The Future of PredictiveThe Future of PredictiveToxicology in a Flattening World:Toxicology in a Flattening World:Barriers to greater validation and acceptanceBarriers to greater validation and acceptanceof QSAR models in regulatory applicationsof QSAR models in regulatory applications

Ann RichardAnn RichardNational Center for Computational ToxicologyNational Center for Computational Toxicology

US Environmental Protection AgencyUS Environmental Protection Agency

Toxicity Risk Assessment

NO2

ToxicityToxicityPredictionPredictionProblemProblem

Toxicity Risk Assessment

NO2 increasing complexity

increasing relevance to RA

increasing uncertainty

SARSARstructure-activitystructure-activity

relationshipsrelationships

ToxicityToxicityPredictionPredictionProblemProblem

A (Q)SAR model for regulatory purposes shouldA (Q)SAR model for regulatory purposes shouldbe associated with the following information:be associated with the following information:

•• a defined endpointa defined endpoint

•• an unambiguous algorithm, i.e. transparency an unambiguous algorithm, i.e. transparency

•• a defined domain of applicability a defined domain of applicability

•• appropriate measures of goodness-of-fit, robustness, and appropriate measures of goodness-of-fit, robustness, andpredictivitypredictivity

•• a mechanistic interpretation, if possible a mechanistic interpretation, if possible

OECD Principles OECD Principles ((SetubalSetubal, 2002), 2002)::

A (Q)SAR prediction for regulatory purposes should be placed in alarger context of information to assess the utility of the prediction.

EPAEPA’’s Revised Cancer Risks Revised Cancer RiskAssessment GuidelinesAssessment Guidelines

Mode of Action: Mode of Action: ““Use of mode of action information in hazardUse of mode of action information in hazardcharacterization ... is a central part of the proposed approach ...characterization ... is a central part of the proposed approach ...””

Structural Analogue Data:Structural Analogue Data: ““Confidence in conclusions is a functionConfidence in conclusions is a functionof how similar the analogues are to the agent under review inof how similar the analogues are to the agent under review instructure, metabolism, and biological activity.structure, metabolism, and biological activity.””

Structural-Activity Relationships: Structural-Activity Relationships: ““SAR analyses and models canSAR analyses and models canbe used to predict molecular properties, surrogate biologicalbe used to predict molecular properties, surrogate biologicalendpoints, and carcinogenicity. ... Suitable SAR analysis [ofendpoints, and carcinogenicity. ... Suitable SAR analysis [ofchemicals that do not bind covalently to DNA] requires knowledge orchemicals that do not bind covalently to DNA] requires knowledge orpostulation of the probable mode(s) of action ...postulation of the probable mode(s) of action ...””

Narrative: Narrative: ““biological plausibilitybiological plausibility””, , ““mode of action consistent withmode of action consistent withgenerally agreed-upon principles and understanding ofgenerally agreed-upon principles and understanding ofcarcinogenicitycarcinogenicity””

Validation: Validation:What is the performance of model outside of the trainingWhat is the performance of model outside of the trainingset? in different chemical domains?set? in different chemical domains?

Domain(sDomain(s) of applicability:) of applicability:When should a model be applied?When should a model be applied?When not?When not?

Confidence level: Confidence level:What confidence can one place in the prediction?What confidence can one place in the prediction?

Important SAR Modeling Concepts:Important SAR Modeling Concepts:

Knowledge includes knowing what you don’t knowKnowledge includes knowing what you don’t know

Toxicity prediction

Global SAR Toxicity Prediction Model

Q

Chemical Structures

Descriptors

Activities

low

high

Confidence inprediction

Chemical Structures

Descriptors

Activities

Activity predictionConsistent with known mechanism of actionBiological & chemical plausibilityModel performance for analoguesIdentified analoguesRelevant descriptorsCoverageStatistical certainty

low

high

Confidence inprediction

Q

Global SAR Toxicity Prediction Model

Model assumptionsTraining databaseData qualityStatistical performance measuresProspective predictivityModel failuresModel complexityInterpretability

Example: Example: DEMETRA ProjectDEMETRA ProjectHybrid System for prediction of aquatic toxicityHybrid System for prediction of aquatic toxicity

(combination of 5 Neural Network models)(combination of 5 Neural Network models)

-3

-1

1

3

5

7

-3 -1 1 3 5 7

Observed

Predicted

TRAINING SETTRAINING SET

y = 0.90x + 0.26y = 0.90x + 0.26

RR22 = 0.76 = 0.76

TEST SET

y = x + 0.52

R2 = 0.72

What sorts of chemicals areWhat sorts of chemicals are well predicted? well predicted? poorly predicted? poorly predicted?

