provided higher clearance rates than gill fractions. For norethindrone and propranolol, a

significant difference was observed between liver and gill CLm within each species.

However, a difference between species was not observed as the CLm of trout and

catfish liver and the CLm of trout and catfish gill were not significantly different.

Ibuprofen metabolism did not follow the trend of higher liver clearance rates. The

difference between liver and gill fractions was not considered significantly different.

While gill clearance rates for ibuprofen were in the same range as those observed for

norethindrone and propranolol, ibuprofen liver clearance rates were significantly less

than those of norethindrone and propranolol. Ibuprofen metabolism was also tested in

uninduced Sprague-Dawley rat and CD-1 mouse S9 to provide further perspective on

the lower intrinsic clearance rates found in fish liver S9. CLm of rat and mouse were not

50

significantly different than those of fish. Thus, ibuprofen appears to have a lower

clearance rate across species than propranolol and norethindrone.

Fig. 7. Mean loss of parent compound in S9 fractions over 90 minute study time period.

51

Fig. 8. Mean CLm of S9 fractions. “*” indicates significant difference from S9 of the

same species.

52

Table 5

Comparison of intrinsic clearance rate (CLm), extrapolated metabolic transformation rate (kM), BCFM and BCF0. Mean and

standard error reported. †Represents four assay replicates of single lot of pooled rat or mouse S9.

Norethindrone Propranolol Ibuprofen

Trout Liver S9

Trout Gill S9

Catfish Liver S9

Catfish Gill S9

Trout Liver S9

Trout Gill S9

Catfish Liver S9

Catfish Gill S9

Trout Liver S9

Trout Gill S9

Catfish Liver S9

Catfish Gill S9

Rat Liver S9

Mouse Liver S9

n 10 6 8 4 6 7 6 5 7 6 8 6 † †

CLm 77.4 ± 13.2

11.9 ± 3.1

105.1 ± 8.7

23.8 ± 8.8

180.9 ±

13.8

460.6 ±

16.8

149.1 ± 12.5

61.2 ± 15.9

46.7 ±

12.3

48.9 ±

18.1

41.0 ± 6.0

74.5 ± 22.1

89.7 ± 16.0

46.72 ± 7.1

kM 0.26 ± 0.04

0.03 ± 0.01

0.52 ± 0.03

0.02 ± 0.006

1.75 ±

0.07

0.50 ± 0.13

2.45 ± 0.12

0.17 ± 0.04

0.56 ±

0.12

0.69 ±

0.12

0.82 ± 0.10

0.17 ± 0.05

2.02 ± 0.35

1.1 ± 0.17

BCFM 84.9 ±

1.6 96.1 ±

0.3 76.8 ±

0.9 96.3 ±

0.3

0.55 ±

0.003

0.59 ± 0.004

0.54 ± 0.003

0.60 ± 0.002

0.97 ±

0.02

0.97 ±

0.005

0.96 ± 0.005

0.99 ± 0.003

0.88 ± 0.02

0.97 ± 0.004

BCFM(total) 83.5 ± 1.6 76.2 ± 0.9 0.53 ± 0.005 0.53 ± 0.004 0.93 ± 0.02 0.95 ± 0.006 0.88 ± 0.02

0.97 ± 0.004

BCF0 97.3 0.60 1.0

53

2.3.3. Metabolic transformation rate

As described in section 2.8, the metabolic transformation rate (kM, d-1) was

extrapolated from loss of parent (Table 5). Fish tissue fractions with higher CLm had

correspondingly higher kM as the physiological parameters entered for trout and catfish

were not greatly different. However, the increased liver to body weight, TW and CO of

the rat enhanced its calculated kM.

2.3.4. BCF

Not all tissue fractions consistently contributed to a significantly reduced BCF.

Predicted BCF0 for norethindrone, propranolol and ibuprofen were 97.3, 0.60 and 1.0

respectively. BCFM including trout liver kM were highly significantly less than the original

predicted BCF0 for all three compounds. Catfish liver BCFM was also significantly less

than BCF0 for all compounds. Trout gill metabolism significantly lowered BCFM for

ibuprofen and propranolol; however, its impact on norethindrone was considered not

significant. Catfish gill did not significantly reduce BCF in any of the compounds.

Mean gill and liver kM were combined additively to obtain kM(total), representing

total fish metabolic transformation. Trout BCFM(total) was significantly smaller than BCFM

based on liver alone (BCFM(liver)) for propranolol (0.53 ± 0.005) and ibuprofen (0.93 ±

0.02). Trout BCFM(total) was not significantly less than trout BCFM(liver) for norethindrone

(83.5 ± 1.6) due to minimal gill clearance. Conversely, a significant difference was

noted between catfish BCFM(total) and BCFM(liver) for norethindrone (76.2 ± 0.9).

Significant differences were not observed with propranolol (0.53 ± 0.004) and ibuprofen

(0.95 ± 0.006). Lastly, in continuing with mammalian comparisons for ibuprofen, rat

54

liver BCFM (0.88 ± 0.02) was significantly less than trout and catfish BCFM(total).

However, mouse BCFM (0.97 ± 0.004) was not significantly different.

2.4. Discussion and Conclusions

This study aimed at finding a simple method for determining kM in vitro. The

method proved reliable in providing reproducible intrinsic clearance rates between

different batches of tissue homogenates. A significant difference was not observed

between the CLm within a tissue fraction type, supporting the use of tissue homogenates

to reduce interfish variability. Additionally, the fish used during this study were procured

at different times over the course of two years. Variability from the assorted fish

procurement batches was not seen.

As intrinsic clearance rates were scaled to metabolic transformation rates, the

physiological parameters used in the model influenced calculated kM values in different

ways. For example, mean TW differed between liver and gill with liver accounting for a

larger proportion of body weight than gill. The larger liver TW increased kM, and thus

correspondingly decreased BCFM. The same is true for tissue protein levels (PL). Liver

had larger PL than gill, which increased kM. Differences in cardiac output between the

two fish species was not great enough to result in significant differences in kM.

However, a 10-fold increase in cardiac output, as seen in the rat, did significantly

increase kM.

Differences in metabolism and CLm were observed between tissue types

throughout the study. For norethindrone and propranolol, gill CLm was smaller than that

of liver. Other studies have suggested that gill metabolic activity is less than that of the

liver. Arachidonic acid metabolism in scup (Stenotomus chrysops) gill microsomes was

55

found to be 10- to 30-fold less than that of scrup liver microsomes (Schlezinger et al.,

1998). Metabolic activity in rainbow trout gill epithelial cells was observed at between

seven- and 60-fold less than isolated hepatocytes, depending on the substrate (7-

ethoxycoumarin, aniline and testosterone) (Leguen et al., 2000). Others have reported

similar metabolic differences (Pesonen and Andersson, 1991; Lindstrom-Seppa et al.,

1981).

Although differences in CLm were noted between tissues, differences in intrinsic

clearance rates were not found between species. In contrast, significant differences

were seen in BCFM values between species in some cases. The biological and

ecological relevance of these differences in BCFM must be put into perspective. For

instance, BCFM for propranolol was significantly different between trout and catfish for

both liver and gill. Nonetheless, these differences were less than a 0.1 change in BCF

and are most likely not biologically or ecologically relevant to either fish species.

Overall, the inclusion of gill and liver kM decreased norethindrone BCFs by 14.2%

in trout and 21.7% in catfish. Propranolol BCFs decreased by 11.7% in both fish

species. Ibuprofen BCFs decreased by 7.0%, 5.0%, 12.0% and 3.0% for trout, catfish,

rat and mouse respectively. The inclusion of kM brought predicted BCFs closer to

observed in vivo BCFs (Nallani et al., 2009).

Although the test compounds are not expected to bioconcentrate due to their

small KOW, they demonstrate the metabolic activity of fish S9 fractions and the potential

for fish to clear xenobiotics. Furthermore, they serve to advance method development

of in vitro metabolism assays. The application of in vitro metabolism assays in an

integrated ADME bioconcentration model has the potential to derive more realistic BCF

56

values while dramatically reducing study time, cost and the number of animals required

(OECD, 1996; de Wolf et al., 2007; Weisbrod et al., 2007).

2.5. References

Arnot, J. A., Gobas, F. A., 2004. A food web bioaccumulation model for organic

chemicals in aquatic ecosystems. Environ. Toxicol. Chem. 10, 2343–2355.

Arnot, J., Gobas, F. A., 2006. A review of bioconcentration factor (BCF) and


Environ. Rev. 14, 257-297.

Arnot, J. A., Mackay, D., Parkerton, T., & Bonnell, M., 2008. A database of fish

biotransformation rates for organic chemicals. Environ. Toxicol. Chem. 27, 2263-

2270.

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

R. S., 2009. A quantitative structure-activity relationship for predicting metabolic

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

1168-1177.




Cowan-Ellsberry, C., Dyer, S. E., Bernhard, M., Roe, A., Dowty, M., & Weisbrod, A.,

2008. Approach for extrapolating in vitro metabolism data to refine

bioconcentration factor estimates. Chemosphere 70, 1804-1817.

Davies, B., Morris, T., 1993. Physiological parameters in laboratory animals and

humans. Pharm. Res. 10, 1093-1095.

57





Dyer, S., Bernhard, M., Cowan-Ellsberry, C., Perdu-Durand, E., Demmerle, S., Cravedi,

J., 2009. In vitro biotransformation of surfactants in fish. Part II--Alcohol

ethoxylate (C16EO8) and alcohol ethoxylate sulfate (C14EO2S) to estimate

bioconcentration potential. Chemosphere 76, 989-98.

Gearhart, J. M., Jepson, G. W., Clewell, H. J., Andersen, M. E., Conolly, R. B., 1994.

Physiologically Based Pharmacokinetic Model for the Inhibition of

Acetylcholinesterase by Organophosphate Esters. Environ. Health Perspect. 102

Suppl., 51-60.



Modelling 69, 1-17.

Houston, J. B., 1994. Utility of in vitro drug metabolism data in predicting in vivo

metabolic clearance. Biochem. Pharmacol. 47, 1469-1479.



Janssen, B., Debets, J., Leenders, P., Smits, J., 2002. Chronic measurement of cardiac

output in conscious mice. Am. J. Physiol. Regulatory Integrative Comp. Physiol.

282, 928-935.

58

Kelly, S. P., Fletcher, M., Part, P., Wood, C. M., 2000. Procedures for the preparation

and culture of 'reconstructed' rainbow trout branchial epithelia. Methods Cell Sci.

22, 153-163.




110, 56-66.



cells in culture. Aquat. Toxicol. 48, 165–176.



259–263.

Masubuchi, Y., Hosokawa, S., Horie, T., Suzuki, T., Ohmori, S., Kitada, M., Narimatsu,

S., 1994. Cytochrome P450 isozymes involved in propranolol metabolism in

human liver microsomes. The role of CYP2D6 as ring-hydroxylase and CYP1A2


Mohutsky, M. A., Chien, J. Y., Ring, B. J., Wrighton, S. A., 2006. Predictions of the in

vivo clearance of drugs from rate of loss using human liver microsomes for phase

I and phase II biotransformations. Pharm. Res. 23, 654-662.

Nallani, G., Paulos, P., Vaz, A., Constantine, L., Venables, B., Huggett, D., 2009.

Bioconcentration potential of ibuprofen in fathead minnow (Pimephales

59

promelas) and channel catfish (Ictalurus punctatus) [Abstract No. UP012].

Presented at SETAC North America 30th Annual Meeting, New Orleans, LA.

Obach, R. S., 1999. Prediction of human clearance of twenty-nine drugs from hepatic

microsomal intrinsic clearance data: an examination of in vitro half-life approach

and nonspecific binding to microsomes. Drug Metab. Dispos. 27, 1350-1359.



Paris, France.



Xenobiotica. 21, 461–471.






Weisbrod, A., Burkhard, L., Arnot, J., Mekenyan, O., Howard, P., Russom, C.,





60

CHAPTER 3

IBUPROFEN METABOLISM IN THE LIVER AND GILL OF RAINBOW TROUT,

Oncorhynchus mykiss

3.1. Introduction

Ibuprofen is a nonsteroidal anti-inflammatory drug (NSAID) found in over-the-

counter and prescription pharmaceuticals. Ibuprofen is typically administered as a

racemic mixture of (S)-(+)- and (R)-(-)-enantiomers (Rodrigues 2005), with (R)-(-)-

ibuprofen undergoing unidirectional chiral inversion to (S)-(+)-ibuprofen (Davies, 1998).

Both enantiomers are readily metabolized through phase I and phase II pathways. In

human and rat liver, cytochrome P450 (CYP) 2C9 and 2C8 contribute to the majority of

Phase I biotransformation of ibuprofen (Jacqz-Aigrain and Anderson 2006). Uridine

diphosphate glucuronyl-transferase (UGT) 1A9, 1A3, 2B1 and 2B7 are known to

conjugate ibuprofen (Ritter 2000). Studies have shown direct glucuronidation to

account for approximately 14% of (S)-(+)-ibuprofen metabolism with 2-hydroxy-

ibuprofen (2-OH-IBP) and 3-hydroxy-ibuprofen (3-OH-IBP) metabolites accounting for

28% and 45%, respectively in human microsomes (Davies 1998; Rodrigues 2005; Rudy

et al. 1991). 3-OH-IBP is quickly biotransformed to 3-carboxy ibuprofen via cytosolic

dehydrogenases (Rudy et al. 1991; Davies 1998). As fish do not contain the same

CYP2C isoforms as mammals (Buhler and Wang-Buhler 1998), it cannot be assumed

that piscine biotransformation will be equivalent to that of mammals.

