ENDOCRINE DISRUPTING POTENTIAL OF ENVIRONMENTAL CHEMICALS CHARACTERIZED BY HIGH-THROUGHPUT SCREENING

Daniel M. Rotroff

A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Environmental Sciences and Engineering.

Chapel Hill 2013

Approved by:

Ivan Rusyn, M.D. Ph.D.

David J. Dix, Ph.D

Thomas Knudsen, Ph.D.

Fred Wright, Ph.D.

Rebecca Fry, Ph.D.

i

© 2013 Daniel M. Rotroff ALL RIGHTS RESERVED

ii

ABSTRACT

DANIEL M. ROTROFF: Endocrine Disrupting Potential of Environmental Chemicals Characterized by High-Throughput Screening (Under the direction of Dr. David J. Dix)

Over the past 20 years, an increased focus on detecting environmental chemicals that pose a risk of endocrine disruption and congressional legislation have driven the creation of the U.S. EPA Endocrine Disruptor Screening Program (EDSP). Several thousand chemicals are subject to the EDSP, which will require millions of dollars and decades to process using current test batteries. In order to identify opportunities for increased chemical throughput, we initially investigated how well EPA ToxCast in vitro high-throughput screening (HTS) assays relevant for estrogen, androgen, steroidogenic, and thyroid disrupting mechanisms could identify compounds relative to in vitro and in vivo data collected from studies related to the EDSP Tier 1 screen. An iterative, balanced optimization model was implemented and indicated that ToxCast HTS assays measuring estrogen receptor (ER) and androgen receptor

(AR) activation classify compounds with estrogenic and androgenic activity in guideline studies with a high degree of accuracy, respectively. The ER signaling pathway involves a wide array of molecular initiating events and cellular processes. This dissertation examined whether active chemicals in ToxCast ER transactivation assays could indicate chemical- induced upregulation of the ER pathway through ligand binding leading to induced changes in T47D growth kinetics. Considering the complex set of ER in vitro assays in toto increased the overall sensitivity of detection for ER reference chemicals. In addition, this research

iii highlighted important aspects of the biological response such as non-ER specificity in the cell growth assay. These nuances are likely important considerations for the construction of a predictive model. In effort to accurately predict the estrogenic potential of environmental chemicals in a high-throughput format, multiple orthogonal in vitro ER assays were used to develop a predictive model for ~2000 chemicals. Model results indicate a high degree of predictivity for both the uterotrophic in vivo assay and ER reference chemicals. The information provided by the model will aid in understanding how environmental chemicals contribute to endocrine-related human health consequences and predict the estrogenic potential of chemicals through the use of in vitro assays, limiting the need to run more costly and animal-intensive in vivo bioassays.

iv

ACKOWLEDGEMENTS

First and foremost I would like to express my deep gratitude to my research advisor,

Dr. David Dix, Ph.D. Without his support and expertise this would not have been possible.

His academic and professional guidance has been invaluable, and will continue to be so in all of my future endeavors. I also owe a great deal of appreciation to my academic advisor, Dr.

Ivan Rusyn, M.D. Ph.D., who made sure that I stayed on target throughout this entire process, despite my occasional efforts to test certain boundaries. I would like to express my appreciation to the additional members of my committee, Dr. Thomas Knudsen, Ph.D., Dr.

Fred Wright, Ph.D., and Dr. Rebecca Fry, Ph.D. Their diverse knowledge and experience pushed me to be a better student and a better researcher.

I owe a great deal of gratitude to Dr. Richard Judson, Ph.D., who kept his door open to me and spent many hours helping me formulate new and innovative ideas. His amazing work ethic sets a high standard, and through his support and mentorship I have benefitted greatly. Lastly, I would like to show my gratefulness to my colleagues, collaborators, and managers at the U.S. Environmental Protection Agency National Center for Computational

Toxicology. Their continued effort to push science forward and willingness to share from their vast knowledge and their distinct backgrounds are what have made this experience so significant.

The research described here has been partially supported by the U.S. Environmental

Protection Agency. The views expressed in this paper are those of the author and do not

v necessarily reflect the views or policies of the U.S. Environmental Protection Agency.

Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

vi

TABLE OF CONTENTS

LIST OF TABLES ...... x

LIST OF FIGURES ...... xi

LIST OF ABBREVIATIONS ...... xiii

Chapter

I. INTRODUCTION ...... 1

Endocrine Physiology ...... 1

Human Endocrine Disruption and Pathology ...... 3

Environmental Impact of Endocrine Disruption ...... 6

Endocrine Disruptor Screening ...... 8

Computational Toxicology and Alternatives to Traditional Test Methods ...... 10

Estrogen Signaling Pathways in Predictive Toxicology ...... 13

Predictive Modeling of Endocrine Disruptors ...... 17

References ...... 24

II. USING IN VITRO HIGH THROUGHPUT SCREENING ASSAYS TO IDENTIFY POTENTIAL ENDOCRINE-DISRUPTING CHEMICALS ...... 32

Introduction ...... 32

Methods ...... 36

Results ...... 41

Discussion ...... 44

Figures ...... 55

References ...... 59

vii

III. REAL-TIME GROWTH KINETICS MEASURING HORMONE MIMICRY FOR 1816 UNIQUE TOXCAST CHEMICALS IN T-47D HUMAN DUCTAL CARCINOMA CELLS ...... 64

Introduction ...... 64

Methods ...... 66

Results ...... 74

Discussion ...... 78

Tables ...... 84

Figures ...... 89

References ...... 96

IV. ENDOCRINE TESTING IN THE 21 ST CENTURY: USING IN VITRO ASSAYS TO PREDICT ESTROGEN RECEPTOR SIGNALING RESPONSES...... 101

Methods ...... 103

Discussion ...... 118

Tables ...... 124

Figures ...... 126

References ...... 137

V. GENERAL DISCUSSION ...... 141

Advances in In Vitro Technology ...... 141

Phenotypic Anchoring ...... 143

Combining In Vitro Data to Test for In Vivo Effects ...... 145

Limitations ...... 148

References ...... 152

VI. FUTURE DIRECTIONS ...... 155

Assay and Model Development to Address Biological Gaps ...... 155

Providing In Vivo Context ...... 157

viii Potential for Prioritization and Targeted Testing ...... 157

References ...... 160

ix LIST OF TABLES

Table

2.1 Summary of Endocrine Related HTS Assays ……………………………. 50

2.2 Summary of Endocrine Literature Survey ……………………………….. 51

2.3 Chemical Results from Overlapping HTS-E and Guideline Reports …… 52

2.4 Chemical Chemical Results from Overlapping HTS-A and Guideline Reports ……………...... 53

2.5 Test Species in Guideline and Non-Guideline In Vitro and In Vivo Studies ……………………………………………………………………. 54

3.1 ER Reference Chemicals for Sensitivity and Specificity Analysis ………. 84

3.2 Chemical Library Results Summary and Replicate Concordance ……….. 85

3.3 25 Most Potent and Efficacious Compounds in the Cell Growth Assay at 80 h ……………………………………………………………………….. 86

3.4 Paraben Compounds – T-47D Cell Growth Potency and Efficacy at 80 h.. 87

3.5 Estrogen Receptor Binding Conditional Probabilities …………………… 87

3.6 Logistic Regression Model Performance…………………………………. 88

4.1 ER In Vitro Assay Annotation and Model Grouping………………….….. 124

4.2 15 Chemicals with Highest ER Interaction Scores ………………………. 125

x LIST OF FIGURES

Figure

1.1 Schematic representation of the HPG, HPA, and HPT axes……...... 23

2.1 Overlap between EDSP T1S assays and ToxCast phase I assays by endocrine MOAs …………………………………………………………. 55

2.2 Illustration of the balanced optimization model used to analyze predictive capacity of endocrine-related ToxCast assays ……………….. 56

2.3 Forest plot illustrating the performance —as measured by sensitivity, specificity, and BA—of ToxCast endocrine-related assays for predicting outcomes captured in EDSP/OECD guideline studies …………………. 57

2.4 Forest plot illustrating the performance —as measured by sensitivity, specificity, and BA—of ToxCast endocrine-related assays for predicting outcomes captured in non-guideline endocrine studies …………………. 58

3.1 RTCA Cell Growth Assay …………...... 89

3.2 Example of Growth Curve Translation to Concentration Response Curve. 90

3.3 Representative growth curves of various test compounds measuring changes in normalized cell index over time…...…………………………. 91

3.4 Comparison of Cell Growth Results with Additional ER Assays ……… 92

3.5 Reciever operating characteristic plot for uterotrophic comparisons …… 93

3.6 Reciever operating characteristic plot for ER reference chemical comparisons ……………………………………………………………… 94

3.7 Combination Treatment of Electron Transport Chain Inhibitors with ER Antagonist, ICI 182,780………………...... 95

xi Figure

4.1 ER Signaling Pathway Overview (Overlayed with ToxCast Assays)…………………………………………………..………………... 126

4.2 Graphical Representation of the Logical Structure of the Model………… 127

4.3 Composite Curve Example …………………………………..…………... 128

4.4 Heatmap of Composite Scores ………………...………………………… 139

4.5 Impact of Linear Transformation on the Distributions of 2 R ………………….……………………………………………………… 130

4.6 Agonist Model Stability …………...…………………………………….. 131

4.7 Agonist/Antagonist Volcano Plot ………………………………………... 132

4.8 ER α/ER β Volcano Plot ……………………………………………...…… 133

4.9 Uterotrophic Analysis ……………………………………………………. 134

4.10 Reference Chemical Spectrum …………………………………………... 135

4.11 Reference Chemical Analysis…………………………..………………… 136

xii LIST OF ABBREVIATIONS

AC50 – Activity concentration at 50% efficacy

AF-1 – Activation function 1

AF-2 – Activation function 2

Agonist_CS – Agonist Composite Score

AhR – Aryl hydrocarbon receptor

Antagonist_CS – Antagonist Composite Score

AR – Androgen receptor

BA – Balanced accuracy bER – Bovine estrogen receptor

Binding_CS – Binding Composite Score

CASRN – Chemical Abstracts Service Registry Number

CDK – Cyclin dependent kinase

CI – Cell index

CRH – Corticotropin releasing hormone

CYP19 – Aromatase cytochrome P-450

DBD – DNA Binding Domain

DBP – Dibutyl Phthalate

DES - Diethylstilbestrol

DHEA - Dehydroepiandrosterone

DHT - Dihydrotestosterone

DMSO – Dimethyl sulfoxide

xiii E2 - 17 β-estradiol

EDC – Endocrine disrupting chemical

EDSP – Endocrine Disrupter Screening Program

EMAX- Maximum efficacy

EPA –Environmental Protection Agency

ER – Estrogen receptor

ERE – Estrogen response element

ERES – Estrogen Receptor Expert System

FBS – Fetal bovine serum

FDA – Food and Drug Administration

FFDCA – Federal Food, Drug and Cosmetic Act

FN – False negative

FP – False positive

FQPA – Food Quality and Protection Act

FSH - Follicle stimulating hormone

GHRH – Growth hormone-releasing hormone

GnRH - Gonadotropin-releasing hormone

GR – Glucocortocoid receptor

Growth_CS – Growth Composite Score hCG - Human chorionic gonadotropin hER – Human estrogen receptor

HPA – Hypothalamic-pituitary-adrenal

HPG - Hypothalamic-pituitary-gonadal

xiv HPT – Hypothalamic-pituitary-thalamic

HPV – High production volume

HTS – High Throughput Screening

HTS-A – High Throughput Screening-Androgen

HTS-E – High Throughput Screening-Estrogen

HTS-S – High Throughput Screening-Steroidogenesis

HTS-T – High Throughput Screening-Thyroid

LBD – Ligand binding domain

LEC – Lowest effective concentration

LH - Lutenizing hormone

LOC – Level of concern mERa – Murine estrogen receptor-α

MOA – Mode-of-Action

NCI – Normalized cell index

NR – Nuclear receptor

OECD – Organization for Economic Co-operation and Development

OR – Odds ratio

PCA – Protein complementation assay

PMID – PubMed Identifier

PR – Progesterone receptor

QSAR – Quantitative structure-activity relationship r – Correlation coefficient

RTCA – Real-time cell analysis

xv RTU-reporter transcription unit

SDWA – Safe Drinking Water Act

SERM – Selective estrogen receptor modulator

SGK-1 – Serum/glucocortocoid regulated kinase-1

SMA – Simple moving average

T1S – Tier 1 Screening

T2S – Tier 2 Screening

T3 - Triiodothyronine

T4 - Thyroxine

TDS – Testicular Dysgenesis Syndrome

TN – True negative

TP – True positive

TPO – Thyroid peroxidase

TRH – Thyrotropin-releasing hormone

TSH – Thyroid stimulating hormone

UAS – upstream activator sequence

xvi

CHAPTER 1

INTRODUCTION

Endocrine Physiology

The endocrine system consists of glands that secrete hormones that regulate and influence almost every organ and cell type. These hormones are secreted into the blood stream and activate signaling cascades to regulate and maintain a diverse set of biological functions. The various functions include glucose metabolism, sexual differentiation, bone density, blood pressure, and digestion (Cooper and Kavlock 1997; Dupont et al. 2000; Lodish et al. 2009; de Mello et al. 2011; Ng et al. 2001).

The endocrine system is commonly described in three axes: the hypothalamic- pituitary-gonadal (HPG) axis, the hypothalamic-pituitary-adrenal (HPA) axis, and the hypothalamic-pituitary-thyroid (HPT) axis (Figure 1.1). These physiological axes represent three different feedback systems and involve multiple, highly interrelated, signaling pathways occurring between key organs to maintain homeostasis of bodily functions. The ultimate control center, the hypothalamus, secretes gonadotropin-releasing hormone (GnRH), growth hormone-releasing hormone (GHRH), thyrotropin-releasing hormone (TRH), corticotrophin-releasing hormone (CRH), and other important determinants of endocrine function into the hypophyseal portal system (Jessop 1999; Knol 1991). These hormones act directly on the anterior pituitary to stimulate its release of gonadotropins, growth hormone, and thyroid stimulating hormone (TSH), respectively (Fox 2004). 1

Gonadotropins are a group of anterior pituitary hormones that include follicle- stimulating hormone (FSH) and luteinizing hormone (LH) (Figure 1.1a) (Fox 2004). These peptide hormones, along with the steroid hormones estrogen and progesterone, control the ovarian cycle in females (Fox 2004). In males, FSH stimulates primary spermatocytes to undergo meiosis and LH triggers testosterone production (Walker and Cheng 2005). Gonadal stimulation by FSH induces the release of the peptide factor ‘inhibin’, which in turn acts back on the anterior pituitary to inhibit FSH production. Increases in serum concentration of sex steroids (estrogen, testosterone) suppress GnRH secretion by the hypothalamus and FSH and

LH secretion by the anterior pituitary gland. The coordination of these hormones in both positive and feedback circuits is necessary for proper homeostasis of the HPG axis (Fox

2004).

The HPT axis is primarily involved in regulating growth, metabolism and development (Figure 1.1b) (Zoeller et al. 2007). Thyroid follicular cells, transport iodine and secrete triiodothyronine (T3) and thyroxine (T4) via thyroid peroxidase (TPO),when stimulated by TSH from the anterior pituitary. The main role of TSH is to free T3 and T4 from thyroglobulin so they can be released into the blood stream (Zoeller et al. 2007). T3 is critical to fetal neurodevelopment, and postnatally helps to regulate metabolism and cardiac output (Bernal, 1984; Oppenheimer and Schwartz, 1997). T3 and T4 work synergistically with growth hormone (GH) and consequently (an inhibitor of GH) to further down-regulate

TSH secretion from the anterior pituitary (Figure 1.1b) (Zoeller et al. 2007).

Hormones produced through activating the HPA axis are necessary for reproductive health, glucose metabolism, and blood pressure (Figure 1.1c) (Fox 2004). Hypothalamic

CRH secretion stimulates ACTH release from the anterior pituitary. The ACTH target tissue

2 is the adrenal cortex. Its outer zone (zona glomerulosa), middle zone (zona fasciculate), and inner zone (zona reticularis) produce different hormones in response to ACTH stimulation and all three are synthesized from cholesterol. Although not entirely distinct from one another, the zona glomerulosa primarily secretes mineralocorticoids such as aldosterone (Fox

2004). Aldosterone regulates blood pressure through renal retention of sodium and water

(Brewster and Perazella 2004). The zona fasciculata and reticularis are responsible for synthesizing the glucocorticoids cortisol and corticosterone, which upregulate gluconeogenesis and lypolysis for bioenergy production (Fox 2004). The two innermost zones are also responsible for synthesizing androstenedione, a precursor molecule to both testosterone and estrogen. The enzymes 17 β-hydroxysteroid-dehydrogenase and aromatase

(CYP19) convert androstenedione to testosterone and estradiol, respectively. These enzymatic conversions typically occur in the testis and ovary, respectively; however, aromatase is also expressed in non-gonadal tissues such as fat, which can further promote estradiol synthesis (Simpson and Davis 2001). Increased serum concentrations of these hormones have the ability to down-regulate CRH release from the hypothalamus and ACTH from the anterior pituitary (Figure 1.1c).

Human Endocrine Disruption and Pathology

The endocrine system is integral to many physiological functions. Disruption of the endocrine system can have varying severity depending on the etiology. Many endocrine pathologies occur in the form of neoplasms or cellular hyperplasia, causing overproduction of that tissue’s hormone. They also occur in the form of atrophic conditions preventing the appropriate levels of hormone to be secreted. Because homeostatic endocrine processes are

3 highly interrelated, treating one type of endocrine deficiency can have unintended consequences on another endocrine process. For example, hyperaldosteronism can be a result of adrenal hyperplasia or adrenal carcinoma and can cause increased water and sodium retention resulting in subsequent hypertension (Calhoun et al. 2002; Lindsay and Atkinson

2009). One treatment option for hyperaldosteronism is to administer a mineralocorticoid antagonist, such as spironolactone; however, this drug is a weak androgen receptor (AR) and glucocortocoid receptor (GR) antagonist that also displaces testosterone and estradiol from sex hormone binding globulin. Manifestations of this disruption in males is include gynecomastia and testicular atrophy, and in females altered menstruation (Lubbos HG 1998; de Souza et al. 2010).

Epidemiological evidence suggests an increasing incidence of poor semen quality, testicular cancer, and other male reproductive disorders including undescended testis

(cryptorchidism) and urethral fusion defects (hypospadias) (Auger et al. 1995; Carlsen et al.

1992; Skakkebaek et al. 2001). Cryptorchidism and hypospadias are common human malformations with an incidence in liveborn infants of 2-9% and 0.2-1%, respectively

(Toppari et al. 2010). The collective term for developmental defects of the male tract is

Testicular Dysgenesis Syndrome (TDS). TDS is diagnosed by the co-occurrence of poor semen quality, testicular germ line tumors, hypospadias, and/or cryptorchidism in various combinations (Skakkebaek et al. 2001). Studies have demonstrated associations of increased incidences of TDS with exposure to certain environmental chemicals (Toppari et al. 2010).

The evidence is unequivocal that certain pharmaceuticals have potential to disrupt endocrine function and may lead to reproductive abnormalities. One example, diethylstilbesterol (DES), was first applied as a cattle feed supplement and later prescribed to

4 women to lower the risk of miscarriage (Palmlund et al. 1993). Approximately 5 million women were prescribed DES during pregnancy in the United States alone (Palmlund et al.

1993). DES not only failed as a therapeutic for spontaneous pregnancy loss but also led to premature labor and neonatal death. Some females exposed in utero displayed increased incidences of cervical and vaginal clear cell adenocarcinoma, vaginal adenosis, and genital tract abnormalities (Palmlund et al. 1993). Males exposed in utero demonstrated increased incidences of cryptorchidism, hypospadias, infertility, poor semen quality, and rete testis adenocarcinoma among other disorders (Toppari et al. 2010).

Some environmental chemicals have been shown to interfere with male reproductive development through endocrine mechanisms. Vinclozolin is one example. This fungicide was used to treat many types of berries, grapes, and other crops (U.S. EPA 2000). Current exposures to vinclozolin are considered to be very low after the voluntary termination of uses and import tolerances (U.S. EPA 1998). Its antiandrogenic properties are well characterized in both in vitro and in vivo rodent bioassays. Vinclozolin is a weak AR antagonist but is quickly metabolized in serum to active metabolites, M1 and M2 (Sierra-Santoyo et al. 2012).

Numerous studies demonstrated the ability of vinclozolin to compete with testosterone and

DHT for AR (Fang et al. 2003; Kojima et al. 2003; Scippo et al. 2004). In vitro transactivation assays have also demonstrated that M1 and M2 can downregulate transcriptional activation of AR (Freyberger et al. 2010; Kojima et al. 2003; Vinggaard et al.

2005). Male rats exposed to vinclozolin in utero have higher incidences of vaginal pouch, cryptorchidism, seminiferous tubular atrophy, nipple retention, hypospadias and reduced anogenital distance (Gray et al. 1994). Peripubertal male rats given vinclozolin experienced delays in sexual maturation, such as increased age at preputial separation. Although studies

5 testing vinclozolin for antiandrogenic and TDS phenotypes have been conducted using animal studies, a mode-of-action framework created to determine the relevance of these animal effects for human risk assessment identified the vinclozolin-induced malformations of the male reproductive tract as being highly plausible in humans (Kavlock and Cummings

2005).

Environmental Impact of Endocrine Disruption

Endocrine disruption not only poses a risk to human health, but also poses a significant risk to environmental health (Caliman and Gavrilescu 2009; Kiesecker et al.

2001). Furthermore, the environment often serves as a sentinel for potential human health hazards (Caliman and Gavrilescu 2009). Pesticide runoff or drift from crops, as well as pharmaceutical presence in wastewater have potential for significant adverse health effects on wildlife (Sparling and Fellers 2009; Tavera-Mendoza et al. 2002). An active area of research for the impact of environmental chemicals on wildlife is the significant global decline of amphibians (Hof et al. 2011).

Atrazine, for example, is a common chlorotriazine herbicide that has been detected frequently in U.S. surface waters and drinking water sources (U.S. EPA 2011b). Many studies have been conducted implicating atrazine’s potential as an endocrine disrupting chemical (EDC) by altering the HPG axis (Cooper et al. 2000; Hayes et al. 2003; Stoker et al.

2000; U.S. EPA 2011b). Several studies demonstrated a link between atrazine exposure in various strains of rats with reduced serum testosterone, effects on male accessory tissues, and altered spermatogenesis (Abarikwu et al. 2010; Kniewald et al. 2000; Stoker et al. 2000).

Although the mechanism by which atrazine suppresses testosterone is not known, evidence

6 suggests that decreases in the secretion of LH is the primary cause of testicular consequences.

Atrazine is not believed to directly affect LH, because LH was induced in atrazine- treated rats administered GnRH. This implies an alternate neuroendocrine mechanism

(Cooper et al. 1996, 2000). A study conducted by Shafer et al. supported this hypothesis by showing that atrazine and cyanazine non-competitively inhibited the binding of a benzodiazepine to GABA A receptors (Shafer et al. 1999). Furthermore, GABAA receptors have been shown to be important regulators of GnRH release (Terasawa 1998). Leydig cells respond to LH by synthesizing testosterone, which can then be converted to dihydrotestosterone (DHT) by 5 α-reductase. Additional data suggests that atrazine can inhibit the conversion of testosterone to DHT by 5 α-reductase (Simic et al. 1991). Although the effects on 5 α-reductase may not be the primary mechanism for alterations in spermatogenesis, the important role DHT plays in the development and maintenance of tissues such as the prostate and seminal vesicles may exacerbate the effects from LH suppression (Cai et al. 1994).

The U.S. EPA currently estimates the aquatic ecosystem level of concern (LOC) for atrazine as approximately 10 ppb (10 µg/L) over a 60-day period (U.S. EPA 2009a).

However, in some rivers concentrations of atrazine have reached 21µg/L and areas near fields utilizing atrazine can reach 250 µg/L (Solomon et al. 2009). Studies have shown effects on amphibian larval development and demasculinization after atrazine exposures. A study by Tavera-Mendoza et al. (2002) found that exposures of 21 µg/L resulted in decreased testicular volume in male amphibians and testicular aplasia in tadpoles. Another study by

Hayes et al. (2002) exposed amphibian larva to levels of (0.01-200 ppb), but found no effects

7 on time to metamorphosis or mortality; however, effects at all doses higher than .01 ppb produced multiple gonads or were hermaphrodites with multiple testes and ovaries, and displayed decreased plasma testosterone.

Environmental exposures of EDC’s that play a role in the decline of wildlife amphibian populations may also be relevant for human health. Cragin et al. (2011) provided epidemiological evidence of statistically significant trends in reports of irregular menstrual cycle lengths in areas with high levels of atrazine drinking water contamination versus women residing in areas with low levels of atrazine contamination. In order to maintain healthy wildlife areas and prevent adverse human health effects from environmental exposures to EDC’s, it is critical that a complete characterization of the potential interactions pharmaceuticals and environmental chemicals could have on healthy endocrine physiology is performed.

Endocrine Disruptor Screening

Environmental chemicals such as bisphenol-A, phthalates, and parabens have exhibited potential to interfere with the endocrine system and are nearly ubiquitous in the environment. These compounds have commercial applications in food and drink packaging, polycarbonate plastics, detergents, polystyrene tubes, cosmetics, and shampoos (Calafat et al.

2005; Smith et al. 2012; Sonnenschein and Soto 1998; U.S. HHS 2008). The potential for adverse health and environmental effects due to EDC exposure has lead to increased public awareness of safe products and increased pressure for political and regulatory action.

The Federal Food, Drug, and Cosmetic Act (FFDCA) was passed in 1938 following the death of more than 100 people in what is known as The Elixer Sulfanilamide Incident of

8 1937 (Ballentine 1981). The incident occurred after the drug, sulfanilamide, was exposed with diethylene glycol. Subsequently, Congress passed the FFDCA and provided authority to the Food and Drug Administration (FDA) to regulate safety requirements for food, drugs and cosmetics (Ballentine 1981). In 1996, the Food Quality Protection Act (FQPA) and the Safe

Drinking Water Act (SDWA) were passed. These two acts amended the FFDCA, and required that the U.S. EPA determine whether certain substances may have an effect in humans similar to a naturally occurring estrogen, or other endocrine related effects (21

U.S.C. 346a 1996; U.S. Public Law 1996). To address these requirements, the EPA formed the Endocrine Disruptor Screening Program (EDSP) (www.epa.gov/endo/) (U.S. EPA

2012a).

EDSP requires that chemical manufacturers provide data from specific in vitro and in vivo assays while following strict guidelines to insure testing is performed consistently and reliably. EDSP consists of two tiers of testing. The first is composed of in vitro and in vivo assays designed to measure effects on estrogenic, androgenic, thyroid and steroidogenesis modes of action (MOA). A weight-of-evidence approach has been proposed as a decision making tool for determining what compounds will go from tier 1 (T1S) to tier 2 (T2S). The selection of assays in T2S has not been finalized, but the assays under consideration include more resource intensive assays such as the mammalian two-generation, avian two-generation, and others (U.S. EPA 2012a).

Under the FFDCA and SDWA amendments of 1996, approximately 10,000 chemicals are subject to testing by EDSP (U.S. EPA 2012a). Since the inception of EDSP, however, orders for testing only about 200 chemicals have been issued. Decisions on how chemicals

9 will be selected for T2S testing or what assays will be included in T2S testing remain to be seen.

In an effort to increase throughput, the EPA’s EDSP21 project is aiming to use high- throughput in vitro and in silico approaches to prioritize which chemicals should be tested in

T1S first (U.S. EPA 2011a, 2012b). Goals of the EDSP21 work plan include replacing certain resource intensive T1S assays within the next five years and full replacement of T1S with high-throughput in vitro and in silico assays as a long-term goal (U.S. EPA 2011a). In order to meet these goals, methods for validating in vitro assays, and models that combine in vitro assays to optimize for predictive capabilities must be developed.

The U.S. EPA National Center for Computational Toxicology’s ToxCast TM program and the cross-agency Tox21 program are currently screening chemicals in hundreds of high- throughput in vitro assays, some of which are endocrine related (Kavlock et al. 2012). The current ToxCast and Tox21 chemical libraries represent approximately 17% and 53% of the

EDSP chemical universe, respectively. Successful predictive models have been developed using the assays and chemicals from these projects (Judson et al. 2012; Martin et al. 2011;

Sipes et al. 2011). Furthermore, tools for prioritizing chemicals have also been developed using these data sets (Reif et al. 2010). Therefore, it stands to reason that these programs are uniquely poised to develop predictive models for endocrine related targets that would be valuable in augmenting the EDSP screening effort.

Computational Toxicology and Alternatives to Traditional Test Methods

Although the study of toxic substances has been ongoing for centuries, a rapid expansion of the chemical landscape has forced toxicology into a new dimension. Many

10 thousands of chemicals are now being manufactured, and due to the backlog of chemicals in regulatory testing, the majority of chemicals have yet to undergo full toxicological evaluation

(Judson et al. 2009). Due to ethical limitations, toxicologists have always had to rely on extrapolating toxicities observed in animals to potential adverse health effects in humans with an equivalent exposure. Because animals are intrinsically different than humans, this process can be quite uncertain. Furthermore, deciding on an appropriate animal model or reconciling contradictory results between animal models can further pose a challenge for decision makers.

Today, toxicology finds itself in the midst of a paradigm shift. Traditionally, the primary source of data relating chemical exposures to adverse outcomes in humans came from in vivo animal models. Study designs using mostly rats, mice, and rabbits are used for testing chemicals for carcinogenic, reproductive, developmental, chronic, and acute health effects (Knudsen et al. 2009; Martin et al. 2009b, 2009a). In addition to the difficulty of extrapolating human health effects from animal tests, these studies can require up to two years and $2 million for an individual chemical. Although these studies offer the advantage of an intact organism to test for toxicity, they often require high doses and overt effects before a chemically induced effect can be observed.

Recent advances in in vitro biotechnology can now help meet the need to test more chemicals and provide information about their interactions with many human molecular targets. The need for better mechanistic understanding and species extrapolation has led to development of technologies such as toxicogenomics. Sets of genes that are activated or suppressed after a toxic insult can be grouped and used to phenotypically anchor genetic changes to more traditional toxicological endpoints (Thomas et al. 2001). This is useful

11 because transcriptional activation/suppression is thought to be one of the earliest cellular changes in an adverse response. The ability to sensitively measure these changes can allow for experiments to be conducted at more realistic exposure levels than many traditional toxicology tests, which rely on measurements of overt toxicity from high doses (Nuwaysir et al. 1999). However, this technology comes with certain disadvantages and challenges. One of the disadvantages is that not all genes perturbed after a chemical exposure may be involved in the toxicity pathway, leading to results that can be difficult to interpret (Green et al. 2007).

Furthermore, it is difficult to distinguish the primary genes regulated by a chemical versus the less-specific secondary or tertiary gene changes and these results can vary by assay technology and laboratory practice (Beyer et al. 2007).

Many studies have attempted to use in vitro assay data to demonstrate associations with human and rodent adverse health effects using a wide variety of methods and informatics techniques. Studies that use machine learning algorithms with data covering large amounts of chemical and biological space that do not incorporate prior biological knowledge show poor predictive capabilities (Benigni et al. 2010; Thomas et al. 2012). On the other hand, methods which group assays based on biological pathways have shown promising potential to predict in vivo outcomes (Judson et al. 2012; Martin et al. 2011; Sipes et al.

2011). It is important to remember that many of the in vitro assays used in these models are conducted using human cell lines. In the near future, predictive models from in vitro assays may be used to prioritize chemicals for further testing with the long term goal of replacing the more resource intensive assays.

12 The ToxCast program and the cross-agency Tox21 program are currently evaluating the use of high-throughput in vitro assays to prioritize chemicals for testing in more traditional toxicology bioassays (Judson et al. 2010; Kavlock et al. 2012). The enormous influx of data provided by these technologies on thousands of chemicals has many advantages, but also comes with certain challenges. Not the least of which is the integration of these molecular initiating events with new and existing in vivo data, and relating these effects to relevant human outcomes (Collins et al., 2008; Kavlock et al., 2009; Judson et al.,

2010).

In an attempt to identify informative endocrine-related in vitro assays, many studies demonstrate their utility by comparing the results to known reference compounds or compare the results to other in vitro assay results (Kloas et al. 1999; Soto et al. 1995). However, until now no resource for comparing a large number of chemicals for in vitro to in vivo endpoints was available for endocrine-specific targets. The present body of work develops a library of quantitative in vivo and in vitro data from a wide variety of endocrine studies, including validated study designs (as described in Chapter 2). This dataset provides the first resource for phenotypically anchoring endocrine related assays for a large set of chemicals and the following work described in Chapter 2, tests whether HTS assays, relevant for key endocrine targets, can classify chemicals consistently with guideline endocrine studies captured in this dataset.

Estrogen Signaling Pathways in Predictive Toxicology

Testosterone and androstendione are synthesized in the adrenal cortex of males and females from a cholesterol precursor. These two androgens can be converted to endogenous

13 estrogens, 17 β-estradiol and estrone, by the aromatase enzyme (CYP19A1). Aromatase is present in a wide variety of tissues including ovaries, placenta, brain, and adipose tissue

(Simpson and Davis 2001; Simpson et al. 1994). Circulating estrogen can bind and modulate the ER which in part regulates sexual differentiation, bone density, fertility, cell proliferation, and inflammatory responses (Harnish 2006; Kobayashi et al. 1996; Krege et al. 1998).

