Emerging Technologies in Toxicity Testing:How will this Impact Risk Assessment?

Dr Christopher J PortierDr. Christopher J. PortierSenior Advisor to the Director

Head, Laboratory of Environmental Systems BiologyHead, Laboratory of Environmental Systems BiologyNational Institute of Environmental Health Sciences

Communication

EconomicsResearch

Test Hypotheses

ScienceResearch

Test Hypotheses

Risk AnalysisTranslationEvaluation

Test HypothesesInterpretation

Test HypothesesInterpretation

EvaluationCharacterizationImplementationCommunication

RiskAssessment Benefit

Assessment

SocietyDecision

Cost/BenefitRisk

ManagementSet Priority

Manage

Management

Evaluation of the weight of the evidence - IARCCancer inh

Cancer ini t l i l

Mechanistic andth l t d thumans

� Sufficient evidence� Limited evidence� Inadequate evidence

experimental animals

� Sufficient evidence� Limited evidence� Inadequate evidence

other relevant data

• Mechanistic data “weak,” “moderate,” or “strong”?

� Inadequate evidence� Evidence suggesting lack

of carcinogenicity

� Inadequate evidence� Evidence suggesting lack

of carcinogenicity• Mechanism likely to be

operative in humans?

Overall evaluation

� Group 1 Carcinogenic to humans� Group 2A Probably carcinogenic to humans� Group 2B Possibly carcinogenic to humans

G f� Group 3 Not classifiable as to its carcinogenicity to humans� Group 4 Probably not carcinogenic to humans

Human data predominates, but all data is usedEVIDENCE IN EXPERIMENTAL ANIMALS

Group 1

Sufficient Limited Inadequate ESLC

Sufficient

strong evidence inS

Group 2B (exceptionally, Group 2A)Limited

strong evidence inexposed humans …agent acts through relevant mechanism

Group 2A

HU

MA

NS

belongs to a mechanistic class

with supporting evidence from Group 3Inadequate

strong evidence in exposed humans

strong evidencmechanism also operates in humansE

NC

E IN

consistently and strongly supported by a broad range of mechanistic and other relevant data

mechanistic and other relevant data

Group 3Inadequate operates in humansGroup 2B

strong evidence … mechanism does not operate in humans

EV

IDE

Group 3

relevant data

Group 4ESLC

Modified from Vincent Cogliano, IARC

Systems Biology for the Individual

Gohlke and Portier (2006)

R h d th H M d lResearch and the Human Model

EpidemiologyHuman ClinicalLaboratory

AnimalModels

TissueCultures

CellCulturesMolecular

Biology

Gohlke and Portier (2006)

Biology

Boverhof and Zacharewski (Tox. Sci., 2006)

R h d th H M d lResearch and the Human Model

EpidemiologyHuman ClinicalLaboratory

AnimalModels

TissueCultures

CellCulturesMolecular

Biology

Gohlke and Portier (2006)

Biology

Boverhof and Zacharewski (Tox. Sci., 2006)

Emerging Screening Tools and the Risk Assessment Process

High ThroughputS i

Is this thing likely to be toxic?

Are humansexposed?

Screening

Tissue Cultures

What kinds of Toxicity?

How much exposure?

AlternativeModes

Computational

How much toxicityis there?

Tools

Omics and multiplexedTechnologies

GWAS and otherHuman Studies

InformationInformationTechnologies

UnknownT i it

High Priority Further Testing as a Group

Test a FewTreat as a Functional

Family

Toxicity

Mid Priority

a Group

FurtherIn-vitro HTS

Second Tier

Confirm Same Group Activity

UnknownHigh Priority

Mid PriorityInitial

Screening

Further Testing as a Group“Omics”

TestingUnknown

Carcinogenicity

Mid PriorityInitial

ScreeningMutiple

Activities

Test a FewLow Priority Further Testing as

Low Priority

Screening Activities

Test a FewThousands of ChemicalsHundreds of

Testing as a Group

Thousands of Chemicals Hundreds of Chemicals Treat as a

FunctionalFamily

Hundreds of Chemicals

What constitutes a positive? Should it be a single number?

