topical scientific workshop on new approach methodologies...

• Topical Scientific Workshop on New Approach Methodologies in Regulatory Science

19-20 April 2016

Dr Tomasz Sobański

European Chemicals Agency,Finland

Feedback

• Paper forms available on the side table: online survey link on the workshop website

4

Reports from break-outsessions

Rapporteurs:

Dr Norbert Fedtke, ECHA

Dr Elisabet Berggren, European Commission

Dr Karel de Raat, advisor to ECHA

Break-out session 1

Case study from SEURAT-1

Perfluorinated alkyl acids: direct acting toxicant category supported by ToxCast evidence

Chair:

Dr Watze de Wolf, Chairman of the Member State Committee, ECHA, Finland

Presenter:

Ms Sharon Stuard, Procter & Gamble,

United States of America

Rapporteur:

Dr Norbert Fedtke, ECHA, Finland

Topical Scientific Workshop on

New Approach Methodologies in Regulatory Science

19 – 20 April, 2016

4/20/2016 7

Question block 1: Case description

1. Are the chemical structures from the source substances and target substances sufficiently described to determine the structural (dis)similarities?

Yes, impurity profiles would be needed though (no further consideration for the current case)

2. What is the regulatory purpose for which the case is prepared?

Filling data gap for 90-day RDT in the context of REACH registration

3. What is the property for which a prediction is attempted?

90-day RDT with the critical effect liver toxicity with the purpose to identify a NOAEL and subsequently derive a DNEL

4. What is the hypothesis under which the prediction is attempted?

Parent substance is the acting chemical structure, no metabolism. Liver toxicity as key effect mediated via receptor interaction (PPAR). Variation in strength of effects

Case description (2)

• 5. What information obtained in animal studies is provided to support the prediction?

90-day study with source substance, “flanking information” on C 6, C11, C12 supporting the prediction, but not in the category.

• 6. What are the weak points of the justification, which are identified by the assessment according to the RAAF?

• Borders of the category; justification could be improved for the selection of category members.

• C8 selected as worst case for liver toxicity. Really worst case for the set of parameters in a 90-day study? Issues are: toxicokinetic information and possible other effects, e.g. thyroid function disruption, haematological, clear statement missing on effect doses for those possible effects in vivo

• Documentation of the weight of the mechanistic evidence.

•4/20/2016 •8

• 7. What supporting evidence is still missing or could be added to increase the confidence in the prediction?

• Data table for ToxCast, other NAM data; further toxicokinetic information

• Scientifically relevant question, regulatory-wise obligation met without NAM

• Systematic assessment of quality of all information provided

• Further detailed description of the methods used in Toxcast and of the results obtained

•4/20/2016 •9

4/20/2016 10

Question block 2: Contribution of NAM information

8. What types of NAM information have been used in the case to increase the confidence in the prediction?

Mechanistic understanding of the liver toxicity, ToxCast assays

9. Were the weak points in the prediction addressed or at least partly addressed by NAM, and has the condifence in the prediction increased

Lacking further toxicokinetic information remains a weak point, NAM data currently in the case study not supposed to address this.

4/20/2016 11

Question block 3: Detailed characteristics of the NAM information provided

10. Are the NAM methods used in the case study generally available?

no

11. What scientific limitations are evident for the NAM information in the case study?

Solubility issues of test substances, applicability domain due to phys-chemissues

12. What could be the barriers in using or generating the necessary NAM data?

Cost currently, expected to decrease in importance in future , time, uncertainty on acceptance (from the panel discussion), missing reporting standards, potential for predicting absence of effect

Break-out session 2

Case study from SEURAT-1

β-Unsaturated alcohols: indirect acting toxicant category supported by SEURAT-1

Chair: Dr Derek Knight, Senior Scientific Advisor, ECHA, Finland

Presenter: Dr Andrea Richarz, European Commission, Joint Research Centre, Italy

Rapporteur: Dr Elisabet Berggren, European Commission, Joint Research Centre, Italy



19 – 20 April, 2016

Major Uncertainties

• Complexity of structures → Similarity / category boundaries and members

• Details on study design / quality of in vivo data, choice of NOAEL

• Potency of effects, order of reactivity

→ proving the worst case for source compound

• Transformation mechanism (other than via ADH)/rates?

