danish epa copenhagen 2002-06-27 - miljøstyrelseneng.mst.dk/media/mst/69087/qsar pbt final...

SHC/TS 2-3/029

Danish EPA Copenhagen 2002-06-27 File: QSAR PBT rev8.doc

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Identification of potential PBTs and vPvBs by use of

QSARs.

Henrik Tyle¤¤

, Henrik S. Larsen¤, Eva B. Wedebye

¤, Dick Sijm

#, Thomas Pedersen Krog

¤&

Jay Niemelä¤

¤¤ to whom correspondence should be addressed ([email protected]).

¤ Danish Environmental Protection Agency, Strandgade 29, DK-1401 Copenhagen K

# RIVM, Bilthoven, the Netherlands.

Summary. Very Persitent and Very Bioaccumulative substances (vPvBs) and Persistent, Bioaccumulative and Toxic substances

(PBTs) are generally considered to be substances of very high concern. They have therefore recently been proposed to

be included under a new authorisation scheme under REACH (the new draft EU policy on chemicals). QSARs should

be used for initial identification of potential vPvBs and PBTs without sufficient experimental data according to the

principles of the draft revised TGD for risk assessment of new and existing chemicals and biocides. The substances

considered were basically all discrete organic substances on EINECS (around half of all of the substances on EINECS,

i.e. around 50.000 chemicals). Most focus was put on substances with a total European production volume of more than

10 tons per year per manufacturer, because this production volume trigger was considered suitable for an initial

identification of the substances with a significant environmental release and exposure potential.

Five different QSAR algorithms for persistence have been considered. Justifications are provided for recommending a

combination of three different biodegradation QSAR models for selection of substances that are potentially very

persistent (vP) or persistent (P). This combination of QSAR models for initial identification of potentially persistent

substances without experimental data has been recommended in the revised TGD.

Three different QSAR models for prediction of bioaccumulation have then been employed on the potentially persistent

substances. Two of these models are recommended and justification for this proposal is provided. One model (BCF-

Syracuse) is used for the primary selection of potentially very bioaccumulating (vB) or bioaccumulating (B)

substances. The other model (BCF-Connell) is used for identifying additional substances of possible concern in

combination with expert evaluation for their potential for biotransformation

Relevant QSAR predictions of toxicity to mammals and aquatic species in relation to the T criteria of the draft revised

TGD are included. Furthermore a preliminary environmental release and exposure scoring of the selected substances is

also included. The environmental release scoring was based on information from the Product Register of Denmark,

Finland, Norway and Sweden following the same provisional scoring methodology as used in a recent similar EU

priority setting exercise on CMRs

A preliminary comparison of experimental data on high production volume chemicals and QSAR predictions indicates

that employment of QSARs may provide reliable information that may prove useful. Potential vPvBs and PBTs

without sufficient experimental data should undergo further testing according to the over all sequential testing scheme

agreed in the revised TGD: P, B and T.

The performed QSAR exercise on selection of potential vPvB/PBTs indicates that the number of potential vPvB and

PBT with a production volume in the EU countries greater than 10 tpa per manufacturer is relatively small (134

substances). A preliminary environmental release and exposure scoring based on the data from the Nordic Product

Registers on these substances indicates that only 66 out of the 134 potential vPvBs/ PBTs are actually registered as used

in chemical products on the Nordic marked. Furthermore the exercise suggests that the number of potential vPvBs/PBT

with significant environmental release from use in these chemicals products is even lower ( 18 to 32 substances

depending of the exact definition of what constitutes a significant environmental release potential). Potential vPvBs and

PBTs may possibly also be significantly released to the environment from industrial processes and chemicals not

registered in the Product Registries of the Nordic countries. Industry is therefore encouraged to provide supplementary

information about all 134 vPvB /PBT-candidates in relation to their potential for environmental release and exposure

during their whole life-cycle (i.e. during manufacturing, processing, use and disposal). Only relatively few vPvB / PBT

candidates are identified using QSARs and the total number of vPvB/PBT-candidates with a significant release potential

will clearly be even lower. Thus use of QSARs on untested chemicals and inclusion of vPvBs and PBTs under an

authorisation scheme in an attempt to avoid unacceptable risks seems to be feasible.

SHC/TS 2-3/029

Introduction.

The PBT and vPvB criteria1.

A new set of criteria for identification of PBTs and vPvBs is under discussion in the EU in relation

to the revision of the TGD for new and existing substances and biocides. Such criteria will also be

used in relation to the new EU policy on chemicals2 according to Competent Authorities for new

and existing substances (cf. “An interim Strategy for management of PBT and VPVB substances”,

ENV/D/432048/01, NOTIF/36/2001, Brussels 08/06/2001 and the EU Commissions White Paper

Strategy for a future Chemicals Policy (COM(2001) 88 final, Brussels 27.2.2001).

The need for employment of an efficient identification scheme with criteria relating to persistence

(P), bioaccumulation (B) and toxicity (T) is caused by the fact, that for PBT-substances

employment of the traditional quantitative PEC/PNEC-concept is considered too uncertain. The

long persistency, the bioaccumulative behaviour and indications of severe chronic toxicity make

both the traditional exposure and the effects assessment of such substances extremely uncertain.

This may especially be the case in relation to potential risks for the marine environment.

This is explained in the latest version of the new chapter on the marine environment in the draft

revised TGD in this way:

"While this [PEC/PNEC-]approach must clearly also apply to the marine environment, it must be

recognised that the concepts and methodologies have largely been developed with the local and

regional spatial scales in mind, rather than the potential for global impact. There are, therefore,

additional concerns for the risk assessment of the marine environment which may not be adequately

addressed by the methodologies used by the existing standard EU risk assessment. These are:

a. the concern that hazardous substances may accumulate in parts of the marine environment

and that:

(i) the effects of such accumulation are unpredictable in the long-term;

(ii) that such accumulation would be practically difficult to reverse;

b. the concern that remote areas of the oceans should remain untouched by hazardous

substances resulting from human activity, and that the intrinsic value of pristine environments

should be protected.

Of these additional concerns (a) above may be seen as the principle concern. This is characterised by a

spatial and temporal scale not covered by existing approaches to risk assessment. It is a concern that

chemical substances which can be shown both to persist for long periods and bioaccumulate in biota,

can give rise to toxic effects after a greater time and at a greater distance than chemicals without these

properties. While this is also true for the freshwater environment, the additional concern in the marine

environment is that once the chemical has entered the open seas, any cessation of emission will not

necessarily result in a reduction in chemical concentration and hence any effects become difficult to

reverse. Equally, because of the long-term exposures and long life-cycle of many important marine

species, effects may be difficult to detect at an early stage.

To meet these concerns, which principally relate to substances that are considered as Persistent,

Bioaccumulative and Toxic (referred to as PBTs), or have other properties which give rise to a

similar level of concern, an assessment approach will be detailed that will give special consideration

to this new protection goal. The objective for these substances, therefore, is to reduce their levels in

the marine environment to the lowest level practically possible. In this context, the assessment of

risk fulfils the purpose of determining how the above objective can be achieved; specifically, what

1 The newest May 2002 version of the revised TGD has been checked and taken into account in relation to the content

of this paper. 2 Cf. http://www.europa.eu.int/comm/environment/chemicals/index.htm

SHC/TS 2-3/029 are the sources, routes and pathways to the marine environment and which programmes and

measures are the most effective in order to reduce the levels."