Although there are still many unanswered questions regarding the ER signaling pathway, the information gained from understanding the relationship between chemical behaviors and endogenous hormone structure has led to the development of successful pharmaceutical compounds(Gabriel and Jatoi 2012; O’Regan and Jordan 2001). These drugs are most often used to treat various forms of ER-responsive cancers, although they can also be used to treat individuals with hormone insufficiency as well. The success of these chemotherapeutics has been dampened by discovery of side-effects, such as increased incidences of endometrial cancer after tamoxifen treatment (Fisher et al. 1994). These discoveries demonstrate the complexities of ER signaling, and contributed to the understanding that some chemicals that target ER have opposite behaviors in different tissues. These compounds are called selective estrogen receptor modulators (SERM) (Lewis and Jordan 2005; Tee et al. 2004).

Two ER subtypes are known: ER α and ER β. The ligand binding domain (LBD) between the two receptors is only 59% homologous; however, the activation function 2 (AF-

2), where the ligand binds, is almost identical with only two amino acids differing between the two subtypes. The DNA binding domain (DBD) differs by only 3% (Shanle and Xu

2011). In addition, the activation function 1 (AF-1) domain can regulate transcriptional activation independently of ligand binding. The AF-1 domain shares only 18% similarity

14 between the two subtypes. Therefore, many ligands that can bind both receptor subtypes may do so with varying affinities or cellular consequences. Furthermore, ER α and ER β are expressed at different basal levels in different tissues. With only a 17% overlap in the genes regulated by both receptors (Tee et al. 2004), this expression implies differential biological effects to either receptor subtype. Finally, many different cofactors can bind to further regulate these differential responses and themselves vary in cellular concentration and cellular expression (Shanle and Xu 2011). As such, it is not surprising that slight variants in chemical structure can still engage the ligand binding domain (LBD) but invoke distinct responses.

Steroid hormones produced by the adrenal cortex and the gonads are synthesized from a cholesterol precursor. For that reason, steroid hormones share a similar structure with three 6-carbon rings and one 5-carbon ring (Fox 2004). Xenobiotic compounds known to target ER, such as DES, genestein, ICI 182,780, o,p’ -DDT, p,p’ -DDT, and tamoxifen, have structural similarity to 17 β-estradiol (they all have at least three 6-carbon rings). However, these compounds differ from one another by potency and pharmacological activity as agonists, antagonists, or both. Part of this differential activity may be due to the presence of side-chains or functional groups that protrude from the ligand binding pocket (Wu et al.

2005). An AF-2 H12 motif typically wraps around the ligand inside of the binding pocket, and the side-chain or functional group present on a particular chemical structure can sterically alter the H12 motif to decrease ER α stability and, in turn, alter the binding affinity to coactivators and corepressors at the AF-2 cleft (Wu et al. 2005). ICI 182,780, a pure ER antagonist, has a bulky side-chain that blocks the association between H12 and LBD.

Tamoxifen, an antagonist in mammary tissue and an agonist in uterine tissue, has a shorter

15 side-chain that allows H12 to more closely associate with the LBD (Wu et al. 2005). Many estrogens are structurally similar; however, small changes in chemical structure can drastically alter the phenotypic response.

Alterations in receptor mediated activity are responsible for the different phenotypic profiles observed with various estrogenic compounds. Characteristically, SERMs exhibit

‘partial agonist’ ER activity. These compounds compete for the ligand binding domain and will result in a reduction in overall signal by ER (Zhu 2005). However, if no full agonist is present, these SERM compounds will cause an overall increase in signal by ER. For example, tamoxifen given to premenopausal women can accelerate osteoporosis by competing with endogenous 17 β-estradiol. However, tamoxifen can help prevent bone loss in postmenopausal since endogenous levels of 17 β-estradiol are greatly reduced (Dardes et al.

2002).

Another contributor to the differential responses caused by xenoestrogens results from the ratio of ER α/ER β. Tissues with high ER α/ER β ratios, such as breast tissue, proliferate when exposed to estrogens. Other tissues, such as the colon, have a low ER α/ER β ratio and experience suppressed growth when exposed to estrogens (Fiorelli et al. 1999).

Endocrine disrupting compounds that operate through receptor-mediated mechanism(s) tend to have common chemical structure characteristics. The knowledge of structural characteristics and selective activity of ER has resulted in effective pharmaceutical SERMs; however, because the slight variations among these chemical structures alter receptor activity and cellular consequences, and because downstream effects vary by cell state and tissue type, less is known about the potential for environmental chemicals to act as SERM compounds.

16 Predictive Modeling of Endocrine Disruptors

With the growing number of manufactured chemicals, there is an increasing need for high-throughput and computational techniques to test chemicals for their potential to cause adverse health effects. Traditional regulatory toxicology tests are too expensive and are too slow to effectively screen all necessary chemicals. Biotechnology has improved significantly in the past 15 years, and in vitro assays are now capable of testing many human molecular targets with known toxicological implications for thousands of chemicals (Kavlock et al.

2012). These assays must be qualified as to their predictive capabilities in terms of sensitivity

(identifying positives) and specificity (differentiating positives from negatives) in order to be applicable for chemical prioritization in lieu of a traditional low-throughput regulatory paradigm.

In order to successfully predict and model biological endpoints, data is needed to anchor and validate toxicities predicted by computational toxicology. Although a recent transformation for the field of regulatory toxicology, high-throughput assays and predictive models have been used extensively in drug screening (Bibette 2012). For example, based on rodent 28-day repeat-dose studies using 15 male Sprague–Dawley rats treated with known renal toxicants, Fielden et al. (2005) demonstrated a toxicogenomic signature of 35 genes that correctly classified 76% of compounds that caused renal tubular degeneration. As with any in vivo bioassay or in vitro assay, there are distinct advantages and limitations that must be considered when extending a classification schema into a predictive signature. Genomic perturbations may classify compounds correctly, but the target may not be directly related to the toxicological endpoint. The gene may be activated or deactivated by a secondary mechanism which might limit its ability to predict the toxicological endpoint.

17 Quantitative structure activity relationship (QSAR) models that make use of chemical structure to predict biological outcomes have historically shown marginal success for some outcomes but become less effective for toxicities of reproduction and development. One reason for this may be a limited capacity to reproduce the entire cellular and mechanistic complexity of developmental and reproductive processes. An integrative approach with

QSAR and in vitro assays may, however, improve certain predictions of in vivo adverse effects, including those relevant for reproductive and developmental toxicity (Sedykh et al.

2011; Zhu et al. 2008). Because the endocrine system is highly complex and interconnected with many different targets capable of affecting steroid hormone production and function, it is not plausible that any single assay would accurately predict the endocrine disrupting potential across a large and diverse chemical landscape. One method of addressing these limitations is to use an integrative approach of developing models composed of multiple assays for each endocrine MOA. The work presented in this dissertation aims to combine multiple in vitro assays into a model capable of detecting compounds that will interfere with the estrogen MOA.Ultimately combining multiple models across each relevant endocrine

MOA may provide the greatest opportunity for comprehensively detecting endocrine disrupting chemicals.

Computational models capable of predicting chemical impacts on steroid biosynthesis using the adrenocortical cell line, H295R, have been developed. A study by Breen et al.

(2010) demonstrated that a computational model of the steroidogenesis pathway could accurately reproduce the effects of CYP11 β-hydroxylase inhibitor, metyrapone. Additional efforts to make the model generalizable, and a larger validation set, are needed to qualify this type of predictive model for broader translation into regulatory toxicology. An in vitro

18 screening assay in H295R cells measuring effects on estrogen and androgen synthesis is already included in the EDSP T1S, but in a 24 well format (U.S. EPA 2012a) thus limiting this to a medium throughput platform. The ToxCast program is currently exploring methods for a high-throughput version of this assay.

For detecting chemicals with the potential to interact with ER, the EPA has developed the estrogen receptor QSAR/rule-based expert system (ERES). ERES uses structural characteristics, ER binding affinity and fish vitellogenin as indicators of estrogenic activity, along with certain decision making criteria (U.S. EPA 2009b). Although this approach may be useful for prioritizing or screening chemicals for estrogenic activity, there are several shortcomings highlighted by the 2009 EPA Scientific Advisory Panel. 1) The model was constructed using pesticide inert ingredients and antimicrobial pesticides that may not be applicable to other chemical domains. 2) The ER binding assay does not distinguish agonist from antagonist, or ER α from ER β activity. 3) There are examples of non-receptor mediated estrogenic activity that may go undetected in this model. 4) Lastly, there was no automatic approach to deriving decision rules (U.S. EPA 2009b).

One method for combining in vitro assays to predict reproductive toxicants was explored by the 6 th European Union’s ReProTect project (Schenk et al. 2010). This study investigated this approach to determine the feasibility of developing an integrated testing strategy for reproductive and developmental toxicants. Ten blinded chemicals with well characterized reproductive profiles were tested in 14 in vitro assays. The two major requirements for chemical selection included well characterized in vivo effects and no

CYP450-mediated metabolic activation. The assays selected were designed to target male and female infertility, implantation, or embryonic development. The activity concentration at

19 50% response (AC50) was calculated and ranked in comparison to reference chemical responses in each assay. The entire study was conducted in a double-blind fashion, and the chemicals were only revealed after running the model. Effect types were split into three groups (female fertility, male fertility, and developmental toxicity). Overall, the 10 chemicals in the 3 groups resulted in 26/30 correct classifications. The results indicate that for this small set of chemicals, the rankings are highly correlated with their known toxicological profile

(Schenk et al. 2010). This study successfully demonstrated that models based on combining in vitro assays can correctly classify compounds that interfere with complex mechanisms of reproduction and fetal development in vivo .

In order for the long-term goal of replacing in vivo assays with in vitro and in silico based approaches to be successful, a method for deriving human oral equivalent doses based on activity concentrations will be needed. A computational approach for this was developed in Rotroff et al. (2010) and was later applied to a larger chemical library in Wetmore et al.

(2012). This approach uses human serum albumin and metabolic clearance to derive mg/kg/day conversion factors for in vitro measures of bioactivity. Comparisons to published

PBPK models demonstrated that the oral equivalent values were reasonably concordant

(Rotroff et al. 2010; Wetmore et al. 2012). Additional work by Hays and Aylward (2011) demonstrated a method to derive an estimated serum concentration for doses for which adverse effects were observed in rodent studies. Both methods can be used to enhance the use of assays for human health effects, but the model described by Hays and Aylward (2011) will create an equivalent for overt toxicity; whereas the model described by Rotroff et al. (2010) derives equivalents for molecular events.

20 A wide variety of methods and technologies exist for developing predictive models. If modeling efforts are to succeed for endocrine screening, data to anchor predictions will not only need to be high quality, but will need to exist for enough chemicals to determine the predictive capability of the model. This can be challenging since there are limited chemicals for which testing has been completed relative to the numbers screened in high-throughput efforts. Previous studies investigating a chemical response in multiple ER assays compare the assay responses equally and usually only take into account potency measurements (e.g. averaging AC50 values) (Kloas et al. 1999; Schenk et al. 2010).

The methods outlined in this dissertation are novel in that they characterize endocrine-related i n vitro assays by their entire concentration response, and take into account potency, efficacy, and uncertainty (as described in Chapter 4). Furthermore, the assays were previously characterized to determine causes for false positives and false negatives, and then combined in a manner that minimizes the impact of assay discrepancies (as described in

Chapters 3 and 4). Strong reference chemicals are detected by most in vitro assays; however, accounting for sources of false positives and false negatives can help to address inconsistencies for weakly active or negative chemicals. The availability of large chemical libraries for both guideline in vivo studies and in vitro assays makes these comparisons possible. Chapter 2 aims to use a data-driven approach to identify candidate MOA for predictive modeling efforts, which subsequently will be used to prioritize chemicals for further endocrine-related testing. This study tests the hypothesis that a chemical’s in vitro activation of the estrogen or androgen receptor will indicate an increased likelihood of estrogen and androgen related effects in in vivo bioassays, respectively. Secondly, Chapter 3 describes the evaluation of 1816 chemicals of environmental relevance for their potential to

21 alter cell growth kinetics through steroid hormone pathways in T47D cancer cells to test the hypothesis that compounds that activate ER transcription in the Attagene transactivation assay represent compounds upregulating the ER pathway through ligand binding and will result in compound-induced cellular changes in T47D growth kinetics. Finally, Chapter 4 aims to develop a biologically-based model capable of determining estrogenic potential based on in vitro measurements from multiple components of the estrogenic pathway. This study tests the hypothesis that estrogen related disruption of reproductive health by environmental chemicals occurs by activating multiple molecular initiating events, these events are measurable in vitro and can be used to accurately determine estrogenic likelihood.

22 Figures Figure 1.1 (A) A schematic representation of the HPG axis. The hypothalamus triggers a series of hormones resulting in sex steroid production from the gonads. Increased serum levels inhibit the further production of sex steroids. (B) A schematic representation of the HPT axis. The hypothalamus, anterior pituitary, and thyroid gland regulate thyroid hormone (T3/T4) production. (C) The HPA axis is responsible for regulating a wide variety of hormones including corticosteroids, mineralocorticoids. The hypothalamus triggers a series of hormones resulting in sex steroid production from the gonads.

HPT axis HPA axis HPG axis C. A. B. Hypothalamus Hypothalamus Hypothalamus

Somatostatin TRH CRH GnRH 23 Anterior Pituitary Anterior Pituitary Anterior Pituitary

TSH ACTH FSH LH

Thyroid Gland Adrenal Cortex Sex Inhibin Gonads Steroids

Corticosteroids Sex Steroids T3/T4 Mineralocorticoids Sperm Production or Ovulation

Metabolic Increase, Heart Rate, Blood-glucose Male levels, Growth and Development Blood pressure reproductive metabolism tissues, converted to estrogen in females

23 References

Abarikwu SO, Adesiyan AC, Oyeloja TO, Oyeyemi MO, Farombi EO. 2010. Changes in sperm characteristics and induction of oxidative stress in the testis and epididymis of experimental rats by a herbicide, atrazine. Archives of environmental contamination and toxicology 58: 874–882.

Auger J, Kunstmann JM, Czyglik F, Jouannet P. 1995. Decline in semen quality among fertile men in Paris during the past 20 years. New England Journal of Medicine 332: 281–285.

Ballentine C. 1981. Taste of raspberries, taste of death: the 1937 elixir sulfanilamide incident. FDA Consumer magazine.

Benigni R, Bossa C, Giuliani A, Tcheremenskaia O. 2010. Exploring in vitro/in vivo correlation: Lessons learned from analyzing phase I results of the US EPA’s ToxCast project. Journal of Environmental Science and Health, Part C 28: 272–286.

Beyer RP, Fry RC, Lasarev MR, McConnachie LA, Meira LB, Palmer VS, et al. 2007. Multicenter study of acetaminophen hepatotoxicity reveals the importance of biological endpoints in genomic analyses. Toxicological sciences 99: 326–337.

Bibette J. 2012. Gaining confidence in high-throughput screening. PNAS 109:649–650; doi:10.1073/pnas.1119350109.

Breen MS, Breen M, Terasaki N, Yamazaki M, Conolly RB. 2010. Computational model of steroidogenesis in human H295R cells to predict biochemical response to endocrine- active chemicals: model development for metyrapone. Environmental health perspectives 118: 265.

Brewster UC, Perazella MA. 2004. The renin-angiotensin-aldosterone system and the kidney: effects on kidney disease. The American journal of medicine 116: 263.

Cai LQ, Fratianni CM, Gautier T, Imperato-McGinley J. 1994. Dihydrotestosterone regulation of semen in male pseudohermaphrodites with 5 alpha-reductase-2 deficiency. Journal of Clinical Endocrinology & Metabolism 79: 409–414.

Calafat AM, Kuklenyik Z, Reidy JA, Caudill SP, Ekong J, Needham LL. 2005. Urinary Concentrations of Bisphenol A and 4-Nonylphenol in a Human Reference Population. Environ Health Perspect 113:391–395; doi:10.1289/ehp.7534.

Calhoun DA, Nishizaka MK, Zaman MA, Thakkar RB, Weissmann P. 2002. Hyperaldosteronism among black and white subjects with resistant hypertension. Hypertension 40: 892–896.

Caliman FA, Gavrilescu M. 2009. Pharmaceuticals, personal care products and endocrine disrupting agents in the environment–a review. CLEAN–Soil, Air, Water 37: 277–303.

24 Carlsen E, Giwercman A, Keiding N, Skakkebæk NE. 1992. Evidence for decreasing quality of semen during past 50 years. British medical journal 305: 609–613.

Cooper RL, Kavlock RJ. 1997. Endocrine disruptors and reproductive development: a weight-of- evidence overview. Journal of Endocrinology 152: 159–166.

Cooper RL, Stoker TE, Goldman JM, Parrish MB, Tyrey L. 1996. Effect of atrazine on ovarian function in the rat. Reproductive Toxicology 10: 257–264.

Cooper RL, Stoker TE, Tyrey L, Goldman JM, McElroy WK. 2000. Atrazine disrupts the hypothalamic control of pituitary-ovarian function. Toxicological sciences 53: 297–307.

Cragin LA, Kesner JS, Bachand AM, Barr DB, Meadows JW, Krieg EF, et al. 2011. Menstrual cycle characteristics and reproductive hormone levels in women exposed to atrazine in drinking water. Environmental research.

Dardes RC, Schafer JM, Pearce ST, Osipo C, Chen B, Jordan VC. 2002. Regulation of estrogen target genes and growth by selective estrogen-receptor modulators in endometrial cancer cells. Gynecologic oncology 85: 498–506.

Dupont S, Krust A, Gansmuller A, Dierich A, Chambon P, Mark M. 2000. Effect of single and compound knockouts of estrogen receptors alpha (ERalpha) and beta (ERbeta) on mouse reproductive phenotypes. Development 127: 4277–4291.

Fang H, Tong W, Branham WS, Moland CL, Stacy L, Hong H, et al. 2003. Study of 202 natural, synthetic, and environmental chemicals for binding to the androgen receptor. Chemical research in toxicology 16: 1338–1358.

Fielden MR, Eynon BP, Natsoulis G, Jarnagin K, Banas D, Kolaja KL. 2005. A gene expression signature that predicts the future onset of drug-induced renal tubular toxicity. Toxicologic pathology 33: 675–683.

Fiorelli G, Picariello L, Martineti V, Tonelli F, Brandi ML. 1999. Functional estrogen receptor β in colon cancer cells. Biochemical and biophysical research communications 261: 521– 527.

Fisher B, Costantino JP, Redmond CK, Fisher ER, Wickerham DL, Cronin WM. 1994. Endometrial Cancer in Tamoxifen-Treated Breast Cancer Patients: Findings From the National Surgical Adjuvant Breast and Bowel Project (NSABP) B-14. JNCI J Natl Cancer Inst 86:527–537; doi:10.1093/jnci/86.7.527.

Fox SI. 2004. Human Physiology . 9th ed. Mcgraw-Hill (Tx).

Freyberger A, Witters H, Weimer M, Lofink W, Berckmans P, Ahr H-J. 2010. Screening for (anti) androgenic properties using a standard operation protocol based on the human stably transfected androgen sensitive PALM cell line. First steps towards validation. Reproductive Toxicology 30: 9–17.

25 Gabriel EM, Jatoi I. 2012. Breast cancer chemoprevention. Expert Review of Anticancer Therapy 12:223–228; doi:10.1586/era.11.206.

Gray LE, Ostby JS, Kelce WR. 1994. Developmental effects of an environmental antiandrogen: the fungicide vinclozolin alters sex differentiation of the male rat. Toxicology and applied pharmacology 129: 46–52.

Green ML, Singh AV, Zhang Y, Nemeth KA, Sulik KK, Knudsen TB. 2007. Reprogramming of genetic networks during initiation of the Fetal Alcohol Syndrome. Developmental Dynamics 236:613–631; doi:10.1002/dvdy.21048.

Harnish DC. 2006. Estrogen receptor ligands in the control of pathogenic inflammation. Current opinion in investigational drugs (London, England: 2000) 7: 997.

Hayes T, Haston K, Tsui M, Hoang A, Haeffele C, Vonk A. 2003. Atrazine-induced hermaphroditism at 0.1 ppb in American leopard frogs (Rana pipiens): laboratory and field evidence. Environmental Health Perspectives 111: 568.

Hayes TB, Collins A, Lee M, Mendoza M, Noriega N, Stuart AA, et al. 2002. Hermaphroditic, demasculinized frogs after exposure to the herbicide atrazine at low ecologically relevant doses. Proceedings of the National Academy of Sciences 99: 5476–5480.

Hays SM, Aylward LL. 2011. Interpreting human biomonitoring data in a public health risk context using Biomonitoring Equivalents. International journal of hygiene and environmental health.

Hof C, Araújo MB, Jetz W, Rahbek C. 2011. Additive threats from pathogens, climate and land- use change for global amphibian diversity. Nature.

Jessop DS. 1999. Review: Central non-glucocorticoid inhibitors of the hypothalamo-pituitary- adrenal axis. J Endocrinol 160:169–180; doi:10.1677/joe.0.1600169.

Judson R, Richard A, Dix DJ, Houck K, Martin M, Kavlock R, et al. 2009. The Toxicity Data Landscape for Environmental Chemicals. Environ Health Perspect 117:685–695; doi:10.1289/ehp.0800168.

Judson RS, Houck KA, Kavlock RJ, Knudsen TB, Martin MT, Mortensen HM, et al. 2010. In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project. Environ Health Perspect 118:485–492; doi:10.1289/ehp.0901392.

Judson RS, Mortensen HM, Shah I, Knudsen TB, Elloumi F. 2012. Using pathway modules as targets for assay development in xenobiotic screening. Molecular BioSystems 8: 531– 542.

Kavlock R, Chandler K, Houck K, Hunter S, Judson R, Kleinstreuer N, et al. 2012. Update on EPA’s ToxCast Program: Providing High Throughput Decision Support Tools for Chemical Risk Management. Chem. Res. Toxicol. 25:1287–1302; doi:10.1021/tx3000939.

26 Kavlock R, Cummings A. 2005. Mode of action: Inhibition of androgen receptor function- Vinclozolin-induced malformations in reproductive development. CRC Critical Reviews in Toxicology 35: 721–726.

Kiesecker JM, Blaustein AR, Belden LK. 2001. Complex causes of amphibian population declines. Nature 410:681–684; doi:10.1038/35070552.

Kloas W, Lutz I, Einspanier R. 1999. Amphibians as a model to study endocrine disruptors: II. Estrogenic activity of environmental chemicals in vitro and in vivo. The Science of the total environment 225: 59.

Kniewald J, Jakomini ć M, Tomljenovi ć A, \vSimi ć B, Romac P, Vrane\vsi ć Ð, et al. 2000. Disorders of male rat reproductive tract under the influence of atrazine. Journal of applied toxicology 20: 61–68.

Knol BW. 1991. Stress and the endocrine hypothalamus-pituitary-testis system: A review. Veterinary Quarterly 13: 104–114.

Knudsen TB, Martin MT, Kavlock RJ, Judson RS, Dix DJ, Singh AV. 2009. Profiling the activity of environmental chemicals in prenatal developmental toxicity studies using the US EPA’s ToxRefDB. Reproductive toxicology (Elmsford, NY) 28: 209.

Kobayashi S, Inoue S, Hosoi T, Ouchi Y, Shiraki M, Orimo H. 1996. Association of bone mineral density with polymorphism of the estrogen receptor gene. Journal of Bone and Mineral Research 11: 306–311.

Kojima H, Iida M, Katsura E, Kanetoshi A, Hori Y, Kobayashi K. 2003. Effects of a diphenyl ether-type herbicide, chlornitrofen, and its amino derivative on androgen and estrogen receptor activities. Environmental health perspectives 111: 497.

Krege JH, Hodgin JB, Couse JF, Enmark E, Warner M, Mahler JF, et al. 1998. Generation and reproductive phenotypes of mice lacking estrogen receptor β. Proceedings of the National Academy of Sciences 95: 15677–15682.

Lewis JS, Jordan VC. 2005. Selective estrogen receptor modulators (SERMs): Mechanisms of anticarcinogenesis and drug resistance. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 591:247–263; doi:10.1016/j.mrfmmm.2005.02.028.

Lindsay JR, Atkinson AB. 2009. 49 Adrenal Causes of Cushing’s Syndrome. Clinical Endocrine Oncology 378.

Lodish MB, Sinaii N, Patronas N, Batista DL, Keil M, Samuel J, et al. 2009. Blood pressure in pediatric patients with Cushing syndrome. Journal of Clinical Endocrinology & Metabolism 94: 2002–2008.

Lubbos HG HS. 1998. Adverse effects of spironolactone therapy in women with acne. Arch Dermatol 134: 1162–1163.

27 Martin MT, Judson RS, Reif DM, Kavlock RJ, Dix DJ. 2009a. Profiling Chemicals Based on Chronic Toxicity Results from the U.S. EPA ToxRef Database. Environ Health Perspect 117:392–399; doi:10.1289/ehp.0800074.

Martin MT, Knudsen TB, Reif DM, Houck KA, Judson RS, Kavlock RJ, et al. 2011. Predictive model of rat reproductive toxicity from ToxCast high throughput screening. Biology of reproduction 85: 327–339.

Martin MT, Mendez E, Corum DG, Judson RS, Kavlock RJ, Rotroff DM, et al. 2009b. Profiling the reproductive toxicity of chemicals from multigeneration studies in the toxicity reference database. Toxicological Sciences 110: 181–190.

Mello WG de, Morais SRL de, Dornelles RCM, Kagohara Elias LL, Antunes-Rodrigues J, Bedran de Castro JC. 2011. Effects of neonatal castration and androgenization on sexual dimorphism in bone, leptin and corticosterone secretion. Bone.

Ng PC, Lam CWK, Fok TF, Lee CH, Ma KC, Chan IHS, et al. 2001. Refractory hypotension in preterm infants with adrenocortical insufficiency. Archives of Disease in Childhood-Fetal and Neonatal Edition 84: F122–F124.

Nuwaysir EF, Bittner M, Trent J, Barrett JC, Afshari CA. 1999. Microarrays and toxicology: The advent of toxicogenomics. Molecular Carcinogenesis 24: 153–159.

O’Regan RM, Jordan VC. 2001. Tamoxifen to raloxifene and beyond. Seminars in Oncology 28:260–273; doi:10.1016/S0093-7754(01)90119-8.

Palmlund I, Apfel R, Buitendijk S, Cabau A, Forsberg JG. 1993. Effects of diethylstilbestrol (DES) medication during pregnancy: report from a symposium at the 10th international congress of ISPOG. Journal of Psychosomatic Obstetrics & Gynecology 14: 71–89.

Reif DM, Martin MT, Tan SW, Houck KA, Judson RS, Richard AM, et al. 2010. Endocrine Profiling and Prioritization of Environmental Chemicals Using ToxCast Data. Environmental Health Perspectives 118: 1714–1720.

Rotroff DM, Wetmore BA, Dix DJ, Ferguson SS, Clewell HJ, Houck KA, et al. 2010. Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicological Sciences 117: 348–358.

Schenk B, Weimer M, Bremer S, Der Burg B Van, Cortvrindt R, Freyberger A, et al. 2010. The ReProTect Feasibility Study, a novel comprehensive in vitro approach to detect reproductive toxicants. Reproductive toxicology 30: 200–218.

Scippo M-L, Argiris C, De Weerdt C Van, Muller M, Willemsen P, Martial J, et al. 2004. Recombinant human estrogen, androgen and progesterone receptors for detection of potential endocrine disruptors. Analytical and bioanalytical chemistry 378: 664–669.

28 Sedykh A, Zhu H, Tang H, Zhang L, Richard A, Rusyn I, et al. 2011. Use of in Vitro HTS- Derived Concentration-Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity. Environmental health perspectives 119: 364–370.

Shafer TJ, Ward TR, Meacham CA, Cooper RL. 1999. Effects of the chlorotriazine herbicide, cyanazine, on GABAA receptors in cortical tissue from rat brain. Toxicology 142:57–68; doi:10.1016/S0300-483X(99)00133-X.

Shanle EK, Xu W. 2011. Endocrine Disrupting Chemicals Targeting Estrogen Receptor Signaling: Identification and Mechanisms of Action. Chem. Res. Toxicol. 24:6–19; doi:10.1021/tx100231n.

Sierra-Santoyo A, Angeles-Soto E, Lourdes López-González M de, Harrison RA, Hughes MF. 2012. In vitro metabolism of the anti-androgenic fungicide vinclozolin by rat liver microsomes. Archives of toxicology 1–9.

Simic B, Kniewald Z, Kniewald J, Davies JE. 1991. Reversibility of the inhibitory effect of atrazine and lindane on cytosol 5. alpha.-dihydrotestosterone receptor complex formation in rat prostate. Bulletin of Environmental Contamination and Toxicology;(United States) 46.

Simpson ER, Davis SR. 2001. Minireview: aromatase and the regulation of estrogen biosynthesis—some new perspectives. Endocrinology 142: 4589–4594.

Simpson ER, Mahendroo MS, Means GD, Kilgore MW, Hinshelwood MM, Graham-Lorence S, et al. 1994. Aromatase cytochrome P450, the enzyme responsible for estrogen biosynthesis. Endocrine reviews 15: 342–355.

Sipes NS, Martin MT, Reif DM, Kleinstreuer NC, Judson RS, Singh AV, et al. 2011. Predictive models of prenatal developmental toxicity from ToxCast high-throughput screening data. Toxicological Sciences 124: 109–127.

Skakkebaek NE, Rajpert-De Meyts E, Main KM. 2001. Testicular dysgenesis syndrome: an increasingly common developmental disorder with environmental aspects: Opinion. Human reproduction 16: 972–978.

Smith KW, Braun JM, Williams PL, Ehrlich S, Correia KF, Calafat AM, et al. 2012. Predictors and Variability of Urinary Paraben Concentrations in Men and Women, Including before and during Pregnancy. Environmental health perspectives.

Solomon KR, Baker DB, Richards RP, Dixon KR, Klaine SJ, Point TW La, et al. 2009. Ecological risk assessment of atrazine in North American surface waters. Environmental Toxicology and Chemistry 15: 31–76.

Sonnenschein C, Soto AM. 1998. An updated review of environmental estrogen and androgen mimics and antagonists. The Journal of steroid biochemistry and molecular biology 65: 143–150.

29 Soto AM, Sonnenschein C, Chung KL, Fernandez MF, Olea N, Serrano FO. 1995. The E- SCREEN assay as a tool to identify estrogens: an update on estrogenic environmental pollutants. Environmental health perspectives 103: 113.

Souza F de, Muxfeldt E, Fiszman R, Salles G. 2010. Efficacy of spironolactone therapy in patients with true resistant hypertension. Hypertension 55: 147–152.

Sparling DW, Fellers GM. 2009. Toxicity of two insecticides to California, USA, anurans and its relevance to declining amphibian populations. Environmental Toxicology and Chemistry 28: 1696–1703.

Stoker TE, Laws SC, Guidici DL, Cooper RL. 2000. The effect of atrazine on puberty in male Wistar rats: an evaluation in the protocol for the assessment of pubertal development and thyroid function. Toxicological Sciences 58: 50–59.

Tavera-Mendoza L, Ruby S, Brousseau P, Fournier M, Cyr D, Marcogliese D. 2002. Response of the amphibian tadpole Xenopus laevis to atrazine during sexual differentiation of the ovary. Environmental Toxicology and Chemistry 21: 1264–1267.

Tee MK, Rogatsky I, Tzagarakis-Foster C, Cvoro A, An J, Christy RJ, et al. 2004. Estradiol and Selective Estrogen Receptor Modulators Differentially Regulate Target Genes with Estrogen Receptors α and β. Mol. Biol. Cell 15:1262–1272; doi:10.1091/mbc.E03-06- 0360.

Terasawa E. 1998. Cellular Mechanism of Pulsatile LHRH Release1. General and comparative endocrinology 112: 283–295.

Thomas RS, Black MB, Li L, Healy E, Chu T-M, Bao W, et al. 2012. A Comprehensive Statistical Analysis of Predicting In Vivo Hazard Using High-Throughput In Vitro Screening. Toxicological Sciences 128: 398–417.

Thomas RS, Rank DR, Penn SG, Zastrow GM, Hayes KR, Pande K, et al. 2001. Identification of toxicologically predictive gene sets using cDNA microarrays. Molecular Pharmacology 60: 1189–1194.

Toppari J, Virtanen HE, Main KM, Skakkebaek NE. 2010. Cryptorchidism and hypospadias as a sign of testicular dysgenesis syndrome (TDS): environmental connection. Birth Defects Research Part A: Clinical and Molecular Teratology 88: 910–919.

U.S. EPA. 2012a. Endocrine Disruptor Screening Program.

U.S. EPA. 2011a. Endocrine Disruptor Screening Program for the 21st Century: EDSP21 Work Plan.

U.S. EPA. 2000. R.E.D Facts: Vinclozolin.

30 U.S. EPA. 2011b. Re-evaluation of the Human Health Effects of Atrazine: A review of Non- Cancer Effects, Drinking Water Monitoring Frequency and Cancer Epidemiology. FIFRA Scientific Advisory Panel Meeting. July 2011.

U.S. EPA. 2009a. The Ecological Significance of Atrazine Effects on Primary Producers in Surface Water Streams in the Corn and Sorghum Growing Region of the United States (part II).

U.S. EPA. 2009b. The Use of Structure Activity Relationships of Estrogen Binding Affinity to Support Priorization of Pesticide Inert Ingredients and Antimicrobial Pesticides for Screening and Testing.

U.S. EPA. 2012b. U.S. Environmental Protection Agency Endocrine Disruptor Screening Program Comprehensive Management Plan.

U.S. EPA. 1998. Vinclozolin; Voluntary Termination of Uses.

U.S. HHS. 2008. NTP-CERHR Monograph on the Potential Human Reproductive and Developmental Effects of Bisphenol-A.

