danish experience on regulatory use of qsar predictions

Danish EPA

Ministry of Environment

MST

ECHA, September 23-24, 2010

Eva Bay Wedebye (DTU) & Henrik Tyle (Danish EPA)

Danish experience on regulatory use of QSAR predictions

ECHA workshop 23-24 September 20102 DK EPA and DTU Food

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• The Danish QSAR database

- internet version

- updated in-house version

• Examples on regulatory use

- contributions to OECD SIAM

- the Danish advisory self-classification list

Considerations regarding uncertainty

• Plans on further REACH related QSAR developments

- short term (2011)

- longer term (2013)

Content


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Danish QSAR prediction database

Commercial, in-house or publicly available models for

• Physico-chemical properties

• Absorption, distribution, metabolism

• Degradation, bioconcentration, ecotoxicity (fish, daphnia, algae, tetrahymena)

• Human health endpoints, e.g. acute toxicity, skin irritation, sensitization, teratogenicity, endocrine disruption, mutagenicity, cell transformation, cancer

QMRF’s with validation results and training sets for our own models submitted to

the OECD (Q)SAR Application Toolbox - and we are in the process of submitting them to the EU JRC QSAR Model Database

QMRF: QSAR Model Reporting Format,

a standardised format for documenting (Q)SAR models, expected to be a communication tool between industry and the authorities under REACH


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Danish QSAR database- internet version (1.1.2004)

Predictions from 60 QSAR models for around 166.000 substances- with AD information for all DTU Food models

• Free access: http://qsar.food.dtu.dk,

• Manual: http://ecbqsar.jrc.it

• And included in the OECD (Q)SAR Application Toolbox

Search:

• CAS and 2D structure

• Predictions

• Combinations of predictions (and/or/not)

Models are made in or obtained from mainly

• MultiCASE

• Episuite

• Equations based on literature


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Danish QSAR database- updated in-house version

Updates includes:

• More structures (185,000)

• 3D descriptors

• More endpoints (e.g. antiandrogenicity, hERG, 5 CYPs (binding, inhibition), acute mammalian tox.)

• More model systems (Leadscope, SciQSAR, Pharma toxboxes)

5 pages with predictions per chemical


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How good are the (Q)SARs?

For our own models we find:

• It depends (of course) on the endpoint

– models are dependant on the underlying data

• Often the experimental variance is unknown

• Overall accuracy generally between 70-85%

• Domain 30-80% of discrete organic EINECS chemicals

Work is ongoing to make a battery approach by use of SciQSAR, Leadscope and MultiCASE to improve accuracy and/or domain


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• Purpose:

– How do QSAR predictions fit with internationally assessed test data?

– Limitations/questions/possibilities of using QSAR predictions as compared with test data

• Basis :

• 184 discrete organic chemicals discussed at SIAM11 to 18

• Danish EPA QSAR database

• comparison not a scientific validation

Example 1: Comparison of SIDS test data with (Q)SAR predictions


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Selected endpoints:

• Biodegradability

• Acute toxicity to aquatic organisms:

– algae

– Daphnia

– fish

• Mutagenicity

Example 1: Comparison of SIDS test data with (Q)SAR predictions


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• Some of the 184 SIDS test data were not in agreement with the experimental training set data of the models

• This introduces an additional type of uncertainty - only connected to test data - besides the limitations of modelling itself

• The biodegradation models performed as in validation studies in the past (i.e. very well)

• When combination of such models are used, when they agree, the sensitivity and specificity increase - but on the expense of the number of chemicals for which predictions can be made

• The QSARs for acute aquatic toxicity generally works well within an order of magnitude - QSAR performance: Fish> Daphnia > algae

- but test data also vary pretty much

• QSARs for Ames test mutagenicity also works very well - but in a proper validation a more balanced test set with more positive chemicals is needed

Overall conclusion of the comparison of QSAR predictions with SIDS test data


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Advisory self-classification list 2010

• Aim: making QSAR based recommendations to help manufacturers / importers to consider self-classification of substances not already having a harmonised classification

• Approach: fit the classification criteria for selected endpoints with appropriate combinations of QSAR model predictions

