Office of Research and Development

ToxCast and Tox21: Update 2012 Richard Judson U.S. EPA, National Center for Computational Toxicology Office of Research and Development

The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA

UNCG September 2012

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The Tox21 Community

Tox21 Goals

• Identify patterns of compound-induced biological response in order to:

− characterize toxicity/disease pathways

− facilitate cross-species extrapolation

− model low-dose extrapolation

• Prioritize compounds for more extensive toxicological evaluation

• Develop predictive models for biological response in humans

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Problem Statement

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Too many chemicals to test with standard animal-based methods

– Cost, time, animal welfare

Need for better mechanistic data - Determine human relevance

- What is the Mode of Action (MOA) or Adverse Outcome Pathway (AOP)?

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ToxCast / Tox21 Goals

• Identify molecular targets or biological pathways linked to toxicity – Chemicals perturbing these can lead to adverse events

• Develop assays for these targets or pathways – Assays probe “Molecular Initiating Events” or “Key Events” [MIE / KE]

• Develop predictive models: in vitro → in vivo

– “Toxicity Signature” – Extend to inform biomarkers or bioindicators for key events

• Use signatures: – Prioritize chemicals for targeted testing (“Too Many Chemicals” problem) – Suggest / distinguish possible AOP / MOA for chemicals

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Signature Generation

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In Vitro Data

In Vivo Data

Predictive Models – “Signatures”

ToxCast HTS Assays: >1100 readouts / effects

Species Human

Rat Mouse

Zebrafish Sheep Boar

Rabbit Cattle

Guinea pig

Cell Format Cell free Cell lines

Primary cells Complex cultures

Free-living embryos

Detection Technology qNPA and ELISA

Fluorescence & Luminescence Alamar Blue Reduction

Arrasyscan / Microscopy Reporter gene activation

Spectrophotometry Radioactivity

HPLC and HPEC TR-FRET

Readout Type Single

Multiplexed Multiparametric

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Assay Provider ACEA

Apredica Attagene BioSeek

CellzDirect NCGC/Tox21

NHEERL MESC NHEERL NeuroTox NHEERL Zebrafish

NovaScreen Odyssey Thera

Assay Design viability reporter

morphology reporter conformation reporter

enzyme reporter membrane potential reporter

binding reporter inducible reporter

Biological Response cell proliferation and death

cell differentiation mitochondrial depolarization

protein stabilization oxidative phosphorylation reporter gene activation gene expression (qNPA)

receptor activity receptor binding

Tissue Source Lung Breast Liver Vascular Skin Kidney Cervix Testis Uterus Brain

Intestinal Spleen Bladder Ovary Pancreas Prostate Inflammatory Bone

Target Family Response Element

Transporter Cytokines

Kinases Nuclear Receptor CYP450 / ADME Cholinesterase Phosphatases

Proteases XME metabolism

GPCRs Ion Channels

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ToxCast: HTS Data Timelines

Set Chemicals Assays Endpts Completion Available ToxCast Phase I 293 ~600 ~1100 2011 Now

ToxCast Phase II 767 ~600 ~1100 Fall 2012 Beginning 12/12

E1K (endocrine) 880 ~50 ~120 Fall 2012 Beginning 12/12

Tox21 8,193 ~25 ~50 Ongoing Beginning 12/12

Chemicals

Assa

ys

~600

0 1000 1800 8,200

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Tox21 qHTS 10K Library

NCGC –Drugs

–Drug-like compounds

–Active pharmaceutical ingredients

EPA

• Pesticides actives and inerts

• Industrial chemicals

• Endocrine Disruptor Screening Program

• OECD Molecular Screening Working Group

• FDA Drug Induced Liver Injury Project

• Failed Drugs

NTP

• NTP-studied compounds

• NTP nominations and related compounds

• NICEATM/ICCVAM validation reference compounds for regulatory tests

• External collaborators (e.g., Silent Spring Institute, U.S. Army Public Health Command)

• Formulated mixtures

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Predictive Model Development from ToxCast and Other Data