An advantage of implementing a special strategy for identification of persistent, bioaccumulative

and toxic substances and other types of substances with intrinsic very harmful properties causing a

similar concern is, that this would assist in focussing on a limited number of relevant substances to

be targeted. At the first stage a number of suspected PBTs may be identified based on easily

obtainable and available information, including information from screening tests and QSAR

predictions. These PBT candidates may then within a short time-frame be considered for

confirmatory testing / information gathering to be able to determine whether they really are PBTs

that should be phased out. If further testing is not considered worthwhile for some of the PBT

candidates it may simply for these substances be considered regulating them in a way, so that

future environmental exposure will be significantly reduced, including considerations of limitations

or prohibition of marketing and use.

Thus the implementation of such a strategy seems appropriate not only because of the EU countries

obligation to target POPs in their legislation on new and existing substances according to the UNEP

POP Convention. It is also a response to the recent criticism of the current EU chemical policy,

which relies heavily on the resource intensive quantitative PEC/PNEC risk assessment paradigm.

Finally it is a response to the needs of OSPAR and other marine conventions to reduce losses and

releases of PBTs as soon as possible and to eliminate releases within one generation.

The currently proposed PBT / vPvB-criteria are (cf. the draft revised TGD, chapter on the marine

environment):

Criteria for identification of PBT and vPvB substances.

PBT-criteria vPvB-criteria

P*

Half-life > 60 d in marine water

or in freshwater* > 40 d or in

marine sediment > 180 d or in

freshwater sediment > 120 d

Half-life > 60 d marine or freshwater or

>180 d in marine or fresh water sediment

B BCF > 2000 BCF > 5000

T Chronic NOEC < 0.01 mg/l or

CMR**

or endocrine disrupting

effects

Not applicable

* including data for estuaries** chronic toxicity defined as toxic to mammals and classifiable with

R48 or chronicly toxic to birds (i.e. avian chronic NOEC < 30 mg kg food); R: reproductive toxic to

birds or mammals, the latter defined as cat. 1, 2, & 3 reproductive toxic substances, i.e. classifiable

with R60, 61, 62, 63 or 64; C and M: carcinogenic or mutagenic, defined as cat. 1 &2, and cat. 3,

the latter being based on a case by case evaluation of the evidence, i.e. classifiable with R45 & 46

and as regards R40 case by case in relation to the evidence.

In principle substances are selected, when they fulfil the criteria for all three inherent properties P,

B and T. However, a certain flexibility is required in their application for instance in cases where

one criterion is marginally not fulfilled but the others are exceeded considerably. This includes for

example significantly bioaccumulating substances which do not fulfil the persistence criteria but is

measured in marine biota distant from anthropogenic sources. Another example may be substances

SHC/TS 2-3/029 that have a significantly longer degradation half-life but are somewhat less bioaccumulating than

the BCF cut offs of the criteria.

The PBT-assessment has links to similar concepts discussed in other fora such as the ECE LRTAP

and UNEP POPs Conventions3 and Marine Conventions like the OSPAR Convention. The overall

concerns are the same even though the PBT criteria may differ slightly, because of the different

scale and scope these international agreements refer to. Reference is made to the discussion of these

concerns and proposed selection of POPs and PBTs in e.g. Henrik Tyle & Jay Niemelä: "Use of

QSARs for selection of POPs. Working paper to the CEG II-participants in Vienna", Danish EPA

(1999) and "Briefing Document on the Work of DYNEMEC and the DYNAMEC Mechanism for

Selection and Prioritisation of Hazardous Substances", The OSPAR Secretariat, (OSPAR 00/5/2-E,

June 2000) and WWF and Seas at Risk: "Hazardous Substances identified by OSPAR for cession of

discharges, emissions and losses before 2020" (OSPAR 00/5/16-E(L), June 2000).

a) In relation to the proposed EU PBT- & vPvB-criteria it is currently being considered:How the

criteria for long-range environmental transport in the of the LRTAP and UNEP POPs

Stockholm Convention could be interpreted and used in certain cases as supporting evidence to

include substances that are borderline to the PBT criteria.

b) How to implement an overall testing strategy in relation to available data, including QSAR data

and screening types of experimental data. The generally agreed testing strategy is to confirm

indicative (screening) data indicating that the substance may be a PBT with data from more

advanced long-term or simulation tests that are more directly linked to the PBT-criteria above.

Furthermore it is generally agreed that the testing sequence for candidate PBTs and vPvBs

should be P-> B -> T. There are two main reasons for this:

persistency is the basic property and of relevance both as an indicator for potential long

range environmental transport / occurrence over larger distances in the environment, and for

continued occurrence over longer time periods.

Further testing for confirming persistence, i.e. long degradation half-lives, does not – unlike

most further testing for bioaccumulation and toxicity - require testing in animals. Thus

confirmatory testing of persistency may in any way not come into conflict with animal

welfare considerations concerning laboratory animals.

Lower costs of biodegradation tests than testing for bioaccumulation and for chronic toxicity

c) How the T criteria should be interpreted, e.g. how to perform the case by case evaluation of the

evidence of substances classified for C or M in category 3, and how to evaluate substances

which are borderline in relation to the T-criteria

d) How in detail to use screening data on P, B and T, how to apply QSARs and expert judgement

in the selection of PBT- and vPvB-candidates

Other issues likely to be discussed in future include for example how to interpret the subset of the T

criterion referring to endocrine disturbing chemicals (EDCs). Currently only few testing methods

are available and even less confirmatory experimental data exist, even on chemicals that are

suspected for causing endocrine disturbance. One starting point could possibly be to use the

provisional EU list of EDCs, which was also used in establishment of the OSPAR priority list of

substances for action.

In the coming years further discussion is also foreseen in relation to other issues such as how to

interpret testing results of advanced long term tests / simulation tests for P, B and T, including how

to interpret variations observed in different studies with the same endpoint in relation to the PBT-

criteria and their cut off values ? More specifically for example relating to e.g. simulation studies of

degradation, how in relation to the half-life criterion to interpret a lag phase ? Another issue may be,

how to interpret “tailing” of degradation curves ? (cf. e.g. ISO/DIS 14592-1, OECD TG 307, 308

3 Cf. http://www.unece.org/env/popsxg & http://www.chem.unep.ch.pops

http://www.unece.org/env/popsxg

SHC/TS 2-3/029 and draft OECD TG “Aerobic Mineralisation in Surface Water – Simulation Biodegradation Test

(2002)). How should a range of different half-lives in different studies with micro-organisms from

surface waters located at different places be interpreted? Similarly as regards bioaccumulation:

How should different BCFs in the same type of organisms be interpreted ? How should different

BCFs between different types of aquatic organisms be interpreted ? Could better estimations be

made of the potential for bioaccumulation also via the food chain ? How should indications from

available field measurements of concentrations in biota relative to measurements of water

concentrations be weighed into the evaluation ? It is also foreseeable that a lot of interpretation and

discussion will take place in relation to the T-criteria and available aquatic and mammalian toxicity

data. This will presumably be the case because much of the most relevant types of data (chronic and

lift time exposure data) may not have been obtained by using standard test methods. Identification

of certain substances exerting special types of chronic effects like mutagenicity and carcinogenicity

may quite reliably be predicted by use of QSAR models. Another related discussion may therefore

also take place in future on the applicability of such QSAR predictions in relation to the PBT issue.

