So Many Chemicals, So Little Time Evolution of

So Many Chemicals, So Little Time…Evolution of Computational Toxicology

Rusty ThomasDirectorNational Center for Computational Toxicology

North Carolina State Seminar

January 17, 2017

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

Comp Chem, HTTK, HTS, Systems Tox, HT Exposure

National Center forComputational Toxicology

Regulatory Agencies Need to Make A Range of Decisions on Chemicals…

• Multiple drivers shape type of assessment– Regulatory demands– Economic considerations– Multiple applications

• Chemical assessments are “fit-for-purpose”– Prioritization (e.g., EDSP, PMN, SNUR)– Screening-level assessments (e.g., CCL,

GreenChem)– Provisional assessments (e.g., PPRTVs)– Toxicity assessments (e.g., IRIS)– Risk assessments (e.g., MCLs,

pesticides)


2

Current Testing Limited by Traditional Models and Regulatory System

• Traditional toxicity tests have been added as new hazards were recognized

• In 1976, the U.S. Toxic Substances Control Act put burden on EPA to demonstrate “unreasonable risk” for industrial chemicals

• Increased recognition of lack of safety data for many environmental/industrial chemicals

– 1984 US NRC Report

• Guidance from internal reports and expert committees highlighted need for increased throughput and computational approaches

– 2005 EPA CompTox Report

– 2007 US NRC Report

0

10

20

30

40

50

60

70

Perc

ent o

f Che

mic

als

Acute CancerGentox Dev ToxRepro Tox

Judson, et al EHP (2010)


3

• Early beginnings of computational toxicology started with QSARs and PBPK models in 1970s

• Use of the term ‘computational toxicology’ appeared in the literature in the late 1990s

• The field of computational toxicology has since undergone rapid evolution

Evolution of Computational Toxicology

c. 1970s - 2002 2003 - 2006 2007 - 2010 2011 - 2016c.1500 – 1960s…




4

What Traits Have Been Under Selection?

• Increased throughput and biological coverage

• Interrogation of effects at the molecular and pathway level

• Increasingly relevant test systems

• Putting results in a dose/exposure context

• Characterization of uncertainty

• Computational modeling to integrate experimental data

• Delivery of data and models through decision support tools

• Transparency and performance-based validation


5

Increased Throughput Required Shift to Molecular/Pathway Approaches

ToxCast

Concentration

Res

pons

e

~600 Cell & biochemical

assays

~1,000 Chemicals

Tox21

~30 Cell & biochemical

assays

~8,000 Chemicals

Set Chemicals Assays Completion

ToxCast Phase I 293 ~600 2011

ToxCast Phase II 767 ~600 2013

ToxCast Phase III 1001 ~100 Ongoing

E1K (endocrine) 880 ~50 2013


6

Broad Success Derived from High-Throughput Screening Approaches

Provide Mechanistic Support for Hazard ID

Group Chemicals by Similar Bioactivity and Predictive Modeling

Prioritization of Chemicals for Further Testing

Assays/Pathways

Che

mic

als

IARC Monographs 110, 112, 113

FIFRA SAP, Dec 2014


7

Continuing Pressure Towards Increased Biological Coverage

Picture of Everything - Howard Hallis

Whole Transcriptome Technologies

Diverse Cell Types & Culture

Systems


8

Searching for a Platform

Low Coverage RNA-seq

MCF7

6 chemicals1 concentration

5 Replicates@ 6 hours

TempO Seq & TruSeq

MCF7

6 chemicals1 concentration

5 Replicates@ 6 hours Low Coverage

RNA-seq

MAQCRNA Stds

TempO Seq & TruSeq

MAQCRNA Stds

4 RNA Pools

5 ReplicatesLow Coverage

RNA-seq

4 RNA Pools

5 Replicates

Targeted RNA-seq

Requirements:• Whole genome • 384 well • Automatable• Low cost

Technical Comparison

Technical Comparison

Functional Comparison

Functional Comparison


9

Technical Performance of the Three Sequencing Platforms

TruSeqr2 0.74

TempO-Seqr2 0.75

Low Coverager2 0.83


10

Functional Performance of the Three Sequencing Platforms

TrichostatinGenistein

Low Coverage TruSeq TempOSeqCorrectCMAP 0/5 (0%) 0/5 (0%) 4/5 (80%)

• Large scale screen of 1,000 chemicals (ToxCast I/II) in single cell type

• Additional screens across multiple cell types/lines• Additional reference chemicals and genetic

perturbations (RNAi/CRISPR/cDNA)


11











Increasing Relevance of Test Systems Necessary at Multiple Levels

Relevant Metabolism

Relevant Tissue and Organ Responses

Relevant Species Responses


13

Assessing Cross-Species Differences in Response

Houck et al., Unpublished

Multispecies Attagene Trans Reporter Assay

• Host cell: human HepG2• Stimulation with EC20 of 6a-

fluorotestosterone for detection of androgen receptor antagonists

• 100 chemicals with ER, AR, PPAR activity tested in concentration-response

• Data calculated as fold-change over control (6a-fluorotestosterone/DMSO)


14

Cross-Species Differences in Nuclear Receptor Responses

-log AC50 (uM)

= agonist= antagonist


15

Adding Relevant Metabolic Activity

-3 -2 -1 0 1 2 30

50

100

150

CYP1A2

[Compound] log µM

Perc

ent A

ctiv

ity

-3 -2 -1 0 1 2 30

50

100

150

CYP2B6

[Compound] log µM

Perc

ent A

ctiv

ity

-3 -2 -1 0 1 2 30

50

100

150

CYP2C9

[Compound] log µM

Perc

ent A

ctiv

ity

-3 -2 -1 0 1 2 30

50

100

150

CYP3A4

[Compound] log µM

Perc

ent A

ctiv

ity

3

Furafylline

Thio-TEPA

Tienilic Acid

Ketoconazole

DeGroot and Simmons, Unpublished


16











The Need for High(er) Throughput TK Approaches

0

100

200

300

400

500

600

700

800

Total Number Chemicals with InVivo TK

HTTK (Hamner &EPA)

