Building Libraries of Adverse 

Outcome Pathways

Daniel L. Villeneuve, Ph.D., Aquatic Toxicologist

Toxic Effects Characterization Research Branch, MED, NHEERL, ORD, EPA

5/26/2011 1

U.S. Environmental Protection Agency
5/26/2011 U.S. Environmental Protection Agency 2

I. Paradigm Shift in Toxicology – Predicting Adversity

II. Adverse Outcome Pathways – definition and example

III. AOPs and alternative testing strategies

IV. Strategies for AOP discovery and development

V. Applying AOPs

VI. Conclusions

Outline
3

Mode of action – based

Focused/targeted in vivo testing

•QSARs

•Chemical Categories

•High throughput screening

•In vitro

•Biomarkers

•Genomics

I. Paradigm Shift in Toxicology
•QSARs

•Chemical Categories

•High throughput 

screening

•In vitro

•Biomarkers

•Genomics

Use in hazard/risk assessment has been limited by lack of well defined (predictive) 

linkages between these alternative types of data and adverse outcomes traditionally 

considered relevant to ecological risk assessment.

Molecular Cellular Tissue Organ

Organ 

System Individual Population Ecosystem

•Survival

•Growth/development

•Reproduction

I. Paradigm Shift in Toxicology
An Adverse Outcome Pathway (AOP) is a conceptual framework that portrays existing knowledge 

concerning the linkage between a direct molecular initiating event and an adverse outcome, at a 

level of biological organization relevant to risk assessment.

(Ankley et al. 2010, Environ. Toxicol. Chem., 29(3): 730-741.)

Chemical

Properties

Receptor/Ligand

Interaction

DNA Binding

Protein Oxidation

Gene 

activation

Protein 

production

Altered 

signaling

Altered 

physiology

Disrupted 

homeostasis

Altered tissue 

development/ 

function

Lethality

Impaired 

Development

Impaired 

Reproduction

Toxicant

Cellular 

Responses

Organ

Responses

Organism

Responses

Structure 

Recruitment

Extinction

Population

Responses

MacroMolecular

Interactions

Molecular Cellular Tissue Organ Organ 

System Individual Population Ecosystem

II. Adverse Outcome Pathways –

definition and example
6

Biologic

inputs

“Normal” 

Biological

Function

Adverse

Outcomes

(e.g., mortality, 

Reproductive

Impairment)

Cell inury, 

Inability to 

regulate

Adaptive

Responses

Early cellular

changes

Exposure

Uptake-Delivery to Target Tissues

Perturbation

Cellular response pathway

Molecular

initiating event

Perturbed cellular 

response pathway

Adverse outcome

relevant to

risk assessment

Toxicity Pathway

Adverse Outcome Pathway

II. Adverse Outcome Pathways –

definition and example
0

1

2

3

4

5

6

7

0.1 1 10 100

fadrozole (uM)

fmol/h/mg

IC50 = 6.93 +/- 0.80 μM

A

B

*C*

*D*

*E*

Ovary

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0.01 0.1 1 10 100 1000

fadrozole (uM)

fmol/h/mg

IC50=8.82 +/- 1.58 μM

A

*B*

*C*

*D*

*E*

*F*

Brain

Aromatase Inhibition

Villeneuve et al. 2006. Aquat. Toxicol. 76:353-368

In vitro assay with small fish tissues

II. Adverse Outcome Pathways –

definition and example
Sanderson et al. 2001, EHP, 109: 1027-1031

High throughput assay with human cell line

II. Adverse Outcome Pathways –

definition and example
SAR and QSAR in Environmental Research, 20: 657-678.