For a test chemical prediction:For a test chemical prediction:

What are closest model analogs? What are closest model analogs?

How accurate are predictions for How accurate are predictions forthe closest the closest modelmodel analogs?analogs?

How well are the closest How well are the closest chemicalchemicalanalogs predicted?analogs predicted?

Are descriptors well within range Are descriptors well within rangeof dataset?of dataset?

Is prediction plausible? Is prediction plausible?

Mechanistic basis High confidence

Limited domain ofapplicability

Draize rabbit eye test scores: 68 Bulk liquids – direct eye exposure Modified maximum average scores (MMAS)

Human eye irritation thresholds: 23 compounds - vapor phase exposure Eye Irritation Threshold (EIT)

Adjusted by liquid-saturated vapor pressure, PAdjusted by liquid-saturated vapor pressure, P0 0 ::

Log (1/EIT) = log (MMAS/ PLog (1/EIT) = log (MMAS/ P0 0 ))

Single QSAR for combined set of 91Single QSAR for combined set of 91compoundscompounds

::5 solubility-related parameters, R5 solubility-related parameters, R22=0.936=0.936

Supports relevance ofDraize test to humans

Mechanistic basis ofQSAR: passive transfer

Applicable to low vpchemicals

Applicable tohazardous chemicals

Extends range of bothdatasets

Organophosphate

Combining SAR and Biofunctional InformationPredicting Carcinogenicity of Organophosphates

In vitrogenotoxicity?

+

-

- High acutetoxicity?

MarRapidhydrolysis? +

-M

...

+

-

Increase serumalkalinephosphatase?

LM

- HM

SAR:Structuresuggestiveof alkylation?

Strong chelating property?

LM

??

+

+

??

????

In vivogenotoxicity?

L

SAR

SAR SAR

SAR

Woo et al. (1996) Environ. Woo et al. (1996) Environ. CarcCarc. & . & EcotoxEcotox. Revs., C14:1-42. Revs., C14:1-42

CHEMICAL XPrioritized for weight of evidence

Determination of Genotoxic Carcinogenicity

Available Empirical DataOn Carcinogenicity

Available Eimpirical Dataon Genotoxicity

QSAR Model Predictions For Carcinogenicity

SAR Model PredictionsFor Carcinogenicity

QSAR Model Predictionsfor Genotoxicity

SAR Model Predictionsfor Genotoxicity

WEIGHT OF EVIDENCECONCLUSION FORCARCINOGENICITY

WEIGHT OF EVIDENCECONCLUSION FOR

GENOTOXICITY

Model RobustnessIndicators

Model RobustnessIndicators

Sufficient Weight of Evidence for/against

Carcinogenicity?

WEIGHT OF EVIDENCECONCLUSION FOR

GENOTOXIC CARCINOGENICITY

NO

NO

NO

NO

YES

YES

YES

YES

Sufficient Weight of Evidence –

Positive or Negative - for

Carcinogenicity?

Sufficient Weight of Evidence for/against

Genotoxicity ?

Sufficient Weight of Evidence –

Positive or Negative - for Genotoxicity ?

Coutesy of Bette Meek, Health Canada

ComHaz Preliminary Weight of Evidence – Data/(Q)SAR

SAR

SAR

Integrated Testing Scheme:Integrated Testing Scheme:New Chemical Hazard AssessmentNew Chemical Hazard Assessment

80% prediction accuracy:Propose use with reduced

confirmatory assay

Pilot Study (10 chem)Pilot Study (10 chem)In vivo results availableIn vivo results available

(use DEREK, TOPKAT, misc. SAR models)(use DEREK, TOPKAT, misc. SAR models)

Implementationwould lead to 37%reduction in use

of animals

Gubbels-van Hal et al, Regul. Tox. Pharm., 2005, 42:284-295.