Lack of experimental data and the need for using QSARs. Even though experimental data on P, B and T are generally more likely to be available on

substances produced in high volumes4 (> 1000tpa/EU-manufacturer), even for high production

volume chemicals experimental data are frequently not available (cf. the table on page 6).

It is obvious that experimental data in general will be even more limited on substances produced in

smaller quantities. For example substances produced in volumes between 10 and 1000 tpa/EU-

manufacturer (MPVCs5) have been reported to the ECB, but only scarce information about their

intrinsic hazardous properties have generally been provided in the form of their proposed hazard

classification.

It has been recognised that lack of experimental data does not necessarily mean lack of concern, i.e.

in this case that a substance does not deserve attention in relation to the PBT-issue. Based on this it

has been agreed that the identification of vPvB- and PBT-candidates of HPVC, and in principle

also MPVC, should be based on QSARs, if no experimental data are available. The Danish EPA has

volunteered to assist the ECB in this regard (NOTIF/36/2001). At the first stage ECB will identify

potential PBTs & vPvBs exclusively based on experimental data of the IUCLID database on the

HPVC As an independent exercise the DK EPA is identifying PBT- and vPvB-candidates based on

QSARs. At the next phase it will be investigated how these two approaches can supplement each

other, e.g. how QSARs can be used to supplement missing experimental data on substances, where

the experimental data set is incomplete in relation to the PBT-criteria.

In both cases the more detailed criteria and data interpretation rules in the Chapter on the Marine

environment of the draft TGD have been used for this work

4 High Production Volume Chemicals (HPVCs)

5 Medium Production Volume Chemicals

SHC/TS 2-3/029

Availability of experimental data in IUCLID-data on high tonnage substances (HPVCs):

3

Environmental Fate and Pathways

3.1.1 Photodegradation 48 %

3.1.2 Stability in Water 41 %

3.1.3 Stability in Soil 23 %

3.2 Monitoring Data (Environment) 23 %

3.3.1 Transport between Environ. Compart. 25 %

3.3.2 Distribution 31 %

3.4 Mode of Degradation in Actual Use 26 %

3.5 Biodegradation 61 %

3.6 BOD5, COD or BOD5/COD Ratio 26 %

3.7 Bioaccumulation 30 %

3.8 Additional Remarks 25 %

4 Ecotoxicity

4.1 Acute/Prolonged Toxicity to Fish 68 %

4.2 Acute Tox. to Aquatic Invertebrates 55 %

4.3 Toxicity to Aquatic Plants e.g. Algae 46 %

4.4 Tox. to Microorganisms e.g. Bacteria 57 %

4.5.1 Chronic Toxicity to Fish 14 %

4.5.2 Chronic Tox. to Aquatic Invertebrates 18 %

4.6.1 Toxicity to Soil Dwelling Organisms 30 %

4.6.2 Toxicity to Terrestrial Plants 32 %

4.6.3 Tox. to Other Non-mamm. Terr. Species 33 %

4.7 Biological Effects Monitoring 26.%

4.8 Biotransformation and Kinetics 26 %

4.9 Additional Remarks 36 %

5 Toxicity

5.1.1 Acute Oral Toxicity 77 %

5.1.2 Acute Inhalation Toxicity 51 %

5.1.3 Acute Dermal Toxicity 53 %

5.1.4 Acute Toxicity, Other Routes 35 %

5.2.1 Skin Irritation 73 %

5.2.2 Eye Irritation 73 %

5.3 Sensitisation 48 %

5.4 Repeated Dose Toxicity 58 %

5.5 Genetic Toxicity in Vitro 67 %

5.6 Genetic Toxicity in Vivo 38 %

5.7 Carcinogenicity 44 %

5.8 Toxicity to Reproduction 26 %

5.9 Developmental Toxicity/Teratogenicity 32 %

5.10 Other Relevant Information 52 %

5.11 Experience with Human Exposure 56 %

Ref: "Public Availability of Data on EU High Production Volume Chemicals", (2000) Remi

Allanou, Bjorn G. Hansen and Yvonne van der Bilt, European Commission, Joint Research Centre,

Institute for Health and Consumer Protection, European Chemicals Bureau, TP 280, Ispra (VA),

21020, Italy

The data available indicated above may or may not be of high quality

SHC/TS 2-3/029

Employment of QSARs for identification of vPvB- & PBT-

candidates.

Persistency: According to the draft TGD a combination of BIOWIN 1 and BIOWIN 3

6should be considered (cf.

also TGD/EN/MA/BG/DK4, incl. Annex 1 and Howard et al (1992)”Predictive Model for Aerobic

Biodegradability Developed from a file of evaluated Biodegradation Data”, Environ.Toxicol.

Chem., 11, 595-603)

However in relation to use of QSAR biodegradation models for prediction of not ready

biodegradability, we prefer to use the BIOWIN2 (i.e. the non-linear) model instead of the

BIOWIN1 (linear) model. Thus we propose that the suggested QSAR based selection of potentially

persistent substances employs a combination of a prediction of not ready biodegradability by use of

BIOWIN 2 and a prediction that the half-life should be in the range of months, i.e. BIOWIN 3 with

a score of < 2.2. (calibrated as explained in the Draft TGD, marine Chapter and in

TGD/EN/MA/BG/DK4 by use of a model score of 2.2 from the prediction concerning the well

investigated slowly degradable substance1,2,4-trichlorobenzene).

The reason for preferring BIOWIN 2 over BIOWIN 1 is that the performance of BIOWIN 2 is

somewhat better, because this model does not exclude the identification of so relatively many not

readily degradable substances as BIOWIN 1. This was shown in previous external validations (cf.

TemaNord 1995:581, cf. p. 159-62). This was also shown in a recent external validation on a larger

set of MITI I test data on 453 (330 not readily and 123 readily degradable) substances made by Jay

Niemelä, DK EPA. (This internal working paper, "Comparison of Aerobic Biodegradation models

by External validation", Jrm/12/05.03.01, is available on request). The latter also showed that at the

same time BIOWIN 2 gave a lower percentage false predictions (11%) of not ready

biodegradability that BIOWIN1 (13%). The former external validation showed that BIOWIN 2 only

predicts marginally more readily biodegradable substances erroneously to be not readily

biodegradable. Thus in conclusion BIOWIN 1 misses identification of more than half of all not

readily degradable substances (57-59%), whereas BIOWIN 2 misses a bit less than half (47-50%).

Both models have virtually the same low level of false predictions of not ready biodegradability.

Based on this we propose to use BIOWIN 2 instead of BIOWIN 1 i.e. the combination BIOWIN 2 <

0.5 AND BIOWIN 3< 2.2, (P-selection 1)

We furthermore suggest to include consideration of employment of other available QSAR models

that could minimise the impact of the quite high number of falsely predicted readily degrading

substances by employment of the BIOWIN2 model (cf. above). Care should at the same time be

taken to avoid that such a model does not significantly increase the number of substances falsely

predicted to be not readily biodegradable.

