US Army Corps of Engineers

BUILDING STRONG®

Ed Perkins

Senior Scientist

US Army Engineering

Research and Development

Center

Vicksburg, MS

Development and Use of Quantitive

AOPs to Support Decision Making

Reducing complexity to enable decisions

Reducing complexity to enable decisions

MIE KE 1 KE 2 KE 3 KE 4 AO

Receptor Molecular level

Cellular level

Organ level

Tissue level

Individual level

MIE KE 1 KE 2 KE 3 KE 4 AO 3 2 1 3 2

Indirect KER

3

KER1 KER2 KER3 KER4 KER5

AOP

AOP with

weighted KER

MIE KE 1 KE 2 KE 3 KE 4 AO 2 2 3 1

3

2

Evidence for

chemical X

causing or

initiating AOP

BRIEF BACKGROUND OF AOP FRAMEWORK Builds and expands upon prior pathway-based approaches to

predictive toxicology

Motivated, in part, by NAS (2007) “Toxicity Testing in the 21st

Century” emphasis on greater use of mechanistic data

Framework focuses on translation of mechanistic data into

endpoints meaningful to risk assessment

Emphasis initially on ecotoxicology, but equally applicable to

human health

*Ankley et al. 2010. Adverse outcome pathways: A framework to support

ecotoxicology research and risk assessment. Environ. Toxicol. Chem. 29: 730-

741.

MIE KE 1 KE 2 KE 3 KE 4 AO

Receptor Molecular level

Cellular level

Organ level

Tissue level

Individual level

MIE KE 1 KE 2 KE 3 KE 4 AO 3 2 1 3 2

Indirect KER

3

KER1 KER2 KER3 KER4 KER5

AOP

AOP with

weighted KER

MIE KE 1 KE 2 KE 3 KE 4 AO 2 2 3 1

3

2

Evidence for

chemical X

causing or

initiating AOP

STANDARDIZED, REVIEWED AND OECD

PUBLISHED INDIVIDUAL AOPS

Describes how measureable events lead to a regulatory outcome

•2012 launch of OECD AOP development programme•2013 OECD Guidance on Developing and Assessing AOPs

•Introduce standardization and rigor to AOP development•Conventions, terminology and information content•Weight of evidence evaluation based on modified Bradford-Hill criteria•Support development of AOP Knowledgebase

Adopted by EPA and promoted n EU

AOP Development and Description

Case Studies

Users’ handbook supplement to OECD guidance document for developing and assessing AOPs.

•March 2014 – Advancing AOPs for Integrated Toxicology and Regulatory Applications Workshop

Regulatory Impact of AOP Concept

AOP KB AS A CATALOG OF TOXICOLOGICAL

PATHWAYS AND ADVERSE OUTCOMES

Adverse Outcome Pathway Knowledge Base (AOP-KB)

