THE TRANSFER OF ETHYL GLUCURONIDE IN THE DUALLY PERFUSED EX VIVO PLACENTAL PERFUSION MODEL: IMPLICATIONS FOR ALCOHOL SCREENING

DURING PREGNANCY

by

Jeremy Norman Matlow

A thesis submitted in conformity with the requirements for the degree of Master of Science

Graduate Department of Pharmacology and Toxicology University of Toronto

©!Copyright by Jeremy Norman Matlow (2012)!

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ABSTRACT

THE TRANSFER OF ETHYL GLUCURONIDE IN THE DUALLY PERFUSED EX VIVO PLACENTAL PERFUSION MODEL: IMPLICATIONS FOR ALCOHOL

SCREENING DURING PREGNANCY

Master of Science (2012) Jeremy Norman Matlow

Department of Pharmacology, University of Toronto

Alcohol consumption during pregnancy can lead to Fetal Alcohol Spectrum Disorder,

and because maternal self-reports are often unreliable, a biomarker of alcohol use during

pregnancy is needed to accurately determine fetal exposure. Ethyl glucuronide (EtG) is a

direct metabolite of ethanol that has been detected in the meconium of infants born to mothers

who consumed alcohol during pregnancy. In the current study, a method was developed and

validated for EtG detection in placental perfusate and tissue using gas chromatography-mass

spectrometry. Subsequently, the ex vivo human placental perfusion model was used to

investigate whether EtG crosses the human placenta. The validated GC-MS method showed

sufficient sensitivity in detecting EtG in placental perfusate and tissue. EtG crossed the

placenta slowly and transfer was incomplete after 3 hours of perfusion. EtG appears to cross

the human placenta and, hence, to represent both maternal and fetal exposure to alcohol.

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ACKNOWLEDGEMENTS

As I sit at my desk with a completed manuscript and just the acknowledgements page to

fill in, I cannot help but appreciate how lucky I am to have had such a dedicated group of

supporters over the past 2 years. Firstly, I offer my sincerest gratitude to Dr. Gideon Koren,

whose experience working with graduate students is anything short of remarkable. Always

creative, open-minded, and understanding, I could not have asked for a better supervisor.

I would not have been able to advance very far with my project without the expertise of

two people. Thank you to Angelika Lubetsky, the placental perfusion guru, for showing me

the ins and outs of the experimental model with patience and compassion. Thank you also to

Dr. Katarina Aleksa for teaching me how to approach developing and validating my analytical

method. I wish both of you the very best in all of your future endeavours.

I was exceedingly fortunate for the opportunity to work in an inter-disciplinary

environment like Motherisk. With counselors, clinical fellows, laboratory staff, and other

students around every corner, I broadened my horizons and made many friends along the way.

Thank you to everyone in this wonderful department for your warmth and kindheartedness. A

special thank you needs to go out to Janine Hutson, who has helped me over the last 2 years so

many times that I have lost count long ago.

To my mom, dad, and Cory: thank you for being a constant pillar of support. It is such

a comfort to know that I have such a loving family on my side all the time. Lastly, I would like

to thank my Toronto contingency!Nathan, David, Danielle, Aaron, Hayley, and Hillary!for

being my first line of encouragement during my time in Toronto. My successes were only

possible because of your unyielding support and I am truly blessed to have an amazing group

of people with whom I can share these memorable moments.

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TABLE OF CONTENTS ABSTRACT .................................................................................................................... ii ACKNOWLEDGEMENTS .......................................................................................... iii TABLE OF CONTENTS .............................................................................................. iv LIST OF TABLES......................................................................................................... vi LIST OF FIGURES...................................................................................................... vii LIST OF ABBREVIATIONS ..................................................................................... viii LIST OF APPENDICES ................................................................................................ x

CHAPTER 1. INTRODUCTION.................................................................................. 1 1.1. STATEMENT OF THE PROBLEM ............................................................................... 1 1.2. PURPOSE AND STUDY OBJECTIVES......................................................................... 2 1.3. HYPOTHESES AND RATIONALE............................................................................... 3

CHAPTER 2. REVIEW OF THE LITERATURE...................................................... 4 2.1. FETAL ALCOHOL SPECTRUM DISORDER .................................................................. 4

2.1.1. Description and characteristics...................................................................... 4 2.1.2. Prevalence and disease burden...................................................................... 6 2.1.3. Etiology and risk factors ............................................................................... 7

2.2. FATTY ACID ETHYL ESTERS ................................................................................. 10 2.2.1. Description .................................................................................................. 10 2.2.2. Fatty acid ethyl esters and pregnancy ......................................................... 11 2.2.3. Utility of fatty acid ethyl esters in clinical practice .................................... 12

2.3. ETHYL GLUCURONIDE .......................................................................................... 14 2.3.1. Pharmacokinetics ........................................................................................ 14 2.3.2. Ethyl glucuronide in pregnancy .................................................................. 16 2.3.3. Advantages of ethyl glucuronide ................................................................ 18

2.4. PLACENTAL PERFUSION AS A MEANS OF QUANTIFYING DRUG TRANSFER ............. 23 2.4.1. Placental anatomy ....................................................................................... 23 2.4.2. Mechanisms of placental drug disposition .................................................. 24 2.4.3. Utility of the ex vivo placental perfusion model ......................................... 28 2.4.4. Quantitative analysis of the ex vivo placental perusion model.................... 30

CHAPTER 3. MATERIALS AND METHODS ........................................................ 35

3.1. PLACENTAL PERFUSION ....................................................................................... 35 3.1.1. Ex vivo perfusion of a single placental cotyledon ....................................... 35 3.1.2. Pre-control phase......................................................................................... 37 3.1.3. Experimental phase ..................................................................................... 37 3.1.4. Measurements of placental viability ........................................................... 38 3.1.5. Statistical analysis ....................................................................................... 41

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3.2. SAMPLE ANALYSIS............................................................................................... 42 3.2.1. Materials and equipment ............................................................................. 42 3.2.2. Preparation of stock solutions and standards .............................................. 42 3.2.3. Sample preparation...................................................................................... 43 3.2.4. Method optimization ................................................................................... 45 3.2.5. Method validation ....................................................................................... 45 3.2.6. GC/MS instrumentation ............................................................................. 46

CHAPTER 4. RESULTS ............................................................................................. 48 4.1. METHOD VALIDATION.......................................................................................... 48 4.2. DETERMINANTS OF PLACENTAL VIABILITY AND INTEGRITY ................................. 53 4.3. PLACENTAL DISPOSITION OF ETHYL GLUCURONIDE.............................................. 55

CHAPTER 5. DISCUSSION ....................................................................................... 58 5.1. VALIDATION OF GC-MS METHOD FOR ETHYL GLUCURONIDE DETECTION.............. 58

5.1.1. Limitations to the study............................................................................... 60 5.2. PLACENTAL PERFUSION OF ETHYL GLUCURONIDE................................................ 63

5.2.1. Limitations to the study............................................................................... 68 5.2.2. Ethyl glucuronide as a biomarker of alcohol use during pregnancy ........... 71

CHAPTER 6. CONCLUSIONS AND FUTURE STUDIES ..................................... 73 6.1. CONCLUSIONS...................................................................................................... 73 6.2. FUTURE STUDIES .................................................................................................. 74

6.2.1. False EtG results due to sample contamination .......................................... 74 6.2.2. EtG immunoassay in meconium ................................................................. 75 6.2.3. Additional concordance studies between FAEE and EtG........................... 75

REFERENCES ............................................................................................................. 77 LIST OF PUBLICATIONS, ABSTRACTS, AND CONFERENCE PRESENTATIONS ...................................................................................................... 89 APPENDICES............................................................................................................... 90

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LIST OF TABLES Table 1. Harmonization of Institute of Medicine nomenclature and 4-Digit Diagnostic code ranks to screen for FASD in newborns...................................... 5 Table 2. Summary of current studies that have measured ethyl glucuronide in meconium, fetal remains, and placental tissue ............................................... 17 Table 3. Effect of consuming ethanol-containing foods and using self-care products on the detection of ethyl glucuronide................................................... 19 Table 4. Physiological and pharmacokinetic changes that occur in pregnant women compared to non-pregnant adults........................................................... 26 Table 5. Comparison of techniques used to analyze drug disposition across the placenta ......................................................................................................... 29 Table 6. Analyte ions and retention times for developed GC-MS program.................. 47 Table 7. Summary of protocols used to optimize method of EtG extraction and detection from placental perfusate and tissue .............................................. 49 Table 8. Summary of inter-day variability, intra-day variability and experimental recovery for final protocol .................................................................................. 52 Table 9. Method sensitivity ............................................................................................ 52 Table 10. Measurements of placental integrity and viability during perfusion experiments......................................................................................................... 53 Table 11. Triplicate measurements of EtG concentration in each perfused cotyledon .. 56 Table 12. Percent EtG recovery...................................................................................... 57

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LIST OF FIGURES

Figure 1. Facial phenotype in children with Fetal Alcohol Syndrome............................ 5 Figure 2. Ethanol metabolism........................................................................................ 10 Figure 3. Anatomy of the human term placenta ............................................................ 24 Figure 4. Schematic diagram of the ex vivo placental perfusion set-up at the

Motherisk laboratory ............................................................................................. 35 Figure 5. Sample chromatographs of the quantifying ion for EtG and EtG-d5 extracted from perfusate and tissue....................................................................... 51 Figure 6. Antipyrine concentration in maternal and fetal reservoirs during the experimental phase of the perfusions .................................................................... 54 Figure 7. EtG concentration in maternal and fetal reservoirs during the experimental phase of the perfusions after addition of 1 µg/mL EtG to the maternal reservoir.................................................................................................. 55 Figure 8. Fetal-to-maternal ratios for antipyrine and EtG during the experimental phase of the perfusions.......................................................................................... 56

viii

LIST OF ABBREVIATIONS

AAG Alpha-1 acid glycoprotein

ABCG2 Breast cancer receptor protein

ADH Alcohol dehydrogenase

ARBD Alcohol-related birth defect

ARND Alcohol-related neurodevelopmental disorder

ATP Adenosine triphosphate

BCRP Breast cancer resistance protein

CL Clearance

Cmax Peak plasma concentration

CNS Central nervous system

CV Coefficient of variability

CYP Cytochrome P450

EtG Ethyl glucuronide

F:M Fetal-to-maternal

FA Fetal artery

FAEE Fatty acid ethyl ester

FAS Fetal alcohol syndrome

FASD Fetal alcohol spectrum disorder

FV Fetal vein

GC Gas chromatography

GLUT-1 Glucose transporter 1

hCG Human chorionic gonadotropin

HFBA heptafluorobutyric acid

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IS Internal standard

LC Liquid chromatrography

LOD Limit of detection

LOQ Limit of quantification

MA Maternal artery

MRP-2 Multi-drug resistance protein 2

MS Mass spectrometry

MV Maternal vein

PBS Phosphate buffered saline

PDMS Polydimethylsiloxane

PFPA Pentafluoropropionic acid

pKa Log dissociation constant

SD Standard deviation

SEM Standard error of the mean

SPME Solid phase microextraction

Tmax Time to peak plasma concentration

UDPGA Uridine diphospho-glucuronic acid

UGT Uridine diphophate glucuronosyltransferase

x

LIST OF APPENDICES

Appendix I. Consent form ............................................................................................. 91

Appendix II. Composition of M199 Medium................................................................ 95

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CHAPTER 1. INTRODUCTION

1.1. STATEMENT OF THE PROBLEM

It is well known that heavy consumption of alcohol during pregnancy may lead to the

development of Fetal Alcohol Spectrum Disorder (FASD) in offspring, which manifests itself

in the form of structural malformations, cognitive dysfunction, and, most frequently,

neurobehavioural abnormalities. Proper diagnosis and treatment of FASD can reduce future

health and societal burdens; however early intervention is infrequent because pregnant women

tend not to disclose alcohol consumption use due to fear of stigmatization, blame, and losing

custody of the child. Therefore, objective biomarkers of alcohol use during pregnancy are

needed to accurately screen for children at risk of FASD.

Fatty acid ethyl esters (FAEE) are direct alcohol metabolites that have been detected in

infant meconium and, since they do not cross the human placenta, they serve as biomarkers of

fetal exposure to ethanol. Several studies have uncovered sources of false results with FAEE

analysis, and therefore other biomarkers of ethanol use during pregnancy are being investigated

to compliment FAEE analysis. Ethyl glucuronide (EtG) is another direct biomarker of ethanol

that has been detected in meconium, placental tissue, and fetal remains of terminated

pregnancies. Whether these matrices contain EtG because it crosses the placenta or because

the fetal liver converts ethanol to EtG is unknown. To better assess the utility of EtG as a

biomarker of alcohol use during pregnancy, its disposition across the maternal-placental-fetal

unit needs to be quantified.

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1.2. PURPOSE AND STUDY OBJECTIVES

The purpose of this study was to assess the disposition of EtG at levels typical of

moderate alcohol consumption across the maternal-placental-fetal unit. The following study

objectives were established for this investigation:

Objective 1: To develop and validate a gas chromatography-mass spectrometry method that

can accurately quantify EtG disposition in maternal perfusate, fetal perfusate, and placental

tissue.

Objective 2: To determine if EtG crosses the human placenta when present at a concentration

indicative of moderate alcohol consumption by means of the ex vivo placental perfusion model.

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1.3. HYPOTHESES AND RATIONALE

The following hypotheses were tested according to the established study objectives:

Hypothesis 1: We hypothesized that a gas chromatography-mass spectrometry method could be

established for the detection of EtG disposition in maternal perfusate, fetal perfusate, and

placental tissue. We hypothesized that the method could be sufficiently sensitive to detect EtG

concentrations indicative of moderate alcohol consumption in these matrices.

Hypothesis 2: Previous studies have detected EtG in meconium, placental tissue, and fetal

remains. Based on this information, we hypothesized that EtG would cross the human placenta

at concentrations indicative of moderate alcohol consumption.

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CHAPTER 2. REVIEW OF THE LITERATURE

2.1. FETAL ALCOHOL SPECTRUM DISORDER

2.1.1. Description and characteristics

Fetal alcohol spectrum disorder (FASD) is an umbrella term that covers physiological,

developmental, and behavioural outcomes in children associated with maternal consumption of

alcohol in utero. While, historically, there have been many case reports of alcohol-exposed

infants born with growth and morphological abnormalities, the term Fetal alcohol syndrome

(FAS) was initially coined in 1973 (Jones & Smith, 1973). FAS is currently considered the

most severe form of FASD, whereas the United States Institute of Medicine has also developed

the terms alcohol-related birth defects (ARBD) and alcohol-related neurodevelopmental

disorders (ARND) when only physiological or neurological abnormalities are detected,

respectively (Stratton et al., 1996).

Children born with FAS display specific growth and physiological impairments

(Stratton et al., 1996). Specifically, newborns may have growth restriction, which can be

measured as low birth weight, low weight-to-height ratio, decreased cranial size, and

microcephaly. Additionally, newborns with full FAS display 3 characteristic facial features:

short palpebral fissures (horizontal eye length), flattened philtrum, and thin vermillion of the

upper lip (Figure 1). Astley and Clarren (2000) developed a 4-Digit Diagnostic Code to rank

the severity of growth restriction, facial features, CNS damage and maternal exposure to

alcohol on a scale from 1 to 4. Recently, the terminology used by the Institute of Medicine and

the ranking system developed by Astley and Clarren have been synthesized to allow for

standardized characterization and diagnosis of newborns (Table 1).

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Figure 1. Facial phenotype of children with Fetal alcohol syndrome. Reprinted from Journal of Child Neurology, Vol. 27(3), Paintner et al. Fetal alcohol spectrum disorders—implications for child neurology, part 2: diagnosis and management. Pg 355-62. Copyright 2012 with permission from Sage publications.

Table 1. Harmonization of Institute of Medicine nomenclature and 4-Digit Diagnostic code ranks to screen for FASD in newborns. Reprinted from Canadian Medical Association Journal, Vol. 172(Supplement 5), Chudley et al. Fetal alcohol spectrum disorder: Canadian guidelines for diagnosis. Pg S1-S21. Copyright 2005 with permission from Canadian Medical Association publications.