We have considered whether a BIODEG model developed by Jay Niemelä, DK-EPA could be used

for this purpose in a combination with the BIOWIN 2-model. The BIODEG model has the

advantage, that it defines whether or not each prediction it makes, is reliable according to the

domain of the model. This model however predicts virtually all not readily biodegradable

substances as being not readily degradable, but contrary to BIOWIN 2 and MITIDEG 2 it also

6 BIOWIN1: Biodegradation Probability Program, linear model

BIOWIN2: Biodegradation Probability Program, non-linear model

BIOWIN3: Biodegradation Probability Program, ultimate biodegradability timeframe

BIOWIN4: Biodegradation Probability Program, primary biodegradability timeframe

SHC/TS 2-3/029 predicts relatively many readily degradable substances as not readily degradable. Thus it was

concluded that this model would probably not be of help in this exercise.

Other models that seems to be more suitable for this purpose may however be the MITIDEG -

models.7 These models were created by Helene Loonen and further developed by Phil Howard and

co-workers and are like the BIOWIN-models freely available for use 8. The MITIDEG-models

predict both ready and not ready biodegradability with a high probability (> 80 %, c.f. Tunkel J. et

al. (1999): “Predicting Ready Biodegradability in the Japanese Ministry of International Trade and

Industry Test”, Environ. Toxicol. Chem, 19,(10), 2478-85). The non-linear model (MITIDEG 2)

seems to make slightly more false predictions of not readily biodegradable substances compared to

the linear model. On the other hand it seems also to have a slightly higher percentage of correctly

predicted not readily biodegradable substances (Cf. also a working paper on validation of

biodegradation models by Jay Niemelä, Danish EPA9). Thus employment of the non-linear

MITIDEG 2-model is preferred for use in combination with BIOWIN 2 in the selection process.

Such an application would not be likely to generate a significant number of falsely predicted

substances fulfilling the P-criterion, first of all because both BIOWIN 2 and MITIDEG 2 each has a

low number of falsely predicted not ready biodegradable substances. The other reason is that they

are employed in combination with the BIOWIN 3 predictions of long half-lives. Even if the

combined use of BIODEG 2 and MITIDEG 2 in a few cases falsely predict a substance to be not

ready biodegradable, it is not likely that the BIOWIN 3-prediction for this substance would also be

that the half-life is long enough, so that the substance would be selected.

Thus, as the second option to identify potentially persistent substances, we propose to use

predictions of not ready biodegradability in either BIOWIN 2 or MITIDEG 2 combined with the

agreed employment of BIOWIN 3, i.e. (BIOWIN 2 < 0.5 or MITIDEG 2 < 0.5) AND BIOWIN 3 <

2.2 (P-selection 2)10

.

A third option for identification of potentially persistent substances has been suggested, which is to

employ the MITIDEG 2 model predictions alone alternatively to the combination of BIOWIN 2

model and MITIDEG 2 predictions. Thus this P-selection 3 algorithm would be to use MITIDEG 2

< 0.5 and BIOWIN 3 < 2.2.

As a fourth option for selecting a special class of even more persistent substances it has been

suggested, when the potential for bioaccumulation should only be based on the lipophilicity (cf. the

section on bioaccumulation potential below). A suitable P-selection 4 could possibly be similar to

the one used previously for initial selection of candidate POPs of global concern, i.e. BIOWIN 2 <

0.0036 and BIOWIN 3< 2.2. (cf. "Use of QSARs for selection of POPs", where it is explained that

the cut off value of both QSAR models were calibrated for exactly including the well known

substance, 1,2,4-trichlorobenzene for consideration, but where the linear BIOWIN 1 was used

instead of the non-linear model BIOWIN 2) .

Finally as a fifth option is has recently been proposed to use the P-selection 5 algorithm: BIOWIN

2 < 0.5 or MITIDEG 2 < 0.5, i.e. to omit using BIOWIN 3. The reasons for this suggestion were

based on that this model and the trigger score of 2.2 has not been externally validated and on a

concern that the P-selection algorithm 2 would exclude too many not readily biodegradable

substances.

The number of substances selected by using these alternative selection procedures are shown in the

tables below.

7 MITIDEG1:MITI biodegradation, linear model

MITIDEG2:MITI biodegradation, non- linear model 8 All the mentioned models are included in the EPISUITE Program package

9 available on request

10 recommended by the revised TGD for prediction of potentially persistent substances according to the vPvB/PBT-

criteria

SHC/TS 2-3/029 The number of substances included in the selection process was restricted to those discrete

organic substances with a SMILES notation, where also a 3-D structure using SMILES could be

created by CHEM-X and where the substance thus could be entered into the Danish EPA QSAR

database. This procedure lowered the number of substances somewhat, but in the same time assured

that only substances with high quality SMILES notation available were included (cf. further

explanation in the paper “Use of QSARs for selection of POPs”). By the described procedure

substances with either wrong SMILES or a non-specific 3-D structure are excluded. As an example

of the latter, this method does at present not include a substance like hexabromocyclododecane

(CAS no. 25637-94-4), which is presently undergoing EU risk assessment, because this substance

does not have one definite chemical structure)

Number of substances: EINECS: MPVC: HPVC:

With SMILES & 3-D: 46706 4165 1351

With SMILES

(out of a total of):

52269

(100116)

4948

(7840)

1421

(2617)

The number of

Substances fulfilling:

EINECS: MPVC: HPVC:

P-selection1:

BIOWIN2 < 0.5 AND BIOWIN3< 2.2

6906

623

91

P-selection2:

(BIOWIN2 < 0.5 or MITIDEG 2 <

0.5) AND BIOWIN3 < 2.2

7666

730

97

P-selection 3:

MITIDEG 2 < 0.5 AND

BIOWIN 3 < 2.2

7647

728

97

P-selection 4:

BIOWIN 2 < 0.0036 AND

BIOWIN 3 < 2.2.

(only in combination with log P >5for

high bio-accumulation potential)

4928

486

74

P-selection 5:

BIOWIN 2 < 0.5 or

MITIDEG 2 < 0.5:

15191

2707

773

From this it appears that the employment of the P-selection 4 algorithm is identifying around 30 %

fewer substances than the other P-selection algorithms. The P selection 4 algorithm is however only

recommended for further consideration - if at all - and when used in combination with log Kow-

triggers for bioaccumulation. The reason for why the employment of P-selection 4 is questionable is

that theBIOWIN 2-cut off value used has been chosen arbitrarily. (cf. further in sections on

bioaccumulation and comparison of selections).

P-selection 5 on the other hand identifies considerably more substances than P-selection 2. The

number of selected substances is 2 to 7 times higher (depending of whether the substances

considered are high, medium or low production volume substances, cf the above table). P-selection

5, i.e. to exclude BIOWIN 3 predictions, is however not recommended. The reason is the general

good agreement of predictions of not ready biodegradability (use of BIOWIN 2 & MITIDEG 2) and

the prediction of long environmental half-lives (use of BIOWIN 3) (cf. further in Appendix I,

Annex 6, point 5, and the sections on bioaccumulation and comparison of selections).

P-selection 2 (BIOWIN 3 in combination with BIOWIN2 or MITIDEG 2) selects around 10 %

more substances than the number of substances selected by application of P-selection 1 (i.e. the

combination of the BIOWIN 2 and BIOWIN 3-models that misses some of the not readily

SHC/TS 2-3/029 degradable substances.) P-selection 3 (MITIDEG 2 in combination with BIOWIN 3) includes even

fewer substances than P-selection 2, however only two medium production volume substances less

and none high production volume substances. An analysis has however been made of the usefulness

of using the combination of BIOWIN 2 and MITIDEG 2 predictions in general for increasing the

number identified not readily biodegradable substances (cf. Appendix I, Annex 6, point 4). This

analysis indicates that use of the combination of BIOWIN 2 and MITIDEG 2 predictions in

combination may be preferable to use of MITIDEG 2 predictions only, even though the difference

for selection of potential vPvBs/PBTs is very small.

