:Computational Toxicology New Approaches to Improve

Environmental Health Protection

Presentation to the 4th International Academic Conference on Environmental and Occupational Medicine

Kunming, ChinaOctober 17, 2006

Computational Toxicology: New Approaches to Improve

Environmental Health Protection

Presentation to the 4th International Academic Conference on Environmental and Occupational Medicine

Kunming, ChinaOctober 17, 2006

Are you ready for the revolution?

D Butler, Nature Feb 15 2001; 409, 758 - 760Are you ready for the revolution?

D Butler, Nature Feb 15 2001; 409, 758 - 760

“small experiments driven by individual investigators will give way to a world in which multi-disciplinary teams….emerge as the key players…..in the era of systems biology in which the ability to create mathematical models describing the function of networks of genes and proteins is just as important as traditional lab skills.”

“the research teams that will be most successful….are those that switch effortlessly between the lab bench and the suite of sophisticated computational tools.”

DNA

mRNA

Protein

Metabolites

Transcription

Translation

Metabolism

Molecular Biology

Enabling TechnologiesEnabling Technologies3

“…to integrate modern computing and information technology with molecular

biology to improve Agency prioritization of data requirements and risk assessment

of chemicals”

“…to integrate modern computing and information technology with molecular

biology to improve Agency prioritization of data requirements and risk assessment

of chemicals”

www.epa.gov/comptox

What’s It All AboutWhat’s It All About

Digitization Legacy data

Dispersed data

Scale Chemicals

Biological space

Levels of biological organization

Quantifying Physiology, biochemical pathways and networks, biology

Data mining and management

The Source to Outcome ContinuumThe Source to Outcome Continuum

Source/Stressor Formation

Environmental Conc.

External Dose Target Dose

Biological Event

Effect/Outcome

Historically the problem has been approached one chemical at a time, one

stage at a time, with little progress in predicting across the stages and across chemicals. Current demands on the EPA are making this an untenable approach.

Computational Toxicology was initiated to provide new thinking to overcoming the

bottlenecks.

The Source to Outcome ContinuumThe Source to Outcome Continuum

GENOMICS, PROTEOMICS, and METABONOMICS:IDENTIFY/CHARACTERIZE

Source/Stressor Formation

Environmental Conc.

External Dose Target Dose

Biological Event

Effect/Outcome

COMPUTATIONAL METHODS:PREDICT/PRIORITIZE

ToxicologyGenomics

SystemsBiology

Informatics

HighThroughput

Screens

8

Toxicity of conazoles in Integrated Toxicogenomics Study

Toxicity of conazoles in Integrated Toxicogenomics Study

Chemical Liver

ToxicityLiver

TumorsThyroid

DisruptionTesticular Toxicity

Myclobutanil

NO NO NO YES

Propiconazole

YES YES NO NO

Triadimefon

YES YES YES YES

Common MOA across conazoles, tissues, cancer/non-cancer?

Use combination of toxicology and genomics to answer these questions.

Enriched for: Azole antifungals, p<4.9E-11

SP_Hepatomegaly, early gene expression:SV0412215R5RU:

-4 -3 -2 -1 0 1 2 3

-3

-2

-1

0

1

2

Sca

lar

pro

du

ct s

core

s fo

r P

XR

ac

tiv

atio

n s

ign

atu

re

Scalar product scores for hepatomegaly signature

MYCLOBUTANILPROPICONAZOLETRIADIMEFON

10Iconix genomics signatures combine with their reference Iconix genomics signatures combine with their reference database to correctly group EPA compounds with other database to correctly group EPA compounds with other

conazole antifungalsconazole antifungals

Martin, et al Toxicol. Sci. in press

Flu PropiMyclo Tri

A: Liver CYPs

CYP2g1

CYP1a2

CYP3a3

CYP CMF1b

CYP2c

CYP4F4

CYP4A2

CYP3a9

Flu PropiMyclo Tri

B: Liver CAR/PXR

CYP1a2

CYP3a3

Alas1

Ahr

Por

CYP3a9

Udpgtr2

Flu PropiMyclo Tri

C: Testis CYPs

CYP2j4

CYP11b3

CYP2j3p1

CYP2d2

CYP2e1

CYP11

Flu Propi Myclo Tri

Gtf2a2

Hsd17b4

Psbp1

Sult4a1

Nr1d2

Nr3c1

SOCS-2

Nr1d1

Nr4a2

Hsd11b1

D: Testis Steroidogenesis

Up

Down

CYP and CAR/PXR are differentially expressed in conazole treated rat livers

Toxicological Information GapsToxicological Information Gaps

>10,000 Chemicals in Need of Evaluation

>10,000 Chemicals in Need of Evaluation

Antimicrobials 750

EDCs 1500

HPVs 3300

Pesticidal Inerts 3310

MPVs 5400

H2O CCLs 7324

Large Data Gaps Exist for Many Chemical Types

Large Data Gaps Exist for Many Chemical Types

Bioactivity Profile of Environmental Chemicals

0

20

40

60

80

100

120

-2 -1 0 1 2 3 4 5 6 7 8

Log Conc (nM)