→ reactive potency vs kinetics (size of the category)

• Variation of metabolic pathways (aldehydes/ketones)

→ possible other effects via further metabolitespresent (kinetics of transformations)

• Toxic reactivity mechanism? Map on AOP?

Read-across issues discussed:

• Toxicokinetics is challenging also in terms of what data is needed to support a given hypothesis.

• How to identify the category, in reality depending on interest from the manufacturer.

4/20/2016 14INTERNAL

Information Added and Uncertainties Reduced by NAM

• Metabolism of βOAs to toxicant, reactivity of metabolites as opposed to parent compounds, link with structure (ex vivo perfused liver, in silico, in chemico)

• in chemico data: only quantitative data available to show clustering of reactivity potency (supported by in silico/in vitro)

• Mechanism of adverse effect evidence strengthened by in chemico/in silico/ SEURAT-1 in vitro data

• in particular: HSC activation markers in hepatic organoids confirm MoA hypothesis of metabolic-mediated fibrosis

How can NAM/non-animal data support read-across? (1)

• NAM is providing mechanistic information that together with other information, e.g. in vivo data, assist in establishing the hypothesis.

• Evaluate whether the NAM data is relevant to the specific read-across case.

• To split the case into a two-step procedure:

o First addressing metabolic rate, e.g. metabolic profile in human hepatocytes or HepRG.

o Secondly the reactivity, e.g. evidence could be molecular mechanism


How can NAM/non-animal data support read-across? (2)

• Be creative using different evidence, e.g. skin sensitization can be sued to help inform on chemical reactivity and biological availability and activation.

• In vivo omics data could assist to bridge in between the in vitro and in vivo situation.

• Evidence from non vertebrate species, knowing which biological pathways are relevant to human. This would be an opportunity to bridge in between cell assays and organism level.


What are barriers and limitation in using NAM in read-across?

• Difficult to make general guidance how to apply NAM evidence and how to understand what is fit for purpose.

• Cost-efficiency, how to stimulate manufacturers to use alternatives, too much expertise and resources needed.

• Almost impossible to get an overview and understanding of all NAM and how they can be applied and provide useful data.

• The adverse effect is a complex process including multiple cellular networks and dose response dependence difficult to interpret as well as relevant time points for sampling.


4/20/2016 19

Conclusions from break-out session 2

How to progress together:

• Template to provide data for read-across assessment.

• Training and education.

• Develop a REACH submission based on a case study developed with NAM for example under the EU-ToxRiskproject, but with invitation to other groups of expertise to collaborate.

Break-out session 3

Case study from BASF

Read-across with metabolomics for phenoxy herbicides

Chair: Dr Tomasz Sobański, ECHA, Finland

Presenter: Dr Bennard van Ravenzwaay, BASF, Germany

Rapporteur: Dr Karel de Raat, advisor to ECHA,

The Netherlands



19 – 20 April, 2016

4/20/2016 21


1. Are the chemical structures from the source substances and target substances sufficiently described to determine the structural (dis)similarities?

No real problems. It is clear that the substances are not the racemic mixtures of the past. However, it is not clear from the text which optical isomers are the subject of the read-across. This can easily be addressed. One participant desired a better underpinning of the choice of the substances based on chemometrics.

2. What is the regulatory purpose for which the case is prepared?

It is not clear from the document what the regulatory purpose is. The substances are herbicides with a lot of data. They have just been chosen to demonstrate the possibilities of the NAM. So in itself the case does not serve a regulatory purpose. However, the case was discussed as if it was aimed at meeting a regulatory data requirement, and not at screening or prioritization.

3. What is the property for which a prediction is attempted?

Ninety day repeated-dose oral toxicity with the rat. OECD 408


4. What is the hypothesis under which the prediction is attempted?

The scenario used for the RAAF assessment was either 2 for analogue approach or scenario 4 for category approach.