QSAR, chemical categories

II. Adverse Outcome Pathways –

definition and example
10

Pregnenolone

Progesterone

17a-OH-Pregnenolone

17a-OH-Progesterone

CYP17 

(hydroxylase)

CYP11A

3b-HSD 3b-HSD

DHEA

Androstenedione

Testosterone

CYP17 

(lyase)

3b-HSD

17b-HSD

CYP19

17b-estradiol

Cholesterol

20b-HSD

17α20β-dihydroxy-4-

pregnen-3-one

CYP17 

(hydroxylase)

CYP17 

(lyase)

estrone

CYP19

17b-HSD

11β-OHTestosterone

CYP11B1

11-Ketotestosterone

11βHSD

11-deoxycorticosterone

CYP21

corticosterone

CYP11B1

aldosterone

CYP11B2

11-deoxycortisol

CYP21

cortisol

CYP11B2

CN

N

N

II. Adverse Outcome Pathways –

definition and example
A bit of fish reproductive biology

11

Agonism

Estrogen

Receptor

Vtg

production

Hepatocyte

Oocyte

development

Ovary

Ovulation

& spawning

Female

Stable or

increasing 

trajectory

Population

Estradiol 

ER

Agonist

HO

OH

HO

OH

ERE-Vtg

Cellular response pathway

Biologic

inputs

“Normal” 

Biological

Function

II. Adverse Outcome Pathways –

definition and example
12

CN

N

N

Fadrozole

Fadrozole (μg / L)

0 50

Aromatase Activity

(fmol/mg

-1 hr

-1

)

0

75

150

Male 

Female 

a

b

c

c

Fadrozole (μg / L)

0 50

Aromatase Activity

(fmol/mg

-1 hr

-1

)

0

75

150

Male 

Female 

a

b

c

c

8

0

2

4

6 E2 (ng/ml)

*

*

0

10

20

Vtg (mg/ml)

*

*

*

Control 2 10 50

Fadrozole (μg/l)

8

0

2

4

6 E2 (ng/ml)

*

*

0

10

20

Vtg (mg/ml)

*

*

*

Control 2 10 50

Fadrozole (μg/l)

-20 -18 -16-14-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20

Exposure (d)

0

2

4

6

8

10

(Thousands)

Cumulative Number of Eggs

Control

2

10

50

Fadrozole (ug/L)

*

*

*

-20 -18 -16-14-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20

Exposure (d)

0

2

4

6

8

10

(Thousands)

Cumulative Number of Eggs

Control

2

10

50

Fadrozole (ug/L)

*

*

*

Biochemical

Reproduction

(Key Events)

Ankley et al., 2002, Toxicol. Sci. 67:121-130

Perturbed fish reproductive biology

II. Adverse Outcome Pathways –

definition and example
5/26/2011 13

Chemical

Property

Profile

Receptor/Ligand

Interaction

DNA Binding

Protein Oxidation

Gene Activation

Protein Production

Altered Signaling

Protein Depletion

Altered Physiology

Disrupted 

Homeostasis

Altered Tissue 

Development

or Function

Lethality

Impaired 

Development

Impaired 

Reproduction

Cancer

Toxicant Macro-Molecular

Interactions

Cellular 

Responses

Organ

Responses

Individual

Responses

Structure 

Recruitment

Extinction

Population

Responses

Adverse Outcome Pathway

CN

N

N

Aromatase 

inhibition

8

0

2

4

6 E2 (ng/ml)

*

*

0

10

20

Vtg (mg/ml)

*

*

* Control 2 10 50 Fadrozole (μg/l)

8

0

2

4

6 E2 (ng/ml)

*

*

0

10

20

Vtg (mg/ml)

*

*

* Control 2 10 50 Fadrozole (μg/l)

-20 -18 -16-14-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20

Exposure (d)

0

2

4

6

8

10

(Thousands)

Cumulative Number of Eggs

Control

2

10

50

Fadrozole (ug/L)

*

*

*

-20 -18 -16-14-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20

Exposure (d)

0

2

4

6

8

10

(Thousands)

Cumulative Number of Eggs

Control

2

10

50

Fadrozole (ug/L)

*

*

*

Reduced E2, 

Vtg synthesis

Impaired 

vitellogenesis Reduced fecundity

II. Adverse Outcome Pathways –

definition and example

Molecular

initiating event

Key events or predictive

relationships spanning

levels of biological 

organization 

Adverse outcome

relevant to 

risk assessment
5/26/2011 U.S. Environmental Protection Agency 14

Adverse outcome pathway: a conceptual framework that portrays existing knowledge 

concerning the linkage between a direct molecular initiating event and an adverse 

outcome, at a level of biological organization relevant to risk assessment. –

(eco; demographic relevance)

Mode of action: the sequence of key events, starting with the interaction of an 

agent with the target cell, through molecular, biochemical, cellular, functional and 

anatomical changes, resulting in cancer or other adverse health effects.