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Univariate Analysis

DATABASES

ToxCastDBin vitro

ToxRefDBin vivo

ASSAY SELECTION

ASSAY AGGREGATION

ASSAY SET REDUCTION

MULTIVARIATE MODEL

p-value statistics

Condense by gene, gene family, or pathway

Reduce by statistics (e.g. correlation)

LDAModel Optimization

x

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11 Martin et al 2011

Reproductive Rat Toxicity Model Features

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36 Assays Across 8 Features

Balanced Accuracy

Training: 77% Test: 74%

+ -

Martin et al 2011

Reproductive Rat Toxicity Model Features

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Example: Cancer Signatures Non-genotoxic carcinogens

• Use insights from Hallmarks of Cancer – Hanahan and Weinberg 2000, 2011 – Cancer is a multi-step progressive disease – Virtually all cancers display all hallmark processes

• We observe that most chemicals perturb multiple pathways

• Hypothesis: – A chemical that perturbs many pathways related to cancer hallmark

processes will be more likely to cause cancer in the lifetime of an animal than a chemical that perturbs few such pathways

– Chemicals can increase cancer risk through many different patterns of pathway perturbations

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Hallmarks of Cancer Hanahan and Weinberg (2000)

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PPARα

p53

CCL2 ICAM1

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Hallmarks of Cancer Hanahan and Weinberg (2011)

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IL-1a IL-8 CXCL10

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Pathway Hits Raise Risk of Multiple Cancer Types

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Hallmark-related ADME-related Endpoint

Level 2: Preneoplastic Level 3: Neoplastic

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Published Predictive Toxicity Models Predictive models: endpoints

liver tumors: Judson et al. 2010, Env Hlth Persp 118: 485-492 hepatocarcinogenesis: Shah et al. 2011, PLoS One 6(2): e14584 cancer: Kleinstreuer et al. 2012, submitted rat fertility: Martin et al. 2011, Biol Reprod 85: 327-339 rat-rabbit prenatal devtox: Sipes et al. 2011, Toxicol Sci 124: 109-127 zebrafish vs ToxRefDB: Sipes et al. 2011, Birth Defects Res C 93: 256-267

Predictive models: pathways

endocrine disruption: Reif et al. 2010, Env Hlth Persp 118: 1714-1720 microdosimetry: Wambaugh and Shah 2010, PLoS Comp Biol 6: e1000756 mESC differentiation: Chandler et al. 2011, PLoS One 6(6): e18540 HTP risk assessment: Judson et al. 2011, Chem Res Toxicol 24: 451-462 angiogenesis: Kleinstreuer et al. 2011, Env Hlth Persp 119: 1596-1603

Continuing To Expand & Validate Prediction Models Generally moving towards more mechanistic/AOP-based models

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Understanding Success and Failure

• Why In vitro to in vivo can work: – Chemicals cause effects through direct molecular interactions that

we can measure with in vitro assays

• Why in vitro to in vivo does not always work: – Pharmacokinetics issues: biotransformation, clearance (FP, FN) – Assay coverage: don’t have all the right assays (FN) – Tissue issues: may need multi-cellular networks and physiological

signaling (FN) – Statistical power issues: need enough chemicals acting through a

given MOA to be able to build and test model (FN) – Homeostasis: A multi-cellular system may adapt to initial insult

(FP) – In vitro assays are not perfect! (FP, FN) – In vivo rodent data is not perfect! (FP, FN)

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Systems Models

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Beyond in vitro to in vivo signatures

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Structure Clusters Chemical Categories

In vitro Assays

Adverse Outcome

Pharmacokinetics

In Vitro-In Vivo Signatures

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Combining Chemical Structure and In Vitro Assays

• Structure clustering based on chemical fragments – FP3, FP4, MACCS, PADEL, PubChem (~2700 total) – Hierarchical clustering and then set variable cutoffs – For examples: ~12 chemicals / cluster

• Goals

– Find clusters that are highly predictive of each assay (read-across) – Assay structure alerts: alternatives assessments – Assay QC

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Cluster Assay Endpoint

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Clusters 80% predictive of assay hit