The actual mechanisms by which the three substances are expected to cause the same effects are established by metabolome pattern ranking. This pointed clearly to liver toxicity (PPAR alpha) and kidney toxicity. This toxicity was observed for both sources. About the same metabolome changes were found with the target substance. It was therefore concluded that this substance would cause the same effects via the same mechanisms.

The metabolome analysis combines the formulation of the hypothesis, its confirmation to an acceptable level of confidence and, in this case, a quantitative prediction.

•4/20/2016 •22


5. What information obtained in animal studies is provided to support the prediction?

28-day studies with the two sources and the target. However, the doses of two of them (one source and the target) were too low. ADME studies.

6. What are the weak points of the justification, which are identified by the assessment according to the RAAF?

Uncertainty about the occurrence of other effects of the target that are not reflected in its metabolome profile. The presenter emphasized during the BOS that most (nearly all?) of the effects observed in 90-day RDT studies give rise to metabolome changes and that these changes are more sensitive than the effects. However, this is not really elaborated upon in the case-study report.

•4/20/2016 •23


Metabolome analysis was based on 28-day studies and used for RA between 90-day studies. According to the presenter, all effects that can be seen in a 90–day study give already metabolome changes within about 14 days.

An other uncertainty was caused by the low dose-levels applied in two crucial underpinning 28-day studies.

Moreover, there was some discussion about the of effects observed. In particular effects on the testes and the adrenals, which did not fit the mechanistic explanation. However, the presenter attributed the former to reduced food intake and did not regard the latter (discoloration) as relevant/adverse.

•4/20/2016 •24


7. What supporting evidence is still missing or could be added to increase the confidence in the prediction?

Information on the general qualitative and quantitative sensitivity of the metabolome analysis to reflect the effects that can be observed in an oral 90-day RDT with rats.

This to build more confidence that the target will not show ‘other’effects that are not reflected in the metabolome. A validation point.

Detailed insight in the results of the/a 90-day study with 2,4 D. The absence of ‘other’effects for this substance would make the case stronger.

Combination with other NAM (bottom-up/top-down).

Etc.

•4/20/2016 •25

4/20/2016 26

Question block 2: Contribution of NAM information

8. What types of NAM information have been used in the case to increase the confidence in the prediction?

Metabolome profiling formed the core of the case. It was not an extra. Without the NAM there would not have been a viable hypothesis/mechanistic explanation.

9. Were the weak points in the prediction addressed?

Without the NAM, the case is ’empty’. It completely depends on the NAM.

4/20/2016 27


10. Are the NAM methods used in the case study generally available?

The NAM depends on advanced AC analysis, statistical ‘processing’ and the availability of a database. The first two can be implemented by a well-equipped CRL. The database is not available.

More transparency is necessary to get the NAM broadly accepted for read-across purposes. The assessing scientists from regulatory agencies find it important that they can judge the validity of the methods in (much) more detail.

The current document does not provide this level of detail. This holds in particular for the database on which the NAM is used.

In this respect it is also important to mention the need of standardization.


11. What scientific limitations are evident for the NAM information in the case study?

Assuming that a detailed assessment of the underlying information would not give rise to problems (see Question 10), the scientific limitations of the NAM are in THIS PARTICULAR CASE OF POSITIVE READ-ACROSS limited. The outspoken character of the results gives confidence.

Only the uncertainty of the occurrence of ‘other’ effects remains. Based on the statements of the presenter it seems that this uncertainty is not great.

•4/20/2016 •28


12. What could be the barriers in using or generating the necessary NAM data?

Lack of transparency and detailed info;

Dependence of profile changes on experimental conditions and strain (see below);

The toxicological scope;

Need for standardization;

Need for validation;

Costs?????

Required technical facilities?

Required expertise (also for assessment)?

•4/20/2016 •29

Additional discussion points

The relevance of high Tanimoto scores;

Chemical read-across versus biological read-across;

Dependence of metabolome profiles on experimental conditions, in particular animal strains,

Targeted and untargeted (fingerprinting) metabolome analysis; this case is a mixture;

Acceptance of the case and scoring; large majority in favour of acceptance.