4-digit diagnostic code ranks

IOM nomenclature Growth

deficiency FAS facial phenotype

CNS damage or dysfunction

Gestational exposure to

alcohol FAS (with confirmed exposure) 2, 3, or 4 3 or 4 3 or 4 3 or 4

FAS (without confirmed exposure) 2, 3, or 4 3 or 4 3 or 4 2 Partial FAS (with confirmed

exposure) 1, 2, 3, or 4 2, 3, or 4 3 or 4 3 or 4

ARND (with confirmed exposure) 1, 2, 3, or 4 1 or 2 3 or 4 (2 for < 6

years)

3 or 4

Scoring: 1 = No symptoms/no risk of exposure to alcohol during pregnancy 2 = Mild symptoms/unknown risk of exposure to alcohol during pregnancy 3 = Moderate symptoms/some risk of exposure to alcohol during pregnancy 4 = Severe symptoms/high risk of exposure to alcohol during pregnancy

While children born with FAS show characteristic growth and physiological

abnormalities at birth, the majority of children with FASD show few, if any, of the

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pathognomonic signs of full-blown FAS. Instead, many affected children will only display

neurobehavioural abnormalities, which are often detected first in the school environment

(Koren & Todorow, 2010). The most commonly reported behavioural and cognitive

impairments include attention deficits, memory problems, hyperactivity, poor judgement,

difficulty abstracting, disorientations in time and space, and impulsivity (Streissguth, 1997).

The full blown personal and economic burdens associated with FASD arise when these

symptoms are left undetected and untreated, such that children grow up to develop secondary

characteristics of FASD: mental health problems, disrupted school experiences, trouble with

the law, confinement, inappropriate sexual behaviour, and alcohol and other drug problems

(Streissguth, 1997). Unfortunately, many children who do not show physiological impairments

are unlikely to receive the early diagnosis and interventions necessary to reduce the risk of

developing these harmful secondary characteristics (Streissguth et al, 1996).

2.1.2. Prevalence and disease burden

In their handbook of behavioural teratology, Riley and Vorhees (1986) note that “FAS

represents the largest environmental cause of behavioural teratogenesis yet discovered and,

perhaps the largest single environmental cause that will ever be discovered.” Indeed, since its

characterization over 40 years ago, countless research has been conducted on the

epidemiology, etiology, diagnosis and treatment of FASD. Since 50% of pregnancies are

unplanned (Forrest, 1994), exposure to alcohol in the first trimester is commonplace.

Additionally, approximately half of American women of childbearing age consume alcohol to

some degree, with up to 13% reporting binge drinking (Centers For Disease Control and

Prevention, 2009).

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In terms of numbers, the incidence of overall FASD in Canada has been recently

approximated at 1 in 100 live births (Chudley et al., 2005), with an average individual cost of

$21,642/year and a nationwide burden of $5.3 billion/year (Stade et al., 2009). Prevalence data

is dependent on a variety of social factors that will be discussed later. For example, cultural

norms regarding alcohol consumption can influence the number of children born with FASD.

The region with the highest documented FASD prevalence is in a wine county in the Western

Cape province of South Africa, where the incidence was 40.5-46.4 per 1000 children aged 5-9

(de Sanctis et al., 2011). Furthermore, the relationship of the child with his or her caregiver

(biological, adoptive, or foster parent) can influence the severity of disease and therefore the

individual and societal burden (Chudley et al., 2005).

2.1.3. Etiology and risk factors

While not fully understood, FASD has been proposed to be the product of numerous

alcohol-induced effects on both the fetal brain and the placenta. Goodlett et al. (2005)

summarized the mechanisms of teratogenesis on the fetal brain into 6 distinct categories based

on a multitude of animal and human studies:

1. Disrupted cellular energetics, such as altered glucose utilization and transport, impaired

DNA and protein synthesis, and oxidative stress

2. Impaired cell acquisition (cell cycle alterations, impaired development of specific

cellular bodies), dysregulated developmental timing of cell generation, migration,

outgrowth, synaptogenesis and myelination

3. Altered regulation of gene expression by specific transcription factors

4. Disrupted cell-cell interaction through impairment of specific adhesion molecules

5. Altered cell signaling pathways

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6. Cell damage and death caused by apoptosis, oxidative stress, and excitotoxicity

Additionally, mechanisms of ethanol-induced placental injury and susceptibility have been

proposed to influence placental teratogenicity. Ethanol decreases levels of the vasodilatory

eicosinoid prostacyclin in a dose-dependent manner in human umbilical veins (Randall &

Saulnier, 1995), and this alteration can lead to imbalances in placental vascular function, blood

flow, and oxygen delivery to the fetus (Altura et al., 1982). Interestingly, variable exposure to

ethanol has been demonstrated in an analysis of ethanol biomarkers detected in human

dizygotic twins and a litter of guinea pigs (Gareri et al., 2009). This indicates that, given the

same maternal dose and alcohol pattern, dizygotic twins may show different exposure to

ethanol, thus potentially implicating the placenta as a selective factor mediating ethanol

exposure to individual fetuses.

There are a variety of perinatal and postnatal risk factors of FASD severity. The major risk

factors that affect the fetus are genetic predisposition, alcohol consumption pattern and

maternal characteristics. Firstly, certain maternal polymorphisms appear to influence the

prevalence of alcohol-related birth defects, such as the ADH2*3 polymorphism, which has a

protective effect against such outcomes by coding for a more efficient alcohol dehydrogenase

(McCarver et al., 1997). In terms of alcohol pattern, the dose of alcohol consumed will greatly

affect fetal alcohol concentrations, since maternal, fetal, and amniotic fluid ethanol

concentrations are similar within minutes of ethanol consumption (Idanpaan-Heikkila et al.,

1972). While no safe amount of alcohol has been determined during pregnancy, binge

drinking!or consumption of 5 or more drinks in one sitting!is considered a more harmful

consumption pattern than low or moderate use (Abel, 1998). As well, timing of alcohol

exposure is important. Drinking in the first trimester can lead to structural morphologies,

9

while drinking in the later stages of pregnancy can cause growth restriction (Streissguth, 1997).

Since it develops throughout pregnancy (Koren, 2011), the fetal brain is considered the most

susceptible organ to alcohol damage and exposure at any time can influence functional

development.

Many maternal characteristics have been identified that are linked to an increased

likelihood of alcohol consumption during pregnancy and therefore of FASD. For example,

assuming a woman discontinues alcohol use upon pregnancy recognition, timing of recognition

plays an important role in determining how long a child may have been inadvertently affected

(Kim et al., 2010). Secondly, psychological conditions such as depression are strong

predisposing factors for problem drinking that can affect severity (Chander & McCaul, 2003).

Parity is also a risk factor for problem drinking, as women who consume alcohol in their first

pregnancy are likely to continue during subsequent pregnancies (Berenson et al., 1991).

Partner alcohol and drug use increases the likelihood of maternal alcohol use during pregnancy

(Quinlivan & Evans, 2005). Other risk factors that are associated with higher FASD

prevalence are maternal smoking, maternal age, poor nutrition during pregnancy, and poor

prenatal care (Kim et al., 2010; Paintner et al., 2012).

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2.2 FATTY ACID ETHYL ESTERS

2.2.1. Description

Fatty acid ethyl esters (FAEE) are formed by the esterification of fatty acids and

ethanol via FAEE synthases throughout the body (Laposata & Lange, 1986). The group of

FAEE consists of more than 20 compounds of different chain length, however, when used for

analysis, most laboratories use the cumulative concentration of 4-7 compounds (Hartwig et al.,

2003). Figure 2 shows some of the pathways of ethanol elimination in the body, with FAEE

formation comprising part of the minor non-oxidative pathway. FAEE are considered direct

ethanol metabolites since they still contain the two carbon atoms of ethanol.

Figure 2. Ethanol metabolism. For the purposes of simplicity, only the non-oxidative pathways relevant to this paper are depicted in the figure. For a full list of ethanol metabolites and biomarkers, please consult Joya et al., 2012.

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2.2.2. Fatty acid ethyl esters and pregnancy

The most simple and direct method of screening for alcohol use in pregnant women is

self-report. There are a variety of questionnaires that can be administered to pregnant women

to screen for problem drinking (Russell et al., 1996; Sokol et al., 1989; Wurst et al., 2008), but

they often prove impractical as a solitary method of screening because women often

underreport their alcohol use during pregnancy due to fear of stigmatization, blame, and losing

custody of the child. Thus, the combined use of a questionnaire and analysis of objective

alcohol biomarkers is the most effective way to screen for alcohol use during pregnancy and,

by extension, potential fetal exposure (Wurst et al., 2008).

There are no studies that have investigated FAEE levels in blood in pregnant women.

Instead, many studies focus on FAEE detection in maternal hair and neonatal meconium,

which is the first stool of life. These matrices are useful for detecting exposure to drugs over a

long time frame. Hair grows at a rate of approximately 1 cm/month (Pragst & Balikova, 2006),

and meconium begins forming in the fetal gut as early as 12 weeks gestation (Ostrea & Naqvi,

1982). Thus, in obstetric populations, the measurement of alcohol biomarkers in these

matrices can show maternal alcohol consumption over several months, representing the late

stages of pregnancy.

FAEE detection in maternal hair has been documented in many studies (Kulaga et al.,

2009; Kulaga et al., 2010; Pragst & Balikova, 2006). The current cut-off for excessive alcohol

consumption is 0.5 ng FAEE/mg hair (Auwarter et al., 2001). Similarly, several general

population studies have measured FAEE in meconium (Chan et al., 2003; Gareri et al., 2008;

Hutson et al., 2010) and 2 nmol FAEE/g meconium has been established as the cut-off for

excessive alcohol use during pregnancy using four FAEE (Pragst & Balikova, 2006). Of

12

considerable importance, using the placental perfusion model, FAEE have been shown to not

cross the human placenta (Chan et al., 2004) and thus detection in meconium is indicative of

fetal exposure to and metabolism of ethanol exclusively.

2.2.3. Utility of fatty acid ethyl esters in clinical practice

The measurement of FAEE in meconium as a determinant of fetal alcohol exposure has

become common practice in Canada and is one of the key tools for diagnosing FASD (Goh et

al., 2008). There are a variety of advantages to using FAEE in clinical practice. Firstly, FAEE

do not cross the human placenta and, as such, their detection in fetal matrices such as

meconium indicate direct exposure to and metabolism of ethanol (Chan et al., 2004). In

addition, positive FAEE results correlate with clinically relevant indicators of FASD, such as

lower APGAR scores and lower executive functioning (Peterson et al., 2008).

Unfortunately, analysis of the cumulative FAEE compounds by chromatographic

methods is time consuming, complicated, and expensive. Additionally, there are several

sources of false FAEE results such as prenatal vitamin use, olive oil use, and contamination of

meconium with post-natal stool (Chan et al., 2003; Zelner et al., 2012). These sources of

FAEE false positives warrant investigation into other biomarkers that could supplement FAEE

analysis. While maternal hair FAEE concentrations above 0.5 ng/mg can distinguish excessive

drinkers, there is no way to differentiate between teetotalers and social drinkers who consume

less than 30 g ethanol per day (Morini et al., 2010b). For example, abstinent mothers can still

test between the limit of detection and the 0.5 ng/mg cut-off (Auwarter et al., 2001), likely due

to several sources of false positives, such as use of certain hair care products (Gareri et al.,

2011). Similarly, meconium samples can test positive for FAEE in the infants of abstinent

mothers, and possible sources of false positives include frequent use of olive oil, microbial

13

infection, gestational diabetes, and increased use of prenatal vitamins (Chan et al., 2003).

Interestingly, infants whose meconium tests negative for FAEE may test positive in subsequent

bowel movements due to carbohydrate fermentation by gut flora in postnatal stool (Zelner et

al., 2012). This stresses the need to collect the first postnatal passing, which can be difficult to

time.

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2.3. ETHYL GLUCURONIDE

2.3.1. Pharmacokinetics

Similar to FAEE, ethyl glucuronide (EtG) is a minor, direct, non-oxidative metabolite

of ethanol (Figure 2). The advantage of using non-oxidative metabolites is that they remain in

the body longer than its major oxidative metabolites (Peterson, 2004), and can therefore be

used to measure alcohol consumption after ethanol has been eliminated from the body. EtG is

formed by the net addition of UDP-glucuronic acid (UDPGA) to ethanol, a reaction catalyzed

by the UDP-glucuronosyltransferase (UGT) family (Foti & Fisher, 2005). Several UGT

isoforms have been implicated in EtG formation, however inhibition studies with adult human

liver microsomes and recombinant UGTs have shown that UGT1A1 and 2B7 contribute the

most to EtG formation (Foti & Fisher, 2005).

EtG was initially detected in human blood and urine and several studies have correlated

detection of EtG in these matrices with alcohol use (Halter et al., 2008; Hoiseth et al., 2007;

Hoiseth et al., 2009a). Importantly, compared to ethanol, EtG is stable in blood and urine for

longer periods and can therefore provide a larger window of alcohol consumption. In a highly

controlled pharmacokinetic study (Hoiseth et al., 2007), healthy adults were given 0.5 g/kg

ethanol, and blood and urine samples were collected up to 14 and 50 hours, respectively.

Approximately 0.02% of the initial ethanol dose is converted to EtG collected in urine on a per

mole basis. Time to peak blood concentration (Tmax) for ethanol is 1 hour, ethanol is detectable

in blood for only up to 6 hours, and is eliminated at a rate of 0.14 g/L/h. In urine, Tmax and

detection time for ethanol is 2.1 and 6.9 hours, respectively. In contrast, EtG parameters in

blood (half-life = 2.2 h, Tmax = 4 h, detection time = 10 hours) and in urine (Tmax = 4.75 hours,

detection time = 30 hours) are more extended, indicating that EtG may have clinical utility in

15

detecting alcohol consumption hours to days after exposure. Using urine concentrations and

volumes to determine dose excreted, renal clearance of EtG is 8.32 L/h and volume of

distribution is 0.28 L/kg. The assumption for this calculated volume of distribution was that

EtG is exclusively eliminated in the urine, which is an appropriate approximation since the

calculated total body clearance for EtG is in the same order of magnitude as the renal clearance

for EtG.

To elucidate the effect of dosing on pharmacokinetic parameters, a subsequent study

was conducted that compared consumption of a mild-moderate (0.5 mg/kg) to a moderate-

heavy (1.0 g/kg) dose of ethanol (Hoiseth et al., 2010b). EtG pharmacokinetics were similar to

those previously reported for the 0.5 mg/kg ethanol group (Hoiseth et al., 2007), with a half-

life of 2.83 hours and detection in urine for over 24 hours. Interestingly, after doubling the

ethanol dose, maximal EtG concentrations tripled from 0.36 mg/L to 1.06 mg/L, and area

under the curve measurements for EtG increased by a factor of nearly 3.5. These findings

suggest that the correlation between ethanol concentration and EtG production may not be

linear, and that EtG may be a more sensitive marker of high ethanol concentrations. Indeed,

previous studies have shown that UGT1A1 and 2B7 enzymes responsible for the formation of

EtG are not saturated at higher ethanol concentrations (Hoiseth et al., 2008; Rosano & Lin,

2008). On the contrary, it is possible that other ethanol metabolic pathways are saturated with

high doses of ethanol, and EtG is therefore produced in higher quantities than expected based

solely on dose ratios.

An important conclusion from these pharmacokinetic studies is that EtG reveals more

information on alcohol use than does ethanol. Firstly, since ethanol has such a short Tmax in

blood, if a subject has decreasing ethanol concentrations in two consecutive blood draws, this

16

only excludes ethanol consumption within the past 30-60 minutes (Hoiseth et al., 2007). This

can be problematic in cases of drunk driving, where suspects claim they consumed alcohol

after the incident in question. Indeed, samples are generally taken approximately 2.5 hours

after a car incident (Hoiseth et al., 2009a), by which time ethanol measurements may not be

useful. In terms of time frames, EtG is generally detectable in blood for up to 24 hours

(Hoiseth et al., 2009a), while detection in urine has been reported up to 5 days (Borucki et al.,

2005).

2.3.2. Ethyl glucuronide and pregnancy

While blood and urine are useful matrices for the detection of EtG and for the

elucidation of alcohol consumption over hours to days, they are of limited value in overall

pregnancy cases where information needs to be collected on alcohol consumption over several

months. Importantly, it is important to determine the extent of alcohol consumption during the

second and third trimesters, as this is the period where most women are aware of their

pregnancy and thus continued heavy use of alcohol is indicative of problem drinking (Sarkar et

al., 2010). Thus, EtG analysis in obstetric populations has focused on measurements in

maternal hair and fetal matrices.