Ac

tiv

ity

Su

m

Specific Mechanism

Unknown/Multiple Targets

Non-Toxic

NOEC

Primary Toxicology

Target

Target Gene

Family

Gene Family Promiscuity

Non-specific Assay Interference

HTS Target Assay Panels

Goal: assesses broad biological effect patterns and correlate with the patterns of known toxicants for forecasting potential for hazard

ToxCast – A New EPA Research Program (Dix et al, Toxicol Sci. 2006 in press)

ToxCast – A New EPA Research Program (Dix et al, Toxicol Sci. 2006 in press)

Physical-Chemical Indicators

LogP pKA ……

Chemical 1

Chemical 2

Chemical 3

.......

Bio-Computational Indicators

PASS

Molecular Receptor Docking

#1

……

Biochemical Based Indicators

GPCRsHMG-CoA reductase

……

Cellular Based Indicators

H4IIE H295R ……In Vitro Omics Indicators

Primary Heptatocyte

s

Stem Cells

……Model Organisms

Response 1 Response 2 ……

Chemical Grouping

Bin 1

Bin 2

Bin 3

….

….

Bin ….

Increasing Biological Relevance

Increasing Cost

Pesticides as Proof of ConceptPesticides as Proof of Concept

~800 Registered in the United States

Wealth of Toxicological Information Developmental, reproductive, chronic, etc

Represent broad range of chemistries Azoles, carbamates, pyrethroids, triazines, etc.

Designed with biological activity in mind Receptor binding, enzyme inhibition, cytoskeletal,

etc.

Have created a library of 400 for study

Molecular Docking Experiments & virtual-HTS

A

Target

+

Complex

A

Affinity grid for hPPAR-

PDB ID: 1I7GPDB ID: 1I7G

nM M mM

Affinity increase

Lig

and

s in

mu

ltip

le c

har

ge

stat

esL

igan

ds

in m

ult

iple

ch

arg

e st

ates

PPAR- agonist Fluorocarbons

PPAR Receptor and Molecular Docking

Docking Animation: PFOAs & PPAR

ToxCast – Beyond Proof of Concept

ToxCast – Beyond Proof of Concept

Availability of a science-based system to categorize chemicals of like properties and activities

Increasing confidence as database grows

Once operational, Mode of Action leads for new chemicals

Provide EPA Program Offices with a relatively inexpensive predictive tool box that heretofore has been seriously lacking

Improve the efficiency and effectiveness of the use of animals in hazard identification and risk assessment

ToxCast

Assays

ToxCastDatabase

HazardPrediction

ChemicalChemical

ChemicalChemical

ToxCast

Assays

PrioritizeChemical

YES

NO

No FurtherTesting

LOW

FurtherScreening

And Testing

HIGH

MEDIUM

ToxCast

Assays

ToxCastDatabase

HazardPrediction

ChemicalChemical

ChemicalChemical

ToxCast

Assays

PrioritizeChemical

YES

NO

No FurtherTesting

LOW

FurtherScreening

And Testing

HIGH

MEDIUM

Chemical

Procurement-

Management

Metabolomic

Profiling

Genomic

Profiling

Biochemical

Assays

Cell-Based

Screening

High-Content

Analysis

Microelectronic

Monitoring

Model

Organisms

Toxicity

Predictor

ToxCastDatabase

Chemical

Procurement-

Management

Metabolomic

Profiling

Genomic

Profiling

Metabolomic

Profiling

Genomic

Profiling

Biochemical

Assays

Cell-Based

Screening

High-Content

Analysis

Microelectronic

Monitoring

Model

Organisms

Toxicity

Predictor

ToxCastDatabase

Drinking Water

Contaminants

IRIS

Ecotox, A

ster, Teratox

TS

CA

Su

bstance

Registry List

Pe

stic

ide

A

ctiv

es

Pe

stic

ide

O

the

r In

gre

die

nts

High P

roduction

Volu

me

Informa

tion S

ystem

EPA’s Chemical Data Islands

Green C

hemistry

Air

To

xic

o-

ge

no

mic

s

SMILES

Chemical Structures

Chemical NamesCAS Registry Nos.