PhyschemPhyschem Properties PropertiesMP, BP, MP, BP, solubsolub, , LogPLogP, VP, .., VP, ..

Tox PropertiesTox Properties acute oral acute oral subacutesubacute toxtox (28 day) (28 day) acute dermal/inhalation acute dermal/inhalation eye/skin irritation eye/skin irritation skin sensitization skin sensitizationin vitro in vitro mutagmutag (2 tests) (2 tests)

EcotoxEcotox Properties Properties biodegbiodeg, acute daphnia, , acute daphnia, …… acute fish acute fish toxtox

80-90% predictionaccuracy:

Propose use with singleconfirmatory assay

Poor prediction accuracy (10%)No reduction in animal use

Develop newSAR model

Structure-based Screening & Structure-based Screening & Prioritization:Prioritization:

Chemical(s)of concern

Apply existingSAR model

DataData

DataData

DataData

DataData

DataData

DataData

Place in context ofexisting data andunderstanding

Mine existingdata forstructural &biofunctionalanalogs

Chemistry-based Data Mining Chemistry-based Data Mining & Exploration:& Exploration:

Chemical(s)Chemical(s)of concernof concern

StructuralStructuralanalogsanalogs

Chemical-Chemical-specificspecific

datadata

PropertyPropertyanalogsanalogs

Biological/Biological/mechanisticmechanistic

analogsanalogs

Structure-Activity Relationships

http://ntp.niehs.nih.gov/

CASRN or ChemicalName search

Off-site to search structure oranalogs (NLM ChemID Plus)

View study results:Genetic toxicity studies:Salmonella

5 study categories

Summary Call

View study details

View detailed data

Individualexperiment results

Individualexperiment results

Cannot download entire list of NTP Cannot download entire list of NTPchemicals and test summary datachemicals and test summary data

Cannot structure or substructure-search Cannot structure or substructure-searchdatabasedatabase

Cannot download subsets of data: Cannot download subsets of data: list of TA98 pos data list of TA98 pos data

list of all thyroid tumor carcinogens list of all thyroid tumor carcinogens

Cannot ask relational questions of data: Cannot ask relational questions of data: what chemicals are TA100 what chemicals are TA100 negneg + TA98 pos? + TA98 pos?

list all chemicals with positive rat liver tumor list all chemicals with positive rat liver tumorfindings in cancer bioassay that are also non-findings in cancer bioassay that are also non-mutagenicmutagenic

Archival Relationalvs.

Short-TermBioassays

CAS, Chemical Name

NTP Database Site Map

Short-TermBioassays

CAS, Chemical Name

Developmental Studies

Embryo/fetalmalformations

NTP Database Site Map

““Shared standards are a huge flattener,Shared standards are a huge flattener,because they both force and empower morebecause they both force and empower morepeople to communicate and innovate overpeople to communicate and innovate overmuch wider platforms.much wider platforms.””

The World is FlatThe World is Flat - Thomas Friedman, 2004. - Thomas Friedman, 2004.

Gene expression

BioactivityProtein expression

Cell-basedassays

Chronicwholeanimalstudies

Chemical Structures

Neurotox

Repro &Develop Tox

Cancer

Genetox

DDistributedistributed

SStructuretructure--SSearchableearchable

ToxToxicityicityPublicPublicDatabaseDatabaseNetworkNetwork

DDistributedistributed

SStructuretructure--SSearchableearchable

ToxToxicityicityPublicPublicDatabaseDatabaseNetworkNetwork

Chemical structure-annotation

Data standards and integration

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

-nature & CAS of mixturecomponents,-tautomers,-sterechemistry,-error in source info-replicate 2D, CAS, parent-quaternary ammonium