EtG can be detected in maternal hair by gas chromatography-mass spectrometry (GC-

MS) or, less frequently, by liquid chromatography-mass spectrometry (LC-MS), with detection

limits that distinguish heavy alcohol consumption of 2 pg/mg for GC-MS and 50 pg/mg for

LC-MS (Pragst & Balikova, 2006). Unlike FAEE, by measuring EtG concentrations in hair, it

is also possible to distinguish between teetotalers, social drinkers, and heavy drinkers (Yegles

et al., 2004).

17

Five populations have been studied with respect to EtG analysis in fetal matrices: 4

with the use of meconium and 1 with fetal remains and placental tissue (Table 2). Limit of

detection (LOD) or limit of quantification (LOQ) for the LC-MS-MS method used are reported

along with percentage of samples above LOD/LOQ and sample range to give an indication of

population trends.

Table 2. Summary of current studies that have measured ethyl glucuronide in meconium, fetal remains, and placental tissue.

Matrix Cohort Cutoff used (ng/g)

Fraction (%)

samples above cutoff

Range of positive samples (ng/g)

Reference

602 samples from Department of Obstetrics and Gynecology

at University of Erlangen-Nuremberg

LOD = 10 97/596 (16.3)

LOD – 10,230

(Bakdash et al., 2010)

18 samples from 4 Antwerp hospitals LOQ = 50 5/18

(27.8) LOQ –

980 (Tarcomnicu et al., 2010)

185 samples from NICU in Arcispedale Santa Maria

Nuova, Reggio Emilia, Italy and Pediatric Service of

Hospital del Mar, Barcelona, Spain

LOQ = 5 153/180 (85.0)

LOQ - 2331

(Morini et al., 2010a)

Meconium

607 samples from 7 hospitals across Italy

Cutoff for heavy alcohol consumption

= 444

48/607 (7.9)

Cutoff - 888

(Pichini et al., 2012)

Fetal remains

35 samples from voluntary interruptions of pregnancy at

12th week at hospital in Murcia, Spain

LOQ = 5 4/35 (11.4) 33 - 391 (Morini et

al., 2011)

18

Placental tissue

35 samples from voluntary interruptions of pregnancy at

12th week at hospital in Murcia, Spain

LOQ = 5 4/35 (11.4)

112 – 1305

(Morini et al., 2011)

The results of these studies show that different geographical regions exhibit variability

in EtG levels in meconium, suggesting distinct maternal drinking patterns among populations.

For example, when comparing the meconium samples from Barcelona and Reggio Emilia,

median EtG concentrations in meconium were 15.6 ng/g in the Reggio Emilia cohort vs. 101.5

ng/g in the Barcelona cohort (Pichini et al., 2009). Additionally, while no samples in the

Italian cohort tested above 400 ng/g (approximately equal to the 2 nmol/g cut-off used to

distinguish heavy alcohol consumption), the prevalence of samples above this value was a

striking 21% in the Spanish cohort. This variability can even be seen in different regions of the

same country, as shown by maternal alcohol consumption prevalence data in Italy alone that

ranged from 0% in Verona to nearly 30% in Rome (Pichini et al., 2012). These data suggest

that EtG analysis in meconium can show which specific geographical regions are more likely

to contain heavily drinking obstetric patients, and subsequently, are more likely to have a

higher risk of fetal alcohol exposure.

The detection of EtG in placental tissue and in fetal remains suggests that these

matrices may be additional sources of information regarding alcohol use during pregnancy,

particularly in forensic cases where blood and urine are not readily available (Morini et al.,

2011). Finally, even though neonatal hair was analyzed in one study (Morini et al., 2010b),

there was not enough hair to complete the analysis. To date, the many complications

associated with drug analysis in neonatal hair (ex. absence of or too little neonatal hair at birth,

poor understanding of neonatal hair physiology) preclude its use in many laboratories.

19

However, due to the success of EtG testing in maternal hair, EtG analysis in neonatal hair still

represents an unmet potential in alcohol monitoring during pregnancy.

2.3.3. Advantages of ethyl glucuronide

Based on research in adults and, specifically, pregnant women, and because of the

inherent complications with FAEE analysis, EtG may serve as an effective additional

biomarker for the detection of alcohol consumption during pregnancy. Two of the major

advantages of EtG testing are the reduction of confounding variables that can lead to false

results and the availability of different analytical methods that can suit the needs of different

laboratories.

2.3.3.1. Reduction of confounding variables with EtG analysis

Since ethanol can be found in various foods and commonly used self-care products, it is

important to determine the extent of EtG formation from these sources to eliminate false

results. Table 3 summarizes some of the potentially confounding sources that have been

analyzed for EtG detection in hair or urine.

Table 3. Effect of consuming ethanol-containing foods and using self-care products on the detection of ethyl glucuronide.

Source investigated Matrix analyzed

Results Reference

22% alcoholic mouthwash Blood and urine

All true negative Hoiseth et al., 2010a

Non-alcoholic wine with 1 g ethanol

Blood and urine

All true negative Hoiseth et al., 2010a

Sip of vodka with 1 g ethanol

Blood and urine

Negative in blood, detectable in some patients in urine

Hoiseth et al., 2010a

Various apple and grape juices (0.3-0.6 g/L ethanol)

Urine All true negative Musshoff et al., 2010

2-3 L non-alcoholic beer (4 Urine Detectable in all 8 samples Musshoff et

20

g/L ethanol) al., 2010

0.75 - 1.3 kg sauerkraut (2 g/kg ethanol)

Urine Detectable in 1/5 specimens Musshoff et al., 2010

600-700 g bananas (5 g/kg ethanol)

Urine Detectable in 2/6 specimens Musshoff et al., 2010

Hand sanitizer (62% ethanol) for 3 days (10 hours/day) every 5 minutes

Urine Detectable up to 2 µg/mL in samples collected at end of day

Reisfield et al., 2011

Baker’s yeast and sugar Urine Positive in 2 abstinent adults Thierauf et al., 2010

Hair care products with 10-95% ethanol content

Hair Negative in abstinent subjects Gareri et al., 2011

Bleaching Hair False negatives in social and heavy drinkers

Morini et al., 2010c

Of note, it appears that both the dose and timing of ethanol use are important

determinants of EtG detection in urine. For example, urine samples from subjects consuming

1 g ethanol via large quantities of non-alcoholic wine were negative while those of some

subjects consuming the same 1 g ethanol via a sip of vodka were positive (Hoiseth et al.,

2010a). These findings indicate that the Cmax for ethanol may be more important than the dose

itself when determining if an alcohol exposure will cause a false positive for EtG in urine. In

addition, urine results show that consumption of foods containing baker’s yeast and sugar may

be falsely positive for EtG due to glucose fermentation (Thierauf et al., 2010).

Of higher importance in screening pregnant populations are the false results in hair.

The results from Table 3 show that hair bleaching can lead to false negatives in medium to

heavy alcohol consumers, likely due to considerable ion suppression after direct sample

injection using LC-MS-MS (Morini et al., 2010c). Ion suppression can be counteracted by

cleanup with solid phase extraction prior to injection into the LC. False results were not

detected with the use of hair colouring products, suggesting that EtG metabolic and melanin

21

pathways do not interact (Appenzeller et al., 2007). Lastly, Gareri et al. (2011) discovered

that, unlike FAEEs, there is no incorporation of EtG into hair after washing with alcohol-based

hair products (ex. shampoos, conditioners, mousses, gels). While some foods and products

have led to false EtG results, these cases are rare in general obstetric populations, as

demonstrated by random sampling from obstetric patients showing a high true negative rate

within these populations (Bakdash et al., 2010).

2.3.3.2. Analytical methods for EtG detection

Unlike FAEEs, EtG is a single molecule that affords quick and simple analysis via

chromatography and enzyme immunoassay. The current gold standard for EtG detection in

hair and meconium is LC-MS-MS. Briefly, samples are prepared and mixed with internal

standard in an aqueous solvent, samples are then ultrasonicated, centrifuged, and the

supernatant is directly injected into the LC (Bakdash et al., 2010; Morini et al., 2006). With

respect to meconium analysis, several methods have been proposed to increase the utility of the

analytical protocol. Currently, meconium samples generally do not require clean up with solid

phase extraction, small sample sizes can be used, and total run time for EtG in meconium is

only 8 minutes with an EtG retention time of 3.3 minutes (Morini et al., 2008). This method is

fast, simple, selective for EtG, and quite sensitive. Methods for EtG detection in meconium via

GC-MS-MS have also been well established. Samples are prepared in the same way, but

require clean up with solid phase extraction, evaporation, and derivatization before injection

(Wurst et al., 2004).

Chromatographic equipment is large, expensive and requires specialized technicians,

especially when coupled with mass spectrometry. As such, an enormous advantage of EtG

analysis has been the development of an enzyme immunoassay that contains a fluorescently

22

linked anti-EtG antibody (Jung et al., 2009). Currently developed for EtG urinalysis, the

immunoassay can be implemented in nearly any general laboratory, requires minimal specimen

volume, does not require extraction or derivatization, and allows for quick run time and

analysis of output (Wright & Ferslew, 2012). Alongside its many advantages, the EtG

immunoassay has shown good concordance with the gold standard LC-MS-MS in clinical and

postmortem urine samples (Turfus et al., 2012). LOD for the assay is 50 ng/mL in urine, and a

positive specimen is determined at an EtG concentration greater than 500 ng/mL. Currently,

the immunoassay is being developed for meconium analysis, and since the positive cut-off in

meconium has been reported at 444 ng/g (i.e. 2 nmol/g) with LC-MS-MS (Pichini et al., 2012),

the sensitivity of EtG analysis should not be compromised with the conversion to analysis by

immunoassay. With some adaptations to produce clean extracts and to avoid matrix

interferences, the implementation of meconium EtG analysis via enzyme immunoassay proves

to be an invaluable addition to screening for in utero exposure to alcohol.

23

2.4. THE HUMAN PLACENTA: PHYSIOLOGY AND EXPERIMENTAL PROCEDURES

2.4.1. Placental anatomy

The placenta plays a diverse array of roles to ensure a healthy pregnancy. It is

responsible for supplying the developing fetus with nutrients and oxygen, clearing waste from

the fetal circulation, and producing hormones necessary for pregnancy (Syme et al., 2004).

The functional unit of the placenta is called a cotyledon, and each of a placenta’s 20-40

cotyledons is independently perfused via maternal and fetal vasculature (Syme et al., 2004).

The basic anatomy of the human placenta is outlined in Figure 3 (Myren et al., 2007).

Maternal arteries invade the decidual surface and supply the intervillous space with maternal

blood. Blood drains from the space via maternal veins that form openings in the decidual

plate. The umbilical cord generally consists of two fetal arteries that supply deoxygenated

blood from fetus to placenta, and one fetal vein that delivers nutrients and oxygen to the fetus.

The fetal arteries branch into the chorionic spiral arteries and end in networks of capillaries

called villous trees. Each cotyledon contains one villous tree bathed in maternal blood, and it

is at this region where the rate-limiting maternal-fetal transfer of drugs occurs.

24

Figure 3. Anatomy of the human term placenta. (1) Maternal arteries; (2) Maternal veins; (3) Decidua basalis; (4) Cytotrophoblast; (5) Intervillous space; (6) Villous tree; (7) Syncitiotrophoblast; (8) Umbilical cord. Reprinted from Toxicology in Vitro, Vol. 21(7), Myren. The human placenta—an alternative for studying foetal exposure. Pg 1332-40. Copyright 2007 with permission from Elsevier.

Each villous tree is composed of fetal endothelial cells, villous stroma and a trophoblast

layer (Syme et al., 2004). Devoid of a basement membrane, the area of transfer between

circulations is minimal. In the first trimester, the thickness of the trophoblast layer is 50-100

µm, but due to shedding of the syncitiotrophoblast this thickness decreases to 4-5 µm at term,

increasing the ease of passive diffusion at term (van der Aa et al., 1998).

2.4.2. Mechanisms of placental drug disposition

The transfer of xenobiotics and endogenous compounds across the placenta is

accomplished by several mechanisms:

25

2.4.2.1. Passive diffusion and physiochemical properties of a drug

The process of passive diffusion across the fetal endothelium requires no energy and

transfer is determined by an established concentration gradient between two compartments

(Syme et al., 2004). Aside from the maternal dose, which establishes the concentration

gradient between maternal and fetal circulations, there are specific drug qualities, called

physiochemical properties, which determine the rate and extent of passive diffusion of a

particular drug across the placenta. These properties are lipophilicity, size, ionization, and

protein binding.

Generally, lipophilic molecules can dissolve in membrane lipids and can therefore

diffuse across the endothelial membrane at the maternal-fetal interface more readily than

hydrophilic molecules (Reynolds & Knott, 1989). Size does not heavily influence the diffusion

of lipophilic drugs, however, for hydrophilic drugs, diffusion becomes increasingly impaired as

the size of the drug increases (Syme et al., 2004). Only the un-ionized form of a drug can

cross the placenta via passive diffusion (Syme et al., 2004) and therefore the extent of

ionization in maternal blood is important. The log dissociation constant (pKa) of a particular

drug gives information on its degree of ionization. Weak acids with low pKa’s (<< pH 7.4)

and weak bases with high pKa’s (>> pH 7.4) are highly ionized in maternal blood and passive

diffusion of these drugs is impaired. Finally, drugs are found in the maternal circulation in

either free form, bound to plasma proteins, or bound to red blood cells. Only the free form of a

drug can cross the placenta, so drugs that are highly bound to plasma proteins or red blood

cells in the maternal circulation may transfer more slowly than would be predicted solely by

the drug’s other physiochemical properties (Giaginis et al., 2011).

26

2.4.2.2. Maternal pharmacokinetics and physiological changes during pregnancy

There are a variety of changes that occur during pregnancy that can alter drug

pharmacokinetics and influence the transfer of drugs across the placenta (Table 4).

Table 4. Physiological and pharmacokinetic changes that occur in pregnant women compared to non-pregnant adults. Reprinted from Placenta, Vol. 27(8), Gedeon and Koren. Designing pregnancy centered medications: drugs which do not cross the human placenta. Pg 861-8. Copyright 2006 with permission from Elsevier.

Selecting some functions from Table 4 as examples, an increase in total body water

during pregnancy leads to an increase in volume of distribution, particularly for hydrophilic

drugs. This can reduce the maternal plasma drug concentration and decrease the initial

concentration gradient between mother and fetus that drives transfer via passive diffusion

(Gedeon & Koren, 2006). Also from Table 4, renal flow and glomerular filtration rate are

increased during pregnancy. These changes can cause extensive drug elimination from the

maternal circulation before placental transfer occurs, thereby limiting fetal exposure. This

phenomenon is particularly important for drugs that already transfer slowly across the placenta

due to their physiochemical properties (Giaginis et al., 2011).

Function Change Cardiac output Increased Tidal volume Increased

Pulmonary blood flow Increased Gastric pH Increased

Glomerular filtration rate Increased Renal drug elimination Increased

Hepatic drug elimination Increased, decreased, or unchanged Clearance Increased

Total body water Increased Volume of distribution Increased

Steady state plasma concentration Decreased Peak serum concentration Decreased

Intestinal motility Decreased Protein binding capacity Decreased

27

2.4.2.3. Active transport

Aside from passive diffusion, the other major process that influences drug disposition

across the placenta is active transport. Active transport requires energy, often in the form of

adenosine triphophate or via an electrochemical gradient generated by H+, Na+ or Cl- (Syme et

al., 2004). Often, active transport occurs against a concentration gradient, concentrating

necessary nutrients in fetal tissue via influx or preventing transfer of certain maternal drugs via

efflux. A variety of active transporters are located on both the brush-border apical and

basolateral membranes of the placenta and serve to pump substances away from or through the

syncitiotrophoblast (Syme et al., 2004). Endogenous substrates for these transporters include

amino acids, hormones, and vitamins, and structurally similar drugs may compete for binding

sites (Ganapathy et al., 2000). An example of the influence of active transport in drug

disposition is the case of glyburide transfer across the placenta. In a placental perfusion

experiment, fetal concentrations of the anti-diabetic glyburide were significantly increased in

the presence of an inhibitor of the apical efflux transporter encoded by ABCG2 (breast cancer

receptor protein), indicating that this transporter plays an important role in the prevention of

glyburide transfer to the fetus (Pollex et al., 2008).