Shockley et. al.. SOT 2010

HTS Analyses Heat Map

10−1

10−3

10−2

licat

e k

valu

e, m

M

10−5

10−4

Sec

ond

dupl

ic

>50 uM10 50 M

10−6

10−5

10−4

10−3

10−2

10−1

10−6

First duplicate k value, mM

10-50 uM1-10 uM<1 uM

fibroblastsneuroblastoma

Renal Rodent

SpeciesReproducibility

Species Concordance

ToxScores: combining information to set priorities

Kavlock, 2009

Commercial Tools: GeneGo (an example)

• Chemical ReportsChemical Reports

– Major Metabolites

– 76 QSAR predictions

• Protein binding

• Cancer

• Alzheimer’s

• Etc

– Compounds predicted to have similar activity

F b th t d t b lit• For both parent and metabolites

– Possible cellular (receptors, proteins, etc.) targets

• Both parent and metabolites

– Functional pathways likely to be targeted

Comparison of the average performance of eight different statistical classificationstatistical classification methods

Thomas et. Al. Toxicological Sciences 112(2), 311–321 (2009)

How good is good enough?

The predictive capacity of the 3T3 Neutral Red Uptake assay to The predictive capacity of the 3T3 Neutral Red Uptake assay to identify substances with acute oral LD50 > 2000 mg/kgidentify substances with acute oral LD50 > 2000 mg/kg

TP FP FN TN Sens Spec Acc PPV NPV

JRC 23 14 2 12 92 46 69 62 86HSL 18 14 1 11 95 44 66 56 92IIVS 24 17 1 12 96 41 67 59 92

TP: True PositiveFP: False Positive FN: False NegativeTN: True Negative

Prieto et. Al., JRC, SOT 2010

Id tif i I t t Di P th

H G tiIdentify Pathways R l t d t H

Identifying Important Disease PathwaysNew ‘omics

DataHuman Genetics

+Human Disease

Related to Human Disease

PredictChemical-Gene-

Disease Interactome

High ThroughputHigh Content

Set Priorities

Develop Screens

PredictRisks

Sets of PathwaysHigh Content

Chemical-Specific Data

Develop Hypotheses

Fingerprint Toxicants

Genetics, Pathways and Human Disease

• ObservationsObservations

– Genetic polymorphisms associated with human diseases

• Example: ALDH2 isoenzyme polymorphism (mutated form) ALDH2*2 is unable to complete phase 2 metabolism of alcohol leading to increasedunable to complete phase 2 metabolism of alcohol leading to increased acetaldehyde levels in blood resulting in increased blood pressure and flushed appearance

– Part of a pathway related to glycolysis and gluconoegenesis

– Other aspects of this pathway can lead to increases in acetaldehyde

• So can biofuels?

– All of these should be associated with the same symptoms (or phenotype orAll of these should be associated with the same symptoms (or phenotype or disease)

– If chemicals block the activity of ALDH2, they should also increase risk of the same phenotype

Genetic Association Databasegeneticassociationdatabase.nih.gov

Human Genetics+

Human Disease

Identify Pathways Related to Human

Disease

• The Genetic Association Database is a gene-centered archive of published scientific papers on human genetic association studies.

D t b C t t• Database Contents

– 28347 records on human gene-phenotype (mostly complex disease) relationships

Sets of Pathways

• Data used in our analysis

– Manual phenotype grouping and better annotation

– 8,825 unique associations between 2088 genes and 208 disease phenotypes

Becker et al. (2004) Nature Genetics 36(5)

Unsupervised Clustering of Pathways with

Gohlke et al. (BMC Systems Biology, 2009)

CardiovascularRenin-angiotensonPPAR

Pathways with Phenotypes

CardiovascularMetabolic

CancerCell cycleErbb

Insulin

ImmuneToll like receptorCytokine

Erbb

Endocrine

Immune CytokineJak-Stat

Calcium

Steroid hormone

Neuropsychiatric

CalciumVEGFTyrosine metabolism

ism

biot

ics

by C

YP

set

abol

ism

olis

mne

Deg

rada

tion

oids

way

way

Met

abol

ism

Sign

alin

g Sy

stem

hway

Form

atio

n

alin

g Pa

thw

ay

tion

hway

thw

ay

thw

ay

Cancer Pathways

Glu

tath

ione

Met

abol

iM

etab

olis

m o

f Xen

obA

rach

idon

ic A

cid

Me

Lino

leic

Aci

d M

etab

oLi

mon

ene

and

Pine

nB

iosy

nthe

sis

of S

tero

Sul

phur

Met

abol

ism

Fola

te B

iosy

nthe

sis

Cel

l Cyc

leP

53 S

igna

ling

Path

wW

nt S

igna

ling

Pat

hwno

sito

l Pho

spha

te M

Pho

spha

tidyl

inos

itol

Erb

-B S

igna

ling

Path

Dor

sal-V

entra

l Axi

s F

Axo

n G

uida

nce

B c

ell R

ecep

tor S

ign

Tigh

t Jun

ctio

nLo

ng-T

erm

Pot

entia

tG

nRH

Sig

nalin

g Pa

thM

elan

ogen

esis

mTO

R S

igna

ling

Pat

Adh

eren

s Ju

nctio

nns

ulin

Sig

nalin

g Pa

t

DNA DamageColorectal CancerBladder CancerEsophageal CancerGastric CancerHead and Neck Cancer