Vinggaard AM, Jacobsen H, Metzdorff SB, Andersen HR, Nellemann C. 2005. Antiandrogenic effects in short-term in vivo studies of the fungicide fenarimol. Toxicology 207: 21–34.

Walker WH, Cheng J. 2005. FSH and testosterone signaling in Sertoli cells. Reproduction 130: 15–28.

Wetmore BA, Wambaugh JF, Ferguson SS, Sochaski MA, Rotroff DM, Freeman K, et al. 2012. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicological sciences 125: 157–174.

Wu Y-L, Yang X, Ren Z, McDonnell DP, Norris JD, Willson TM, et al. 2005. Structural basis for an unexpected mode of SERM-mediated ER antagonism. Molecular cell 18: 413–424.

Zhu BT. 2005. Mechanistic explanation for the unique pharmacologic properties of receptor partial agonists. Biomedicine & Pharmacotherapy 59:76–89; doi:10.1016/j.biopha.2005.01.010.

Zhu H, Rusyn I, Richard A, Tropsha A. 2008. Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure–activity relationship models of animal carcinogenicity. Environmental health perspectives 116: 506.

Zoeller RT, Tan SW, Tyl RW. 2007. General background on the hypothalamic-pituitary-thyroid (HPT) axis. CRC Critical Reviews in Toxicology 37: 11–53.

31

CHAPTER 2

USING IN VITRO HIGH THROUGHPUT SCREENING ASSAYS TO IDENTIFY POTENTIAL ENDOCRINE-DISRUPTING CHEMICALS 1

Introduction

Endocrine hormones regulate a diverse set of physiological responses, some of which include sexual dimorphism, reproductive capacity, glucose metabolism, and blood pressure

(Cooper and Kavlock 1997; Dupont et al. 2000; Lodish et al. 2009; de Mello et al. 2011; Ng et al. 2001). The many types of responses regulated by hormones makes them of particular concern for disruption by xenobiotics (Ankley and Giesy 1998; Colborn and Clement 1992; Soto and

Sonnenschein 2010; Tilghman et al. 2010).Endocrine disruption can lead to many adverse consequences, some of which include altered reproductive performance and hormonally mediated cancers (Birnbaum and Fenton 2003; Kavlock et al. 1996; Soto and Sonnenschein

2010; Spencer et al. 2012). Endocrine disruption can also have adverse effects on the fetus or newborn because of the delicate balance of hormones required during critical developmental windows (Bigsby et al. 1999; Chandrasekar et al. 2011; Cooper and Kavlock 1997; Mahoney and

Padmanabhan 2010). For example, studies have demonstrated that thyroid hormone insufficiency during pregnancy may lead to adverse neurological outcomes in children (Haddow et al. 1999).

1 Previously published as Rotroff DM, Dix DJ, Houck KA, Knudsen TB, Martin MT, McLaurin KW, Reif DM, Crofton KM, Singh AV, Xia M, Huang R, Judson RS. (2012). Using in Vitro High Throughput Screening Assays to Identify Potential Endocrine-Disrupting Chemicals. Environ Health Perspect. 2012 Sep 28.

32 The Federal Food, Drug, and Cosmetic Act (FFDCA , 1996), as amended by the Food

Quality Protection Act (FQPA, 1996), and the Safe Drinking Water Act Amendments (SDWA,

1996), requires the U.S. Environmental Protection Agency (EPA) to determine whether certain substances may have an effect in humans similar to that produced by a naturally occurring estrogen, or other such endocrine effects (FFDCA , 1996). In response, the U.S. EPA formed the

Endocrine Disruptor Screening Program (EDSP) (U.S. EPA 2012a). The EDSP is a two-tiered program that requires chemical manufacturers to submit or generate data on a suite of both in vivo and in vitro assays. The first phase of EDSP assays are designated as the Tier 1 screening

(T1S) battery (U.S. EPA 2012b). These tests identify chemicals with the potential to interact with endocrine pathways or mechanisms, and focus on disruption of estrogen, androgen, and thyroid hormone pathways. Based on a weight-of-evidence approach, chemicals showing positive activity in T1S assays could then be subject to more complex Tier 2 tests (U.S. EPA

2012a). The European Commission is continuing the implementation of the European Union’s

Community Strategy for Endocrine Disrupters, which includes the establishment of a priority list of substances for further evaluation and assay development and validation (European

Commission 2012). In addition, the European Commission is working toward defining specific criteria to identify endocrine disruptors within a legislative framework, drawing on current scientific opinion (Kortenkamp et al. 2011).

The U.S. EPA estimates that the statutory requirements and discretionary authorities through passage of the FQPA and its amendments and the SDWA will require the EDSP to screen as many as 9,700 environmental chemicals. Generating this data required under the current testing guidelines will be expensive and time-consuming, and it will require significant animal resources (U.S. EPA 2011a). To date, chemicals have been nominated by the U.S. EPA

33 for EDSP T1S on the basis of exposure potential or registration status. Because of fiscal and time constraints, the U.S. EPA is considering using endocrine-related in vitro high throughput screening (HTS) assays and in silico models to prioritize chemicals for testing in T1S (U.S. EPA

2011a). There has been a significant improvement in HTS technologies since the U.S. EPA began work on developing and implementing the EDSP. In 2007, the National Research Council

Report Toxicity Testing in the 21st Century: A Vision and a Strategy (National Research Council

2007) acknowledged these advances and recommended that the agency develop a strategy to use modern molecular-based screening methods to reduce, and ultimately replace, the reliance on whole-animal toxicity testing. The U.S. EPA’s ToxCast program (U.S. EPA 2012d), and the U.S. government’s cross-agency Tox21 program (U.S. EPA 2012c) are using HTS assays and developing computational tools to predict chemical hazard, to characterize a diverse set of toxicity pathways, and to prioritize the toxicity testing of environmental chemicals (Huang et al.

2011; U.S. EPA 2012c). Included in these programs are assays that cover toxicity pathways involving estrogen, androgen, and thyroid hormone receptors, as well as targets within the steroidogenesis pathway. The current ToxCast chemical library covers approximately 17% of the chemicals subject to the EDSP, and the larger Tox21 chemical library covers approximately 53% of the chemicals subject to EDSP. Assay technologies include competitive binding, reporter gene, and enzyme inhibition assays. The comparison of HTS assays, endocrine-related modes of action (MOA) and EDSP T1S is shown in Figure 2.1. An endocrine MOA consists of a series of molecular initiating events relevant for estrogen, androgen, thyroid, or steroidogenic pathways.

These assays do not represent their respective MOA in its entirety, but are used to detect chemicals capable of perturbing a particular MOA. In the present study, we investigated the predictive ability of ToxCast HTS assays for end points tested in EDSP T1S, and we tested the

34 hypothesis that if a chemical activates the estrogen or androgen receptor in vitro, estrogen-and androgen-related effects will occur in in vivo bioassays. Ideally, HTS tests should be highly reproducible and yield a minimal number of false-positive (specificity) and false-negative

(sensitivity) chemicals.

Previous studies have suggested the use of HTS assays for identifying endocrine disrupt- ing potential. For example, the ReProTect project developed within the 6th European Framework

Program tested 14 in vitro assays using 10 prototype compounds to determine feasibility for a reproductive screening program (Schenk et al. 2010). Those in vitro assays were grouped into three segments of the reproductive cycle: endocrine disruption, fertility, and embryonic development. The results of ReProTect showed, at least for the 10 prototype chemicals, that appropriate in vitro assay selection can effectively group compounds based on known reproduc- tive toxicity (Schenk et al. 2010).

HTS assays are useful for identifying chemical impacts on molecular initiating events in biological or toxicological pathways. Combinations of HTS assays measuring competitive ligand binding, reporter gene activation, and enzyme inhibition can be used to characterize chemical potential for endocrine disruption. These chemical characterizations can then be quantitatively evaluated by investigating associations with guideline EDSP T1S assay results. The aim of the present study was to use this data-driven approach to identify candidate MOAs for predictive modeling efforts, which subsequently will be used to prioritize chemicals for further endocrine- related testing.

35

Methods

Chemical Selection

In this study we used data from the ToxCast Phase I chemical library, containing data for

309 unique chemical structures (U.S. EPA 2012e). Most of these chemicals are either current or former active ingredients in food-use pesticides that were designed to be bioactive, or they are industrial chemicals that are environmentally relevant. Details of the chemical library were reported by Judson et al. ( 2009). Data on an additional 23 reference chemicals were included that were tested in a separate study (Judson et al. 2010), 17 of which were not in the ToxCast

Phase I library. CAS registry numbers (CASRN) for the ToxCast Phase 1 chemicals and the additional 17 chemicals are available online in Supplemental_File_1.csv (Rotroff et al. 2013).

Guideline and Non-Guideline Endocrine Assays

Data from guideline endocrine-related in vitro and in vivo studies were extracted from

EDSP Tier 1 validation reports from the U.S. EPA EDSP web site (U.S. EPA 2012a). Non- guideline studies were obtained from open literature by querying PubMed

(http://www.ncbi.nlm.nih.gov/pubmed) and Google Scholar (http://scholar.google.com/) using the following terms: (any chemical name or CASRN in the 309) AND (“ in vitro” OR “ in vivo” )

AND (“estrogen” OR “androgen” OR “uterotrophic” OR “Hershberger” OR “steroidogenesis”

OR “thyroid hormone”). The automated search found a wide variety of studies representing

2,113 individual studies. The list of studies was manually curated to remove studies that did not contain data usable for the current analysis, leaving 248 unique studies (e.g., studies of mixtures without testing compounds individually, studies that mentioned the chemical but did not test it in

36 a bioassay, studies measuring bioaccumulation). Studies that identified their methods as following the Organisation for Economic Co-operation and Development (OECD) guidelines

(Kanno et al. 2001, 2003; OECD 1999, 2001, 2003, 2007) or EDSP protocols were grouped together with EDSP T1S data for the guideline analysis. When available, PubMed identifiers

(PMID) were used as unique annotations for each report. For the few instances when no PMID was available or for each EDSP T1S validation report, a unique identifying number was gen- erated. The citation information for all documents used in the analysis is available online in

Supplemental_File_2.txt (Rotroff et al. 2013).

Guideline endocrine-related assays gathered from EDSP validation reports and OECD guideline studies were categorized according to whether they tested estrogen-, androgen-, steroidogenesis-, or thyroid-related MOAs (guideline-E, guideline-A, guideline-S, guideline-T, respectively). Additional information captured included study type (e.g., amphibian metamorphosis, reporter gene), assay type (e.g., serum levels, organ weight), species, strain, cell type, target, and whether or not it was an EDSP/OECD guideline study. Chemical potency [e.g., concentration at half-maximum activity (AC50), lowest effective concentration] for a given end point was captured as it was represented in the study report along with the maximum concentration/dose tested. In addition, agonist or antagonist responses were noted when appli- cable. Data from guideline and non-guideline studies were dichotomized as either active if a response was observed, or inactive if no response was observed. If a study investigated multiple end points for a given endocrine MOA and produced at least one statistically significant end point, then that study–chemical–MOA combination was considered active. Activity/inactivity was determined based on the presence of a statistically significant response or was based on the study author’s conclusion. Data were further annotated as having a hit value of either 1 or 0 for

37 active and inactive, respectively. We combined all guideline and non-guideline literature studies so as to have a single hit value for each study–chemical–MOA combination. Data that were con- flicting or otherwise unclear were included in the data table but annotated as such, and removed from analyses. The data obtained from guideline endocrine-related studies and other non- guideline literature reports are available online Supplemental_File_3.csv (Rotroff et al. 2013).

ToxCast In Vitro Assays

HTS competitive binding, enzyme inhibition, and reporter gene assays representing estrogen-, androgen-, steroidogenesis-, or thyroid-related end points (HTS-E, HTS-A, HTS-S,

HTS-T, respectively) were selected as a subset of the > 500 HTS assays generated by the

ToxCast program (ToxCastDB v.17; U.S. EPA, 2012e) [see Supplemental_File_1.csv (Rotroff et al. 2013)]. The details and a description of each assay are reported in Table 2.1.

For chemicals that produced a statistically significant and concentration-dependent response in a given assay, the AC50 was recorded. The criteria for determining the activity of a compound are assay platform. The data were then dichotomized so that if an AC50 was present for a given chemical end point concentration, a 1 was reported; if no response was observed, a 0 was reported. Chemicals tested in triplicate for quality control purposes were designated 1 or 0 on a majority basis. Chemicals that were run in duplicate with at least one sample producing an

AC50 were designated as a 1.

Model Development

We performed an iterative, balanced optimization analysis to determine the ability of

ToxCast HTS assays to correctly classify the results of guideline endocrine-related assays while maintaining balance between sensitivity and specificity. The process for this analysis is illustrated in Figure 2.2. Because each HTS endocrine MOA may have multiple ToxCast HTS

38 assays, we used disjunctive logic employing varied weight-of-evidence thresholds to determine optimal predictive performance. This model tested variable thresholds for the HTS ToxCast assay results represented as unweighted binary data, while the guideline or non-guideline endocrine-related assay results remained static. Initially, the model began with a threshold criterion of one positive ToxCast HTS assay out of the total number of ToxCast HTS assays for a chemical to be considered to perturb a given MOA. Once calculated, the model was then re-run with increasing increments of one assay until all ToxCast HTS assays for a given endocrine

MOA were required to be positive for a chemical to be considered to perturb the given MOA. As the threshold for a positive call was increased, a larger weight of evidence was required for a chemical to be considered a “hit” for perturbing the given endocrine MOA. An exception was made for guideline pubertal studies and the ToxCast NVS_NR_hAR assay. Guideline pubertal studies test for effects that can arise through multiple different endocrine-related pathways. For this reason, if a chemical was considered positive in the pubertal assay and the result conflicted with other guideline studies (e.g., receptor binding, reporter gene), the pubertal assay was not included in the weight of evidence. The ToxCast NVS_NR_hAR assay is a human androgen receptor binding assay in the LNCaP prostatic cell line. The androgen receptor in this cell line is known to bind to steroid hormones other than androgens (Veldscholte et al. 1992). For this reason, if a compound was negative in all other HTS-A assays, the result for the NVS_NR_hAR assay was not included in the weight-of-evidence.

For a specific set of criteria across all overlapping chemicals, we calculated sensitivity, specificity, and balanced accuracy (BA) as measures of model performance (Figure 2.2B). The guideline analysis was performed comparing ToxCast HTS assays and guideline endocrine assays gathered from EDSP validation reports and OECD guideline studies. We also conducted a

39 separate non-guideline analysis comparing ToxCast HTS assays with assays from non-guideline studies. Many of the EDSP/OECD guideline studies and those reported in non-guideline litera- ture used multiple studies/assays for each chemical–MOA combination. Because separate studies are not always in agreement relative to a chemical–MOA perturbation, the model was run using two scenarios: a) Any positive report for a chemical resulted in a positive call for the chemical–

MOA combination; or b) > 50% (threshold > 0.50) of guideline or non-guideline endocrine- related studies or assays must report the chemical to be active for a given endocrine MOA.

For each threshold criteria the number of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) were calculated. A TP was any chemical determined to be positive in the ToxCast HTS assays and was also positive in guideline endocrine reports. A

FP was positive in ToxCast but reported as negative in the guideline endocrine reports. If a chemical was determined to be negative in the ToxCast HTS assays and positive in the guideline endocrine reports, then it was recorded as a FN. Last, a TN was any chemical negative in the

ToxCast HTS assays and negative in the guideline endocrine reports. At each threshold combination, all of the available chemicals were classified as TP, FP, TN, or FN and were used to calculate sensitivity, specificity, and BA as a measure of model performance.

Statistical Analysis

To identify statistically significant BA values, we performed a permutation test. The test randomized which ToxCast assays were associated with guideline endocrine studies or biomedical literature for each endocrine MOA in order to determine whether or not a randomly chosen set of assays from the > 500 ToxCast end points would likely produce a similar association. The BA calculation based on random assay associations was performed using the same number of ToxCast assays as the model and with the same threshold criteria. Assays were

40 permuted 10,000 times to build the random BA population distribution, and the percentile where the model BA fell among this distribution was calculated to provide a p-value. A p-value of <

0.01 was considered statistically significant. The distributions developed from the permutation tests were used to define the confidence intervals in Figures 2.3 and 2.4.

Results

Data Collection

Data covering guideline endocrine-related in vitro and in vivo assays was extracted from documents used in EDSP Tier 1 validation or conducted according to OECD guidelines. We found a total of 40 studies covering 154 unique chemicals, resulting in a total of 1,246 captured end points. Table 2.2 shows the chemical overlap between the ToxCast chemical library and the chemicals captured from guideline and non-guideline studies. Twenty-one chemicals available from EDSP validation documents and other OECD guideline studies covering the guideline-E

MOA overlapped with the ToxCast HTS-E assays. Thirteen chemicals overlapped in the corresponding guideline-A assays, 8 in the guideline-T assays, and 17 in the guideline-S assays.

We extracted additional data used in a separate analysis from a total of 215 non-guideline studies

[see Supplemental_File_3.csv available online (Rotroff et al. 2013)].

Model Results .

The results presented in Figure 2.3 demonstrate the predictive ability of ToxCast HTS-E and HTS-A assays relative to the corresponding endocrine MOA in the guideline endocrine- related studies. Detailed results from the univariate model with guideline studies are available online in Supplemental_File_4.csv (Rotroff et al. 2013).

41 Comparison of HTS and guideline endocrine assays. For HTS-E end points, we obtained an optimal BA of 0.91 ( p < 0.001) with a sensitivity of 0.89 and a specificity of 0.92, a threshold of two positives for ToxCast HTS-E assays, and > 50% for guideline-E studies (Figure 2.3). This means a minimum of two ToxCast HTS-E assays must report an AC50 value for a chemical to be considered positive, and > 50% of guideline-E assays must be reported as positive in the

EDSP validation reports or OECD guideline studies. Overlapping HTS-E and HTS-A chemicals and corresponding performance in the HTS and guideline studies is provided in Tables 2.3 and

2.4. Twenty-one guideline-E–related chemicals overlapped with ToxCast Phase I chemicals. One chemical, chlorpyrifos-methyl (CASRN 5598-13-0), was misclassified as a positive (FP) and one chemical, prochloraz (CASRN 67747-09-5), was misclassified as a negative (FN) by this set of

ToxCast assays. If the goal was to optimize sensitivity, a threshold criteria of one ToxCast

HTS-E assay and > 50% of guideline-E would produce a perfect sensitivity of 1, but specificity drops to 0.5 across this set of ToxCast HTS-E assays [see Supplemental_File_4.csv available online (Rotroff et al. 2013)]. An additional analysis was conducted in which the threshold criteria for the guideline-E assays lowered from > 50% to any single positive report resulted in a positive call. This lowers the sensitivity from 0.89 to 0.5, and the overall BA drops to 0.75 (Figure 2.3).

Figure 2.3 demonstrates the predictive ability of the ToxCast HTS-A assays and the guideline-A results. The optimal predictive ability of the ToxCast HTS-A assays was reached with a threshold of one HTS-A assay and a threshold > 50% for the guideline-A assays. This set of criteria produced a BA of 0.92 ( p < 0.001), with a sensitivity of 0.83 and specificity of 1

(Table 2.4). The results for HTS-S and HTS-T were not statistically significant among any of the analyses, with BAs of 0.56 ( p > 0.01) and 0.50 ( p > 0.01), respectively [see

Supplemental_File_4.csv available online (Rotroff et al. 2013)].

42 Comparison of HTS and uterotrophic and Hershberger assays. A separate analysis was conducted to determine the predictive capability of the ToxCast HTS-E assays to detect positive and negative chemicals reported in EDSP/OECD guideline uterotrophic assays (Figure 2.3). 18

Eighteen chemicals were available for comparison, and the optimal thresholds for HTS-E produced a BA of 0.9 ( p < 0.001), with a sensitivity and a specificity of 0.88 and 0.9, respectively.

In addition, we determined the predictive ability of ToxCast HTS-A assays for

EDSP/OECD guideline Hershberger results. Although, only six chemicals were available for comparison, the analysis resulted in a BA of 1 ( p < 0.001), with a perfect measure of sensitivity and specificity with thresholds of one positive assay required for both HTS-A and EDSP/OECD guideline Hershberger reports (Figure 2.3).

Comparison of HTS and non-guideline study assays. Predictive modeling results for non- guideline studies in the biomedical literature are presented in Figure 2.4. All results from the analysis with non-guideline studies are available online in Supplemental_File_5.csv (Rotroff et al. 2013). The HTS-E MOA produced a maximum BA of 0.74 ( p < 0.01), with at least one

ToxCast assay being positive (ToxCast HTS-E threshold of 1) and a literature threshold of >

50%. These criteria produced a sensitivity of 0.75 and a specificity of 0.72. Because of the wide range of test conditions, assay technologies, and species present in the open-literature, sensitivity was lower than in the guideline studies. This is apparent because of the model optimization that occurred with only one HTS-E assay required for a positive classification, compared with optimizing at two assays in the guideline analysis. We observed an overall concordance of 0.7 between the guideline-E assay results and the estrogen-related literature results given the stated thresholds (data not shown).

43 The optimal BA reached 0.65 ( p > 0.01) with the ToxCast HTS-A assays threshold of 1 and and androgen-related literature threshold > 50%. At these thresholds, sensitivity was low

(0.3) but specificity was 1 (Figure 2.4). There was a concordance between chemical classifications for guideline-A reports and non-guideline reports of 0.77 at the reported thresholds of > 50% (data not shown).

Discussion

The results of this study demonstrate that ToxCast in vitro assays perform adequately to prioritize chemicals for further EDSP T1S for estrogen and androgen activity, and these HTS assays are predictive of the likelihood of a positive or negative finding in more resource- intensive assays. Additional HTS assays will be needed to predict steroidogenic and thyroid activity of chemicals. Methods for prioritizing chemicals based on a broad range of ToxCast

HTS assays, in combination with physical–chemical properties, have been previously developed

(Reif et al. 2010). Other efforts are also under way to develop more sophisticated, pathway-based predictive models that would be more suitable for supporting regulatory decision making. The present study demonstrates the MOA for which these models would be expected to succeed, and for which areas need additional technologies before a sufficient screening tool would be expected to be successful. This information can now be used for more focused follow-up efforts to identify endocrine-related MOAs for prioritization.

The HTS-E and HTS-A assays demonstrate a high degree of association with the guideline-E and guideline-A assays. The two types of misclassifications, FP and FN, are important because they highlight shortcomings in the model or further specify the domain of applicability. FPs are compounds predicted to be active but that were not active in this analysis

44 based on the threshold of EDSP/OECD reports or literature data. These are significant because a

FP could lead to unnecessary testing in more resource intensive assays, and FNs are of concern because they represent potentially active chemicals that would have gone undetected.

The HTS-E model correctly classified 90% of chemicals, and only 2 of 21 chemicals were misclassified as FP or FN. Chlorpyrifos-methyl was an FP, meaning that it was predicted to be estrogenic by ToxCast HTS-E assays but was not positive in the only guideline-E report, which was a uterotrophic study by Kang et al. (2004) (Table 2.3). This same chemical was reported to be inactive in all of the extracted non-guideline-E literature data (active in 0 of 4 available assays). Chlorpyrifos-methyl was inactive in all ToxCast HTS-E assays except for the

Attagene ER α TRANS and CIS reporter gene assays, which resulted in the subsequent positive call.

Non-guideline estrogen-related literature for prochloraz reported observations of ER α antagonism in some reporter gene and proliferation assays (Bonefeld-Jorgensen et al. 2005;

Kjærstad et al. 2010), but other studies did not observe activity in reporter gene assays (Kojima et al. 2004; Lemaire et al. 2006; Petit et al. 1997; Andersen et al. 2002) or proliferation assays

(Andersen et al. 2002; Vinggaard et al. 1999) [see Supplemental_File_3.csv available online

(Rotroff et al. 2013)]. Prochloraz was a FN in this analysis as because it was active in the NCGC

ER α NCGC_ERalpha_Antagonist assay but negative in all other ToxCast HTS-E binding and reporter gene assays [see Supplemental_File_1.csv available online (Rotroff et al. 2013)].

Prochloraz tested positive in the only guideline-E assay available (Table 2.3). This EDSP/OECD fathead minnow assay showed altered fecundity, vitellogenin, and oocyte atresia after prochloraz treatment (U.S. EPA 2007). Prochloraz is known to disrupt steroidogenesis through inhibition of

CYP (cytochrome P450) 17 hydroxylase and aromatase, preventing the critical conversion of

45 progesterone to 17 α-hydroxyprogesterone and testosterone to 17 β-estradiol, respectively

(Blystone et al. 2007; Sanderson et al. 2002). The fathead minnow assay likely detected this non–receptor-mediated mechanism of estrogen disruption, and this mechanism of action would not have been expected to be detected in the current set of ToxCast HTS-E assays. Prochloraz was the only compound misclassified in the HTS-A analysis, and the effects observed in the reproductive study in male fish are likely a result of the same steroidogenic perturbations.

Prochloraz was correctly identified by the ToxCast aromatase enzyme inhibition assay, which was grouped with the HTS-S–related MOA.

Although a limited number of chemicals was available for comparison, we found a strong association between the ToxCast HTS-E and HTS-A assays with EDSP/OECD guideline uterotrophic and Hershberger studies. Eighteen chemicals were available for comparison between ToxCast HTS-E and guideline uterotrophic assays and only two were misclassified

(Table 2.3). Six chemicals were available for analysis between ToxCast HTS-A assays and

Hershberger responses, and all of these chemicals were classified correctly for a perfect BA of 1

(Table 2.4).

There are several explanations for why a chemical may be misclassified by the ToxCast

HTS models. In some scenarios a chemical may not have been tested at concentrations high enough to exhibit a response in ToxCast assays. Inconsistencies could also result from species, tissue, or cell-type differences between the ToxCast and guideline studies. Most of the ToxCast assays use human cell lines or reporter constructs, and some areas of misclassification may result from species differences between these assays and the rodent bioassays. Comparisons of available species between guideline and non-guideline studies are available in Table 2.5.

Interspecies differences should be taken into consideration because they may be quite

46 substantial. For example, studies have highlighted not only the importance of tissue and cell distribution and context within an organism for both ER and AR (Kolasa et al. 2003; Zhou et al.

2002) but also the presence of ER α and ER β splice variants (Saunders et al. 2002). Most in vitro assays are limited in their metabolic capabilities, so chemicals that require metabolic activation in order to be active may not be detected. However, methoxychlor and vinclozolin, which become more active with metabolism, were both detected in the HTS-E (Table 2.3) and HTS-A

(Table 2.4) assays. Furthermore, in vivo assays may detect chemicals that perturb endocrine- related end points elicited via toxicity in other organs, such as the liver (Leffert and Alexander

1976; Masuyama et al. 2000; Xie et al. 2003). The assays selected for the present study comprise only a small portion of the overall endocrine pathway domain. Alterations in neuroendocrine or other pathways, as well as some feedback mechanisms, could be affected by a compound and would not be detected by these assays. The methods we used to classify compounds may result in different conclusions than those obtained by the EDSP (U.S. EPA 2011b). Despite these limitations, evidence from the present study indicates that very few chemicals that are active in

EDSP T1S go undetected by ToxCast HTS-E and HTS-A assays. Most of the misclassifications appear to be from downstream estrogenic and androgenic effects caused by alterations of upstream steroidogenic enzymes. Most of the active guideline-E and guideline-A chemicals in this data set appear to operate through receptor-mediated pathways and are detectable in vitro .

The non-guideline literature analysis demonstrated that ToxCast HTS assays are also predictive of a broader range of endocrine-related assays. As expected, we observed a loss of accuracy in predicting the non-guideline literature analysis compared with the EDSP/OECD guideline studies because the non-guideline literature studies used a wide variety of species, assay protocols, and technologies. An additional factor that led to the loss of sensitivity in the

47 HTS-A non-guideline analysis was the imbalance of positive to negative reports. The guideline study had 6 positives of 13 total chemicals (46%) at > 50% threshold, and the non-guideline reports had 47 positives of 59 total chemicals (80%) at the same threshold. The sensitivity would be expected to improve with a more balanced data set.

This analysis shows that there is a clear need to develop HTS assays capable of detecting steroidogenesis and thyroid disrupting compounds. The current HTS-S related assay within

ToxCast is limited to a single cell-free aromatase enzyme activity assay. Aromatase is a key enzyme in the biosynthesis of estrogens from androgens (Schuurmans et al. 1991; Stoker et al.

2000a). However, in addition to aromatase inhibition, other mechanisms of steroidogenesis may be impacted by environmental chemicals that are not tested in our current HTS battery (Stoker et al. 2000a, 2000b). Additional assay technologies that may provide a more comprehensive set of steroidogenesis end points are currently being assessed.

The ToxCast HTS-T assays used in our analysis are composed of thyroid hormone receptor binding and reporter gene assays. A limited number of chemicals was available for comparison between the HTS-T assays and the guideline studies. The inability of the ToxCast

HTS-T assay results to associate with compounds thought to disrupt thyroid homeostasis in

EDSP/OECD guideline studies suggests that most of these compounds are not acting through thyroid hormone receptor-mediated mechanisms (Paul et al. 2010; Zorrilla et al. 2009)(Paul et al., 2010; Zorrilla et al., 2009). Thyroid hormone homeostasis has been shown to be altered through enhanced or suppressed clearance of thyroid hormone by metabolic enzymes (Saghir et al. 2008; Zorrilla et al. 2009). ToxCast contains HTS assays that measure nuclear receptor activation and metabolic enzyme activity, which could be relevant for thyroid hormone metabolism. However, many chemicals that were active in these in vitro ToxCast assays were

48 not associated with adverse outcomes in the in vivo literature we reviewed, and the subsequent lack of specificity for thyroid-active chemicals led to their exclusion from this analysis (data not shown).

From these findings, we conclude that most chemicals chosen to validate EDSP T1S assays alter estrogen- and/or androgen-related end points through nuclear receptor-mediated mechanisms and are capable of being efficiently detected by the ToxCast HTS assays. For the purpose of prioritization, it is important to establish sufficient confidence that the assays being utilized are specific and sensitive so that chemicals prioritized for EDSP T1S include those most likely to be active. Although further efforts are needed to improve detection of steroidogenic and thyroid- disrupting chemicals with in vitro test systems, our results indicate that ToxCast endocrine assays are highly predictive of chemicals with estrogenic and androgenic receptor-based endocrine MOAs, and that their use in predictive models for endocrine testing would allow efficient prioritizing of chemicals for further testing.

49 Tables

Table 2.1. Summary of Endocrine Related HTS Assays

Unique EDSP/OECD Overlapping Assigned Chemical Overlapping active chemicals MOA Species Assay Target Assay Technology s Tested Chemicals in ToxCast ToxCast Assay ATG_AR_TRANS HTS-A Human Androgen Receptor-Agonist Multiplexed reporter gene assay 309 a 13 0 NCGC_AR_Agonist HTS-A Human Androgen Receptor-Agonist GAL4 BLAM Reporter gene assay 309 13 0 NCGC_AR_Antagonist HTS-A Human Androgen Receptor-Antagonist GAL4 BLAM Reporter gene assay 309 13 5 NVS_NR_hAR HTS-A Human Androgen Receptor Competitive Binding 309 13 6 NVS_NR_rAR HTS-A Rat Androgen Receptor Competitive Binding 309 13 1 ATG_ERa_TRANS HTS-E Human Estrogen Receptor-α Multiplexed reporter gene assay 326 b 21 12

Estrogen Receptor Response b

50 ATG_ERE_CIS HTS-E Human Multiplexed reporter gene assay 326 21 11 Element ATG_ERRa_TRANS HTS-E Human Estrogen Related Receptor-α Multiplexed reporter gene assay 326b 21 0 ATG_ERRg_TRANS HTS-E Human Estrogen Related Receptor-γ Multiplexed reporter gene assay 326 b 21 0 NCGC_ERalpha_Agonist HTS-E Human Estrogen Receptor-α-Agonist GAL4 BLAM Reporter gene assay 326 b 21 7 NCGC_ERalpha_Antagonist HTS-E Human Estrogen Receptor-α-Antagonist GAL4 BLAM Reporter gene assay 309 15 4 NVS_NR_bER HTS-E Bovine Estrogen Receptor Competitive Binding 316 b 17 1 NVS_NR_hER HTS-E Human Estrogen Receptor Competitive Binding 326 b 21 4 NVS_NR_mERa HTS-E Mouse Estrogen Receptor-α Competitive Binding 316 b 17 1 NVS_ADME_hCYP19A1 HTS-S Human Aromatase Enzyme Inhibition 309 17 1 Thyroid Hormone Receptor-β- NCGC_TRbeta_Agonist HTS-T Human GAL4 BLAM Reporter gene assay 309 8 0 Agonist Thyroid Hormone Receptor-β- NCGC_TRbeta_Antagonist HTS-T Human GAL4 BLAM Reporter gene assay 309 8 0 Antagonist Thyroid Hormone Receptor-β- NVS_NR_hTRa HTS-T Human Receptor Activation 309 8 0 Antagonist a Additional reference compounds from Judson et al. (2010) were run but not included because this is the only androgen-related HTS assay that tested these chemicals; b Includes additional reference compounds from Judson et al. 2010

50

Table 2.2. Summary of Endocrine Literature Survey

No. of Unique No. of Chemicals Endocrine Modes No. of No. of Data Chemicals from Overlapping with of Action Documents Points Literature Survey ToxCast Estrogenicity 18 (108) 410 (979) 104 (158) 21 (143) Androgenicity 22 (54) 571 (301) 60 (73) 13 (59) Steroidogenesis 10 (32) 123 (251) 44 (61) 17 (55) Thyroid 7 (48) 142 (190) 27 (57) 8 (47) ALL 40 (215) 1246 (1721) 154 (182) 35 (157) * Values represent guideline (non-guideline) studies.

