RELIABILITY OF SAR PREDICTIONS

FOR TTC RISK ASSESSMENT OF NEW

INGREDIENTS

EMGS RISK ASSESSMENT SIG – TUESDAY 24TH SEPT 2013

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DIANA SUAREZ-RODRIGUEZ, PAUL FOWLER AND ANDREW SCOTT Safety & Environmental Assurance Centre

OVERVIEW

Thresholds of Toxicological Concern – their development and use

Role of in silico prediction models

Summary and Conclusions

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WHAT IS THE THRESHOLD OF TOXICOLOGICAL CONCERN (TTC)?

A “pragmatic” risk assessment tool based on the principle of establishing human exposure threshold values below which there is no appreciable risk to human health for a chemical where specific toxicity data may be limited

Originally derived for food contact materials (Frawley 1967)

Cramer, Ford and Hall (1978) developed a decision tree that classifies chemicals on the basis of their chemical properties - Cramer Rules

Three classes: Class I - Low concern chemicals

Class II - Substances less innocuous than Class I, but don’t

contain structural features suggestive of toxicity

Class III - High concern chemicals

Bar Chart

Binned log(NOEL) (1)

Class III

Class II

Class I

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EXPOSURE-DRIVEN RISK ASSESSMENT AND USE OF THE TTC

For food additives, a Threshold of Regulation was derived (1995) - 1.5mg/person/day provided there are no structural alerts for genotoxicity/carcinogenicity

Munro (1996) developed generic thresholds for non-cancer endpoints using a data set of 613 compounds and their related systemic exposure data

Cramer Class 5th Percentile of the NOEL Human exposure threshold

(mg/person/day)

I 1800

II 540

III 90

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CURRENT USE OF THE TTC

Threshold of regulation adopted by FDA on food contaminants (food contact materials)

The TTC approach can be applied to low concentrations in food of chemicals with insufficient toxicity data – Adopted by JECFA on flavouring substances

TTC being investigated for cosmetics ingredients (Blackburn et al 2007, Kroes et al 2007)

Drivers:

Exposure-based risk assessment

Chemicals with insufficient data

Unable to carry out in vivo testing

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TTC DECISION TREE - COSMETICS

Decision tree taken from Kroes et al, 2007, Food Chem. Toxicol., 45, 2533-2562

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EFSA 2012 & SCCS/SCHER/SCENIHR 2012

Removal of the Threshold of Regulation (1.5 mg/person/day)

Re evaluation of Cramer class 2

EFSA (2012). Available from: http://www.efsa.europa.eu/en/efsajournal/pub/2750.htm

SCCS/SCHER/SCENIHR (2012). Available from: http://ec.europa.eu/health/scientific_committees/consumer_safety/docs/sccs_o_092.pdf

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Expressed in terms of kg bw/day

ASSESSMENT OF IN SILICO TOOLS

TTC approach relies on in silico structural alerts to identify genotoxic or carcinogenic potential of an unknown material

In general, in silico tools such as Derek are known to perform well for mutagenicity

No guidance provided by EFSA or SCCS/SCHER/SCENIHR on what approach should be adopted to determine structural alerts

This study aimed to assess the utility of a suite of in silico prediction models as predictive tools for genotoxicity and carcinogenicity using two data sets containing Ames, in vivo MN and CARC data

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ASSESSMENT OF IN SILICO TOOLS

A data set was compiled from publicly available (ISS) and proprietary data sets (Leadscope Enterprise)

A total of 399 compounds with data across the three endpoints

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ISS DATA SET http://www.iss.it

TD50

SMILES

Ames

CARC

Overal l call

214 cpds

in vivo MN

ILSI DATA SET (FDA data extracted

from Leadscope)

TD50

Ames

CARC

Overall call

381 cpds

in vivo MN

SMILES

COMBINED ISS + ILSI

TD50

Ames

CARC

Overall call

399 cpds

in vivo MN

SMILES

GENOTOXICITY DATA SET: DETAILS

Endpoint Positives Negatives Equivocal Inconclusive

Carcinogenicity 265 134

Mutagenicity 160 238 1

in vivo MN 151 241 2 5

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Carcinogens Non-carcinogens

48% are +ve in the Ames 75% are –ve in the Ames

44% are +ve in the in vivo MN 72% are –ve in the in vivo MN

56% +ve in either the Ames or in vivo MN

IN SILICO PREDICTIVE TOOLS

TOXTREE version 2.5.4

DEREK NEXUS version 2.0.3

OECD (Q)SAR TOOLBOX version 3

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IN SILICO PERFORMANCE - TOXTREE