2.4.2.4. Placental metabolism

The placenta is a metabolically active organ, containing Phase I and II enzymes,

specifically cytochrome P450s (CYPs), uridine diphosphate glucuronosyltransferases (UGTs),

glutathione-S-transferases, and sulfotransferases (Syme et al., 2004). Despite their presence,

there are only approximately half the levels of these enzymes in the placenta as in the adult

liver and the metabolic capability of the placenta is generally not of great clinical relevance

(Reynolds & Knott, 1989). However, there are a variety of methods utilized for studying

28

placental metabolism. In some placental perfusion experiments (Dickinson et al., 1989;

Fowler et al., 1989; Hutson et al., 2011b), the placental production and transfer of an

experimental drug’s metabolites are also measured. Additionally, placental microsomes can be

prepared and metabolic studies can be conducted using these fractions. For example,

microsomal studies in first trimester placentae from terminated pregnancies have demonstrated

placental expression of CYP1A, CYP2E1, UGT, and "-glucuronidase (Collier et al., 2002b),

and studies conducted in term placentae have shown that the placenta can metabolize FAEE

(Chan et al., 2004), azidothymidine (Collier et al., 2004) and bilirubin (Serrano et al., 2002).

Current studies are looking at the protective role of metabolizing enzyme induction by

exogenous toxins such as alcohol and cigarette smoke (Collier et al., 2002b), particularly in the

sense that this induction may be compensatory in the late stages of pregnancy when drugs can

more readily cross the thinner maternal-fetal barrier.

2.4.3. Utility of the ex vivo placental perfusion model

The human ex vivo placental perfusion model was initially developed in 1967 (Panigel

et al., 1967), and later modified to the form that is currently used in the Motherisk laboratory

(Schneider et al., 1972). A thorough description of the protocol will be given in chapter 3.

Briefly, the perfusion model simulates maternal and fetal blood flow to and from the placenta

with the use of buffer solutions in place of blood, roller pumps to establish blood flow and

flasks that act as maternal and fetal “reservoirs” (i.e. systemic circulations). Experimental

drugs can be added to the system and samples can be taken over time to monitor placental

transfer. Since the method’s technical adaptations were made in 1972 and because of many

advantages over other methods utilized in placental research, there has been a steady increase

29

in the number of papers published on or using the perfusion model over the past 40 years

(Omarini et al., 1992).

The placental perfusion model resolves many of the issues associated with other

techniques: ethical issues associated with in vivo human studies, issues of species specificity

associated with animal studies, physiological discrepancies with cell cultures, and

standardization with in silico studies. There are, however, conditions whereby other placental

techniques may be beneficial. Table 5 outlines some of the key advantages and disadvantages

between the placental perfusion model and other techniques. To overcome the negative

characteristics, many techniques are often employed for a specific drug to give a well-rounded

depiction of drug disposition across the placenta.

Table 5. Comparison of techniques used to analyze drug disposition across the placenta. Table adapted from Giaginis et al. (2011) and Hutson et al. (2011a).

Technique Advantages Disadvantages In vivo human studies

• Direct levels from cord and maternal blood allow for exact answers to immediate questions

• Drug monitoring over long periods of time

• Ethical issues if samples taken before delivery

• Cannot provide information on drug distribution within maternal-fetal tissues

• Inter-individual variability can preclude generalizations

In vivo animal studies

• Reduce ethical and inter-individual issues • Toxicology studies can be performed

throughout gestation • Drug accumulation in specific tissues can

be studied

• Due to placental physiology, extrapolation of kinetic information to human data is difficult

In vitro • Large variety of cell cultures to choose from according the specific needs of the study

• Useful for the study of drug uptake, efflux, and metabolism

• Tissue cultures are intact, so cell-cell structures and communications are maintained

• Expression of metabolizing enzymes or of transporters can vary across cell lines

• Regulatory mechanisms may not be present in the preparation

• No standardization procedures established to reduce inter-laboratory variability

Ex vivo • Structures are intact and most closely resemble in vivo data

• Can only mimic transfer of substances at term

30

• Can take measurements over time • Sampling is available from all circulations

and from placental tissue • Use of a standard compound (ex.

antipyrine) reduces inter-laboratory variability and differences in blood flow, placental weight, and surface area for exchange

• Trauma to the tissue and surrounding membranes may prevent utility

• Tedious procedure, time-consuming, and potentially expensive

In silico • Can help improve or create new experimental procedures

• High throughput screening of potentially fetotoxic candidates possible due to well-established software

• As of yet, it is not able to properly address issues of placental metabolism or active transport

With current studies comparing the perfusion protocol between laboratories (Myllynen

et al., 2010) and developing quantitative techniques to account for inter-laboratory differences

(Mose et al., 2012), the placental perfusion model promises to offer objective measures of drug

disposition.

2.4.4. Quantitative analysis of the ex vivo placental perfusion model

2.4.4.1. The fetal-to-maternal ratio

One method of using the placental perfusion model to quantify maternal-fetal transfer is

to collect data once the system has reached steady state!that is, once there is no net transfer in

either direction. The standard parameter used ubiquitously in perfusion experiments to

measure both transfer and drug kinetics is the fetal-to-maternal ratio (F:M ratio) (Frederiksen et

al., 2010). This parameter is often measured at various time points throughout the perfusion

and used in secondary analyses described later in this section, however, the F:M ratio at steady

state provides substantial information. In terms of drug transfer, limited transfer is often

indicated as F:M < 0.1, transfer as F:M between 0.1 and 1.0, and fetal accumulation as F:M >

31

1.0 at steady state (Hutson et al., 2011a). Clearance rates and time to steady state!as depicted

as the time to F:M ratio plateau!both give an indication of the rate of a drug’s transfer.

Certain limitations of the perfusion system preclude its utility in accurately predicting

in vivo data. Notably, disparities between perfusion and in vivo data can be attributable to both

the extent of protein binding of a drug and the difference in the drug’s ionization between

maternal and fetal circulations (Hutson et al., 2011a). Protein binding can greatly influence the

trans-placental disposition of drugs as only the non-bound form can cross (Reynolds & Knott,

1989). However, only in certain circumstances are plasma proteins added to the perfusion

system, and even then, their use is an approximation due to variations throughout pregnancy

and between individuals. Due to this discrepancy, perfusion and in vivo F:M ratios may differ

drastically for certain drugs. For example, due to the high albumin concentrations in term fetal

plasma, fetal albumin can serve as a depot for certain acidic drugs (ex. diazepam,

sulfonamides, salicylates), thus leading to an increased F:M ratio in vivo and a potential

underrepresentation in the perfusion model (Reynolds & Knott, 1989). Conversely, certain

basic drugs that bind extensively to #1-acid glycoprotein will be highly bound in the maternal

circulation and may demonstrate slower transfer in vivo than would be predicted solely by the

drug’s physiochemical properties (Reynolds & Knott, 1989). In terms of drug ionization, the

difference between maternal and fetal pH can lead to ion trapping of weakly basic drugs in the

slightly more acidic fetal plasma (Hutson et al., 2011a). When analyzing term placentae, this

phenomenon can be responsible for adverse events in newborns whose mothers were treated

with basic anesthetics during delivery (Reynolds & Knott, 1989).

Researchers have attempted to reduce discrepancies between techniques by

synthesizing perfusion and in vivo data. Garland et al. (2008) developed an equation for in

32

vivo drug transfer that was later adapted by Hutson et al. (2011a) to approximate the F:M ratio

in vivo based on the perfusion F:M ratio, protein binding, and the effect of pH difference

between fetal and maternal circulations:

!

F :M =%unboundM%unboundF

x1+10pKa" pHF

1+10pKa" pHMx

CLMFCLFM +CLF

where %unbound is the proportion of unbound drug in maternal or fetal plasma, pKa is the log

dissociation constant for the drug, CLMF/CLFM is the F:M ratio at steady state in the closed

circuit configuration or the clearance rates in the open circuit configuration, and CLF is the

non-placental fetal clearance of the drug, which is assumed to be negligible. In a systematic

review of the perfusion method, Hutson et al. (2011a) found 26 drugs with a documented

perfusion steady state F:M ratio, protein binding data, and in vivo cord and maternal drug

concentrations drawn after delivery. There was a correlation between the in vivo cord-to-

maternal blood ratio and the calculated F:M ratio using the above equation, indicating that,

with the appropriate alterations, the perfusion model can be used to predict in vivo drug

disposition between mother and infant.

2.4.4.2. Secondary measurements of transfer

The perfusion system allows for sampling of many different compartments, including

the maternal artery (MA), maternal vein (MV), fetal artery (FA), fetal vein (FV), and the

placental tissue itself (Ala-Kokko et al., 2000). As such, other parameters often used to

measure concentration changes between maternal and fetal circulations take into account this

availability and are able to give additional insight into the trans-placental gradient for each

33

lobule (Challier, 1985). Parameters of placental gradient establishment are the transport

fraction and the extraction fraction:

Transport Fraction = (CFv – CFa)/(CMa-CFa) and

Extraction Fraction = (CMa-CMv)/(CMa-CFa),

where C = concentration; M = maternal perfusate; F = fetal perfusate; a = artery; v = vein

Additionally, a mass balance calculation can be used to account for the distribution of

an experimental drug at steady state (Frederiksen et al., 2010). This involves measuring the

drug’s concentration in maternal perfusate, fetal perfusate, and placental tissue at the end of the

experiment and determining the fractions of initial dose distributed to each compartment. Not

only does this give a measurement of the degree of drug transfer alternative to the F:M ratio,

but it can also give insight into the binding and storing capacity of the placenta itself. By

summing the fractions of initial dose recovered in these 3 compartments as well as the samples

taken throughout the experiment for analysis, the mass balance calculation also serves as a

percent yield and gives an indication of drug recovery. This can be an important determinant

of the extent of drug leakage during the experiment, which is related to placental integrity.

Lastly, several measurements can be used before a drug has necessarily reached steady

state and can give an indication of how transfer is likely to occur. These include the indicative

permeability coefficient, which is the slope of the F:M ratio vs. time curve between 0 and 30

minutes (Frederiksen et al., 2010); the area under the curve of the F:M ratio vs. time curve

between 0 and 120 minutes for both experimental drug and test substance (see section 3.2.2.

for test substances and antipyrine) (Mose et al., 2012); and the corrected transfer index, which

gives a ratio of the percentage of initial dose transferred to fetal circulation of experimental

34

drug compared to test substance (Mose et al., 2012). After 120 minutes, not all substances

have reached steady state, so these measurements are predictive of drugs that are suspected of

transferring primarily via passive diffusion. The test substance antipyrine is expected to have

reached equilibrium by 120 minutes, so, by using ratios, these early measurements provide

further information on the quality of the perfusion and the appropriateness of the selected flow

rates, which can help guide decision making for subsequent perfusions (Mose et al., 2012).

35

CHAPTER 3. MATERIALS AND METHODS

3.1. PLACENTAL PERFUSION

3.1.1. Ex vivo perfusion of a single placental cotyledon

Term placentae were obtained from scheduled elective Caesarian sections at the

obstetrics ward at St. Michael’s Hospital in Toronto, Ontario. Research ethics board approval

was obtained from the hospital and mothers gave written consent prior to delivery (Appendix

I).

The placental perfusion protocol has been previously explained in detail (Miller et al.,

1985) and adapted in our laboratory (Derewlany et al., 1991; Pollex et al., 2010). Figure 4

outlines the key features of the perfusion system used at the Motherisk laboratory.

Figure 4. Schematic diagram of the ex vivo placental perfusion set-up at the Motherisk laboratory. Reprinted from Clinical Pharmacology and Therapeutics, Vol. 90(1), Hutson et al. The human placental perfusion model: a systematic review and development of a model to predict in vitro transfer of therapeutic drugs. Pg 67-76. Copyright 2011 with permission from Nature Publishing Group.

36

All perfusions were started within 30 minutes of delivery. Immediately after delivery,

placentae were transported to the on-site perfusion laboratory at St. Michael’s Hospital in ice-

cold heparinized phosphate buffered saline (PBS). An artery/vein pair on the fetal side

supplying a clearly defined cotyledon was isolated and the maternal side was checked for

trauma and an intact decidual plate. After cannulation of the fetal vessels, fetal flow of

perfusate was established from a reservoir containing 150 mL fetal perfusate. The lobule was

clamped fetal side down in a chamber containing PBS (1 M, pH 7.4) kept at 37°C, and excess

placental tissue was removed. Maternal circulation was established from a round boiling flask

containing 250 mL maternal perfusate by inserting blunt tipped needles 2-3 mm below the

decidual surface and venous outflow was collected from small openings in the decidual plate.

Both circuits were closed once blood had been entirely cleared and replaced with fresh

perfusate.

Perfusate consisted of 10.9 g/L M199 tissue culture medium (Sigma Aldrich, St. Louis,

MO; see Appendix II for ingredients), dextran (maternal, 7.5 g/L; fetal, 30.0 g/L), glucose

(maternal, 2.77 mM), heparin (2000 U/I), and kanamycin (100 mg/L). Antipyrine (1 mM) was

added to the maternal perfusate as a flow-dependent marker of passive diffusion (Schneider et

al., 1972) and to allow for comparisons between perfusions with different flow rates

(Mathiesen et al., 2010). While antipyrine has been shown to reduce maternal venous

prostaglandin levels, these reductions are not associated with changes in maternal or fetal

blood flow or oxygen content (Cashner et al., 1986). To mimic physiological conditions in

maternal and fetal blood (Reynolds & Knott, 1989), maternal and fetal perfusates were

buffered to pH 7.4 and 7.35 with 30 mM and 25 mM NaHCO3, respectively. Maternal and

fetal flows were established independently by the use of two roller pumps and flow rates were

kept at 14 and 2 mL/min, respectively. Maternal perfusate was equilibrated with 95% O2/5%

37

CO2 and fetal with 95% N2/5% CO2. Throughout the experiment, measurements of placental

viability were taken from sampling ports extending from sections of the circuits corresponding

to the fetal artery (FA), fetal vein (FV), maternal artery (MA), and maternal vein (MV) (Figure

4).

3.1.2. Pre-control phase

Prior to the addition of EtG, there was a 1-hour control phase where fresh perfusate was

added to both reservoirs and baseline measurements of placental integrity and viability were

established. O2 pressure, CO2 pressure, pH, and glucose concentration were determined by

sampling from the 4 sampling ports and measuring every 15 minutes via an on-site Blood Gas

Analyzer (Radiometer ABL 725, Copenhagen, Denmark). Samples were taken directly from

the maternal and fetal reservoirs every 15 minutes for analysis of human chorionic

gonadotropin (hCG) secretion and antipyrine transfer. Fetal arterial inflow pressure, fetal

volume, and maternal and fetal flow rates were recorded every 15 minutes as measures of

placental integrity. Throughout the experiment, pH was altered to maintain physiological

levels as needed by addition of small amounts of HCl or NaOH. The experiment was

discontinued if inflow pressure deviated from 40-60 mmHg for an extended period of time or if

fetal volume loss exceeded 4 mL/hour. At the end of the pre-control phase, roller pumps were

turned off and final fetal and maternal volumes were recorded.

3.1.3. Experimental phase

Prior to commencement of the experimental phase, reservoirs were refilled with 150

and 250 mL of fresh fetal and maternal perfusate. For perfusion, stock EtG powder (Medichem

Diagnostica, Steinenbronn, Germany) was diluted in methanol to 1 mg/mL and stored at -20°C

38

until use. The 3-hour experimental phase began after adding 250 µL (1 mg/mL) EtG to the 250

mL maternal reservoir (final concentration = 1 µg/mL), mixing the flask, and turning on the

roller pumps. The use of 1 µg/mL EtG for the perfusions is based on blood EtG levels detected

in healthy adults who consumed a moderate dose (1 mg/kg) ethanol (Hoiseth et al., 2010b). A

3-hour time frame was chosen to allow enough time to detect EtG transfer, but not enough for

placental viability to be compromised. O2 pressure, CO2 pressure, pH, and glucose

concentration were determined by sampling from the 4 sampling ports and measuring every 10

minutes for the first half hour, and then every 30 minutes afterwards. Samples were taken

directly from the maternal and fetal reservoirs every 30 minutes for analysis of hCG secretion

and antipyrine transfer. Samples were also taken from the 2 reservoirs for analysis of EtG

transfer every 10 minutes for the first half hour and then every 30 minutes afterwards. The

experiment was discontinued if inflow pressure deviated from 40-60 mmHg for an extended

period of time or if fetal volume loss exceeded 4 mL/hour. Due to stringent exclusion criteria

for placental viability and integrity needed for a successful perfusion, the success rate for a

fully completed perfusion was approximately 5%. All samples were stored at -20°C until

analysis as per manufacturer recommendations.