Key event: empirically observable, necessary steps in the disease (cancer) or 

adverse outcome process.

Molecular initiating event: the initial point of chemical-biological intersection within an organism that 

starts an AOP. First key event in a mode of action.

Toxicity pathway: cellular response pathways that, when sufficiently perturbed, are expected to result 

in adverse health effects.

II. Adverse Outcome Pathways –

definition and example
5/26/2011 15

II. Adverse Outcome Pathways –

definition and example
5/26/2011 U.S. Environmental Protection Agency 16

III. AOPs and alternative 

testing strategies

How do AOPs support the development of alternative 

testing strategies?
OECD 210, OCSPP 850.1400: 

Fish, Early-life Stage Toxicity Test

• Most frequently used bioassay for predicting fish chronic toxicity

• Supports ERA for pesticides, biocides, pharmaceuticals, industrial chemicals.

•Requires ≥ 360 fish per test

•30-80 days depending on species

•Cost: 50-80K USD/test

• Endpoints

•Hatching

•Mortality

•Morphological/behavioral abnormalities

•Growth (weight, length)

• Product: LOEC, NOEC, and/or EC10

•Most sensitive endpoint as hazard threshold

III. AOPs and alternative 

testing strategies
• >50 scientists

• Academia, gov’t, industry, NGOs

• Europe, North America, Asia

III. AOPs and alternative 

testing strategies
AhR-mediated 

cardiotoxicity

AChE inhibition

Gill narcosis

Volz et al. in preparation

III. AOPs and alternative 

testing strategies
Other FELS 

toxicity pathway 

assays

Thousands

Tens

Hundreds

Volz et al. in preparation

III. AOPs and alternative 

testing strategies
5/26/2011 U.S. Environmental Protection Agency 21

IV. Strategies for AOP 

discovery/development

How many AOPs are there?

How can we systematically discover 

and characterize them?
• USEPA, NERL – Cincinnati, OH

• D. Bencic, M. Kostich, A. Biales, D. Lattier, J. Lazorchak, G. Toth, R. 

Wang, 

• USEPA, NHEERL – Duluth, MN, and Grosse Isle, MI

• G. Ankley, E Durhan, K Jensen, M Kahl, C. LaLone, E Makynen, D. 

Miller, D. Villeneuve

• USEPA, NERL – Athens, GA

• T. Collette, D. Ekman, M. Henderson, K. Ralston-Hooper, Q. Teng 

• USEPA, NHEERL, -RTP, NC

• M. Breen, R. Conolly, S. Edwards, L. Burgoon

• USEPA, NCER, STAR Program

• N. Denslow (Univ. of Florida), E. Orlando, (Univ. Maryland), K. 

Watanabe (Oregon Health Sciences Univ.), M. Sepulveda (Purdue 

Univ.)

• USACE, ERDC – Vicksburg, MS

• E. Perkins, T. Habib, M. Mayo

• Other partners

• Jackson St. University, N. Garcia-Reyero, 

• University of St. Thomas, D. Martinovic

• Birmingham University, Birmingham, UK, F. Falciani, N. Turan

• UC Berkeley, C. Vulpe, A. Loguinov

• UC Santa Barbara, J. Shoemaker, F. Doyle III

• Joint Genome Institute, DOE (Walnut Creek, CA)

• Sandia, DOE (Albuquerque, NM)

• Pacific Northwest National Laboratory (Richland, WA)

• Environment Canada, M. McMaster (Burlington, ON)

SMALL FISH

Linking exposures and effects using transcriptomics, proteomics, and metabolomics with small fish models 
5/26/2011 23

IV. Strategies for AOP 

discovery/development

Adapted from Greely 2002
5/26/2011

24

11βHSD

P45011β.

GnRH

Neuronal 

System

GABA Dopamine

Brain

Gonadotroph

Pituitary

D1 R

D2 R

?

?

Y2 R

NPY

Y1 R

GnRH 

R

?