Aquatic organisms may be exposed to chronic, low dose exposures to ibuprofen.

Ibuprofen has been identified in the environment with concentrations observed as high

as 4239ng L-1 in wastewater effluent and 2370ng L-1 in surface water (Roberts and

61

Thomas 2006; Ternes 2001). Thus, understanding the ability and mechanism of fish to

metabolize ibuprofen further elucidates the effects of ibuprofen in the aquatic

environment.


Chemicals used in the following studies were purchased from Sigma-Aldrich

Corp. (St. Louis, MO) unless noted below. Alamethicin and uridine diphosphate

glucuronic acid (UDPGA) were generously provided by Pfizer Inc. (Groton, CT). The

Bradford Protein Assay was procured from Bio-Rad Laboratories (Hercules, CA).

Uninduced, male Sprague-Dawley rat liver S9 and uninduced, male CD-1 mouse S9

were purchased from Moltox (Boone, NC). Ibuprofen and d3-ibuprofen were sourced

from Toronto Research Chemicals (North York, ON, Canada).

Gill and liver tissue were harvested from mature rainbow trout (Greers Ferry

National Fish Hatchery, Heber Springs, AR). Fish were maintained in dechlorinated tap

water at 16°C in Frigid Units Living Streams under fluorescent lights with a 16 hour

light/8 hour dark photoperiod. Fish were fed ground Purina Trout Chow. Fish were

anesthetized prior to sacrifice. All studies were conducted in accordance with

University of North Texas animal use protocols.

For uninduced fractions, tissues from five to ten fish were composited and

homogenized on ice using an electric homogenizer. The homogenate was centrifuged

at 9,000g for 20 minutes at 4°C. The S9 supernatant was carefully separated from the

pellet and stored at -80°C. To obtain microsomal fractions, S9 underwent centrifugation

at 100,000g for 60 minutes at 4°C. The supernatant was discarded and the pellet

resuspended in homogenization buffer. For induced fractions, rainbow trout (n = 3)

62

were injected with beta-naphthoflavone (BNF, 100mg (kg bw)-1) and sacrificed after 24-

hours. Microsomes were made from the composited gill and liver tissue.

S9 and microsomal fractions were diluted to 2.0 and 1.0mg protein mL-1 with

0.01M phosphate buffer (pH 7.4), respectively. The Bradford Protein Assay determined

the protein content of each sample. Samples (650µL total volume) included an NADPH

regeneration system and 10μM ibuprofen. Samples were run in duplicate or triplicate.

S9 matrix controls and solvent controls were run with each assay to assure loss of

parent was not due to binding effects. Samples were equilibrated for 10 minutes (trout

at 15°C; mammalian at 37°C) in a temperature controlled shaker. To initiate the

reactions, 6.5μL of 50mM NADPH in phosphate buffer was added to each sample.

Samples were then incubated for 60 to 120 minutes. Aliquots of 100μL were removed

throughout the course of the incubation and placed in equal volume cold methanol to

stop biological activity. Deuterated internal standard was then added. Samples were

centrifuged at 2500g for 10 minutes to pellet the denatured protein. The supernatant

was then stored at 4°C until instrumental analysis. All loss of parent studies were

analyzed via GC/MS (see below).

To further investigate UGT specific activity, the above procedure was modified to

include the addition of the cofactor UDPGA. Alamethicin was added at 50μg mg-1

protein to S9 samples prior to equilibration. To initiate the reaction, 5mM UDPGA was

added to the sample along with NADPH. To evaluate the influence of phase I

biotransformation on ibuprofen metabolism, alpha-naphthoflavone (ANF), a CYP1A2

inhibitor, was added prior to incubation at 10µM to BNF-induced microsomes.

Uninhibited samples were run for comparison.

63

Samples of uninduced S9 and induced, uninhibited microsomes were collected

after 60 minutes for metabolite analysis. Metabolites were analyzed via LC/MS (see

below).

For GC/MS analysis, samples were derivatized with boron trifluoride (BF3)-

methanol and analyzed with an Agilent 6890N Network GC System coupled with an

Agilent 5973 inert Mass Selective Detector and an Agilent autosampler (Agilent

Technologies, Inc.). Analytes were detected and quantified in selective ion monitoring

(SIM) mode with the quantitative ions 161 m/z for ibuprofen and 164 m/z for deuterated

ibuprofen. For LC/MS analysis, chromatography was performed with an Agilent solvent

delivery system and autosampler on a Phenomenex Synergi Hydro RP-C18 (4.6mm x

150mm, 4μm) column. Metabolite identification was performed using a Thermo LTQ

equipped with an electrospray interface operating in negative ion mode (Thermo

Electron).


Inc.). Mean and standard error are reported. Previous unpublished data has shown

compositing tissue to reduce the variability in metabolic activity that may be found

between individual fish. The uninduced S9 and microsomes used in these studies

come from the same data set and have been used to evaluate metabolic activity of

other compounds with confidence. Thus, although some assays are replicates of single

tissue homogenates, it is assumed that their activity is representative of the overall

statistical population.

64

3.3. Results and Discussion

In the S9 metabolism study, loss of ibuprofen parent compound was observed in

all trials, indicating the occurrence of metabolism (Fig. 9). The intrinsic clearance rate

(CLm, mL hr-1 (mg protein)-1) was calculated from loss of parent material normalized to

protein content, according to the Eq. (8) based upon Xing et al. (2008).

CLm = Rate loss of parent (µmol hr-1 mg protein-1) * Vol of test solution (mL)

(8) Initial parent concentration (µmol).

Initial parent concentrations were analytically confirmed and not based on nominal

concentrations.

Fig. 9. Relative loss of parent compound over 90 minute study time period. Represents

mean loss of parent. Assays including UDPGA were performed with a single batch

composited tissues. Error bars represent standard error among replicates.

65

Significant differences in CLm were not found between species or tissue type of

uninduced S9 fractions (Fig. 10A). Rat liver CLm, on average, were higher than trout

and mouse CLm. Metabolite analysis of S9 samples at test termination identified 2-OH-

IBP as the primary metabolite in all samples (Fig. 11A). 3-OH-IBP was not detected as

it was most likely converted to 3-carboxy ibuprofen during incubation (Chang et al.

2008). A significant difference was not observed between the relative frequency of 2-

OH-IBP from mouse and trout liver S9. However, the relative frequency of 2-OH-IBP

produced by trout gill S9 was significantly more than mouse liver S9.

Fig. 10. (A) Mean CLm of uninduced S9 fractions. Significant differences were not

observed between species, tissue type or with UDPGA addition. (B) Mean CLm of BNF-

induced and uninduced trout liver microsomes (TLM) and trout gill microsomes (TGM).

Microsomes inhibited with ANF. “*” indicates a significant difference.

66

Fig. 11. Relative frequencies of ibuprofen and its metabolites. (A) “*” indicates

significant differences between fish tissue fraction and mouse tissue fraction. (B) “*”

indicates significant difference between induced and uninduced microsomes.

Glucuronide metabolites were not detected in the trout gill S9 samples. This

indicates that direct glucuronyl conjugation may not play a major role in

biotransformation in gill tissues. Moreover, the addition of UDPGA did not significantly

increase CLm. As UDPGA is often found at levels below the Km of UGTs in uninduced

environments (Ritter 2000), CLm was expected to increase with the addition of the

cofactor. The lack of significant increase reinforces that glucuronidation is not a major

ibuprofen pathway in the gill. Glucuronide metabolites were found in mouse liver S9 at

expected relative frequencies (Chang et al. 2008). No significant difference was found

between the relative frequency of trout liver and mouse liver S9 glucuronide

metabolites.

As phase I biotransformation appears to be the major pathway in ibuprofen

metabolism, studies with induced and/or inhibited microsomes were conducted to

67

further delineate the mechanism behind trout biotransformation of ibuprofen. Metabolic

activity was greatly enhanced in BNF-induced trout liver microsomes (mean 19.6% loss

of parent compound within 20 minutes). This may be due to increased CYP1A activity

(Buhler and Wang-Buhler 1998). Metabolite analysis indicated 2-OH-IBP levels in BNF-

induced liver microsomes to be double that of uninduced liver microsomes (Fig. 11B).

Glucuronide metabolites were not detected in induced liver microsomes. Moreover,

ANF inhibited metabolic activity in induced liver microsomes (Fig. 10B). The remaining

activity is assumed to be glucuronide conjugation. BNF did not enhance metabolic

activity in gill microsomes, nor did ANF significantly inhibit activity. Metabolite analysis

found similar relative frequencies of 2-OH-IBP in gill microsomes, while glucuronide

metabolites were not detected as in gill S9 fractions.

The enhanced metabolism in BNF-induced microsomes and subsequent

inhibition by ANF suggests CYP1A2, rather than a CYP2C homolog, as a metabolic

pathway for ibuprofen in fish. Naproxen, another NSAID, is known to undergo CYP1A2

biotransformation along with CYP2C9 in humans (Miners et al. 1996; Rodrigues et al.

1996). CYP1A2 may oxidize ibuprofen in a similar manner as naproxen.

These data suggest that ibuprofen can be metabolized in fish at similar rates and

with similar end metabolites as mammals. The intrinsic clearance in mouse and rat liver

S9 factions was not significantly different than trout liver CLm. The mechanism

responsible for ibuprofen metabolism may be different (e.g. CYP1A2) than known

mammalian mechanisms (e.g. CYP2C9), yet it may be feasible to read-across some

information from mammalian literature since intrinsic clearance rates were similar. More

68

data are needed to fully understand the similarities and differences between piscine and

mammalian metabolism and its ecological significance.

3.4. References



environmental monitoring. Comp Biochem Physiol. 121C, 107-137.


A. D., 2008. Confirmation that cytochrome P450 2C8 (CYP2C8) plays a minor


36, 2513-2522.

Davies, N., 1998. Clinical pharmacokinetics of ibuprofen: The first 30 years. Clin

Pharmacokinet. 34, 101–154.

Han, X., Nabb, D., Yang, C., Snajdr, S., Mingoia, R., 2009. Liver microsomes and S9

from rainbow trout (Oncorhynchus mykiss): comparison of basal-level enzyme

activities with rat and determination of xenobiotic intrinsic clearance in support of

bioaccumulation assessment. Environ. Toxicol. Chem. 28, 481-488.



Miners, J., Coulter, S., Tukey, R., Veronese, M., Birkett, D., 1996. Cytochromes P450,

1A2, and 2C9 are responsible for the human hepatic O-demethylation of R- and

S-naproxen. Biochem Pharmacol. 51, 1003-1008.


xenobiotic bioactivation reactions. Chem Biol Interact. 129, 171-193.

69



Environ. 356, 143-153.

Rodrigues, A., 2005. Impact of CYP2C9 genotype on pharmacokinetics: are all

cyclooxygenase inhibitors the same? Drug Metab. Dispos. 33, 1567-1575.

Rodrigues, A., Kukulka, M., Roberts, E., Ouellet, D., Rodgers, T., 1996. [O-methyl

14C]naproxen O-demethylase activity in human liver microsomes: evidence for

the involvement of cytochrome P4501A2 and P4502C9/10. Drug Metab. Dispos.

24, 126-136.

Rudy, A., Knight, P., Brater, D., Hall, S., 1991. Stereoselective metabolism of ibuprofen

in humans: administration of R-, S- and racemic ibuprofen. J. Pharmacol. Exp.

Ther. 259, 1133-1139.


aqueous environmental samples. Trends Anal. Chem. 20, 419-434.

70

CHAPTER 4

METABOLISM OF THREE PHARMACEUTICALS IN RAINBOW TROUT AND

CHANNEL CATFISH LIVER AND GILL CELL SUSPENSIONS, AND THE EFFECT ON

PREDICTED BIOCONCENTRATION

4.1. Introduction

Bioconcentration involves uptake of xenobiotics via an organism’s respiratory

surfaces (e.g., gills and/or skin) and the subsequent elimination from the organism

(Gobas and Morrison, 2000). Uptake through the gills is the predominant route for most

organic xenobiotics except extremely hydrophobic organic chemicals, in which uptake

through ingestion of xenobiotics bound to food particles significantly contributes to total

bioaccumulation (Bruggeman et al., 1984; Randall et al., 1998). The bioconcentration

factor (BCF) is a quantitative expression of a compound’s ability to partition from the

aqueous environment into and remain in an organism. Historically, models have utilized

the 1-octanol-water partition coefficient (KOW) as the primary determinant of BCF (Neely

et al., 1974; Veith et al., 1979; Mackay, 1982; Davies and Dobbs, 1984; Oliver and

Niimi, 1985). Single compartment models provide successful estimates for compounds

whose bioconcentration is governed by their lipophilicity and low biologic activity.