•4/20/2016 •30

4/20/2016 31

Conclusions from break-out session 3

• The case convincingly demonstrates the great potential value of metabolome analyses to support read-across;

• The analysis shows that it is highly likely that the target will cause the same effects as were observed for the sources at about the same dose level;

• There is uncertainty about possible other effects of the target that are not reflected in the metabolome;

• Implementation requires more transparency on the methods and database, additional validation studies and standardization.

Discussion on break-outsessions’ reports

Moderator:

Mr Mike RasenbergHead of Unit, Computational Assessment and Dissemination, ECHA, Finland

We are on a break and will return at 10:45

Theme 2

Screening and priority setting

Chairs:

Dr Kerry NugentNational Industrial Chemicals Notification and Assessment Scheme, Australia

Dr Jack de BruijnDirector of Risk Management, ECHA, Finland

Dr Kerry Nugent

National Industrial ChemicalsNotification and Assessment Scheme, Australia

The NICNAS IMAP Program

April, 2016

Dr Kerry Nugent – Principal Scientist

Existing Chemicals Program

NICNAS Australia

NICNAS

• National Industrial Chemicals Notification and Assessment Scheme (NICNAS)

– Commenced 1990

– Covers both human health and environment

– Manages Australian Inventory of Chemical Substances

– Premarket assessment of new chemicals

– Existing Chemicals assessed on priority basis (PEC)

• Includes ingredients in formulated cosmetics

Existing Chemicals Program Review

• Conducted 2003-2006

• Primary recommendation was to accelerate prioritisation and assessment of 38000 grandparented chemicals

• Implementation planning and program design phase

• Inventory Multitiered Assessment and Prioritisation (IMAP) program commenced June 2012

• 3000 chemicals identified for assessment

Beyond Prioritisation

• Minimum read across/QSAR hazard data objective for all chemicals

– Not “no data, no hazard”

• Integration of exposure and hazard throughout

• Preparation of a report on available information for most chemicals

• Where possible, use this information to support risk management recommendations

Exposure data (or otherwise!)

• Canada had use/volume data from DSL compilation

– No similar data for AICS

• After consultation, program designed around lack of “real” data

– Uses inferred from international information

– Mostly default volume

• Based on combination of use and volume

– See Nazaroff et al 2012 (EHP Vol 120 p1678)

Three level problem formulation

1. Is there a problem?

– Can concerns be dismissed based on readily available information?

2. Identify magnitude of problem.

– Assemble known data

– Look for straightforward resolutions based on objective knowledge

3. If needed, fully analyse problem and potential solutions.

Tier I

Tier II

Tier III

The role of the levels

• Tier I – identify chemicals not needing resourcing (de-cluttering!)

– Lack of hazard

– Lack of use

– Hazards controlled given known uses

– End result is statement of Tier I criteria met

• Tier II – Provide more information

– Identification of chemicals for which risk management can control known risk

– Propose further assessment for remainder

Tier I Assessment

Tier II Assessment

Tier III Assessment

• Chemical safety information published for community, industry and government

• Risk management recommendations supported by objective data

• Risk management recommendations for chemicals of concern

• Detailed published assessments on specific issues relating to these chemicals

• Community and industry can identify safe chemicals

• High level assessment information published

Outcomes

Tier I Matrix

Exposure Bands

• Use broadly categorised as:

– Cosmetic (100% of chemical available for exposure)

– Domestic

– Commercial

– Site Limited (0.1% available for exposure)

• Multiplied by introduction volume (known for some chemicals including HVIC) to obtain score

• Under default volumes, cosmetics = band 4 to site limited = band 1

Hazard Bands

Hazard band Hazard indicators (aligned with GHS)

Hazard band 4 CarcinogenicMutagenicReproductive/developmental toxicityEndocrine disruptionNeurotoxicity

Hazard band 3 Fatal (acute)Significant Toxicity (repeat)Respiratory sensitisationFull thickness destruction of skin tissue (limited exposure)