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ER Assays

Estrogens

Conazoles

CYP Binding Assays

Alkyl Phenols

Surfactants

GPCR Binding Assays

Alachlor …

Captan …

Inflammation Assays

Surfactants

Chemical Set 2

Chemical Set 1

Assays

Data Set Incomplete

Azoles

Tetracycline …

Endosulfans Steroids

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Adding Pharmacokinetics Reverse ToxicoKinetics (rTK)

Human Hepatocytes

(10 donor pool)

Add Chemical (1 and 10 µM)

Remove Aliquots at 15, 30, 60, 120 min

Analytical Chemistry

-5

-4

-3

-2

-1

0

1

2

3

0 50 100 150

Ln C

onc

(uM

)

Time (min)

Hepatic Clearance

Human Plasma

(6 donor pool)

Add Chemical (1 and 10 µM)

Analytical Chemistry

Plasma Protein Binding

Equilibrium Dialysis

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Combine experimental data with PK Model to estimate dose-to-concentration scaling

Collaboration with Thomas et al.., Hamner Institutes Publications: Rotroff et al, ToxSci 2010, Wetmore et al, ToxSci 2012

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Etox

azol

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Triclosan Pyrithiobac-sodium

log

(mg/

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ay)

Rotroff, et al. Tox.Sci 2010 Wetmore et al Tox Sci 2012

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Range of in vitro AC50 values converted to human in vivo daily dose

Actual Exposure (est. max.)

margin

Combining in vitro activity and dosimetry

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Initial Prioritization Application: EDSP21 Use high-throughput in vitro assays and modeling tools to prioritize chemicals for EDSP Tier 1 screening assays

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ER / AR Focus: EDSP21 • Endocrine Disruptor Screening Program

– FQPA, SDWA 1996 contain provisions for screening for chemicals and pesticides for possible endocrine effects

– Test pathways: estrogen, androgen, thyroid, steroidogenesis (EATS) – Universe of chemicals: 5000-6000

• Tier 1 screening battery (T1S): 11 in vitro & in vivo assays – Development and validation > 10 years – >$1 M per chemical – Current throughput < 100 chemicals / year

• EDSP21 goal: – Prioritize chemicals for T1S – Hypothesis: EATS (in vitro)+ more likely to be T1S+ – Use many EATS in vitro assays – Combine with modeling, use, occurrence and exposure information

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Characterizing chemicals for estrogen signaling pathway activity

• Active vs. inactive • Potency and efficacy spectrum across assays • Agonist … Antagonist • Partial … full Agonist / Antagonist • ERα vs. ERβ • Metabolically activated or deactivated • Cell type specificity • ER-mediated or not

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All Data is preliminary and unpublished

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Pro-ligand

ER Active ligand

Cofactor

ER-regulated gene expression

Cell proliferation

Oxidative stress

pathways

Non-ER-mediated cell proliferation

pathways

Non-ligand-mediated activation of ER activity

Attagene Attagene NCGC ACEA

Odyssey Thera

Odyssey Thera

Novascreen

Using multiple lines of evidence to test for ER activity

Odyssey Thera and Attagene assays have metabolic capacity

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Estrogen signaling pathway assays

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source_name_aid source condition organism tissue Cell Format Cell Type

