Office of Research and Development

Using Tox21 Data for Risk Assessment and Alternatives AssessmentRichard JudsonU.S. EPA, National Center for Computational ToxicologyOffice 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

Mid-Atlantic SOT, May 2014

Office of Research and DevelopmentNational Center for Computational Toxicology

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 Overall Strategy

• Identify targets or pathways linked to toxicity (AOP focus)• Develop high throughput assays for these targets or pathways• Develop predictive systems models

– in vitro → in vivo

–in vitro → in silico

• Use predictive models (qualitative):–Prioritize chemicals for targeted testing –Suggest / distinguish possible AOP / MOA for chemicals

• High Throughput Risk Assessments (quantitative)• High Throughput Exposure Predictions

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Testing under ToxCast and Tox21Chemicals, Data and Release Timelines

Set Chemicals Assays Endpoints Completion Available

ToxCast Phase I 293 ~600 ~700 2011 Now

ToxCast Phase II 767 ~600 ~700 03/2013 10/2013

ToxCast Phase IIIa 1001 ~100 ~100 Just starting 2014

E1K (endocrine) 880 ~50 ~120 03/2013 10/2013

Tox21 8,193 ~25 ~50 Ongoing Ongoing

Chemicals

Ass

ays

~600

~8,2000

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ToxCast Phase II:1051 Chemicals x 791 Assay Readouts

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ACEA: redAttagene: orangeApredica: blackBioSeek: greenNovascreen: grayTox21: violetOT: blue

Assays

Chem

icals

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ToxCast and the Endocrine Disruptor Screening Program

http://www.epa.gov/endo/pubs/edsp21_work_plan_summary%20_overview_final.pdf

EPA Research provides basis for improving the suite of assays and models to advance chemical

prioritization and screening

The universe of chemicals passes through each version of the HTS/in silico

pipeline to evaluate chemicals in refined tests, or for new pathways, to evaluate improve and validate methods.

Chemical PrioritizationIncludes registration review timeline, physico-chemical properties, exposure estimates, in vitro assays and computer models (QSAR, expert systems, systems biology models).

Screening DecisionsNear Term = Incorporates HTS/in silico prioritization methods for post EDSP List 2Intermediate = Run subset of T1S assays indicated by HTS and in silico predictions Long Term = Full replacement of EDSP T1S Battery

Chemicals Of Regulatory Interest

in vitro HTS/ in silico (P1)Current EDSP T1S Battery

Test+

Test-

Near Term(<2 yrs)

FocusedEDSP Tier 2 Tests

WOE+

WOE-

Test-

in vitro HTS/ in silico (P2)in vitro/in silico focuses

subset of EDSP T1STest+Intermediate

Term (2-5 yrs)

WOE+

WOE-

in vitro HTS/ in silico (full replacement of Tier 1)Longer Term (>5 yrs) WOE+

WOE-

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Computational Model

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Example 1 – BPA: true agonist (AUC=0.66)

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Example curves

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True Agonist True Antagonist

Negative-BAI Negative-NAI

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Reference Chemical Classification

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AUC heat map for Reference chemicals

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Major theme – all assays have false positives and negative

Much of this “noise” is reproducible, i.e. it is “assay interference”

Result of interaction of chemical with complex biology in the assay

Our chemical library is only partially “drug-like”-Solvents-Surfactants-Intentionally cytotoxic compounds-Metals-Inorganics

Assays cluster by technology,suggesting technology-specific

non-ER activity

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Example illustrating assay data

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Most chemicals display a “burst” of activity at same concentration as cytotoxicity

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Most chemicals cause activity in many assays near the cytotoxicity threshold

Cell-stress related assay interference

“Hit” (AC50) in burst region is less likely to result from specific activity (e.g. binding to receptor or enzyme)

Z-score: # of SD from burst center-High Z: more likely to be specific-Low Z: less likely to be specific