However, hydrophobicity models have been hypothesized to overpredict the BCF of

ionizable, readily metabolized compounds, such as pharmaceuticals.

Recent BCF modeling efforts have proposed a multi-compartment model based

on the principles of absorption, distribution, metabolism and elimination (ADME) (Arnot

et al., 2009; Arnot and Gobas, 2004; Cowan-Ellsberry et al., 2008). The model depicted

as BCF = (k1 + kU) / (k2 + kG +kM +kE), includes: uptake via gills (k1), dietary ingestion

71

(kU), elimination across gills (k2 ), growth (kG), metabolism (kM) and egestion (kE)

(Gobas, 1993). By far, a majority of efforts have focused on the role of metabolism on

the process of bioconcentration (Arnot et al., 2008; Cowan-Ellsberry et al., 2008; Dyer

et al., 2009). Building on concepts developed within the human pharmaceutical sector,

in vitro fish metabolism data may be able to predict whole fish clearance, and thus,

contribute to a more realistic calculated BCF (Obach, 1999; Mohutsky et al., 2006;

Cowan-Ellsberry et al., 2008).

In vitro biotransformation assays provide several advantages to full scale in vivo

studies. As BCF has gained increased regulatory significance over the past decade,

the need for cost effective techniques that take into consideration refinement, reduction

and replacement in animal studies has also grown. In vitro biotransformation assays

allow for quick, reproducible results without the expense of in vivo studies. Moreover,

the primary cell suspensions used in this study permit the evaluation of heterogeneous

mixtures of cell types. This is advantageous when studying tissues, such as the gill,

which contain multiple cell types that could potentially play a role in xenobiotic

metabolism. Lastly, in vitro studies satisfy the societal desire to reduce the use of

animals in toxicological testing (Bols et al., 2005; de Wolf et al., 2007; Weisbrod et al.,

2007).

This study evaluates the ability of in vitro fish metabolism data from liver and gill

cell suspensions to determine whole fish intrinsic clearance, using ibuprofen (a weak

acid), norethindrone (a neutral compound) and propranolol (a weak base) as the test

compounds. Test compounds were chosen as they are readily metabolized in human

and mammalian liver (Masubuchi et al., 1994; Chang et al., 2008; Korhonen et al.,

72

2008) and have been found in the aquatic environment at trace levels (Huggett et al.,

2003; Roberts and Thomas, 2006; Fernandez et al., 2007).


4.2.1. Materials

All materials used in the isolation of cells were purchased from Invitrogen Corp.

(Carlsbad, CA). Chemicals used during cell incubation and neutral red uptake assay

were purchased from Sigma-Aldrich Corp. (St. Louis, MO). (±)-Ibuprofen, norethindrone

and their deuterated internal standards were sourced from Toronto Research Chemicals

(North York, ON, Canada). (±)-Propranolol was supplied by TOCRIS Bioscience

(Ellisville, MO). Deuterated propranolol was purchased from CDN Isotopes Inc. (Pointe-

Claire, Quebec, Canada).

4.2.2. Fish Cultures

Rainbow trout were procured from Greers Ferry National Fish Hatchery (Heber

Springs, AR) while channel catfish were procured from Pond King Inc. (Gainesville, TX)

as juveniles and raised under laboratory conditions. Fish were maintained in

dechlorinated tap water at the appropriate temperature (16°C for rainbow trout and

room temperature (~25°C) for channel catfish) in Frigid Units Living Streams under

fluorescent lights with a 16 hour light/8 hour dark photoperiod. Fish were fed ground

Purina Trout Chow. All studies were conducted in accordance with University of North

Texas animal use protocols.

73

4.2.3. Cell Isolation

Prior to harvesting gill tissue, fish were placed in sterile reconstituted hard water

for 20 minutes. Gill tissues were washed thoroughly with Hank’s Balanced Salt Solution

(HBSS) following the protocol described in Kelly et al. (2000). Livers were perfused with

HBSS for ten minutes and then carefully excised. Gill, liver and whole fish wet weights

were collected prior to further processing. Fish were anesthetized with MS-222 during

sacrifice.

Cell isolation was carried out in the same manner for both gill and liver. Three

tissues were finely minced and composited to reduce interfish variability. Two to three

lots of individual tissue composites (n= 3) were prepared to evaluate lot variability.

Tissues were placed in 50mL conical centrifuge tubes and 5-10mL of 0.05%

trypsin with 0.2g L-1 EDTA•4Na in Hanks' Balanced Salt Solution (Part et al., 1993; Kelly

et al., 2000). Tissues were then placed in a temperature controlled shaker (15°C for

trout; 25°C for catfish) for 20 minutes. Cells were then transferred through a 100µm cell

strainer into a new conical centrifuge tube containing 20mL 10% fetal bovine serum

(FBS) in HBSS. Trypsinization was repeated 3-4 times as needed. The strained cells

were centrifuged at 500g for 10 minutes to pellet the cellular contents. The supernatant

was aspirated off and a pre-chilled solution containing 2.5% FBS in HBSS was added.

The pellet was resuspended with mechanical agitation. The resuspended cells were

centrifuged at 500g for 10 minutes. The supernatant was removed and the pellet was

resuspended in10mL of Leibovitz’s L-15 medium (containing L-glutamine).

74

4.2.4. Cell deactivation

Enzymatically deactivated cells served as control cells to ensure loss of parent

was not due to binding effects. Cells were pelleted via centrifugation at 500g for 5

minutes. Media was removed and 3:1 acetonitrile:MQ water was added. Cells were

resuspended and then placed at 4°C for 15 minutes. Cells were centrifuged at 500g for

10 minutes. The acetonitrile:MQ supernatant was removed and cells were resuspended

in the original volume of media.

4.2.5. Cell Suspension Incubation

Cell suspensions were diluted to 2.5x106 total cells mL-1 with media. Cell counts

were determined via hemocytometer. Test compounds were dissolved in ethanol to

create 100X stock solutions. To reduce any reaction interference from the ethanol, only

6.5μL was added to the cell suspension to bring the concentration to 10μM test

compound (650µL total test volume, 1% ethanol). Samples were run in duplicate.

Media controls, solvent controls and deactivated cell controls were run with each assay

to assure that loss of parent was not due to binding effects. Trout cell suspensions

were placed in a temperature controlled shaker at 15°C (25°C for catfish) and incubated

for 120 minutes. Aliquots of 100μL were removed at 0, 15, 30, 60, 90 and 120 minute

time points for each test material. Aliquots were placed in equal volume cold methanol,

vortexed and placed at -20°C to stop biological activity. Deuterated internal standard

(d3-ibuprofen, d6-norethindrone or d7-propranolol) was then added. Samples were

centrifuged at 500g for 10 minutes to pellet the denatured protein. The supernatant was

carefully removed via pipette and stored at 4°C until instrumental analysis.

75

4.2.6. Instrumental Analysis

Samples were dried under a gentle stream of nitrogen and reconstituted in 100

µL 1:1 methanol:MQ water. LC-MS and LC-MS/MS were carried out using a Waters

2695 Separations Module and autosampler. Chromatography was performed on a

Waters Sunfire C18 (2.1mm x 50mm, 3.5µm) column (Waters). Analyte identification

was performed using a Micromass Quattro Ultima mass spectrometry system with an

electrospray interface run in positive mode for norethindrone and propranolol and in

negative mode for ibuprofen. The flow rate for all applications was 0.2mL min-1 with a

column temperature of 25°C and injection volume of 10µL.

The mobile phase for norethindrone analysis consisted of MQ water with 0.1%

formic acid (solvent A) and methanol with 0.1% formic acid (solvent B1). Initial mobile

phase conditions of 70% solvent A: 30% solvent B1 were held for one minute and then

solvent B1 was increased to 70% over three minutes. Solvent B1 was further increased

to 95% over 30 seconds and then decreased to 70% over two minutes. Initial mobile

phase conditions were returned over 30 seconds and held for three minutes. For

propranolol analysis, the mobile phase comprised of solvent A and 2:1

acetonitrile:methanol with 0.1% formic acid (solvent B2). Initial mobile phase proportion

of 90% (A) / 10% (B2) was held for 30 seconds. Solvent B2 linearly increased to 90%

over 9.5 minutes. Initial conditions were then linearly resumed over the course of two

minutes. For ibuprofen, the initial mobile phase consisted of MQ with10µM ammonium

formate (solvent C) and unmodified methanol (solvent D) at 50% each. Solvent D

increased linearly to 95% at seven minutes and was held for two minutes. Initial mobile

phase conditions were returned over two minutes.

76

Individual tune files were created for each analyte to optimize detection.

Norethindrone settings consisted of 4.00kV capillary voltage, 60V cone voltage and

30eV collision energy. For propranolol, capillary voltage was set at 4.00kV with cone

voltage at 45V and collision energy at 17eV. Ibuprofen held tune settings of 3.00kV

capillary voltage and 10V cone voltage. Protonated ions were dissociated with argon

for norethindrone (m/z 298.5>108.2), d6-norethindrone (m/z 304.1>113.8), propranolol

(m/z 260.1>183.4) and d7-propranolol (m/z 267.1>190.1) with detection in multiple

reaction monitoring (MRM) mode. Ibuprofen was run in single reaction monitoring

(SRM) mode with m/z 205.1 and m/z 208.1 monitored for ibuprofen and d3-ibuprofen,

respectively. Six-point calibration curves of analyte standards were used for

quantification.

4.2.7. Neutral Red Uptake Assay

For quality control purposes, a neutral red uptake assay adapted from Repetto et

al. (2008) was used to determine cell viability. The neutral red uptake assay evaluates

cell viability based on the ability of viable cells to incorporate and bind the weak cationic,

supravital neutral red dye. The neutral red dye penetrates cell membranes via nonionic

diffusion and accumulates in lysosomes. Cell suspension were diluted to 2.5x106 cells

mL-1 with media. Neutral Red Solution, 0.33% in DPBS (50µL) was added to 450µL of

cell suspension in a microcentrifuge tube. Samples were placed in a shaker at the

appropriate temperature for two hours. Cells were then pelleted via centrifugation at

500g for 10 minutes. The supernatant was carefully removed and 200µL of Neutral Red

Assay Fixative (0.1% CaCl2 in 0.5% formaldehyde) was added to each sample.

Samples were gently shook for no more than two minutes as care was taken not to lyse

77

cells. Samples were centrifuge at 500g for five minutes. The supernatant was

discarded and 500µL of Neutral Red Assay Solubilization Solution (1% acetic acid in

50% ethanol) was added to each sample. Samples were vortexed to resuspend the

pellet and release neutral red dye into the solution. In a clear 96-well plate, 200µL of

sample was pipetted into wells in duplicate. Media blanks and deactivated control cells

were also analyzed. Absorbance was measured 540nm with background absorbance

at 690nm. Results confirmed cell viability or deactivation in each cell lot (data not

shown).

4.2.8. In vitro metabolic transformation rates of the gill and liver

In vitro metabolic transformation rates of the gill and liver were determined using

the bioconcentration model described by Cowan-Ellsberry et al. (2008) (Figure 12). The

model extrapolates in vitro test data to determine a whole body metabolic

transformation rate using key fish physiological information (Table 6). This extrapolation

is based on concepts commonly applied in mammalian pharmacokinetics (Obach,

1999). Intrinsic clearance rate of parent compound (CLm) was determined from in vitro

cellular suspension data, in which CLm = loss of parent material (µmol hr-1 cell-1) * total

reaction volume (mL) / the analytically confirmed initial parent concentration (µmol).

Multiplication of tissue weight (TW) and cellularity (Cp, cells (g tissue)-1) determined

intrinsic clearance in tissue (CLi), which was extrapolated to tissue clearance (CLt) by

78

Fig. 12. Extrapolation of in vitro primary cell suspension metabolism assay to metabolic

transformation rate (kM) based upon Cowan-Ellsberry et al. (2008). [CLm, intrinsic

clearance rate of parent compound (mL hr-1 cell-1) ; CLi, intrinsic clearance in tissue (L d-

1 kg-1); TW, tissue weight (g tissue (kg body weight)-1); Cp, cellularity (cells (g tissue)-1);

CLt, tissue clearance (L d-1 kg-1); CO, cardiac output (mL min-1 kg-1); TF, fraction of

blood flow through tissue; fu, free fraction correction term; Vd, volume of distribution (L

kg-1)].

79

Table 6

Physiological and physical characteristic used to extrapolate in vitro metabolic

transformation within the BCF model. Mean values are reported below. Values were

determined during the course of the study or in other unpublished laboratory studies.

Trout Gill Trout Liver Catfish Gill Catfish Liver

Body weight (kg) 0.211 0.211 0.057 0.057

Cellularity (cells (g tissue)-1) 1.17x108 3.41x108 1.24x108 3.03x108

Cardiac output (mL min-1 kg-1) 16.9 16.9 26.2 26.2

Fraction of blood flow through tissue

1.0 0.20 1.0 0.20

Body composition

lipid 0.10 0.10 0.10 0.10

non-lipid organic 0.20 0.20 0.20 0.20

water 0.70 0.70 0.70 0.70

Blood composition

blood lipid 0.01 0.01 0.01 0.01

non-lipid organic 0.15 0.15 0.15 0.15

water 0.84 0.84 0.84 0.84

Temperature of environment 15°C 15°C 25°C 25°C

including cardiac output (CO), fraction of blood flow through tissue (TF) and a free

fraction correction term (fu). Division by the volume of distribution (Vd) then produced

80

the metabolic transformation rate (kM). BCF was then calculated, incorporating kM

(BCFM) and with kM equal to zero (BCF0). BCF calculations also included predicted k1,

k2, kE and kG values based upon KOW, total fish weight and fish lipid content.