Carcinogenicity and mutagenicity rulebase

A decision tree for estimating carcinogenicity and mutagenicity, based on the rules published in the document: “The Benigni / Bossa rulebase for mutagenicity and carcinogenicity – a module of Toxtree”, by R. Benigni, C. Bossa, N. Jeliazkova, T. Netzeva, and A. Worth. European Commission Report EUR 23241 EN

TOXTREE EXPERIMENTAL CARCINOGENICITY

Positive Negative Total

Positive 169 95 264* Sensitivity = 64%

Negative 46 88 134 Specificity = 66%

*1 carcinogen was not processed in Toxtree – Pb2+ 12

IN SILICO PERFORMANCE - DEREK NEXUS

Knowledge-base expert system

Process against all genotoxicity endpoints: mutagenicity, chromosome damage, genotoxicity and carcinogenicity

DEREK NEXUS EXPERIMENTAL CARCINOGENICITY

Positive Negative Total

Positive 174 91 265 Sensitivity = 66%

Negative 64 70 134 Specificity = 52%

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IN SILICO PERFORMANCE - OECD (Q)SAR TOOLBOX

Freely available tool developed by the OECD – not predicting the carcinogenicity

DNA binding profiling

OECD TOOLBOX EXPERIMENTAL CARCINOGENICITY

Positive Negative Total

Positive 173 91 264* Sensitivity = 65%

Negative 76 58 134 Specificity = 57%

*1 carcinogen was not processed in OECD Toolbox – Pb2+ 14

CONSENSUS MODELLING - PREDICTIONS

DEREK Nexus, OECD toolbox and TOXTREE

Integration of the predictions from the three models

25 carcinogens are not predicted (2 of these are metals – excluded from TTC approach)

A total of 23 carcinogens would be missed, i.e. 9%

Number of Compounds

Ames in vivo MN Carcinogenicity

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7

3

1

Positive

Negative

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SUMMARY OF CARCINOGENIC CHEMICALS MISSED BY THE IN SILICO APPROACH

Number of Chemicals

Clastogenicity in vitro and in vivo

in silico predictivity compared with in vitro genotoxicity

2 Yes – clear positive in vitro and in vivo

Would be predicted by in vitro genetic tox tests but not QSAR

1 No, but Ames positive Would be predicted by in vitro genetic tox tests but not QSAR

3 Negative in vitro assays. Weak positive / questionable in vivo MN assays.

Negative in in vitro genetic tox tests and also QSAR.

An evaluation of 2 of the chemicals indicated that these were negative in genotoxicity assays, which suggests they were falsely categorised.

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CONCLUSIONS

If take worst case view 5 genotoxic carcinogens (positive in vivo MN data) were not predicted by in silico approaches Three of these were not detected by in vitro genetic tox methods

Additional 4 genotoxic and carcinogenic materials (positive Ames) with no alert in silico

9 in total

2% probability (based on this dataset) of supporting a genotoxic carcinogen (at exposures of 90 mg/person/day or above), based on Cramer classification

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1.5 mg/kg bw/day

CONCLUSIONS

The TTC approach is a pragmatic exposure-driven risk assessment tool, and is of particular use where compound specific data may be limited

The presence of structural alerts for genotoxicity/carcinogenicity restricts the internal exposure to 0.15mg/person/day

An integrated suite of 3 in silico prediction models (DEREK Nexus, OECD (Q)SAR TOOLBOX and TOXTREE) could be useful as a screen for potential genotoxicity/carcinogenicity, with an absence of alerts used to support chemicals at higher exposure levels using the Cramer decision tree

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0.0025 mg/kg bw/day

ACKNOWLEDGEMENTS

Nora Aptula

Phil Carthew

Catherine Clapp

Claire Davies

Paul Fowler

David Mason

Claire Moore

Diana Suarez Rodriguez

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