3.1.4. Measurement of placental viability

3.1.4.1. Antipyrine detection

A detailed method for antipyrine detection has been described elsewhere (Brodie and

Axelrod, 1949). Briefly, standards with known antipyrine concentrations were analyzed using

UV-visible recording spectrophotometer W-160A (Shimadzu, Tokyo, Japan) at 350 nm and a

standard curve was generated. Samples from the perfusion were analyzed in duplicate and

sample absorbance was used to determine sample concentration with the following formula:

39

Antipyrine concentration = (Absorbance – y-intercept)/slope in µmol/L, where

y-intercept and slope are derived from the standard curve.

Antipyrine concentrations were converted to µmol/g tissue and mean values were reported.

The slope of the first hour of the concentration vs. time graph was reported as the rate of

antipyrine appearance in and disappearance from the fetal and maternal circulations,

respectively.

3.4.1.2. Human chorionic gonadotropin

Human chorionic gonadotropin (hCG) was measured in maternal and fetal samples via

an ELISA kit (Alpha Diagnostic International, San Antonio, TX) and a Biotek Synergy HT

microplate reader (Biotek instruments, Winooski, VT) at 450 nm. A standard curve was

generated with known concentrations of hCG and concentrations from samples throughout the

perfusion were determined by the following equation:

hCG concentration = (Absorbance – y-intercept)/slope in mIU/mL, where y-

intercept and slope are derived from the standard curve

hCG concentrations were expressed as mIU/g tissue.

3.4.1.3. Glucose

Blood glucose levels (mg/dL) were measured in maternal and fetal artery and vein

throughout the experiment via an on site Blood-Gas Analyzer (Radiometer ABL 725,

Copenhagen, Denmark). Values were converted to µmol/g tissue and rates of glucose

appearance or disappearance were recorded for each perfusion as the slope of the concentration

vs time graph for the first hour.

40

3.4.1.4. Oxygen

Blood oxygen partial pressure (pO2) was measured in maternal and fetal artery and vein

throughout the experiment via an on site Blood-Gas Analyzer (Radiometer ABL 725,

Copenhagen, Denmark). Oxygen content, delivery, transfer, and consumption were calculated

according the following calculations (Challier et al., 1976):

I. Oxygen Content of Perfusate Samples

O2 Content = 0.939/(BP – 47) x pO2 in µmol O2/mL perfusate, where

0.939 – solubility of oxygen expressed as µmol O2/mL fluid at 37°C and 1

atmosphere dry gas pressure;

BP – barometric pressure in mmHg;

47 – saturated vapour pressure of water at 37°C in mmHg;

pO2 – pO2 of sample in mmHg

II. Maternal Oxygen Delivery

O2 Delivery = MA x Qm/WT in µmol O2/min/g, where

MA – O2 content of the maternal arterial perfusate sample in µmol O2/mL perfusate;

Qm – flow rate (mL/min) of the perfusate on the maternal side of the placenta;

WT – weight of the perfused lobule expressed in grams

III. Rate of Transplacental Oxygen Transfer

O2 Transfer = Qf x (FV-FA)/WT in µmol O2/min/g, where

Qf – flow rate (mL/min) of the perfusate on the fetal side of the placenta;

FV – O2 content of the fetal venous perfusate sample in µmol O2/mL perfusate;

FA - O2 content of the fetal arterial perfusate sample in µmol O2/mL perfusate;

WT – weight of the perfused lobule expressed in grams

41

IV. Placental Oxygen Consumption

O2 Consumption = [(MA-MV)*Qm/WT] – O2 Transferred in µmol O2/min/g,

where

MA - O2 content of the maternal arterial perfusate sample in µmol O2/mL perfusate;

MV – O2 content of the maternal venous perfusate sample in µmol O2/mL perfusate;

Qm – flow rate (mL/min) of the perfusate on the maternal side of the placenta;

WT – weight of the perfused lobule expressed in grams;

O2 Transferred – O2 transferred to the fetal side of the placenta as calculated in III

above expressed in µmol O2/min/g

3.1.5. Statistical analysis

All data is presented as mean ± SEM unless stated otherwise and comparisons between

pre-control and experimental phases were analyzed using a two-tailed Student’s T-test with

significance determined at a p-value less than or equal to 0.05.

42

3.2. SAMPLE ANALYSIS

3.2.1. Materials and Equipment

Stock ethyl glucuronide and internal standard penta-deuterated ethyl glucuronide (EtG-

d5) solutions were purchased from Cerilliant (Round Rock, TX). The water used in all

experimental procedures was obtained from a Milli-Q Advantage A10 Ultrapure Water

Purification System (Millipore, Billerica, MA). Stock grade methanol, formic acid, and

heptafluorobutyric and pentafluoropropionic anhydride derivatizing agents were purchased

from Sigma-Aldrich (St. Louis, MO). Columns used for solid phase extraction were UCT

Clean Screen Extraction Columns (200mg/3mL/50pkg; Chromatographic Specialties Inc.,

Brockville, ON), Aminopropyl NH2 Columns (Sopachem, Eke, Belgium), and OASIS MAX

columns (Waters Corporation, Milford, MA). An Optima L-80 XP Ultracentrifuge (Beckman

Coulter) was used for blended tissue centrifugation. All samples were analyzed using a

Shimadzu QP2010 Plus GC-MS coupled to an AOC-5000 Autosampler (Shimadzu, Columbia,

MD, USA) and integration was performed with Shimadzu GCMSsolution version 2.50

software. Splitless liners (2mm x 5 x 95) were purchased from Chromatographic Specialties

Inc and SPME 100 µm polydimethylsiloxane red and black fibers were purchased from

Supelco Analytical (Bellefonte, PA).

3.2.2. Preparation of stock solutions and standards

Working stock solutions of EtG were prepared by making 1:10 serial dilutions from

100 µg/mL stock EtG in methanol (stock solutions were 10 µg/mL, 1 µg/mL, 100 ng/mL, and

10 ng/mL). These stock solutions covered the range of concentrations used for standards,

validation, and calculation of limit of detection (LOD) and limit of quantification (LOQ).

43

Working stock solution of 1 µg/mL EtG-d5 was prepared by diluting 100 µg/mL stock EtG-d5

in methanol. All stock solutions were stored at -20°C as per the manufacturer’s

recommendations (Cerilliant, Round Rock, TX).

For calibration standards, 1 mL standards were prepared with blank perfusate, 50

ng/mL EtG-d5, varying concentrations of EtG ranging from 0-500 ng/mL for fetal standards

and 0-1000 ng/mL for maternal standards, and 50 µL formic acid. For tissue standards, blank

1.00 ± 0.01 g tissue samples were suspended in 3 mL deionized water with 50 ng/g EtG-d5,

varying concentrations of EtG (0, 5, 10, 50, 100, 250, 500 ng/g) and 150 µL formic acid.

Blank standards, containing neither EtG nor EtG-d5, were also prepared for each batch as a

control for equipment functionality.

3.2.3. Sample preparation

3.2.3.1. Preparation of perfusate samples

Maternal and fetal samples collected for EtG analysis during the experimental phase of

the perfusions were thawed, and 950 µL sample and 50 µL (1 µg/mL) EtG-d5 were transferred

to labeled Eppendorf tubes. Standards were prepared as described in section 3.2.2. Formic

acid (50 µL) was added to each tube in preparation for solid phase extraction.

3.2.3.2. Preparation of placental tissue samples

For each placenta analyzed, one sample from an adjacent unperfused lobule and 3

samples from the perfused lobule were prepared by weighing 1.00 ± 0.01 g tissue, suspending

samples in 3 mL deionized water, and adding 50 µL (1 µg/mL) EtG-d5 to each sample. Formic

acid (150 µL) was added to each tube and samples were vortexed thoroughly. Standards were

44

prepared as described in section 3.2.2. Standards and samples were blended on ice for three 30

second intervals each using a POLYTRON PT 10-35 laboratory homogenizer (Kinematica

Inc., Littau/Lucerne, Switzerland) on level 7. Standards and samples were transferred to

centrifuge tubes and spun using an Optima L-80 XP Ultracentrifuge (Beckman Coulter, Brea,

CA) with a Ti 50.2 rotor for 30 minutes at 28,700 g and 4°C. The supernatant was collected

for subsequent solid phase extraction.

3.2.3.3. Solid phase extraction

Perfusate and tissue standards and samples were extracted through UCT Clean Screen

Extraction Columns (200 mg/3 mL, UnitedChem, Bristol, PA) via a vacuum manifold. The

protocol for extraction was as follows:

1. Condition cartridges with 1 mL 1 % formic acid solution.

2. Add 1 mL sample and pull through slowly, leaving vacuum on 5 kPa for 5 minutes.

3. Add 1 mL water and pull through slowly, leaving vacuum on 10 kPa for 15 minutes.

4. Change collection vials and elute with 2 mL 2% formic acid in methanol solution,

pulling though slowly.

3.2.3.4. Derivatization

Eluted standards and samples were transferred to SPME vials and dried with N2 gas on

a 35°C hot plate. Each vial was then derivatized with 40 µL heptafluorobutyric anhydride

(HFBA) and heated at 80°C for 15 minutes. Finally, samples were dried briefly with N2 gas

and loaded onto the GC tray for injection.

45

3.2.4. Method optimization

Several conditions were optimized to maximize peak chromatographic areas counts.

Solid phase extraction was carried out using protocols for OASIS MAX (Kerekes et al., 2009),

Aminopropyl NH2 (Yegles et al., 2004) and UCT Clean Screen (Agius et al., 2010) cartridges.

Derivatization with both heptafluorobutyric anhydride (HFBA) and pentafluoropropionic

anhydride (PFPA) was evaluated. Additionally, 3 methods for introducing sample into the

GCMS were compared: direct injection, headspace injection, and injection after solid phase

microextraction (SPME). For the latter, injection using 100 µm polydimethylsiloxane red and

black fibers were compared.

3.2.5. Method validation

Quantification of all perfusate and tissue standards was done by taking the ratio of the

peak area for the quantifying ion for EtG to that of the quantifying ion for EtG-d5 (see Table 6).

3.2.5.1. Limit of detection (LOD) and limit of quantification (LOQ)

To determine the LOD and LOQ, 11 low levels of concentration were prepared in

triplicate as follows: 0.25, 0.5, 0.75, 1, 2.5, 5, 7.5, 10, 15, 20, 25 ng/mL for perfusate and 0.5,

0.75, 1, 2.5, 5, 7.5, 10, 15, 20, 25, 50 ng/g for tissue. Each concentration was prepared

independently. Standard curves were generated by calculating the EtG:EtG-d5 quantifying ion

ratio of peak area count for each sample and plotting it as a function of EtG concentration. At

least 5 points were used for each curve. Regression lines were generated and regression

analysis was performed. LOD and LOQ were calculated as LOD = 3$/S and LOQ = 10$/S

(Aleksa et al., 2011). $ represents the standard deviation of the linear regression line and S

46

represents the slope of the line. LOD and LOQ averages of the 3 lines generated for each

matrix were calculated.

3.2.5.2. Precision

Inter-day precision was assessed in triplicate using low, medium, and high

concentrations from standard curves. Concentrations used were 10, 100, and 500 ng/vial.

Intra-day variation was assessed by running each of the 3 concentrations consecutively in

triplicate. The coefficient of variability (CV) was calculated for each concentration and matrix

as CV = $/A * 100, where $ represents the standard deviation and A represents the average of

each triplicate.

3.2.5.3. Experimental recovery

Experimental recovery was assessed by comparing the EtG quantifying ion peak area

counts of triplicate tissue and perfusate standards to counts from a sample that had the same

amount of EtG added directly to the SPME vial prior to GC-MS analysis. Concentrations used

were 10, 100, and 500 ng/vial.

3.2.6. GC-MS instrumentation

All samples were analyzed using a Shimadzu QP2010 Plus GC-MS coupled to an

AOC-5000 Autosampler (Shimadzu, Columbia, MD, USA). The GC-MS was operating in

negative chemical ionization mode and samples were analyzed using Shimadzu GCMSsolution

version 2.51 software. After drying off excess HFBA, samples were further derivatized using a

CTC-agitator with the following parameters: pre-incubation at 2 minutes with 1 minute of

agitation and 15 second stop intervals, fiber extraction for 10 minutes with 1 minute of

47

agitation and 15 second stop intervals, 5 minutes desorption, agitator speed 250 rpm and

agitator temperature 90°C.

Samples were passed through a splitless liner and separated using a DB-1HT column

(10 µm thickness, 15 m length, 0.25 mm diameter; Agilent, Mississauga, ON) with helium as

the carrier gas. The GC oven temperature ramp was: 70°C, hold for 2 minutes, increase to

280°C at a rate of 12°C/minute. The injection temperature was 260°C and the column flow

was 1.2 mL/minute. Ion source and interface temperatures were both 250°C. Detector voltage

was 0.7 kV above the calibrated baseline. The MS was operating in SCAN acquisition mode

and was programmed to analyze an ion window of 200-425 (m/z). Analyte ions and retention

times are summarized in Table 6. The total run time was 19.5 minutes.

Table 6. Analyte ions and retention times for developed GC-MS program. Ions 399 and 404 (underlined) were unique for EtG and EtG-d5, respectively, and were therefore used for sample quantification, while ions 288 and 213 were used for additional qualification of the analytes.

Analyte Ions (m/z) Retention Time (minutes)

Ethyl glucuronide 399, 288, 213 9.486

Ethyl glucuronide d5 404, 288, 213 9.453

48

CHAPTER 4. RESULTS

4.1. METHOD VALIDATION

A variety of methods were tested to achieve maximum peak area count for EtG and

EtG-d5 and to minimize surrounding noise at the ions analyzed (Table 7). The following

parameters were tested and compared according to protocols in the existing literature: method

of solid phase extraction, injection method, derivatizing agent used, pre-injection parameters,

SPME fiber used, GC temperature ramp speed, and GC column flow speed. For each section

of Table 7, the final protocol presented in each parameter yielded the best result and was used

for analysis of perfusate and tissue samples. Sample chromatograms of the quantifying ion for

EtG and EtG-d5 extracted from perfusate and tissue using the final protocol are shown in

Figure 5.

Our method was validated to detect EtG in placental perfusate and tissue matrices.

Standard curves of low range concentrations were linear from 1-25 ng/mL for placental

perfusate and 5-50 ng/g for placental tissue. Inter- and intra-day variability and recovery for

samples at low, medium, and high concentrations are summarized in Table 8. All three

parameters were calculated in triplicate at each concentration and for both perfusate and tissue.

Inter-day variability was calculated over 3 consecutive days. The limit of detection was 1.6

ng/mL for placental perfusate and 13.7 ng/g for placental tissue (Table 9).

49

Table 7. Summary of protocols used to optimize method of EtG extraction and detection from placental perfusate and tissue.