4.2.9. Statistical Analysis


Inc.). Two-way repeated measures ANOVA confirmed statistical significance of loss of

parent compound over time while Bonferroni post tests were used to evaluate the

differences between tissue homogenate batches at each sample time point. CLm, kM

and BCF were analyzed either via an unpaired t-test or one-way ANOVA with

Bonferroni's multiple comparison post test. Mean and standard error are reported.

4.3. Results

4.3.1. Intrinsic clearance rate of parent compound

A significant loss was not found in deactivated controls cells indicating that loss

due to binding did not greatly contribute to loss of parent compound (Fig. 13).

Fig. 13. Mean loss of parent compound in deactivated control cells.

Loss of parent in active cell suspensions occurred at varying rates among

species and cell types (Figure 14). In general, the intrinsic clearance rate (CLm; mL hr-1

cell-1) was higher in hepatocytes than gill cell suspensions (Figure 15A). For ibuprofen,

81

trout and catfish hepatocytes had significantly higher CLm than the gill cell suspensions.

A difference was not observed between species (i.e., trout hepatocyte CLm was not

different than catfish hepatocyte CLm and trout gill CLm was not different that catfish gill

CLm). For norethindrone, catfish hepatocytes had a significantly higher mean CLm than

catfish gill cells. Trout hepatocytes had a slightly higher, yet not statistically different

mean CLm than gill cells. A significant difference was not observed between species.

For propranolol, a species difference was noted in that catfish hepatocyte CLm was

significantly greater than trout hepatocyte CLm. Catfish gill CLm was greater for

propranolol, yet not significantly greater, than trout gill CLm.

Fig. 14. Mean loss of parent compound over 120 minute study time period.

82

Fig. 15. (A) Mean intrinsic clearance rate of parent (CLm) and (B) mean extrapolated

metabolic transformation rate (kM). “*” indicates significant difference between

hepatocyte and gill cell suspensions of the same species.

83

4.3.3. Metabolic transformation rate (kM)

As described in section 2.8, the metabolic transformation rate (kM, d-1) was

extrapolated from loss of parent (Figure 15B). Although hepatocytes had greater CLm,

gill kM was significantly larger in both species for all three compounds. A larger fraction

of blood flow through the gills (TF) lead to greater tissue clearance (CLt), and thus a

greater kM, despite the liver having greater cellularity and thus greater intrinsic

clearance in the tissue (CLi). While the liver may more effectively metabolize

compounds once they have reached the liver, the larger gill TF allows for greater

potential to act upon compounds. Hence, hepatocyte kM is limited by hepatic blood

flow. As a pre-systemic tissue, the gill has greater opportunity to eliminate xenobiotics

through metabolism.

4.3.4. BCF

Including kM, significantly reduced BCF in all species and cell types. Predicted

BCF0 (i.e., predicted BCF without kM) for norethindrone, propranolol and ibuprofen were

97.3, 0.6 and 1.0 respectively. For propranolol, a species difference was not observed

between the BCFM calculated from liver kM or gill kM (Table 7). For norethindrone,

species differences were not noted in BCFM calculated from liver kM. However, trout gill

BCFM was significantly less than catfish gill BCFM even though mean trout gill kM was

less than that of catfish gill.

Mean gill and liver kM were combined additively to obtain kM(total), representing

total fish metabolic transformation. Propranolol BCFM(total) was significantly smaller than

BCFM based on liver alone (BCFM(liver)) for trout (0.29 ± 0.011) and catfish (0.29 ±

0.000). BCFM(total) for norethindrone was also significantly less than BCFM(liver) for trout

84

(28.0 ± 0.1) and catfish (34.0 ± 3.7). Trout BCFM(total) was considered significantly less

than catfish for norethindrone. Furthermore, Ibuprofen BCFM(total) for trout (0.50 ± 0.000)

and catfish (0.53 ± 0.002) were significantly different than BCFM calculated with liver

alone.

4.4. Discussion and Conclusions

This study aimed at finding a simple method for determining kM in vitro. The

method proved reliable in providing reproducible intrinsic clearance rates between

different lots of primary cells. A significant difference was not observed between the

CLm within a cell type. This supports the use of composited tissues to reduce interfish

variability in primary cell suspensions.

Fig. 16. Intrinsic clearance of parent compound (CLm) between cell suspension lots.

Differences in metabolism and CLm were observed between cell types throughout

the study. For all three compounds, hepatocyte CLm was greater than that of gill cell

suspensions. Previous studies (Chapter 2) with trout and catfish liver and gill S9

85

Table 7

Comparison of intrinsic clearance rate of parent (CLm), extrapolated metabolic transformation rate (kM), BCFM and BCF0.

Mean and standard error reported. n refers to the number of cell lots used in the study. Assays were run in duplicate

within the cell lots.


Trout Liver

Trout Gill

Catfish Liver

Catfish Gill

Trout Liver

Trout Gill

Catfish Liver

Catfish Gill

Trout Liver

Trout Gill

Catfish Liver

Catfish Gill

n 3 3 3 2 3 3 3 2 3 3 3 2

CLm 23.9 ±

2.6 19.2 ±

1.9 37.8 ±

7.1 7.6 ± 2.6

25.6 ± 6.0

12.0 ± 3.3

50.3 ± 4.1

32.6 ± 7.1

112.9 ± 5.1

41.0 ± 6.4

122.4 ± 4.4

30.0 ± 6.1

kM 0.89 ± 0.002

3.42 ± 0.087

1.37 ± 0.004

4.10 ± 0.627

3.44 ± 0.003

15.0 ± 0.958

5.3 ± 0.001

26.1 ± 0.175

2.83 ± 0.000

13.77 ± 0.058

4.38 ± 0.000

21.30 ± 0.167

BCFM 64.4 ± 0.050

32.8 ± 0.564

76.8± 0.059

41.6 ± 3.75

0.50 ± 0.000

0.33 ± 0.011

0.51 ± 0.000

0.32 ± 0.000

0.86 ± 0.000

0.55 ± 0.000

0.87 ± 0.000

0.57 ± 0.002

BCFM(total) 28.0 ± 0.1 34.0 ± 3.7 0.29 ± 0.011 0.29 ± 0.000 0.50 ± 0.000 0.53 ± 0.002

BCF0 97.3 0.6 1.0

86

fractions on norethindrone, propranolol and ibuprofen also found liver S9 fractions to

provide higher clearance rates than gill S9 fractions. Several other studies have further

suggested that gill metabolic activity is less than that of liver (Lindstrom-Seppa et al.

1981; Pesonen and Andersson 1991; Schlezinger et al. 1998; Leguen et al. 2000).

Although differences in CLm and kM were noted between species for propranolol,

the biological and ecological relevance of these differences must be kept in perspective.

Catfish hepatocyte CLm was twice that of trout with a mean 42.0% loss of parent after

two hours compared to only 24.6% for trout. Additionally, catfish gill more readily

metabolized propranolol than trout gill. Yet, these results translated to equivalent BCFM

for the two species. Thus, any biological significance is most likely not related to

bioconcentration, but in catfish’s ability to better metabolize propranolol, which may

have significance in chronic, low level exposure scenarios.

Overall, the inclusion of gill and liver kM decreased norethindrone BCFs by 71.3%

in trout and 65.0% in catfish. Propranolol BCFs decreased by 51.0% in trout and 51.2%

in catfish. Ibuprofen BCFs decreased by 49.5% and 47.3% for trout and catfish,

respectively. Thus, the inclusion of kM brought predicted BCFs closer to observed in

vivo BCFs (Nallani et al., 2009).

The BCF model uses additional ADME parameters to calculate BCF. k1, k2, kG

and kE are based upon whole fish weight, temperature and/or KOW. Of these

parameters, KOW is static while the 10°C temperature variation between species has

little effect. The disparity in whole fish weights caused the unexpected results. Even

though the trout and catfish studied were at similar non-sexually reproducing life stages,

the increased trout body weight decreased k1, k2, kG and kE, which allowed for kM to

87

have a greater influence in BCF calculations. Thus, trout gill BCFM was significantly

less than catfish gill BCFM.

Although the test compounds were not expected to significantly bioconcentrate

(i.e., BCF>2000) due to their small KOW, they demonstrate the ability to measure

metabolic activity in fish cell suspensions through loss of parent. Furthermore, they

serve to advance method development of in vitro biotransformation assays and their

application in an integrated ADME bioconcentration model. As discussed in Cowan-

Ellsberry et al. (2008), the sensitivity of kM and BCFM to extrapolation parameters

underscores the need for further physiological data collection and analysis. Overall, the

inclusion of metabolism allows for more realistic BCF while reducing study time, cost

and animal use (de Wolf et al., 2007; OECD, 1996; Weisbrod et al., 2007).

4.5. References



Arnot, J. A., Mackay, D., Parkerton, T., Bonnell, M., 2008. A database of fish


2270.




1168-1177.


ecotoxicology of fish. Piscine cell lines in environmental toxicology, in Mommsen,

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T., Moon, T. (Eds.), Biochemistry and Molecular Biology of Fishes, Vol. 6.

Elsevier Science, Amsterdam, pp. 43-84.






36, 2513-2522.

Cowan-Ellsberry, C., Dyer, S. E., Bernhard, M., Roe, A., Dowty, M., Weisbrod, A., 2008.

Approach for extrapolating in vitro metabolism data to refine bioconcentration

factor estimates. Chemosphere 70, 1804-1817.

Davies, R. P., Dobbs, A. J., 1984. The prediction of bioconcentration in fish. Water Res.

18, 1253-1262.


Egmond, R., Weisbrod, A., Whale, G., 2007. Animal use replacement, reduction,

and refinement: Development of an integrated testing strategy for

bioconcentration of chemicals. Integr. Environ. Assess. Manag. 3, 3-17.







89



Modelling 69, 1-17.

Gobas, F., Morrison, H., 2000. Bioconcentration and biomagnification in the aquatic

environment. in: Boethling, R., Mackay, D. (Eds.), Property Estimation Methods

for Chemicals: Environmental and Health Sciences. CRC Press LLC, Boca

Raton, FL, pp. 189-232.



Pollut. 121, 199-205.



22, 153-163.




110, 56-66.



cells in culture. Aquat. Toxicol. 48, 165–176.



259-263.

90

Mackay, D. 1982. Correlation of bioconcentration factors. Env. Sci. Technol. 16, 274-

278.














1113-1115.






Paris, France.

91

Oliver, B. G., Niimi, A. J., 1985. Bioconcentration Factors of Some Halogenated

Organics for Rainbow Trout: Limitations in their Use for Prediction of

Environmental Residues. Environ. Sci. Technol. 19, 842-849.

Part, P., Norrgren, L., Bergstrom, E., Sjoberg, P., 1993. Primary cultures of epithelial

cells from rainbow trout gills. J. Exp. Biol. 175, 219-232.



Xenobiotica 21, 461–471.

Randall, D. J., Connell, D. W., Yang, R., Wu, S. S., 1998. Concentrations of persistent

lipophilic compounds in fish are determined by exchange across the gills, not

through the food chain. Chemosphere, 37, 1263-1270.

Repetto, G., del Peso, A., Zurita, J. L., 2008. Neutral red uptake assay viability/

cytotoxicity. Nat. Protoc. 3, 1125-1131.



Environ. 356, 143-153.




Veith, G.D., D.L. Defoe and B.V. Bergstedt. 1979. Measuring and estimating the

bioconcentration factor of chemicals on fish. J. Fish. Res. Board Can. 36:1040-

1048.

92

Weisbrod, A., Burkhard, L., Arnot, J., Mekenyan, O., Howard, P., Russom, C.,





93

CHAPTER 5

CONCLUSIONS

5.1. Introduction

The evaluation of metabolic potential provides mechanistic insight into how

aquatic species may process xenobiotics in their environment. The ability to metabolize

and clear potentially deleterious substances may afford an advantage to the organism.

This dissertation focused on the influence of metabolic transformation on

bioconcentration. An in vitro biotransformation assay was refined across two fish

species with three compounds. Loss of parent was measured in S9 fractions,

microsomes (trout only) and primary cells suspensions to assess metabolic activity.

The intrinsic clearance rate of parent compound (CLm) was extrapolated to the

metabolic transformation rate (kM) and inserted into a BCF model.

5.2. Comparison of Subcellular Fractions and Cell Suspensions

5.2.1. First Order Kinetics

To evaluate loss of parent compound, first order kinetics was assumed for each

reaction. Remaining parent compound concentrations were plotted versus time (Fig.

15). Data points were fit to an exponential curve with the following integrated equation

where k is the first order loss constant and t is time.

µmoles lost = intercept * e-kt (9)

94

Fig. 17. Example of first order kinetics in catfish hepatocyte assay results. Ibuprofen

concentrations (ng mL-1) are plotted against time (minutes).

