# Section 3.1:

Androgen Receptor Model Nicole Kleinstreuer, PhD Integrated Laboratory Systems NTP Interagency Center for the Evaluation 

# of Alternative Toxicological Methods (NICEATM)

Slide 1 of 41

# Presentation Roadmap

Androgen Receptor Bioactivity Estrogen Receptor Bioactivity Introduction

Exposure Toxicokinetics/Dosimetry

Integrated Bioactivity Exposure Ranking

Future Directions FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 2 of 41

# Androgen Receptor Model

Combines results from multiple in vitro assays 

#  Following the example of ER  Multiple assays on AR pathway • Different technologies 

# • Different points in pathway • Assay Interference • False Positives/Negatives 

# • Noise, Cytotoxicity
 Use model to integrate assays


#  Evaluate model against reference chemicals Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 3 of 41

# Radioligand Biochemical Assays (Novascreen) Assay ID

Assay Name

Gene Target Normalized Data Type

Assay Design Type

Biological Process Target

Detection Technology

Detection Technology Subtype Measurement Timepoint (Hours) Organism Tissue Cell Format Cell Line Name Assay Footprint

A1 NVS_NR_hAR AR percent activity radioligand

binding receptor

binding Lysate - based

radiodetection Scintillation

counting 18 human NA cell -free NA 96 -well plate A2 NVS_NR_cAR AR percent activity radioligand

binding receptor

binding Filter- based

radiodetection Scintillation

counting 18 chimp NA cell -free NA 96-well plate 

### Section 3.1

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# Protein Complementation Assays (Odyssey Thera ) Assay ID

Assay Name

Gene Target Normalized Data Type

Assay Design Type

Biological Process Target

Detection Technology

Detection Technology Subtype Measurement Timepoint (Hours) Organism Tissue Cell Format Cell Line Name Assay Footprint

A3 OT_AR_ARSRC1_0 480 AR percent activity protein fragment

complementation assay protein

stabilization Protein fragment Complementa tion Fluorescence

intensity 8 human kidney cell line HEK2 93T microplate : 384-well plate A4 OT_AR_ARSRC1_0 960 AR percent activity protein fragment

complementation assay protein

stabilization Protein fragment Complementa tion Fluorescence

intensity 16 human kidney cell line HEK2 93T microplate : 384-well plate 

# RNA Transcription Activation Assays (Attagene )

A5 ATG_AR_TRANS_up AR log2 fold induction mRNA induction regulation of transcription factor activity RT- PCR and

Capillary electrophore sis Fluorescence

intensity 24 human liver cell line HepG 2 microplate : 24-well plate 

### Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 5 of 41

# Protein Transcription Activation Assays (Tox21) Assay ID

Assay Name

Source GeneTarget Normalized Data Type Assay Design Type

Biological Process Target

Detection Technology

Detection Technology Subtype Measurement Timepoint (Hours) Organism Tissue Cell Format Cell Line Name Assay Footprint

A6 Tox21_AR_BLA_Agonist_ ratio Tox21 AR percent activity beta

lactamas e induction regulation

of gene expression GAL4 b lactamase reporter gene Fluorescence

intensity 24 huma

n kidney cell

line HEK293 T microplat

e: 1536 - well plate A7 Tox21_AR_LUC_MDAKB2 _Agonist Tox21 AR percent activity luciferase

induction regulation

of gene expression Luciferase coupled ATP quantitation Bioluminesce

nce 48 huma

n ovary cell

line MDAkb2 microplat

e: 1536 - well plate A8 Tox21_AR_BLA_Antagoni st_ratio Tox21 AR percent activity beta

lactamas e induction regulation

of gene expression GAL4 b lactamase reporter gene Fluorescence

intensity 24 huma

n kidney cell

line HEK293 T microplat

e: 1536 - well plate A9 Tox21_AR_LUC_MDAKB2 _Antagonist Tox21 AR percent activity luciferase

induction regulation

of gene expression Luciferase coupled ATP quantitation Bioluminesce

nce 48 huma

n ovary cell

line MDAkb2 microplat

e: 1536 - well plate


### Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 6 of 41

### Section 3.1, Figure 3.4

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# AR 

# Assays

Higher intensity color indicates greater activity.AR Receptor Binding (Agonist)
Dimerization Cofactor Recruitment DNA Binding RNA Transcription Protein Production R3