Drinking Water

Contaminants

IRIS

Ecotox, A

ster, Teratox

TS

CA

Su

bstance

Registry List

The world of chemical data

Pe

stic

ide

A

ctiv

es

Pe

stic

ide

O

the

r In

gre

die

nts

High P

roduction

Volu

me

Informa

tion S

ystem

EPA’s Chemical Data Islands

Green C

hemistry

Air

To

xic

o-

ge

no

mic

s

SMILES

Chemical Structures

World Wide WebNational

Toxicology Program

European Chemicals Bureau (SIDS)

National Library of Medicine

Chemical NamesCAS Registry Nos.

PubChem

Chemical structures

Drinking Water

Contaminants

IRIS

Ecotox, A

ster, Teratox

TS

CA

Su

bstance

Registry List

The world of chemical data

Pe

stic

ide

A

ctiv

es

Pe

stic

ide

O

the

r In

gre

die

nts

High P

roduction

Volu

me

Informa

tion S

ystem

EPA’s Chemical Data Islands

Green C

hemistry

Air

To

xic

o-

ge

no

mic

s

SMILES

PubChemWorld Wide Web

National Toxicology

Program

European Chemicals Bureau (SIDS)

National Library of Medicine

Chemical NamesCAS Registry Nos.

Chemical structures

Chemical Structures

DS

ST

ox

ChemicalChemicalstructuresstructures

http://www.epa.gov/ncct/dsstox

Capturing the Legacy DataCapturing the Legacy Data

Correlating Domain OutputsCorrelating Domain Outputs

Cell based Assays

Nuclear Receptors

Toxicology Endpoints

Physical chemical propertiesProfile Matching

27

ToxCast Training Set – Pesticide Actives

ToxCast Training Set – Pesticide Actives

correlations correlationsToxRef DB

ToxicologicalProfiles

DSSTox

Chemical Structural Features

27

HTS Profiles

ToxCastDB

ArrayTrackDB

Genomics Profiles

Myclobutanil ? ? + + - -

Propiconazole ? ? - - + -

Triadimefon ? ? + - + +

Rat Thyroid Tumor

HTS Profile Correlation

Genomic Profile CorrelationChemical

MaleFertility

Testicular Atrophy

Mouse Liver Tumors

ToxCast Chemical Classificationof Environmental Chemicals

ToxCast Chemical Classificationof Environmental Chemicals

correlations correlationsToxRef DB

ToxicologicalProfiles

DSSTox

Chemical Structural Features

Chemical Classification Based on Known or Predicted Toxicity

Profiles

Tox

Correlation

Present

Liver

Carcinogen

Kidney

Carcinogen DNT NT

Liver

Enzymes Etc….

Chemical A + - - - + - -

Chemical B + - - + - - -

Chemical C + + - - - + -

Chemical D + + - - - + -

Chemical E + - - + + - -

Chemical F - - - - - - -

28

HTS Profiles

ToxCastDB

ArrayTrackDB

Genomics Profiles

Gas Exchange

Arterial

Brain

GI Tract

Liver

Kidney

Fat

Slowly Perfused

Rapidly Perfused

Metabolism Ven

ous

Rats:

pnd10: 20 g

Adult: 200 g (60 day)

Aged: 450 g (2 yr)

Predicted Venous Concentrations in rats – 6 hr 500 ppm

Predicted Venous Concentrations in rats – 6 hr 500 ppm

Modeling uses age-appropriate physiological and chemical specific (e.g., partition

coefficients, metabolism rates) parameters

0 5 10 15 20 250

50

100

150

200

250

PND10AdultAged

time (hrs)

Ve

no

us

co

nc

en

tra

tio

n (

mg

/L)

Methyl ethyl ketone

0 5 10 15 20 250

5

10

15

20

25

30

35

PND10AdultAged

time (hrs)

Ve

no

us

co

nc

en

tra

tio

n (

mg

/L) Trichloroethylene

The Virtual Liver:A multiscale , computational model

Molecular CircuitryMolecular Circuitry

Hanahan and Weinberg, Hallmarks of Cancer, Cell, 100, 57-70, 2000.

Reduction of uncertainty in risk assessment

Reduction of uncertainty in risk assessment

(Policy-based approach)

Risk

RfC Range ofuncertainty

(Mechanism-based approach)

Information

ToxRefDB

ArrayTrack

VirtualLiver

NCCT

ToxCast

34

AcknowledgementsAcknowledgements

DSSTox Ann Richard

ToxCast and RefToxDB David Dix Keith Houck Richard Judson Matt Martin

Molecular Modeling Jim Rabinowitz Rocky Goldsmith Tom Transue

Life Stages Hugh Barton Chester Rodriquez

Virtual Liver Jerry Blancato Rory Conolly Imran Shah