51 Table 2.3. Chemical Results from Overlapping HTS-E and Guideline Reports

Chemicals Results Overlapping HTS-E and Guideline-E Chemical Information Model Parameters and Results HTS Assays Guideline c

a

a a a a b a b b,c a a a b b

a a ATG_ERa_TRANS ATG_ERE_CIS ATG_ERRa_TRANS ATG_ERRg_TRANS NCGC_ERalpha_Agonist NCGC_ERalpha_Antagonist NVS_NR_bER NVS_NR_hER NVS_NR_mERa E_Binding Repro E_Fish -E_Pubertal F E_Reporter Gene E_Uterotrophic CASRN Name Modality Endocrine From ToxCast Required Positives From Literature Required Positives BA Sens Spec ToxCast Guideline 1912-24-9 Atrazine E 2 >50% 0.91 0.89 0.92 0 0 0 0 0 0 0 0 0 0 0 0/1 NA 1/0 0/1 1/0

52 17804-35-2 Benomyl E 2 >50% 0.91 0.89 0.92 0 0 0 0 0 0 0 0 0 0 0 0/1 NA NA 0/1 1/1

80-05-7 Bisphenol A E 2 >50% 0.91 0.89 0.92 1 1 1 1 0 0 1 1 1 1 1 1/0 N A 0/1 NA 8/1 2425-06-1 Captafol E 2 >50% 0.91 0.89 0.92 0 0 0 0 0 0 0 0 0 0 0 1/0 NA NA 0/1 0/1 5598-13-0 Chlorpyrifos-methyl E 2 >50% 0.91 0.89 0.92 1 0 1 1 0 0 0 0 0 0 0 NA NANANA0/1 84-74-2 Dibutyl phthalate E 2 >50% 0.91 0.89 0.92 0 0 1 0 0 0 0 0 0 0 0 NA NA NA NA0/3 117-81-7 Diethylhexyl phthalate E 2 >50% 0.91 0.89 0.92 0 0 0 0 0 0 0 0 0 0 0 1/0 NANA0/10/1 66230-04-4 Esfenvalerate E 2 >50% 0.91 0.89 0.92 0 0 1 0 0 0 0 0 0 0 0 NA NA NA NA 0/1 60168-88-9 Fenarimol E 2 >50% 0.91 0.89 0.92 1 1 1 1 0 0 1 1 0 0 0 NA N A 1/0 NA NA 72-43-5 Methoxychlor E 2 >50% 0.91 0.89 0.92 1 1 1 1 0 0 1 1 0 0 0 NA N A 1/0 NA 7/0 40487-42-1 Pendimethalin E 2 >50% 0.91 0.89 0.92 1 1 1 1 0 0 0 0 0 0 0 NA NA NA NA 1/0 52645-53-1 Permethrin E 2 >50% 0.91 0.89 0.92 0 0 1 0 0 0 0 0 0 0 0 NA NA NA NA 0/1 67747-09-5 Prochloraz E 2 >50% 0.91 0.89 0.92 0 1 0 0 0 0 0 1 0 0 0 NA 1/0 NA NA NA 3380-34-5 Triclosan E 2 >50% 0.91 0.89 0.92 0 0 0 0 0 0 0 0 0 0 0 NA NA 1/0 NA 0/1 50471-44-8 Vinclozolin E 2 >50% 0.91 0.89 0.92 0 0 0 1 0 0 0 0 0 0 0 N A 0/1 NA NA NA 104-40-5 4 Nonylphenol E 2 >50% 0.91 0.89 0.92 0 0 0 1 0 0 0 NA 0 0 0 1/0 NA NA NA NA 140-66-9 4-(tert-octyl)Phenol E 2 >50% 0.91 0.89 0.92 1 1 1 1 0 0 1 NA NA 1 NA NA 1/0NA NA 2/0 521-18-6 5a-androstan-17b-ol-3-one E 2 >50% 0.91 0.89 0.92 1 1 1 1 0 0 1NANA0NA NA NANANA1/0 50-28-2 b-estradiol E 2 >50% 0.91 0.89 0.92 1 1 1 1 0 0 1 NA NA 1 NA 1/0 NA NA NA 3/0 13311-84-7 Flutamide E 2 >50% 0.91 0.89 0.92 0 0 0 0 0 0 0 NA 0 0 0 0/ 1 NA NA 0/1 0/1 446-72-0 Genistein E 2 >50% 0.91 0.89 0.92 1 1 1 1 0 0 1 NA NA 1 NA 1/ 0 NA NA NA 5/0 a 1 indicates positive result, 0 indicates negative result b (# of positive reports/# number of negative reports) c Conflicting pubertal studies were ignored (See methods)

52 Table 2.4. Chemical Results from Overlapping HTS-A and Guideline Reports

Chemicals Results Overlapping HTS-A and Guideline-A Chemical Information Model Paramters and Results HTS Assays Guideline c,d b a a a b b,c a,d b a b

a a ATG_AR_TRANS NCGC_AR_Agonist NCGC_AR_Antagonist NVS_NR_hAR NVS_NR_rAR intact male adult A_15-day A_Binding Repro A_Fish A_Hershberger A_Pubertal-M CASRN Name Modality Endocrine From ToxCast Required Positives From Literature Required Positives BA Sens Spec ToxCast Guideline 1912-24-9 Atrazine A 1 >50% 0.92 0.83 1 0 0 0 0 0 0 0 NA 0/1 NA NA 2/0 53 80-05-7 Bisphenol A A 1>50% 0.92 0.83 1 1 1 0 0 1 1 0NA1/0NANA NA 84-74-2 Dibutyl phthalate A 1 >50% 0.92 0.83 1 0 0 0 0 0 0 0 0/1 NA NA NA 1/0 117-81-7 Diethylhexyl phthalate A 1 >50% 0.92 0.83 1 0 0 0 0 0 0 0 NA 0/1 NA NA NA 66230-04-4 Esfenvalerate A 1 >50% 0.92 0.83 1 0 0 0 0 0 0 0 NA NA NA 0/1 NA 122-14-5 Fenitrothion A 1 >50% 0.92 0.83 1 1 1 0 0 1 1 1 NA NA NA 1/0 NA 330-55-2 Linuron A 1 >50% 0.92 0.83 1 1 1 0 0 1 1 0 1/0 1/0 NA 9/0 1/0 72-43-5 Methoxychlor A 1>50% 0.92 0.83 1 1 1 0 0 1 0 0NA1/0NANA 0/1 52645-53-1 Permethrin A 1 >50% 0.92 0.83 1 0 0 0 0 0 0 0 NA NA NA 0/2 NA 67747-09-5 Prochloraz A 1 >50% 0.92 0.83 1 0 1 0 0 0 1 0 NA NA 1/0 NA NA 7696-12-0 Tetramethrin A 1 >50% 0.92 0.83 1 0 0 0 0 0 0 0 NA NA NA 0/1 NA 3380-34-5 Triclosan A 1 >50% 0.92 0.83 1 0 0 0 0 0 1 0 NA NA NA NA 0/1 50471-44-8 Vinclozolin A 1 >50% 0.92 0.83 1 1 1 0 0 1 1 0 1/0 1/0 1/0 4/0 1/0 a 1 indicates positive result, 0 indicates negative result b (# of positive reports/# number of negative reports) c Conflicting pubertal studies were ignored (See methods) d Additional positives from other assays required for positive call (See methods)

53 Table 2.5. Test Species in Guideline and Non-Guideline In Vitro and In Vivo Studies

Organism Non-Guideline-E Guideline-E Non-Guideline-A Guideline-A Human Bovine Atlantic Croaker Kelp Bass Rat Fathead Minnow Mouse Rat Rainbow Trout Rabbit Chicken Green Anole Channel Catfish Zebrafish Xenopus Caiman Whiptail Lizard Largemouth Bass

54 Figures

Figure 2.1. Overlap between EDSP T1S assays and ToxCast phase I assays by endocrine MOAs. Abbreviations: A, androgen; E, endocrine; NA, not applicable; T, thyroid. Colors indicate the type of endocrine MOA data.

55 Figure 2.2. Illustration of the balanced optimization model used to analyze predictive capacity of endocrine-related ToxCast assays. Multiple assays and study reports were available for each chemical–MOA combination. (A) Snapshot of a step in this modeling/optimization process, in which chemical X is positive in three of five HTS assays and two of three guideline reports. In this example, the dynamic HTS threshold is at least two positive assays and the guideline threshold is at least 50% positive reports, so chemical X is considered a true positive (TP). With less than two positive assays, chemical X would be a false negative (FN); < 50% positive reports would produce a false positive (FP); and if both were negative according to this criteria, then chemical X would be a true negative (TN). (B) Method for tabulating results for all chemicals (e.g., chemical X would be counted in the TP portion of the contingency table) to arrive at an estimate of balanced accuracy for each of the threshold parameters.

56 Figure 2.3. Forest plot illustrating the performance —as measured by sensitivity, specificity, and BA—of ToxCast endocrine-related assays for predicting outcomes captured in EDSP/OECD guideline studies. Symbols represent the optimal BA obtained across all threshold combinations and the corresponding sensitivity and specificity at the same threshold. Gray boxes indicate 95% confidence intervals around permuted BA distributions. Analyses designated “All” include all available assays for the stated endocrine MOA. A value of > 50% “required guideline positives” indicates that > 50% of the studies had to report a positive result for a chemical to be considered a positive in the analysis. If the “required guideline positives” value is designated 1, then any study reporting a positive resulted in the chemical being considered positive in the analysis. A separate analysis compared only uterotrophic and Hershberger analyses (right). Statistical significance is reported as * p < 0. 1, ** p < 0.05, *** p < 0.001 by Fisher’s exact test.

57

57 Figure 2.4 Forest plot illustrating the performance—as measured by sensitivity, specificity, and BA—of ToxCast endocrine-related assays for predicting outcomes captured in non-guideline endocrine studies. Symbols represent the optimal BA obtained across all threshold combinations and the corresponding sensitivity and specificity at the same threshold. Gray boxes indicate 95% confidence intervals around permuted BA distributions. A value of > 50% “required non-guideline positives” indicates that > 50% of the studies had to report a positive result for a chemical to be considered a positive in the analysis. If the “required non-guideline positives” column value is designated 1, then any study reporting a positive resulted in the chemical being considered positive in the analysis. Statistical significance is reported as * p < 0.1, ** p < 0.05, *** p < 0.001 by Fisher’s exact test. 58

58 References

Ankley GT, Giesy JP. 1998. Endocrine disruptors in wildlife: A weight of evidence perspective. Principles and Processes for Assessing Endocrine Disruption in Wildlife. SETAC, Pensacola, FL, USA 349–368.

Bigsby R, Chapin RE, Daston GP, Davis BJ, Gorski J, Gray LE, et al. 1999. Evaluating the effects of endocrine disruptors on endocrine function during development. Environmental Health Perspectives 107: 613.

Birnbaum LS, Fenton SE. 2003. Cancer and developmental exposure to endocrine disruptors. Environmental health perspectives 111: 389.

Blystone CR, Furr J, Lambright CS, Howdeshell KL, Ryan BC, Wilson VS, et al. 2007. Prochloraz inhibits testosterone production at dosages below those that affect androgen- dependent organ weights or the onset of puberty in the male Sprague Dawley rat. Toxicological sciences 97: 65–74.

Bonefeld-Jorgensen EC, Grünfeld HT, Gjermandsen IM. 2005. Effect of pesticides on estrogen receptor transactivation in vitro: a comparison of stable transfected MVLN and transient transfected MCF-7 cells. Molecular and cellular endocrinology 244: 20–30.

Chandrasekar G, Arner A, Kitambi SS, Dahlman-Wright K, Lendahl MA. 2011. Developmental toxicity of the environmental pollutant 4-nonylphenol in zebrafish. Neurotoxicology and teratology 33: 752–764.

Colborn T, Clement C. 1992. Chemically-induced alterations in sexual and functional development: the wildlife/human connection . Princeton Scientific Pub. Co.

Cooper RL, Kavlock RJ. 1997. Endocrine disruptors and reproductive development: a weight-of- evidence overview. Journal of Endocrinology 152: 159–166.

Dupont S, Krust A, Gansmuller A, Dierich A, Chambon P, Mark M. 2000. Effect of single and compound knockouts of estrogen receptors alpha (ERalpha) and beta (ERbeta) on mouse reproductive phenotypes. Development 127: 4277–4291.

European Commission. 2012. Endocrine Disruptors Website: How the European Commission Addresses Challenges Posed by Endocrine Disruptors. Endocrine Disruptors Website. Available: http://ec.europa.eu/environment/endocrine/index_en.htm [accessed 3 July 2012].

FFDCA (Federal Food, Drug, and Cosmetic Act). 1996. Tolerances and Exemptions for Pesticide Chemical Residues. 21USC346a.

FQPA. 1996. FQPA (Food Quality Protection Act of 1996) Public Law 104-170.

59 Haddow JE, Palomaki GE, Allan WC, Williams JR, Knight GJ, Gagnon J, et al. 1999. Maternal thyroid deficiency during pregnancy and subsequent neuropsychological development of the child. New England Journal of Medicine 341: 549–555.

Huang R, Xia M, Cho M-H, Sakamuru S, Shinn P, Houck KA, et al. 2011. Chemical genomics profiling of environmental chemical modulation of human nuclear receptors. Environmental health perspectives 119: 1142.

Judson R, Richard A, Dix DJ, Houck K, Martin M, Kavlock R, et al. 2009. The Toxicity Data Landscape for Environmental Chemicals. Environ Health Perspect 117:685–695; doi:10.1289/ehp.0800168.

Judson RS, Martin MT, Reif DM, Houck KA, Knudsen TB, Rotroff DM, et al. 2010. Analysis of eight oil spill dispersants using rapid, in vitro tests for endocrine and other biological activity. Environmental science & technology 44: 5979–5985.

Kang HG, Jeong SH, Cho JH, Kim DG, Park JM, Cho MH. 2004. Chlropyrifos-methyl shows anti-androgenic activity without estrogenic activity in rats. Toxicology 199: 219–230.

Kanno J, Onyon L, Haseman J, Fenner-Crisp P, Ashby J, Owens W. 2001. The OECD program to validate the rat uterotrophic bioassay to screen compounds for in vivo estrogenic responses: phase 1. Environmental Health Perspectives 109: 785.

Kanno J, Onyon L, Peddada S, Ashby J, Jacob E, Owens W. 2003. The OECD program to validate the rat uterotrophic bioassay. Phase 2: dose-response studies. Environmental health perspectives 111: 1530.

Kavlock RJ, Daston GP, DeRosa C, Fenner-Crisp P, Gray LE, Kaattari S, et al. 1996. Research needs for the risk assessment of health and environmental effects of endocrine disruptors: a report of the US EPA-sponsored workshop. Environmental health perspectives 104: 715.

Kjærstad MB, Taxvig C, Nellemann CL, Vinggaard AM, Andersen HR. 2010. Endocrine disrupting effects in vitro of conazole antifungals used as pesticides and pharmaceuticals. Reproductive Toxicology 30: 573–82.

Kojima H, Katsura E, Takeuchi S, Niiyama K, Kobayashi K. 2004. Screening for estrogen and androgen receptor activities in 200 pesticides by in vitro reporter gene assays using Chinese hamster ovary cells. Environmental Health Perspectives 112: 524.

Kolasa A, Wiszniewska B, Marchlewicz M, Wenda-Ró żewicka L. 2003. Localisation of oestrogen receptors (ERalpha and ERbeta) in the human and rat epididymides. Folia Morphologica 62: 467–466.

Kortenkamp A, Martin O, Faust M, Evans R, McKinlay R, Orton F, et al. 2011. State of the Art Assessment of Endocrine Disrupters. Final Report.[Online] Available at: http://ec. europa. eu/environment/endocrine/documents/4_SOTA% 20EDC% 20Final% 20Report 20: V3.

60 Leffert HL, Alexander NM. 1976. Thyroid hormone metabolism during liver regeneration in rats. Endocrinology 98: 1241–1247.

Lemaire G, Mnif W, Mauvais P, Balaguer P, Rahmani R. 2006. Activation of α-and β-estrogen receptors by persistent pesticides in reporter cell lines. Life sciences 79: 1160–1169.

Lodish MB, Sinaii N, Patronas N, Batista DL, Keil M, Samuel J, et al. 2009. Blood pressure in pediatric patients with Cushing syndrome. Journal of Clinical Endocrinology & Metabolism 94: 2002–2008.

Mahoney MM, Padmanabhan V. 2010. Developmental programming: impact of fetal exposure to endocrine-disrupting chemicals on gonadotropin-releasing hormone and estrogen receptor mRNA in sheep hypothalamus. Toxicology and applied pharmacology 247: 98– 104.

Masuyama H, Hiramatsu Y, Kunitomi M, Kudo T, MacDonald PN. 2000. Endocrine disrupting chemicals, phthalic acid and nonylphenol, activate pregnane X receptor-mediated transcription. Molecular Endocrinology 14: 421–428.

Mello WG de, Morais SRL de, Dornelles RCM, Kagohara Elias LL, Antunes-Rodrigues J, Bedran de Castro JC. 2011. Effects of neonatal castration and androgenization on sexual dimorphism in bone, leptin and corticosterone secretion. Bone.

National Research Council. 2007. Toxicity Testing in the 21st Century: A Vision and a Strategy. Washington, DC: National Academies Press.

Ng PC, Lam CWK, Fok TF, Lee CH, Ma KC, Chan IHS, et al. 2001. Refractory hypotension in preterm infants with adrenocortical insufficiency. Archives of Disease in Childhood-Fetal and Neonatal Edition 84: F122–F124.

OECD. 2003. OECD Draft Report of the Validation of the Rat Uterotrophic Bioassay: Phase 2. Testing of Potent and Weak Oestrogen Agonists by Multiple Laboratories.

OECD. 2007. OECD Guideline for Testing of Chemicals. Uterotrophic Bioassay in Rodents: A Short-term Screening Test for Oestrogenic Properties-Test Guideline 440.

OECD. 2001. Progress of Work on the Validation of the Rodent Uterotrophic Bioassay: Phase Two Status Report (Including Statistical Evaluation of the Results).

OECD. 1999. Validation Protocol for the Uterotrophic Assay.

Paul KB, Hedge JM, DeVito MJ, Crofton KM. 2010. Short-term exposure to triclosan decreases thyroxine in vivo via upregulation of hepatic catabolism in young Long-Evans rats. Toxicological Sciences 113: 367–379.

Petit F, Goff P Le, Cravedi JP, Valotaire Y, Pakdel F. 1997. Two complementary bioassays for screening the estrogenic potency of xenobiotics: recombinant yeast for trout estrogen receptor and trout hepatocyte cultures. Journal of molecular endocrinology 19: 321–335.

61 Raun Andersen H, Vinggaard AM, Høj Rasmussen T, Gjermandsen IM, Cecilie Bonefeld- Jørgensen E. 2002. Effects of Currently Used Pesticides in Assays for Estrogenicity, Androgenicity, and Aromatase Activity in Vitro. Toxicology and applied pharmacology 179: 1–12.

Reif DM, Martin MT, Tan SW, Houck KA, Judson RS, Richard AM, et al. 2010. Endocrine Profiling and Prioritization of Environmental Chemicals Using ToxCast Data. Environmental Health Perspectives 118: 1714–1720.

Rotroff DM, Dix DJ, Houck KA, Knudsen TB, Martin MT, McLaurin KW, et al. 2013. Using in Vitro High Throughput Screening Assays to Identify Potential Endocrine-Disrupting Chemicals. Environmental Health Perspectives 121: 7.

Saghir SA, Charles GD, Bartels MJ, Kan LH, Dryzga MD, Brzak KA, et al. 2008. Mechanism of trifluralin-induced thyroid tumors in rats. Toxicology letters 180: 38–45.

Sanderson JT, Boerma J, Lansbergen GW, Berg M van den. 2002. Induction and inhibition of aromatase (CYP19) activity by various classes of pesticides in H295R human adrenocortical carcinoma cells. Toxicology and applied pharmacology 182: 44–54.

Saunders PT, Millar MR, Macpherson S, Irvine DS, Groome NP, Evans LR, et al. 2002. ER β1 and the ER β2 splice variant (ER βcx/ β2) are expressed in distinct cell populations in the adult human testis. Journal of Clinical Endocrinology & Metabolism 87: 2706–2715.

Schenk B, Weimer M, Bremer S, Der Burg B Van, Cortvrindt R, Freyberger A, et al. 2010. The ReProTect Feasibility Study, a novel comprehensive in vitro approach to detect reproductive toxicants. Reproductive toxicology 30: 200–218.

Schuurmans ALG, Bolt J, Veldscholte J, Mulder E. 1991. Regulation of growth of LNCaP human prostate tumor cells by growth factors and steroid hormones. The Journal of steroid biochemistry and molecular biology 40: 193–197.

SDWA. 1996. SDWA (Safe Drinking Water Act Amendments of 1996) Public Law 104-182.

Soto AM, Sonnenschein C. 2010. Environmental causes of cancer: endocrine disruptors as carcinogens. Nature Reviews Endocrinology 6: 363–370.

Spencer TE, Dunlap KA, Filant J. 2012. Comparative developmental biology of the uterus: Insights into mechanisms and developmental disruption. Molecular and cellular endocrinology 354: 34–53.

Stoker TE, Laws SC, Guidici DL, Cooper RL. 2000a. The effect of atrazine on puberty in male Wistar rats: an evaluation in the protocol for the assessment of pubertal development and thyroid function. Toxicological Sciences 58: 50–59.

Stoker TE, Parks LG, Gray LE, Cooper RL. 2000b. Endocrine-disrupting chemicals: prepubertal exposures and effects on sexual maturation and thyroid function in the male rat. A focus on the EDSTAC recommendations. CRC Critical Reviews in Toxicology 30: 197–252.

62 Tilghman SL, Nierth-Simpson EN, Wallace R, Burow ME, McLachlan JA. 2010. Environmental hormones: Multiple pathways for response may lead to multiple disease outcomes. Steroids 75: 520–523.

U.S. EPA. 2012a. Endocrine Disruptor Screening Program.

U.S. EPA. 2011a. Endocrine Disruptor Screening Program for the 21st Century (EDSP21 Work Plan). The Incorporation of In Silico Models and In Vitro High Throughput Assays in the Endocrine Disruptor Screening Program (EDSP) for Prioritization and Screening.

U.S. EPA. 2012b. Tier 1 Screening Battery. Available: http://www.epa.gov/endo/pubs/assayvalidation/tier1battery.htm [accessed 3 July 2012].

U.S. EPA. 2012c. Tox21. Available: http://www.epa.gov/ncct/Tox21/ [accessed 3 July 2012].

U.S. EPA. 2012d. ToxCast: Screening Chemicals to Predict Toxicity Faster and Better. Available: http://epa.gov/ncct/toxcast/ [accessed 3 July 2012].

U.S. EPA. 2012e. TOXCST: Research Chemical Inventory for EPA’s ToxCastTM Program: Structure-Index File.

U.S. EPA. 2007. Validation of the Fish Short-Term Reproduction Assay: Integrated Summary Report.

U.S. EPA. 2011b. Weight-of-Evidence: Evaluating Results of EDSP Tier 1 Screening to Identify the Need for Tier 2 Testing.

Veldscholte J, Berrevoets CA, Ris-Stalpers C, Kuiper G, Jenster G, Trapman J, et al. 1992. The androgen receptor in LNCaP cells contains a mutation in the ligand binding domain which affects steroid binding characteristics and response to antiandrogens. The Journal of steroid biochemistry and molecular biology 41: 665–669.

Vinggaard AM, Breinholt V, Larsen JC. 1999. Screening of selected pesticides for oestrogen receptor activation in vitro. Food Additives & Contaminants 16: 533–542.

Xie W, Yeuh M-F, Radominska-Pandya A, Saini SP, Negishi Y, Bottroff BS, et al. 2003. Control of steroid, heme, and carcinogen metabolism by nuclear pregnane X receptor and constitutive androstane receptor. Proceedings of the National Academy of Sciences 100: 4150–4155.

Zhou Q, Nie R, Prins GS, Saunders PT, Katzenellenbogen BS, Hess RA. 2002. Localization of androgen and estrogen receptors in adult male mouse reproductive tract. Journal of andrology 23: 870.

Zorrilla LM, Gibson EK, Jeffay SC, Crofton KM, Setzer WR, Cooper RL, et al. 2009. The effects of triclosan on puberty and thyroid hormones in male Wistar rats. Toxicological Sciences 107: 56–64.

63

CHAPTER 3

REAL-TIME GROWTH KINETICS MEASURING HORMONE MIMICRY FOR 1816 UNIQUE TOXCAST CHEMICALS IN T-47D HUMAN DUCTAL CARCINOMA CELLS

Introduction

Many xenobiotic chemicals are able to interact with molecular targets in the endocrine system, making screening for potential endocrine disrupting compounds (EDCs) an important issue (Soto and Sonnenschein 2010; Tilghman et al. 2010). One possible effect of endocrine disrupting chemicals is perturbation of cell growth through pathways linked to cell cycle regulation (Davis et al. 1997; Doisneau-Sixou et al. 2003). Activation of the estrogen receptor

(ER) signaling pathway, for example, is one possible mechanism that underlies cell proliferation in hormonally-sensitive tissues such as mammary and endometrial tissue (Doisneau-Sixou et al.

2003; Shiozawa et al. 2004). The ER signaling pathway is involved in a number of complex signaling cascades that can influence many genes, e.g., up to 26% of the transcriptome in MCF-7 human mammary ductal carcinoma cells (Hah et al. 2011). Furthermore, the role of steroid hormones in the regulation of some mammary tumors has been well established and has motivated the development of estrogen pathway-based chemotherapeutics (Crowder et al. 2009;

Jordan and Morrow 1999; Oh et al. 2006; Vogel et al. 2006).

The EPA formed the Endocrine Disruptor Screening Program (EDSP) in 1996, in response to The Federal Food, Drug, and Cosmetic Act (FFDCA), as amended by the Food

Quality Protection Act (FQPA), to test substances for their potential to mimic the effect produced by a naturally occurring estrogen, or other such endocrine effects

64 (www.epa.gov/endo/). EDSP is a tiered program requiring chemical manufacturers to submit or generate data for regulatory decision making. The Tier 1 screening battery (T1S) consists of both in vitro and in vivo assays to test chemicals for their potential for estrogen, androgen, thyroid or steroidogenesis modes-of-action. The Tier 2 assay battery has not yet been determined, but will consist of more complex in vivo tests. The initial list of 67 chemicals for EDSP testing was finalized in 2009 and has yet to complete the T1S. There are thousands of chemicals subject to the T1S, and it is estimated that the T1S will cost ~$1 million per chemical (U.S. EPA 2011a,

2011b).

Due to the amount of resources required to meet the current test guidelines, the EPA launched the EDSP21 project, which is evaluating a new strategy using computational or in silico models, and molecular-based, in vitro assays to prioritize and eventually replace the more resource-intensive T1S assays (U.S. EPA 2011a). Previous studies have demonstrated that in vitro assay data can be used to accurately classify compounds relative to in vivo tests for certain modes of action for endocrine disruption (Rotroff et al. 2013; Schenk et al. 2010). Many of the assays under consideration for EDSP21 testing come from The U.S. EPA’s ToxCast TM program and the interagency Tox21 TM program. ToxCast and Tox21 are designed to use high-throughput in vitro and in silico technologies to prioritize large numbers of chemicals for in vivo regulatory tests. In conjunction with prioritization, the goals of ToxCast are to guide targeted testing strategies and to build predictive models, which may eventually replace more costly, low- throughput assays in a variety of applications beyond prioritizing chemicals in EDSP.

This study analyzes a ToxCast assay that monitors cell growth kinetics in the estrogen responsive, human mammary ductal carcinoma cell line T-47D. An inventory of 1816 unique environmental chemicals was tested in the assay. These include a set of positive and negative

65 reference chemicals for ER activity. This cell line expresses ER α and also contains androgen, glucocorticoid, and progesterone receptors (Keydar et al. 1979). Cell growth kinetics were assessed using real-time cell analysis (RTCA) on the xCELLigence system, which offers a more comprehensive evaluation of cellular growth kinetics compared to traditional in vitro viability and proliferation assays. The assay uses electronic microsensors located at the bottom of the cell culture well to detect changes in cell number, morphology and adhesion through electrical impedance measurement at the electrode-solution interface (Figure 3.1). Here, we compare the results of this cell-impedance assay with other ER related assays in order to better understand the role of xenobiotics in ER mediated cell-level activity and further characterize the ability of in vitro HTS assays to detect these interactions

Methods

Chemical Selection

This study was conducted using data from three ToxCast chemical libraries and reference compounds totaling 1,816 unique chemicals. This inventory includes 293 unique chemical structures from the Phase Iv2 ToxCast chemical library, the majority of which are active ingredients of current or former food-use pesticides, or industrial chemicals of environmental relevance (Judson et al. 2009). An additional 676 unique chemical structures were from the

ToxCast Phase II chemical library. The remaining 880 unique chemical structures were from the

ToxCast E1K chemical library. Individual chemical library information can be found at http://www.epa.gov/ncct/toxcast/chemicals.html. All chemicals were shipped as 20mM stock solutions in DMSO.

66 During screening, 2 M MG132 (Tocris) was used as a positive control for cytotoxicity,

200 and 500 pM 17 -estradiol (Tocris) (E2) was used as a positive control for estrogen response. To characterize the specificity of the assay, progesterone (Sigma), dexamethasone

(Sigma), aldosterone (Sigma) and EGF (Life Technologies) were used as reference compounds for response to progesterone receptor, glucocorticoid receptor, mineralcorticoid receptor and

EGF receptor pathways, respectively.

Experimental Procedures.

The xCELLigence system Multi-E-Plate stations were used to measure the time- dependent response to chemicals described above by RTCA. Each compound was tested in an eight-point, 1:4 serial dilution series starting at a maximum final concentration of 100 M. A maximum starting concentration of 0.5% DMSO was present in the 100 M chemical samples and was diluted along with the test article dilution series. The screen was performed in biological duplicate using two separate, 96-well, E-Plates 96 TM for each dilution series (n=2). Positive controls (MG132 and E2) and a negative control (assay media) were tested in quadruplicate on each testing plate. 0.5% and 0.125% DMSO were tested in duplicates in each plate to serve as solvent controls for the 2 highest concentrations of testing compounds: 100 M and 25 M.

Reference compounds were tested with 8 concentrations with 1:5 serial dilutions. All screening was carried out by ACEA Biosciences, Inc. (San Diego, CA).

T-47D cells purchased from ATCC were maintained in RPMI1640 media supplemented with 10% characterized fetal bovine serum (FBS). Before screening, T-47D cells were pre- conditioned in assay medium: Phenol Red-free RPMI1640 supplemented with 10% charcoal- stripped FBS. Cells were then detached and seeded in E-Plates 96 in assay medium. After overnight monitoring of growth once every hour, compounds were added to T-47D cells and

67 remained in the medium until the end of the experiment. Cellular responses were then recorded once every 5 min for the first 5 hours, and once every hour for an additional 100 hours.

Data Processing

Raw data were collected in the form of Cell Index (CI) for each of the time points before and after compound addition. Data were collated into *.csv files and converted to Normalized

Cell Index (NCI) according to the following equation:

= , = 1,2,3, … ,

where, CI(T k) is the cell index at T k, the last time point before compound addition, CI(T i) is the cell index at T i, the i-th measured time point, N is the number of total measured time points.

Wells flagged for technical issues such as contamination or electronic failure were removed from analysis and substituted with the average of the duplicate chemical’s time points before and after the missing value. Data were then grouped by chemical and a simple moving average (SMA) was used to smooth the data in order to combine replicates for time course analysis:

t-6 + t-5 … + t+6 SMA t= 13 where, SMA t is the simple moving average at time t, NCI t is the NCI at time t.

17 β-estradiol (E2) was run in quadruplicate on each plate, and the maximum average E2 response on each plate was used as a positive control for all the test chemicals on that plate.

Vehicle control (0.5% DMSO) wells were run in duplicate on each plate and the maximum average NCI was used as a negative control for all the test chemicals on that plate. If a chemical sample was run on two different plates, then the maximum NCI values for the positive and negative controls were averaged. All smoothed NCI values for the treatment chemicals were then converted to a percentage of the positive control value.

68 For cell loss, the NCI value at the time of compound administration was considered to represent complete (100%) viability. MG132 (2µM), a proteasome inhibitor and known cytotoxic agent, was used as the positive control for cell loss, and was tested in quadruplicate on each plate. The minimum average response on each plate was used as a positive control for cell loss for all the test chemicals on the corresponding plate. If a chemical sample was run on two different plates, then the minimum NCI values for MG132 were averaged. If an NCI value for

MG132 fell below zero, the response was considered to be below the limit of detection, and was replaced with the minimum value greater than zero across all plates. All smoothened NCI values were then converted to a percentage of positive control, which was considered to represent no

(0%) viability.