STRUCTURE_ChemicalType

STRUCTURE STRUCTURE_Shown TestSubstance_ChemicalName

STRUCTURE_TestedForm_DefinedOrganic

STRUCTURE_MolecularWeight

STRUCTURE_Formula

STRUCTURE_IUPAC

STRUCTURE_SMILES

STRUCTURE_Parent_SMILES

STRUCTURE_InChI

ChemicalNotedefined organicinorganicorganometallic

parent,salt Na, Cl, etccomplex HCl, H2O, mesylate, etc

tested chemical,general form of chemical,active ingredient of formulation,representative isomer inmixture,representative component inmixture,monomer of polymer, simplified to parent

CH3

O

CH3

DSSTox Standard Chemical Fields:DSSTox Standard Chemical Fields:

TestSubstance_CASRN

TestSubstance_Description

single chemical compoundmacromoleculedefined mixture or formulationundefined mixtureunspecified or multiple forms

DSSTox_SID

NCTRlogRBA

ER RBA

ChemClass ERB

Activity GroupERB

RationaleChemClass ERB

MeanChemClass ERB RBA

LogP

F1, F2, …F6

ChemClass FHM

MOA

MOACONF

CLOGP

LC50

LC50NOTE

LC50RATIO

MIXMOA

TOXINDEX

FATS

BEHAVIOR

ChemClass DBP

Concern Level

Rationale

Rational Source

AnalogChemName

AnalogCAS

AnalogSMILES

SAL CPDB

TD50 Rat

TD50Mouse

Target Sites RatMale

Target SitesRat Female

Target SitesMouse Male….Other Species

Integrating Diverse Databases from aIntegrating Diverse Databases from aChemical Structure Perspective:Chemical Structure Perspective:

CPDBCPDB DBPCAN EPAFHM NCTRER DBPCAN EPAFHM NCTRER ……..

NCTRlogRBA

ER RBA

ChemClass ERB

Activity GroupERB

RationaleChemClass ERB

MeanChemClass ERB RBA

LogP

F1, F2, …F6

ChemClass FHM

MOA

MOACONF

CLOGP

LC50

LC50NOTE

LC50RATIO

MIXMOA

TOXINDEX

FATS

BEHAVIOR

ChemClass DBP

Concern Level

Rationale

Rational Source

AnalogChemName

AnalogCAS

AnalogSMILES

SAL CPDB

TD50 Rat

TD50Mouse

Target Sites RatMale

Target SitesRat Female

Target SitesMouse Male….Other Species

Integrating Diverse Databases from aIntegrating Diverse Databases from aChemical Structure Perspective:Chemical Structure Perspective:

CPDBCPDB DBPCAN EPAFHM NCTRER DBPCAN EPAFHM NCTRER ……..

Standard Chemical FieldsStandard Chemical Fields

Standard Tox Fields: Standard Tox Fields: StudyType, Species, Endpoint, RouteStudyType, Species, Endpoint, Route

Activity Fields: Activity Fields: Dose, ActivityCategory, Dose, ActivityCategory, SummaryCallSummaryCall, ..., ...

DSSTox Database Design:DSSTox Database Design:

ToxicologyToxicology ChemistryChemistry

Toxicological Data Chemical

Structures &Properties

ContextContext Utility for SARUtility for SAR

RelevanceRelevance

Toxicologists

Biologists

Risk Assessors

Domain Expertise

SAR Modeling

Machine Learning

Comp. Chemistry

3D QSAR

CPDBAS_v3a_1481_22Oct2005

STRUCTUREDSSTox_SIDDSSTox_ID_FileNameSTRUCTURE_FormulaSTRUCTURE_MolecularWeightSTRUCTURE_ChemicalTypeSTRUCTURE_TestedForm

_DefinedOrganicSTRUCTURE_ShownTestSubstance_ChemicalNameTestSubstance_CASRNTestSubstance_DescriptionChemicalNoteChemicalReplicateCountSTRUCTURE_ChemicalName

_IUPACSTRUCTURE_SMILESSTRUCTURE_Parent_SMILESSTRUCTURE_InChIStudyTypeEndpointSpecies

