Enhancing Acceptance of and Confidence in Predictive Models of AOPs

NC3Rs/Unilever Workshop: Applying Exposure Science to Increase the Utility of Non-Animal Data in Efficacy and Safety Testing

February 15 – 16, 2017London, U.K.

Presented by:M.E. Meek, University of Ottawa

bmeek@uottawa.ca

1

Outline2

• The “drivers” for change• Positioning “exposure”

• Principles of Successful Regulatory Engagement in TransitionPrevious Experience in International Initiatives

• MOA/AOPs, PBPK

• Implications

Evolving International Mandates for Chemicals

Canada

“Categorization” for 23, 000 chemicals & multi tiered assessment (2006)

Europe

Registration, Evaluation and Authorization of Chemicals (REACH) (2007)

Japan Stepwise Assessment under the Chemical Substances Control Law (CSCL)” (2009)

Australia Inventory Multi Tiered Assessment and Prioritization (IMAP) (2012)

U.S. – TOSCA Reform

The need to increase efficiency in chemical risk assessment

3

U.S. NRC Toxicity Testing in the 21st Century

Compounds

Metabolite(s)

AssessBiological

Perturbation

AffectedPathway

Measures ofdose in vitro

Dose ResponseAnalysis for Perturbationsof Toxicity

Pathways

Calibrating in vitro and human

Dosimetry

Human ExposureData

Population BasedStudies

ExposureGuideline

Mode of ActionChemical

Characterization

Dose Response Assessment

Hazard Characterization

Risk Characterization

Exposure Assessment

4

The Need for Evolution and IntegrationTesting/Assessment/Computational

Better predictability

Broader application to larger numbers of chemicals

Higher relevance

Relevant pathways

Relevant doses

Relevant species

Requires early focus & assimilation/integration of testing and assessment in a more mechanistic/computational context

5

Facilitating Regulatory EngagementMeek, M.E. & Lipscomb,

Toxicology 332 (2015) 112–123

Principles:

• 1. transitioning in a familiar context

• 2. tiering to acquire experience and increase confidence

• 3. contextual knowledge transfer to facilitate interpretation and communication in

application

• 4. coordination and development of expertise and

• 5. the importance of continuing challenge

Requires:• Collaboration between the research and regulatory communities to increase

understanding and tailor the documentation

• Transparency and consistency in documentation/public repositories

6

MOA/Human Relevance or Species Concordance

AnalysisObjectives

Drawing maximally and early on mechanistic information

Transparency

Bridging regulatory/research

Doing the right research/testing

Issues impacting regulatory uptake:

Hypothesized MOAs often not well defined

Critical datagaps

Lack of transparency and consistency in documentation

of weight of supporting evidence

7

Tissue Dose Key Event 1Effective Dose

Key Event 2 Key Event 3 Adverse Outcome

Toxicokinetics (tk) Toxicodynamics (td)

8

Hypothesized

mode of action

(key events)

based on

Bradford Hill

considerations

Qualitative and

quantitative human

concordance

Implications for risk

assessment

Assessment-

specific data

generation

From Fig. 1

Level of confidence

Level of confidence

Critic

al da

ta

gaps

iden

tified

Assessment-

specific data

generation Critic

al da

ta

gaps

iden

tified

Key events

(Adverse)

effect

Mode of action

B/H Support Conflict Gaps

1.

2.

3.

4.

5.

Users’ handbook supplement to OECD guidance document for developing and assessing AOPs.Early Examples: Becker et al., 2015

Regulatory Toxicology and

Pharmacology 72 (2015) 514

B/H Def. H M L

B.P.

Essentiality

Empirical .

Adverse Outcome Pathway

Exposure

Target tissue

Absorption, Distribution, Metabolism, Excretion

Chemical Agnostic Pathway

Ch

em

ical

Sp

eci

fic

Toxi

coki

net

ics

Mo

de

of

Act

ion

Edwards et al. (2016) J Pharmacol Exp Ther 356:170

Evolution of AOPs to Support a Range of Regulatory Applications

9

AOPs - Assimilating Biological Data to be More Predictive

KE

KERMolecular Initiating Event

KERKey Event

KERKey Event

KERKey Event Adverse

Outcome

Ambient water

sample

extract

Predicted chemical-gene/chemical-pathway

interaction network

STITCHCTD

Analytesdetected

Toxcast/Tox21

•Predicted hazards•Taxonomic relevance

•Endpoints for targeted monitoring

extract

KERMolecular Initiating Event

“tripped” by a stressor (e.g., chemicals)

KERKey Event

Cellular Responses

KERKey Event

Early & Late Organ Responses

Adverse Outcome

Relevant RegulatoryEndpoint

Mode of Action

Analysis

Integrated Testing and Assessment

Network Descriptions

(Systems Biology)

b

Group or Chemical Specific Absorption, Distribution Metabolism, Excretion

Other AOPs sharing KEs/KERsIntegration and Weighting

with Existing Data to Target Testing

. . .