R1

R6 R5 N1
N2 N3 N4 N5 A1 A2

A3 A4

A5

A6 A7 Receptor (Direct Molecular Interaction) Intermediate Process Assay AR agonist pathway Pseudo - receptor pathway AR antagonist pathway R2

N7 AR Receptor Binding (Antagonist)

A8 A9 Dimerization
N8

N9 DNA Binding Cofactor Recruitment

N10 Antagonist Transcription Suppression R4

R7 RA1 NVS human chimp
OT PCA AR, SRC1

ATG TRANS

Tox21 BLA Tox21 LUC Tox21 BLA Tox21 LUC 

### Section 3.1, Figure 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 8 of 41 

# AR Model

# AR Computational Model

9 

### Penalty enforces physical assumption that chemical will not hit many targets simultaneously

AUC Summarizes results A i 

### is the efficacy of the assay at a given concentration R j 

### is the “true” efficacy which is unobservable F links receptors to assays

Solve a constrained least- squares problem to minimize difference between the measured and predicted assay values

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 9 of 41

# AR AUC Scales Monotonically With AC50 For Pure Agonists/Antagonists Active In All Assays

Agonist AUC=0.1 Equivalent to AC50=100 uM 

### Section 3.1 Antagonist AUC=0.05 Equivalent to AC50=100 uM

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# Selection of AR Reference Chemicals

• ICCVAM final report on the evaluation of in vitro test methods for detecting potential endocrine disruptors (ICCVAM 2003) • OECD draft validation report on the AR - mediated Stably Transfected Transcriptional Activation (AR 

# STTA) Assay (OECD 2010 ) • 23 reference chemicals had consistent in vitro results in both reports, and were tested in the 

# ToxCast library. Section 3.1
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# AR Reference Compounds

CASRN Chemical Name Agonist Activity Antagonist Activity 2392 - 39 -4 Dexamethasone Agonist Inactive 63 - 05 - 8 4-Androstenedione Agonist Inactive 521 - 18 - 6 5a - Dihydrotestosterone Agonist Inactive 58 - 18 -4 Methyl testosterone Agonist Inactive 57 - 85 - 2 Testosterone propionate Agonist Inactive 13311 - 84- 7 Flutamide Inactive Antagonist 140 - 66 - 9 4-tert - Octylphenol Inactive Antagonist 32809 - 16 - 8 Procymidone Inactive Antagonist 80 - 05 - 7 Bisphenol A Inactive Antagonist 50471 -44- 8 Vinclozolin Inactive Antagonist 72 - 55 - 9 p,p' - DDE Inactive Antagonist 52806 - 53 - 8 Hydroxyflutamide Inactive Antagonist 56 - 53 - 1 Diethylstilbestrol Inactive Antagonist 84- 74- 2 Di - n - butyl - phthalate Inactive Inactive 117 - 81 - 7 Diethylhexyl phthalate Inactive Inactive 1912 - 24- 9 Atrazine Inactive Inactive 427 - 51 - 0 Cyproterone acetate SARM SARM 50 - 28 - 2 17 - β - Estradiol SARM SARM 53 - 16 - 7 Estrone SARM SARM 330 - 55 - 2 Linuron SARM SARM 52 - 01 - 7 Spironolactone SARM SARM 57 - 83 - 0 Progesterone SARM SARM 206 -44- 0 Fluoranthene SARM SARM 