SAL_CPDBTD50_Rat_mg/kg/dayTD50_Rat_mmol/kg/dayTargetSites_Rat_Male, Female, Both SexesTD50_Mouse_mg/kg/dayTD50_Mouse_mmol/kg/dayTargetSites_Mouse_Male, Female, Both SexesTD50_Hamster_mg/kg/dayTD50_Hamster_mmol/kg/dayTargetSites_Hamster_Male, Female, Both SexesTD50_Dog_mg/kg/dayTargetSites_DogTD50_Rhesus_mg/kg/dayTargetSites_RhesusTD50_Cynomolgus_mg/kg/dayTargetSites_CynomolgusActivityCategory_SingleCellCallActivityCategory_MultiCellCallNTP_TechnicalReportToxicityNote

adr = adrenal gland;bon = bone;cli = clitoral gland;eso = esophagus;ezy = ear/Zymbal’s gland;gal = gall bladder;hag = harderian gland;hmo = hematopoietic system;kid = kidney;lgi = large intestine;liv = liver;lun = lung;meo = mesovarium;mgl = mammary gland;mix = mixture;myc = myocardium;nas = nasal cavitynrv = nervous system;orc = oral cavityova = ovary;pan = pancreas;per = peritoneal cavity;pit = pituitary gland;pre = preputial gland;pro = prostate;ski = skin;smi = small intestine;spl = spleen;sto = stomach;sub = subcutaneous tissue;tba = all tumor bearing animals;tes = testes;thy = thyroid gland;ubl = urinary bladder;ute = uterus;vag = vagina;vsc = vascular system.

NTPGTZ_v1a_1921SAL_TA100, rat S9, hamster S9, w/o S9, sumSAL_TA102, …SAL_TA104SAL_TA1535SAL_TA1537SAL_TA97SAL_TA98

IMMTOX:IMMTOX:Immunotoxicity TestImmunotoxicity TestBatteryBattery

88 chemicals88 chemicals

18 immunotox measures18 immunotox measures

Summary calls Summary calls

Usage categories Usage categories AntibodyResponse AntibodyResponse NaturalKillerCells NaturalKillerCells LymphocyteProliferation LymphocyteProliferation MixedLeukocyteResponse MixedLeukocyteResponse LeukocyteCountLeukocyteCount ThymusBodyWt ThymusBodyWt SpleenBodyWt SpleenBodyWt Lipopolysaccaride Lipopolysaccaride DelayedTypeHypersensitivity DelayedTypeHypersensitivity CytotoxicTLymphocyte CytotoxicTLymphocyte SurfaceMarkers SurfaceMarkers ContactSensitizationContactSensitization HostResistanceAssaysHostResistanceAssays (6) (6)

Up next ...Up next ... IMMTOXIMMTOX: Immunotox Battery Database : Immunotox Battery Database Expanded from 1992 publicationExpanded from 1992 publication

reporting a battery of immunotox results for 88 chemicals, most from the NTP.reporting a battery of immunotox results for 88 chemicals, most from the NTP.

NCTRARNCTRAR: FDA: FDA’’s National Center for Toxicological Research - Androgens National Center for Toxicological Research - AndrogenReceptor Binding Database Receptor Binding Database Androgen receptor relative binding affinities tested inAndrogen receptor relative binding affinities tested ina common in vitro assay for 202 chemicals, provided with chemical class-baseda common in vitro assay for 202 chemicals, provided with chemical class-basedstructure activity features.structure activity features.

NTPGTZNTPGTZ: NIEHS National Toxicology Program Gene-Tox Database (E.: NIEHS National Toxicology Program Gene-Tox Database (E.Zeiger) Zeiger) Battery of genetic toxicity test results for over 1900 chemicals from historicalBattery of genetic toxicity test results for over 1900 chemicals from historicalNTP studies.NTP studies.

UNLVSSUNLVSS: UniLever: UniLever’’s Skin Sensitization Databases Skin Sensitization Database Skin sensitization resultsSkin sensitization resultsfor over 200 chemicals from Unilever studies.for over 200 chemicals from Unilever studies.