Hazard band 2 Toxic (acute)Harmful (repeat)Skin sensitisationFull thickness destruction of skin tissue (prolonged exposure)Eye damage

Hazard band 1 Harmful (acute)Irritant

Product Life Cycle

• In IMAP Tier I, product life cycle may be relevant

– Chemical manufacture and formulation

– Use by consumers only in dilute formulations

• May consider the relevant hazard information for the chemical as used during each life cycle stage

Risk 21 comparison

• Clearly a different matrix to Risk 21

– Does not meet the criterion of same scale on both axes

– Based on even lower data availability

• However, covers a broader range of hazards, quantitative and non-quantitative

• Uses surrogates for exposure and hazard

• Can be considered a lower tier again

Tier II

• More “classical”

• Provides information for stakeholders

• Considers whether existing risk management is appropriate

• Proposes risk management where this can be done on objective grounds

– Classification

– “Scheduling”

Objective data

• Much of the data sourced in IMAP can be considered “objective”

– Results of well-reported guideline studies

– Conclusions from well-regarded international sources

• These data can be referred to risk management agencies for consideration under their processes

Quantification

• IMAP – almost total lack of quantitative exposure data

• Can determine chemicals to be priorities to obtain data from industry…

• But can justify risk management in many cases without quantification

– “Scenarios”

What do we mean by

“risk assessment”?

Risk Assessment

Chemical

Risk

Exposure

Assessment

Hazard

Characterisation

Dose

Response

Risk Assessment

Chemical

Risk

Scenario

Identification

Hazard

Characterisation

Dose

ResponseQuantificationXX

“Risk”

Scenarios

• Almost total lack of volume and concentration data

• Uses largely estimated from international sources

• Are there circumstances where the identified hazards lead to clear risks under identified scenarios?

– Sensitising hair dyes

• Risk shown to be real but magnitude uncertain

Hazard characterisation

• Objective data

• Expert judgement using:

– Read across/grouping

– (Q)SAR (mostly mechanistic support)

– Bioelution (metals)

– Other hazard data/mechanistic

• Can accommodate future data types

– Particularly hazard characterisation

Safe Work Australia1474

Environment 73

Impact for risk management

IMAP Tier II Recommendations

At February 2016 2614 recommendations were made for 2045 unique chemicals assessed at

Tier II.

1635349

161

270

199

Tier II RecommendationsTranches 1 to 16

Safe Work Australia

Scheduling

ACCC

Tier III

Environment

Classification

Public Health

Product Safety

Tier III

Environment

Further risk management?

• Good outcomes where:

– Risk management aligned with GHS criteria

– Non-quantitative hazard eg sensitisation, genotoxic effects

– Proposal for adoption of international standard (eg SCCS)

– When seen to be proportionate

• May need further assessment and information gathering for quantification

• Question – revisiting control banding/TTC using more up to date hazard characterisation?

www.nicnas.gov.au 1800 638 528

Questions?

Dr Richard Judson

Endocrine Disruptor ScreeningProgram, United States Environmental Protection Agency, USA

Office of Research and Development

Application of computational and high-throughput in vitro screening for prioritization

Richard JudsonU.S. EPA, National Center for Computational ToxicologyOffice of Research and Development

The views expressed in this presentation are those of the author and do not

necessarily reflect the views or policies of the U.S. EPA

ECHA Read-Across Workshop

19-20 April 2016, Helsinki

20 minutes

Office of Research and DevelopmentNational Center for Computational Toxicology

Major Points

• EDSP has a mismatch between resources needed for

Tier 1 and number of chemicals to be tested

–~10,000 chemicals in EDSP Universe

–~$1M per chemical for Tier 1, 50-100 year backlog

• Need new approach

–Prioritize chemicals

–Replace low-throughput assays with high-throughput variants

• Demonstrate new approach: Estrogen receptor

–Multiple high-throughput in vitro assays

–Demonstrate use to prioritize chemicals and replace selected

Tier 1 assays

•64


In Vitro Estrogen Receptor ModelCombines results from multiple in vitro assays

•65

• Use multiple assays per pathway

• Different technologies

• Different points in pathway

• No assay is perfect

• Assay Interference

• Noise

• Use model to integrate assays

• Evaluate model against reference chemicals

• Methodology being applied to other pathways

Judson et al: “Integrated Model of Chemical Perturbations of a Biological Pathway