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Examine Z-scores by assay

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Bimodal

Cytotox / Cell Stress“True” activity

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Non-specificity with cytotox is general

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Having cytotoxicity @<100 uM greatly increases number of hits

Chemicals with cytotoxicity @<100 uM have many hits, but few are outside of burst

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* =Reference chemicals

- These chemicals should be near the right of the gene score distribution

- Most assays show reference chemicals to be potent and specific

- Gives confidence that novel chemicals active in the assay are perturbing that pathway

“Weak Validation”

After removing burst, most assays detect reference chemicals - 95% success

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Promiscuity: Highest for chemicals designed to be bioactive

Least Promiscuous Chemical Classes

0.1-0.3% of assays are active

None designed to be bioactive

Most Promiscuous Chemical Classes

2-3% of assays are active

All designed to be bioactive

Category NchemMean Hit Ratio p-cold

alcohol primary 10 0.0011 0.00021phthalate 17 0.0032 0.00084carboxylate di 15 0.0028 0.0029carboxylate 7 0.0015 0.0042

Category NchemMeanHit Ratio p-hot

conazole (triazoles) 13 0.034 3.5E-06Pharma Class 4.86 10 0.031 1.1E-05Pharma Class 4.58 11 0.029 4.1E-05conazole (imidazoles) 6 0.031 0.003Pharma Class 3.292 5 0.039 0.0049steroid P 5 0.022 0.0052Pharma Class 4.43 7 0.020 0.0067

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High throughput pharmacokinetic (HTPK) in vitro methods have been developed by pharmaceutical industry for predicting efficacious doses in clinical trials

In Wetmore et al. (2012) the same methods are used to approximately convert ToxCast in vitro bioactive concentrations (µM) into daily doses needed to produce similar levels in a human (mg/kg BW/day)

These doses can then be directly compared with exposure data, where available

Egeghy et al. (2012) and National Academy Report: “Exposure Science in the 21st

Century” points out that not much exposure information is out there

e.g. Judson et al., (2011)

Potential Exposure from

ExpoCast

mg/kg BW/day

Potential Hazard from ToxCast with

Reverse Toxicokinetics

LowRisk

MedRisk

HighRisk

High Throughput

Pharmacokinetics and Exposure

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ExpoCast Coverage of the ToxCast

Phase II Chemicals

ToxCast Oral Equivalents based on unpublished data from Barbara Wetmore

Ora

l Eq

uiv

alen

t D

ose

s an

d E

stim

ated

Exp

osu

res

(mg

/kg

/day

)

Predictions from Wambaugh et al. (2013) ExpoCast model with USEtox, RAIDAR, and near field/far field heuristic

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Chemical use information informs exposure scenarios

• CPCat : Chemical and Product categories– Public use information on 40,000 chemicals– http://actor.epa.gov/cpcat

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Approach for Rapid Validation and Application of Alternatives

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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) 23

SystemsModels

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AcknowledgementsEPA NCCTRusty ThomasKevin CroftonKeith HouckAnn RichardRichard JudsonTom KnudsenMatt MartinWoody SetzerJohn WambaughMonica LinnenbrinkJim RabinowitzSteve Little

Agnes ForgacsJill FranzosaChantel NicolasBhavesh AhirNisha SipesLisa TruongMax LeungKamel MansouriEric WattCorey Strope

EPA NCCTNancy BakerJeff EdwardsDayne Filer Jayaram KancherlaParth KothiyaJimmy PhuongJessica LiuDoris SmithJamey VailHao TruongSean WatfordIndira ThillainadarajahChristina Baghdikian

NIH/NCATSMenghang XiaRuili HuangAnton Simeonov

NTPWarren CaseyNicole KleinstreuerMike DevitoDan ZangRay Tice

EPA CollaboratorsKathie DionisioKristin IsaacsPeter EgeghyDavid DixAlan DixonScott LynnPatience BrownDon BergfeltLes Touart