• DK EPA list with 34,292 chemicals with one or more advisory classifications for the following endpoints:

– Skin irritation (R38)

– Skin sensitization (R43)

– Acute (mammalian) oral toxicity (R22, R25 & R28)

– Mutagenicity (Mut3;R68)

– Carcinogenicity (Carc3;R40)

– Reproductive toxicity / possible harm to the unborn child (Repr3;R63)

– Danger to the aquatic environment (N;R50, N;R50/53, N;R51/53, N;R52/53)


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Advisory self-classification list 2010

• Aim: information from the QSAR database to help manufacturers / importers to assess hazard of untested substances not already classified

• Approach: fit the classification criteria for selected endpoints with appropriate combinations of QSAR model predictions

• DK EPA list with 34,292 chemicals with one or more advisory classifications for the following endpoints:

– Skin irritation (R38)

– Skin sensitization (R43)

– Acute (mammalian) oral toxicity (R22, R25 & R28)

– Mutagenicity (Mut3;R68)

– Carcinogenicity (Carc3;R40)

– Reproductive toxicity / possible harm to the unborn child (Repr3;R63)

– Danger to the aquatic environment (N;R50, N;R50/53, N;R51/53, N;R52/53)


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QSAR algorithm used for self-classification CARC. cat. 3 Assessment of carcinogenicity

Prediction of in vivo tests:

• Carcinogenicity, male rat• Carcinogenicity, female rat• Carcinogenicity, male mouse• Carcinogenicity, female mouse

Positive prediction according to the FDA ICSAS method*, or > 1 positive test

Genotoxicity screeningPredictions of in vitro tests

• Reverse mutation test (Ames)• Chromosomal aberration (CHO/CHL)• Mouse lymphoma

Advisory classification Carc3; R40

Positive prediction or positive test in at least one model

*: 2 or more pos. predictions for chemicals without significant deactivating fragments


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Number of substances with QSAR basedadvisory classifications

Number of substances with advisory classifications

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

Number 5.742 3.726 4.036 13.873 1.184 168 9.669 8.005 2.381 7.376 6.063 2.989

Mut3; R68

Carc3; R40

Rep3; R63

Xn;R22 T;R25 Tx;R28 R43 Xi;R38 N;R50N;R50/

53N;R51/

53R52/53

Download the documentationreport or searchonline in the list at http://www.mst.dk/English/Chemicals/Substances_and_materials/The_advisory_list_for_selfclassification/The-advisory-list-for-selfclassification.htm


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• test data are also uncertain ( & often of unknown size)

• Use high quality QSAR models (with QMRF)

• Use validated QSAR models & predictions within the AD

• Use combinations of QSAR predictions relating to the same regulatory endpoint

• When close to regulatory ”cut off” : need for higher precision & confidence

• Generally easier to identify chemicals with effects than ”innocent ones”

(”absence of evidence of effects is not evidence of absence of effects”)

• For genotoxicity (mut. and genotox. carc.) : NTA offer great possibilities to significantly supplement available test information

• this may also be used for minimizing reprotox testing (REACH: no reprotox testing on genotoxic chemicals with appropriate RM )

Some of our experiences on the use of QSAR in relation to uncertainty


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• Now: Use of current QSAR prediction database and its possibilities for making ”combined predictions” with and / or / not as shown with the earlier cancer example

• 2011:

– new QSAR prediction database for more REACH substances (discrete organics where structure information is available, EU FP7 project)

– updated model predictions for CMR- and PBT-related properties

• 2013 (provided that the last part of applied funding is granted):

– new public QSAR prediction database

– increase considerably the number of chemicals

– if / where possible improve training sets for models

– battery approach by using a number of different QSAR model tools for each endpoint (e.g. Leadscope, MultiCASE, SciQSAR)

Plans on further REACH related QSAR developments


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• Supplementary input to other relevant priority setting / screening / evaluation tools, such as:

- Other QSAR/non-testing approaches

OECD Application Toolbox,

ECHA tools

Tools provided by other MS

- Tools / data related to exposure/release

monitoring databases

Exposure/fate models

Product Register Information

- REACH Registration information handled by ECHA (CASPAR)

In relation to various REACH tasks

DK QSAR contributions under REACH


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Thank you!