ACEA_T47D ACEA human breast Cell line T47D

ATG_ERa_TRANS Attagene human liver Cell line HepG2

ATG_ERE_CIS Attagene human liver Cell line HepG2

Tox21_ERa_BLA_Agonist Tox21 human kidney Cell line HEK293T

Tox21_ERa_BLA_Antagonist Tox21 human kidney Cell line HEK293T

Tox21_ERa_LUC_BG1_Agonist Tox21 human ovarian Cell line BG1

Tox21_ERa_LUC_BG1_Antagonist Tox21 human ovarian Cell line BG1

NVS_NR_bER Novascreen bovine uterus tissue extract

NVS_NR_hER Novascreen human breast Cell line: cell extract

NVS_NR_mERa Novascreen mouse uterus tissue extract

OT_ER_ERaERa Odyssey Thera +/- S9 human kidney Cell line HEK293T

OT_ER_ERaERb Odyssey Thera +/- S9 human kidney Cell line HEK293T

OT_ER_ERbERb Odyssey Thera +/- S9 human kidney Cell line HEK293T

OT_ERa_GFPERa_ERE Odyssey Thera +/- S9 human cervix Cell line HeLa

OT_ERa_ERE_LUC_Agonist Odyssey Thera human Cell line: bulk

transiently transfected

CHO-K1

OT_ERa_ERE_LUC_Antagonist Odyssey Thera human Cell line: bulk

transiently transfected

CHO-K1

OT_ERb_ERE_LUC_Antagonist Odyssey Thera human Cell line: bulk

transiently transfected

CHO-K1

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NCGC ER BG1-LUC vs. BLA Agonist Assays

NCGC Screening System 1: BSL1/Kalypsys

Capacity: 3.0MM Assay Wells 5.0MM Compound

Wells

Throughput: 1400 plates/day

Readers: ViewLux (2) Acumen (2) Envision (2)

New Capabilities:

Automatic Loading and Unloading stations using commercially

available plate stackers Dispense Inspection

Systems using integrated CCD

cameras

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Metabolic Capacity: +/- S9 for metabolism

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AssayName

CellType

AssayDesign

TargetType

TargetFamily

GeneTarget

DetectionTechnology

Apoptosis_PARP U-2 OS cellular CELL CYCLE FluorescenceATR_Chk1Cdc25C HEK293T PCA pathway KINASE; PHOSPHATASE CHEK1; CDC25C Fluorescenceb2AR_b2ARbARR2 HEK293T PCA pathway GPCR; PROTEIN ADRB2; ARRB2 FluorescenceCellCycle_Cdc2Cdc25C PCA pathway KINASE; PHOSPHATASE CDC2; CDC25C MicroscopyCXCR4_CXCR4bARR2 HEK293T PCA pathway CYTOKINES CXCR4; ARRB2 FluorescenceGRP78_HeLa HeLa elisa: sandwich pathway NA GRP78 MicroscopyH2AX_gH2AX U-2 OS elisa: sandwich pathway DNA BINDING H2AX MicroscopyNFKb_p50p65 HEK293 PCA pathway PROTEIN; PROTEIN RELA; NFKB1 Fluorescencep38MAPK_Mnk1p38 HEK293T PCA pathway KINASE; KINASE MKNK1; MAPK14 Fluorescencep53_Mdm2p53 HEK293T PCA pathway PROTEIN; PROTEIN MDM2; TP53 Fluorescencep53_Pin1p53 HEK293T PCA pathway ISOMERASE; PROTEIN PIN1; TP53 FluorescencePI3K_Akt1PDk1 HEK293 PCA pathway KINASE; KINASE AKT1; PDK1 FluorescenceRhoRock_Limk2Cofilin1 HEK293T PCA pathway KINASE; PROTEIN LIMK2; CFL1 FluorescenceNURR1_NURR1RXRa HEK293T PCA pathway NUCLEAR RECEPTOR NR4A2; RXRA FluorescencePPARg_PPARgSRC1 HEK293T PCA pathway NUCLEAR RECEPTOR PPARG; SRC FluorescenceSRC1_SRC1FXR HEK293T PCA pathway NUCLEAR RECEPTOR FXR; SRC FluorescenceAR_ARE_LUC_Agonist luciferase induction pathway NUCLEAR RECEPTOR AR LuminescenceAR_ARSRC1 HEK293T PCA pathway NUCLEAR RECEPTOR AR; SRC FluorescenceER_ERaERa HEK293T PCA pathway NUCLEAR RECEPTOR ESR1 FluorescenceER_ERaERb HEK293T PCA pathway NUCLEAR RECEPTOR ESR1; ESR2 FluorescenceER_ERbERb HEK293T PCA pathway NUCLEAR RECEPTOR ESR2 FluorescenceERa_ERE_LUC_Agonist luciferase induction pathway NUCLEAR RECEPTOR ESR1; ERE LuminescenceERa_ERE_LUC_Antagonist luciferase induction pathway NUCLEAR RECEPTOR ESR1; ERE LuminescenceERa_GFPERaERE HeLa fluorescent protein induction pathway NUCLEAR RECEPTOR ESR1; ERE MicroscopyERb_ERE_LUC_Antagonist luciferase induction pathway NUCLEAR RECEPTOR ESR2; ERE LuminescenceHEK293T_LDH HEK293T protease activity cellular CELL DEATH spectrophotometryHepatocyte_LDH human hepatocyte protease activity cellular CELL DEATH spectrophotometry