Concentration Response Curves

All concentration-response curves were fit using non-linear least squares regression in the open-source statistical software, R and the sfsmisc package (Maechler 2011; R Development

Core Team 2011). For the cell growth analysis, cytotoxicity was ascribed to a particular chemical-concentration level when the response fell below 15% of the next lower test concentration. This concentration and all higher concentrations were flagged and excluded from the concentration response data prior to curve fitting. If a minimum of four responses remained after cytotoxicity filtering, then concentration response curves were fit for each time-point using the Hill-equation:

− = − 1 + 50 where, Y is the response variable (% E2), T is the upper asymptote, B is the lower asymptote, X is the concentration, AC50 is equal to the concentration at which 50% of activity (response)

69 occurs, and W is the Hill-slope coefficient. The Hill-slope coefficient was constrained between 1 and 8. The minimum value of Y produced was recorded as the minimum efficacy (E MIN ), and the maximum value of Y produced was recorded as the maximum efficacy (E MAX ). If the E MAX reached 25% of the window between the maximum growth attained by the negative control

(0.5% DMSO) and the maximum growth of the positive control (200 pM E2 for Phase II chemicals, and 500 pM E2 for PhaseIv2 and E1K chemicals), then the chemical was assessed as

‘active’ for T-47D cell growth. To ensure the quality of the controls, the ratio of active chemicals to the dynamic range of the negative and positive controls was calculated for each plate. Plates that were 3 standard deviations from the mean were substituted with the average negative and positive control values across all plates. This helped prevent false positives due to controls with a low dynamic range. After substituting with the average controls, the concentration responses for these curves were re-fit. If the E MIN for a chemical was greater than the threshold for activity

(25%), then the chemical’s AC50 was set to the minimum concentration. A final manual curation was performed where active and inactive calls could be adjusted if the systematic curve fitting did not accurately reflect the data. This step was performed for all 80 h concentration response curves. An example of how the growth profiles translate into concentration response curves can be seen in Figure 3.2.

A separate but similar approach was taken to assess cell loss. Concentration-response curves were fit for each time-point using the same Hill equation as the growth curves, but replaced with a negative Hill-slope coefficient. B was constrained to 0 (100% MG132), and the minimum value of Y produced was recorded as the E MIN . If the E MIN value dropped an arbitrary

25% below the starting value of 100%, then the chemical was considered active by cell loss.

70 Replicate Concordance.

All active and inactive chemicals at 80 h after compound administration that had at least two blinded duplicates across all three chemical libraries were used to assess replicate concordance. If a chemical produced either all active or inactive responses across available replicates, then that chemical was given a concordance value of 1. If a chemical produced 50% active responses, then that chemical was given a concordance value of 0. For all other combinations the concordance value was based on the number of active responses divided by the total number of replicates. This analysis was performed separately for cell growth and cell loss.

ER Assay Comparison.

Data on ToxCast Phase I and Phase II, totaling to 957 unique chemicals, from ER transactivation assays and ER binding were compared to the results from the cell growth analysis at 80 h. For the transactivation assay, all chemicals were tested in 8-point concentration-response format using 3-fold serial dilutions from a top concentration of 100µM (Phase I) or 200µM

(Phase II) in Attagene Inc.’s cellular biosensor system (Factorial™). This system combines libraries of cis- and trans-regulated transcription factor reporter constructs with a highly homogeneous method of detection, enabling simultaneous evaluation of multiplexed transcription factor activities. The ER α assay endpoint from the TRANS system

(ATG_ERa_TRANS) and the corresponding estrogen receptor response element from the CIS system (ATG_ERE_CIS) comprised the two ER transactivation assays used in all subsequent assay comparisons (Martin et al. 2010; Romanov et al. 2008). The human ER binding assay

(hER) (Caliper, a PerkinElmer company, Hanover, MD) assessed whether the test compound could potentially interrupt estradiol binding with the estrogen receptor (Knudsen et al. 2011).

The hER biochemical assay was performed with human estrogen receptor proteins, tritium-

71 labeled estradiol, and the test compound up to 50 µM. Changes in radioactivity between no test chemical and test chemical determined whether the chemical interfered with estradiol-estrogen receptor binding.

Chemicals with no AC50 value were assigned an arbitrary value of 1M. All AC50 values were transformed according to the following formula:

AC50 = - (AC50 ) transformed log original + 6

This transformed AC50 increases with potency and sets inactive compounds to 0. Compounds that were positive in the hER binding assay were tabulated as “Binding” and all others were tabulated as “Non-Binding.” Pearson correlation coefficients (r) were calculated for the transformed AC50 values between the growth assay and transcriptional activation assays for all, binding, and non-binding categories independently.

Conditional Probabilities

Conditional probabilities were calculated to determine whether or not chemicals classified as positive for ER binding had increased probability of testing positive in the

ATG_ERa_TRANS, ATG_ERE_CIS, or the T-47D cell growth assay compared to the chemicals classified as ER non-binding. Chemicals were split into two categories depending on whether they were active in the hER binding assay (Binding), or inactive in the hER binding assays (Non-

Binding). For each of the categories (Binding or Non-Binding) the total number active in either

0,1,2 or all 3 ER assays (T-47D cell growth, ATG_ERa_TRANS, or ATG_ERE_CIS) was divided by the total number of active chemicals in that category to calculate conditional probabilities.

72 Logistic Regression Model

In order to determine if increased potency and efficacy in the T-47D cell growth,

ATG_ERa_TRANS, and ATG_ERE_CIS increased the likelihood of being positive in the hER binding assay, logistic regression was performed using a generalized linear model implemented using the R statistical software. The AC50 transformed and efficacy normalized to E2 from the cell growth assay at 80 h and both transactivation assays were used to model the binary dependant variable for ER receptor binding (Binding = 1; Non-Binding=0). Odds ratios were then calculated by exponentiating the coefficient for each input variable.

Sensitivity and Specificity Analysis

Comparisons were made to results from guideline uterotrophic rodent bioassays and all combinations of the T-47D cell growth, ATG_ERa_TRANS, and ATG_ERE_CIS assays.

Uterotrophic data was captured from EDSP T1S validation and OECD guideline studies.

Additional details regarding the uterotrophic assays used for this analysis can be found in Rotroff et al., (2013). A comparable analysis was performed using 25 ER reference chemicals with activities based on NICEATM and literature reports. The list of ER reference chemicals and associated calls can be found in Table 3.1. Combinations of all in vitro assays were analyzed to determine if adding assays improves capabilities for predicting the uterotrophic or reference chemical classifications. All in vitro assays in a given group were required to be active in order for the chemical in the corresponding group to be considered active. Sensitivity, specificity and balanced accuracies were calculated from counts. A Fisher’s exact test was performed on the counts and used to determine the reported p values.

73

Results

A total of 2016 chemical samples including reference chemicals were screened for effects on cell growth. This chemical library included 1816 unique chemicals from the various ToxCast libraries and 4 embedded reference compounds. A total of 264 replicated samples consisting of

69 chemicals were added as duplicates or triplicates, or else were contained in multiple ToxCast libraries. The overall concordance of these replicates was 0.84 and 0.89 for cell growth and cell loss at 80 h, respectively (Table 3.2). A total of 204 of 1816 chemicals caused increased cell growth at 80 h after treatment. The efficacy values ranged from 156% to 28% and the potency values spanned the full extent of concentrations tested here. With regards to inhibition of cell growth, a total of 360 chemicals were flagged for >25% drop in impedance at 80 h after treatment. Maximum inhibition ranged from 26% of the negative control down to 100%. Some chemicals caused both an increase in cell proliferation at lower concentrations with higher concentrations causing inhibition of cell growth.

Growth curves were created for all compounds, and Figure 3.2 illustrates representative profiles seen throughout the chemical set. Glucocorticoids, mineralocortocoids, and progestins caused a distinctive biphasic response profile with an initial increased impedance lasting approximately 24 h followed by a return to near baseline growth rates (Figure 3.3A-F), compared to the more linear growth curve seen by some androgens and estrogens (Figure 3.3G-I). It should be noted however, that some estrogens at high-concentrations also had tendencies to show biphasic growth characteristics. A few chemicals, such as 5 α-dihydrotestosterone, produced both the initial increase followed by a significant linear growth increase at later time points. The biphasic response observed for certain endogenous steroid hormones, such as aldosterone,

74 progesterone, and corticosterone, can also be observed for some synthetic steroids. Treatment with triamcinolone, a synthetic corticosteroid, and norgesterel, a synthetic progestin, also caused biphasic growth responses (Fig. 3C,F). Although some estrogens caused biphasic growth curves, such as the synthetic estrogen, 17 α-ethinylestradiol, most of these responses had a more linear growth profile (Figure 3.2, Figure 3.3G-I).

The 25 most efficacious compounds can be seen Table 3.3. Several phenolic compounds including bisphenol B, bisphenol A, 4,4-sulfonyldiphenol, dodecylphenol, 4-(1,1,3,3- tetramethylbutyl)phenol, and 4-nonylphenol (branched) were among the most efficacious.

Though only butylparaben, with a four carbon side-chain, was among the 25 most efficacious compounds, the 9 unique paraben chemicals all tested positive except for methylparaben. Results from paraben compounds can be seen in Table 3.4. Parabens with longer side-chains appeared to be more potent inducers of ER than parabens with shorter side chains.

In order to determine the concordance among chemical responses across the subset of

ToxCast ER assays, the potency values (AC50), between the T-47D cell growth assay and two

ToxCast transcriptional activation assays (ATG_ERa_TRANS, ATG_ERE_CIS) were compared

(Figure 3.4C). The ATG_ERa_TRANS and ATG_ERE_CIS assays displayed the highest correlation across all values with an correlation coefficient of 0.64, indicating that compounds

‘active’ in one assay were likely to be ‘active’ in the other, and also have similar potency (AC50) values between the two assays. This is expected because both are measuring similar, but not identical, ER-mediated transcriptional activation. Although, both assays are measuring ER transcriptional activity, the ATG_ERE_CIS assay uses a full-length reporter construct, whereas the ATG_ERa_TRANS contains only the ligand binding domain. An additional comparison was made to determine if there was a difference in correlation between these two transcriptional

75 assays and chemicals that were also positive in the ER binding assay. AC50 values for chemicals that were positive for ER binding were highly correlated (r=0.98, p < .001), compared to chemicals that were positive in both ER transactivation assays but tested negative for ER binding

(r=0.59, p < .001) (Figure 3.4C). This suggests that chemicals active for both transactivation assays and ER receptor binding have an increased likelihood of activating the ER signaling pathway through similar mechanisms, i.e., through ER binding. A complementary analysis was performed between the T-47D cell growth assay and each transactivation assay. The cell growth assay results demonstrated a weak correlation coefficient of 0.42 ( p < .001) and 0.35 ( p < .001) across all chemicals for ATG_ERE_CIS and ATG_ERa_TRANS assays, respectively (Figure

3.4A-B). However, when non-binding chemicals were excluded, the correlation coefficient increased to 0.70 ( p < .001) and 0.72 ( p < .001) for ATG_ERE_CIS and ATG_ERa_TRANS assays, respectively (Figure 3.4A-B). Because these chemicals were detected as positive for ER binding, and were active for both ER transactivation and T-47D cell growth at similar potencies, these chemicals would be expected to have a higher likelihood of activating the ER signaling pathway through a common mechanism. There were a total of 51 (5%) chemicals that were active in the T-47D cell growth assay and both transactivation ER assays out of the available 957 unique chemicals from ToxCast Phase I and Phase II chemical libraries. There were a total of 20

(2%) unique chemicals that were considered positive for ER binding, 15 (2%) of which were positive for all three cell growth and transactivation assays. Out of the 20 unique chemicals that were positive for binding in the hER binding assay, 3 were considered negative in all three cell growth and transactivation assays. Overall, this indicates that the hER binding assay is less sensitive, but highly specific.

76 Table 3.5 shows the conditional probabilities of being active in the cell growth or transactivation assays if also positive in the hER binding assay. Compounds that were negative for ER binding have a decreased probability of being active in either the cell growth or transactivation assays (Table 3.5). Likewise, compounds active in the hER binding assay have a conditional probability of 0.75 for being active in all three cell growth and transactivation assays

(Table 3.5).

A logistic regression model revealed that the demonstrated potency (AC50) and efficacy

(EMAX) for a test compound were highly associated with the likelihood of the compound being active in the ER binding assays (p = 3.04x10 -7). Odds ratios were calculated using the logistic regression coefficients for both the AC50 and EMAX values to determine the contribution of each variable for predicting ER binding. Using the AC50 yielded an odds ratio (OR) of 14.57

(95% CI 3.50 – 109.04), and using the EMAX resulted in a weaker and non-statistically significant OR of 1.02 (95% CI 0.96 – 1.07), signifying that chemical potency is a stronger indicator of ER receptor binding than efficacy for this set of assays (Table 3.6).

Results from the uterotrophic sensitivity and specificity analysis are reported in Figure

3.5. All comparisons were statistically significant ( p < .01) and had balanced accuracies (BA) >

0.7. The lowest BA was 0.76 for the ATG_ERa_TRANS and the highest BA of 0.815 occurred with the T-47D Cell Growth-ATG_ERa_TRANS, T-47D Cell Growth-ATG_ERE_CIS, and the

T-47D Cell Growth-ATG_ERa_TRANS-ATG_ERE_CIS combinations. Because all assays in a group must be positive in order for a chemical to be marked active, sensitivity decreases and specificity increases with the addition of assays. Comparisons to the ER reference set in Table

3.1 are made in Figure 3.6. A total of 25 chemicals were available for analysis, including four negative compounds (dibutyl phthalate (DBP), flutamide, linuron, progesterone). Overall, the in

77 vitro assays classified compounds more accurately with the ER reference set, than the uterotrophic assays. All assay combinations were statistically significant ( p < 0.01) by Fisher’s exact tests. The T-47D performed the lowest with a BA of 0.85 ( p=0.005). The highest BA

(0.95) was obtained with the T-47D Cell Growth-ATG_ERa_TRANS, T-47D Cell Growth-

ATG_ERE_CIS, and the T-47D Cell Growth-ATG_ERa_TRANS-ATG_ERE_CIS combinations.

Discussion

The results from the present study add an important functional component to measuring

ER activation, namely T-47D cell growth kinetics, and expand the chemical space to many compounds for which no estrogenic information has been thus far captured. 32 (3%) unique compounds among 957 test compounds tested negative in all other ER assays, and tested positive in the T-47D cell growth assay. This subset of chemicals may have induced T-47D cell growth through non-classical ER signaling pathways (Boonyaratanakornkit et al. 2007; Matés et al.

2008). The other 172 active chemicals would be expected to have a higher likelihood of perturbing the estrogenic pathway through more conventional ligand-activated signaling network. Comparisons of chemical classifications in the T-47D cell growth assay to chemical classifications from uterotrophic studies and ER reference chemicals demonstrated a high-degree of concordance (Figures 3.5 and 3.6). Although, all combinations of assays were statistically significantly associated with both uterotrophic classifications and ER reference chemical classifications, balanced accuracies were generally higher when multiple assays were included.

One advantage to the real-time capabilities of the xCELLigence platform is the capacity to differentiate between chemical growth profiles that may be indistinguishable from testing strategies that use traditional proliferation assays with fewer time points. Biphasic growth curves

78 were observed in some cases, whereby increased impedance occurred, followed by decreased impedance around 30 h after compound administration, with a subsequent increase in impedance sustained throughout the remaining sampling time. Impedance can be impacted by alterations in cell morphology due to cytoskeletal changes and alterations in cellular adhesion machinery, in addition to cell proliferation and cell loss (Atienza et al. 2005). The biphasic growth curves appeared predominantly after exposures to progestins, mineralcorticoids, glucocorticoids and some androgens.

Two isoforms of the progesterone receptor (PR) have been identified, PRA and PRB, and both are expressed in T-47D cells with an approximate ratio of 0.5-0.9 PRA:PRB (Groshong et al. 1997; McGowan and Clarke 1999; Sartorius et al. 1994). Activation of PRA is associated with morphological changes, such as cell rounding and loss of cell adhesion, and is thought to contribute to increased malignancy in tumors expressing high ratios of PRA:PRB; whereas PRB is associated with a proliferative response (Boonyaratanakornkit et al. 2007; McGowan and

Clarke 1999). Biphasic proliferative responses have been observed in T-47D cells expressing only the PRB isoform, with increased numbers of cells retained in G2/M+S until approximately

15-48 h, upon which a late arrest in G1 occurred, roughly corresponding to the biphasic growth curves observed in the current study (Figure 3.4) (Boonyaratanakornkit et al. 2007; Groshong et al. 1997). Furthemore, PRB has been shown to regulate cell cycle promoters, cyclin D1 and cyclin dependent kinase (CDK), and cell cycle inhibitors including p21, in a biphasic manner corresponding with late G1 arrest following progesterone exposure (Boonyaratanakornkit et al.

2007; Groshong et al. 1997). PR, AR, and the glucocorticoid receptor (GR) have been identified as inducers of serum and glucocorticoid regulated kinase -1 (SGK-1), which regulates cell proliferation and cell cycle progression and may explain the similar growth profile observed by

79 androgens, glucocorticoids, and progestins (Amato et al. 2009; Boonyaratanakornkit et al. 2007;

Shanmugam et al. 2007). Cyclin D1 has been shown to both activate transcription of ER in the absence of estradiol, and be induced by ER transactivation (Altucci et al. 1996; Neuman et al.

1997; Roy and Thompson 2006). This positive feedback system may explain the sustained growth profile observed by estrogen treatments. Though it is not clear what caused the biphasic growth profile, T-47D cells do express several steroid hormone receptors, and hormones are known to impact cell cycle through both receptor-mediated and receptor-independent mechanisms. In addition, electron transport chain inhibitors, pyridaben and rotenone, also potently induced cell growth in this assay. An ancillary experiment was conducted that demonstrated, unlike estradiol, the growth induced by these compounds was not attenuated by the ER antagonist, ICI 182,780. The inability for ICI 182,780 to block the proliferative response indicates that these compounds are not inducing cell growth through an ER mechanism (Figure

3.7).

Many of the most efficacious compounds in the T-47D cell growth assay included phenolic compounds (Table 3.3). The ability for phenolic compounds to bind and activate ER has been demonstrated in a number of studies (Blair et al. 2000; Gaido et al. 1997). Some phenolic compounds, such as bisphenol-A and 4-nonylphenol are high-production volume

(HPV) chemicals used in a wide variety of applications, including food and drink packaging, flame retardants, polycarbonate plastics, detergents, and polystyrene tubes (Calafat et al. 2005;

Sonnenschein and Soto 1998; U.S. HHS 2008). Potential for human exposure to these HPV chemicals is relatively high due to their use in a wide range of manufacturing applications.

Bisphenol-A, for example, has been detected in blood and urine samples ubiquitously in the population (Calafat et al. 2005). Although the estrogenic potential of compounds like bisphenol-

80 A and 4-nonylphenol has been previously studied, limited data exists on the potential estrogenicity for many chemicals tested in this chemical library, such as butam. Butam is an herbicide that tested positive in the T-47D cell growth, ATG_ERa_TRANS, and ATG_ERE_CIS assays.

The paraben chemical class is composed of commonly used preservatives found in a wide range of household items, some of which include cosmetics, shampoos, pharmaceuticals, and food additives (Smith et al. 2012). Parabens have demonstrated activity in a variety of ER assays, and an increased paraben side chain length positively correlates with ER activation potency

(Blair et al. 2000; Okubo et al. 2001). This was highly consistent with the T-47D cell growth assay described herein, in which parabens with longer side chains achieved greater potency than parabens with shorter side chains (Table 3.4). Relative efficacies for most of the parabens were between 60% and 90% of estradiol, but no structurally-related trend for efficacy was apparent.

Parabens all demonstrated linear growth profiles, suggesting the activation of different pathways compared to the compounds associated with biphasic growth profiles.

The ratio of ER α to ER β is an important factor to determining the phenotypic response after estrogenic exposure (Gustafsson 1999; Kuiper et al. 1997; Pravettoni et al. 2007).

Genistein, a naturally occuring phytoestrogen, resulted in a growth response similar to 17 β- estradiol, albeit less potent; however, genistein has been associated with decreased cell proliferation in certain cell lines, e.g., Caco2-BBe, MCF-7, and PC-3 (Chen and Donovan 2004;

Peterson and Barnes 1996). Although, genistein may bind to ER α, it preferentially binds to ER β

(Kuiper et al. 1997). ER β has been shown to counter the proliferative response typically associated with ER α activation (Gustafsson 1999; Pravettoni et al. 2007). T-47D cells have a much higher basal ER α/ ER β ratio, resulting in increased proliferation after genistein exposure

81 (Sotoca et al. 2008; Strom et al. 2006). Sotoca et al. (2008) used a T-47D-ER β recombinant cell line with enforced expression of ER β and managed to attribute the observed anti-proliferative effect to ER β activation. This suggests that the high ratio of ER α: ER β in T-47D cells is the likely cause for the observed proliferative response by genistein in this study. In addition to the impact of ER α to ER β ratios, cofactor availability may be tissue and cell type dependent, and could alter the observed response (Shang et al. 2000; Thenot et al. 1999).

The high-throughput hER binding assay is less sensitive compared to the T-47D cell growth, ATG_ERa_TRANS, and ATG_ERE_CIS assays (Figure 3.4). The hER binding assays were exposed to chemical concentrations of up to 50 µM, and the T-47D cell growth,

ATG_ERa_TRANS, and ATG_ERE_CIS assays were tested with chemical concentrations up to

100 µM. This provides a partial explanation for why the hER binding assays did not detect the weakly active compounds; however, many of the chemicals that were positive in the T-47D cell growth, ATG_ERa_TRANS, and ATG_ERE_CIS assays produced AC50 values < 50 µM and were not detected in the hER binding assay. The cross-assay comparisons provide a better understanding of each assay’s strengths and weaknesses in order to better characterize an individual chemical’s estrogenic potential.

When considered in context of other ER in vitro assays, the T-47D cell growth assay provides an important functional measurement of estrogenic activity. This assay may be an important indicator for non-genomic mechanisms of ER activation; the importance of these mechanisms in regulating proliferative responses are becoming increasingly apparent

(Björnström and Sjöberg 2005; Falkenstein et al. 2000; Watson et al. 2011). It is possible that some of the discrepancies between active and inactive responses between the cell growth assay and the two transcriptional activation assays may be due to an estrogenic response not mediated

82 by gene expression through the estrogen response element. Future work is needed to include a more comprehensive panel of assays, covering other key molecular initiating events in the ER pathway. Currently, efforts within the ToxCast program are underway to include assays that measure ER signaling before and after treatment of the test chemical with a liver S9 fraction to determine effects of xenobiotic metabolism and to include assays that measure receptor dimerization and nuclear translocation for both ER α and ER β. Overall, the T-47D cell growth assay provides important information relating duration of exposure, potency, and efficacy to effects on cell growth or cell loss. In addition, the data-driven characterization of ER in vitro assays provides an important step towards developing a more comprehensive and integrated approach for testing chemicals for their potential modulation of ER signaling.

83 Tables

Table 3.1. ER Reference Chemicals for Sensitivity and Specificity Analysis.

Activity CASRN Chemical Name Call

57-63-6 17alpha-ethynyl estradiol Positive 50-28-2 17beta-estradiol Positive 56-53-1 Diethylstilbestrol (DES) Positive 77-09-8 Phenolphthalein Positive 4-(1,1,3,3- 140-66-9 Tetramethylbutyl)phenol Positive 599-64-4 4-Cumylphenol Positive 80-05-7 Bisphenol A Positive 77-40-7 Bisphenol B Positive 85-68-7 Butylbenzyl phthalate Positive 94-26-8 Butylparaben Positive 60168-88-9 Fenarimol Positive 446-72-0 Genistein Positive 143-50-0 Kepone Positive 84852-15-3 Nonylphenol (branched) Positive 789-02-6 o,p'-DDT Positive 120-47-8 Ethylparaben Positive 1912-24-9 Atrazine Positive

56-55-3 Benz[a]anthracene Positive 115-32-2 Dicofol Positive 72-43-5 Methoxychlor Positive 72-55-9 p,p’-DDE Positive 84-74-2 Dibutyl phthalate (DBP) Negative 13311-84-7 Flutamide Negative 330-55-2 Linuron Negative 57-83-0 Progesterone Negative

84

Table 3.2. Chemical Library Results Summary and Replicate Concordance

No. Active Replicate Replicate No. No. Percentage No. Active Percentage Chemical Chemicals - Concordance - Concordance Chemical Unique Active - Cell Chemicals - Active - Cell Library Cell Cell Growth - Cell Loss Samples Chemicals Growth (%) Cell Loss Loss (%) Growth Phase Iv2 311 293 16 5.5 0.96 74 25.3 0.96 Phase IIab 700 676 81 12.0 0.78 130 19.2 0.82 E1K 1000 880 123 14.0 0.88 156 17.7 0.92 Total 2016 a 1820 a,b 204 11.2 0.84 360 19.8 0.89 a Four Additional reference chemicals were included b Some compounds were run in more than one chemical library

85

85 Table 3.3. 25 Most Potent and Efficacious Compounds in the Cell Growth Assay at 80 h.

Time Since Response Cutoff for Time Since Treatment AC50 EMAX (% EMIN CASRN Chemical Name Activity (% Growth above Treatment (h) When First (µM)a E2) (% E2) Starting Value) Active (h)

77 -40 -7 Bisphenol B b 80.0 46.2 0.283 128.93 25.28 47.45 50-28-2 17beta-Estradiol b 79.8 44.9 ≤0.006 128.72 84.56 59.66

57-91-0 17alpha-Estradiol 80.0 44.0 ≤0.006 127.35 92.59 49.76 446 -72 -0 Genistein b 79.7 48.9 0.082 123.09 34.56 46.99 27193-86-8 Dodecylphenol 80.1 44.1 0.229 117.50 41.08 57.00 80-05-7 Bisphenol A b 79.5 47.1 0.387 116.70 32.73 51.63 84 -16 -2 meso -Hexestrol 79.7 46.7 ≤0.006 115.46 93.70 56.14 57-63-6 17alpha-Ethinylestradiol b 79.7 13.7 ≤0.006 114.87 104.75 47.14 72-33-3 Mestranol 80.1 41.3 ≤0.006 114.18 89.55 54.32 84852-15-3 4-Nonylphenol, branched b 80.0 46.4 0.304 110.94 26.33 48.34 17924-92-4 Zearalenone 80.0 52.1 0.022 109.84 64.00 64.34 105624-86-0 5HPP-33 79.8 49.9 0.386 108.81 20.47 48.04 86 53-16-7 Estrone 79.5 52.4 ≤0.006 105.17 85.66 51.97 474-86-2 Equilin 79.7 50.0 ≤0.006 103.63 79.63 53.64 2,2-Bis(4-hydroxyphenyl)-1,1,1- 2971-36-0 trichloroethane 79.7 50.7 0.102 101.65 35.61 57.65 521-18-6 5alpha-Dihydrotestosterone 79.6 43.1 0.181 100.35 45.01 55.11

50-27-1 Estriol 80.1 56.4 ≤0.006 97.87 78.38 49.36

56-53-1 Diethylstilbestrol b 79.0 48.0 ≤0.006 95.87 87.12 46.46

486-66-8 Daidzein 79.5 56.1 0.400 90.25 30.34 54.21 68-22-4 Norethindrone 79.9 40.9 ≤0.006 89.26 53.55 51.56 1478-61-1 Bisphenol AF 79.7 52.1 0.118 88.41 30.88 55.79 10161-33-8 17beta-Trenbolone 79.7 40.7 ≤0.006 88.21 59.43 55.57 140-66-9 4-(1,1,3,3-Tetramethylbutyl)phenol 79.7 52.0 0.230 82.83 26.68 49.09 57-83-0 Progesterone 79.8 43.8 0.079 77.86 48.35 49.06 104-43-8 4-Dodecylphenol 80.0 65.0 0.244 75.21 20.90 60.22 a Compounds that were saturated at lowest concentration tested are denoted with “ ≤” b Compounds that were present in multiple chemical libraries, the one with the highest EMAX was included in this table.

86 Table 3.4. Paraben Compounds – T-47D Cell Growth Potency and Efficacy at 80 h

Time Since Response Cutoff for Activity Treatment EMAX (% Growth above Starting CASRN Chemical Name (h) AC50 (µM) (% E2) Value) 5153-25-3 2-Ethylhexylparaben 80.1 0.607 82.55 54.08 1219-38-1 Octylparaben 80.1 1.143 90.91 54.32 17696-62-7 Phenylparaben 80.1 1.332 65.78 54.08 1085-12-7 Heptylparaben 79.9 1.492 82.94 50.21 94-26-8 Butylparaben a 80.0 1.776 99.23 51.39 94-18-8 Benzylparaben 80.1 1.871 69.70 61.94 94-26-8 Butylparaben a 79.7 3.426 116.36 53.61 94-13-3 Propylparaben 80.0 4.329 88.88 49.14 120-47-8 Ethylparaben a 80.1 7.617 63.35 52.77 120-47-8 Ethylparaben a 80.1 7.860 65.69 52.78 99-76-3 Methylparaben 80.0 NA 40.98 49.14 a Chemical was included in two different ToxCast chemical inventories

Table 3.5. Estrogen Receptor Binding Conditional Probabilities

No. No. of Assays Chemical Conditional No. Chemicals Conditional Detected as s Detected Probability for Detected as ER Probability for Non- Active a as ER Binders Non-Binders b Binders Binders b 0 3 0.15 664 0.70 1 1 0.05 152 0.16 2 1 0.05 93 0.10 3 15 0.75 36 0.04 a Assays include T-47D Cell growth, ATG_ERa_TRANS, and ATG_ERE_CIS; b Based on the hER binding assay

87 Table 3.6. Logistic Regression Model Performance

No. Odds Ratio Input Coefficient Std. Chemical (95% CI) P-value Variable s ( β) Error sa 4.16x10 -8 (5.23x10 -13 – Intercept 51 -16.90 4.84 8.72x10 -5) 0.0004 Mean Efficacy (EMAX) 51 0.02 0.03 1.02 (0.96 – 1.07) 0.53 Mean Potency (AC50) 51 2.68 0.86 14.57 (3.50 – 109.04) 0.0012 Overall 51 Model 3.04x10 -7 a Only chemicals active in all three assays were used (Cell growth, ATG_ERa_TRANS, and ATG_ERE_CIS)

88

Figures

Figure 3.1. RTCA Cell Growth Assay. 96-well E-plates have electrodes at the bottom of each well. Cells are seeded and the impedance signal is altered by the confluence or morphological changes of cells in each well. Increases in cell density correspond to increased impedance (Z).

89

Figure 3.2. Example of Growth Curve Translation to Concentration Response Curve. In this example, the effect of treatment with genistein on cell proliferation was plotted over time and concentration. As the cell number increased, the impedance increased. The treatment had an apparent effect on cell growth or viability which can be measured through the relative decrease in electrical impedance. The circles on the growth curve correspond to the time of the concentration response curve plotted on the bottom. 90

90

Figure 3.3. Representative growth curves of various test compounds measuring changes in normalized cell index (NCI) over time (h). Biphasic growth curves can be seen for some endogenous and synthetic steroid hormones including corticosteroids (B,F), progestins (A,C), and some androgens (D,E), whereas linear growth curves are observed for compounds known to bind to the estrogen receptor (ER) (G-I).

91 Figure 3.4. Comparison of Cell Growth Results with Additional ER Assays. Chemical AC50 values were log transformed so that the axis increases with potency. The potency values were compared with two transactivation assays (ATG_ERa_Trans and ATG_ERE_CIS). Chemicals positive for one of three ER binding assays were colored red, and chemicals testing negative in all three binding assays were colored red. Linear regression demonstrated a higher linear correlation between compounds that were also detected to bind to ER.

92

Figure 3.5. Receiver operating characteristic (ROC) plot for uterotrophic comparisons. Symbols represent groups of assays. A chemical must be classified as active in all assays in the given group for a chemical to be considered active. Active and inactive chemicals were then compared to classifications of uterotrophic activity to determine sensitivity, specificity, and BA. P values were determined by Fisher’s exact test.

93

Figure 3.6. ROC plot for ER reference chemical comparisons. Symbols represent groups of assays. A chemical must be classified as active in all assays in the given group for a chemical to be considered active. Active and inactive chemicals were then compared to classifications of uterotrophic activity to determine sensitivity, specificity, and BA. P values were determined by Fisher’s exact test.

94 Figure 3.7. Combination Treatment of Electron Transport Chain Inhibitors with ER Antagonist, ICI 182,780. Two known mitochondrial electron chain inhibitors, rotenone and pyridaben, caused a potent increase in the Normalized Cell Index (NCI). In order to investigate if this observation was occurring through the activation of ER, the study was repeated using a combination of chemical treatment alone, and chemical treatment in combination of prototypical ER antagonist, ICI 182,780. The response induced by 17 β- estradiol was completely attenuated with the addition of ICI 182,780. However, addition of ICI 182,780 had no effect on the response induced by rotenone or pyridaben treatment, suggesting the response causing increased impedance is not mediated by ER.

17β– estradiol 0.13pM 17β– estradiolICI182780 + ICI 182,780 2.5 ctrl 2.5 ctrl AB10nM 40uM 2nM 8uM 2 Normalization point 2 Normalization point (cmpd addition) 400pM (cmpdaddition) 1.6uM 80pM 320nM 1.5 16pM 1.5 64nM 3.2pM 12.8nM NCI NCI 0.64pM 2.56nM 1 1 0.13pM 0.51nM ICI182780 0.5 0.5

0 0 0 30 60 90 120 0 30 60 90 120 Time (hrs) Time (hrs)

Pyridaben Pyridaben + ICI 182,780 0.51nM ctrl ICI182780 2.5 2.5 ctrl C 40uM D 40uM 8uM Normalization point Normalization point 8uM 2 2 (cmpd addition) 1.6uM (cmpd addition) 1.6uM 320nM 320nM 64nM 1.5 1.5 64nM 12.8nM 12.8nM

2.56nM NCI 2.56nM NCI 1 0.51nM 1 0.51nM ICI182780 0.5 0.5

0 0 0 30 60 90 120 0 30 60 90 120 Time (hrs) Time (hrs)

Rotenone0.51nM RotenoneICI182780 + ICI 182,780 2.5 ctrl 2.5 ctrl E 40uM F 40uM 8uM Normalization point 8uM 2 Normalization point 2 (cmpd addition) (cmpd addition) 1.6uM 1.6uM 320nM 320nM 1.5 1.5 64nM 64nM 12.8nM NCI 12.8nM 2.56nM 1 2.56nM NCI 1 0.51nM 0.51nM ICI182780 0.5 0.5

0 0 0 30 60 90 120 0 30 60 90 120 Time (hrs) Time (hrs)

95 References

Altucci L, Addeo R, Cicatiello L, Dauvois S, Parker MG, Truss M, et al. 1996. 17beta- Estradiol induces cyclin D1 gene transcription, p36D1-p34cdk4 complex activation and p105Rb phosphorylation during mitogenic stimulation of G (1)-arrested human breast cancer cells. Oncogene 12: 2315.