. . .

Example Applications

AOPs - Envisaged Applications

11

Addressing the Research-Regulatory Interface:

AOPs/Knowledge Base

OECD

AOP devt and assessment (2012)

Test Guidelines

Hazard Evaluation

AOPKB.org

AOPWIKI.org

Facilitating research collaboration: Addressing regulatory needs:

• Systematically organized and documented, including the extent of supporting evidence and quantitation for application

• Avoiding duplicative effort

• Integration and analysis

• Building networks

• Accessible and searchable

Tailoring Description and Identifying data gaps relevant to application

12

13

Formalizing AOP Descriptions and Assessment to Support Regulatory Application

• OECD Guidance on Developing and Assessing

AOPs and Associated Handbook(2013, 2014)

• Conventions and terminology

• Information content of an AOP description

• Weight of supporting evidence

(WOE)/confidence evaluation to support

application

AOP Development

and Description Case Studies

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

How certain

are we?

http://aopkb.org/common/AOP_Handbook.pdfAOPWIKI.org

Weight of Supporting Evidence/Quantitation of KERs

Qualitative WOE – Extent of Supporting Data

To provide basis for consideration of and consistent communication of

relative confidence in the supporting data for various parts of the pathway,

based on:

a limited no. of critical elements

clarification of the nature of supporting data through:

defining questions

criteria & examples

Quantitation of KERs• quantitation of the KERs, as a basis for developing predictive

response-response models

How much change in KEup is needed to evoke some unit of change in KEdown?

14

Biological Plausibility – KERs

Biology of the pathway

Essentiality – KEs within AOP

Necessity of Key Events

Experimental support from specialized studies to

block or modify key events, stop/recovery studies

Empirical Support – KERs

Pattern of Quantitative Associations among Key

Events tested through application of stressors

More

important

Less

important

Qualitative WOE for AOPs

15

AOP Page

Section 1 - Title

Section 4 – Abstract

Section 5a – Summary of the AOP

MIE

KEs

AO

Key Event Relationships

Section 6 – Scientific evidence supporting the linkages in the AOP

Applicability domain(s) of the AOP

Life-stage Taxonomic

Sex

Section 7 – Overall Assessment of the AOP

Modified Bradford Hill Considerations

KE Pages

KER Pages

MIE Page

AO Page

• Description • Measurement/

detection • Taxonomic

applicability

• Description • Measurement/

detection • Taxonomic

applicability • Evidence for

chemical initiation

Chemical initiator(s)

• Description • Measurement/

detection • Taxonomic

applicability • Regulatory relevance

Section 5b – MIE, KE, and AO descriptions Figure 2. Overview of the organization of content pages in the AOP-wiki relative to sections of the AOP template. Sections 1, 4, 5a, and 7 are found on the main page for an individual AOP. Information related to sections 5b and section 6 are entered into separate content pages that can be linked to multiple individual AOP pages.

• Title • Description • Biological plausibility • Empirical support • Inconsistencies and

uncertainties • Quantitative

understanding

Linkage table

AOP

WikiSlide courtesy of Steve Edwards,

EPA

Annex 1

Consideration Defining

Questions

High

(Strong)

Moderate Low

(Weak)

Biological

Plausibility of

KERs

Support for

Essentiality of

KEs

Empirical

Support for

KERs

16

Molecular Initiating Event

(MIE)

KE 1 to KE n Key EventsCellular to Organ

Response

Adverse Outcome(AO)

Alkylation of DNA

(Male pre-meiotic germ cells)

Offspring: Increased numbers of mutations in all tissues

MutationsInsufficient or incorrect DNA

repairInherited

mutations

KER1 KER2 KER3

iKER4

iKER5

MIE AOKE1 KE2

Alkylation of DNA leading to heritable mutations

KER KER

17

18 Activation of estrogen receptor potentially leading to adverse outcomes

Statistical model based on suite of MIE/early KE in vitro (HTP)assays

Used for screening/prioritization in USEPA EDSP program

Inhibited thyroid hormone signaling leading to developmental effects

Mechanistic network model of MIEs/shared KEs affecting TH levels

Applications to pesticide regulation (trigger values) and EDSP screening

Covalent chemical interactions leading to skin sensitization in humans

Steroid synthesis inhibition leading to reproductive effects in fish

Biologically Based Predictive Models

U.S. NRC Toxicity Testing in the 21st Century

Compounds

Metabolite(s)

