Building Scientific Confidence in the Development and Evaluation of - - PowerPoint PPT Presentation

National Center for Computational Toxicology

Building Scientific Confidence in the Development and Evaluation of Read-Across

GenRA: Evaluating local validity for read-across prediction using chemical and biological information

Grace Patlewicz National Center for Computational Toxicology

Studies

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

• rcid.org/0000-0003-3863-9689

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Outline

• Definitions
• Workflow for category development and read-

across

• Identifying the sources of uncertainties associated

with read-across and practical strategies to address these

• Quantifying uncertainties and assessing

performance of read-across

• From research to implementation
• Summary

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Definitions: Read-across

Known information on the property of a substance (source chemical) is used to make a prediction of the same property for another substance (target chemical) that is considered “similar” i.e. Endpoint &

• ften study specific

Source chemical Target chemical Property

 

 

Reliable data Missing data

Predicted to be harmful Known to be harmful Acute fish toxicity?

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Chemical category and read-across: General Workflow

• 1. Decision context
• 2. Data gap analysis
• 3. Overarching hypothesis
• 4. Analogue identification
• 5. Analogue evaluation

– Data gap filling

• 6. Uncertainty assessment

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Chemical category and read-across: General Workflow

• 1. Decision context
• 2. Data gap analysis
• 3. Overarching hypothesis
• 4. Analogue identification
• 5. Analogue evaluation

– Data gap filling

• 6. Uncertainty assessment

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• 1. Decision context
• Prioritisation e.g. PMN
• Screening level hazard assessment
• Risk Assessment e.g PPRTV
• Different decision contexts will dictate

the level of uncertainty that can be tolerated

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• 6. Sources of Uncertainty
• Analogue or category approach? (#analogues)
• Data quality
• Overarching hypothesis/Similarity rationale –

how to identify similar analogues and justify their similarity for the endpoint of interest

• Address the dissimilarities and whether these

are significant from a toxicological standpoint

• Presence vs absence of toxicity
• Toxicokinetics – including Metabolism

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Identifying Uncertainties

• Several publications that guide the

construction and assessment of categories and use of read-across

– Guidance and examples (OECD, 2014; ECHA, 2008; ECETOC TR 116, 2012) – Frameworks for identifying analogues e.g. Wu et al, 2010, Patlewicz et al, 2013 – Frameworks for assessing read-across (ECHA – RAAF, Blackburn and Stuart, 2014, LERAT (Patlewicz et al, 2015)

SCIRADE

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Addressing uncertainties - 1

• Search and Selection of analogues
• Using metabolism information
• Presence or absence of toxicity
• Using in vitro data such as HTS data to

enhance read-across

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Search and selection of analogues

• Explored the use of different structure-based

approaches (Pubchem, Chemotyper and MoSS MCSS with Tanimoto index as a measure of similarity) to identify hindered phenol analogues and evaluate their validity for reading across Estrogenicity

• Make a read-across Estrogenicity prediction

for each target hindered phenol

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Read-across predictions

Filtering 1 (Log Pow & MV) Filtering 2 (No. of Data Sources)

See poster from P Pradeep

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Case study conclusions

• Initial selection of analogues based on different

descriptor sets (for this example) was invariant to the read-across prediction performance

• Evaluating analogue validity paying close attention

to the quality of the underlying analogue data and relevant physchem properties did significantly improve read-across predictive performance

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Metabolism - 1

• Do we always need to do a detailed assessment of

metabolism for read-across?

• Or can we identify sufficiently based on existing tools

and data?

• Skin sensitisation ~ 22-25% of skin sensitisers

require some level of activation

– Whether activation is by oxidation (pre) or due to metabolism (pro) is less well understood – Tools e.g.TIMES-SS, OECD Toolbox can be helpful in diagnosing whether a substance is direct acting or indirect acting (pre- or pro- hapten) – For a dataset of 127 substances, non-animal methods could correctly identify the majority of pre and pro- haptens

See associated poster

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• Read-across acceptance is context dependent –

based on subjective expert judgement assessment – potential lack of harmonised or reproducible decisions

• No clear understanding of what constitutes

success

• Do we know what the performance of a read-

across is really like on a more general level? Critical need is an objective measure of uncertainty in a read-across prediction

Addressing uncertainities - 2

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• GenRA (Generalised Read-Across) is a “local validity”

approach

• Predicting toxicity as a similarity-weighted activity
• f nearest neighbours based on chemistry and

bioactivity descriptors

• Initial focus relied on standard guideline studies
• Toxicity effects recorded as binary outcomes

Quantifying uncertainty & Assessing performance of read- across

Shah et al, (2016)

α ij k j tox j α ij k j tox i

s Σ x s Σ = y

bc} bio {chm, = α ,

Where

tox j

x

, in this case, is the in vivo toxicity of chemical j

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GenRA: Nominal cluster

Explore performance as a function of number of nearest neighbours or similarity index

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• Tested and compared
• 1. Chemical descriptors
• 2. Bioactivity descriptors
• 3. Hybrid of chemical and bioactivity descriptors
• No preselection of descriptors was performed
• Bioactivity descriptors were often found to be

more predictive of in vivo toxicity outcomes

• The approach enabled a performance baseline for

read-across predictions of specific study outcomes to be established

• But still context dependent on the endpoint and

the chemical neighbourhood under study

Quantifying uncertainty & Assessing performance of read-across

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Next steps in progress:

• Use of other chemical descriptor sets that

encode more expert knowledge of SARs

• Incorporating TK information

Quantifying uncertainty & Assessing performance of read- across

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0.8 1

s=0.81 k=4

1

1 1

Analysing local neighborhood of a chemical

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0.7 2

s=0.72 k=6

2

1 2 2 1

Analysing local neighborhood of a chemical

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0.63

s=0.63 k=10

3

1 2 3 3 2 1

Analysing local neighborhood of a chemical

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1 2 3 3 2 1

θ

Analysing local neighborhood of a chemical

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From Research to Implementation

• Public accessible tool building on the iCSS Chemistry

Dashboard under development

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From Research to Implementation

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Summary

• Still many challenges remain in read-across
• Quantifying the uncertainty of read-across

prediction is a critical issue

• Have illustrated a handful of the research

directions being taken

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Acknowledgements

• Imran Shah
• Tony Williams
• Richard Judson
• Rusty Thomas
• Prachi Pradeep
• Participants of the JRC Expert meeting on pre and

pro haptens held Nov 10-11, 2015