Category Materials used Method Results Reference

Aminopropyl NH2 Columns

• Condition with 3 mL methanol, 3 mL water, 3 mL acetonitrile • Add sample • Wash with 1 mL n-hexane • Elute with 2 mL 2% NH3

No signal Yegles et al., 2004

OASIS MAX Cartridges

• Condition with 2 mL methanol, then 2 mL water • Add sample • Wash with 1 mL NH4OH, 2 mL methanol • Elute with 2 mL 2% formic acid in methanol

Poor signal, unable to integrate individual peaks

Kerekes et al., 2009

Solid Phase Extraction

UCT CleanScreen Cartridges

• Condition with 1 mL 1% formic acid • Add sample • Wash with 1 mL water • Elute with 2 mL 2% formic acid in methanol

Clear individual peaks for EtG and internal standard

Agius et al., 2010

Direct injection

Dry samples, derivatize for 30 minutes, reconstitute in 50 µL ethyl acetate, inject 2 µL

Background noise masked signal

Kerekes et al., 2009

Headspace SPME Agitation for 20 minutes, inject 2.5 mL at 36 µL/sec and 90°C

Poor signal, unable to integrate individual peaks

Aleksa et al., 2011 Injection

Method

SPME Dry samples, derivatize for 15 minutes, analyte adsorbs to SPME fiber before fiber injection

Highest area counts, effective peak separation

Agius et al., 2010

Derivatizing Agent PFPA Used 10 µL for comparison to HFBA

Poor signal for internal standard quantifying ion

Jurado et al., 2004

50

HFBA Used 10 µL for comparison to PFPA

Strong EtG and internal standard peaks

Agius et al., 2010

- Derivatized samples pre-incubated in agitator for 5 minutes followed by fiber extraction for 20 minutes

Good area counts, SPME fiber swelled after each batch

Aleksa et al., 2011 Pre-injection

Parameters -

Derivatized samples dried with N2 briefly, pre-incubated in agitator for 2 minutes followed by fiber extraction for 10 minutes

Same area counts as other method, SPME fiber lasted longer

Agius et al., 2010

Carboxen/ PDMS fiber (75µm, black)

-

Higher counts than red fiber, however fiber stripped at higher EtG concentrations

Agius et al., 2010

SPME Fiber

PDMS fiber (100 µm, red) -

Counts not as high as black fiber, however, fiber lasted for more runs

Agius et al., 2010

- GC oven temperature increased from 70°C to 280°C at 15°C/minute

Peaks did not drop to baseline, difficult to integrate

- Ramp Speed

- GC oven temperature increased from 70°C to 280°C at 12°C/minute

Peaks dropped to baseline -

- 1.0 mL/min Internal standard peak incorporated into nearby M199 peak

- Column

Flow - 1.2 mL/min

Effective separation of internal standard and M199 peaks

-

51

Figure 5. Sample chromatographs of the quantifying ion for EtG (399) and EtG-d5 (404) after extraction of (A) 100 ng/mL EtG from perfusate; (B) 100 ng/mL EtG-d5 from perfusate; (C) 100 ng/g EtG from tissue; (D) 100 ng/g EtG-d5 from tissue

52

Table 8. Summary of inter-day variability, intra-day variability and extraction efficiency for final protocol (n=3 for each measurement).

Low (10 ng/vial)

Medium (100 ng/vial)

High (500 ng/vial)

Intra-day CV (%)

Perfusate 15 15 5

Tissue 35 14 23

Inter-day CV (%)

Perfusate 8 13 17

Tissue 38 28 17

Experimental recovery (%)

Perfusate 12 9 10

Tissue 8 5 3

Table 9. Method sensitivity. Limits of detection and quantification are measured in ng/mL perfusate and ng/g tissue.

Matrix Mean R2 SD CV (%) LOD LOQ

Perfusate 0.9985 0.001 0.11 1.6 4.8

Tissue 0.9948 0.006 0.70 13.7 41.6

53

4.2. DETERMINANTS OF PLACENTAL INTEGRITY AND VIABILITY

A total of 4 cotyledons from different placentae were perfused with 1 µg/mL EtG and

parameters of placental integrity and viability are presented in Table 10. Mean lobule weight

of the 4 perfused lobules was 19.03 ± 1.29 g and maternal and fetal flow rates were 13.65 ±

0.68 and 2.11 ± 0.06 mL/min, respectively. During all 4 perfusions, fetal volume loss was

never greater than 4 mL/h and pH values remained within physiological ranges throughout the

pre- and experimental phases. Oxygen, glucose, hCG, antipyrine and fetal arterial inflow

pressure measurements were not statistically different between the control and experimental

phases (Table 10). Antipyrine equilibrated between the two circulations after 3 hours with a

final F:M ratio of 0.62 ± 0.13 (Figure 6), which is similar to previous perfusion experiments

(Annola et al., 2008; Myllynen et al., 2008). The rates of antipyrine disappearance from

maternal circulation and appearance in fetal circulation were not statistically different (0.02 ±

0.01 vs 0.02 ± 0.00 µmol/g/min; p=0.91).

Table 10. Measurements of placental integrity and viability during perfusion experiments.

Viability Marker Pre-Control (mean ± SEM)

Experiment (mean ± SEM)

p-value

Fetal arterial inflow pressure (mmHg) 33.75 ± 4.59 36.78 ± 1.59 0.56

hCG production (mIU/g/min) 18.98 ± 3.42 10.91 ± 1.65 0.08

Oxygen Transfer from maternal to fetal (!mol O2/g/min)

0.00 ± 0.00 0.01 ± 0.00 0.45

Oxygen delivery to placenta (!mol O2/g/min)

0.43 ± 0.03 0.41 ± 0.03 0.75

Oxygen consumption by placenta (!mol O2/g/min)

0.21 ± 0.05 0.22 ± 0.01 0.92

Glucose consumption (!mol/g/min) 0.26 ± 0.05 0.21 ± 0.07 0.57

54

Figure 6. Antipyrine concentration (mean ± SEM) in maternal (closed circles) and fetal (open circles) reservoirs during the experimental phase of the perfusions (n=4). Initial maternal antipyrine concentration is 1 mM.

To elucidate whether EtG utilizes placental glucose transporters for transfer, the rates of

glucose disappearance from maternal circulation and glucose appearance in fetal circulation

were compared between pre-control and experimental phases of the perfusion. Neither the

rates of glucose disappearance from maternal circulation (0.26 ± 0.05 vs 0.21 ± 0.07

µmol/g/min, p = 0.57) nor the rates of glucose appearance in fetal circulation (0.02 ± 0.03 vs

0.07 ± 0.02 µmol/g/min, p = 0.26) were statistically different between pre-control and

experimental time points.

55

4.3. PLACENTAL DISPOSITION OF ETHYL GLUCURONIDE

After addition of 1 µg/mL EtG to the maternal circulation, transfer was slow and

incomplete after 3 hours of perfusion (Figure 7).

Figure 7. EtG concentration (mean ± SEM) in maternal (closed circles) and fetal (open circles) reservoirs during the experimental phase of the perfusions after addition of 1 µg/mL EtG to the maternal reservoir (n=4). Note: error bars are included for mean fetal concentrations.

The initial rate of disappearance from maternal circulation was rapid over the first 30

minutes and diminished for the remainder of the experiment. EtG was first detected in the fetal

circulation after 20 minutes. The fetal concentration after 3 hours was 229.88 ± 19.85 ng/mL

and the fetal-to-maternal ratio at 3 hours was 0.29 ± 0.02. This ratio is not indicative of steady

state parameters since net maternal-to-fetal transfer was still occurring after 3 hours. This can

be seen by comparing the antipyrine and EtG F:M ratios in Figure 8. Lines have been fitted

through the first 4 data points (0-90 minutes), during which time transfer is expected to occur

only in the maternal-to-fetal direction. Lines have been extended to 180 minutes to show that,

56

while antipyrine has reached steady state by the end of the experiment, EtG concentrations still

fit the line indicative of unidirectional transfer. This indicates that EtG transfer is incomplete

after the 3 hour experiment.

Figure 8. Fetal-to-maternal ratios (mean ± SEM) for antipyrine (closed circles) and EtG (open circles) during the experimental phase of the perfusions (n=4).

Averages of EtG concentration in triplicate samples of 1.00 ± 0.01 g samples of

perfused placental tissue ranged from 140-415 ng/g (Table 11). Placental tissue samples taken

from a cotyledon not perfused with EtG were below the LOD for EtG.

Table 11. Triplicate measurements of EtG concentration (ng/g) in each perfused cotyledon (n=4).

Trial Placenta 1 Placenta 2 Placenta 3 Placenta 4

1 401.27 <LOD 320.31 283.78

2 392.33 229.48 217.99 57.91

3 450.48 168.88 247.84 79.58

Average 414.69 199.18 262.05 140.43

CV (%) 7.55 21.51 20.08 88.75

57

The following equation was used to determine the % recovery of EtG from all

measurable compartments:

% Recoverycomp = (concentrationcomp * volumecomp)/250 µg, where

concentrationcomp is the EtG concentration of that compartment in ng/mL or ng/g,

volumecomp is the volume or mass (for lobule compartment) of the compartment in mL or g,

and 250 µg is the initial dose of EtG introduced to the maternal circulation at time 0

Percent recoveries from each compartment of the perfusion apparatus as well as total EtG

recovery are shown in Table 12.

Table 12. Percent EtG recovery.

Source of Recovery (%)

Placenta 1

Placenta 2

Placenta 3

Placenta 4

Average ± SEM

Removed from maternal circulation

10.17 10.77 10.21 11.95 10.78 ± 0.41

Remaining in maternal circulation

54.40 61.19 38.77 67.20 55.39 ± 6.13

Removed from fetal circulation

0.91 1.10 1.50 1.07 1.15 ± 0.13

Remaining in fetal circulation

7.84 8.19 12.04 8.94 9.25 ± 0.96

Extracted from lobule post-perfusion

2.64 1.16 1.61 1.42 1.71 ± 0.32

Total recovered 75.97 82.41 64.13 90.59 78.28 ± 5.58

Net EtG disappearance

24.03 17.59 35.87 9.41 21.72 ± 5.58

Note: The sum of the samples taken throughout the experiment for analysis of EtG, antipyrine and hCG is called the “removed from maternal circulation” and “removed from fetal circulation” compartments. Perfusate remaining in the reservoirs and tubing after the 3 hour experiment are called the “remaining in maternal circulation” and “remaining in fetal circulation” compartments.

58

CHAPTER 5. DISCUSSION

5.1. VALIDATION OF GC-MS METHOD FOR ETHYL GLUCURONIDE DETECTION

The first objective of the current study was to develop and validate a method for

detecting EtG in placental perfusate and tissue using GC-MS. While the current gold standard

for EtG detection in plasma and placental tissue is LC-MS-MS, our laboratory is equipped with

GC-MS and method development was more practical. In any case, there are several GC-MS

methods available for detecting EtG in hair, and components of these methods were easily

transferrable to the current protocol. Notably, the hair method used by Agius et al. (2010) was

adapted to EtG detection in this study by means of solid phase extraction protocol, method of

injection, SPME fiber used, and derivatizing agent used.

This is the first method to use GC-MS to measure EtG in placental perfusate and in

placental tissue. Perfusate is essentially human plasma without plasma proteins and since EtG

is not expected to bind to erythrocytes or plasma protein (Hoiseth et al., 2009b), comparisons

with studies conducted in plasma or even whole blood are appropriate. There are several

studies that have measured EtG in blood with LC-MS, including all the blood pharmacokinetic

studies mentioned in Chapter 2. Only three papers to date have analyzed EtG in blood via GC-

MS (Janda & Alt, 2001; Schmitt et al., 1995; Shen et al., 2009), all of which used similar

methods for EtG extraction and detection.

In these three EtG blood studies with GC-MS, the LODs ranged from 37-100 ng/mL

and one study reported an LOQ of 173 ng/mL (Janda & Alt, 2001). Comparatively, the LOD

and LOQ in this thesis were approximately 2 ng/mL and 5 ng/mL. Several factors might

contribute to the comparatively lower limits seen in this study compared to others. Firstly,

perfusate is a “cleaner” matrix than whole blood and does not require initial protein denaturing

59

and centrifugation to remove cellular fractions. Secondly, these previous methods introduced

sample into the GC via direct injection, whereas in this study samples were introduced via

SPME. This latter method utilizes a porous fiber coated in a solid sorbent that allows for

sample adsorption and subsequent equilibrium between the sorbent and the SPME vial

(Pawliszyn, 1999). Thus, SPME allowed for introduction of a concentrated amount of analyte

into the GC, compared to only 1-2 µL of a reconstituted solution of analyte, as seen with direct

injection (Aleksa et al., 2011). Lastly, the lower limits in this study may be due to the

derivatizing agent and protocol utilized. Adapted from a hair protocol, where EtG LOD was

0.6 pg/mg hair (Agius et al., 2010), samples in this study were derivatized with both PFPA and

HFBA to determine that HFBA produced higher area counts at anticipated peaks, and samples

were derivatized at an optimal temperature for 30 minutes. The low LOQ established for this

method was essential for accurately measuring the initial appearance of EtG in the fetal

perfusate over the first hour, where concentrations ranged from 0-100 ng/mL. Analytical

sensitivity allowed for the detection of EtG in the fetal circulation within 20 minutes, which

gives some insight on potential tissue binding and saturation during the initial 20 minutes.

Only one previous study has measured EtG in placental tissue, however the study

analyzed first trimester placentae using LC-MS (Morini et al., 2011). Of the 35 placentae

analyzed from women undergoing voluntary termination, 4 tested above the LOQ (~5 ng/g) for

EtG, with values of 122, 215, 435, and 1305 ng/g. Despite differences in the protocols, the

average EtG concentrations of triplicate samples for the 4 placentae analyzed in this study after

perfusing with 1 µg/mL EtG (140, 199, 262, 415 ng/g) were similar to those reported by

Morini et al. The LOQ in this study (42 ng/g) was much higher than the one reported by

Morini et al., potentially due to the increased sensitivity of LC-MS over GC-MS for detecting

60

EtG in placental tissue. However, this LOQ was sufficient for capturing 11/12 of the tissue

samples (triplicate for each placenta) in this study.

Aside from differences in the method used and placental age, there are other important

differences between this thesis and the study conducted by Morini et al. in terms of measuring

EtG in placental tissue. Firstly, the dose of alcohol consumed and therefore the amount of EtG

produced was unknown in Morini et al., whereas all placentae in this study were perfused with

the same dose of EtG (1 µg/mL). Secondly, there was high variability amongst the

concentrations of the triplicate samples of each cotyledon in this study, suggesting that the 3-

hour timeframe of the perfusion may not have been sufficient for EtG to distribute evenly

throughout the lobule. Lastly, comparisons between concentrations in whole placentae vs.

single lobules may not be accurate because the increase in blood flow is disproportionate to the

increase in surface area, which means that, given the same dose, in vivo whole placentae may

have a higher drug concentration compared to a single ex vivo cotyledon. Regardless of these

differences, both studies were able to detect tissue EtG concentrations indicative of moderate

alcohol consumption, which is the objective of biomarker screens in hair and meconium, and of

this study.

5.1.1. Limitations to the study

As demonstrated in Table 7, the validation of this method was lengthy, as many

changes were required to obtain a final method that was specific, accurate, and sensitive

enough to detect EtG in placental perfusate and tissue. The two major limitations to the

validation of this method were the higher degree of inter- and intra-day variability, and the low

extraction efficiency. Inter- and intra-day variability was 8-17% and 5-15% for perfusate and

17-38% and 14-35% for tissue. Variability in tissue samples was greater than in perfusate

samples likely due to the inherent compositional complexity and heterogeneity of tissue.

61

Another reason for high variability in tissue is that triplicates were prepared for each

concentration, while other studies often use more than 3 replicates in their precision analyses to

reduce the coefficient of variability (Aleksa et al., 2011). Lastly, tissue samples contained

different final volumes of supernatant after ultracentrifugation. This suggests that placental

segments were not homogeneous and therefore, in some segments, water and EtG may have

been incorporated into the sub-cellular pellet and subsequently discarded.

SPME itself is a source of variability and several sources of imprecision have been

proposed (Pawliszyn, 1997). Heterogeneity among tissue samples will lead to different

degrees of adsorption to the SPME fiber and competition with EtG for adsorption sites. Fiber

use can also lead to variability, as there can be carryover of background substances to

subsequent samples. A more detailed optimization of fiber adsorption/desorption times and

temperatures could reduce this carryover.

Experimental recovery was calculated for this experiment as the ratio of detector count

for analyte extracted from either perfusate or tissue to detector count for pure un-extracted

analyte. For the latter, EtG was added directly from the stock solution to the vial, evaporated,

derivatized, and injected. The experimental recoveries for perfusate and tissue at varying EtG

concentrations were 9-12% and 3-8%, respectively, indicating that overall recovery was rather

poor. As a comparison, Janda et al. (2001) measured EtG extracted from blood with GC-MS

and calculated a mean extraction efficiency of 85%, however larger sample volumes were used

for SPE and the aminopropyl NH2 cartridges used by Janda et al. were unsuccessful for EtG

detection in the present thesis (Table 7). Agius et al. (2010) used the same SPE cartridges and

protocol as in this study for their analysis of EtG in hair with GC-MS, with extraction

efficiencies of 63-76%. This finding suggests that the matrices themselves!perfusate and

tissue!may be contributing to the low recoveries.

