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

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 4 of 41
Protein Complementation Assays (Odyssey Thera)

Assay ID

Assay Name

Gene Target

Normalized Data Type

Assay Design Type

Biological Process Target

Detection TechnologyDetection 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

Proteinfragment 

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

Proteinfragment 

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 blactamase 

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

Luciferasecoupled 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 blactamase 

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

Luciferasecoupled 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

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 7 of 41

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

 

j

AiFijRj

Aiis the efficacy of the assay at a given concentration

Rjis the “true” efficacy which is unobservable

F links receptors to assays

( ) ( )

2 2

A A penalty R

i

meas

i

pred

i



   

Solve a constrained least-squares problem 

to minimize difference between the 

measured and predicted assay values

2

0

2

2

( )

[1,0]

SR SR

SR penalty R

A

pred

i













 

Nconc

i

j i

conc

j

sign slope R conc

N

AUC

1

( ) ( )

1

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

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 10 of 41
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 (ARSTTA) Assay (OECD 2010)

• 23 reference chemicals had consistent in vitro 

results in both reports, and were tested in the 

ToxCast library.

Section 3.1

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 11 of 41
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

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 14 of 41
AR Example – Testosterone propionate

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 3.1

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 15 of 41
AR Example – Hydroxyflutamide

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 3.1

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 16 of 41
AR Example – Vinclozolin

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 3.1

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 17 of 41
AR Example – Procymidone

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 3.1

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 18 of 41
AR Example – p,p’-DDE

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 3.1

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 19 of 41
AR Example – Cyproterone acetate

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 3.1

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 20 of 41
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 41
AUC Range 0.05 to 1 0.01 to 0.05

Agonist 25 18

Antagonist 144 84

R3 17 14

R4 11 13

R5/A5 6 0

R6 4 2

R7 130 32

A1 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

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 24 of 41
FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

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

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 25 of 41
“Cytotoxicity Burst” Review

10 100 1000 0.1 1

AC50 (mM)

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 41
FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 3.1

AR Example – Antimicrobials

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 27 of 41
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

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide28 of 41
First Drivers of Pseudo-receptor Bioactivity:

Low T (non-normal noise)

Low Z (cytotoxicity-related)

PseudoReceptorN |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

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 30 of 41
Z-distributions for Z-significant Assays

Section 3.1

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 31 of 41
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 PseudoReceptorsCorrelated

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 A1, A2 A2 0.76 4.5 2.90E-09

Novascreen NR A1, A2 A8 0.76 2.1 0.00015

Novascreen NR A1, A2 Agonist 0.76 4.6 1.50E-10

Novascreen NR A1, A2 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 PseudoReceptorsCorrelated

Pseudo- Receptor

Reference

Mean

PseudoReceptor

mean

p-value

Tox21 BLA.Agonist A6, R6 A8 0.46 1.2 0.00039

Tox21 BLA.Agonist A6, R6 Antagonist 0.46 1.5 0.00044

Tox21 BLA.Agonist A6, R6 R7 0.46 2.1 0.00019

Tox21 BLA.Antagonist A8, R7 A2 0.28 0.74 0.007

Tox21 BLA.Antagonist A8, R7 A8 0.28 1.9 4.60E-10

Tox21 BLA.Antagonist A8, R7 Agonist 0.28 0.68 0.026

Tox21 BLA.Antagonist A8, R7 Antagonist 0.28 1.7 3.90E-05

Tox21 BLA.Antagonist A8, R7 R7 0.28 1.9 7.40E-07

Tox21 LUC.Agonist A7, R6 Agonist 0.71 1.2 0.012

Tox21 LUC.Antagonist A9, R7 A2 0.16 0.84 9.60E-05

Tox21 LUC.Antagonist A9, R7 A8 0.16 0.96 3.60E-08

Tox21 LUC.Antagonist A9, R7 Antagonist 0.16 1.2 2.40E-05

Tox21 LUC.Antagonist A9, R7 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

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Slide 35of 41
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.

If 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 modelagonist 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 modelactives (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