G M A L L B S F C P W In P E D A B T L G M m A In

Head and Neck CancerCancer (not otherwise specified)Lung CancerBreast CancerProstate CancerOvarian CancerLeukemiaLymphomaBrain CancerChronic Obstructive Pulmonary DiseaseCervical CancerMelanomaUlcerative ColitisPancreatic CancerPancreatic CancerHepatitus CMultiple SclerosisRheumatic ArthritisEndometrial CancerThyroid Cancer

A Subway Map of Cancer Pathways H h d W i b N t R i 2002A Subway Map of Cancer Pathways Hahn and Weinberg, Nature Reviews, 2002

Comparative Toxicogenomics Databasehttp://ctd.mdibl.org/

• Interactions between environmental factors and genes/proteins in diverse organisms are curated from the published literature using both algorithm based methods as well as manual curation.

• Environmental factor identifiers used in the literature are annotated using MeSH chemical terms.

Identify Pathways Related to Human

Disease

Mattingly et al. 2006. Toxicol Sci. 92(2):587-95.

High ThroughputHigh Content

Chemical-Specific Data

Gohlke et al. (BMC Systems Biology, 2009)

Metabolic Syndrome Cluster

Significance in Adipocytokine and PPAR i li d fi thi l tPPAR signaling define this cluster:

- 13 are retinoids (including 4 antineoplastic agents).

- 4 are known PPAR alpha/gamma p gantagonist/agonists:

- 2 are used to treat hyperlipedemia and insulin resistence

- 1 is a Bisphenol A derived epoxy (BADGE)(BADGE)

- 2 are anti-psychotics known to cause weight gain

- Essential omega 3 fatty acids

Neurologic Disease Cluster

• Phenotypes • Exposuresyp– Aggression

– Alcohol Abuse

– D 21266 (amine)

– Plicamycin

Nafenopin

– Migraine

– Mood Disorders

– Anxiety Disorder

– Attention Deficit Disorder

– Autism

– Nafenopin

– Naphthyridines

– Nordihydroguaiaretic Acid

– Obsessive Compulsive Disease

– Personality Traits

– Bipolar Affective Disorder

– Depression

Huntington’s Disease

– Nystatin

– Organophosphorus Compounds

– Schizophrenia

– Substance Abuse

– Suicide– Huntington s Disease

– Oxygen

– Pentazocine

– Pentobarbital

– Tourette Syndrome

e oba b a

– PilocarpineGohlke et al. (BMC Systems Biology, 2009)

Screening new chemicals for activity

• Pure prediction from a computational modelPure prediction from a computational model

• Screening using HTS, geneomics and a variety of other tools

– Look to see what it predicts for activity from a model like ours

– Look to see what chemicals have the same activity and use a toxic equivalency factors approach

Chemicals linked to breast cancer

Enriched Human Susceptibility PathwaysBreast Cancer

Estradiol Interactome

Identifying and Screening PathwaysMAP Kinase

p53 signaling

y g g y

Insulin signaling

Retinol metabolism

Insulin signaling

ErbB signaling # Env. Factors

#

Di

Cell cycle

Signal transduction Metabolism

iseases

Avg. of 16 diseases associated with each

signaling pathway

gAvg. of 4 diseases

associated with each metabolic pathway

Finding targets for screeningSelective gene over-expression

Focal Adhesion Signaling

Creighton et al., Cancer Research, 2006

Commercial Tools: GeneGo (an example)