Activin

Inhibin

GABAA

R

GABAB

R

Y2 R

D2 R

GnRH

Circulating Sex Steroids / Steroid 

Hormone Binding Globlulin

PACAP

Follistatin

Activin PAC1 R

Activin R

FSHb LHb

GPa

Circulating LH, FSH 

LH R FSH R

Circulating LDL, HDL

LDL R

HDL R

Outer mitochondrial 

membrane

Inner mitochondrial 

membrane

StAR 

P450scc

pregnenolone 3bHSD

P450c17

androstenedione

testosterone

Cholesterol

estradiol

Gonad

(Generalized, gonadal, 

steroidogenic cell)

Androgen / Estrogen 

Responsive Tissues

(e.g. liver, fatpad, gonads)

ER AR

3

4

5

6

Blood

Blood

Compartment

17βHSD

P450arom

11-ketotestosterone

17 progesterone α-hydroxyprogesterone

17α,20β-P (MIS)

20βHSD

Fipronil (-)

Muscimol (+)

Apomorphine (+)

Haloperidol (-)

Trilostane (-)

Ketoconazole (-)

Fadrozole (-)

Prochloraz (-,-)

Vinclozolin (-)

Flutamide (-)

17β-Trenbolone (+)

17α-Ethynylestradiol (+)

7

8

9

10

11

12

2

1

Chemical Probes
Fathead minnow 21 d, reproduction • Fecundity (NOEC / LOEC)

• Plasma steroids and vtg

• Targeted qpcr analyses

Zebrafish

24, 48, 96 h time course •Targeted qpcr analyses

• Microarray

Fathead minnow

1, 2, 4, 8 d exposed, 1, 2, 4, 8 d dep. 

• Plasma steroids and vtg

• Ex vivo steroid production

• Targeted qpcr analyses

• Microarray

• Metabolomics

Mode of action

Aromatase inhibitor

Anti-androgen 

(metabolites)

Dopamine 2 receptor 

antagonist

Steroidogenesis

inhibitor

Steroidogenesis

inhibitor

Androgen receptor 

agonist

Steroidogenesis

inhibitor

Anti-androgen 

(metabolites)

Test chemical

Fadrozole (FAD)

Flutamide (FLU)

Haloperidol (HAL)

Ketoconazole (KTC)

Prochloraz (PRO)

17β trenbolone(TRB)

Trilostane (TRI)

Vinclozolin (VIN)

IV. Strategies for AOP 

discovery/development
Test chemical Mode of action 8 d time course

Conc (μg/L)

21 d fecundity

LOEC & NOEC (μg/L) 

Fadrozole (FAD) Aromatase inhibitor L: 4.1±0.13

H: 35±1.1

NOEC: ND

LOEC: 2.0 

Flutamide (FLU) Anti-androgen (metabolites) L: 56±3.0

H: 557±37

NOEC: 50

LOEC: 500

Haloperidol

(HAL)

Dopamine 2 receptor 

antagonist

57.3 ± 4.7 NOEC: 20

LOEC: ND

Ketoconazole (KTC) Steroidogenesis inhibitor L: 32.7±3.4

H: 311±36

NOEC: 6.0d

LOEC: 25d

Prochloraz (PRO) Steroidogenesis inhibitor L: 23.6±1.4

H: 288±24

NOEC: 30

LOEC: 100

17β trenbolone (TRB) Androgen receptor agonist L: 0.032±0.007

H: 0.493±0.055

NOEC: 0.005

LOEC: 0.050

Trilostane (TRI) Steroidogenesis inhibitor L: 282±38 

H: 1620±190 

NOEC: 300

LOEC: 1500

Vinclozolin (VIN) Anti-androgen (metabolites) L: 177±30 

H: 502±43

NOEC: ND

LOEC: 60

EDC Exposures (8 chemicals x 2 fhm assays)

IV. Strategies for AOP 

discovery/development
17βtrenbolone

(trb-acetate)

AR binding

AR-dependent 

somatic cell 

proliferation

Tubercle and fatpad

formation in 

females

Ankley et al. 2003 ET&C 22:1350-1360

IV. Strategies for AOP 

discovery/development
Molecular Cellular Tissue Organ Organ 

System Individual Population Ecosystem

17βtrenbolone

(trb-acetate)

AR binding

Negative feedback, 

hypothalamic 

neurons

Reduced 

steroidogenesis, 

vitellogenesis

Reduced 

fecundity

Ankley et al. 2003 ET&C 22:1350-1360

IV. Strategies for AOP discovery/development
17βtrenbolone

(trb-acetate)