Data sets with a coefficient of determination (R2 ) less than 0.5 were excluded

from future analysis. Table 8 provides the mean R2 for each data set. Most assay

replicates met the requirements for first order kinetics.

Table 8

Mean coefficient of determination (R2 ) for exponential curves fit to concentration versus

time. “M” indicates microsomes. “Cell” indicates primary cell suspension.


S9 M Cell S9 M Cell S9 M Cell

Trout Liver 0.72 0.94 0.82 0.82 0.95 0.91 0.72 0.69 0.72

Trout Gill 0.64 0.75 0.77 0.72 0.54 0.71 0.57 0.51 0.81

Catfish Liver 0.77 --- 0.72 0.81 --- 0.91 0.73 --- 0.79

Catfish Gill 0.85 --- 0.71 0.62 --- 0.68 0.78 --- 0.81

95

5.2.2. Variability

In some data sets, such as trout gill microsome metabolism of ibuprofen, the

mean percent loss over 90 minutes was small (e.g., 6.9%). Although, this slight loss of

parent was considered significant over time and exceeded the minimum detectable

difference (MDD) due to a small variance (s2) between replicates, the mean loss of

ibuprofen was only 138.5ng mL-1 over the course of the assay. Data sets with greater

loss of parent often had a greater s2 and thus a larger MDD. For example, catfish liver

S9 had an average 65.9% loss of propranolol over the course of the study and an MDD

of 6.7% loss. The loss of ibuprofen in trout gill microsomes would not be statistically

verifiable with the same s2 and MDD as the propranolol catfish liver S9 assay. Variation

in more metabolically active data sets is due to greater variation at the intermediate time

points. Continuing with the previous example, at the 90 minute time point, a mean

65.9% loss with standard deviation of 4.9% was observed. The standard deviation of

the proceeding time points (15, 30, 45 and 60 minutes) ranged from 18.6% to 29.1%.

Thus, the source of variation was the loss rate at which the replicates approached the

substrate concentration asymptote.

5.2.3. Norethindrone

As discussed in previous chapters, loss of parent norethindrone was observed in

all subcellular fractions and cell suspensions. In general, hepatic subcellular fractions

and cell suspensions provided for a greater percent loss of parent compound (Fig. 16).

The greatest percent loss was seen in trout liver microsomes, followed by trout and

catfish liver S9 and then trout and catfish hepatocytes. A difference in loss of parent

was not as distinguishable in gill subcellular fractions and cell suspensions.

96

Fig. 18. Mean percent loss of norethindrone across liver and gill S9, microsomes and

cell suspensions.

CLm of cell suspension assays was adjusted to mL hr-1 (106 cells)-1 to better

compare with the subcellular fractions. Trout liver microsomes were statistically greater

than trout liver S9 and trout hepatocytes (Fig. 17). This may be in part to the

normalization of microsomes to S9. Microsomes were diluted to 1mg protein mL-1 while

S9 was diluted to 2mg protein mL-1. Some evidence indicate that the microsomal pellet

contains four to five times greater P450 activity than S9 (Pearce et al., 1996). Thus,

microsomes would be expected to have higher CLm. A significantly higher CLm was

also found in trout gill microsomes.

Hepatocyte suspension CLm were significantly lower than liver S9 CLm in both

species. Although the same trend was seen in catfish gill suspensions, the difference

97

was not statistically significant. Conversely, trout cell suspension CLm was slightly, yet

not significantly, larger than trout S9 CLm.

Fig. 19. Comparison of mean intrinsic clearance rate (CLm) of norethindrone in liver and

gill subcellular fractions and primary cell suspensions. “*” indicates significance from S9

CLm of the same species.

98

Fig. 20. Comparison of mean metabolic transformation rate (kM) of norethindrone in liver

and gill subcellular fractions and primary cell suspensions. “*” indicates significance

from S9 kM of the same species.

The extrapolation of CLm to kM provided for a wide range of values, in which cell

suspension kM was significantly greater than S9 kM in all species and tissue types (Fig.

18). This was due to differences in the extrapolation modeling. In order to calculate kM

from CLm, different formulas were used to determine intrinsic clearance in the tissue

(CLi). The tissue weight (TW) was multiplied by CLm and then further multiplied by

either the subcellular fraction protein level (PL) or cell suspension cellularity (Cp).

CLi = TW * (PL or Cp) * CLm (11)

99

Protein levels ranged from 0.024 to 0.094 g protein (g tissue)-1. Cellularity was

much greater with a range of 1.17x108 to 3.41x108 cells (g tissue)-1. This is an

enormous disparity in magnitude that heavily influences kM. Thus, in vitro cell

suspension assays will have a greater influence on BCF than in vitro subcellular fraction

assays due to the manner in which kM is calculated.

5.2.4. Propranolol

Propranolol was well metabolized in most assay replicates with the highest

percent loss of parent in hepatic subcellular fractions and the least in trout gill cell

suspensions (Fig. 19).

Liver S9 and microsomal fractions had significantly higher CLm than hepatocyte

suspensions (Fig. 20). The same trend was seen in gill subcellular fractions. However,

a significant difference could not be obtained due to the variance in gill S9 and

microsome CLm. Increased metabolism in S9 and microsomes over cell suspensions

may be due to either greater quantity or greater substrate accessibility to P450

isozymes in the reaction vessel. Differential centrifugation forms microsomes from the

endoplasmic reticulum (ER), which isolates ER membrane-bound P450s from other

cytosolic enzymes. S9 contains both microsomes and cytosol. CYP2D6 readily

metabolizes propranolol in mammalian microsomes (Masubuchi et al., 1994). Although

direct CYP2D homologues have not been identified in fish (Buhler and Wang-Buhler,

1998), the biotransformation pathway is assumed to be P450-mediated. The similar

metabolism of propranolol in S9 and microsomes supports a P450-mediated pathway.

100

Fig. 21. Mean percent loss of propranolol across liver and gill S9, microsomes and cell

suspensions.

Fig. 22. Comparison of mean intrinsic clearance rate (CLm) of propranolol in liver and



101

Fig. 23. Comparison of mean metabolic transformation rate (kM) of propranolol in liver

and gill subcellular fractions and primary cell suspensions. “*” indicates significance

from S9 kM of the same species.

A similar phenomena was observed with propranolol extrapolation to kM as in

norethindrone (Fig. 21). The calculation of CLi favored greater kM in cell suspensions

than subcellular fractions. However, propranolol kM values were often two to four times

greater than corresponding norethindrone kM due to greater loss of parent in propranolol

assays. Cell suspension kM was significantly greater than subcellular fraction kM,

particularly in the gill where cell suspension kM were almost two orders of magnitude

greater.

102

5.2.5. Ibuprofen

Unlike norethindrone and propranolol, ibuprofen also undergoes phase II

biotransformation via glucuronidation to form an acyl-glucuronide metabolite (Ritter

2000). Studies have shown direct glucuronidation to account for approximately 14% of

(S)-(+)-ibuprofen metabolism with 2-hydroxyl-ibuprofen and 3-hydroxyl-ibuprofen

metabolites accounting for 28% and 45%, respectively in human microsomes (Rudy et

al., 1991; Davies, 1998; Rodrigues, 2005). In mammalian systems, the hydroxyl-

metabolites are formed by CYP2C9 and CYP2C8 (Jacqz-Aigrain and Anderson, 2006).

As discussed in Chapter 3, an alternative phase I pathway involving CYP1A2 is

hypothesized for piscine metabolism.

Fig. 24. Mean percent loss of ibuprofen across liver and gill S9, microsomes and cell

suspensions.

103

Hepatocyte suspensions metabolized a significantly greater percent of parent

compound than subcellular fractions (Fig. 22). Increased availability to UDP-

glucuronosyltransferase (UGT) in the cellular suspensions may have lead to the

increased metabolism. UGTs are similar to P450s in that they are membrane bound

enzymes located in ER. However, unlike P450s, the UGT active site is in the lumen of

the ER (Remmel et al., 2008). Highly charged uridine diphosphate glucuronic acid

(UDPGA) requires transport across the membrane to the UGT active site. Membrane

integrity is maintained in microsomes (Kobayashi et al., 2006), limiting UDPGA and

ibuprofen access. Membrane disruptors, such as alamethicin, are required to allow for

membrane leakage in microsomal incubations. However, the effectiveness of

membrane disruptors is often substrate specific (Lett et al., 1992). In the ibuprofen

studies described in Chapter 3, alamethicin was added to S9 incubations with

supplemental UDPGA. Although some studies have found the addition of alamethicin to

double or triple UGT activity (Fisher et al., 2000), the addition of alamethicin and

UDPGA did not significantly enhance ibuprofen metabolism in S9 compared to

unaugmented S9. In cell suspensions, UDPGA-ER transport remains functional. Thus,

the increased ibuprofen metabolism in hepatocyte suspensions may be due to

increased glucuronidation.

Comparison of CLm between subcellular fractions and cell suspensions was

similar to that of norethindrone and propranolol (Fig. 23). Hepatocyte suspensions had

significantly greater CLm. A significant difference was not found between gill CLm.

104

Fig. 25. Comparison of mean intrinsic clearance rate (CLm) of ibuprofen in liver and gill

subcellular fractions and primary cell suspensions. “*” indicates significance from S9


Fig. 26. Comparison of mean metabolic transformation rate (kM) of ibuprofen in liver and


kM of the same species.

105

For ibuprofen, kM values were in the same range as propranolol (Fig. 24). Lower

kM were noted in norethindrone. Cell suspension kM was significantly greater than

subcellular fraction kM, particularly in the gill .

5.3. Objectives and Hypotheses

5.3.1. Objective 1


metabolic rate constant (kM) for the test compounds in gill S9 fractions.



Two-way repeated measures ANOVA analyzed loss of parent compound over

time for statistical significance. For all three compounds, the effect of time was

considered extremely significant (p<0.0001) for all gill subcellular fractions. Thus, the

null hypothesis was rejected.


values. α = 0.05

One-way ANOVA with Bonferroni post tests was used to determine significance

between BCF0 and BCFM. Significance was not found with all gill subcellular fractions,

thus, the null hypothesis cannot be rejected in its entirety (Table 9). The influence of

catfish gill kM was not significant in decreasing the BCFM of any of the three compounds.

A significant difference was observed in trout gill S9 for propranolol and ibuprofen, but

106

not for trout gill microsomes. The opposite was found with norethindrone. Significance

was noted in trout gill microsomes, but not trout S9.

5.3.2. Objective 2


metabolic rate constant (kM) for the test compounds in liver S9 fractions.





considered extremely significant (p<0.0001) for all liver subcellular fractions. Thus, the

null hypothesis was rejected.


values. α = 0.05


between BCF0 and BCFM. Significance was found in all liver subcellular fractions, thus,

the null hypothesis can be rejected (Table 9).

107

Table 9

Influence of kM on predicted BCF. "ns” indicates no significant difference when

compared to BCF0. “*” indicates p<0.05. “**” indicates p<0.01. “***” indicates

p<0.0001.


Trout Liver S9 *** *** **

Trout Gill S9 ns ** **

Trout Liver Microsomes *** *** **

Trout Gill Microsomes * ns ns

Catfish Liver S9 *** *** ***

Catfish Gill S9 ns ns ns

5.3.3. Objective 3


metabolic rate constant (kM) for the test compounds in primary gill cell suspensions.





considered extremely significant (p<0.0001) for all primary gill cell suspensions. Thus,

the null hypothesis was rejected.

108


values. α = 0.05


between BCF0 and BCFM. Significance was found in all gill cell suspensions

(p<0.0001), thus the null hypothesis can be rejected.

5.3.4. Objective 4


metabolic rate constant (kM) for the test compounds in primary hepatocyte suspensions.





considered extremely significant (p<0.0001) for all primary hepatocyte suspensions.

Thus, the null hypothesis was rejected.


values. α = 0.05


between BCF0 and BCFM. Significance was found in all hepatocyte suspensions

(p<0.0001), thus the null hypothesis can be rejected.

109

5.3.5. Objective 5

Compare in vitro metabolism data to in vivo whole fish bioconcentration data.

Hypothesis 9: HO: Calculated BCF including in vitro kM values will not

significantly differ from in vivo BCF. α = 0.05


between BCF0 and BCFM(total) (Table 10). In vivo BCF data was graciously provided by

Gopi Nallani (Nallani et al., 2009). In vivo trout BCF data was not available for

propranolol or norethindrone. In vivo catfish data was also not available for propranolol.

Although BCFM(total) values were closer to in vivo BCF than BCF0, a statistical difference

remained between the bioconcentration factors (Fig. 25). The null hypothesis failed to

be rejected.

Table 10

Statistical analysis between predicted in vitro BCFM(total) and in vivo BCF. "ND” indicates

no data available. “***” indicates p<0.0001.

Norethindrone Ibuprofen

BCFM(total) in vivo BCFliver in vivo

BCFmuscle in vivo BCFliver

in vivo BCFmuscle

Trout S9 *** *** ND ND

Trout Cell Suspension *** *** ND ND

Catfish S9 *** *** *** ***

Catfish Cell Suspension *** *** *** ***

110

Fig. 27. Comparison of in vitro and in vivo BCF. (A) “*” indicates significant difference

between BCF0 or BCFM and the in vivo BCF values of the same species. (B) No in vivo

BCF data available for comparison. (C) “*” indicates significant difference from in vivo

catfish BCF.