Amato R, D’Antona L, Porciatti G, Agosti V, Menniti M, Rinaldo C, et al. 2009. Sgk1 activates MDM2-dependent p53 degradation and affects cell proliferation, survival, and differentiation. J Mol Med 87:1221–1239; doi:10.1007/s00109-009-0525-5.

Atienza JM, Zhu J, Wang X, Xu X, Abassi Y. 2005. Dynamic monitoring of cell adhesion and spreading on microelectronic sensor arrays. Journal of biomolecular screening 10: 795–805.

Björnström L, Sjöberg M. 2005. Mechanisms of estrogen receptor signaling: convergence of genomic and nongenomic actions on target genes. Molecular Endocrinology 19: 833– 842.

Blair RM, Fang H, Branham WS, Hass BS, Dial SL, Moland CL, et al. 2000. The estrogen receptor relative binding affinities of 188 natural and xenochemicals: structural diversity of ligands. Toxicological Sciences 54: 138–153.

Boonyaratanakornkit V, McGowan E, Sherman L, Mancini MA, Cheskis BJ, Edwards DP. 2007. The role of extranuclear signaling actions of progesterone receptor in mediating progesterone regulation of gene expression and the cell cycle. Molecular Endocrinology 21: 359–375.

Calafat AM, Kuklenyik Z, Reidy JA, Caudill SP, Ekong J, Needham LL. 2005. Urinary Concentrations of Bisphenol A and 4-Nonylphenol in a Human Reference Population. Environ Health Perspect 113:391–395; doi:10.1289/ehp.7534.

Chen AC, Donovan SM. 2004. Genistein at a concentration present in soy infant formula inhibits Caco-2BBe cell proliferation by causing G2/M cell cycle arrest. The Journal of nutrition 134: 1303–1308.

Crowder RJ, Phommaly C, Tao Y, Hoog J, Luo J, Perou CM, et al. 2009. PIK3CA and PIK3CB inhibition produce synthetic lethality when combined with estrogen deprivation in estrogen receptor–positive breast cancer. Cancer research 69: 3955– 3962.

Davis DL, Telang NT, Osborne MP, Bradlow HL. 1997. Medical hypothesis: bifunctional genetic-hormonal pathways to breast cancer. Environmental health perspectives 105: 571.

Doisneau-Sixou SF, Sergio CM, Carroll JS, Hui R, Musgrove EA, Sutherland RL. 2003. Estrogen and antiestrogen regulation of cell cycle progression in breast cancer cells. Endocrine-related cancer 10: 179–186.

96 Falkenstein E, Tillmann HC, Christ M, Feuring M, Wehling M. 2000. Multiple actions of steroid hormones—a focus on rapid, nongenomic effects. Pharmacological reviews 52: 513–556.

Gaido KW, Leonard LS, Lovell S, Gould JC, Babaı̈ D, Portier CJ, et al. 1997. Evaluation of chemicals with endocrine modulating activity in a yeast-based steroid hormone receptor gene transcription assay. Toxicology and applied pharmacology 143: 205– 212.

Groshong SD, Owen GI, Grimison B, Schauer IE, Todd MC, Langan TA, et al. 1997. Biphasic regulation of breast cancer cell growth by progesterone: role of the cyclin- dependent kinase inhibitors, p21 and p27Kip1. Molecular Endocrinology 11: 1593– 1607.

Gustafsson JA. 1999. Estrogen receptor beta--a new dimension in estrogen mechanism of action. J Endocrinol 163:379–383; doi:10.1677/joe.0.1630379.

Hah N, Danko CG, Core L, Waterfall JJ, Siepel A, Lis JT, et al. 2011. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell 145: 622–634.

Jordan VC, Morrow M. 1999. Tamoxifen, Raloxifene, and the Prevention of Breast Cancer. Endocrine Reviews 20:253–278; doi:10.1210/er.20.3.253.

Judson R, Richard A, Dix DJ, Houck K, Martin M, Kavlock R, et al. 2009. The Toxicity Data Landscape for Environmental Chemicals. Environ Health Perspect 117:685– 695; doi:10.1289/ehp.0800168.

Keydar I, Chen L, Karby S, Weiss FR, Delarea J, Radu M, et al. 1979. Establishment and characterization of a cell line of human breast carcinoma origin. European Journal of Cancer (1965) 15: 659–670.

Knudsen TB, Houck KA, Sipes NS, Singh AV, Judson RS, Martin MT, et al. 2011. Activity profiles of 309 ToxCast TM chemicals evaluated across 292 biochemical targets. Toxicology 282: 1–15.

Kuiper GGJM, Carlsson BO, Grandien KAJ, Enmark E, Häggblad J, Nilsson S, et al. 1997. Comparison of the ligand binding specificity and transcript tissue distribution of estrogen receptors $\alpha$ and $\beta$. Endocrinology 138: 863–870.

Maechler M. 2011. sfsmisc: Utilities from Seminar fuer Statistik ETH Zurich. R package version 1.0–15.

Martin MT, Dix DJ, Judson RS, Kavlock RJ, Reif DM, Richard AM, et al. 2010. Impact of environmental chemicals on key transcription regulators and correlation to toxicity end points within EPA’s ToxCast program. Chemical research in toxicology 23: 578– 590.

97 Matés JM, Segura JA, Alonso FJ, Márquez J. 2008. Intracellular redox status and oxidative stress: implications for cell proliferation, apoptosis, and carcinogenesis. Archives of toxicology 82: 273–299.

McGowan EM, Clarke CL. 1999. Effect of overexpression of progesterone receptor A on endogenous progestin-sensitive endpoints in breast cancer cells. Molecular Endocrinology 13: 1657–1671.

Neuman E, Ladha MH, Lin N, Upton TM, Miller SJ, DiRenzo J, et al. 1997. Cyclin D1 stimulation of estrogen receptor transcriptional activity independent of cdk4. Molecular and cellular biology 17: 5338–5347.

Oh DS, Troester MA, Usary J, Hu Z, He X, Fan C, et al. 2006. Estrogen-regulated genes predict survival in hormone receptor–positive breast cancers. Journal of clinical oncology 24: 1656–1664.

Okubo T, Yokoyama Y, Kano K, Kano I. 2001. ER-dependent estrogenic activity of parabens assessed by proliferation of human breast cancer MCF-7 cells and expression of ERalpha and PR. Food and chemical toxicology: an international journal published for the British Industrial Biological Research Association 39: 1225.

Peterson G, Barnes S. 1996. Genistein inhibits both estrogen and growth factor-stimulated proliferation of human breast cancer cells. Cell growth & differentiation: the molecular biology journal of the American Association for Cancer Research 7: 1345.

Pravettoni A, Mornati O, Martini PGV, Marino M, Colciago A, Celotti F, et al. 2007. Estrogen receptor beta (ERbeta) and inhibition of prostate cancer cell proliferation: studies on the possible mechanism of action in DU145 cells. Molecular and cellular endocrinology 263: 46–54.

R Development Core Team. 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.

Romanov S, Medvedev A, Gambarian M, Poltoratskaya N, Moeser M, Medvedeva L, et al. 2008. Homogeneous reporter system enables quantitative functional assessment of multiple transcription factors. Nature methods 5: 253–260.

Rotroff DM, Dix DJ, Houck KA, Knudsen TB, Martin MT, McLaurin KW, et al. 2013. Using in Vitro High Throughput Screening Assays to Identify Potential Endocrine- Disrupting Chemicals. Environmental Health Perspectives 121: 7.

Roy PG, Thompson AM. 2006. Cyclin D1 and breast cancer. The Breast 15: 718–727.

Sartorius CA, Groshong SD, Miller LA, Powell RL, Tung L, Takimoto GS, et al. 1994. New T47D breast cancer cell lines for the independent study of progesterone B-and A- receptors: only antiprogestin-occupied B-receptors are switched to transcriptional agonists by cAMP. Cancer research 54: 3868–3877.

98 Schenk B, Weimer M, Bremer S, Burg B van der, Cortvrindt R, Freyberger A, et al. 2010. The ReProTect Feasibility Study, a novel comprehensive< i> in vitro approach to detect reproductive toxicants. Reproductive Toxicology 30: 200–218.

Shang Y, Hu X, DiRenzo J, Lazar MA, Brown M. 2000. Cofactor dynamics and sufficiency in estrogen receptor–regulated transcription. Cell 103: 843–852.

Shanmugam I, Cheng G, Terranova PF, Thrasher JB, Thomas CP, Li B. 2007. Serum/glucocorticoid-induced protein kinase-1 facilitates androgen receptor- dependent cell survival. Cell Death & Differentiation 14: 2085–2094.

Shiozawa T, Miyamoto T, Kashima H, Nakayama K, Nikaido T, Konishi I. 2004. Estrogen- induced proliferation of normal endometrial glandular cells is initiated by transcriptional activation of cyclin D1 via binding of c-Jun to an AP-1 sequence. Oncogene 23: 8603–8610.

Smith KW, Braun JM, Williams PL, Ehrlich S, Correia KF, Calafat AM, et al. 2012. Predictors and Variability of Urinary Paraben Concentrations in Men and Women, Including before and during Pregnancy. Environmental health perspectives.

Sonnenschein C, Soto AM. 1998. An updated review of environmental estrogen and androgen mimics and antagonists. The Journal of steroid biochemistry and molecular biology 65: 143–150.

Soto AM, Sonnenschein C. 2010. Environmental causes of cancer: endocrine disruptors as carcinogens. Nature Reviews Endocrinology 6: 363–370.

Sotoca AM, Ratman D, Saag P Van der, Ström A, Gustafsson JA, Vervoort J, et al. 2008. Phytoestrogen-mediated inhibition of proliferation of the human T47D breast cancer cells depends on the ERalpha/ERbeta ratio. The Journal of steroid biochemistry and molecular biology 112: 171.

Strom A, Hartman J, Foster JS, Kietz S, Wimalasena J, Gustafsson J. 2006. Estrogen receptor beta inhibits 17beta-estradiol-stimulated proliferation of the breast cancer cell line T47D. PROCEEDINGS-NATIONAL ACADEMY OF SCIENCES USA 103: 8298.

Thenot S, Charpin M, Bonnet S, Cavailles V. 1999. Estrogen receptor cofactors expression in breast and endometrial human cancer cells. Molecular and cellular endocrinology 156: 85–93.

Tilghman SL, Nierth-Simpson EN, Wallace R, Burow ME, McLachlan JA. 2010. Environmental hormones: Multiple pathways for response may lead to multiple disease outcomes. Steroids 75: 520–523.

U.S. EPA. 2011a. Endocrine Disruptor Screening Program for the 21st Century (EDSP21 Work Plan). The Incorporation of In Silico Models and In Vitro High Throughput Assays in the Endocrine Disruptor Screening Program (EDSP) for Prioritization and Screening.

99 U.S. EPA. 2011b. Response to Corrective Action Plan for OIG Report No. 11-p-0215, EPA’s Endocrine Disruptor Screening Program Should Establish Management Controls to Ensure More Timely Results.

U.S. HHS. 2008. NTP-CERHR Monograph on the Potential Human Reproductive and Developmental Effects of Bisphenol-A.

Vogel VG, Costantino JP, Wickerham DL, Cronin WM, Cecchini RS, Atkins JN, et al. 2006. Effects of tamoxifen vs raloxifene on the risk of developing invasive breast cancer and other disease outcomes: the NSABP Study of Tamoxifen and Raloxifene (STAR) P-2 trial. JAMA 295:2727–2741; doi:10.1001/jama.295.23.joc60074.

Watson CS, Jeng YJ, Guptarak J. 2011. Endocrine disruption via estrogen receptors that participate in nongenomic signaling pathways. The Journal of Steroid Biochemistry and Molecular Biology 127: 44–50.

100

CHAPTER 4

ENDOCRINE TESTING IN THE 21 ST CENTURY: USING IN VITRO ASSAYS TO PREDICT ESTROGEN RECEPTOR SIGNALING RESPONSES

Introduction

The estrogen receptor (ER) regulates a vast array of physiological responses through a highly complex network of signaling mechanisms. Disruption of ER signaling or estrogen biosynthesis has been shown to cause adverse effects on reproductive success and fetal development, and can exacerbate certain types of cancer (Bigsby et al. 1999; Birnbaum and

Fenton 2003; Cooper and Kavlock 1997; Kavlock et al. 1996; Mahoney and Padmanabhan 2010;

Soto and Sonnenschein 2010). New research, still in its infancy, is also demonstrating the important role of ER in healthy neurological functioning (Isoe-Wada et al. 1999; Sampei et al.

2000; Tan et al. 2012; Xin et al. 2012). The significant role of ER and its involvement in a diverse set of health effects has led to a large body of scientific research focused on elucidating

ER’s intricate pathway.

Activation of ER signaling has been demonstrated by some environmental chemicals,

(e.g. bisphenol-A, parabens); however, thousands of chemicals exist with little to no data regarding their toxicological potential (Judson et al. 2009). In 1996, the U.S. Environmental

Protection Agency (EPA) was tasked with screening chemicals for their potential to interact with estrogen, androgen, steroidogenesis, and thyroid mechanisms(U.S. EPA 2012). In response, the

EPA created the Endocrine Disruptor Screening Program (EDSP) with the aim of testing the many thousands of chemicals for endocrine disrupting potential using a two-tiered testing

101 strategy. Thousands of chemicals are subject to Tier 1 screening (T1S), at an estimated cost of

~$1 million per chemical (U.S. EPA 2011a, 2011b). The initial list of 67 chemicals was finalized in 2009 and has yet to complete T1S. Due to the prohibitive costs of testing all necessary chemicals, EPA has launched the EDSP21 project with the immediate goal of chemical prioritization using computational and in vitro approaches, and the long-term goal of replacing more resource-intensive T1S assays with cost effective testing strategies.

One hypothesis is that by using multiple in vitro assays measuring orthogonal targets in a signaling pathway, one gains increased confidence in a chemical’s potential to perturb the given pathway. However, this is complicated by the fact that biological implications of ER activation are heavily dependent on cell and tissue type, and in vitro assays are tested using a variety of technologies in multiple cell types (Kolasa et al. 2003; Saunders et al. 2002). For example, the two isoforms of ER (ER α and ER β) result in somewhat opposite physiological effects. ER α activation generally results in a proliferative response, whereas, ER β activation generally results in an anti-proliferative and anti-inflammatory response (Gustafsson 1999; Pravettoni et al. 2007).

Chemicals that bind to ER will oftentimes bind to both isoforms but with differential affinities

(Kuiper et al. 1997). Furthermore and contrary to traditional thought, a growing body of evidence suggests that many ER ligands are not easily classifiable as agonists or antagonists, but that weak/partial agonists can act as antagonists under certain conditions (Dutertre and Smith

2000). Therefore, it is critical for any modeling effort where a key component is the aggregation of assay results, to incorporate the distinct biological characteristics of individual assays in order to obtain a biologically relevant model. Development of the model was built around the conceptual ER signaling pathway in Figure 4.1.

102 In the present study, we used an aggregation of in vitro assay results to develop an ER interaction score, indicating the likelihood of a chemical interacting with ER, the process by which the model was constructed and evaluated is shown in Figure 4.2. We used distinct assay characteristics to form chemical-activity spectrums for putative ER α/ER β and antagonism/agonism responses. One challenge to developing this model was the limited number of chemicals available for comparison; nevertheless, model comparisons with available in vivo data and ER reference chemical classifications indicate that the model is capable of predicting estrogenic likelihood with a high degree of accuracy.

Methods

Chemical Selection

The present study was conducted using data from three ToxCast chemical libraries which consisted of 1800 unique chemicals. This inventory includes 293 chemical structures from the

Phase Iv2 ToxCast chemical library, the majority of which are active ingredients of current or former food-use pesticides, or industrial chemicals of environmental relevance (Judson et al.

2009). An additional 676 chemical structures were from the ToxCast Phase II chemical library.

The remaining 880 chemical structures were from the ToxCast E1K chemical library. Individual chemical library information can be found at http://www.epa.gov/ncct/toxcast/chemicals.html.

All chemicals were shipped as 20mM stock solutions in DMSO.

In Vitro Assays

The NovaScreen HTS competitive binding assays for human, bovine, and mouse estrogen receptor (hER, bER, mERa, respectively) were developed and run by Caliper Discovery

Alliances and Services (Hanover, MD). A more complete description of the large set of HTS

103 assays from which these are taken is provided in Knudsen et al. (2011). The hER, bER, mERa receptor binding assays (Catalog Nos. 100-0127, 100-0126, 100-0897) were conducted on extracts of human breast cancer cells, bovine, and mouse uterine membranes, respectively. The

ER radioligand assays measure displacement of [ 3H]-estradiol at final ligand concentrations of

0.1 nM (hER) and 0.7 nM (bER) with the positive reference 17 β-estradiol. Reactions were carried out in 10 mM TRIS-HCI (pH 7.4 containing 1.5 mM EDTA, 1.0 mM dithiothreitol and

25 mM sodium molybdate at 0-4 ºC for 18 hr. The reaction is terminated with dextran-coated charcoal and incubated for 20 min at 0-4 ºC to adsorb unbound radioactivity. After centrifugation, the radioactivity remaining bound in the supernatant fraction is determined and compared to reference control values in order to ascertain any interactions of test compound with the ligand-binding site. The competitive binding assays were initially run in duplicate at a single concentration. Assay-chemical combinations meeting a pre-defined threshold of 30%, from the vehicle (DMSO) control signal or if the Z score was at least 2.0 median absolute deviations from the median (30% inhibition or MAD2) were then run in a follow-up screen in singleton concentration–response format with maximum concentration of 50µM (Knudsen et al. 2011).

Concentration response curves in the follow-up screen constrained the upper and lower asymptotes of the curve between 0- and 20% activity and between 100- and 120% activity, respectively, to allow for consistent extrapolation of the concentration at 50% activity (AC50) across assay-chemical combinations. Extrapolated AC50s above the highest concentration tested were allowed if the Emax was greater than 25% activity. Emax is defined in this analysis as the maximal tested response minus the lower asymptote. In order for a response to report an AC50 and be established as a hit, an Emax of 25% and an R-squared filter of 0.5 must be obtained

(Knudsen et al. 2011).

104 The Odyssey Thera assays utilizes protein-fragment complementation assay (PCA) technology which consists of a reporter protein rationally dissected into two fragments and fused to two proteins known to interact within a signaling complex. The reporter protein is fully assembled when the two test proteins dimerize. Chemical activity is measured via changes in signal intensity and location. For the assays in the present study, the fused proteins were expressed in human HEK293T kidney cell lines and target estrogen receptors alpha (ER α) and beta (ER β) homo- and heterodimerization. Assays were run at 24 h across three dimerization conditions (ER α-ER α, ER α-ER β, ER β- ER β) and represent assays, OT_ERaERa_1440,

OT_ERaERb_1440, OT_ERbERb_1440, respectively. Normalization of assay results was performed as percent of the 17 β-estradiol, and the baseline for each plate-wise normalization was the median raw plate response including all DMSO wells. Concentration response data was fit to a Hill model with criteria for generating AC50 values including: Hill curves R 2values ≥ 0.5, p- value ≤ 0.01 (from a test of significant difference between the top and bottom of the curve fit), and Emax (maximum activity) ≥ 30% baseline activity.

The Attagene assays describe a large collection of transcription factor assays, including two ER assays (ATG_ERa_TRANS, ATG_ERE_CIS). This collection of a multiplexed reporter gene assays and data on 309 environmental chemicals are described in Martin et al. (Martin et al., 2010). Attagene Inc. (RTP, NC), under contract to the U.S. EPA (Contract Number EP-W-

07-049), provided multiplexed reporter transcription unit (RTU) assays consisting of 48 human transcription factor DNA binding sites transiently transfected into the HepG2 human liver hepatoma cell line (Romanov et al. 2008). In addition to the Cis-acting reporter genes (CIS), a modification of the approach was used to generate a trans-system (TRANS) with a mammalian one-hybrid assay consisting of an additional 25 RTU library reporting the activity of nuclear

105 receptor (NR) superfamily members (Martin et al. 2010).The human ligand-binding domain of each nuclear receptor was expressed as a chimera with the yeast GAL4 DNA-binding domain that activated in trans a 5XUAS-TATA promoter, which regulated the transcription of a reporter sequence unique to each NR RTU. To ensure the specificity of detection, each individual trans-

RTU system including both receptor and reporter gene was separately transfected into suspended cells followed by pooling and plating of the transfected cells prior to screening. A major difference between the CIS and TRANS system is that in CIS activities of endogenous transcription factors are measured, whereas the TRANS assay evaluates changes in activities of exogenous, chimeric NR-Gal4 proteins. A cytotoxicity assessment was performed at the higher concentrations, qualitatively, to remove confounding data from the downstream analysis process.

Additional details on how the cytotoxicity assessment was performed are provided in Rotroff et al. (Rotroff et al. 2013a).

The ACEA data (ACEA_T47D_80hr_Positive )was generated using the xCELLigence system Multi-E-Plate stations which measure the time-dependent growth response by real-time cell analysis. All screening was carried out by ACEA Biosciences, Inc. (San Diego, CA). T-47D cells purchased from ATCC were maintained in RPMI1640 media supplemented with 10% characterized fetal bovine serum (FBS). Before screening, T-47D cells were pre-conditioned in assay medium: Phenol Red-free RPMI1640 supplemented with 10% charcoal-stripped FBS.

Cells were then detached and seeded in E-Plates 96 in assay medium. Cellular responses were then recorded once every 5 min for the first 5 hours, and once every hour for an additional 100 hours. Each chemical was tested in an eight-point, 1:4 serial dilution series starting at a maximum final concentration of 100 µM. A maximum starting concentration of 0.5% DMSO was present in the 100 µM chemical samples and was diluted along with the test article dilution

106 series. The screen was performed in biological duplicate using two separate, 96-well, E-Plates

96 TM for each dilution series (n=2). Positive control, 17 β-estradiol, was tested in quadruplicate on each testing plate. 0.5% and 0.125% DMSO were tested in duplicates in each plate to serve as solvent controls for the 2 highest concentrations of testing compounds: 100 µM and 25 µM.

Reference compounds were tested with 8 concentrations with 1:5 serial dilutions. Raw data were collected in the form of Cell Index (CI) for each of the time points before and after compound addition. All data was normalized to the CI value at the time of compound administration.

Following background subtraction of the DMSO solvent control, the values were represented as

% of 17 β-estradiol based on the positive and negative controls on each plate. For the present study, only the 80 h time point was used for incorporation into the model. Cytotoxicity was ascribed to a particular chemical-concentration level when the response fell below 15% of the next lower test concentration. This concentration and all higher concentrations were flagged and excluded from the concentration response data prior to concentration response curve fitting. If the upper asymptote of the concentration response curve reached 25% of 17 β-estradiol, then the chemical was assessed as ‘active’ for T-47D cell growth. Additional details of the ACEA data processing and analysis can be found in Rotroff et al. (Rotroff et al. 2013a).

The Tox21 assays consist of two high-throughput ER reporter gene assays run in both agonist and antagonist mode. GeneBLAzer® ER α-UAS-bla GripTiteTM cell line was obtained from Invitrogen (Carlsbad, CA, USA). Each of these lines stably express the ligand-binding domain (LBD) of the specific human nuclear receptor fused to the DNA-binding domain (DBD) of GAL4 and a beta-lactamase reporter gene under the transcriptional control of an upstream activator sequence (UAS). Binding of agonist to the LBD of the GAL4 (DBD)-NR (LBD) fusion protein causes the fusion protein to bind to the UAS, resulting in expression of beta-lactamase. In

107 antagonist mode, an AC50 concentration of the reference ligand was included in the assay. Cells were cultured in medium containing 2% charcoal/dextran-treated FBS, 0.1 mM NEAA and 1 mM sodium pyruvate overnight in the flasks before the assay. The assay was performed in clear bottom black Greiner 1536-well plates. 17 β-estradiol was used as a positive control. Library compounds were measured for their ability to either stimulate or inhibit (in the presence of an

AC50 of the appropriated agonist) the reporter gene activity. Compounds were screened in a 15- point titration series from 1 nM to 76 µM in 1536-well format and reporter gene activity determined as previously described (Huang et al. 2011). Data were normalized relative to positive controls (20 nM, 100%, for agonist mode and 0.5nM, 0%, for antagonist mode), and

DMSO-only wells (basal, 0% for agonist mode and -100% for antagonist mode). Concentration- response titration points for each compound were fitted to the Hill equation yielding concentrations of half-maximal stimulation (EC 50 ), half-maximal inhibition (IC 50 ) and maximal response (% of control) values (Huang et al. 2011). For agonist activity, the maximal response needed to be at least 20% of the maximal response of the positive control to be considered agonist activity.

Data Processing

Concentration response curves were aggregated according to their assigned group within the ER pathway(Figure 4.1 and Table 4.1). The NVS_hER, NVS_mERa, and NVS_bER binding assays were grouped together because both agonist and antagonist activity are detectable and indistinguishable. Agonist assays, OT_ERaERa_1440, OT_ERaERb_1440, OT_ERbERb_1440,

Tox21_ERa_BLA_agonist, Tox21_ERa_LUC_BG1_agonist, ATG_ERa_TRANS_perc, and

ATG_ERE_CIS_perc were grouped together. Antagonist assays, Tox21_ERa_BLA_antagonist and Tox21_ERa_LUC_BG1_antagonist were grouped together. Because the ACEA_T47D_80h

108 assay responds to growth inducing chemicals other than estrogens, it was given a group of its own (Rotroff et al. 2013a). Groups of assays are annotated in Figure 4.1 and tabulated in Table

4.1.

In order to minimize the variability occurring from different assay technologies, assays within groups were linearly transformed. Linear transformation was performed by selecting an anchor assay, and selecting a potent and weak reference chemical that was positive in all assays within the group. The AC50 value was used as the point of reference for the transformation. 17b- estradiol and benzyl butyl phthalate were used as reference chemicals for the agonist group. For the antagonist group, fulvestrant was used as the potent reference chemical and no weak reference chemical available. Because two points are required for the linear transformation, a dummy chemical was created for the weak reference in the antagonist group, and was given a value of 100 µM for each assay. Therefore, no transformation occurred for chemicals active at high concentrations, but appropriate adjustments were made at lower concentrations. For each group, the differences between the anchor assay’s AC50 and each individual assay’s AC50 for the two reference chemicals were fit using a linear regression model, and the subsequent slope and intercept were used as correction factors and applied across all chemicals in the given group.

This transformation does not adjust the shape of the concentration response or the y-axis. The only adjustments made are adjustments to the x-axis, so that chemicals can be adequately compared across assays. No transformation was applied to the binding assays since they are tested using the same technology platform, or the cell growth group because it is composed of a single assay.

109 Composite Concentration Response Curves

All chemicals for each assay technology were fit using non-linear least squares regression to a hill-model in a separate analysis, as previously described. These analyses included filtering for cytotoxicity and outliers where applicable. All assay responses were normalized to 17 β- estradiol, except those in the antagonist group, which were normalized to 4-hydroxytamoxifen.

Next, non-linear least squares regression in the open-source statistical software, R and the sfsmisc package were used to fit a single, composite concentration response curve through all individual concentration response curves for a given group using a hill-model (Maechler 2011; R

Development Core Team 2011). A representation of a composite model can be seen in Figure

4.3. The four-parameter Hill equation is represented as:

− = − 1 + 50 T was constrained to +/- 20% of the maximum response observed across all assays in the group in order to minimize convergence errors. B was constrained to 0, and W was constrained to

1. In order to determine the uncertainty around the composite model, 100 composite models were generated by sampling individual assays with replacement. Each size of each sample was consistent with the number of unique assays, and a composite model was fit through each sample. A 99% confidence interval of the bootstrapped samples was calculated at each interpolated concentration. Because the growth group only contained a single assay, a 90% prediction interval was calculated from the sum of squared errors. In order to obtain uncertainty estimates when there were not enough data points available to calculate the prediction interval within the growth group, interpolated data points were added to the response but were weighted so that they did not impact the error calculation.

110 The Efficacy Score (%) is calculated from the maximum value of the lower-window of the bootstrapped confidence interval. If the window does not overlap with zero, then a response is considered to have statistically significant response and will have an Efficacy Score > 0. If the maximum value of the lower-window overlaps with 0, then the Efficacy Score will equal 0. The first concentration at which the lower window of the confidence interval was greater than zero was annotated as the lowest effective concentration (LEC) (µM). The Potency Score (%) was obtained by taking the lowest and highest concentrations across all chemicals as 100% and 0%, respectively, and calculating the percentage at which the LEC occurs. If the lower-window of the confidence interval never increased above 0, then the Potency Score was set to 0. A separate

ER α and ER β Potency Score was calculated using the AC50 from the OT_ERaERa_1440 and the OT_ERbERb_1440, respectively.

If both the Potency and Efficacy Scores were > 0, then the mean of the Potency Score and the Efficacy Score was assigned as the Composite Score for the chemical-group. Otherwise the

Composite Score is set to 0. A graphical representation of these parameters is shown in Figure

4.3. Composite Scores were then scaled to the maximum Composite Score within each group. If all individual assays within a group were active for a chemical, but the composite score was 0, then the chemical was flagged for follow-up analysis.

The ER Interaction Score is calculated based on the following logic:

(1) IF: > 0 >

THEN: = + + /3

(2) IF: > 0 >

THEN: = + /2

(3) IF: = 0 = 0 > 0

111 THEN: = + + 0/3

(4) IF: = 0 = 0 = 0

THEN: = 0

Where CS agonist is the scaled agonist Composite Score, CS antagonist is the scaled antagonist

Composite Score, CS binding is the scaled binding Composite Score, and CS growth is the scaled growth Composite Score.

Hierarchical Clustering

Hierarchical clustering was performed using the “pheatmap” package in statistical software, R (R Development Core Team 2011; Raivo Kolde 2012). Two separate heatmaps were generated, one with all chemicals, and one with only chemicals that had Composite Scores > 0 for at least one group (binding, agonist, antagonist, or growth). Clustering was performed using

Euclidean distance and Ward’s method.

Model Evaluation

The impact of the linear transformation was determined by comparing the distributions of

R2 values from all chemicals in the agonist and antagonist groups, respectively, with and without the linear transformation. Statistical significance was determined using a one-tailed Wilcoxon test ( α= 0.05). The analysis was also repeated with only chemicals that produced ER Interaction

Scores > 0.

Model stability for the agonist group was tested by comparing the CS agonist after removing a single assay and was then repeated for each assay. Each model with a single assay removed was compared to the model with all assays by fitting a linear regression model to the

2 two sets of CS agonist . The percentage of compounds with CS agonist > 0 was recorded, and R and linear regression ( p < 0.01) were used to characterize the goodness-of-fit between each model.

112 We compared chemicals with available uterotrophic data in order to investigate the associations with ER Interaction Scores and in vivo rodent estrogenicity. Uterotrophic OECD guideline and EDSP T1S validation studies were used and chemical activity was classified consistently with Rotroff et al. (Rotroff et al. 2013b) as either being active or inactive for agonist or antagonist activity based on changes in rodent uterine weight. An additional comparison to 33

ER reference chemicals from the EDSP21 working-group, obtained by expert review of the literature and NICEATM consensus, were compared to the ER Interaction Scores. Reference chemicals were classified for expected ER activity as strong, strong-moderate, moderate, weak, very weak/ agonist (metabolism required), or negative. ER Interaction Scores were binned by reference chemical categories, and the distributions were plotted as box plots. Each group of binned ER Interaction Scores was tested for significance using the Mann-Whitney U test. A binned group was considered statistically significantly different from another group if the Mann-

Whitney U test resulted in a p < 0.1. The overall trend was tested for significance by Kruskal-

Wallis one-way ANOVA.

Results

A total number of 1800 chemicals were available for modeling based on existing data from the assays in Table 4.1. Assays were split into four groups, binding, agonist, antagonist, and growth, depending on the type of estrogenic activity reported. Chemicals with composite scores

> 0 were considered to be statistically significant for ER activity within the respective group based on these criteria. Overall, 50 (2.7%), 186 (10.3%), 109 (6.1%), 144 (8%) chemicals produced composite scores > 0 for CS binding , CS agonist , CS antagonist , and CS growth , respectively. These scores resulted in a total of 305 (16.9%) chemicals with ER Interaction Scores > 0. Chemicals

113 that produced composite scores > 0 in only a single group totaled 16, 107, 79, and 52 for

CS binding , CS agonist , CS antagonist , and CS growth , respectively. Chemicals with a composite score > 0 in at least one group are clustered in Figure 4.4. Clustering of all chemicals is shown in the inset of

Figure 4.4 to demonstrate that the vast majority (83.1%) of chemicals do not show any activity in any group.

The 15 chemicals with the highest ER Interaction Scores are tabulated in Table 4.2.

These include a both known agonist and antagonist chemicals. 17 α-estradiol, raloxifene hydrochloride, 4-hydroxytamoxifen, 17 β-estradiol, and 17 α-ethinylestradiol had ER Interaction

Scores > 80. 5HPP-33 was the only chemical with both CS agonist and CS antagonist > 0, suggesting the possibility of antagonist and agonist activity. Most of the chemicals in this subset had both high ER α and ER β scores indicating a strong affinity for both isoforms, but with some chemicals displaying slight selectivity.