AR binding

Negative feedback, 

hypothalamic 

neurons

AR-dependent 

somatic cell 

proliferation

Tubercle and 

fatpad formation in 

females

Reduced 

steroidogenesis, 

vitellogenesis

Reduced fecundity

Adverse Outcome Pathway: Human

17βtrenbolone

(trb-acetate)

AR binding

Negative feedback, 

hypothalamic 

neurons

AR-dependent 

somatic cell 

proliferation

Increased body 

hair in females

Testicular atrophy, 

impaired sperm 

production

Infertility

Adverse Outcome Pathway: Small Fish

Molecular Cellular Tissue Organ Organ System Individual Population Ecosystem

Cross-species comparisons
5/26/2011 U.S. Environmental Protection Agency 30

•As AOPs are developed, AOP information needs to be 

assembled into a properly curated, accessible, searchable, 

and adaptable format.

•Important step toward contextualizing AOP networks

IV. Strategies for AOP 

discovery/development
AOPs depicted as linear

AOPs function in networks

• intersections

• shared nodes

• can influence on another

AOP networks function in a broader systems biology context

IV. Strategies for AOP 

discovery/development
Role for Toxicogenomics

• Toxicogenomics - can survey a broader range of “biological space” 

• Discovery - adverse effects mediated through unknown, or poorly 

characterized, mechanisms of action. 

• Unraveling interactions between biological pathways. 

• Pathway perturbation motifs - Identifying those features that respond to 

a given stressor/types of stress. 

Chemical 

exposure

Gene expression 

response

Reverse engineering of 

interaction network

Refinement of network 

with binding interactions

Computational model of 

network architecture

Defining 

interaction 

network

Relate network to known 

pathways and outcomes

IV. Strategies for AOP 

discovery/development
Fathead minnow 21 d, reproduction • Fecundity (NOEC / LOEC)

• Plasma steroids and vtg

• Targeted qpcr analyses

Zebrafish

24, 48, 96 h time course •Targeted qpcr analyses

• Microarray

Fathead minnow

1, 2, 4, 8 d exposed, 1, 2, 4, 8 d dep. 

• Plasma steroids and vtg

• Ex vivo steroid production

• Targeted qpcr analyses

• Microarray

• Metabolomics

Mode of action

Aromatase inhibitor

Anti-androgen 

(metabolites)

Dopamine 2 receptor 

antagonist

Steroidogenesis

inhibitor

Steroidogenesis

inhibitor

Androgen receptor 

agonist

Steroidogenesis

inhibitor

Anti-androgen 

(metabolites)

Test chemical

Fadrozole (FAD)

Flutamide (FLU)

Haloperidol (HAL)

Ketoconazole (KTC)

Prochloraz (PRO)

17β trenbolone(TRB)

Trilostane (TRI)

Vinclozolin (VIN)

IV. Strategies for AOP 

discovery/development
5/26/2011 U.S. Environmental Protection Agency 34

Mutual information a measure of how much one random variables tells 

us about another -can be thought of as the reduction in uncertainty about 

one random variable given knowledge of another 

Expression effects

Expression effects 

across many 

experimental 

conditions

IV. Strategies for AOP 

discovery/development
Physiological measurements

Receptor hubs

Targets

Mutual information network

IV. Strategies for AOP 

discovery/development
mRNA processing

Cell cycle

Protein secretion

DNA damage response

none

none

Metabolic processes

Inflammatory response

Regulation of biol. processes

Mapping Functional Modules onto Network none

system development 

organ development 

developmental process 

anatomical structure development 

intrinsic to plasma membrane 

IV. Strategies for AOP 

discovery/development
Mutual information network

Clusters of genes with statistically-coordinated expression profiles

• Identify characterize novel biological pathways/functions mediating adverse outcomes.

• Anchor to features in graphical systems models

• Accelerate the discovery of new AOPs and improve understanding of AOPs in a network/systems context

IV. Strategies for AOP 

discovery/development
fipronil

GABA receptor antagonist

cyproterone acetate

anti-androgen

17b-trenbolone

androgen

vinclozolin

anti-androgen

17a-ethynylestradiol (EE2)

estrogen

bisphenol A (BPA)

estrogen 

and anti-androgen ?