APPLICATION OF TOXICOLOGICAL

MODELS IN CHEMICAL RISK ASSESSMENT

Robust predictive models offer cost-effective option to assessing

chemical risk when empirical data are limited

Occasionally utilized for screening-level assessments (e.g.,

narcosis QSARs), but rarely applied to regulatory decision-making

When higher-level predictive modeling has been employed

involves “data rich” high-visibility chemicals of human health

concern

Lack of use involves multiple factors

Availability of “generic” (as opposed to chemical-specific) BBDR

models

Accessibility of focused biological knowledge (and data) for the

array of endpoints/species of potential concern

Difficulty in formulating transparent, causal models

ROLE OF AOPS IN DEVELOPMENT OF

PREDICTIVE TOXICOLOGICAL MODELS

Provide transparent conceptual framework upon which to base

models

Options for many taxa/pathways/endpoints available through

resources such as the AOP-wiki

Clearly defined, measurable nodes (MIE, KE) directly indicative

of perturbation of pathways of concern

KERs provide basis for quantitative response-response relationship

model selection/parameterization

KERs and overall AOPs subjected to WoE based evaluation of

plausibility/causality

Moving from an AOP to qAOP

Descriptive

A qAOP captures response-response relationships

Hazard Assessment

Risk Assessment

Hazard assessment vs. Risk Assessment

AOPs are conceptual models for qAOPs

KE

1

KE

2

KE

3

KE

4

KR

5

A

O

KE

1

KE

2

KE

3

KE

4

KR

5

A

O

KE

1

KE

2

KE

3

KE

4

KR

5

A

O

EC

a

EC

b

EC

c

EC

d

EC

e

AOP

May not model all details of the AOP

Or they could have

more detail

qAOP

Must incorporate the AOP, but …

Description of Key Event RelationshipRelevant Models

& AnalysesTypical Data Needs

AOP

Directed: 𝐾𝐸𝐴 → 𝐾𝐵e.g. all KERs in AOP represent a causal linkage

Network/graph

analyses

techniques

Graph structure

providing the

connectivity between

KEs

Directed and signed relationship: 𝐾𝐸𝐴 →±𝐾𝐸𝐵

e.g. increasing and decreasing, i.e. ↑ 𝐾𝐸𝐴 ⇒↓ 𝐾𝐸𝐵

[a]

Direction and scalar-weighted relationship:

𝐾𝐸𝐴 →±𝑤𝐴,𝐵

𝐾𝐸𝐵e.g. simple weights

[a] Expert judged weights

qAOP

Direction and functional relationship: 𝐾𝐸𝐴 →𝑓𝐾𝐸𝐵

Probabilistic, e.g. probability of KE activation Bayesian

Networks

Expert judged

probabilities e.g. P(KE1

active given KE2 active)

Experimental data on

the KEs (e.g.

active/inactive)

Non-linear, e.g. saturable response Regression

modelling

Experimental data on

KEs under different levels

of perturbation

Time-dependent, e.g. acute vs chronic KE

activation

ODE, IBMs,

ABMs,

Leslie projection

matrix

Independent parameter

measurement

temporal data

Steatosis qAOP Bayesian Network

Rapid development of qAOP for screening

NFE

2/Nrf

2

LRH-1FX

RSHP

PPAR

-alpha

LX

RFAS PPAR-

gamma

HSD17b

4FA beta

oxidationCytosolic

FA

Stea

-

tosis

Lipo-

genesi

s

mTORC-

1LFAB-

P

AK

TaPKC

PI3K

SREBP

-1SCD

1

mTORC-

2

Insulin

receptor

+ -

+ |95 5

- | 5 95

+ -

+ | 5 95

- | 95 5

+ -

+ |95 5

- | 5 95

+ -

+ |95 5

- | 5 95

+ -

+ |95 5

- | 5 95

+ -

+ |95 5

- | 5 95

+ -

+ | 5 95

- | 95 5

+ -

+ |99 1

- | 1 99

Lipo & LFAB-P

++ -+ +- --

+ |99 99 99 1

- | 1 1 1 99

FXR & SHP & LXR

+++ -++ +-+ --+ ++- -+- +-- ---

+ | 50 50 50 1 99 50 50 1

- | 50 50 50 99 1 50 50 99

CytoFA & Fab-ox.

++ -+ +- --

+ | 1 1 99 99

- | 99 99 1 1

AKT + PI3K

++ +- -+ --

+ |95 5 50 5

- | 5 5 50 95

+ -

+ |99 1

- | 1 99

+ -

+ |100 0

- | 0 100

LRH-1 & LXR & PPAR-g

+++ -++ +-+ --+ ++- -+- +-- ---

FAS + | 95 75 75 50 75 50 50 1

FAS - | 5 25 25 50 25 50 50 99

mTORC1 & aPKC

++ -+ +- --

+ |95 5 95 5

- | 5 95 5 95

+ -

+ |95 5

- | 5 95

+ -

+ |95 5

- | 5 95

Binary State Bayesian

Network qAOP model

Predicting effect of assay measurements of events in an AOP network

Steatosis causal AOP network

Inhibition

here

Steatosis causal AOP network

Inhibition

here Causes

Fadrozole Inhibition Agonism

Estrogen

ReceptorReduced Vtg

production

Hepatocyte

Impair oocyte

development

Ovary

Impairovulation

& spawning

Female

Declining

trajectory

PopulationAromatase

EnzymeReduced

E2 synthesis

Granulosa

CellAromatase

inhibitor

Inhibition of aromatase by fadrozole well

characterized from molecular to individual level

From D. Villeneuve

Mechanistic qAOP development

Fadrozole Inhibition Agonism

EstrogenReceptor

Reduced Vtgproduction

Hepatocyt

e

Impair oocytedevelopment

Ovary

Impairovulation

& spawning

Female

Decliningtrajectory

PopulationAromataseEnzyme

ReducedE2 synthesis

GranulosaCell

Aromataseinhibitor

Fold

ch

an

ge

re

lative

to

co

ntr

ol (l

og

2)