### Section 3.1, Table 3.2 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 12 of 41

# Reference Chemical Performance
 All pure agonist and antagonist AR reference chemicals were correctly identified.
 All SARMs except Linuron 

# and Fluoranthene were predicted as both agonist and antagonist actives

Section 3.1, Figure 3.2 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 13 of 41

# Assay/Model Legend

(Curves in Appendix 7 & 8) Section 3.1, Appendix 7/8

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# AR Example – Testosterone propionate

FIFRA SAP: Integrated Bioactivity Exposure

Ranking Dec 2 - 5, 2014 

### Section 3.1

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# AR Example – Hydroxyflutamide

FIFRA SAP: Integrated Bioactivity Exposure

Ranking Dec 2 - 5, 2014 

### Section 3.1

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# AR Example – Vinclozolin

FIFRA SAP: Integrated Bioactivity Exposure

Ranking Dec 2 - 5, 2014 

### Section 3.1

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# AR Example – Procymidone

FIFRA SAP: Integrated Bioactivity Exposure

Ranking Dec 2 - 5, 2014 

### Section 3.1

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# AR Example – p,p’ - DDE

FIFRA SAP: Integrated Bioactivity Exposure

Ranking Dec 2 - 5, 2014 

### Section 3.1

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# AR Example – Cyproterone acetate

FIFRA SAP: Integrated Bioactivity Exposure

Ranking Dec 2 - 5, 2014 

### Section 3.1

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# AR Example – Linuron

FIFRA SAP: Integrated Bioactivity Exposure

Ranking Dec 2 - 5, 2014 

### Section 3.1

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 21 of 41

# AR Example – Fluroanthene

FIFRA SAP: Integrated Bioactivity Exposure

Ranking Dec 2 - 5, 2014 

### Section 3.1

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 22 of 41AUC Range 0 . 05 to 1 0 . 01 to 0 . 05 Agonist 

### 25 18 Antagonist 

### 144 84 R3 

### 17 14 R4 

### 11 13 R 5/A5 

### 6 0 R6 

### 4 2 R7 

### 130 32 A 1 

### 49 11 A2 

### 50 28 A3 

### 16 9 A4 

### 45 36 A6 

### 19 11 A7 

### 8 4 A8 

### 184 90 A9 

### 36 20 

# AR Bioactivity of 1855 Chemicals

Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 23 of 41

Higher intensity color indicates greater activity.

#  14/30 conazoles in the library had high Antagonist (R2) AUC values

Section 3.1 

# AR Example – Conazoles

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### Section 3.1 

# AR Example – Antimicrobials

 Triclosan activity in AR antagonist assays begins prior to cytotoxicity



###  Triclocarban activity in AR antagonist assays concurrent with 

### cytotoxicity

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# “Cytotoxicity Burst” Review

1000 100 10 1 0.1 AC50 ( m M ) Number of Hits Burst Region Cytotoxicity Range 3 MAD Tested Concentration Range

21 18 15 12 9 6 3 0 - 3 - 6 - 9 Z Number of Hits Burst

Region Cytotoxicity Range 3 MAD 

### Concentration Space Z- Space

Activity inferred 

### Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 26 of 41FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 

### Section 3.1 

# AR Example – Antimicrobials

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# Pseudo- receptor Diagnostics  Model separates chemicals into AR bioactive, AR 

# inactive, and pseudo - receptor active (individual assays/technologies)  Aim: diagnose cause of pseudo - receptor activity 

#  Diagnostics: • Non - normal distribution of baseline noise / T • Cell - stress / cytotoxicity / Z 

# • Crosstalk / cross - reactivity • Pan - active compounds (PAINS) • Structure class trends • Physico - chemical property trends 

#  Work in progress Section 3.1
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# First Drivers of Pseudo- receptor Bioactivity:

Low T (non - normal noise ) 