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Extra slides (not presented)


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Example of a substance in the in-houseQSAR prediction database


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Page 2


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Page 3


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Page 4


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Biodegradability: outcome of comparison

• The performance of the individual models generally similar to that found in actual validation studies done in the past

• Combined predictions (when models agree) used:

– increased sensitivity and specificity - but

– also decrease in the overall number of chemicals for which predictions can be made

Example 1: Comparison of SIDS test data with (Q)SAR predictions – further information


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Acute aquatic toxicity: outcome of comparison

• Predicted L(E)C50 values were within one order of magnitude relative to SIDS test data for:

– 4 out of 5 of the fish LC50-values

– 3 out of 4 of the Daphnia EC50-values

– 2 out of 3 of the algae EC50-values



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Mutagenicity:

• The SIDS test data were strongly biased towards ”negatives” for all types of mutagenicity tests (e.g. Ames test: 142 negatives, 23 positives)

• Equivocal results were excluded

• Only for the Ames test the number of chemicals was sufficient to make a reasonable comparison

Outcome of comparison:

– specificity around 95 %

– Sensitivity around 80% (but somewhat uncertain due to the low number of positives not incl. in the training set of the QSAR model (5))

i.e. Ames QSAR model identifies correctly far most Ames test negatives and also (far) most positives


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Persistency: 3 models in an algorithm predicting

- not ready biodegradability (BIOWIN 2 or 6 < 0.5)

AND

- estimated long environmental half life (BIOWIN 3 <2.2 )

⇒ P

Bioaccumulative: 2 models regarding BCF in fish

- BCFWIN > 2000 ⇒ B

OR

- BCFWIN < 2000 but BCF(Connell) > 2000 AND positive expert judgements (e.g based on hindrance for uptake due to molecular dimension and the potential for metabolisation)

⇒ B

⇒ un-tested PB-candidates

Regulatory use: Example 3 PBT screening


MST4 6 7 0 6 E IN E C SS u b s ta n c e s

1 ,2 %

5 ,5 %8 4 %

7 %

1 0 %

E IN E C S P P + B c 2 -5 0 0 0 P + B c > 5 0 0 0

4 6 7 0 6 E IN E C SS u b s ta n c e s

0 ,7 %

1 ,3 %

1 4 %

2 %

8 4 %

E IN E C S P P + B s 2 -5 0 0 0 P + B s > 5 0 0 0

4 1 6 5 M P V S u b s ta n c e s

1 ,4 %

6 ,1 %8 3 %

8 %

1 0 %

M P V P P + B c 2 -5 0 0 0 P + B c > 5 0 0 0

4 1 6 5 M P V S u b s ta n c e s

0 ,7 %

1 ,3 %

1 6 %

2 %

8 2 %

M P V P P + B s 2 -5 0 0 0 P + B s > 5 0 0 0

1 3 5 1 H P V S u b s t a n c e s

0 ,7 %

1 ,4 %9 1 %2 %

7 %

H P V P P + B c 2 -5 0 0 0 P + B c > 5 0 0 0

1 3 5 1 H P V S u b s ta n c e s

0 ,1 %

0 ,4 %

7 %

1 %9 3 %

H P V P P + B s 2 -5 0 0 0 P + B s > 5 0 0 0

PBs: B according to Connells equation: B according to BCFWIN – model:


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• No of substances (> 10 tpa/ EU producer) evaluated: 5716

• Total no. of PBT/vPvB candidates as identified by QSARs (PB): 134

• No. registered in Nordic Product Registers: 66

• No with possible release due to widespread use in products: 16 - 32

(depending of the definition of significant environmental release potential)

not considered: PBT/vPvB candidates released from (industrial) processes &

multi-constituent chemicals (mixtures), nevertheless:

�

number of relevant PBT/vPVBs is relatively small according to this QSAR based screen

Further evaluation:

Use of Nordic Product Register information on environmental release potential

danish experience on regulatory use of qsar predictions

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