Based on these comparisons P-selection 2 seems to be the preferable QSAR selection algorithm

for identifying potentially highly persistent substances, when this selection is used for selection of

potential vPvBs/PBTs and combined with QSAR-models for bioaccumulation. This has also been

agreed by the EU working group that developed the revised TGD and thus incuded in this

guidance document

Potential for bioaccumulation:

Two alternative BCF-models are the primary recommended selection tools for further identification

of substances that both fulfil the criteria for potential persistency (cf. above) and potential for a high

bioaccumulation according to the two cut off triggers of the PBT criteria for B (i.e. BCF 2000 and

5000, respectively):

The BCF(Syracuse): This model is available as the EPIWIN model (cf. Meyland W.M. et al

(1999):” Improved Method for Estimating Bioconcentration/Bioaccumulation Factor from

Octanol/Water Partitioning Coefficient” Environ.Toxicol.Chem. 18(4), 664-72.) The model is an

empirically based fragmentation model, which takes into account that certain structural and

molecular factors influence bioaccumulation. Some chemicals may undergo metabolism or may

be sterically hindered for uptake in fish, e.g. because of large molecular size. Such substances

may therefore not bioconcentrate to an extent predicted by their log Kow. Another advantage by

using BCF-Syracuse instead of log Kow is, that the latter is only a fairly good predictor for

bioconcentration in fish for substances with a log Kow value below 6 and in general not a good

predictor above 6. Generally the BCF-Syracuse seems for BCFs > 2000 (log Kow between

around 4.5 and 7.5) to identify the same types of substances, which are also identified by using

the bilinear BCF equation by Bintein-model (Bintein S. et al. (1993): "Non-linear Dependence of

Fish Bioconcentration on n-Octanol/water Partition Coefficient", SAR and QSAR in

Environmental Research 1, 29-39). The exceptions are certain chemical classes, where the BCF-

Syracuse model, by employing its fragmentation methodology, predicts a lower BCF-value due

to hindrance for uptake and/or metabolisation.

The BCF (Connell): This model was slightly modified (recalculated) and recommended in the

context of risk assessment of existing chemicals in EU (Cf. TGD, Part III, European

Commission, 1996). This QSAR model predicts BCF in a more precautionary way, because it

predicts a higher BCF value for chemicals with a log Kow value above 6 (ECETOC, 1999). The

models developed by Bintein et al gave a more accurate assessment of the BCFs of highly

lipophilic chemicals than the BCF (Connell)-model. This indicates that the latter BCF-model

makes frequent over-prediction of BCF for highly lipophilic substances (ECETOC Technical

Report No. 74 (1998):”QSARs in the Assessment of the Environmental Fate and Effects of

Chemicals").

Annex 5 shows the number of substances that would have been selected by using a log Kow trigger

of 4.0 and 5.0 (cf. the rev. TGD (Aug. 2001, version, p. 48) and the estimated log Kow by

LOGKOW-model (EPIWIN). Another PB selection approach based on log Kow was suggested in

the subgroup developing the Marine section of the revised TGD. This option is to consider

identifying potential PBs by employment of P-selection 4 (for even more persistent substances) in

SHC/TS 2-3/029 combination with a log Kow between 4.5 and 9 (i.e. the selection algorithm (BIOWIN 2 < 0.0036

and BIOWIN 3 < 2.2) AND (4.5. < log Kow < 9)). The shortcoming of this approach is the same as

the one by using the BCF(Connell model): unreliability as a predictor for highly lipophilic

substances with a log Kow > 6 and no account of occurrence of substructures that indicate potential

for less bioaccumulation (due to less uptake and/or metabolisation). Furthermore this log Kow-

approach is combined with a requirement of a potential for more extreme persistency, however with

a chosen cut off value of the BIODEG 2 model that is arbitrary. Based on this, we do not support

general use of log Kow-based approaches for triggering confirmatory testing / further attempts to

obtain experimental BCF-data.

Likewise we will not suggest use of the so-called "BCF-plateau model" for such use. This model

does not give such extremely high BCF-values than the linear log Kow-BCF-model (e.g. the Veith

& Kosian-equation). It however still neglects the available - but off course rather limited and to

some extent uncertain - experimental BCF-data, the basis on which the other BCF-models are

developed. Finally the available experimental BCF-data also indicate a general "inverse

relationship" (i.e. a decrease) between bioconcentration in fish and log Kow for organic substances

with a log Kow above 6 (cf. also Howard et. al. 1999, Bintein et al. 1993, ECETOC (1998) ).

In the tables below the number of selected substances are shown by using the different QSAR

modelling approaches for potential persistence in combination with the different BCF-models

described above. Included for illustrative purposes are also the approaches that are not

recommended for general use - e.g. PB selection 4 using a non-validated QSAR prediction for

even higher persistence combined with lipophilicity and PB-selection 5, which does not employ the

BIOWIN 3 model at all.

P-selection 1: BIOWIN2 < 0.5 AND BIOWIN3< 2.2

i.e. PB-selection 1:

EINECS: 2000 < BCF < 5000 BCF > 5000

P & BCF Connell: 521 2288

P & BCF Syracuse: 291 529

MPVC: 2000 < BCF < 5000 BCF > 5000


P & BCF Syracuse 27 46

HPVC: 2000 < BCF < 5000 BCF > 5000



SHC/TS 2-3/029 P-selection 2: (BIOWIN2 < 0.5 or MITIDEG 2 < 0.5) AND BIOWIN3 < 2.2:


EINECS: 2000 < BCF < 5000 BCF > 5000



MPVC: 2000 < BCF < 5000 BCF > 5000



HPVC: 2000 < BCF < 5000 BCF > 5000



P-selection3: MITIDEG 2 < 0.5 AND BIOWIN3 < 2.2:


EINECS: 2000 < BCF < 5000 BCF > 5000



MPVC: 2000 < BCF < 5000 BCF > 5000



HPVC: 2000 < BCF < 5000 BCF > 5000



P-selection 4: BIOWIN2 < 0.0036 AND BIOWIN3 < 2.2:


4.5 < log Kow < 5 5 < log Kow < 9

EINECS: 318 1352

MPVC: 33 133

HPVC: 6 14

SHC/TS 2-3/029 P-selection 5: BIOWIN2 < 0.5 or MITIDEG 2 < 0.5


EINECS: 2000 < BCF < 5000 BCF > 5000



MPVC: 2000 < BCF < 5000 BCF > 5000



HPVC: 2000 < BCF < 5000 BCF > 5000



Comparison of selection algorithms.

Comparison of P-selections: P-selection 4 is only proposed for possible consideration in combination with use of log Kow as a

trigger for bioaccumulation (cf. below). Apparently this identifies a number of substances that are in

between the number of substances identified by P-selection 2 in combination with BCF-Connell and

BCF-Syracuse, respectively. Basically P-selection 4 is rejected because to that a non validated cut

off for persistency is used (cf. further reason in the section on bioaccumulation below)

P-selection 5 identifies considerably more substances than P-selection 2, i.e. the number of selected

substances is 2 to 7 times higher. P-selection 5, i.e. to exclude BIOWIN 3 predictions, is however

not recommended. One reason is as previously mentioned the general good agreement of

predictions of not ready biodegradability and the prediction of long environmental half-lives.