111

5.4. BCF Model Evaluation

The BCF model used was developed through a collaborative effort with the

International Life Sciences Institute – Human and Environmental Sciences Institute

(ILSI/HESI). The multi-compartment model was based on the principles of absorption,

distribution, metabolism and elimination (ADME), including: k1 (uptake via gills), kU

(dietary ingestion), k2 (elimination across gills), kG (growth), kM (metabolism) and kE

(egestion) (Gobas, 1993).

BCF = (k1 + kU) / (k2 + kG +kM +kE) (11)

5.4.1. Dietary Ingestion (kU)

The influence of kM has been discussed in detail in the preceding chapters. The

parameters k1, kU, k2, kG and kE also impact both BCF0 and BCFM. In this model, BCF0

included k1, k2, kG and kE. BCFM included k1, k2, kG, kE and kM. kU was not included in

either calculation as BCF is a measure of accumulation through non-dietary routes.

BAF would require the input of kU.

As noted in Eq. (4), BCF can be extrapolated to BAF via a food chain multiplier

(FCM). However, further study is necessary in order to further extrapolate the current

model to BAF when including both respiratory (i.e., gill) and oral routes of exposure,

metabolism and elimination. The current model only assumes uptake via diffusion into

the gill (k1), in which the entire concentration of compound is present at the site of gill

metabolism. Thus full gill kM can be incorporated into the model. When considering

BAF, uptake via dietary ingestion eliminates the ability to fully incorporate gill kM for

several reasons: (1) presystemic metabolism by the gill would not be applicable to

112

ingested compounds; (2) first-pass metabolism in the gut and liver reduces the amount

of compound available for systemic circulation to the gill; (3) during systemic circulation,

compounds may be stored in fatty tissues prior to reaching the gill. For simple BAF

extrapolations with a FCM, predicted BCFs containing gill kM may underestimate BAF

since they include the metabolism of a tissue in which the compound may never reach.

In a multi-compartment BAF model, the inclusion of kU would necessitate an uptake

ratio between k1 and kU. To prevent the over representation of metabolism, additional

consideration is needed in determining the fraction metabolized by each tissue.

5.4.2. Uptake Via Gills (k1)

(12)

In determining k1, the weight of the fish plays an important role. Larger fish are

assumed to have larger gill surface area and thus, uptake greater levels of xenobiotics

across the gill. As observed in the cell suspension studies, the BCF model is sensitive

to differences in whole fish weight through its influence on uptake and elimination

parameters. Thus, it may be necessary to establish a standardized weight criteria for

model organisms to avoid excess variability in predicted BCF results.

5.4.3. Elimination across gills (k2)

(13)

Elimination across the gills is based on a compound’s ability to

partition from blood to water, where Vlb is the fractional lipid content of whole fish body.

k1 =

1

(0.01 + ( 1

) * W0.4 KOW

k2 = k1

Vlb * KOW

113

k2 is based on the assumption that increased fish lipid content and compound

lipophilicity result in storage of the compound in fatty tissue, rather than systemic

circulation and subsequent elimination across the gill. Thus, Vlb and KOW decrease k2

while the ability to partition into the gill bloodstream (k1) is considered equivalent to the

ability to partition out of the bloodstream. Vlb is expected to vary from 2-11% dependent

upon fish species, age and location (Dyer et al., 2000; Arnot and Gobas, 2004; Cowan-

Ellsberry et al., 2008). For purposes of comparison, Vlb was held constant for each

species at 0.1 (i.e., 10%) (Cowan-Ellsberry et al., 2008). Variation in Vlb can

significantly affect modeling results. For example, changing the Vlb in the extrapolation

of a typical trout liver S9 biotransformation assay alters not only k2, but also kM, BCFM

and BCF0 (Table 11).

Table 11

Example of the influence of the fractional lipid content of whole fish body on

extrapolation of in vitro biotransformation data.

Fractional lipid content of whole fish body (Vlb)

0.02 0.10 0.11

k2 8.77 1.75 1.59

kM 1.14 0.29 0.27

BCFM 17.3 83.4 91.7

BCF0 19.5 97.3 107.0

114

4.3. Egestion (kE)

(14)

Egestion regards the expulsion of undigested material, generally food, from the

organism. This parameter is positively influenced by temperature (T) and inversely

influenced by the weight of the fish (W) and hydrophobicity of the compound. It is

assumed that more lipophilic substances will be stored in the organism and not

eliminated through the gut.

5.4.4. Growth (kG)

kG = 0.0251 * W-0.2, if T>12°C (15)

kG = 0.000502 * W-0.2, if T<12°C (16)

The current weight of the organism influences its potential to grow and thus dilute

the concentration of the xenobiotic compound. Several assumptions are made with this

parameter. Larger fish are assumed to have a slower growth rate than smaller fish.

This may over predict kG for smaller species that have reached adult size with no future

expectation of significant growth. Additionally, exposure is assumed to occur as a

single event. A dilution effect may not have the same significance in a continuous

exposure environment. In a continuous exposure scenario, k1 would increase along

with W. Thus the increased gill surface area may contribute more significantly to uptake

than growth to dilution. Lastly, organisms in cooler environments (<12°C) are expected

kE = 0.125 * 0.02 * W-0.15 * e0.06T

(5x10-8 * KOW) + 2

115

to have slower growth rates than those in warmer environments (>12°C). Additional

study of kG may be necessary in the prediction of BCF for smaller species (e.g., fathead

minnow (Pimephales promelas)) or organisms in environments with dynamic

temperature ranges and/or continuous exposure.

5.5. Conclusion

Overall, extrapolation of the in vitro biotransformation assay data lowered

predicted BCF values, placing them closer, yet still statistically different, to in vivo BCFs.

Refinement of the remaining parameters k1, k2, kG and kE are needed to further improve

the BCF model. Additionally, primary cell suspensions appear to more effectively

contribute to kM. As seen in ibuprofen, cell suspensions are more appropriate for

compounds that undergo glucuronidation and may serve as a better representation for

compounds undergoing other phase II conjugations.

In vitro biotransformation assays are not limited to providing inputs for BCF

models. They also impart knowledge on how aquatic organisms metabolize

xenobiotics. Continued research into the fields of bioconcentration modeling and

piscine biotransformation will allow for better understanding of how xenobiotic

compounds affect aquatic organisms.

5.6. References


chemicals in aquatic ecosystems. Environ. Toxicol. Chem. 10, 2343-2355.

116




Cowan-Ellsberry, C., Dyer, S. E., Bernhard, M., Roe, A., Dowty, M., Weisbrod, A., 2008.

Approach for extrapolating in vitro metabolism data to refine bioconcentration

factor estimates. Chemosphere 70, 1804-1817.

Davies, N., 1998. Clinical pharmacokinetics of ibuprofen: The first 30 years. Clin.


Dyer, S. D., White-Hull, C. E., Shephard, B. K., 2000. Assessment of chemical mixtures

via toxicity reference values overpredict hazard to Ohio fish communities.

Environ. Sci. Technol. 34, 2518-2524.

Fisher, M., Campanale, K., Ackermann, B., Vandenbranden, M., Wrighton, S., 2000. In

vitro glucuronidation using human liver microsomes and the pore-forming peptide

alamethicin. Drug Metab. Dispos. 28, 560-566.



Kobayashi, T., Sleeman, J. E., Coughtrie, M. W., 2006. Molecular and functional

characterization of microsomal UDP-glucuronic acid uptake by members of the

nucleotide sugar transporter (NST) family. Biochem. J. 400(Pt. 2), 281-289.

Lett, E., Kriszt, W., de Sandro, V., Ducrotoy, G., Richert, L., 1992. Optimal detergent

activation of rat liver microsomal UDP-glucuronosyltransferases toward morphine

and 1-naphthol: Contribution to induction and latency studies. Biochem.

Pharmacol. 43, 1649-1653.

117

Masubuchi, Y., Hosokawa, S., Horie, T., Suzuki, T., Ohmori, S., Kitada, M., Suzuki, T.,

Narimatsu, S.. 1994. Cytochrome P450 isozymes involved in propranolol

metabolism in human liver microsomes. The role of CYP2D6 as ring-hydroxylase

and CYP1A2 as N-desisopropylase. Drug Metab. Dispos. 22(6), 909-915.



promelas) and channel catfish (Ictalurus punctatus). New Orleans: North America

SETAC Annual Meeting.

Pearce, R., McIntyre, C. J., Madan, A., Sanzgiri, U., Draper, A. J., Bullock, P., Cook, D.

C., Burton, L. A., Latham, J., Nevins, C., Parkinson, A., 1996. Effects of freezing,

thawing, and storing human liver microsomes on cytochrome P450. Arch.

Biochem. Biophys. 331, 145-169.

Remmel, R. P., Zhou, J., Argikar, U. A., 2008. UDP-Glucuronosyltransferases. in:

Rodrigues, A. (Ed.), Drug-Drug Interactions, 2nd Ed. Informa Healthcare USA,

Inc., New York, NY, pp. 87-134







Ther. 259, 1133-1139.

118

APPENDIX A

PROTOCOLS

119

A.1 In vitro Biotransformation Assay – S9 and Microsomes

1. Combine:

Test Article Blank Solvent Control S9 Control

--- --- 600µL methanol ---

600µL S9 600µL S9 --- 600µL S9

20µL isocitrate dehydrogenase




6µL isocitrate 6µL isocitrate 6µL isocitrate 6µL isocitrate

Test material --- Test material Test material

Test material may be:

6.5µL of 1mM propranolol stock solution (10µM final),

6.5µL of 1mM ibuprofen stock solution (10µM final), or

6.5µL of 1mM norethindrone stock solution (10µM final).

2. Vortex. Equilibrate in incubator at 15°C (trout), 25°C (catfish) or 37°C (mammalian)

for 10 minutes. Shake at low speed.

3. Add 6µL of 50mM NADPH.

4. Vortex well (~30 seconds).

5. Immediately place 100µL of test article, blank, solvent control and S9 control in

100µL methanol (time 0 min). Place denatured samples in refrigerator (4°C).

6. Place remaining blank and controls in incubator or water bath for remainder of test.

Shake at low speed. Place 100µL of blank and controls in 100µL methanol for the

last time point. Place denatured samples in refrigerator (4°C).

120

7. Place test articles in incubator or water bath until the next time point. Time points

should be 10 to 30 minutes apart and cover an appropriate range of time to

determine the intrinsic metabolic clearance rate (i.e., 1-3 hours). Place 100µL of test

article in 100µL methanol for each time point. Place denatured test articles in

refrigerator (4°C).

8. Spin all samples at 2500g for 10 minutes. Carefully remove supernatant and place

in 2mL RAM vial.

For S9 Assay – Ibuprofen Metabolites

9. Combine:

Test Article Blank Solvent Control S9 Control

--- --- 600µL methanol ---

600µL S9 600µL S9 --- 600µL S9





6µL isocitrate 6µL isocitrate 6µL isocitrate 6µL isocitrate

Test material --- Test material Test material

15µL alamethicin 15µL alamethicin 15µL alamethicin 15µL alamethicin

Test material = 6.5µL of 1mM ibuprofen stock solution (10 µM final)

10. Vortex. Equilibrate in incubator at 15°C (trout), 25°C (catfish) or 37°C (mammalian)

for 10 minutes. Shake at low speed.

11. Add 6µL of 50mM NADPH. Add 12µL UDPGA.

12. Vortex well (~5 full seconds).

121

13. Immediately place 100µL of test article, blank, solvent control and S9 control in

100µL cold methanol (time 0 min). Place denatured samples in refrigerator (4°C).

14. Place remaining blank and controls in incubator for remainder of test. Shake at low

speed. Place 100µL of blank and controls in 100µL methanol for the last time point.

Place denatured samples in refrigerator (4°C).

15. Place test articles in incubator until the next time point. Time points should be 10 to

30 minutes apart and cover an appropriate range of time to determine the intrinsic

metabolic clearance rate (i.e., 1-3 hours). Place 100µL of test article in 100µL cold

methanol for each time point. Place denatured test articles in refrigerator (4°C).

16. For metabolite samples, take only a 60 minute time point. Place 600µL methanol in

sample to stop reaction. Transfer to amber RAM vial. Store in refrigerator (4°C).

17. Add d3-Ibuprofen internal standard to loss of parent samples and controls (not

metabolite samples).

18. Spin all samples (except metabolite samples) at 2500g for 10 minutes. Carefully

remove supernatant and place in 2mL RAM vial.