# Low Z (cytotoxicity- related) PseudoReceptor N |T| |T| (Comparator)* p(T) |Z| |Z| (Comparator)* p(Z) R3 17 88 88 NS 2.7 3.5 NS R4 11 45 54 0.056 2.7 1.9 NS R7 130 101 101 NS 0.4 0.99 0.013 A1 49 86 93 0.056 3.5 5 0.033 A2 50 81 85 NS 3.8 2.6 NS A3 16 48 50 NS 2.4 2 NS A4 45 47 56 0.035 1 1.8 NS A8 184 93 100 0.0053 0.95 1.6 0.034 A9 36 96 101 NS - 0.076 0.22 NS A6 12 97 109 NS 3.1 11 0.0017



### *Comparator is Antagonist for most assays, Agonist for A6 Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 29 of 41

# T-distributions for T-significant Assays

Section 3.1
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# Z-distributions for Z-significant Assays

Section 3.1

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# Crosstalk / Cross- reactivity

 Crosstalk – Activity in one target of a multiplexed assay causes other readouts to fire (Ex: ATG) 

#  Cross - reactivity – A compound potent against one receptor is weakly potent against another • True active, but some other process is probably more important

Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 32 of 41

# Quantify Cross- reactivity
 Are pseudo - receptor actives more likely to also be active in many assay readouts than are AR - inactives ?

 Test for cross- reactivity in ATG CIS, ATG TRANS, 

# NVS NR, Tox21 BLA agonist, Tox21 LUC agonist, Tox21 BLA antagonist, Tox21 LUC antagonist

Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 33 of 41

# Cross- reactivity Results (ATG & NVS)

Non -AR Assay Set Expected PseudoReceptors Correlated

Pseudo- Receptor Reference

Mean PseudoReceptor mean p-value Attagene TRANS A5 A8 1.8 4.5 6.30E - 05 Attagene TRANS A5 Agonist 1.8 2.1 0.038 Attagene TRANS A5 Antagonist 1.8 3.3 0.017 Attagene TRANS A5 R7 1.8 5.4 0.00098 Novascreen NR A 1 , A 2 A2 0.76 4.5 2.90E - 09 Novascreen NR A 1 , A 2 A8 0.76 2.1 0.00015 Novascreen NR A 1 , A 2 Agonist 0.76 4.6 1.50E - 10 Novascreen NR A 1 , A 2 Antagonist 0.76 3.1 7.90E -06 

### Example : Chemicals active in only A2 ( NVS_NR_cAR ) and no other AR assays have a mean of 4.5 hits in the NVS NR battery, vs. an mean of 0.76 hits for chemicals inactive in all AR assays 

### Interpretation : These chemicals are potentially promiscuous across multiple NR assays

Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 34 of 41

# Cross- reactivity Results (Tox21)

Non -AR Assay Set Expected PseudoReceptors Correlated

Pseudo- Receptor Reference

Mean PseudoReceptor mean p-value Tox21 BLA.Agonist A6, R 6 A8 0.46 1.2 0.00039 Tox21 BLA.Agonist A6, R 6 Antagonist 0.46 1.5 0.00044 Tox21 BLA.Agonist A6, R 6 R7 0.46 2.1 0.00019 Tox21 BLA.Antagonist A8, R 7 A2 0.28 0.74 0.007 Tox21 BLA.Antagonist A8, R 7 A8 0.28 1.9 4.60E - 10 Tox21 BLA.Antagonist A8, R 7 Agonist 0.28 0.68 0.026 Tox21 BLA.Antagonist A8, R 7 Antagonist 0.28 1.7 3.90E - 05 Tox21 BLA.Antagonist A8, R 7 R7 0.28 1.9 7.40E - 07 Tox21 LUC.Agonist A7, R 6 Agonist 0.71 1.2 0.012 Tox21 LUC.Antagonist A9, R 7 A2 0.16 0.84 9.60E - 05 Tox21 LUC.Antagonist A9, R 7 A8 0.16 0.96 3.60E - 08 Tox21 LUC.Antagonist A9, R 7 Antagonist 0.16 1.2 2.40E - 05 Tox21 LUC.Antagonist A9, R 7 R7 0.16 1.6 9.70E - 10 