Furthermore new not yet published data on more than 300 new substances notified to US EPA and

experimentally tested for ready biodegradability have been compared with QSAR model predictions

made by BIOWIN 1, 2, 3 & 4 and MITIDEG 1 and 2. This investigation indicates that the BIOWIN

3 model predictions can be used for prediction of not ready biodegradability with the same high

reliability as use of BIOWIN 2 and MITIDEG 2. The trigger values used were the same as those

used here, except that a trigger of 2.25 instead of 2.2 was used when applying BIOWIN 3. (pers.

com with Bob Boethling, USEPA). Thus this investigation also indicates that P-selections

employing a combination of these models, including BIOWIN 3, is justifiable and preferred to P-

selection without BIOWIN 3 (i.e. P-selection 2 is preferable to P-selection 5, cf. also Appendix 1,

Annex 6, point 5 to 8).

In relation to the alternative P-selections 1, 2 and 3, it appears not unexpectedly that most

substances are identified by using the persistency criteria which includes BIOWIN3- and

BIOWIN2- or MITIDEG 2-predictions (P-selection 2). The reason is, as explained above, that in

this combination of application of QSAR models minimises the number of not readily degradable

substances, that are falsely predicted to be readily biodegradable compared to P-selection 1,

indicating preference of P-selection 2 over P-selection 1. The alternative P-selection 3 selects only

marginally fewer substances compared to P-selection 2 when this P-selection is combined with one

of the models (e.g. BCF-Connell). Because P-selection 2 generally increases the number of

correctly identified not readily biodegradable substances we prefer P-selection 2 instead of P-

selection 3. (cf. further in Appendix I, Annex 6, point 4).

All together this confirms that the most appropriate selection algorithm for persistency is P-

selection 2.

SHC/TS 2-3/029

Comparison of B-selections: In relation to the BCF-models it appears that they select different number of substances.

Employment of the BCF-Connell model selects around two to four times the number of substances

selected by the BCF-Syracuse model. The latter model is generally preferred, however there is

concern that this model may underpredict the BCF for slowly metabolisable substances or

substances in the high log Kow range. Therefore lists of HPC and MPVC that were created by

employing P-selection 2 and the two BCF-models have been compared by expert judgement: the

substances selected by employing the BCF (Syracuse)-model have been supplemented by relevant

additionally selected substances by employing the BCF (Connell)-model. This evaluation has been

performed by available EU experts, i.e. Dick Sijm (RIVM, NL) and Jay Niemelä (Danish EPA).

First an evaluation was performed by Dick Sijm and briefly done in this way:

CAS no was used to run the EPISUITE-program, then the molecular structure was evaluated along

with the P and B properties by using expert judgement in relation to experience in microbial

degradation, bioaccumulation and biotransformation. The rules and reasons used were not in all

cases necessarily quantitatively applied. The evaluation took account of the following:

Size of the molecule, both length and width. This assuming that either too big (> 1 nm) or too

lengthy (> 4 nm) molecules will slowly, if at all, pass gill or other respiratory membranes. Thus

such a substance will either reach steady-state very slowly and maybe not even within the

lifetime of an (gill-breathing) aquatic organism, or will be taken up slowly, if at all, via the

gastro-intestinal tract, within the lifetime of any aquatic (mammal or gill-breathing or other)

organism.

Any structural indication that the substance may be biotransformed. This includes an aromatic

ring with at least two adjacent unsubstituted places, (long) alkyl chains, polar substituents (e.g. a

hydroxy, nitro, or aminogroup), that may be conjugated, etc. When, however, possible steric

hindrance may occur, e.g. when a big substituent, such as a bromine molecule is attached in the

vicinity of a polar substituent, this is taken into account in the judgement (although not always

purely quantitatively). In this ‘rule’ biotransformation by micro-organisms as well as by higher

organisms (e.g. mammals and fish) are thus taken into consideration, i.e. it thus includes

metabolism and biodegradation potential.

Application of the employed rules will not provide definitive answers, cf. the outcome of the

SETAC workshop “Biotransformation in Environmental Risk Assessment” (Sijm, D.T.H.M., J. de

Bruijn, P. de Voogt and W. de Wolf (1997): "Biotransformation in environmental risk

assessment". Proceedings of a SETAC-Europe Workshop, held in Noordwijkerhout, 28 april - 1

may, SETAC-Europe, Brussels, Belgium, 1996)). For example interspecies differences in

biotransformation are the rule rather than the exception. Furthermore, any sign of possible

biotransformation is not equal to mineralisation.

However, if the purpose is to pick out those substances that are likely to be the most persistent

and least biotransformed, application of such general rules can be defended for such purposes.

The same holds for size-limited uptake and bioaccumulation. Some of the presently known

potentially hazardous big substances (e.g. decabromodiphenylether) appear to cause human health

related effects, or are found in human blood. In these latter cases it is obvious (very likely) that

these big molecules somehow do pass biological membranes.

In this way the substances evaluated could be attributed to the following three classes of substances:

1) those potentially both persistent and bioaccumulative (‘connell OK’),

2) those potentially less persistent and bioaccumulative (‘inbetween’), and

3) those potentially not persistent and bioaccumulative (‘syr OK, size’ or ‘syr OK, metabolism’, or

‘syr OK, metabolism + size’) .

SHC/TS 2-3/029 After this first evaluation Jay Niemelä went through all of the above mentioned substances allocated

to class 1 and 2. This expert judgement exercise was assisted by use of a QSAR program

METABOL (ver. 1.0) where potential for mammalian metabolism is included by indicating scores

for metabolic pathways and involved enzymatic reactions. A classification of potential for

mammalian metabolism was then allocated in a similar manner as described above. Those classified

in group 2 were those chemicals, where some metabolisation seems to take place but where

potentially persistent metabolites occur or where metabolites cycles back to other metabolites closer

to the parent compound in the metabolic pathway). In few cases, and based on a preliminary

trigger11

for doing this, the molecular dimensions (at the least energy state) for possibility of

hindrance for uptake were checked by use of the Program Chem-X. This check was however due to

time constrains not fully pursued and had no influence on the final selection.

The two involved experts went then through two more rounds of expert evaluation focussing each

time on those judgements that still differed. In a few cases the experts had in the final end still a

different judgement. In these cases the substance was provisionally included on the final list of

selected potential PB substances but with a footnote indicating the difference in expert judgement.

In this way the identification of potentially persistent and bioaccumulative HPVC and MPVC by

use of P-selection 2 and employment of the BCF-Syracuse model was supplemented by relevant

substances identified by BCF-Connell and expert judgement related to potential for uptake and

biotransformation (cf. Annex 1, table 1). Appendix I, Annex 7 and 8 include some details of the

performed expert judgements on the HPVC and MPVC of class 1 and 2 above, including the results

of the two expert judgements.

Annex 1, table 1 includes key data on all of the 134 selected potential vPvBs/PBTs. The relatively

low number of substances implies that the total number of vPvBs & PBTs (including those where

experimental data are available) is also relatively low. This indicates that inclusion of vPvBs and

PBTs under the authorisation scheme for avoiding unreasonable risks are feasible.

Toxicity.