Odyssey Thera Assay Battery

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Antagonist behavior in OT-PCA (ICI)

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ERb-ERb

ERa-ERa

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-S9 +S9

Activation

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-S9 +S9

ERα/ERβ

Deactivation

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Metabolic Impact on ER Activity

ChemicalCount

% of Total Hits (n=250)

Examples

evidence of metabolic activition 4 1.6

TriphenylethyleneMestranolMethoxychlorButylene carbonate

evidence of metabolic activition (confounded) 8 3.2

4-NitrosodiphenylamineDiphenylamineQuercetin2-Amino-5-azotolueneDiphenylamineNorethindrone

evidence of metabolic deactivation 55 22

Benzylparaben4-Butylphenol17beta-Trenbolone17alpha-EstradiolBisphenol BGenistein

no evidence of metabolic activation/deactivation 15 6

EstriolBisphenol ABiochanin ATamoxifenMifepristone

not determined 168 67.2

Octyl gallate4-Octylphenol4-Nonylphenolp,p-DDEDibutyl phthalate

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White (-S9) Black (+S9)

Comparing Odyssey Thera assays across potent estrogens

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T47D Cell Growth Kinetics (ACEA)

• Real time cell growth measurements • Human T47D breast carcinoma cell

line – Estrogen-responsive – General cytotoxicity

• Concentration-response testing – 8 conc/3-fold serial dilutions – Duplicate wells

• Real-time measurements during exposure (0-72 hr)

• AC50 for proliferation and inhibition calculated

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GenisteinN

orm

aliz

ed C

ell

Inde

x (N

CI)

Perc

ent E

2 (%

)

Time (Hrs)

Time-Course

Time (Hrs) Time (Hrs)

Concentration (µM) Concentration (µM) Concentration (µM)

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Progesterone

)

Corticosterone Norgesterel

Butyl Paraben Bisphenol A Diethylstilbesterol

5α-Dihydrotestosterone Cyproterone Acetate Triamcinolone

Time (Hrs)

Nor

mal

ized

Cel

l Ind

ex (N

CI)

A. B. C.

G. H. I.

D. E. F.

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Combining Chemical Structure and In Vitro Assays

• Structure clustering based on chemical fragments – FP3, FP4, MACCS, PADEL, PubChem (~2700 total) – Hierarchical clustering and then set variable cutoffs – For examples: ~12 chemicals / cluster

• Goals

– Find clusters that are highly predictive of each assay (read-across) – Assay structure alerts: alternatives assessments – Assay QC

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Cluster Assay Endpoint

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1822 Chemicals ToxCast+e1K

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1822 Chemicals ToxCast+e1K Tanimoto >0.3

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1822 Chemicals ToxCast+e1K Tanimoto >0.5

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1822 Chemicals ToxCast+e1K Tanimoto >0.7

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• fate and transport models (RAIDAR, USEtox) to predict contribution from manufacture and industrial use to overall exposure

• models predict partitioning into environmental media and describe human interactions with that media (1678 chemicals)

• ground-truth to CDC-NHANES urine data (51 chemicals to develop CI)

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ExpoCast: high-throughput exposure prediction

SOURCE: Wambaugh et al. 2012 (manuscript)

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ToxCast and Tox21 Summary

• Goal: use in vitro assays to screen and prioritize many data-poor chemicals

• Signature generation uses combination of biological insight and statistics

• Initial models point the way to real-world applications

• Further refinements are in the works – More chemicals and assays – Systems-level models – Targeted testing approaches

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Thank You!

The EPA ToxCast Team

Key Collaborators National Toxicology Program NIH Chemical Genomics Center US FDA The Hamner Institutes