Metabolomics

average “exposed” spectrum –minus– average “control” spectrum (using urine from male FHM)

4.0 3.0 Chemical Shift (ppm) 2.0 1.0

IV. Strategies for AOP 

discovery/development
5/26/2011 U.S. Environmental Protection Agency 39

Once AOPs are developed, how can they be applied?

1. Linkage to adversity - relevance of alternative data

2. Support focused testing strategies – associate 

molecular perturbations w/ probable outcomes

3. Identification of uncertainties & research needs/priorities

V. Applying AOPs 
5/26/2011 U.S. Environmental Protection Agency 40

4. Support development of useful QSARs and biomarkers

V. Applying AOPs 

OECD WORKSHOP ON USING MECHANISTIC 

INFORMATION IN FORMING CHEMCIAL 

CATEGORIES TO FILL DATA GAPS FOR 

REGULATORY PURPOSES

US-EPA

One Potomac Yard 

2777 S. Crystal Drive

Arlington, VA 22202, USA

8-10 December 2010
5/26/2011 U.S. Environmental Protection Agency 41

5. Species extrapolation

• The better we understand the biology, the easier it is 

to understand how effects translate across species.

V. Applying AOPs 
 Identify chemicals positive for a defined initiating event – Toxcast

 Use AOP to formulate predictions of expected adverse outcomes 

in non-human organisms

 Test hypotheses against existing data or in new experiments

• EPA, ORD

• US Army, ERDC

• Environment Canada

• Norwegian Institute for Water Research 

(NIVA)

• Okazaki National Research Institute, Japan

V. Applying AOPs 

• Where key features are conserved, foundation for 

applying HH HTS data for wildlife (or v.v.)

5. Species extrapolation
QSAR

Focus area

Xenobiotic

chg. gene, 

mRNA, 

protein

expression

chg. 

hormones; 

induc.VTG;

ova-testis

MOLECULAR

Interactions

CELLULAR

Responses

TISSUE/ORGAN

Responses INDIVIDUAL

Responses

POPULATION

Responses

chg. 2’ sex; 

decr. spawn; 

sex reversal

skewed 

sex ratios;

decr. yr. 

class

Incr. Toxicological 

Understanding

Incr. Risk Assessment 

Relevance Levels of Biological Organization

ER-mediated Reproductive Impairment AOP

with In vitro assay and QSAR focus areas

ER 

binding

In vitro Assay

Focus area

Schmieder et al. – case study; OECD Workshop on using mechanistic information in forming chemical categories....

Test Organism ER 

Conserved

Salmonid Yes

Another fish (different 

order)

Yes

Another vertebrate 

(different order)

Yes

Planktonic crustacean

(daphnid)

No

Benthic crustacean 

(amphipod, crayfish)

No

Insect No

Another non-vertebrate, 

non-arthopod (worm, 

mollusk, rotifer)

ACR > 100,000 No

V. Applying AOPs 

5. Species extrapolation

Basis for predicting which species and/or life-stages may be 

vulnerable/susceptible (or not) to effects.
44

Phylogenetic Sensitivity to EE2

Genus or Species Chronic Value(s) (ng/L) Notes

Zebrafish 0.6, 1.5, <1.1 Life-cycle tests 

Fathead Minnow <0.32, 1.45 Life-cycle tests

Medaka 3.2 F0 from 1 d through spawning

Oncorhynchus <16 Adult exposure, fertilization success

Potamopyrgus 50 Adult exposure; embryo production

Gammarus >7600 100 d test, population size

Daphnia 45,000 Life-cycle test

Tisbe >100,000 Saltwater copepod

Chironomus 320,000 Larval growth and molting schedule

Brachionus 800,000 72 h test, intrinsic rate of increase

U.S. Environmental Protection Agency. 2008. Aquatic life criteria for contaminants of emerging concern. Part 2: Illustration of 

recommendations using data for 17a-ethinylestradiol (EE2). Office of Water, Washington, DC.

V. Applying AOPs 

5. Species extrapolation
45

AOPs assume sufficient perturbation to cause the 

adverse outcome. 

•Hazard assessment – capability to cause an adverse 

outcome

•Screening and prioritization

•Targeted testing 

Risk assessment – probability of an adverse outcome 

under defined circumstances. 