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For tissue, the likely source of low EtG yield was likely analyte loss during

homogenation and centrifugation. For perfusate, where samples were immediately extracted

after preparation, a series of experiments were conducted to determine the extent of matrix

effects on EtG loss. The same GC-MS protocol was used to measure EtG either un-extracted

or extracted from five solvents: water, perfusate, perfusate without heparin, perfusate without

dextran, and perfusate without M199 media. Surprisingly, EtG extracted from water alone

gave only a 30% recovery compared to un-extracted EtG. This recovery is still less than half

of the recovery from hair by Agius et al. The only difference between the two extraction

methods was that Agius et al. ran 2 mL of sample through the cartridges, whereas only 1 mL

sample was used in this study. This is because it was impractical to take more sample volume

during each time point throughout the perfusions at the risk of draining the fetal reservoir

before the end of the experiment, which only contained 150 mL initially. The samples

extracted from perfusate without M199 generated EtG counts closest to those of samples

extracted from water, while extraction from the remaining 3 solvents gave even lower

recoveries. These results indicate that the majority of EtG loss was due to the use of 1 mL

sample instead of 2 mL, while the remainder of the loss was due to interference of one or more

of the ingredients in the M199 media (Appendix II) with the SPE cartridges. While recovery

was low for this study, the detection method was still sensitive enough to accurately measure

all maternal and fetal samples.

5.2. PLACENTAL PERFUSION OF ETHYL GLUCURONIDE

The secondary objective of this study was to determine if levels of EtG indicative of

moderate to high alcohol consumption during pregnancy cross the human placenta. No

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measurements of blood EtG concentrations have been published in pregnant women so the use

of 1 µg/mL EtG for this study was based on an approximation from healthy subjects. In a

previous study conducted in healthy adults who consumed 1.0 g/kg ethanol (Hoiseth et al.,

2010b), which corresponds to approximately 5 standard drinks (13 g ethanol each), median EtG

Cmax was 1.06 µg/mL.

The maternal perfusate used in the perfusion experiments is similar to plasma in its

composition and pH, but does not contain plasma proteins such as albumin and AAG. For

drugs that are highly bound to erythrocytes or plasma proteins, the placental perfusion

experiment may over-estimate transfer, since the entire dose will be free to cross. EtG does not

appear to bind to erythrocytes, as shown by an average serum/blood EtG ratio of 1.69 in 13

postmortem cases (Hoiseth et al., 2009b). This value indicates that EtG is found in higher

concentrations in serum than in whole blood. This finding is understandable for a hydrophilic

molecule such as EtG since serum contains 12-18% more water than whole blood (Barnhill Jr

et al., 2007). Lastly, there are no reports of EtG binding to plasma proteins, so the absence of

these components of whole blood in the perfusate was not expected to influence EtG drug

disposition.

The results of this perfusion study show that EtG crosses the term human placenta

slowly, reaching a F:M ratio of 0.29 after 3 hours of perfusion. Glucuronides have previously

been shown to cross the placenta, resulting in significant fetal concentrations (Dickinson et al.,

1989; Garland et al., 2008). In pregnant baboons injected with morphine-3-glucuronide, F:M

ratios of 0.7-0.9 were measured after 24 hours, by which point the metabolite was presumably

at steady state (Garland et al., 2008). The final F:M ratio of 0.29 is not indicative of the actual

steady state value, since the linear increase in transfer seen in Figure 8 shows that EtG transfer

is still unidirectional after 3 hours. In addition, since EtG has a plasma elimination half-life of

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2-3 hours (Hoiseth et al., 2007; Hoiseth et al., 2009a), the amount of EtG elimination from the

maternal circulation by non-placental clearance may shift the concentration gradient between

maternal and fetal circulations in vivo compared to the ex vivo model that does not account for

non-placental clearance. Regardless, the results show a slow transfer of EtG across the

placenta, which is in contrast to the lack of transfer of FAEE.

The major mechanism of maternal-to-fetal EtG transfer is likely passive diffusion,

which occurs down a concentration gradient (Syme et al., 2004). This would explain the slow

rate of transfer, as EtG’s physiochemical properties preclude its efficient transfer across

biological interfaces. Firstly, since it is a molecule larger than 150 Da, it is unable to diffuse

through aquaporin channels (Kalant et al., 2007). Secondly, as a weak acid with a pKa of 3.21

(Krivankova et al., 2005), it is heavily ionized in maternal plasma. Lastly, and most

importantly, hydrophilicity impairs diffusion through lipid membranes. While the partition

coefficient of EtG has not been directly measured, other perfusion studies have used a shake

flask method to determine the octanol:water coefficient of an experimental drug and its

glucuronide (Dickinson et al., 1989; Fowler et al., 1989). For both phenytoin (Dickinson et al.,

1989) and valproic acid (Fowler et al., 1989), the log octanol:water coefficient is positive for

the parent drug (indicating a preference for octanol) and negative for the glucuronide

(indicating a preference for water). Moreover, octanol:water coefficients are positively

correlated with maternal clearance of drug, with parent drug clearance ratios of 110%

(phenytoin) and 95% (valproic acid) of antipyrine, and metabolite clearance ratios of only 12%

(phenytoin glucuronide) and 13% (valproic acid glucuronide). This correlation between

partition coefficient and clearance rate strongly indicates passive diffusion as the mechanism of

placental transfer. These trends are likely seen with ethanol and EtG, since ethanol has a

positive partition coefficient similar to those of phenytoin and valproic acid when using butanol

65

and pentanol as the solvent (Pienta et al., 1996), and since the placenta does not appear to slow

down the transfer of ethanol to the fetus (Idanpaan-Heikkil et al., 1971). Taken collectively, it

is likely that EtG crosses the placenta via passive diffusion.

Another possibility for EtG transfer across the placental barrier is via facilitated

diffusion, which uses a carrier system to transfer substrates down their concentration gradient.

Due to the structural similarities between glucose and EtG, the glucose transporter family was

hypothesized to be a potential candidate for EtG transfer. Glucose transporter 1 (GLUT-1) is

the dominant isoform in the placenta and is localized in the syncitiotrophoblast,

cytotrophoblast, and fetal endothelial cells (Jansson et al., 1993). Furthermore, it has been

suggested that the transfer of morphine-6-glucuronide across the blood brain barrier occurs

through GLUT-1 transport (Polt et al., 1994), particularly since transfer is decreased 3-fold in

the presence of glucose (Bourasset et al., 2003). Therefore, to test the possibility of GLUT-1

involvement in EtG transfer across the placenta, glucose transfer rates were compared between

control and experimental phases of the placental perfusion. Glucose (2.77 mM) was present in

the maternal perfusate during both phases, however EtG (1 µg/mL) was present in the maternal

perfusate only during the experimental phase. Therefore, if EtG utilizes GLUT-1 for placental

transfer, there should be decreased glucose transfer during the experimental phase due to

competitive inhibition. Neither maternal disappearance nor fetal appearance of glucose was

significantly different between control and experimental phases, suggesting that GLUT-1 does

not play a significant role in the placental transfer of EtG.

Active transport is another potential mechanism for EtG disposition across the

maternal-fetal unit. The placenta contains a variety of transporters that require an energy

source such as ATP to pump substrates against their concentration gradient (Syme et al., 2004).

Multi-drug resistance protein 2 (MRP-2) is one such transporter that is localized to the apical

66

syncitiotrophoblast (St-Pierre et al., 2000) and has a broad specificity for glucuronide

substrates (Gerk & Vore, 2002). Additionally, the biliary excretion of several glucuronide

metabolites is reduced in BCRP-null mouse livers (Zamek-Gliszczynski et al., 2006),

implicating BCRP as another potential EtG efflux transporter. The extent of active transport is

often investigated during perfusion studies when the experimental drug either does not cross

the placenta at all (Pollex et al., 2008) or demonstrates potential fetal risk (Hutson et al.,

2011b). While MRP-2 and BCRP may be involved in efflux of EtG in the placenta, active

transport of EtG was not an objective of this study and was not investigated.

A major source of EtG that was not analyzed in this study is the degree of

glucuronidation of ethanol by the maternal-placental-fetal unit. A previous analysis of EtG

formation in adult liver microsomes determined that the isozymes UGT1A1 and 2B7 are the

predominant enzymes in this metabolic process (Foti & Fisher, 2005). EtG formation by the

placenta has not been studied, however UGT expression and localization studies have been

conducted. First trimester placentae contain UGT1A1, UGT2B7 and "-glucuronidase!the

enzyme that hydrolyzes EtG to ethanol!and UGT is elevated in the placentae of mothers who

smoked or drank alcohol (Collier et al., 2002b). Term placentae express metabolically active

UGT2B7, but not UGT1A1, in the syncitiotrophoblast (Collier et al., 2002a). Despite their

reported activity, placental glucuronidation is generally not considered to be a major source of

drug clearance. For example, only 2% of azidothymidine is cleared via placental

glucuronidation in placental cell lines, primary cultures, and subcellular fractions (Collier et

al., 2004). It is therefore likely that placental metabolism of ethanol to EtG plays a minor role

in EtG transfer to the fetus, secondary to metabolism via the maternal liver.

The final metabolic factor pertaining to EtG formation is fetal metabolic capacity.

There have been no studies examining EtG formation in the fetal liver, however several

67

contrasting studies have looked at the ontogeny-related metabolic potential of UGTs. Early

human and rat microsomal analyses suggest that there is metabolic insufficiency of certain

UGT isoforms in the fetal liver and that the majority of UGT substrates are significantly less

metabolized compared to adults (Coughtrie et al., 1988; Leakey et al., 1987). These results are

in contrast with a study conducted in fetal baboons who were administered morphine, where

the authors reported no differences in the metabolism or elimination of morphine or its 2

glucuronide metabolites with gestational age or with the presence of the UGT2B7 inducer

phenobarbital (Garland et al., 2006). The latter results suggest that fetal UGT2B7 is sufficient

and mature enough for the proper clearance of morphine. While this study was conducted in

baboons, the results suggest that a component of EtG formation may be from metabolism by

UGT2B7 in the fetal liver. Taken collectively with the results of the current study, it is likely

that EtG detected in meconium is mostly of maternal origin, with potentially minor

contributions from placental and fetal metabolism. Future studies measuring EtG metabolic

formation in placental and fetal liver microsomes are needed to elucidate the exact metabolic

contributions of these additional pathways.

Genetic polymorphisms have been reported in 6 out of 16 isoforms of the UGT

superfamily, however only UGT1A1 polymorphisms have been associated with any clinically

relevant phenomena (Minors et al., 2002). Namely, UGT1A1 polymorphisms have been

implicated in impaired bilirubin metabolism leading to the onset of neonatal jaundice (Bartlett

& Gourley, 2011), as well as improper handling of irinotecan (Nagar & Blanchard, 2006). In

terms of UGT2B7, microsomal studies in liver samples have shown no change in metabolic

rates with polymorphic variants (Bhasker et al., 2000). With this information, it is unlikely that

there would be any significant genetic variation in the metabolism of ethanol to EtG in term

placentae, since they do not express quantifiable levels of UGT1A1.

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To measure the efficiency of the perfusion protocol and to account for the initial

maternal EtG dose at the end of the experiments, a recovery analysis was conducted. On

average, by the end of the experiment, 55% of the initial EtG dose was still in the maternal

circulation, 9% was recovered in fetal perfusate, 12% was removed for sample analysis, 2%

was recovered in the cotyledon, and there was a net disappearance of 22%. Overall recovery

was as high as 91% and the average was 78%, which is quite high compared to other perfusion

experiments (Frederiksen et al., 2010). Possible sources of EtG loss could have been

adsorption to tubing or EtG hydrolysis by placental "-glucuronidase (Collier et al., 2002b),

neither of which was measured. However, the most likely source of EtG disappearance is via

spills and leakage from the maternal compartment. Fetal perfusate volume loss is a common

measurement of placental integrity (Pienimaki et al., 1997) and is therefore monitored

frequently. Maternal perfusate volume loss, however, is only measured before and after the 3-

hour experiment, and the discrepancy between the pre- and post-perfusion volumes reached as

high as 14 mL in one experiment, which could account for a 5% overall EtG loss. This loss

could be due to small accidental spills during sample collection or to small leakages through

the decidual plate. With an average recovery of 78%, though, these minor sources of EtG loss

are unlikely to alter or decrease the validity of the experimental results.

5.2.1. Limitations to the study

A limitation to the perfusion component of this study was that only term placentae were

used. For both ethical and physiological reasons, it is not possible to use the ex vivo perfusion

model to measure drug transfer in 1st trimester placentae, and therefore EtG transfer in early

pregnancy was not assessed. The maternal and fetal circulations are separated by a thin layer

of fetal endothelial cells and by a layer of trophoblast cells (Vähäkangas & Myllynen, 2006).

During the early stages of pregnancy this maternal-fetal separation is 50-100 !m thick and

69

decreases to 4-5 !m in the 3rd trimester due to shedding of the syncitiotrophoblast (van der Aa

et al., 1998). It is thus suggested that drug transfer in term placentae may be an over-

representation when extrapolating to 1st trimester placentae (Frederiksen et al., 2010). Since

passive diffusion likely represents the major mechanism of placental transfer for EtG, the

results of this study are likely indicative of the highest degree of EtG transfer during

pregnancy.

There are several other differences between 1st trimester and term placentae that may

lead to variation in transfer of EtG. As the placenta grows throughout gestation, its surface

area increases, thereby allowing a greater area for diffusion (van der Aa et al., 1998). Blood

flow to the placenta also increases with gestation (van der Aa et al., 1998). This increase in

flow does not change the ratio of fetal and maternal concentrations, but can influence the time

needed to reach these concentrations. For EtG, a substance that transfers slowly and has a half-

life of only 2-3 h, changes in flow can greatly influence time to steady state and absolute

concentrations in both circulations. Additionally, levels of fetal albumin increase with

gestation, with an albumin F:M ratio increasing from 0.28 in early pregnancy to 1.20 at term

(Krauer et al., 1984). There are no reports of EtG binding the plasma proteins, however

albumin is known to generally bind acidic compounds (Reynolds & Knott, 1989). As such, if

albumin binds EtG to some extent, fetal accumulation is possible at term. A binding assay as

well as term maternal and cord blood samples would be needed to support this assertion.

Finally, UGT1A1, one of the enzymes that metabolizes ethanol to EtG, is found in greater

proportion in early placentae and is not detectable in term placentae (Syme et al., 2004). While

this may potentially alter the disposition of EtG at different stages of pregnancy, it is unlikely

that the minor degree of placental glucuronidation would lead to any clinically relevant

changes.

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Another limitation to the perfusion study is the inherent differences between ex vivo

perfusion and in vivo drug transfer. Both ex vivo maternal perfusate and in vivo maternal blood

circulate entirely through the system/body in approximately 1 minute, however the flow rates

are different because there is only 250 mL of maternal perfusate compared to the whole volume

of maternal blood in vivo. This leads to an inherently slower placental transfer in the perfusion

model compared to in vivo conditions, and may account for longer times to reach steady state

(Hutson et al., 2011a). Indeed, in the current study, while EtG had not reached steady state

between the two circulations after 3 hours of perfusion, this may not necessarily be the case in

vivo.

Lastly, the use of a single cotyledon compared to the entire placenta influences the rate

of transfer according to Fick’s Law, whereby diffusion is proportional to the transfer surface

area (Kalant et al., 2007). Placental surface area is approximately 20-40 times that of a single

cotyledon, and thus, transfer in the perfusion model may again be attenuated compared to in

vivo conditions. These differences can affect overall drug disposition if the changes are not all

proportional. For example, in this study, tissue binding was calculated as the amount of EtG

recovered in the cotyledon over the total amount of EtG originally in the system (250 !g or 250

mL x 1 !g/mL). Blood volume is approximately 20 times that of the maternal perfusate

volume, however whole placental weight is 20-40 times that of a single cotyledon. Therefore,

EtG tissue binding in vivo may be up to 2 times greater than reported in this study due to

differences in physiological proportions.

5.2.2. Ethyl glucuronide as a biomarker of alcohol use during pregnancy

In a similar perfusion experiment, FAEE were shown to not cross the placenta (Chan et

al., 2004). FAEE were only recovered in maternal perfusate and placental tissue, but not in

fetal perfusate. Additionally, net FAEE disappearances of up to 90% and placental microsomal

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studies confirmed that the placenta is capable of FAEE degradation, thus limiting its transfer

into fetal circulation. These findings implicated FAEE as exceptional biomarkers of fetal

alcohol exposure during pregnancy, since FAEE detected in meconium must be of fetal origin.

FAEE detection in meconium is now widely used in clinical practice and is part of the

Canadian guidelines for screening for FASD in newborns (Goh et al., 2008).

The results of the current perfusion study show that EtG does cross the placenta and

previous detection of EtG in meconium, placental tissue and fetal remains is likely of both

maternal and fetal metabolism of ethanol. Average recovery of EtG after the perfusions was

approximately 78%, suggesting that placental degradation of EtG is still possible.