122

A.2 In vitro Biotransformation Assay – Primary Cell Suspensions

Preparation 1. Autoclave ~5 gallons of RHW. Bucket, 2L glassware, autoclave tape, RHW

2. Make solutions Media, wash soln, trypsin+EDTA soln, stop soln, rinse soln

3. Do not feed fish 18-24 hours prior to sacrifice

4. Clean all dissecting tools and area with 70% ethanol

ethanol

Solutions 1. Wash solution 30 mL HBSS or PBS (pH 7.7) – need to check

Store at 4°C Invitrogen $14 – 14175-095-500mL

2. Trypsin+EDTA Solution 5 mL of 0.5% trypsin 0.5 mL 2% EDTA in PBS or HBSS ~44.5 mL PBS (to 50 mL) Store at 4°C

3. Stop Solution 2 mL fetal bovine serum 18 mL PBS or HBSS (pH 7.7) Store at 4°C

4. Rinse Solution 0.5 mL fetal bovine serum 19.5 mL PBS or HBSS Store at 4°C

5. Media 5 mL fetal bovine serum 95 mL L-15 media (make to 100 mL) Store at 4°C

Leibovitz’s L-15 medium (containing L-glutamine and phenol red)

6. water: acetonitrile: formic acid (92:5:3)

MQ water Acetonitrile Formic acid Store at room temp

123

Procedures 1. For GILLS ONLY - Place fish in autoclaved RHW for 30 min prior to collection.

(See Liver Perfusion Protocol for hepatocyte isolation protocol). Anesthetize fish prior to tissue harvest with MS-222.

RHW, bucket, fish Aquatic Tox Lab

2. Remove tissue under sterile conditions and place in HBSS or PBS (pH 7.4) Scalpel, scissors, HBSS or PBS

Aquatic Tox Lab

3. WASH Place tissue in 10mL Wash Soln for 10 min with manual agitation. Mince gills with scalpel, so as to have 3-5 filaments per cut. Place tissue in

10mL of fresh Wash Soln for 10 min with manual agitation. This step is skipped with perfused liver.

Aspirate wash solution. Add 10 mL of fresh Wash Soln with manual agitation.

Place tissue in 50 mL conical centrifuge tube. Aspirate out Wash Soln.

Petri dish 50 mL conical centrifuge tube Wash Solution Scalpel Aspirator Transfer pipettes

Hood in 280

4. TRYPTIC DIGESTION First digestion

Rinse gill filaments with a few drops of Trypsin+EDTA Soln. Aspirate. Add 5 mL Trypsin+EDTA. Shake for 20 min. Mechanically agitate well with transfer pipette (50x). Place 20 mL of Stop Soln into a new 50 mL conical centrifuge tube. Transfer and strain trypsinized cells through 100µm cell strainer

into the new 50 mL conical tube. Remove remaining filaments from strainer and place in the original

conical tube for further digestion. Second digestion

Add5 mL Trypsin+EDTA. Shake for 20 min. Mechanically agitate well with transfer pipette (50x). Transfer and strain trypsinized cells through a new 100µm cell

strainer into the 50 mL conical tube containing the cells from the first digestion.

Repeat with a third digestion if the tissue in the strainer still appears to contain cellular material or if there is a lot of mucus present in gill tissue. The digested gill filaments should appear translucent.

50 mL conical centrifuge tube Transfer pipettes Aspirator 100µm cell strainer Trypsin+EDTA Solution Stop Solution

5. CELL SUSPENSION Centrifuge

124

Centrifuge the stop solution containing the cells for 10 min at 500g (0-4°C).

Aspirate supernatant. Add pre-chilled Rinse Soln (0-4°C) and resuspend the pellet with

mechanical agitation. Centrifuge again for 10 min at 500g (0-4°C). Aspirate supernatant. Add 10 mL pre-chilled Media (0-4°C) and resuspend the pellet with

mechanical agitation.

Transfer pipettes Aspirator Rinse Solution Media

6. Determine cell yield and viability using a hemocytometer. Adjust count to 2.5+06E cells/mL

Hemocytometer

7. CONTROL CELLS Transfer half the volume of the cells to a separate tube Enzymatically deactivate by adding 50% acetonitrile. Mix gently. Centrifuge again for 10 min at 500g (0-4°C). Pour off supernatant. Resuspend pellet to previous volume with Media.

Acetonitrile

8. BIOTRANSFORMATION ASSAY Samples

Test Article 1 mL cell suspension in Media (2.5 million cells/mL) 10µM test chemical

Control 1 mL deactivated cell suspension in Media (2.5 million

cells/mL) 10µM test chemical

Immediately pull 100µL Time 0 sample and place in 100µL of acetonitrile. Gently rock samples while incubating at the appropriate temperature

(15°C for trout, 25°C for catfish). Pull 100µL aliquots at the appropriate time points for the compounds

tested. Immediately place aliquots in 100µL cold acetonitrile. Complete sample extraction, derivitization and analysis as with the S9

assay for that compound.

Acetonitrile Cell suspension Deactivated cell suspension Test compound Incubator/water bath with shaker

125

A.3 Liver Perfusion Protocol

Purpose: To flush blood from fish liver prior to hepatocyte isolation.

Culture of Fish:

Do not feed for approximately 24 hours prior to test initiation.

This procedure is easier to perform on larger fish (<200g).

Equipment:

1. Balance for weighing tissue weights (0.500 to 20g)

2. Balance for weighing whole fish weights (50 to 2000g)

3. Ice bath

4. Infusion needle; gauge 21 or smaller

5. Syringe (5mL)

6. Surgical scalpel, scissors, forceps, clamps

7. Pre-weighed 15mL falcon tubes for tissues

8. Hank’s Buffered Salt Solution (HBSS; Gibco)

9. Ethanol (for sterilization of equipment)

10. MS-222

126

Perfusion:

1. Fill syringe with chilled HBSS.

2. Anesthetize the animal (in 0.10 g/L MS-222) until opercular movement is minimal

and fish is not responsive.

3. Weigh fish and record.

4. Place fish in ice bath with the ventral surface facing the technician.

5. Make a midline incision from the vent to just below the operculum, taking care

not to cut too deeply into the body cavity.

6. Make a lateral incision at the caudal end of the midline incision extending about

half way up to the dorsal surface. Make a similar lateral incision just caudal to the

operculum. Fold back the resulting flap of the body wall and cut away, exposing

the body cavity.

7. Exposed liver should be dark red (prior to perfusion) and the heart still beating.

8. Locate a ventral branch of the hepatic portal vein and carefully clear away any

obscuring connective tissue. Always perfuse the lower vein closest to you.

9. Carefully insert the syringe needle into the portal vein. If possible, secure in

place with a clamp or thread. You must provide constant gentle pressure to the

syringe. Too much pressure will burst the vein.

10. Peristaltic pump option: a capillary tube extending from the syringe may be

threaded into the vein . A peristaltic pump can then be used to provide a

constant flow of HBSS. This did not work well on fish smaller than 200g as then

portal vein was too thin.

11. Sever the blood vessels leading from the anterior aspect of the liver to the heart.

127

12. Perfuse the liver with HBSS for 5-10 min. Blanching of the liver should be evident

within the first minute or two of perfusion.

13. If necessary, refill syringe and repeat. If outer edges remain pink, cut off with

scissors and do not use.

14. Excise liver and remove gall bladder carefully. Do not allow bile to contact liver.

15. Place in pre-weighed 15mL flacon tube on ice.

16. Weigh and record tissue.

17. Proceed immediately to hepatocyte isolation (trypsinization).

Fig. 1. Insertion of needle into portal vein. Fig. 2. Example of blanched liver.

128

A.4 EROD Assay

Purpose: To determine CYP1A2 activity of subcellular fractions and cellular

suspensions.

Supplies:

1. Cell fraction/suspension – 1mg mL-1 microsomes, 2 mg mL-1 S9, or 2x106 cells

mL-1 suspension

2. Cell suspension media or 0.01M PBS as diluent

3. Microcentrifuge tubes

4. Black 96-well plate

5. 10µL pipette, 200µL pipette, 1000µL pipette

6. Pipette tips

7. Vortex with shaker adaptor

8. Incubator

9. Spectrophotometer

Standard Curve:

1. Assay is light sensitive. Keep samples and stock materials covered with foil and

conduct in dark room.

2. A 6-point resorufin standard curve is needed to determine % resorufin created

during dealkylation of the ethoxyresorufin substrate.

3. Final concentrations of 5, 4, 3, 2, 1 and 0.5µg mL-1.

4. In a black 96-well plate, add 100µL to each well to be used.

129

5. Then add the following for each concentration point. Use a 20µg mL-1 resorufin

stock in DMSO.

a. 5µg mL-1 100µL PBS/media 50µL resorufin stock 50µL MeOH

b. 4µg mL-1 110µL PBS/media 40µL resorufin stock 50µL MeOH

c. 3µg mL-1 120µL PBS/media 30µL resorufin stock 50µL MeOH

d. 2µg mL-1 130µL PBS/media 20µL resorufin stock 50µL MeOH

e. 1µg mL-1 140µL PBS/media 10µL resorufin stock 50µL MeOH

f. 0.5µg mL-1 145µL PBS/media 5µL resorufin stock 50µL MeOH

Procedure:

1. Assay is light sensitive. Keep samples and stock materials covered with foil and

conduct in dark room.

2. Determine time points for assay analysis (0, 10, 20, 40 mins are recommended).

3. For samples, add the following to a microcentrifuge tube:

a. 20µL isocitrate dehydrogenase

b. 6µL isocitrate

c. 12.3µL Ethoxyresorufin (0.5mM stock in DMSO for 10µM total)

d. 568µL of you cell fraction/suspension + diluents to make appropriate

concentration with a final volume of 506.3µL. (NOTE: (v1)(p1)=(v2)(p2))

4. Assay should be run in duplicate or triplicate.

5. Place 12µL ethoxyresorufin in 488µL of PBS/media in a microcentrifuge tube to

act as a control.

6. Add 9µL NADPH (50mM) to samples (not controls) to start the reaction. Total

volume is now 615.3µL.

7. Vortex well for 5 seconds. Immediately take a 150µL aliquot and place in 50µL

cold methanol in a black 96-well plate to stop the reaction. This is your time 0

sample.

8. Place in shaker in incubator at the appropriate temperature (12°C for trout, 25°C

for catfish, 37°C for mammalian).

130

9. Take a 150µL aliquot at each time point and place in 50µL cold methanol to stop

the reaction.

10. After each time point, read the96-well plate. (NOTE: Samples are now diluted

25%).

11. Read plate using the EROD protocol on the spectrophotometer. Read on

fluorescence plate reader (Excitation 550 nm; Emission 590 nm).

12. Plot % resorufin. Run statistical analysis to determine difference between

samples and controls.

131

A.5 Neutral Red Uptake Assay for Cell Viability

Purpose: To qualitatively verify viability of cell suspensions. This protocol is not

designed to provide quantitative data.

Supplies:

1. Cell suspension – 2.5 to 4.5x106 cells mL-1 density.

2. Cell suspension media

3. In Vitro Toxicology Assay Kit, Neutral Red based (TOX-4, Sigma)

4. Microcentrifuge tubes

5. Clear 96-well plate

6. 200µL pipette, 1000µL pipette, pipette tips

7. Vortex with shaker adaptor housed within incubator

8. Eppendorf centrifuge

9. Spectrophotometer

Procedure:

10. Dilute cell suspension to a 2.5 to 4x106 cells mL-1 density.

11. Determine time point for assay analysis (at 2-4 hours is recommended).

12. Place 450µL of cell suspension in a microcentrifuge tube.

13. Place 450µL of deactivated cell suspension in a microcentrifuge tube.

14. Place 450µL of media in a microcentrifuge tube to act as a blank.

15. Assay should be run in duplicate or triplicate.

16. Place 50µL of Neutral Red Solution, 0.33% in DPBS to each microcentrifuge

tube. If needed, filter the Neutral Red Solution to remove particulates.

132

17. Place in shaker within the incubator at the appropriate temperature (15°C for

trout, 25°C for catfish, 37°C for mammalian).

18. 10 minutes prior to the end of the incubation period, place samples in Eppendorf

centrifuge. Spin at 500g for 10 min.

19. Carefully remove and discard supernatant. (Note: The blank should not have a

pellet.)

20. Add 200µL of Neutral Red Assay Fixative, 0.1% CaCl2 in 0.5% Formaldehyde to

each sample. Gently shake to rinse for no more than 5 minutes. (Note: Rough

handling of samples may cause cells to lyse.)

21. Centrifuge at 500g for 5 minutes to repellet samples.

22. Carefully remove and discard supernatant.

23. Add 500µL of Neutral Red Assay Solubilization Solution, 1% Acetic acid in 50%

Ethanol to each sample. Vortex well to resuspend pellet and release neutral red

dye into the solution.

24. Add 200µL of sample in duplicate to interior wells of a clear 96-well plate.

25. Read plate using the Neutral Red protocol on the spectrophotometer. Measure

absorbance at a wavelength of 540nm. Measure the background absorbance at

690nm and subtract from 540 nm measurement. (Note: The closest filter we

have to 690nm is 670nm.)

26. Run a t-test or other statistical analysis to determine difference between active

cells and deactivated cells/blank.

133

A.6 Derivitization

Derivitization of Ibuprofen

1. 500µg/L (ppb) check standard should be derivatized along with samples.

2. Dry down samples under N2.

3. Add 50µL BF3/MeOH and 50µL AcN. Vortex for 30 seconds.

4. Incubate at 85°C for 2 hours.

5. Let samples cool to room temperature.

6. Add 6 drops MQ water. Vortex.

7. Add 500µL 1:1 ethyl acetate:hexane. Vortex. Back extract solvent top layer.

Repeat two additional times.