Toxicity criteria are only relevant for substances with BCFs between 2000 and 5000 (cf. the PBT

criteria on p. 3 above). According to the above selections it seems that the number of potentially

persistent HPVC in that BCF- range is quite low. Therefore it is suggested that available

experimental and QSAR data on the HPVC - and possibly also the MPVC - are simply inspected

and a case by case expert evaluation is performed. A quality check of the experimental data from

IUCLID and other available data sources should be included. The QSAR data of the DK-EPA

QSAR database have been used to obtain predictions of toxicity data that are relevant in relation

to the criteria for toxicity in relation to PBTs (i.e. toxicity data on aquatic species and mammals).

Thus such data have been included on all of the selected substances (vPvBs and PBTs) included in

Annex 1 (cf. table 2 & 3 , cf. further in DK-EPA: "Report on Advisory list for self-classification of

dangerous substances” (August 2001) and “Use of QSARs for selection of POPs” (June 1999)).

Potential for environmental release and exposure

Potential for environmental release and exposure may be relevant for priority setting for testing or

regulation of chemicals identified to be potential PBTs / vPvBs. Such an environmental release and

exposure based priority setting has been done on the substances selected by using the above

mentioned P-selection 2, BCF(Syracuse)-model and the BCF(Connell)-model combined with expert

judgement (cf. Annex 1, table 4). The analysis has been performed based on the occurrence of the

substances in products registered by the Danish, Finnish, Norwegian and Swedish Product

11

The trigger being disagreement between the two expert judgements and MW > 600

SHC/TS 2-3/029 Registers. The scoring was performed in the same way as that currently done on CMR cat 1 & 2

substances12

, i.e. according to this scoring table:

The criteria used as basis for grouping substances as having a “low”, “medium”

or “high” release potential are summarised below:

Volume

(tons/year in the

Product Register)

Preparations

(numbers recorded)

Industry branchs or

NACE codes

(numbers recorded)

Product types

(numbers recorded)

Low

< 1 ton < 6 < 6 < 4

Medium

1-100 tons 6-100 6-20 4-10

High

> 100 tons > 100 > 20 > 10

The preliminary results of the environmental release and exposure potential scoring based on data

from each of the Product Registers of Denmark, Finland, Norway and Sweden are presented in

Appendix 1 Annex 9. It is obvious that the number of potential vPvBs/PBTs with a significant

environmental release score is different between the Nordic countries. These differences may,

besides some differences in use of chemical products in the Nordic countries, also be a result of

differences in the national reporting rules in force for registration of chemical products to the

register. The table below presents the total number of identified potential vPvBs/PBTs with a

significant environmental release potential from products registered in one or more of the Nordic

Product Registers. The integration of information from each product register into an evaluation for

all Nordic countries were done in this way: “high” or “medium” release score from at least one

country means a “high” or “medium” release score for the overall evaluation. Thus both “high” and

“medium” release score are considered as indicators for significant release.

Depending of the exact definition of what constitutes a significant release potential, a different

number of substances were identified.

12

by an EU working group on development of REACH in relation to substances of very high concern requiring

authorisation.

SHC/TS 2-3/029

Overview of the results of environmental release scoring accoring to use of the

potential vPvBs/PBTs in products marketed in Denmark, Finland, Norway and

Sweden *.

Total number of potential vPvBs/PBTs

with a production volume above 10 tpa/ maufacturer in the EU: 134

Total number of potential vPvBs/PBTs with a production volume

above 10 tpa/ maufacturer in the EU and

registered in the Nordic Product Registries: 66.

Of which:

Potential vPvBs/PBTs with probable significant environmental release potential from products:

significant volume and widespread use **:

18

Potential vPvBs/PBTs with possible significant environmental release potential from products:

significant volume or widespread use** : 32

Potential vPvBs/PBTs with potential significant environmental release potential from products:

significant volume but probably not widespread use**: 26

*

: a few vPvB/PBT- candidates, which according to expert judgement are likely to undergo hydrolysis to

non-vPvB/PBT-substances have furthermore been excluded. Cf. Annex 1 Table 4 for chemical name and

CAS number on the substances and Appendix 1, Annex 9 for environmental release scoring results for

potential vPvBs/PBTs registered in products in each of the Product Register of Denmark, Finland, Norway

and Sweden. **

: “widespread use” is here defined as “high” or “medium” release score for number of products, use

categories, or industry categories according to the release score table above.

Based on this analysis of potential environmental release and exposure from use of potential

vPvBs/PBTs in chemical products registered on the Nordic market, it can be concluded that only

less than half of the identified potential vPvBs/PBTs seems to occur in chemical products. The true

number of potential vPvB/PBTs in chemical products on the Nordic market may of course be

slightly higher than that recorded, because of limitations in the reporting rules for the Product

Registers. Nevertheless the number of potential vPvB/PBTs in products on the Nordic market is not

likely to be substantially higher. The number of potential vPvB/PBTs with a significant

environmental release potential from chemical products depends on the definition of significant

environmental release potential. The number amounts however only between less than one sixth and

a one quarter of the total number of identified potential vPvBs/PBTs. Thus the number of potential

vPvBs/PBTs with a significant high release potential from use in chemical products seems to be

very low indeed.

Potential vPvBs and PBTs may possibly also be significantly released to the environment from

certain industrial processes and from products not registered in the Product Registries of the Nordic

countries. Industry is therefore encouraged to provide supplementary information about all 134

SHC/TS 2-3/029 vPvB /PBT candidates in relation to their potential for environmental release and exposure during

their whole life-cycle (i.e. during manufacturing, processing, use and disposal).

Even though further release and exposure data from Industry and from other sources are important

is not likely that this information will increase the number of potential vPvBs/PBTs substantially.

The result of such a refinement of the environmental release evaluation may provide a more

complete basis for a priority setting among the selected vPvB/PBT-candidates for further

information gathering, testing or, if not obtainable, even phasing directly in under the authorisation

scheme of REACH.

In conclusion: The relatively low number of potential vPvBs and PBTs with a production volume

above 10 pta per manufacturer in the EU and an even lower number that are used in products on

the market and which also has a significant environmental release potential, indicates that it would

be feasible using QSARs for evaluation of untested chemicals. Such use of QSARs would only

trigger further in-depth analysis and possible requirement of confirmatory testing on a limited

number of substances. The number of substances that have to be considered as vPvBs/PBTs

because confirmatory testing is not performed is likely to be low. The same applies to the number of

vPvBs/PBTs, which by further testing will be confirmed to be vPvBs/PBTs.

Thus this analysis indicates that only a limited number of vPvBs/PBTs will be phased in under the

authorisation scheme of REACH making the vPvB/PBT- assessment and use of QSARs for this

purpose feasible.

Use of QSARs on substances with more or less experimental data regarding

PBT-properties.

It has off course to be considered how the availability of QSAR predictions may supplement the

incomplete data sets regarding the HPVC in IUCLID / or used in a further data evaluation of

conflicting data or data with questionable data quality in IUCLID13

(cf. also Appendix I, Annex 4

with a provisional comparison between HPVC with experimental data and QSAR predictions

according to P-selection 2 and BCF-Connell).

The preliminary analysis seems to indicate that use of QSARs may play a role in allowing initial

screening of substances with little or no available experimental data . This could be used for a

provisional identification of PBT/vPvB-candidates for further check of the availability of

experimental data beyond the content of IUCLID, and - if not available - for priority setting of

confirmatory testing according to the general testing strategy for potential vPvBs/PBTs.