### Example : Chemicals active in only A8 (Tox21_AR_BLA_Antag) and no other AR assays have a mean of 1.9 hits in the Tox21 BLA Ant battery, vs. an mean of 0.28 hits for AR - inactive chemicals Interpretation : These hits may be driven by cytotoxicity or BLA assay interference 

### Section 3.1

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# PAINs Pan -Assay Interference Compounds
 Some compounds simply cause many assays to be activated 

#  Probably related to cell - stress issues  Known problem in pharmaceutical 

# development  Test whether pseudo- receptor actives (individual assays/technologies) are more 

# likely to be active in many ToxCast assays vs. those inactive in all AR assays Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 36 of 41

# PAINs Results

Receptor HI - Z reference

mean HI -Z pseudoreceptor mean P (HI -Z) LO-Z reference mean LO-Z pseudoreceptor mean P ( LO-Z) A1 0.022 0.023 NS 0.018 0.026 NS A2 0.022 0.0033 NS 0.018 0.08 3.50E -08 A8 0.022 0.0062 NS 0.018 0.11 1.10E - 12 Agonist 0.022 0.049 0.00019 0.018 0.042 4.70E - 10 Antagonist 0.022 0.047 NS 0.018 0.097 5.10E - 06 R7 0.022 0.072 0.0032 0.018 0.091 7.00E - 09 

### Example : In the cytotoxicity region of bioactivity (Z<3), chemicals that are model - antagonists are active in an average of 9.7% of all non -AR ToxCast assays, while chemicals that are inactive 

### in AR assays are active in an average of 1.8% of all non -AR ToxCast assays. I f one excludes all bioactivity for Z<3, chemicals that are active only in the Tox21 antagonist assays (R7) are still active in an average of 7.2% of all non -AR ToxCast assays, while chemicals that are inactive in AR assays are active in an average of 2.2% of all non -AR ToxCast assays.

Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 37 of 41

# Structure Class
 Are particular structure classes more likely to be associated with true or pseudo- receptor activity?  Only significant findings are with AR model agonist chemicals and pseudo- receptor A2 

# (NVS biochemical cell -free assay) • Agonist- associated classes: steroids targeting the androgen receptor, steroids targeting the progesterone receptor • A2 - associated class: phenyl sulfuric acid dyes Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 38 of 41

# Physico-chemical Properties
 Are chemicals in particular ranges of physicochemical properties more likely to be associated with true or pseudo- receptor 

# activity?  Weak but statistically significant trends are seen, but need further refinement to be useful • Overlap between property values for model actives (including AR reference chemicals) and pseudo - receptor chemicals is significant 

# • MW, surface area, volume, pKa, polarizability, logP, logS, Caco - 2 permeability Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 39 of 41

#  Preliminary results for Agonist and Antagonist AUC models using SVM, LDA, and CART approaches and QikProp descriptors provide 75 - 80% cross -validated predictive accuracy. 

#  Expanding library of molecular descriptors and modeling approaches.  Future Application: • Training set (ToxCast ): 1677 chemical structures • Prediction Set: 32,464 chemical structures 

# AR QSAR Model

Section 3.1
FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 40 of 41

# AR Model Conclusions  1855 Chemicals run in 9 high -throughput AR assays 

#  Model was developed to look for consistent agonist or antagonist bioactivity  Assays and model perform well against reference 

# chemicals  A small number of chemicals are predicted to have significant bioactivity against the AR pathway • 25 agonist • 144 antagonists • Majority of these are in well - studied chemical classes (steroids, flutamide - like) • Some environmental chemicals are predicted to act as anti - androgens (pesticides, fungicides, plasticizers) Section 3.1 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 - 5, 2014 Slide 41 of 41