Using 18 In Vitro High Throughput Screening Assays for the Estrogen Receptor” (submitted)


Immature Rat: BPA

In vivo guideline study uncertainty26% of chemicals tested multiple times in the

uterotrophic assay gave discrepant results

Kleinstreuer et al. EHP 2015

LE

L o

r M

TD

(m

g/k

g/d

ay)

Injection Oral

InactiveActive

Uterotrophic

species /

study 1

species /

study 2

Reproduce Does Not

Reproduce

Fraction

Reproduce

rat SUB rat CHR 18 2 0.90

rat CHR dog CHR 13 2 0.87

rat CHR rat SUB 18 4 0.82

rat SUB rat SUB 16 4 0.80

rat SUB dog CHR 11 4 0.73

mouse CHR rat CHR 11 4 0.73

mouse CHR rat SUB 13 7 0.65

dog CHR rat SUB 11 6 0.65

dog CHR rat CHR 13 8 0.62

rat CHR mouse CHR 11 11 0.50

mouse CHR dog CHR 6 6 0.50

rat SUB mouse CHR 13 14 0.48

dog CHR mouse CHR 6 8 0.43

mouse CHR mouse CHR 2 3 0.40

Phenotype X


In vitro assays also have false

positives and negatives

Much of this “noise” is reproducible

- “assay interference”

- Result of interaction of chemical

with complex biology in the assay

EDSP chemical universe is structurally

diverse

-Solvents

-Surfactants

-Intentionally cytotoxic compounds

-Metals

-Inorganics

-Pesticides

-Drugs

Assays cluster by technology,

suggesting technology-specific

non-ER bioactivity

Judson et al: ToxSci (2015)

Chem

icals

Assays


Assay-to-assay variation

•68

Agonist

Antagonist

All appropriate

assays are active

but efficacy and

potency vary

“Noise” or real

variation in biology

between cell types?

Judson et al: ToxSci (2015)

Assay Data Integrated Model


In Vitro Reference

Chemical Performance


Uterotrophic Database

98 Chemicals

442 GL uterotrophic bioassays

Literature Searches:

1800 Chemicals

Data Review:

700 Papers, 42 Descriptors, x2

6 Minimum

Criteria

High-Level

Filter

In Vivo ER Reference Chemicals

30 Active, 13 Inactive

Identifying Uterotrophic Reference

Chemicals from the Literature

Selection

Criteria

“Guideline-Like”

(GL)

Kleinstreuer et al: “A Curated Database of Rodent Uterotrophic Bioactivity” (submitted)


Model predicts in vivo uterotrophic assay as well

as uterotrophic predicts uterotrophic

Rank Order (ER Agonist AUC)

Kaempferol

Active

InactiveUterotrophic

D4

Restrict to chemicals with consistent

results from the literature

Browne et al. ES&T (2015)

ER

Ag

on

ist

AU

C

True Positive 29

True Negative 50

False Positive 1

False Negative 1

Accuracy 0.97

Sensitivity 0.97

Specificity 0.98


Explicitly Add Uncertainty to In Vitro Assay Data

•72

Watt et al. (in prep)

Quantify

uncertainty

Consensus

model


CERAPP: using QSAR for further prioritization

• Collaborative Estrogen Receptor Activity Prediction Project

• Goals:

–Use ToxCast ER score (or other data) to build many QSAR models

–Use consensus of models to prioritize chemicals for further testing

• Assumptions

–ToxCast chemicals cover enough of chemical space to be a good

“global” training set

–Consensus of many models will be better than any one individually

• Process

–Curate chemical structures

–Curate literature data set

–Build many models

–Build consensus model

–Evaluate models and consensus•73

Mansouri et al: “CERAPP: Collaborative Estrogen Receptor Activity Prediction Project” EHP (2016)