Structure-Index Files:

IRISSIIRISSI: EPA: EPA’’s Integrated Risk Information System (IRIS)s Integrated Risk Information System (IRIS)

EPAHPVEPAHPV: EPA: EPA’’s High Production Volume Chemicalss High Production Volume Chemicals

NTPTSINTPTSI: National Toxicology Program: National Toxicology Program’’s Test Resultss Test Results

DSSTox ChemoinformaticsDSSTox ChemoinformaticsTotal Records: 6625Total Records: 6625Total Unique Records: 3967 (no replicates)Total Unique Records: 3967 (no replicates)

0

500

1000

1500

2000

2500

CPDBAS

DBPCAN

EPAFHM

NCTRER

FDAMDD

NTPIMT

NCTRAR

ULVSSDIRISSI

NTPGTZ

Duplicate

Unique

Chemical Class Overlaps:

0.1 1 10 100

% frequency

NTP

Test26

Activity/Chemical Class Overlaps:

DSSTox Coordination/Collaborations:DSSTox Coordination/Collaborations:

DSSTox

Lhasa:VITIC

PubChem

NCI Govt.Databases

StructureStructurerelationalrelationalsearchingsearching

ToxML

NTP

Tox DataTox DataStandardsStandards

CEBS

ArrayExpressArrayExpress,,GEO, CTD, GEO, CTD, ……

StandardStandardChemicalChemicalFieldsFields

LeadScopeSimulation PlusBioRadACD/Labs

Stanford MachineLearning Datasets

Data FilesData Files

ModelsModels

Toxicity Experimental Data Toxicity Experimental Data Summary Data: Summary Data:

ToxML

++ _ _

Activity categories Activity categories

Potency categories Potency categories

Mode of action categories Mode of action categories

Summary callsSummary calls

DSSToxDSSToxStandardStandardChemicalChemical&Tox Fields&Tox Fields

Toxicity Experimental Data Toxicity Experimental Data Summary Data: Summary Data:

ToxML

++ _ _

IntermediateIntermediatetoxicitytoxicity

classificationsclassificationsfor SARfor SAR

Activity categories Activity categories

Potency categories Potency categories

Mode of action categories Mode of action categories

Summary callsSummary calls

DSSToxDSSToxStandardStandardChemicalChemical&Tox Fields&Tox Fields

http://pubchem.ncbi.nlm.nih.gov/search/

850,000 Substances from “Legacy” Public Sources 650,000 Unique Structures 10,000,000 BioAssay Results from NCI / DTP

structure/sub-structure searching links to protein structures links to PubMed bioactivity screens similar activity profiles bioassay data

InChI=1.0/C9H6O6/c10-7(11)4-1-2-5(8(12)13)6(3-4)9(14)15/h1-3H,(H,10,11)(H,12,13)(H,14,15)

Unique Unique

Public Public

Text XML-based Text XML-based

Chemically robust Chemically robust charge state charge state chiralchiral centers centers tautomeric form tautomeric form

InChI:InChI:

PubChem (>3M) NCI (>23 M) KEGG (>10K) NIST Chemical Web Book Berkeley Carcinogenic

Potency Project DSSTox EPA Substance Registry

System

World-Wide WebWorld-Wide Web

““ElectronificationElectronification”” of historical data of historical data

Content annotation of unstructured data Content annotation of unstructured data

Chemical structure-annotation Chemical structure-annotation

Data standardization & integration Data standardization & integration

EPA Data Challenges:

InChI text annotation

EPA Structure-browser

Collaborations with NCI,ATSDR, ECB, FDA, NIEHS

Government-wide dataGovernment-wide data

EPA-wide dataEPA-wide data

CBICBIdatadata DSSTox

NIEHS/National Center for Toxicogenomics:NIEHS/National Center for Toxicogenomics:Chemical Effects in Biological System Knowledge-Chemical Effects in Biological System Knowledge-Base Base (M. Waters and J. (M. Waters and J. FostelFostel))

Metabonomics

Historical NTPToxicity data

Geneexpression

GeneFunction

Proteomics

GenePathways

DSSTox / CEBS Collaboration:DSSTox / CEBS Collaboration:Part 1 Part 1 –– DSSTox Annotation of CEB DSSTox Annotation of CEB