9. Reconstitute in 100µL of hexane.

10. If precipitation or cloudiness occurs, transfer samples to new eppendorf tubes. Spin

samples at 2500g for 10minutes. Carefully remove supernatant and place in 200µL

glass insert.

11. Use the GC/MS ibuprofen method found in Cristi’s folder.

12. The 500ppb check standard should be run between sample series as an indicator of

sample degradation.

Derivitization of Propranolol



3. Add 50µL MSTFA and 50µL AcN. Vortex for 30 seconds.

134

4. Incubate at room temperature for 30 minutes.

5. Incubate at 60°C for 15 minutes.

6. Add 15µL MBTFA. Vortex for 30 seconds.

7. Incubate at 60°C for 15 minutes.



10. Reconstitute in 100µL of methylene chloride.



glass insert.

12. For GC/MS analysis, use the Propranolol.M method found in Cristi’s folder in the

Methods folder.

13. This method uses an external calibration curve.

14. Derivatized propranolol degrades within 6 hours at room temperature. Derivatized

propranolol is stable at 4°C for several days.

15. The 500ppb check standard should be run between sample series as an indicator of

sample degradation. You may want to make multiple check standards if you are

running multiple sample series.

16. Keep samples at 4°C for as long as possible. Only run one sample series on the

GC/MS at a time (i.e., load a series of 6 samples plus check standard, run them, and

then load the next sample series).

135

Derivitization of Norethindrone



3. Add 50µL MSTFA and 50µL AcN. Vortex for 30 seconds.

4. Incubate at 70°C for 2 hours.



7. Reconstitute in 100µL of methylene chloride.



glass insert.

9. Use the Norethindrone SIM method found in Cristi’s folder.

The 500ppb check standard should be run between sample series as an indicator of

sample degradation.

136

APPENDIX B

IN VITRO BIOTRANSFORMATION ASSAY FORM

137

In vitro Biotransformation Assay Date: ______________ By: _________________________

Add: mg/mL protein Species/Tissue Harvested

µL S9/microsomes

Add: Stock Conc. Supplier Lot No.

µL analyte (____________)

µL isocitrate dehydrogenase

µL isocitrate

Vortex.

Equilibrate in incubator at __________°C for __________ minutes.

Time begins: Time ends:

Add: Stock Conc. Supplier Lot No.

µL NADPH

Reaction begins at:

For minutes, stop at:

minutes, stop at:

minutes, stop at:

minutes, stop at:

minutes, stop at:

minutes, stop at:

Reaction stopped in __________ µL of acetonitrile / methanol.

Control contains: Stock Conc. Supplier Lot No.

µL analyte (____________)


µL isocitrate

µL of acetonitrile / methanol to stop the reaction.

Blank contains: Stock Conc. Supplier Lot No.

µL S9


µL isocitrate

µL of acetonitrile / methanol to stop the reaction.

138

APPENDIX C

ILSI/HESI BCF MODEL (FROM MICROSOFT EXCEL)

139

The model is described in detail in the following publication

Cowan-Ellsberry, C.E., Dyer, S.D., Erhardt, S., Bernhard, M.J., Roe, A., Dowty, M.E. and Weisbrod, A.V. 2008.

Approach for extrapolating in vitro metabolism data to refine bioconcentration factor estimates

Chemosphere 70:1804-1817

This is Figure 2 from that publication which shows the flow of the calculation and the variables that are used to calculate k MET and BCF

The details of the BCF calculation are taken from Arnot and Gobas (2003 and 2004)

BCF = k1/(k2+kE+kG+kMET)

where

k1=1/((0.01+(1/(Kow))*(W0.4)))

k2 =k1/(Vlb*(Kow))

kE=0.125*(0.02*(W -0.15)*EXP(0.06*T))/(0.00000005*Kow + 2)

kG=0.00251*W-0.2 If T>12

kG = 0.000502*W -0.2 If T <12

S9 -- Intrinsic clearance rate of parent chemical,

CLm (ml hr-1 (mg protein)-1)

Intrinsic Clearance in Liver (L d-1 kg-1)

CLi = LW * PL * CLm

Hepatic Clearance(L d-1 kg-1)

CLh = (LF * CO * fu * CLi)/(LF * CO) + CLi * fu)

Elimination Rate Constant(d-1)

kMET = CLh/Vd

BCF, Mass Balance Fish BCF model

•Liver Weight, LW (gm kg-1)• Protein per gm of liver, PL (gm (gm of liver)-1)

• Cardiac Output, CO (L d-1 kg-1)• Fraction of blood flow through liver, LF• fu free fraction correction term

• Volume of Distribution, Vd (L kg-1)

Hepatocyte -- Intrinsic clearance rate of parent chemical,

CLm (ml hr-1 cell-1)

•Liver Weight, LW (gm kg-1)•Hepatocellularity, Hp (cells (gm of liver)-1)

Intrinsic Clearance in Liver (L d-1 kg-1)

CLi = LW * Hp * CLm

140

Use if test data is from S9 in vitro test

expressed as loss of parent per gm of active

Species of Fish and hepatic blood flow

assumption Trout w/ arterial hepatic blood flow Trout with arterial and portal hepatic blood flow

Chemical InformationChemical Name

Log Kow

Results of In vitro test

Measured loss rate (µmoles of parent lost/gm of protein * hr)

Final Results

kMET (1/day) =B50 =C50

BCF incorporating kMET estimate (L/kg) =B57 =C57

BCF with kMET = 0 (L/kg) =B58 =C58

Model Parameterization and Calculation Steps

In vitro Experimental ConditionsAmount of parent compound added (µmoles) 0.05 0.05

Volume of test vial (ml) 5 5

Fish Physiological ParametersLW - Liver Weight (gm/kg of fish) 8.7 8.7

PL - Protein per gm of liver (gm/gm liver) 0.079 0.079

CO - Cardiac Output (ml/min*kg) 34.5 34.5

LF - fraction of blood flow through liver 0.029 0.2

Qh - blood flow through liver l/kg * d =B25*B26*24*60/1000 =C25*C26*24*60/1000

W - Fish Weight (kg) 1 1

Vlb - Fish Lipid content (fraction) 0.1 0.1

Vnlb - Fish nonlipid content (fraction) 0.2 0.2

Vwb - Fish Water content (fraction) 0.7 0.7

B - proportionality constant reflecting soprtion capacity of

non-lipid organic mater relative to octanol 0.035 0.035

Vlbl - fractional content of lipid in blood 0.014 0.014

Vnbl - fractional content of non-lipid matter in blood 0.147 0.147

Vwbl - fractional content of water in blood 0.839 0.839

T - Temperature of environment (° C) 12 12

Secondary Parameters Calculated

Kow =10^B7 =10^C7

Vd - volume of distribution (L/kg) =B43/B42 =C43/C42

fu - relative binding term 1 1

Pbw Blood water partition coefficient =B33*B39+B34*B39*B32+B35 =C33*C39+C34*C39*C32+C35

BCFp - fish water partition coef (µg/kg fish/ µg/L in water) =B29*B39+B30*B39*B32+B31 =C29*C39+C30*C39*C32+C31

k M ET Calculation

CLm - Intrinsic clearance in the in vitro test (ml/hr * cells) =B20*B10/B19 =C20*C10/C19

CLi -- Intrinsic Clearance in liver (ml/kg *hr) =B46 * (B23 *B24) =C46 * (C23 *C24)

CLi -- Intrinsic Clearance in liver (L/kg *d) =B47*24/1000 =C47*24/1000

CLh -- Hepatic Clearance incorporating blood flow (L/kg * d) =(B48*(B41*B27))/(B27+(B48*B41)) =(C48*(C41*C27))/(C27+(C48*C41))

kMET (1/d) = (B49)/B40 = (C49)/C40

BCF Calculation

k1 uptake rate (L/kg * d) =1/((0.01+1/10^B7)*B28 0̂.4) =1/((0.01+1/10^C7)*C28 0̂.4)

k2 rate of elimination via resp. (1/d) =B53/(B29*(10^B7)) =C53/(C29*(10^C7))

kE rate of chemical egestion via fecal (kg/kg * d) =0.125*(0.02*(B28 -̂0.15)*EXP(0.06*B36))/(0.00000005*10^B7 + 2) =0.125*(0.02*(C28 -̂0.15)*EXP(0.06*C36))/(0.00000005*10^C7 + 2)

kG is rate of growth (1/d) =IF(B36>12,0.00251*B28 -̂0.2,0.000502*B28 -̂0.2) =IF(C36>12,0.00251*C28 -̂0.2,0.000502*C28 -̂0.2)

BCF = k1/(k2+kE+kG+kMET) =B53/(B54+B55+B56+B50) =C53/(C54+C55+C56+C50)

BCF with kMET = 0 =B53/(B54+B55+B56) =C53/(C54+C55+C56)

141

Use if test data is from hepatocyte in vitro test

expressed as loss of parent per hepatocyte

Species of Fish and hepatic blood flow

assumption Trout w/ arterial hepatic blood flow Trout with arterial and portal hepatic blood flow

Chemical InformationChemical Name

Log Kow

Results of In vitro test

Measured loss rate (µmoles of parent lost/cell * hr)

Final Results

kMET (1/d) =B52 =C52

BCF incorporating kMET estimate (L/kg) =B59 =C59

BCF with kMET = 0 (L/kg) =B60 =C60

Model Parameterization and Calculation Steps

In vitro Experimental ConditionsAmount of parent compound added (µmoles) 10 10

Volume of test tube (ml) 1 1

Fish Physiological ParametersLW - Liver Weight (gm/kg of fish) 8.7 8.7

HP - Hepatocellularity (cells/gm of liver) 255000000 255000000

CO - Cardiac Output (ml/min*kg) 34.5 34.5

LF - fraction of blood flow through liver 0.029 0.2

Qh - blood flow through liver l/kg * d =B25*B26*24*60/1000 =C25*C26*24*60/1000

W - Fish Weight (kg) 1 1

Vlb - Fish Lipid content (fraction) 0.1 0.1

Vnlb - Fish nonlipid content (fraction) 0.1 0.1

Vwb - Fish Water content (fraction) 0.8 0.8

B - proportionality constant reflecting soprtion capacity of

non-lipid organic mater relative to octanol 0.035 0.035

Vlbl - fractional content of lipid in blood 0.014 0.014

Vnbl - fractional content of non-lipid matter in blood 0.147 0.147

Vwbl - fractional content of water in blood 0.839 0.839

T - Temperature of environment (° C) 12 12

Secondary Parameters Calculated

Kow =POWER(10,B7) =POWER(10,C7)

Vd - volume of distribution (L/kg) =B45/B44 =C45/C44

fu - relative binding term = fub/fuh =B43/B42 =C43/C42

fuh- binding in hepatocyte test =(1/(10 (̂0.685*B7-2.237)+1)) =(1/(10 (̂0.685*C7-2.237)+1))

fub - binding in blood =(1/(10 (̂0.609*B7-0.558)+1)) =(1/(10 (̂0.609*C7-0.558)+1))

Pbw Blood water partition coefficient =B33*B39+B34*B39*B32+B35 =C33*C39+C34*C39*C32+C35

BCFp - fish water partition coef (µg/kg fish/ µg/L in water) =B29*B39+B30*B39*B32+B31 =C29*C39+C30*C39*C32+C31

k M ET Calculation

CLm - Intrinsic clearance in the in vitro test (ml/hr * cells) =(B10*B20)/B19 =(C10*C20)/C19

CLi -- Intrinsic Clearance in liver (ml/kg *hr) =B48 * (B23 *B24) =C48 * (C23 *C24)

CLi -- Intrinsic Clearance in liver (L/kg *d) =B49*24/1000 =C49*24/1000

CLh -- Hepatic Clearance incorporating blood flow (L/kg * d) =(B50*(B41*B27))/(B27+(B50*B41)) =(C50*(C41*C27))/(C27+(C50*C41))

kMET (1/d) = (B51)/B40 = (C51)/C40

BCF Calculation

k1 uptake rate (L/kg * d) =1/((0.01+1/10^B7)*B28 0̂.4) =1/((0.01+1/10^C7)*C28 0̂.4)

k2 rate of elimination via resp. (1/d) =B55/(B29*(10^B7)) =C55/(C29*(10^C7))

kE rate of chemical egestion via fecal (kg/kg * d) =0.125*(0.02*(B28 -̂0.15)*EXP(0.06*B36))/(0.00000005*10^B7 + 2) =0.125*(0.02*(C28 -̂0.15)*EXP(0.06*C36))/(0.00000005*10^C7 + 2)

kG is rate of growth (1/d) =IF(B36>12,0.00251*B28 -̂0.2,0.000502*B28 -̂0.2) =IF(C36>12,0.00251*C28 -̂0.2,0.000502*C28 -̂0.2)

BCF = k1/(k2+kE+kG+kMET) =B55/(B56+B57+B58+B52) =C55/(C56+C57+C58+C52)

BCF with kMET = 0 =B55/(B56+B57+B58) =C55/(C56+C57+C58)

142

APPENDIX D

EXPONENTIAL PLOTS OF LOSS OF PARENT DATA

159

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