AssessBiological

Perturbation

AffectedPathway

Measures ofdose in vitro

Dose ResponseAnalysis for Perturbationsof Toxicity

Pathways

Calibrating in vitro and human

Dosimetry

Human ExposureData

Population BasedStudies

ExposureGuideline

Mode of ActionChemical

Characterization

Dose Response Assessment

Hazard Characterization

Risk Characterization

Exposure Assessment

19

Physiologically Based Pharmacokinetic (PBPK) Models in

Risk Assessment

Estimating internal dose measures for extrapolation across

species, groups, routes, doses, time & age, taking into account

Physiology (weights of organs and tissues and blood flows)

Physical – chemical and biochemical constants of the

compounds

E.g., In vivo PK; in vitro or ex vivo partition coefficients, metabolism (Km & Vmax)

Enables better characterization or reduction of uncertainty in

risk assessment

Interspecies differences, human variability

Reducing reliance on animal testing

Use of in vitro data

20

PBPK Models – Evaluation

Level of Confidence (LOC)

LOC

in

PBPK models

t

[c]x x

xx

PERFORMANCE

Dose

metric

RELIABILITY

Exposure

Trustworthy ?

BIOLOGICAL

BASISLung

Blood

Fat

RP

T

PP

T

Inhaled

Chemical

Exhaled

Chemical

Alveolar

space

Dermal

dose

Skin

Oral

dose

Liv

er

G.I.

tract

Lung

Blood

Fat

RP

T

PP

T

Inhaled

Chemical

Exhaled

Chemical

Alveolar

space

Dermal

dose

Skin

Oral

dose

Liv

er

G.I.

tract

WHO Guidance on

Characterization

and Application of

PBPK Models in

Risk Assessment

(2010) & Case

Study Update

Reg. Tox. &

Pharmacol.

66:116–129 (2013)

H M L

H

M

L

UNCERTAINTY

SE

NS

ITIV

ITY

21

Biological Basis

Parameters & structure not consistent

with the biology or the current state of

knowledge on MOA of the chemical

Performance:Model Simulations of Data

Model unable to reproduce the shape

(i.e., bumps, valleys) of the TK time-

course data in test species and humans

Model reproduces consistently all TK

data, including the shape of time-course

profiles in multiple species

Reliability

Model simulations not evaluated against

data on dose metrics; no sensitivity or

uncertainty analyses

Simulations vs data on dose metrics;

sensitivity and uncertainty analyses

indicate high level of confidence in

dose metric predictions

Model parameters & structure

biologically plausible and consistent with

available TK & TD data for the chemical

None HighLEVEL OF CONFIDENCE

Characterizing the Level of Confidence in PBPK Models

22

“Uncertainty” and Data-informed Approaches

Conceptual model

Based on non-specific

empirical observations

Model parameters

Same for all chemicals and test

species

Chemical-specific; applicable to dose,

species, exposure route and lifestage

of relevance to risk assessment

Dose metric

Unknown Identified/hypothesized based on

information on mode of action and

plausibility

Biologically-based; consistent with

mode of action

Low HighABILITY TO REDUCE TK UNCERTAINTY

"Default (Traditional)" " PBPK"

23

Regulatory Uptake of PBPK Models

Recent project of the WHO Chemical Risk Assessment Network

Considered extent of regulatory uptake of PBPK models for development of chemical specific adjustment factors since release of CSAF Guidance (2005)

Literature review and data call

Over 100 CSAF identified; 50% were based on PBPK models (50/100)

Approximately 50% of the PBPK-based values were those adopted by regulatory Agencies (23/50)

The confidence considerations as per the IPCS PBPK Guidance were well addressed in the regulatory Agency adopted values

in the non-regulatory values, inconsistent reporting, lack of transparency and confidence often unaddressed

24

Integrating AEP and AOP frameworks

Exposure Science in the 21st Century: a vision and a strategy (2012)

AEP

AOP

25

Implications Need for exposure driven integrated approaches to testing

requires corollary coordination/effort on exposure

QVIVE

Exposure profiling

Importance of coordination and regulatory engagement

(internationally)

data and resource leveraging

Can build on previous experience in coordinating research

and regulatory (e.g., AOPs; PBPK)

Need for common metrics for assessment to facilitate

purpose specific regulatory application

26

• Brigitte Landesmann

• Anna Price

• Sharon Munn

• Clemens Wittwehr

• Maurice Whelan

•Nathalie Delrue

•Stephen Edwards

•Dan Villeneuve

•Mirjam Luijten

•Bette Meek

•Carole Yauk

•Kristie Sullivan

Acknowledgements27