Day

^ ^

^ ^

#

#

#

# #

# #

* *

*

* *

$ $

$ $

$

$

$

$

3 ug/L Fadrozole

30 ug/L Fadrozole

Time series Microarray, PCR, plasma hormone data, plasma protein data

Putative compensatory mechanism

Time

series

Bayesian

Network

analysis

Data driven understanding of Key events in an AOP

Fadrozole Inhibition Agonism

EstrogenReceptor

Reduced Vtgproduction

Hepatocyt

e

Impair oocytedevelopment

Ovary

Impairovulation

& spawning

Female

Decliningtrajectory

PopulationAromataseEnzyme

ReducedE2 synthesis

GranulosaCell

Aromataseinhibitor

Fold

ch

an

ge

re

lative

to

co

ntr

ol (l

og

2)

Day

^ ^

^ ^

#

#

#

# #

# #

* *

*

* *

$ $

$ $

$

$

$

$

Expert guided modelingData driven modeling

Quantitative Prediction of Reproductive/Population Effectsin Fish: Linking Relevant Models Across an AOP

Animal levelOrgan/tissue level

Vtg productionOocyte development,

ovulation and spawningAromatase

inhibition

Population

declining trajectory

Population level

VTG/fecundity correlation

Fadrozole Inhibition Agonism

Estrogen

ReceptorReduced Vtg

production

Hepatocyte

Impair oocyte

development

Ovary

Impairovulation

& spawning

Female

Declining

trajectory

PopulationAromatase

EnzymeReduced

E2 synthesis

Granulosa

CellAromatase

inhibitor

HPG axis model

Molecular level

Oocyte Growth Dynamics

modelPopulation Dynamics

model

Conolly et al. 2017. Quantitative adverse outcome pathways and their application to predictive toxicology.

Fadrozole 21 day, continuous exposure study. HPG axis model (9) predictions

after 21 days of continuous exposure to fadrozole at concentrations of 0, 1.4, 7.3,

and 57 g/L .

plasma E2plasma VTG

fecundity vs FAD FAD effect on population size

Conolly et al Environ Sci Technol 2017

Read-across of response-response plots to illustrate rapid evaluation of the qAOP-

predicted effects of aromatase inhibition on key events and adverse outcome

Aromatase inhibition vs FAD E2 vs Aromatase inhibition

VTG vs E2 Fecundity vs VTG

Pop size vs Fecundity

50% inhibition of

aromatase

2.3 µM

Conolly et al Environ Sci Technol 2017

Conclusion/Recommendations

AOPs provide a standardized approach relevant to regulatory needs.

AOPs are conceptual models that inform design of qAOP models

Define a specific question before developing a qAOP

The complexity and type of qAOP models is driven by resources and

uncertainty:

Time constrained, Data poor

Extensive response-response data

Biological fidelity

Application

Many different qAOP models can be made

Toxicokinetics+qAOP -> internal exposure at MIE+Hazard

Exposure+Toxicokinetics+qAOP-> external exposure+Hazard

qAOPs can be used to to answer a wide range of questions, but developers

should be transparent and document everything

ACKNOWLEDGEMENTS

USACE-ERDC

Lyle Burgoon, Lynn Escalon, Michael

Mayo, Natalia Garcia-Reyero

FUNDING

US EPA

G. Ankley, M. Breen, J. Cavallin, W.Y. Cheng,

R.Conolly, E. Durhan, K. Jensen, M. Kahl, C.

LaLone, E. Makynen, D. Miller, M. Severson,

K. Stevens, D. Villeneuve