Linear Transformation

In an effort to correct variability due to differences in assay technologies, we performed a linear transformation on the chemical concentrations in the agonist and antagonist groups. The model R 2 values were used as a measure of goodness-of-fit to determine whether the transformation improved the overall model fit. Histograms of R 2 values are shown in Figure 4.5, and were used to characterize the differences between transformed and non-transformed models.

The distribution of R 2 values across all chemicals was not improved for the agonist group after the linear transformation ( p > .05). However, when only the chemicals with ER Interaction

Scores > 0 were included in the distribution of agonist R 2 values, there was a statistically significant improvement in R 2 values for the linear transformed group ( p < .0001). The antagonist group was not improved in either set, likely due to the fact that no weak ER

114 antagonist, active in both assays, was available for transformation. Therefore, the only transformation that would occur for the antagonist group would be at very low concentrations.

Model Stability

In order to test model stability, the model for the agonist group was run with a single assay removed, and repeated for all assays. No statistically significant differences ( p < .01) in the correlations of CS agonist were observed by removing any assay in the agonist group (Figure 4.6).

Removal of the OT_ERaERb_1440 assay resulted in the largest drop (2.3%) of chemicals with

ER Interaction Scores > 0. This change was not statistically significant ( p > .01) and the linear model between the group without the OT_ERa_ERb_1440 assay and all assays in the agonist group produced an R 2 of 0.87, indicating that the inclusion of the OT_ERa_ERb_1440 assay contributed 2.3% of the active compounds in the chemical library. Removal of the

Tox21_ERa_LUC_BG1_Agonist assay increased the number of overall active chemicals to

14.1% from 12.3%. The R2 between the model with all assays and this model was 0.77, and the two were not statistically significantly different ( p=1).

Agonist and Antagonist Comparisons

Eight chemicals produced both CS agonist and CS antagonist > 0. For this subset, one also had significant CS binding and four had significant CS growth . The scaled composite scores for this subset of chemicals ranged from 9.62 -56.73 and 13.06 – 71.58 for CS agonist and CS antagonist , respectively.

Most chemicals were not significantly active for both CS agonist and CS antagonist . The spectrum of activity from agonism to antagonism for all chemicals is presented in Figure 4.7. Estrogens, 17 α- estradiol, 17 β-estradiol, 17 α-ethinylestradiol, 17 α-estradiol, and diethylstilbestrol were among the chemicals with the highest CS agonist . Phytoestrogens, apigenin, daidzien, and genistein were also detected with scaled CS agonist of 70, 61.7, and 51.7, respectively.

115 ER α and ER β Comparisons

The OT_ERaERa_1440 and OT_ERbERb_1440 assays from the agonist group were used to investigate ER α and ER β specific responses, respectively. The results of this analysis can be seen in Figure 4.8. A total of 13 chemicals demonstrated statistically significantly increased potency for ER α; however, only 3 of these chemicals had scaled CS agonist > 0. ER antagonists, 4- hydroxytamoxifen, raloxifene hydrochloride, and clomiphene citrate demonstrated significantly increased potency for ER α, but did not have CS agonist > 0. The ER α scores for 4- hydroxytamoxifen, raloxifene hydrochloride, and clomiphene citrate were 69, 70.8, and 92, respectively, and ER β scores were 23.4, 20.8, 65.6, respectively.

A total of 62 chemicals demonstrated statistically significantly increased potency for

ER β, 26 of which had CS agonist > 0. Most of the chemicals that displayed increased potency for

ER β produced relatively low ER Interaction Scores with only 6 chemicals producing scores > 20.

Dehydroepiandrosterone and silwet L77 had ER Interaction Scores of 47.2 and 25.6, respectively. Of the 62 chemicals significantly elevated for ER β, dehydroepiandrosterone was the only chemical that produced an ER α score > 0.

Comparison to In Vivo Uterotrophic Assay

A subset of chemicals with in vivo data on estrogenic potential from uterotrophic studies are represented with their associated ER Interaction Scores in Figure 4.9. A total of 45 chemicals were available for analysis from 15 separate studies. Out of the 45 available chemicals, 23 were classified as negative, 12 as positive for agonist activity, 2 for antagonist activity, and 8 for both agonist and antagonist activity. Within this 45 chemical subset, 19 produced ER Interaction

Scores of 0, of which 3 were classified as agonist or antagonist in the uterotrophic analysis.

Atrazine and disulfiram were classified as ER antagonists in the uterotrophic analysis, and

116 pendimethalin was classified as an agonist. Furthermore, 7 compounds with ER Interaction

Scores > 0 were classified as negatives in the uterotrophic analysis; however, these 7 chemicals are comprised within the lowest 9 ER Interaction Scores > 0 (Figure 4.9). Out of the 26 chemicals with ER Interaction Scores > 0, 19 were classified as having estrogenic activity in uterotrophic studies. Lastly, out of the 19 chemicals with ER Interaction Scores of 0, 16 were classified as having no estrogenic activity in available uterotrophic studies.

Comparison to ER Reference Chemicals

Thirty three chemicals classified for estrogenic potential by literature review and expert opinion overlapped with the present chemical library and were used to assess the performance of the model (Figures 4.10 and 4.11). Reference chemicals were classified as strong (3), strong- moderate (1), moderate (4), weak (14), very weak (3), and negative (8). One chemical, methoxychlor, was classified as very weak and requires metabolic activation. Eight of the thirty three chemicals produced ER Interaction Scores of zero, and all were classified as negative in the reference. No chemicals classified as negative produced ER Interaction Scores > 0 (Figure 4.10).

In order to quantitatively evaluate the performance of the model relative to the available

ER reference chemical classifications, the distributions of ER Interaction Scores were compared for each reference classification group (Figure 4.11). The negative group was statistically significantly different all other groups ( p < 0.1). We also found a statistically significant difference between the weak group and the moderate, and strong groups ( p < 0.1). The small sample sizes of some groups made statistical comparison challenging. However, a Kruskal-

Wallis test conducted with all the groups suggested a strong association between the reference chemical classifications and the ER Interaction Scores ( p < .001). Overall, the present analysis

117 demonstrates that the ER Interaction Scores are concordant with known activities of ER reference chemicals (Figures 4.10 and 4.11).

Discussion

The present study describes a novel approach for incorporating multiple in vitro assays into a model capable of quantitatively determining chemical potential to interact with a given signaling pathway. The ER signaling pathway is an ideal candidate for demonstrating this approach, because multiple in vitro assays measuring different parts of the ER pathway are available for thousands of chemicals. Furthermore, a relatively well established list of reference chemicals and in vivo assay data are available to validate model predictions. Lastly, there is current need for an ER predictive model that can help resolve the bottle-neck that exists for thousands of chemicals mandated for regulatory endocrine screening. The purpose of the ER

Interaction Score is to provide a metric for ranking chemicals based on their potential to interact with ER regardless of agonist or antagonist tendencies, since some chemicals are not easily defined as such. The individual composite scores and assay characteristics can then be examined to investigate the specific activity associated with each chemical (e.g. agonist/antagonist,

ER α/ER β).

Assay grouping was based on the type of activity that the assays are capable of detecting.

Grouping assays that indiscriminately detect agonists and antagonists, with assays that only detect agonist activity would increase the overall uncertainty around the composite measurements. It was also thought that non-linear least squares regression would be more appropriate for modeling groups of assays with similar activity profiles. The grouping of assays for the agonist group was stable, with no single assay driving the overall conclusions (Figure

4.6). Separating assays into specific groups based on activity profiles provides the advantage of

118 enhancing the individual assays strengths and down-weighting the impact of their weaknesses.

For example, the ACEA_T47D_80hr_Positive assay, used to construct the growth composite curves, detects steroid hormones other than estrogens and also responds to mitochondrial disruptors (Rotroff et al. 2013a). In addition, the ER binding assays are susceptible to false positives from detergent compounds by displacement of the ligand by receptor denaturation, and these compounds can be indentified in the cluster that includes only the actives in the binding group (Figure 4.3).

The impact of these ER false positives can be reduced by taking the intersection of chemicals with CS binding , CS agonist , and CS growth > 0. The cluster that includes active compounds for all three of these groups includes known estrogenic compounds such as, estrone, daidzein,

17 α-ethinylestradiol, and many others (Figure 4.4) (Caldwell et al. 2012; Gutendorf and

Westendorf 2001; Totta et al. 2005; Zhang et al. 2012). The assays in the binding group were only tested up to 50µM; therefore, it is possible that weakly active chemicals would be undetected with these assays alone. Non-ligand mediated ER receptor activation has been documented, although little is known about the ability of xenobiotics to operate through this mechanism (Tremblay et al. 1999; Zwijsen et al. 1998). It is possible that chemical activation of alternative signaling pathways may induce ER signaling via this mechanism, and it would be expected that these chemicals would cluster in this group as well.

The two ER isoforms, ER α and ER β, share a 96% homology between their two DNA binding domains. However, the ligand binding domains are only 58% homologous (Mosselman et al. 1996). Furthermore, activation of these two receptors produces different phenotypic outcomes (Gustafsson 1999; Kuiper et al. 1997; Pravettoni et al. 2007). The differences in phenotypic outcomes have been partially attributed to differences in the F-domains of the two

119 isoforms (Skafar and Zhao 2008). Limited information regarding the preference for ER α and

ER β by environmental chemicals exists; however some studies have investigated the ER selectivity for a small group of chemicals that overlap with the present chemical library. Paech et al. (Paech et al. 1997) investigated the ER α and ER β selectivity of diethylstilbestrol, raloxifene, and tamoxifen. Diethylstilbestrol was not found to significantly prefer ER α or ER β which is consistent with the present model scoring 71.4 and 78.06 for ER α and ER β, respectively. The two selective estrogen receptor modulator (SERM) chemicals, raloxifene and tamoxifen were identified as being selective for ER α (Paech et al. 1997). Both raloxifene hydrochloride and 4- hydroxytamoxifen had significantly elevated ER α scores relative to ER β in the present model ( p

< .01). Genistein, apigenin, and daidzein are phytoestrogens that demonstrate increased affinity for ER β relative to ER α, although they readily activate both receptors (Table 4.2) (Kuiper et al.

1997; McCarty 2006; Muthyala et al. 2004). The present model did not find a statistically significant difference for the chemical selectivity of ER α and ER β for these chemicals, but did find an increased affinity for ER β for daidzein and genistein with ER α/ER β scores of 56.6/74.5 and 36.8/57.3, respectively (Table 4.2). Apigenin had high scores for both isoforms with an

ER α/ER β score of 100/86.5. Lastly, dehydroepiandrosterone (DHEA), a known estrogen agonist that readily binds to both ER α and ER β, but with slightly greater affinity for ER β was detected with an ER Interaction Score of 54.9 and an statistically significant increase for ER β, with an

ER α/ER β score of 28.8/53.0 (Chen et al. 2005). Although, only a small set of chemicals are available for comparisons, it appears that the scores are mostly consistent with the available literature.

Uterotrophic assays are designed to measure estrogenic activity through increased uterine weight in immature or ovariectomized rats (Odum et al. 1997). This assay offers the most direct

120 mammalian in vivo measurement of estrogenicity of any of the regulatory EDSP T1S, and has been used to evaluate a relatively large number of chemicals compared to many other in vivo platforms. The studies used in this analysis were all performed to OECD guidelines allowing for more consistent comparisons across chemicals (Rotroff et al. 2013b). Three chemicals, atrazine, disulfiram, and pendimethalin were classified as having uterotrophic activity and had ER

Interaction Scores of 0. Atrazine was classified as an antagonist in the uterotrophic assay and is known to suppress estrogen synthesis through the inhibition of aromatase and does not act through the estrogen receptor; therefore, it would not have been expected to be detected in these assays (Roberge et al. 2004). Pendimethalin was not active in the

Tox21_ERa_LUC_BG1_Agonist, Tox21_ERa_BLA_Agonist_ratio, or the

OT_ER_ERaERa_1440 assays included in the agonist group; however, and due to the uncertainty around the concentration response curves the model was not considered statistically significant.

We compared a subset of 33 reference chemicals with established estrogen activity to the

ER Interaction Scores in order to quantitatively determine the predictive accuracy of the model

(Figures 4.10 and 4.11). Out of the 33 chemicals, the model correctly classified 33/33 (100%) of the ER reference chemicals as active or inactive (Figures 4.10 and 4.11). Furthermore, the model’s ER Interaction Scores successfully ranked chemicals according to their reference activity profiles of strong, strong-moderate, moderate, weak, very weak, and negative ( p = 9x10 -

5) (Figures 4.10 and 4.11).

The vast majority of chemicals included in this chemical library (83%) did not display indications of interacting with the ER signaling pathway, and would be low priorities for additional ER testing. If maximum sensitivity is desired, the model can be run with narrower

121 confidence intervals around the composite curves, resulting in increased overall false positives and decreased false negatives. However, based on the available reference chemical classifications and in vivo data, the present thresholds demonstrate very few occurrences of false positives or false negatives. (Figures 4.9, 4.10, 4.11). Besides the ability to test large numbers of chemicals at a reduced cost, this approach has some significant advantages over the traditional regulatory tests. Primarily, the assays are performed using human targets, and the measurements can be directly attributed to a target within a molecular signaling pathway. The advantage of measuring molecular events is that they can sensitively identify the potential for an adverse outcome, as opposed to the requirement of frank toxicity or an overt physiological change such as in traditional in vivo bioassays like the uterotrophic or 2-generation reproduction studies.

This approach is amenable to the prioritization methods detailed in Reif et al. (Reif et al.

2010, 2013). If the goal is to prioritize for risk assessment for ER, the ER Interaction Score can be used as a single slice in a ToxPi, with other slices composed of QSAR results and/or exposure information. The incorporation of exposure information into a ToxPi implementation has been previously described (Gangwal et al. 2012). If the goal is to rank chemicals based on endocrine disrupting potential, the ER Interaction Score could be used as a single slice in the ToxPi, with other slices composed of models covering additional MOA (e.g. neuroendocrine, thyroid, steroidogenesis, androgen). We propose the present model as an immediately available tool that is capable of ranking chemicals for potential interactions with ER and can be used to prioritize chemicals for EDSP T1S. The use of in vitro models can help to ensure that the chemicals entering EDSP T1S are the chemicals most likely to require regulatory action. Lastly, the present study offers the advantage of a large chemical library and many assays measuring orthogonal and

122 complimentary mechanisms to better assess the uncertainty around in vitro measurements and further elucidate ER biology.

123

Tables

Table 4.1. ER In Vitro Assay Annotation and Model Grouping

Map to Figure Assay Name Assay Information Group 4.1 1 NVS_NR_hER Human ER binding assay Binding 1 NVS_NR_mERa Murine ER α binding assay Binding 1 NVS_NR_bER Bovine ER binding assay Binding Odyssey Thera ER α-ER α dimerization in agonist 2 OT_ERaERa_1440_agonist Agonist mode after 1440 minutes Odyssey Thera ER α-ER β dimerization in agonist 3 OT_ERa_ERb_480_agonist Agonist mode after 480 minutes Odyssey Thera ER β-ER β dimerization in agonist 4 OT_ERbERb_1440_agonist Agonist mode after 1440 minutes ER α luciferase reporter gene assay in human BG-1 5 Tox21_ERa_LUC_BG1_Antagonist Antagonist ovarian cells in antagonist mode ER α β-lactamase reporter gene assay in human HEK- 5 Tox21_ERa_BLA_Antagonist_ratio Antagonist 293 cells in antagonist mode ER α luciferase reporter gene assay in human BG-1 6 Tox21_ERa_LUC_BG1_Agonist Agonist ovarian cells in agonist mode ER α β-lactamase reporter gene assay in human HEK- 6 Tox21_ERa_BLA_Agonist_ratio Agonist 293 cells in agonist mode Multiplexed ER reporter gene assay using full length 6 ATG_ERE_CIS Agonist receptor in HepG2 cells Multiplexed GAL4 reporter construct with human 6 ATG_ERa_TRANS Agonist ER α ligand-binding domain in HepG2 cells. Cell growth using real-time cell analysis in T47D 7 ACEA_T47D_80h Cell Growth cells at 80 hours

124

Table 4.2. 15 Chemicals with Highest ER Interaction Scores

Scaled Composite Score ER Agonist Agonist Interaction ER α ER β CASRN Chemical Name Binding Agonist Antagonist Growth Score Score Score Raloxifene 82640-04-8 86.64 0.00 100.00 0.00 93.32 70.78 20.81 hydrochloride 57 -91 -0 17 α-Estradiol 94.93 88.03 0.00 76.90 86.62 71.43 75.46 4- 68392-35-8 84.61 0.00 81.06 0.00 82.84 68.99 23.41 Hydroxytamoxifen 50-28-2 17β -Estradiol a 83.67 100.00 0.00 62.17 81.95 68.23 67.87 17 α- 57-63-6 99.41 92.87 0.00 49.12 80.47 70.56 65.63 Ethinylestradiol 84 -16 -2 meso -Hexestrol 99.93 82.23 0.00 54.59 78.92 68.17 68.72 56-53-1 Diethylstilbestrol 98.56 85.20 0.00 46.64 76.80 71.43 78.06 50-41-9 Clomiphene citrate 80.53 0.00 60.45 0.00 70.49 92.03 65.57 50-28-2 17 β-Estradiol b 100.00 87.50 0.00 23.45 70.32 71.73 76.90 1478-61-1 Bisphenol AF 93.19 59.16 0.00 57.38 69.91 47.42 57.27

125 53 -16 -7 Estrone 90.09 63.77 0.00 55.40 69.76 47.7 5 49.38 486-66-8 Daidzein 56.83 61.74 0.00 89.26 69.28 56.59 74.50 22'44' - 131-55-5 Tetrahydroxybenz 70.49 79.62 0.00 46.76 65.62 55.26 60.58 ophenone 105624 -86 -0 5HPP -33 74.60 51.30 38.43 62.70 62.87 100.00 86.53 50-27-1 Estriol 83.26 46.58 0.00 54.33 61.39 56.19 39.90 aTested at concentrations up to 3.8µM; bTested at concentrations up to 200µM

125

Figures

Figure 4.1. ER Signaling Pathway Overview (Overlayed with ToxCast Assays). Schematic of ER signaling pathway used to define model groupings. Numbers identify molecular events with available ToxCast assays, and correspond to the assays in Table 4.1. 1 corresponds to the binding group; 2,3,4,6 correspond to the agonist group; 5 corresponds to antagonist group; 7 corresponds to growth group. Cell proliferation was identified as a functional endpoint of ER activation. Available assays measure events covering several possibilities for a chemical to either upregulate or downregulate ER activity.

126

Figure 4.2. Graphical Representation of the Logical Structure of the Model. The initial step is performed to group assays according to the disjunctive logic of the model signaling pathway in Figure 4.1. Second, composite curves are fit using all available data within each group to model the overall concentration response. Next, composite scores are clustered to identify chemicals with similar activity profiles among groups. ER Interaction Scores are then calculated from the composite scores in each group to rank chemicals according to estrogenic likelihood. Lastly, individual assays and composite scores are investigated for additional ER characteristics (agonism/antagonist, ER α/ER β activity). 127

127 Figure 4.3. Composite Curve Example. A) The concentration response curves for all available assays included in the agonist group. B) The composite model of all the individual concentration response curves from the agonist group for bisphenol-A. The lower window of the model confidence interval is used to calculate the efficacy score, and first concentration for which the lower window of the confidence interval that does not overlap with zero is assigned the LEC. The percentage across all tested concentrations that the LEC occurs is the Potency Score. If both the Potency and the Efficacy Score > 0, then the mean of the Potency and Efficacy score is assigned as the Composite Score for that group. If the AC50 occurs after the LEC then it is blue and considered to be significant, otherwise it is colored gray. 128

128

Figure 4.4. Heatmap of Composite Scores. The scaled composite scores are clustered using Euclidean distance and Ward’s method. Rows are groups of assays and columns are chemicals with at least one scaled composite score > 0. The colors represent the scaled composite scores (0-100). Clusters of activity provide evidence for which mechanisms are being activated along the ER signaling pathway. For example, chemicals with scaled composite scores > 0 for only the growth group are likely to be activating a non-ER mediated cell proliferation pathway. Chemicals with scaled composite scores > 0 for binding, agonist, and growth groups are likely to be capable of ligand mediated ER activation. The inset is the clustering of the entire chemical library to provide context for the ratio of inactive chemicals to active chemicals. 129

129

Figure 4.5. Impact of Linear Transformation on the Distributions of R 2. A) The distributions of R2 values for all chemicals in the agonist and antagonist groups without linear transformation. B) The linear transformation provided little change to the R 2 values in the antagonist group and slightly lowered the R 2 values in the agonist group across all chemicals. C) The distribution of R 2 values for only the active chemicals (composite scores > 0) without linear transformation were similar to the distributions of all chemicals. D) However, the R 2 values for only the active chemicals were significantly improved after linear transformation ( p < .0001). This result suggests that the linear transformation reduced the error associated with active compounds. No weak reference chemical was available for linear transformation of the antagonist group, therefore only very potent compounds would have been adjusted. This limitation explains the lack of change in the distribution of R 2 values for this group.

130

Figure 4.6. Agonist Model Stability. Because the agonist group contains the largest number of assays, an assessment was conducted to determine the contribution of each assay to the overall number of active chemicals. The percentage of active chemicals for all assays in the agonist group, and with the removal of a single assay is shown. The percentage of active chemicals is the most greatly reduced by removing the OT_ERaERb_1440 assay, however this difference is not statistically significant. The R 2 and p values correspond to the linear regression between the composite scores between all assays included and the removal of individual assays. Overall, no single assay in the agonist group significantly contributed to the overall number of active chemicals.

131

Figure 4.7. Agonist/Antagonist Volcano Plot. Agonist and antagonist activity was plotted with the ER Interaction Score on the y-axis and CS agonist and CS antagonist on the x-axis. Chemicals with high ER Interaction Scores and high CS agonist fall to the upper right quadrant, whereas chemicals with high ER Interaction Scores and high CS antagonist are located in the upper left quadrant. The colors are determined by the potency of the LEC value. Some chemicals may have a high ER Interaction Score driven partially by their efficacy relative to another chemical’s more potent response. The chemicals in the middle of the x-axis with ER Interaction Score > 0 had no significant activity in either the agonist or antagonist groups, but were active in the binding assay group Interaction Score > 0 had no significant activity in either the agonist or antagonist groups, but were active in the binding assay group.

132

Figure 4.8. ER α/ER β Volcano Plot. ER α and ER β activity was plotted with the CS agonist on the y-axis and the ER α Potency Scores and ER β Potency Scores on the x-axis. Chemicals with high CS agonist and high ER α scores fall to the upper right quadrant, whereas chemicals with high CS agonist and high ER β scores fall towards the upper left quadrant. The colors are determined by the CS binding . The dashed lines represent 3 σ from the mean. Any chemical outside the dashed lines was considered to show statistically significant increases in activity for either ER α or ER β. As expected 17 β-estradiol and diethylstilbestrol showed no preference for either isoform, and daidzein and genistein were active for both ER α and ER β, but showed a slight preference for ER β.

133 Figure 4.9. Uterotrophic Analysis. A chemical subset with uterotrophic data was plotted with the ER Interaction Score on the y-axis and the CS agonist and the CS antagonist on the x-axis. Chemicals with high ER Interaction Scores and high CS agonist fall to the upper right quadrant, whereas chemicals with high ER Interaction Scores and high CS antagonist are located in the upper left quadrant. Uterotrophic classifications were obtained from Rotroff et al. (2013b) and annotated as either being negative, positive for agonist activity, positive for antagonist activity, or both.

134 Figure 4.10. Reference Chemical Analysis. A chemical subset with ER reference chemical classifications was plotted with the ER Interaction Score on the y-axis and CS agonist and the CS antagonist On the x-axis. Chemicals with high ER Interaction Scores and high agonist composite scores fall to the upper right quadrant. Reference chemical classifications were determined by expert review of the literature and NICEATM consensus. 25 of the 33 reference chemicals were classified as ER actives, and 24 were predicted to have ER activity with ER Interaction Scores > 0. Progesterone and Haloperidol were predicted as having very weak activity, but were classified as negatives in this reference set.

135 Figure 4.11. Quantitative Reference Chemical Analysis. ER model performance was quantitatively evaluated by comparing the ER Interaction Scores of subset of 33 ER reference chemicals with their associated reference classifications. Reference chemicals were classified as strong, strong-moderate, weak, very weak, or negative according to expert literature review and in consensus with NICEATM evaluations. Limited numbers of chemicals for comparison made the statistical analysis challenging; however, the ER Interaction Score significantly distinguished the negative group of chemicals from the very weak, weak, moderate, and strong classifications. A highly significant overall trend was detected between ER Interaction Scores and reference chemical classifications ( p< .0001). The colors of the boxplots correspond to their data points in Figure 4.10.

136 References

Bigsby R, Chapin RE, Daston GP, Davis BJ, Gorski J, Gray LE, et al. 1999. Evaluating the effects of endocrine disruptors on endocrine function during development. Environmental Health Perspectives 107: 613.

Birnbaum LS, Fenton SE. 2003. Cancer and developmental exposure to endocrine disruptors. Environmental health perspectives 111: 389.

Caldwell DJ, Mastrocco F, Anderson PD, Länge R, Sumpter JP. 2012. Predicted-no-effect concentrations for the steroid estrogens estrone, 17 β-estradiol, estriol, and 17 α- ethinylestradiol. Environmental Toxicology and Chemistry 31: 1396–1406.

Chen F, Knecht K, Birzin E, Fisher J, Wilkinson H, Mojena M, et al. 2005. Direct agonist/antagonist functions of dehydroepiandrosterone. Endocrinology 146:4568– 4576; doi:10.1210/en.2005-0368.

Cooper RL, Kavlock RJ. 1997. Endocrine disruptors and reproductive development: a weight-of-evidence overview. Journal of Endocrinology 152: 159–166.

Dutertre M, Smith CL. 2000. Molecular mechanisms of selective estrogen receptor modulator (SERM) action. J. Pharmacol. Exp. Ther. 295: 431–437.

Gangwal S, Reif DM, Mosher S, Egeghy PP, Wambaugh JF, Judson RS, et al. 2012. Incorporating exposure information into the toxicological prioritization index decision support framework. Science of the Total Environment 435: 316–325.

Gustafsson JA. 1999. Estrogen receptor beta--a new dimension in estrogen mechanism of action. J Endocrinol 163:379–383; doi:10.1677/joe.0.1630379.

Gutendorf B, Westendorf J. 2001. Comparison of an array of in vitro assays for the assessment of the estrogenic potential of natural and synthetic estrogens, phytoestrogens and xenoestrogens. Toxicology 166: 79–89.

Huang R, Xia M, Cho M-H, Sakamuru S, Shinn P, Houck KA, et al. 2011. Chemical genomics profiling of environmental chemical modulation of human nuclear receptors. Environmental health perspectives 119: 1142.

Isoe-Wada K, Maeda M, Yong J, Adachi Y, Harada H, Urakami K, et al. 1999. Positive association between an estrogen receptor gene polymorphism and Parkinson’s disease with dementia. Eur. J. Neurol. 6: 431–435.

Judson R, Richard A, Dix DJ, Houck K, Martin M, Kavlock R, et al. 2009. The Toxicity Data Landscape for Environmental Chemicals. Environ Health Perspect 117:685– 695; doi:10.1289/ehp.0800168.

Kavlock RJ, Daston GP, DeRosa C, Fenner-Crisp P, Gray LE, Kaattari S, et al. 1996. Research needs for the risk assessment of health and environmental effects of

137 endocrine disruptors: a report of the US EPA-sponsored workshop. Environmental health perspectives 104: 715.

Knudsen TB, Houck KA, Sipes NS, Singh AV, Judson RS, Martin MT, et al. 2011. Activity profiles of 309 ToxCast TM chemicals evaluated across 292 biochemical targets. Toxicology 282: 1–15.

Kolasa A, Wiszniewska B, Marchlewicz M, Wenda-Ró żewicka L. 2003. Localisation of oestrogen receptors (ERalpha and ERbeta) in the human and rat epididymides. Folia Morphologica 62: 467–466.

Kuiper GGJM, Carlsson BO, Grandien KAJ, Enmark E, Häggblad J, Nilsson S, et al. 1997. Comparison of the ligand binding specificity and transcript tissue distribution of estrogen receptors $\alpha$ and $\beta$. Endocrinology 138: 863–870.

Maechler M. 2011. sfsmisc: Utilities from Seminar fuer Statistik ETH Zurich. R package version 1.0–15.

Mahoney MM, Padmanabhan V. 2010. Developmental programming: impact of fetal exposure to endocrine-disrupting chemicals on gonadotropin-releasing hormone and estrogen receptor mRNA in sheep hypothalamus. Toxicology and applied pharmacology 247: 98–104.

Martin MT, Dix DJ, Judson RS, Kavlock RJ, Reif DM, Richard AM, et al. 2010. Impact of environmental chemicals on key transcription regulators and correlation to toxicity end points within EPA’s ToxCast program. Chemical research in toxicology 23: 578– 590.

McCarty MF. 2006. Isoflavones made simple - genistein’s agonist activity for the beta-type estrogen receptor mediates their health benefits. Med. Hypotheses 66:1093–1114; doi:10.1016/j.mehy.2004.11.046.

Mosselman S, Polman J, Dijkema R. 1996. ER beta: identification and characterization of a novel human estrogen receptor. FEBS Lett. 392: 49–53.

Muthyala RS, Ju YH, Sheng S, Williams LD, Doerge DR, Katzenellenbogen BS, et al. 2004. Equol, a natural estrogenic metabolite from soy isoflavones: convenient preparation and resolution of R- and S-equols and their differing binding and biological activity through estrogen receptors alpha and beta. Bioorg. Med. Chem. 12:1559–1567; doi:10.1016/j.bmc.2003.11.035.

Odum J, Lefevre PA, Tittensor S, Paton D, Routledge EJ, Beresford NA, et al. 1997. The rodent uterotrophic assay: critical protocol features, studies with nonyl phenols, and comparison with a yeast estrogenicity assay. Regul. Toxicol. Pharmacol. 25:176–188; doi:10.1006/rtph.1997.1100.

138 Paech K, Webb P, Kuiper GG, Nilsson S, Gustafsson J-\AAke, Kushner PJ, et al. 1997. Differential ligand activation of estrogen receptors ER α and ER β at AP1 sites. Science 277: 1508–1510.

Pravettoni A, Mornati O, Martini PGV, Marino M, Colciago A, Celotti F, et al. 2007. Estrogen receptor beta (ERbeta) and inhibition of prostate cancer cell proliferation: studies on the possible mechanism of action in DU145 cells. Molecular and cellular endocrinology 263: 46–54.

R Development Core Team. 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.

Raivo Kolde. 2012. pheatmap: Pretty Heatmaps. R package version 0.7.3.http://CRAN.R- project.org/package=pheatmap.

Reif DM, Martin MT, Tan SW, Houck KA, Judson RS, Richard AM, et al. 2010. Endocrine Profiling and Prioritization of Environmental Chemicals Using ToxCast Data. Environmental Health Perspectives 118: 1714–1720.

Reif DM, Sypa M, Lock EF, Wright FA, Wilson A, Cathey T, et al. 2013. ToxPi GUI: an interactive visualization tool for transparent integration of data from diverse sources of evidence. Bioinformatics 29: 402–403.

Roberge M, Hakk H, Larsen G. 2004. Atrazine is a competitive inhibitor of phosphodiesterase but does not affect the estrogen receptor. Toxicol. Lett. 154:61–68; doi:10.1016/j.toxlet.2004.07.005.

Romanov S, Medvedev A, Gambarian M, Poltoratskaya N, Moeser M, Medvedeva L, et al. 2008. Homogeneous reporter system enables quantitative functional assessment of multiple transcription factors. Nature methods 5: 253–260.

Rotroff DM, Dix DJ, Houck KA, Kavlock RJ, Knudsen TB, Sipes NS, et al. 2013a. Real- Time Growth Kinetics Measuring Hormone Mimicry for 1816 Unique ToxCast Chemicals in T-47D Human Ductal Carcinoma Cells. In Review.

Rotroff DM, Dix DJ, Houck KA, Knudsen TB, Martin MT, McLaurin KW, et al. 2013b. Using in Vitro High Throughput Screening Assays to Identify Potential Endocrine- Disrupting Chemicals. Environmental Health Perspectives 121: 7.

Sampei K, Goto S, Alkayed NJ, Crain BJ, Korach KS, Traystman RJ, et al. 2000. Stroke in estrogen receptor-alpha-deficient mice. Stroke 31: 738–743; discussion 744.

Saunders PT, Millar MR, Macpherson S, Irvine DS, Groome NP, Evans LR, et al. 2002. ER β1 and the ER β2 splice variant (ER βcx/ β2) are expressed in distinct cell populations in the adult human testis. Journal of Clinical Endocrinology & Metabolism 87: 2706–2715.

139 Skafar DF, Zhao C. 2008. The multifunctional estrogen receptor-alpha F domain. Endocrine 33:1–8; doi:10.1007/s12020-008-9054-1.

Soto AM, Sonnenschein C. 2010. Environmental causes of cancer: endocrine disruptors as carcinogens. Nature Reviews Endocrinology 6: 363–370.

Tan X, Dai Y, Wu W, Kim H, Barros RPA, Richardson TI, et al. 2012. Reduction of dendritic spines and elevation of GABAergic signaling in the brains of mice treated with an estrogen receptor β ligand. Proc. Natl. Acad. Sci. U.S.A. 109:1708–1712; doi:10.1073/pnas.1121162109.