•Requires an understanding of adaptive/homeostatic capacity of biological systems 

and their limits, relative to concentration and duration of exposure.

V. Applying AOPs 

5. Quantitative RA
46

Biologic

inputs

“Normal” 

Biological

Function

Adverse

Outcomes

(e.g., mortality, 

Reproductive

Impairment)

Cell inury, 

Inability to 

regulate

Adaptive

Responses

Early cellular

changes

Exposure

Uptake-Delivery to Target Tissues

Perturbation

Cellular response pathway

V. Applying AOPs 

5. Quantitative RA
Fathead minnow 21 d, reproduction • Fecundity (NOEC / LOEC)

• Plasma steroids and vtg

• Targeted qpcr analyses

Zebrafish

24, 48, 96 h time course •Targeted qpcr analyses

• Microarray

Fathead minnow

1, 2, 4, 8 d exposed, 1, 2, 4, 8 d dep. 

• Plasma steroids and vtg

• Ex vivo steroid production

• Targeted qpcr analyses

• Microarray

• Metabolomics

Mode of action

Aromatase inhibitor

Anti-androgen 

(metabolites)

Dopamine 2 receptor 

antagonist

Steroidogenesis

inhibitor

Steroidogenesis

inhibitor

Androgen receptor 

agonist

Steroidogenesis

inhibitor

Anti-androgen 

(metabolites)

Test chemical

Fadrozole (FAD)

Flutamide (FLU)

Haloperidol (HAL)

Ketoconazole (KTC)

Prochloraz (PRO)

17β trenbolone(TRB)

Trilostane (TRI)

Vinclozolin (VIN)

V. Applying AOPs 
5/26/2011 48

Control

Low

High

Exposure 

Start (d. 0)

24 h 48 h

Exposure phase (d. 1-8) Depuration/recovery phase (d. 9-16)

96 h 8 d

Exposure 

Ends (d. 8)

+ 24 h + 48 h + 96 h + 8 d
CN

N

N

25oC, 12 h RIA

pituitary

FSH

Ovary

cyp19a1a

E2

vitellogenesis

Villeneuve et al. 2009 Environ. Health Perspect. 117:624-631
Theca

Granulosa

cyp11a

cyp19a1a

T 

sensor

E2 

sensor

TRB

T

E2

E2

T

Ekman et al. 2011, ET&C 30:319-329
Biologically-based modeling

Overshoot

Compensation 

during exposure

V. Applying AOPs 

5. Quantitative RA
5/26/2011 U.S. Environmental Protection Agency 52

Reif et al. Environ Health Perspect 118:1714–1720 (2010). 

doi:10.1289/ehp.1002180 [Online 8 September 2010]

Mixtures and mixed mode of action

V. Applying AOPs 

5. Quantitative RA
Systems biology – The scientific study of the dynamic interactions among the 

integrated networks of genes, proteins and biochemical reactions which give 

rise to emergent functions that support life. 

V. Applying AOPs 

5. Quantitative RA
• Understanding key components of system that must be included in an 

assay or effectively modeled to provide predictive power.

• Critical for understanding and predicting complex durationconcentration-response relationships.

• Ultimately needed to understand and predict effects of individual 

chemicals with mixed MOA or complex mixtures.

V. Applying AOPs 

5. Quantitative RA
5/26/2011 U.S. Environmental Protection Agency 55

VI. Conclusions

Significant advances in systems biology will be needed to 

support a transition from qualitative use of AOPs for hazard 

assessment, screening and prioritization to quantitative risk 

assessment.

AOPs can support development of alternative methods and 

informed extrapolation across taxa.

 Hypothesis-driven research is critical to establish the 

linkages depicted in AOPs

Effective use of „omics‟ and HTS approaches can help 

accelerate AOP discovery, development, and evaluation.
 AOPs provide a systematic framework to support 

the integration of diverse types of data in hazard/risk 

assessment.

 AOPs provide a basis to use rapid, low cost, 

predictive data to focus more costly whole animal 

testing.

VI. Conclusions
5/26/2011 U.S. Environmental Protection Agency 57

VII. Acknowledgements
5/26/2011 U.S. Environmental Protection Agency 58