• Chemical ReportsChemical Reports

– Major Metabolites

– 76 QSAR predictions

• Protein binding

• Cancer

• Alzheimer’s

• Etc

– Compounds predicted to have similar activity

F b th t d t b lit• For both parent and metabolites

– Possible cellulr (receptors, proteins, etc.) targets

• Both parent and metabolites

– Functional pathways likely to be targeted

Id tif i I t t Di P th

Identify Pathways Related to RodentSh t T A

Identifying Important Disease PathwaysShort-Term to Long-

Term Prediction ModelRelated to Rodent

Short-term ResponseShort Term Assay

in Rodents + ‘omics

Predict Chronic

Statistic for Testing

Chronic Exposure Response Dose-

Response

Cancer Response in

Chronic BioassaySets of Pathways

y

Chemical Short Name NTP No. Routea Dose Tumors

Microarray data from B6C3F1 mice following 90 day exposure

1-Amino-2,4-dibromoanthraquinone ADBQ 383 Food 20,000 ppm LiverMethylene Chloride MECL 306 Inhalation 4,000 ppm Liver

N-Methylolacrylamide MACR 352 Gavage (Water) 50 mg/kg LiverTris(2,3-dibromopropyl)phosphate TDPP 76 Food 1,000 ppm Liver

2,2-Bis(bromomethyl)-1,3-propanediol BBMP 452 Food 1,250 ppm Other1 2 Dib th DBET 86 G (CO) 62 /k Oth1,2-Dibromoethane DBET 86 Gavage (CO) 62 mg/kg Other

Ethylene Oxide ETOX 326 Inhalation 100 ppm OtherNaphthalene NPTH 410 Inhalation 30 ppm Other

Vanadium Pentoxide VANP 507 Inhalation 2.0 mg/m3 Other1,4-Dichlorobenzene DCBZ 319 Gavage (CO) 600 mg/kg Liver

Propylene glycol mono t butyl ether PGBE 515 Inhalation 1 200 ppm LiverPropylene glycol mono- t-butyl ether PGBE 515 Inhalation 1,200 ppm LiverTetrafluoroethylene TFEL 450 Inhalation 1,250 ppm Liver

1,2,3-Trichloropropane TCPN 384 Gavage (CO) 60 mg/kg Liver2-Chloromethylpyridine hydrochloride CMPH 178 Gavage (Water) 250 mg/kg No

Diazinon DIAZ 137 Food 200 ppm NoIodoform IODO 110 Gavage (CO) 93 mg/kg NoIodoform IODO 110 Gavage (CO) 93 mg/kg NoMalathion MALA 24 Food 14,800 ppm No

4-Nitroanthranilic acid NAAC 109 Food 10,000 ppm NoTetrafluoroethane TFEA ---d Inhalation 50,000 ppm No

Trichlorofluoromethane TCFM 106 Gavage (CO) 3,925 mg/kg NoAir ACON Inhalation

Corn oil CCON Gavage (CO) Feed FCON Food Water WCON Gavage (Water)

Predicted Liver Cancer from Microarray DataAbove red line - clearly positiveBelow blue line clearly negative

Liver CarcinogensBelow blue line - clearly negativeX - actual data predictionGreen range - x-validation range

Predicted Liver Cancer from Microarray DataAbove red line - clearly positiveBelow blue line clearly negative

Liver CarcinogensBelow blue line - clearly negativeX - actual data predictionGreen range - x-validation range

LiverCancer

ProstateCancer

ColorectalCancer

Evaluating Dose-Response Patterns

Methylene Chloride Naphthalene 1,4-Dichlorobenzene

1,2,3-Trichloropropane Propylene glycol mono-t-butyl ether

New types of biomarkers in blood: tissue-specific relase of mRNA into blood

Wetmore et al., Hepatology 2010 (in press)

Issues to Consider

• Predictions are model dependent

– Do we have a process for model review?

• Broaden the use of human data and human approaches in toxicology

Genetics– Genetics

– Epidemiology

– Gene expression profiling

– GWAS studies

• Validation?

Decisions are context dependent and data dependent– Decisions are context dependent and data dependent

– Is it a question of replacing an assay or not needing one because of other information?

• Scalable Approaches• Scalable Approaches

– Improved systems biology, improved human genetics, genomics, disease linkage, epidemiology data, etc.

Advantages and Disadvantages to improving toxicity testing

• More compounds, more compounds, more compounds• Focus intense testing on chemicals likely to be most important• Screen new commercial formulations (e.g. green chemistry)

before investing in a technology• Dataset for risk assessment will be increasingly complex…

– Need a complex measure for deciding if something is a hazard– NOEL’s are unlikely to be useful in setting standards– Focus may finally come to complex exposuresFocus may finally come to complex exposures – Dose-response in itself will be complex– Societal decisions on whether a pattern of activity is adverse will be

difficult

• Lots of public concern caused by misapplying models to product formulations