Measurement of ethanol concentrations throughout the perfusions would help elucidate the

degree of placental degradation. Taken collectively, the results of this experiment suggest that,

because it crosses the human placenta, EtG is not as effective a biomarker of alcohol exposure

when measured in meconium as FAEE. While EtG detection in meconium may still be of

clinical utility in screening for alcohol-exposed children, its utility will likely be secondary to

that of FAEE.

Studies have already looked at the concordance between FAEE and EtG in meconium

and in maternal hair. Using receiver operating characteristic analysis, Backdash et al. (2010)

found an optimal agreement between EtG and FAEE in 596 meconium samples when using

cut-offs of 274 ng/g and 500 ng/g, respectively. Discordance between the two metabolites only

occurred in 2.7% of samples. In maternal hair, more sensitive cut-offs have been determined

for FAEE and EtG, whereby samples can be categorized as “abstinent”, “social drinker”, or

“excessive/chronic drinker” (Albermann et al., 2011). Additionally, in an analysis of 102 hair

samples from women attending their 2nd trimester ultrasound, 23 samples were positive for EtG

and negative for FAEE, thus excluding abstinence in cases that would have gone undetected

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with FAEE analysis exclusively (Wurst et al., 2008). Thus, while the results of the current

study suggest that EtG may not be an effective first line biomarker of in utero alcohol

exposure, it has shown some promise as an adjunct to FAEE in terms of increasing method

sensitivity and specificity. With the finding that there is induction of UGT in 1st trimester

placentae in mothers who smoke or drink alcohol (Collier et al., 2002b), EtG may indeed be an

effective additional biomarker that can effectively distinguish alcohol-exposed from non-

exposed fetuses.

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CHAPTER 6. CONCLUSIONS AND FUTURE DIRECTIONS

6.1. CONCLUSIONS

Unfortunately, there are several disadvantages to FAEE analysis in meconium,

including products and disease states that can lead to false results, and a short time window for

meconium analysis before endogenous FAEE formation via contamination with postnatal stool.

Over the past few years, EtG has been proposed as a potentially useful biomarker of alcohol

use during pregnancy upon detection in meconium, placental tissue, and fetal remains. As

such, this study aimed to develop a method for detecting EtG in placental perfusate and tissue

via GC-MS and to determine whether EtG crosses the human placenta.

The results of this study show that, unlike FAEE, EtG does cross the human term

placenta and therefore EtG previously detected in fetal and neonatal matrices is likely of

maternal origin principally. As such, the current study demonstrated that EtG may not be the

most direct biomarker of fetal alcohol exposure when measured in meconium, and that FAEE

are likely more suitable biomarkers for such parameters. However, EtG can still play an

important role in evaluating at-risk drinking during pregnancy. There are several methods now

available for detecting EtG in various fetal tissues, including the one developed in this study

for detection in placental tissue and placental prefusate via GC-MS. Investigating the

suitability of these methods alongside FAEE analysis in clinical practice has the potential to

greatly improve current standards and guidelines for screening FASD.

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6.2. FUTURE DIRECTIONS

6.2.1. False EtG results due to sample contamination

A case report described a meconium sample originally negative for FAEE that, after

spiking with ethanol, led to FAEE formation (Klein et al., 1999). This finding raised the

question as to whether sample contamination can lead to false positives. To clarify this issue, a

recent study showed that 19/30 babies whose early meconium samples tested negative later

tested positive for FAEE after analyzing subsequent excretions, and that blank meconium

samples incubated with ethanol later tested positive for FAEE (Zelner et al., 2012). The results

of these two studies reveal that meconium itself can produce FAEE in the presence of glucose

and ethanol-producing organisms, both of which are present in the postnatal gastrointestinal

tract. It is likely that FAEE formation occurs in meconium via both bacterial enzymes and

human lipases secreted into the small intestine (Zelner et al., 2012). Either way, this source of

false results greatly complicates the interpretation of FAEE results in meconium.

It will be important to analyze the potential for false EtG results in meconium in a

similar fashion if this method is going to become commonplace for alcohol screening.

Contamination studies leading to false EtG results have been conducted in urine: negative

samples with confirmed bacterial growth tested positive for EtG after addition of either ethanol

or glucose and yeast (Helander et al., 2007). Since bacteria also contain "-glucuronidase,

samples originally positive for EtG later tested negative due to bacterial hydrolysis. Thus,

diabetes and urinary tract infections have been implicated as possible sources of false results

for EtG in urine (Helander & Dahl, 2005). With this information, it is possible that EtG

formation and degradation in meconium is possible, either by bacterial UGTs and "-

glucuronidases, or by human enzymes located in the gastrointestinal tract. Indeed, UGT1A1

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and 2B7 cDNA is highly expressed in both the adult small intestine and in the colon (Ohno &

Nakajin, 2009). With the presence of dietary ethanol-producing organisms and the cofactor

UDPGA, which readily crosses the placenta (Collier et al., 2004), false positive EtG in

meconium is possible. Testing this hypothesis is necessary for a thorough understanding of

EtG disposition and for accurate analysis of EtG screens.

6.2.2. EtG immunoassay in meconium

One major disadvantage of FAEE analysis is that all the current methods utilize some

form of chromatography, either gas or liquid. Chromatographic apparatus is expensive and

requires extensive technical knowledge to operate. Additionally, analysis of FAEE involves

the cumulative concentration of 4-7 FAEE, depending on the method utilized, which can be

time intensive as well. The development of an immunoassay for EtG (Jung et al., 2009) has

made urinalysis less expensive, quick, and easy to implement in general laboratories (Wright &

Ferslew, 2012). Applying these advantages to alcohol screening by testing EtG in meconium

samples via immunoassay would allow for implementation in more laboratories and therefore

would provide faster turnaround time for results. Immunoassay screening would also reduce

laboratory costs, as only positive samples would need to be confirmed with chromatography.

Currently, an immunoassay for EtG detection in meconium is being developed (Pichini et al.,

2012), and after proper validation, this apparatus could be an invaluable addition to the gamut

of alcohol screening procedures in obstetric populations.

6.2.3. Additional concordance studies between FAEE and EtG

Previous studies have compared FAEE to EtG in meconium (Bakdash et al., 2010;

Tarcomnicu et al., 2010) and in adult hair (Yegles et al., 2004), but there is not a high degree

76

of concordance due to the different chemical properties of FAEE and EtG. In addition, as

shown by this study, EtG is not the most effective biomarker for fetal alcohol exposure when

measured in meconium. Thus, a more appropriate concordance study would involve

comparing FAEE in meconium to EtG in either maternal or neonatal hair. Since EtG detection

has been validated in adult hair by GC-MS and LC-MS (Pragst & Balikova, 2006) and can be

done with extremely high sensitivity, hair may be the optimal matrix for EtG use in obstetric

populations. Neonatal hair is also a promising matrix for EtG detection. This analysis has

been attempted previously, but sample size requirements precluded effective EtG measurement

(Morini et al., 2010b). If the barriers of acquiring sufficient neonatal hair can be overcome, an

EtG study in this matrix could advance knowledge of measuring fetal alcohol exposure,

particularly if a concordance study can be conducted alongside FAEE in meconium.

77

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LIST OF PUBLICATIONS, ABSTRACTS, AND CONFERENCE PRESENTATIONS

Publications Matlow JN. Guidelines and strategies for screening fetal alcohol spectrum disorder in Canada. University of Toronto Medical Journal. 2011; 89(1): 16-21. Abstracts !Matlow J, Aleksa K, Lubetsky A, Koren G. Ethyl glucuronide as a biomarker of alcohol consumption during pregnancy. Clinical Pharmacology & Therapeutics. 2012; 91(S1): S8-9. Matlow J, Aleksa K, Koren G. The effectiveness of ethyl glucuronide as a biomarker of alcohol abuse during pregnancy via perfusion of the human placenta. Journal of Population Therapeutics & Clinical Pharmacology. 2011; 18(2): e349-350. Oral Presentations Ethyl glucuronide crosses the human placenta and represents maternal and fetal exposure to alcohol. Canadian Society of Pharmacology & Therapeutics, Modern Therapeutics 2012, Toronto, Ontario. June, 2012. Ethyl glucuronide as a biomarker of alcohol consumption during pregnancy. American Society of Clinical Pharmacology & Therapeutics, National Harbor, Maryland, USA. March, 2012. The effectiveness of ethyl glucuronide as a biomarker of alcohol abuse during pregnancy via placental perfusion and metabolism studies. Fetal Alcohol Canadian Expertise Satellite Conference, Atlanta, Georgia, USA. June 2011.

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APPENDICES

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APPENDIX I. CONSENT FORM Study Title: The Role of the Placenta in Fetal Toxicology

Investigators: On Site Primary Investigator: Dr Howard Berger, Perinatologist, St. Micheal’s Hospital, Department of Obstetrics and Gynecology, Phone: (416) 867- 7460 Ext. 8408 (Available Monday - Friday 9 am – 4 pm) Off-Site Primary Investigator: Dr.Gideon Koren, The Hospital for Sick Children, Department of Clinical Pharmacology Phone: (416) 813-5781 (Available Monday – Friday 9 am – 4 pm) Introduction: Before agreeing to take part in this research study, it is important that you read the information in this research consent form. It includes details we think you need to know in order to decide if you wish to take part in the study. If you have any questions, please ask the study doctor or study staff to explain any words you don’t understand before signing this consent form. You will also have the opportunity to ask any additional questions on the day of surgery. Make sure all your question s have been answered to your satisfaction before signing this document.

All research is voluntary. You may also wish to discuss the study with your family doctor, a family member or close friend. Background Information: The placenta research laboratory at the Hospital for Sick Children is one of the few laboratories in North America currently studying drug transfer in the human placenta. They employ a technique called “placental perfusion”. This unique technique separates the functions of the placenta from both the maternal and fetal influences. The use of this model will further our understanding of the transport and behavior of certain medications across the human placenta, throughout pregnancy. Purpose of Research: Pregnancy is a special state in which there are many physical changes that occur to both the fetus and the mother. While pregnant, some mothers need to take medication in order to maintain a healthy pregnancy. Some of these compounds can reach the fetus by passing through the placenta. We would like to understand this process better. It is very important for researchers and doctors to better understand how medications cross the placenta, so that in the future we may help women protect their unborn fetus from harm.

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Description of Research: Once your baby is born, the umbilical cord is clamped and the baby is separated from the placenta. The placenta is then delivered and thrown away. If you agree to participate in this study, instead of the placenta being disposed of, we would like to use it to continue our study of the transport of medication across the placenta. These tissues will be studied immediately after birth and then disposed of in the usual fashion. No additional procedures or modifications to your care are required to assess the placenta after delivery. If your treating doctor decides that your placenta requires special testing after delivery, we will not collect it as part of this research study. Potential Harms (Injury, Discomforts or Inconveniences): Collection of these samples will not affect your labour or the delivery of your baby. The placenta will only be assessed for research after it has been delivered. The assessment will be done at the time the placenta is routinely disposed. The collection carries no risk to you or your baby. Potential Benefits: Your consent to collect your placenta will be of no direct benefit to you. The results from this study may improve our understanding of drug transfer in the human placenta. In addition, we hope that the information obtained in this study will allow us to develop new treatment options for women during pregnancy while protecting their unborn fetus. Protecting Your Health Information: These consent forms and data collection forms will be held in the strictest confidence. To protect your anonymity, your name will not appear on any record. Information from this study will be kept in a locked filing cabinet in the locked laboratories at the Hospital for Sick Children for three years. Information from this study will also be kept on a password protected computer database in the research laboratories at the Hospital for Sick Children. Your name will not be used in any publication. None of the research results will be placed in your medical records. Participation and Withdrawal: Your participation in this study is voluntary. If you do not want to participate in this study, or wish to withdraw at any time, you are free to do so and this will in no way affect your present or future care. Potential Cost of Participation and Reimbursement: There are no costs associated with participating in this study. You will not be reimbursed for your participation in this study.

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Compensation for Injury: If you become ill or are physically injured as a result of participation in this study, medical treatment will be provided to you in the same manner as you would ordinarily obtain any other medical treatment. In no way does signing this consent form waive your legal rights nor does it relieve the investigators or involved institutions from their legal and professional responsibilities. Publication of Results: Once the study is complete the information will be summarized and submitted to a medical journal for publication. The outcome of this study may also be presented at conferences, scientific meetings and other public forums. It is important that you are aware that you will not be identified in any of these reports and your confidentiality will be completely maintained. Development for Commercial Gain: Research carried out on your samples by researchers at the Hospital for Sick Children, or their collaborators, may lead to the development of marketable treatments, devices, new drugs or patentable procedures. By participating in this study you will not benefit directly from any such commercial products that will remain with the Hospital for Sick Children and their research partners. Research Ethics Board Contact: If you have any further questions about your rights as a research participant, you may contact Dr. Julie Spence, Chair, Research Ethics Board, 416-864-6060 ext 2557. Futher Questions: You have been given a copy of this information and consent form. If you have any questions about taking part in this study, you may contact Dr. Gideon Koren (The Hospital for Sick Children) at (416) 813-5781 or Dr. Howard Berger (St. Michael’s Hospital) at (416) 867-7460 Ext. 8408.

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CONSENT TO PARTICIPATE IN A RESEARCH STUDY

Study Title: The Role of the Placenta in Fetal Toxicology Consent: I acknowledge that the research study described above has been explained to me and that any questions that I have asked have been answered to my satisfaction. I have been informed of my right not to participate and the right to withdraw without compromising the quality of my medical care at St. Michael’s Hospital. As well, the potential risks, harms and discomforts have been explained to me and I also understand the benefits (if any) of participating in the research study. I understand that I have not waived my legal rights nor released the investigators, sponsors, or involved institutions from their legal and professional duties. I know that I may ask now, or in the future, any questions I have about the study or the research procedures. I have been assured that records relating to me and my care will be kept confidential and that no information will be released or printed that would disclose personal identity. I have been given sufficient time to read and understand the above information. By signing this consent form, I give permission for my placenta to be used for research purposes after delivery. The placenta will be collected and will be processed at the time of delivery and used for the purposes outlined in the description of this research study. I hereby consent to participate and will be given a copy of this consent form. Participant’s Name (Please Print) Participant’s Signature Date Name & Position of Person Signature Date Obtaining Consent

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APPENDIX II. COMPOSITION OF MEDIUM M199 Component mg/L Component mg/L Calcium chloride (anhydrous) 200 DL-A Tocopherol Phospate!Na 0.1 Ferric Nitrate!9H2O 0.72 Thiamine!HCl 0.1 Magnesium Sulfate (anhydrous) 97.67 Adenine Sulfate 1.0 Potassium Chloride 400 Adenosine Monophosphate!Na 0.2385 Sodium Acetate (anhydrous) 50 Cholesterol 0.2 Sodium Chloride 6800 Deoxyribose 0.5 Sodium Phosphate Monobasic 122 Glucose 1000 (anhydrous) Glutathione (reduced) 0.05 DL-Alanine 50.0 Guanine!HCl 0.3 L-Arginine ! HCl 70.0 Hypoxanthine 0.3 DL-Aspartic Acid 60.0 Polyoxyethylenesorbitan Monooleate 20 L-Cysteine!HCl!H2O 0.11 Ribose 0.5 L-Cystine!2HCl 26.0 Thymine 0.3 DL-Glutamic Acid 133.6 Uracil 0.3 Glycine 50.0 Xanthine!Na 0.344 L-Histidine!HCl!H2O 21.88 L-Hydroxyproline 10.0 DL-Isoleucine 40.0 DL-Leucine 120.0 L-Lysine!HCl 70.0 DL-Methionine 30.0 DL-Phenylalanine 50.0 L-Proline 40.0 DL-Serine 50.0 DL-Threonine 60.0 DL-Tryptophan 20.0 L-Tyrosine!2Na!2H2O 57.66 DL-Valine 50.0 Ascorbic Acid!Na 0.0566 D-Biotin 0.01 Calciferol 0.1 Choline Chloride 0.5 Folic Acid 0.01 Menadione (sodium bisulfite) 0.016 Myo-Inositol 0.05 Niacinamide 0.025 Nicotinic Acid 0.025 p-Amino Benzoic Acid 0.05 D-Pantothenic Acid (hemicalcium) 0.01 Pyridoxal!HCl 0.025 Pyridoxine!HCl 0.025 Retinol Acetate 0.14 Riboflavin 0.01