HTTK (In Process)

Num

ber o

f Che

mic

als

ToxCast Phase I ToxCast Phase II


Incorporating a High-Throughput Toxicokinetic Approach

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

Reverse Dosimetry

Oral Exposure

Plasma Concentration

ToxCast AC50Value

Oral Dose Required to Achieve Steady

State Plasma Concentrations

Equivalent to In VitroBioactivity

Human Liver Metabolism

Human Plasma Protein Binding

Population-Based IVIVE Model

Upper 95th Percentile CssAmong 100 Healthy

Individuals of Both Sexes from 20 to 50 Yrs Old

EPA ToxCast Phase I and II Chemicals • Models available through ‘“httk” R package

(https://cran.r-project.org/web/packages/httk/)

https://cran.r-project.org/web/packages/httk/


Comparing Dosimetry Adjusted Bioactivity with Exposure

Wetmore et al., Tox Sci., 2012

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20

Improving Exposure Estimates and Characterization

Log10 (µg/g)

• GCXGC-MS with DCM Extraction• 1606 tentative and confirmed chemical

identifications

100 Consumer Products and Articles of Commerce

423

ToxC

ast a

nd/o

r Com

mon

ly O

ccur

ring

Chem

ical

s*

Wambaugh et al. Unpublished


21











Incorporating Uncertainty Into the Pharmacokinetic Modeling

Bayesian Modeling of Plasma Protein Binding and Intrinsic Clearance

Propagation of Experimental Uncertainty to Steady State Plasma Concentrations

Wambaugh, Unpublished


Impact of Incorporating Uncertainty and Variability is Chemical Specific

Wambaugh, Unpublished

Chem 1 Chem 100

Variability Predominates

Uncertainty Predominates


24











Relatively Simple Models Used to Reduce Assay Interference

ER Receptor Binding(Agonist)

Dimerization

CofactorRecruitment

DNA Binding

RNA Transcription

Protein Production

ER-inducedProliferation

R3

R1

R5

R7

R8

R6

N1

N2

N3

N4

N5

N6

A1

A2

A3

A4

A5

A6

A7

A8

A9

A10

A12

A13

A14

A15

A16

ε3

A11

Receptor (Direct Molecular Interaction)

Intermediate Process

Assay

ER agonist pathway

Interference pathway

Noise Process

ER antagonist pathway

R2

N7

ER Receptor Binding

(Antagonist)

A17

A18

Dimerization

N8

N9DNA Binding

CofactorRecruitment

N10AntagonistTranscriptionSuppression

R4

R9

Computational Modeling of Estrogen Receptor Pathway

Accuracy 0.93 (0.95)

Sensitivity 0.93 (0.93)

Specificity 0.92 (1.0)

Accuracy 0.86 (0.95)

Sensitivity 0.97 (0.97)

Specificity 0.67 (0.89)

Che

mic

als

Assays cluster by technology,suggesting technology-specific non-ER bioactivity

Assays

In Vitro Reference Chemicals* In Vivo Reference Chemicals*

*Values in parentheses exclude inconclusive chemicalsJudson et al., Tox Sci. 2015Browne et al., ES&T. 2015

18 In vitro HTS assays for ER bioactivity


Complex Systems Models to Predict Phenotypic Responses to Chemicals

Leung et al., Repro Toxicol, 2016

Signaling Network Underlying Virtual Genital Tubercle Model (Mouse)

Cell field - androgenSHH field

FGF10 field no androgen

Simulation of Genital Tubercle Closure

Embryonic GT Abstracted GT

GD13.5 – 17.5


27











28

Significant Pressure to Deliver Data and Models in Useful Format

Initial Data Delivered as Flat Files

Progress to Dashboard with Limited Search,

Visualization, and Export Functionality

Currently Providing Cross-Functional and Decision

Support Dashboards


29

New Chemistry Dashboard Delivers Structural and Property Data

https://comptox.epa.gov/dashboard


30

RapidTox Decision Support Dashboard in Development

• Semi-automated decision support tool with dashboard interface for high-throughput risk assessments

• Combining diverse data streams into quantitative toxicity values with associated uncertainty


31

Regulatory Applications Require More Focus on Quality and Transparency

• Public release of Tox21 and ToxCast data on PubChem and EPA web site (raw and processed data)

• Transparent ToxCast data analysis pipeline• Data quality flags to indicate concerns with chemical

purity and identity, noisy data, and systematic assay errors

• Publicly available as an R package

• Tox21 and ToxCast chemical libraries have undergone analytical QC and results publicly available

• Public posting of ToxCast procedures• Chemical Procurement and QC• Data Analysis • Assay Characteristics and Performance

• External audit on ToxCast data and data analysis pipeline

FLAGS:Only one conc above baseline, activeBorderline active


32

Next Phase… Evolution Towards a Truly Predictive Science

c. 1970s - 2002 2003 - 2006 2007 - 2010 2011 - 2016c.1500 – 1960s


2016 - ?


33

Acknowledgements and Questions

Tox21 Colleagues:NTP CrewFDA CollaboratorsNCATS Collaborators

EPA Colleagues:NERLNHEERLNCEA

EPA’s National Center for Computational Toxicology

Documents

So Many Chemicals, So Little Time Evolution of