Metabonomics

HistoricalToxicity data

Geneexpression

Gene Function

Proteomics

GenePathways

Historical NTPToxicity data

DSSTox Standard Chemical Fields

Structure-Structure-searchabilitysearchability

AnalogAnalogsearchingsearching

Cross domainCross domainsearchingsearching

DSSTox / CEBS Collaboration:DSSTox / CEBS Collaboration:Part 1 Part 1 –– DSSTox Annotation of CEB DSSTox Annotation of CEB

Metabonomics

HistoricalToxicity data

Geneexpression

Gene Function

Proteomics

GenePathways

Historical NTPToxicity data

DSSTox Standard Chemical Fields

Structure-Structure-searchabilitysearchability

AnalogAnalogsearchingsearching

Cross domainCross domainsearchingsearching

DSSTox Standard Chemical Fields

DB7DB6DB5DB4 DB3 DB2 DB1

DSSTox Toxicity Data Files

DSSToxDSSToxDatabaseDatabaseNetwork +Network +

Part 2 Part 2 –– Link CEBS to DSSTox Database Network Link CEBS to DSSTox Database Network

DSSTox / CEBS Collaboration:DSSTox / CEBS Collaboration:Part 1 Part 1 –– DSSTox Annotation of CEB DSSTox Annotation of CEB

Metabonomics

HistoricalToxicity data

Geneexpression

Gene Function

Proteomics

GenePathways

Historical NTPToxicity data

Public Genomic Databases

Chemical Inventory Chemical Inventory

Chemical indexing Chemical indexing

Linkages Linkages

Predictive Toxicology

SAR Predictionsbased solely onchemicalstructures &properties:MCASETOPKATDEREK

Toxicitypredictionsbased on geneexpressionprofiles:Gene-LogicIconix

“Glocalization” of Predictive Tox Models

structu

restru

cture

toxicity

toxicity

chemistrychemistry biology

biology

Mech 1

Mech 2

Mech 3

Mech 5

ToxicityEndpoint

ChemicalStructures

Mech 4Biological attributesBiological attributesChemical reactivity Chemical reactivity

PNAS January 11, 2005 vol. 102 no. 2 261–266CHEMISTRY PHARMACOLOGY

1576 compoundsX 92 assays

Azole cluster bybiospectra similarity

Approximate “proteomic” signature

PASS (Prediction of ActivitySpectra for Substances)

Cerep: In vitro bioactivity profiles

Expanded chemical Expanded chemical ““propertiesproperties”” in inrelation to biological activityrelation to biological activity

Databases & models need to Databases & models need toextend beyond pharmaceuticals toextend beyond pharmaceuticals toenvironmental/industrial chemicalsenvironmental/industrial chemicalsspanning toxicity spacespanning toxicity space

Reference dataset of Reference dataset oftoxicity-related chemicals withtoxicity-related chemicals withstructures & bioactivity profilesstructures & bioactivity profiles

NIH/NCGC Roadmap:NIH/NCGC Roadmap:Small Molecules High-Small Molecules High-Throughput Screening InitiativeThroughput Screening Initiative

nihroadmap.nih.gov

> 200K > 200K ““small moleculessmall molecules””

> 200> 200bioactivity &bioactivity &cell-basedcell-basedassaysassays

7 dose7 dosedilutionsdilutions

Sample Sample ““toxicitytoxicity”” chemical chemicalspace:space:

NTP chemicalsNTP chemicals

EPA pesticides, inerts EPA pesticides, inerts

EPA HPV Chemicals EPA HPV Chemicals

DSSTox DSSTox

NCI/ NCI/ChemNavigatorChemNavigator

TOX

Biochemical“target” assays

Toxico-Chemoinformatics:Toxico-Chemoinformatics:Data Standardization, Integration, ExplorationData Standardization, Integration, Exploration

In Vitro assays

In Vivo wholeanimal studies

“In Silico” Predictions

Biochemical“target” assays

Toxico-Chemoinformatics:Toxico-Chemoinformatics:Data Standardization, Integration, ExplorationData Standardization, Integration, Exploration

In Vitro assays

In Vivo wholeanimal studies

“In Silico” PredictionsCalculated structures, properties

Tissue, organs

Short-term tests

Receptors, enzymes, proteins

Genomics

Chronic, acute

Cell-based assays