Total Database

Binders: 3961

Agonists: 2494

Antagonists: 2793

CERAPP Consensus evaluation

Key point: As greater consistency

is required from literature sources,

QSAR consensus model

performance improves


CERAPP Summary

• EDSP Universe (10K)

• Chemicals with known use (40K) (CPCat & ACToR)

• Canadian Domestic Substances List (DSL) (23K)

• EPA DSSTox – structures of EPA/FDA interest (15K)

• ToxCast and Tox21 (In vitro ER data) (8K)

~32K unique structures

5-10% predicted to be ER-active

Prioritize for further testing

75


ER Phenol Read-Across Model

Filtering 1 (Log Pkow & MV) Filtering 2 (No. of Literature Sources >= 3)

Pradeep et al. (in prep)

Accuracy increases as

1. Better data is used in the evaluation

2. Neighbors are closer (structure and physchem)


Moving Towards Regulatory Acceptance

From FIFRA SAP, December 2014

• Can the ER Model be used for prioritization?

– “… the ER AUC appears to be an appropriate tool for chemical prioritization for …

the EDSP universe compounds.”

• Can the ER model substitute for the Tier 1 ER in vitro and uterotrophic

assays?

– “… replacement of the Tier 1 in vitro ER endpoints …with the ER AUC model will

likely be a more effective and sensitive measure for the occurrence of estrogenic

activity …”

– “… the Panel did not recommend that the uterotrophic assay be substituted by

the AUC model at this time. The Panel suggested that the EPA considers: 1)

conducting limited uterotrophic and other Tier 1 in vivo assay testing, using the original

Tier 1 Guidelines (and/or through literature curation)”

• Based on follow-up presented here (FR notice, June 18 2015) …

– “EPA concludes that ER Model data are sufficient to satisfy the Tier 1 ER

binding, ERTA and uterotrophic assay requirements.”

•77


Data Transparency: EDSP21 Dashboard

• Goal: To make EDSP21 data easily available to all

stakeholders

–Assay-by-assays concentration-response plots

–Model scores – AUC agonist and antagonist

–ER QSAR calls

–Other relevant data

• https://actor.epa.gov/edsp21

http://actor.epa.gov/edsp21


Summary

• EDSP is in need of new approach to handle large

testing universe

–Reduce cost, speed throughput

• Estrogen Receptor Model is first example of this

–54 chemicals in low-throughput Tier 1 assays

–1800 chemicals tested and published in high-throughput

–1000 more in queue – 2016 planned release

• Next steps

–Androgen receptor (1800 chemicals tested, modeling and

validation in progress)

–Steroidogenesis (1000 chemicals with preliminary data)

–Thyroid – assay development and testing underway for several

targets (THR, TPO, deiodinases, ...)

•79


AcknowledgementsNCCT Staff Scientists

Rusty Thomas

Kevin Crofton

Keith Houck

Ann Richard

Richard Judson

Tom Knudsen

Matt Martin

Grace Patlewicz

Woody Setzer

John Wambaugh

Tony Williams

Steve Simmons

Chris Grulke

Jim Rabinowitz

NCCT

Nancy Baker

Jeff Edwards

Dayne Filer

Parth Kothiya

Doris Smith

Jamey Vail

Sean Watford

Indira Thillainadarajah

NIH/NCATS

Menghang Xia

Ruili Huang

Anton Simeonov

NTP

Warren Casey

Nicole Kleinstreuer

Mike Devito

Dan Zang

Kamel Mansouri

Nicole Kleinstreuer

Eric Watt

Prachi Pradeep

Patience Browne

NCCT Postdocs

Todor Antonijevic

Audrey Bone

Kristin Connors

Danica DeGroot

Jeremy Fitzpatrick

Jason Harris

Dustin Kapraun

Agnes Karmaus

Max Leung

Kamel Mansouri

LyLy Pham

Prachi Pradeep

Caroline Ring

Eric Watt

Questions?

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