Totta P, Acconcia F, Virgili F, Cassidy A, Weinberg PD, Rimbach G, et al. 2005. Daidzein- sulfate metabolites affect transcriptional and antiproliferative activities of estrogen receptor-beta in cultured human cancer cells. J. Nutr. 135: 2687–2693.

Tremblay A, Tremblay GB, Labrie F, Giguère V. 1999. Ligand-independent recruitment of SRC-1 to estrogen receptor beta through phosphorylation of activation function AF-1. Mol. Cell 3: 513–519.

U.S. EPA. 2011a. Endocrine Disruptor Screening Program for the 21st Century (EDSP21 Work Plan). The Incorporation of In Silico Models and In Vitro High Throughput Assays in the Endocrine Disruptor Screening Program (EDSP) for Prioritization and Screening. Available: http://www.epa.gov/endo/pubs/edsp21_work_plan_summary%20_overview_final.pdf [accessed 12 January 2012].

U.S. EPA. 2012. Endocrine Disruptor Screening Program. Available: http://www.epa.gov/endo/ [accessed 3 July 2012].

U.S. EPA. 2011b. Response to Corrective Action Plan for OIG Report No. 11-p-0215, EPA’s Endocrine Disruptor Screening Program Should Establish Management Controls to Ensure More Timely Results.

Xin J, Zhang J, Gao Y, Xiong L. 2012. Association of estrogen receptor β gene polymorphisms with vascular dementia in women. Neurol. Sci. 33:1029–1035; doi:10.1007/s10072-011-0885-9.

Zhang C-Y, Chen J, Yin D-C, Lin S-X. 2012. The contribution of 17beta-hydroxysteroid dehydrogenase type 1 to the estradiol-estrone ratio in estrogen-sensitive breast cancer cells. PLoS ONE 7:e29835; doi:10.1371/journal.pone.0029835.

Zwijsen RM, Buckle RS, Hijmans EM, Loomans CJ, Bernards R. 1998. Ligand-independent recruitment of steroid receptor coactivators to estrogen receptor by cyclin D1. Genes Dev. 12: 3488–3498.

140

CHAPTER 5

GENERAL DISCUSSION

The implications of endocrine disruption carry a wide range of possible adverse health effects. The realization of sensitive toxicological targets capable of disrupting endocrine physiology, and the increased incidence of reproductive and developmental abnormalities, has led to a major effort to characterize the risk of endocrine disruption by environmental chemicals (Auger et al. 1995; Carlsen et al. 1992; Skakkebaek et al. 2001;

Toppari et al. 2010). However, the current regulatory screening tests are time consuming, require significant animal resources, and are prohibitively expensive to generate regulatory decisions for all the chemicals mandated for testing (National Research Council 2007; U.S.

EPA 2011). The present body of work makes a large library of in vivo data for phenotypic anchoring available, describes approaches for identifying modes-of-action (MOA) with the potential for successful predictive models, in vitro assay assessment and characterization, and construction of a predictive model capable of identifying chemicals with estrogenic potential.

Advances in In Vitro Technology

Technological advances are making the generation of biological data faster and cheaper with high-throughput in vitro assays, in silico approaches, and –omics technologies

(i.e. genomics, metabolomics, and proteomics). These advances are creating new opportunities for chemical safety assessment and improving our understanding of the molecular complexity underlying the adverse effects observed in traditional bioassays.

141 Advantages of HTS assays include the ability to test thousands of chemicals in weeks rather than years. However, with enormous amounts of data generated by this technology, new methods for data analysis and data interpretation must be developed.

A key component to the shift from in vivo to in vitro technology for hazard determination is the shift from observing adverse phenotypic effects in a whole-animal context, to observing molecular changes in a cellular context. One advantage to using HTS assays is that changes in molecular events can be detected more sensitively than identifying overt toxicity. HTS assays measuring changes in transcriptional activation/suppression are thought to be measuring one of the earliest cellular changes in an adverse response (Beyer et al. 2007). The ability to sensitively measure these changes can allow for experiments to be conducted at more realistic exposure levels than many traditional toxicology tests, which rely on measurements of overt toxicity from high doses. However, this technology comes with certain disadvantages and challenges. One of the challenges is that chemical induction or suppression of a molecular target does not necessarily constitute a step toward an adverse outcome (Beyer et al. 2007). The ER Interaction Score for genistein (61.1) is close to the ER

Interaction Score of diethylstilbestrol (81.1) (as described in Chapter 4). However, diethylstilbestrol is a pharmaceutical that was removed from the market due to the debilitating effects it caused to fetal development, whereas, genistein is naturally occurring in soy and is in products consumed globally (Palmlund et al. 1993; Toppari et al. 2010). In addition, 17 β-estradiol, an endogenous estrogen with an ER Interaction Score of 85.8, and is present in females in relatively high concentrations and is critical to wellbeing. There is nothing intrinsically adverse about the activation of ER, it is however, potentially adverse for a chemical to interfere with the tightly regulated estrogen signaling network. Chemicals that

142 preferentially bind to ER α may also pose an increased hazard compared to chemicals the preferentially bind ER β. Unlike ER α, ER β activation is actively being explored for its potential anti-inflammatory and chemotherapeutic properties upon activation (Warner and

Gustafsson 2010).

Phenotypic Anchoring

Because not all changes in bioactivity, represented by in vitro perturbations, indicate an increased hazard upon chemical exposure, it is important to identify which changes in bioactivity can serve as biomarkers for adverse outcomes. An example where gene expression profiling technology was successfully implemented in order to classify compounds based on their toxicological MOA can be seen in the work by Thomas et al.

(Thomas et al. 2001). This study used cDNA microarrays to classify 24 well characterized chemicals into classes of peroxisome proliferators, AhR agonists, noncoplanar PCBs, inflammatory response activators, and hypoxia signal transduction pathway activators. The data was modeled using naïve Bayesian classification with cross-validation, and the results identified 12 transcripts that classified chemicals with an estimated 100% accuracy for the represented toxicological classes (Thomas et al. 2001). Several of the transcripts systematically identified as being significant, have known involvements in toxicological pathways, such as CYP1A2 which is regulated by the aryl hydrocarbon receptor (AhR)

(Schmidt and Bradfield 1996).

Using genomics for identifying potential endocrine disrupters is challenging because of the large number of genes regulated by activation of hormone receptors. A study by Hah et al. (Hah et al. 2011) identified that the ER activated ~ 26% of the transcriptome in MCF-7 cells. ER also has a number of non-genomic mechanisms including ERK and Src

143 phosphorylation, which upon activation did not result in cellular proliferation in MCF-7 cells

(Harrington et al. 2006). These results suggest that the adverse proliferative response of estrogens in certain cancers is due to the genomic mechanisms of ER activation (Harrington et al. 2006). As HTS data continues to be generated on a large scale, tools must be developed in order to perform phenotypic anchoring to determine predictivity for these in vitro endpoints. ToxRefDB is database capturing decades of legacy toxicity data from regulatory testing and is an available tool for phenotypic anchoring of HTS assays (Martin et al. 2009b,

2009a). Additional work by Martin et al. (Martin et al. 2011) and Knudsen et al. (Knudsen et al. 2009) have demonstrated that HTS assays can be used to successfully predict certain effects in traditional toxicology bioassays.

Previous work to identify relationships between chemical induced, endocrine effects observed in vitro could only be linked to in vivo effects for a very limited number of chemicals. The data included in Chapter 2 makes in vivo data available for a wide range of endocrine assays, and allows for easy aggregation and segregation of assay characteristics based on the resolution desired for modeling efforts. The present body of work demonstrates that HTS estrogen and androgen assays can be used as measures of bioactivity that can classify chemicals similarly to regulatory endocrine assays for estrogen and androgen MOA, respectively (as described in Chapter 2). Effects observed in OECD guideline and EDSP T1S validation studies were used to accomplish the phenotypic anchoring. Relative to other available HTS assays, the hypothesized estrogen and androgen HTS assays, measuring receptor binding and activation, were determined to be more informative for chemicals that caused adverse effects in the available estrogen and androgen in vivo assays. Overall, this indicates that active chemicals in estrogen and androgen in vitro assays, tend to also cause

144 adverse effects in in vivo endocrine assays. In addition, available HTS assays may not be sufficient for anchoring to some endocrine phenotypes, such as those produced by affecting thyroid and steroidogenesis MOA (as described in Chapter 2). Many chemicals thought to alter thyroid hormone homeostasis leading to adverse effects are thought to be caused by chemically induced changes in thyroid hormone metabolic clearance; however, many HTS assays measuring these endpoints are active for chemicals lacking an association with thyroid toxicity (Saghir et al. 2008; Zorrilla et al. 2009).

Combining In Vitro Data to Test for In Vivo Effects

Previous efforts comparing chemical effects in multiple in vitro assays do so by aggregating across AC50 values or dichotomizing assay results (Rotroff et al. 2013; Schenk et al. 2010). Although, these analyses perform well for classifying chemicals for activity or comparing assays across MOA, they do not allow for quantitative rankings of predicted chemical activity. In addition, the dose-response context is lost when assay results are dichotomized. Every in vitro and in vivo assay has certain limitations and shortcomings.

Some in vitro assays lack sensitivity, generating false negative results, or may be prone to false positive results. The ER binding assays used in the present work indicate that these assays were less sensitive when compared to the transcriptional factor and cell growth assays

(as described in Chapter 3). Because of limitations within the assay technology, chemicals could not be tested above 50 µM, providing no information about chemicals that were active above these concentrations. The ER binding assays are also susceptible to false positives by detergent compounds that cause denaturation of the receptor, making it appear as if the natural ligand was displaced due to the test chemical. However, most of the chemicals that were active in the ER binding assay were also active in the other ER assays (as described in

145 Chapter 3 and Chapter 4). The T47D cell growth assay is not specific for ER activity and responds to other steroid hormones and mitochondrial chain disrupters. If one was to rely on this single assay to prioritize chemicals for more resource intensive assays, then too many false positives for ER would be tested and resources would be wasted. Alternatively, if the

ER binding assay was the only assay used to prioritize chemicals for endocrine screening, a significant number of ER agonists would go undetected. Because assays technologies have different sensitivities, one way to reduce the noise that occurs when comparing multiple technologies is to perform a linear transformation of the test concentrations. Ideally, this transformation will correct for noise that occurs from the technology platform, and the remaining difference in responses can be explained biologically. This can be observed by the improvement in R 2 values of active chemicals in the agonist group after performing the linear transformation (p < .0001) (as described in Chapter 4).

An important aspect to reducing the statistical noise that occurs when associations are made across a large number of predictor variables is to perform a method of dimensionality reduction. Simply applying machine learning methods to a large set of in vitro assays to identify associations with in vivo endpoints limits the likelihood that the model will have enough statistical power to detect a predictive signature (Thomas et al. 2012). The use of multiple in vitro assays in a model that emphasizes their strengths and minimizes their weaknesses will further reduce the noise around their measurements and enhance the possibility of a strong predictive signal. One method of combining in vitro assays to predict active chemicals in multigenerational reproductive studies was explored by Martin et al.

(Martin et al. 2011). In vitro assays with a univariate association with reproductive endpoints were linked to gene sets, reducing the dimensionality and enhancing the predictive capability

146 of the model (Martin et al. 2011). For modeling effects of ER signaling, assays were assigned to four groups according to assay characteristics for detecting estrogens and anti-estrogens and according to location of their respective mechanism on the branch of the ER signaling pathway (as discussed in Chapter 4). For example, the ER binding assays readily but indistinguishably detect agonists and antagonists. The grouping of this assay with agonist- specific assays would result in increased uncertainty for antagonist compounds in the agonist assays. Incorporating prior knowledge and reducing dimensionality can aid in the construction of successful predictive model.

Appropriate in vivo assays and reference chemical libraries must also be available to effectively characterize the in vitro assay endpoints under investigation. For example, multigenerational reproductive studies can detect estrogenic compounds; however, they also detect androgen and thyroid disrupting compounds, among other MOA. The uterotrophic assay is intended to be an assay targeted for detection of chemicals that mimic estrogen or affect estrogen synthesis (Odum et al. 1997). The uterotrophic assay has also been conducted on a relatively large set of chemicals and has a validated protocol to promote consistency across studies. Lastly, a validated list of ER reference chemicals exists to anchor model predictions, making ER an ideal candidate to demonstrate the effectiveness of combining concentration response data for prediction of estrogenic potential. The results indicate a high degree of concordance between increasing model ER Interaction Scores and activity in uterotrophic studies as being highly associated with ER reference chemicals ( p < .0001) (as described in Chapter 4). The ER Interaction Scores can be ordered to rank chemicals with a high probability to a low probability of interacting with ER.

147 One advantage to the current approach is that it can be re-evaluated with new technologies as they become available. As research continues to refine the signaling pathway for ER, the assay-grouping logic can also be updated to improve the overall biological relevance of the model. The methods are also extendable to other pathways that have orthogonal assay technologies and a common positive control. The work herein describes a generalizable approach for conducting a comparative analysis that is capable of identifying

MOA for in vitro predictive modeling, methods for characterizing the strengths and weakness for in vitro assays for the purpose of model optimization, and a novel approach for combining concentration response data into a pathway-based model capable of predicting chemicals with estrogenic potential.

Limitations

As with any computational model or biological assay, limitations exist and are important for defining the appropriate domain of applicability. In vitro technologies measure molecular events and provide measures of bioactivity, unlike in vivo assays that provide phenotypic outcomes without the mechanistic context. The in vitro assay results in this body of work, represent molecular initiating events of key targets involved in endocrine MOA; however, a chemical’s activation of any of these targets does not indicate that exposure to this chemical would result in an adverse outcome. Furthermore, in vitro assays are limited in their ability to account for the effects of chemical metabolism and other pharmacokinetic properties. Although, efforts to incorporate pharmacokinetic information into a high- throughput platform have been investigated, continued efforts are needed in order to be able to apply these methods to diverse chemical libraries (Rotroff et al. 2010; Wetmore et al.

2012). For any loss-of-signal in vitro assays, such as the antagonist assays used herein,

148 confounding by cytotoxicity is a significant challenge. Although filtered for cytotoxicity, it is difficult to distinguish the weak antagonistic responses in the antagonist assays from early cytotoxic responses.

A major challenge to validating predictive models for their predictive capability is the limited in vivo data available. Because a relatively few number of chemicals have been run in in vivo studies, an appropriate external validation set is not available. In Chapter 2, the open literature was used as an external validation set; however, these studies vary greatly in study quality and study design. The low number of chemicals available for comparison could result in a model that is overfit, for example the HTS-A model produced a balanced accuracy for guideline androgen studies and Hershberger studies of 0.92 and 1, respectively; however, these results were no longer statistically significant in the open-literature analysis (as described in Chapter 2). The decrease in predictivity in the open-literature could be due to either overfitting of the model, or because the external validation set was not representative of the original test set. As additional data becomes available from EDSP T1S, the analysis can be reevaluated to provide further validation of the models.

The analysis in Chapter 3 details an approach for characterizing and assessing the applicability of multiple ER in vitro assays. A major limitation of the cell type, used to assess

ER related induction of cell growth, is that the T-47D cells express additional hormone receptors that upon activation can impact cell growth and cell morphology. Furthermore, this assay responds to mitochondrial disrupters (as described in Chapter 3). Once again only a limited amount of chemicals were available for comparison in vivo and ER reference chemical comparisons, 29 and 25 chemicals respectively. However, this cell line readily

149 responds to estrogens and when used in conjunction with other ER assays, can be used to confirm a functional change.

The in vitro limitations (e.g. metabolism, not necessarily adverse) are also relevant to the modeling results in Chapter 4. In addition, the ER Interaction Score is scaled relative to the other chemicals in the model. The addition of new chemicals would not change the composite score, but would change the scaled composite score and the subsequent ER

Interaction Score. The assays (features) that were selected for the ER model are based on the hypothesis in Chapter 2, that assays measuring ER activation are predictive for estrogen related effects observed in vivo . Permutation demonstrated that these assays were significantly more informative than other assays for estrogen related effects in vivo (as described in Chapter 2).The assay used in the model, and subsequently the model itself, does not take into account chemicals that may alter estrogenic action through interfering with neuroendocrine mechanisms or estrogen biosynthesis. These compounds would be detected in the in vivo estrogen studies, but would be missed with the current approach. Additional models measuring effects on steroidogenesis are needed in order to detect these chemicals.

The logic used to calculate the ER Interaction Score is based on the pathway illustrated in

Figure 4.1, and other mechanisms or pathways may not be accounted for that would impact the ER Interaction Score calculation.

The ER model, presented in Chapter 4, was developed using only in vitro data and was not trained on any external dataset; therefore, the problem of overfitting, such as those experienced with traditional machine learning algorithms, are not particularly applicable.

Although, classic overfitting may not be a major concern, there is a high degree of collinearity between the ER in vitro assays. The addition of other ER assays may

150 significantly change model outcome. Furthermore, the assumptions for deriving activity thresholds of individual assays and the thresholds for significance of the model’s confidence and prediction intervals may not be optimal. Most of the data available for evaluating the performance of the ER model is based on effects observed in rodent studies. It is likely that species differences exist that could confound the results observed in the model validation (as described in Chapters 2 and 4). The model showed strong predictive performance against the available ER reference chemicals; however, this chemical set is relatively small and may not account for all mechanisms of ER activation. Furthermore, this reference set overrepresents active chemicals (76% actives). It is possible that with a larger reference set with equal numbers of active and inactive chemicals the model evaluation would have been altered.

However, this reference set is only used to evaluate the model and not to train the model, and therefore should have a minimal impact on the model efficacy.

As with any model, limitations exist and must be evaluated in the context for which the model is intended. The new in vivo data that will become available throughout EDSP T1S can be extremely valuable in supplementing the currently available in vivo data. New technologies capable of determining metabolic potential in an HTS format are needed to improve the in vivo relevance of predictive models. Until these resources are made available, it is important that the results reported in the present body of work are taken in the context of these limitations.

151 References

Auger J, Kunstmann JM, Czyglik F, Jouannet P. 1995. Decline in semen quality among fertile men in Paris during the past 20 years. New England Journal of Medicine 332: 281–285.

Beyer RP, Fry RC, Lasarev MR, McConnachie LA, Meira LB, Palmer VS, et al. 2007. Multicenter study of acetaminophen hepatotoxicity reveals the importance of biological endpoints in genomic analyses. Toxicological sciences 99: 326–337.

Carlsen E, Giwercman A, Keiding N, Skakkebæk NE. 1992. Evidence for decreasing quality of semen during past 50 years. British medical journal 305: 609–613.

Hah N, Danko CG, Core L, Waterfall JJ, Siepel A, Lis JT, et al. 2011. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell 145: 622–634.

Harrington WR, Kim SH, Funk CC, Madak-Erdogan Z, Schiff R, Katzenellenbogen JA, et al. 2006. Estrogen dendrimer conjugates that preferentially activate extranuclear, nongenomic versus genomic pathways of estrogen action. Mol. Endocrinol. 20:491– 502; doi:10.1210/me.2005-0186.

Knudsen TB, Martin MT, Kavlock RJ, Judson RS, Dix DJ, Singh AV. 2009. Profiling the activity of environmental chemicals in prenatal developmental toxicity studies using the US EPA’s ToxRefDB. Reproductive toxicology (Elmsford, NY) 28: 209.

Martin MT, Judson RS, Reif DM, Kavlock RJ, Dix DJ. 2009a. Profiling Chemicals Based on Chronic Toxicity Results from the U.S. EPA ToxRef Database. Environ Health Perspect 117:392–399; doi:10.1289/ehp.0800074.

Martin MT, Knudsen TB, Reif DM, Houck KA, Judson RS, Kavlock RJ, et al. 2011. Predictive model of rat reproductive toxicity from ToxCast high throughput screening. Biology of reproduction 85: 327–339.

Martin MT, Mendez E, Corum DG, Judson RS, Kavlock RJ, Rotroff DM, et al. 2009b. Profiling the reproductive toxicity of chemicals from multigeneration studies in the toxicity reference database. Toxicological Sciences 110: 181–190.

National Research Council. 2007. Toxicity Testing in the 21st Century: A Vision and a Strategy. Washington, DC: National Academies Press.

Odum J, Lefevre PA, Tittensor S, Paton D, Routledge EJ, Beresford NA, et al. 1997. The rodent uterotrophic assay: critical protocol features, studies with nonyl phenols, and comparison with a yeast estrogenicity assay. Regul. Toxicol. Pharmacol. 25:176–188; doi:10.1006/rtph.1997.1100.

Palmlund I, Apfel R, Buitendijk S, Cabau A, Forsberg JG. 1993. Effects of diethylstilbestrol (DES) medication during pregnancy: report from a symposium at the 10th

152 international congress of ISPOG. Journal of Psychosomatic Obstetrics & Gynecology 14: 71–89.

Rotroff DM, Dix DJ, Houck KA, Knudsen TB, Martin MT, McLaurin KW, et al. 2013. Using in Vitro High Throughput Screening Assays to Identify Potential Endocrine- Disrupting Chemicals. Environmental Health Perspectives 121: 7.

Rotroff DM, Wetmore BA, Dix DJ, Ferguson SS, Clewell HJ, Houck KA, et al. 2010. Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicological Sciences 117: 348–358.

Saghir SA, Charles GD, Bartels MJ, Kan LH, Dryzga MD, Brzak KA, et al. 2008. Mechanism of trifluralin-induced thyroid tumors in rats. Toxicology letters 180: 38– 45.

Schenk B, Weimer M, Bremer S, Burg B van der, Cortvrindt R, Freyberger A, et al. 2010. The ReProTect Feasibility Study, a novel comprehensive in vitro approach to detect reproductive toxicants. Reproductive Toxicology 30: 200–218.

Schmidt JV, Bradfield CA. 1996. Ah receptor signaling pathways. Annu. Rev. Cell Dev. Biol. 12:55–89; doi:10.1146/annurev.cellbio.12.1.55.

Skakkebaek NE, Rajpert-De Meyts E, Main KM. 2001. Testicular dysgenesis syndrome: an increasingly common developmental disorder with environmental aspects: Opinion. Human reproduction 16: 972–978.

Thomas RS, Black MB, Li L, Healy E, Chu T-M, Bao W, et al. 2012. A comprehensive statistical analysis of predicting in vivo hazard using high-throughput in vitro screening. Toxicol. Sci. 128:398–417; doi:10.1093/toxsci/kfs159.

Thomas RS, Rank DR, Penn SG, Zastrow GM, Hayes KR, Pande K, et al. 2001. Identification of toxicologically predictive gene sets using cDNA microarrays. Mol. Pharmacol. 60: 1189–1194.

Toppari J, Virtanen HE, Main KM, Skakkebaek NE. 2010. Cryptorchidism and hypospadias as a sign of testicular dysgenesis syndrome (TDS): environmental connection. Birth Defects Research Part A: Clinical and Molecular Teratology 88: 910–919.

U.S. EPA. 2011. Endocrine Disruptor Screening Program for the 21st Century: EDSP21 Work Plan. Available: http://www.epa.gov/endo/pubs/edsp21_work_plan_summary%20_overview_final.pdf [accessed 12 Jan 2012].

Warner M, Gustafsson J-A. 2010. The role of estrogen receptor beta (ERbeta) in malignant diseases--a new potential target for antiproliferative drugs in prevention and treatment of cancer. Biochem. Biophys. Res. Commun. 396:63–66; doi:10.1016/j.bbrc.2010.02.144.

153 Wetmore BA, Wambaugh JF, Ferguson SS, Sochaski MA, Rotroff DM, Freeman K, et al. 2012. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicological sciences 125: 157–174.

Zorrilla LM, Gibson EK, Jeffay SC, Crofton KM, Setzer WR, Cooper RL, et al. 2009. The effects of triclosan on puberty and thyroid hormones in male Wistar rats. Toxicological Sciences 107: 56–64.

154

CHAPTER 6

FUTURE DIRECTIONS

The development of a model capable of prioritizing chemicals based on their estrogenic likelihood provides and an important step towards assessing the endocrine disrupting potential for hundreds of environmental chemicals. The economic burden of testing all chemicals mandated for endocrine screening in medium- and low-throughput assays has made computational models of in vitro and in silico assays an optimal tool to facilitate the testing of these chemicals. Additional work to refine current assays and models is needed, and new assay technologies capable of alleviating the current limitations with in vitro technology will be required in order to fully transform the current state of toxicological safety assessment.

Assay and Model Development to Address Biological Gaps

The comparative analysis in Chapter 2 identified the available thyroid and steroidogenesis assays as being inadequate for classifying chemicals consistently with the

EDSP T1S assays measuring thyroid and steroidogenesis mechanisms (as described in

Chapter 2). Currently, the ToxCast assay library consists of only a single, aromatase enzyme inhibition assay. Although this is an important component of steroid biosynthesis, regulating the conversion of estrogens to androgens, there are many additional ways a chemical can target steroid biosynthesis including CYP11A1 and CYP17 (Zhang et al. 2005). Currently, efforts are underway to develop a quantitative assay measuring multiple steroid hormones

155 along the steroidogenesis pathway in a 96-well format using H295R cells (Hecker et al.

2006; Zhang et al. 2005). Once the data from this assay becomes available, computational modeling within the framework of the steroidogenesis pathway will need to be developed and the predictive capability will need to be demonstrated.

Thyroid toxicity is a particularly difficult endpoint to screen using in vitro assays. No association was observed with chemicals that caused thyroid toxicity in the EDSP T1S guideline studies and the HTS thyroid hormone receptors assays (as discussed in Chapter 2).

These results suggest that most chemicals known to cause thyroid toxicity are not mediated through the thyroid hormone receptor. Other in vitro assays measuring thyroid function exist, but most of these are thyroid explants assays and are not high-throughput (Hornung et al.

2010). Additional studies have suggested that increased clearance of thyroid hormone by metabolic enzymes after chemical exposure can result in thyroid toxicity (Saghir et al. 2008;

Zorrilla et al. 2009). HTS assays for liver metabolizing enzymes are available, and are generally active for thyroid disrupting compounds; however, they are oftentimes active for many chemicals with no evidence of thyroid toxicity limiting their predictive capabilities (as described in Chapter 2). Therefore, additional HTS need to be developed that can target more toxicologically relevant thyroid endpoints.

The androgen MOA is one with many available HTS assays that would be highly amenable to the approach described for ER. The Hershberger assay is an ideal in vivo assay to use for phenotypic anchoring of the HTS androgen assays. One challenge with AR is that many adverse effects from xenobiotics are observed by antagonizing AR. Available AR antagonist assays exist within ToxCast, but because they are a determined through the loss of a fluorescent signal are prone to confounding by cytotoxicity.

156 Providing In Vivo Context

Ultimately, a chemical risk assessment must result in an estimated level of exposure that is presumed to be safe for the human population. A challenging aspect to interpreting in vitro assay results relative to the in vivo outcomes is the lack of absorption, metabolism, digestion and excretion in in vitro and in silico models. Chemicals that display activity in vitro may be rapidly metabolized and excreted, resulting in no adverse outcome observable in vivo. Conversely, a chemical may not be active in vitro but come active after metabolism in vivo . The conversion of in vitro activity concentrations to a human oral equivalent dose has been previously conducted (Rotroff et al. 2010; Wetmore et al. 2012). These studies identified that incorporating metabolic clearance and plasma protein binding provides estimates that can accurately estimate human oral equivalent doses. However, this method assumes 100% absorption by the gastrointestinal tract and is limited by the analytical methods that exist for only a subset of chemicals. Additional research is needed in this area to further refine this model and to make it widely available for in vitro based risk assessment.

Potential for Prioritization and Targeted Testing

The near-term goal of EDSP21 is to prioritize chemicals for endocrine screening in the EDSP T1S by using computational and molecular-based in vitro assays (U.S. EPA 2011).

The present model serves as one method for ranking chemicals based on in vitro activity for estrogenic potential and could be used as a component in a chemical prioritization. Because of limitations in cell-based assays ( e.g. solubility), not all chemicals mandated for EDSP T1S will be able to be modeled using the approaches described herein. Quantitative structure- activity relationship (QSAR) models have been demonstrated to effectively predict chemicals with the potential to bind to ER, and may be a valuable way to augment the current in vitro

157 limitations (Agatonovic-Kustrin et al. 2011; Li and Gramatica 2010). Another important component for prioritization is chemical exposure. Chemicals with a high likelihood of estrogenic activity, and a high risk of exposure should constitute a higher ranking in a prioritization scheme compared to a chemical with high likelihood of estrogenic activity, but negligible risk of exposure (Gangwal et al. 2012).

The intermediate-term goal of the EDSP21 is to replace some more resource intensive assays with computational models using in silico and in vitro approaches, or use these models so that certain assays may be opted out/in. The analysis presented in Chapter 4 demonstrates that the present ER model predicts chemicals active in uterotrophic assays with a high degree of accuracy. Additional information regarding impacts on estrogen biosynthesis and metabolic potential will be needed to effectively replace the uterotrophic assay. The uterotrophic assay is considered to measure a single MOA (estrogenic effects), other multi-modal assays will require additional models covering other MOA besides estrogenicity before a negative result would be sufficient to opt out of a particular EDSP T1S assay. For example, the pubertal female detects effects on the HPG axis, estrogen, thyroid and steroidogenesis (U.S. EPA 2007, 2012).

The shift towards the increased use of HTS assays for informing regulatory decisions should be viewed as an “improvement to toxicity testing rather than alternatives to animal testing” (Andersen and Krewski 2009). HTS assays offer some critical features not offered by traditional in vivo assays, namely throughput, sensitivity, cost, and human relevance

(National Research Council 2007). In vivo assays offer the determination of adverse outcomes and a multi-organ system for chemical assessment. Currently neither approach offer a superset of features, but the two can complement each other. Ideally, the purpose of

158 the prioritization scheme would be to make sure that the only chemicals being tested in the more resource intensive EDSP T1S assays are active for their respective MOA.

The framework for endocrine disruption screening needs to be able to maximize the strengths and minimize the weaknesses of both the computational and in vivo approaches. As the computational models improve and develop to a superset of features for certain in vivo assays, then replacement of the in vivo assays will be necessary. The endocrine screening effort will require many years to effectively screen all the chemicals mandated for testing, technology will continue to evolve during this time, and the framework for testing needs to be flexible to accommodate improved and validated technologies as they become available.

159 References

Agatonovic-Kustrin S, Alexander M, Morton DW, Turner JV. 2011. Pesticides as estrogen disruptors: QSAR for selective ER α and ER β binding of pesticides. Comb. Chem. High Throughput Screen. 14: 85–92.

Andersen ME, Krewski D. 2009. Toxicity testing in the 21st century: bringing the vision to life. Toxicol. Sci. 107:324–330; doi:10.1093/toxsci/kfn255.

Gangwal S, Reif DM, Mosher S, Egeghy PP, Wambaugh JF, Judson RS, et al. 2012. Incorporating exposure information into the toxicological prioritization index decision support framework. Science of the Total Environment 435: 316–325.

Hecker M, Newsted JL, Murphy MB, Higley EB, Jones PD, Wu R, et al. 2006. Human adrenocarcinoma (H295R) cells for rapid in vitro determination of effects on steroidogenesis: hormone production. Toxicol. Appl. Pharmacol. 217:114–124; doi:10.1016/j.taap.2006.07.007.

Hornung MW, Degitz SJ, Korte LM, Olson JM, Kosian PA, Linnum AL, et al. 2010. Inhibition of thyroid hormone release from cultured amphibian thyroid glands by methimazole, 6-propylthiouracil, and perchlorate. Toxicol. Sci. 118:42–51; doi:10.1093/toxsci/kfq166.

Li J, Gramatica P. 2010. The importance of molecular structures, endpoints’ values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders. Mol. Divers. 14:687–696; doi:10.1007/s11030-009-9212-2.

National Research Council. 2007. Toxicity Testing in the 21st Century: A Vision and a Strategy. Washington, DC: National Academies Press.

Rotroff DM, Wetmore BA, Dix DJ, Ferguson SS, Clewell HJ, Houck KA, et al. 2010. Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicological Sciences 117: 348–358.

Saghir SA, Charles GD, Bartels MJ, Kan LH, Dryzga MD, Brzak KA, et al. 2008. Mechanism of trifluralin-induced thyroid tumors in rats. Toxicology letters 180: 38– 45.

U.S. EPA. 2011. Endocrine Disruptor Screening Program for the 21st Century (EDSP21 Work Plan). The Incorporation of In Silico Models and In Vitro High Throughput Assays in the Endocrine Disruptor Screening Program (EDSP) for Prioritization and Screening. Available: http://www.epa.gov/endo/pubs/edsp21_work_plan_summary%20_overview_final.pdf [accessed 12 January 2012].

U.S. EPA. 2012. Tier 1 Screening Battery. Available: http://www.epa.gov/endo/pubs/assayvalidation/tier1battery.htm [accessed 3 July 2012].

160 U.S. EPA. 2007. Validation of Screening and Testing Assays Proposed for the EDSP. Available: http:// http://www.epa.gov/endo/pubs/edsp_validation_paper_v%205.4.pdf.

Wetmore BA, Wambaugh JF, Ferguson SS, Sochaski MA, Rotroff DM, Freeman K, et al. 2012. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicological sciences 125: 157–174.

Zhang X, Yu RMK, Jones PD, Lam GKW, Newsted JL, Gracia T, et al. 2005. Quantitative RT-PCR methods for evaluating toxicant-induced effects on steroidogenesis using the H295R cell line. Environ. Sci. Technol. 39: 2777–2785.

Zorrilla LM, Gibson EK, Jeffay SC, Crofton KM, Setzer WR, Cooper RL, et al. 2009. The effects of triclosan on puberty and thyroid hormones in male Wistar rats. Toxicological Sciences 107: 56–64.

161