In general the preliminary comparison of IUCLID and QSAR based data also indicates a reasonable

level of concordance for identification of PBs, where both types of data are available.

Acknowledgement. Thanks to Helene Loonen & Jack de-Bruijn (ECB), Sylvain Bintein (French EPA) and Bob

Boethling (US EPA) for valuable comments or input to development of this paper. Also thanks to

Alf Lundgren (Swedish Chemicals Inspectorate), Thoralf Kalland (Norwegian Environmental

Protection Agency) and Jukka Malm (Finnish Environmental Institute) for providing us with

environmental release scoring information based on data from their national Product Register.

13

it is a general experience of the ESR programme that the IUCLID data-set often is incomplete and contains data of

questionable quality/ reliability

SHC/TS 2-3/029

Overview of Annexes:

Annex I (included in this document):

Overall result of selection of potentially persistent bioaccumulators by use of QSAR and

expert judgement:

Table 1: key data of QSAR-predictions of persistency & bioaccumulation.

Table 2: key data of QSAR-predictions of aquatic toxicity

Table 3 : key data of QSAR-predictions of mammalian toxicity.

Table 4: Potential vPvBs/PBTs with significant environmental release and exposure potential

according to Nordic Product Register information.

(Results presented in three columns:

To the right: Potential vPvBs/PBTs with probable significant environmental release potential from

products: significant volume and widespread use.

In the middle: Potential vPvBs/PBTs with possible significant environmental release potential from

products: significant volume or widespread use

To the left: Potential vPvBs/PBTs with potential significant environmental release potential from

products: significant volume but probably not widespread use)

Annex 2: Overview of the QSAR based identification of discrete organic "PBs" on EINECS, The

EU lists on MPVC and HPVC.

SHC/TS 2-3/029

Appendix I: (separate file)

Annex 3 : High production Volume PBs according to P-selection 2 and BCF-Connell

Annex 4: QSAR-predictions and IUCLID:

High production Volume PBs identified by QSAR compared to ECB automated PB selection on

IUCLID data (including summary)

Annex 5: Number of substances selected according to selection 1 and 2 and Log Kow

Annex 6: Persistency:

Usefulness of QSARs for prediction of persistency of vP and P.

Annex 7: Bioaccumulation potential of HPVC: Details of Expert judgements based on the first initial expert judgement of High Production Volume

substances selected by use of P-selection 2 and BCF Connell > 2000 but not according to P-

selection 2 and BCF Syracuse > 2000.

Annex 8: Bioaccumulation of MPVC:

Details of Expert judgements based on the first initial expert judgement of Medium Production

Volume substances selected by use of P-selection 2 and BCF Connell > 2000 but not according to

P-selection 2 and BCF Syracuse > 2000.

Annex 9: vPvBs/PBT-candidates with significant environmental release potential:

vPvB/PBT-candidates in products marketed in Denmark, Finland, Norway and Sweden and with a

significant environmental release potential.

SHC/TS 2-3/029

Annex 1

Table 1 Substance ID and PB properties

Chemical ID Biodegradation Bioconcentration Molecular

weight

CAS CHEMICAL Biowin 2 Biowin 3 Mitideg 2 BCF S14

BCF C15

Log Kow16

MW

50-29-3 Benzene. 1.1'-(2.2.2-trichloroethylidene)bis[4-chloro- 0.00 1.20 0.00 41687 45709 6.79 354.49

58-89-9 Cyclohexane. 1.2.3.4.5.6-hexachloro-. (1alpha.2alpha.3beta.4alpha.5alpha.6beta)-

0.00 1.52 0.00 309 2089 4.26 290.83

77-47-4 1.3-Cyclopentadiene. 1.2.3.4.5.5-hexachloro- 0.00 1.35 0.00 1514 4786 4.63 272.77

78-63-7 Peroxide. (1.1.4.4-tetramethyl-1.4-butanediyl)bis[(1.1-dimethylethyl) 0.00 1.71 0.04 22387 44668 6.55 290.45

79-94-7 Phenol. 4.4'-(1-methylethylidene)bis[2.6-dibromo- 0.00 1.35 0.01 13490 43652 7.20 543.88

81-98-1 7H-Benz[de]anthracen-7-one. 3.9-dibromo- 0.00 2.05 0.02 2951 43652 6.51 388.06

85-22-3 Benzene. pentabromoethyl- 0.00 1.34 0.01 14125 38905 7.48 500.65

87-83-2 Benzene. pentabromomethyl- 0.00 1.37 0.02 47863 45709 6.99 486.62

93-46-9 1.4-Benzenediamine. N.N'-di-2-naphthalenyl- 0.00 2.13 0.00 16596 41687 6.39 360.46

115-27-5 4.7-Methanoisobenzofuran-1.3-dione. 4.5.6.7.8.8-hexachloro-3a.4.7.7a-tetrahydro-

0.00 0.70 0.00 851 5623 4.71 370.83

115-32-2 Benzenemethanol. 4-chloro-alpha-(4-chlorophenyl)-alpha-(trichloromethyl)- 0.00 1.02 0.00 1479 28184 5.81 370.49

116-29-0 Benzene. 1.2.4-trichloro-5-[(4-chlorophenyl)sulfonyl]- 0.00 1.59 0.00 708 12882 5.18 356.05

117-08-8 1.3-Isobenzofurandione. 4.5.6.7-tetrachloro- 0.00 1.74 0.00 759 5012 4.65 285.90

117-18-0 Benzene. 1.2.4.5-tetrachloro-3-nitro- 0.00 1.63 0.00 468 2818 4.39 260.89

118-74-1 Benzene. hexachloro- 0.00 1.33 0.00 5129 29512 5.86 284.78

118-82-1 Phenol. 4.4'-methylenebis[2.6-bis(1.1-dimethylethyl)- 0.00 1.45 0.00 43 5623 8.99 424.67

14

Syracuse BCFWIN BCF 15

Connell BCF 16

KOWWIN

SHC/TS 2-3/029


weight


BCF C15

Log Kow16

MW

119-47-1 Phenol. 2.2'-methylenebis[6-(1.1-dimethylethyl)-4-methyl- 0.15 1.91 0.01 4571 25704 7.97 340.51

128-69-8 Perylo[3.4-cd:9.10-c'd']dipyran-1.3.8.10-tetrone 0.00 1.53 0.00 13183 38905 6.26 392.33

128-83-6 9.10-Anthracenedione. 1-amino-2-bromo-4-[(4-methylphenyl)amino]- 0.00 1.77 0.00 5129 45709 6.81 407.27

128-87-0 9.10-Anthracenedione. 1.1'-iminobis[4-amino- 0.00 1.69 0.00 2188 38905 7.46 459.46

129-73-7 Benzenamine. 4.4'-(phenylmethylene)bis[N.N-dimethyl- 0.02 1.91 0.00 5129 25704 5.72 330.48

133-14-2 Peroxide. bis(2.4-dichlorobenzoyl) 0.00 1.53 0.00 8511 33113 6.01 380.01

133-49-3 Benzenethiol. pentachloro- 0.00 1.54 0.00 7079 30903 5.91 282.40

135-91-1 Benzenamine. 4.4'-methylenebis[N.N-diethyl- 0.01 1.93 0.00 15136 40738 6.34 310.49

139-60-6 1.4-Benzenediamine. N.N'-bis(1-ethyl-3-methylpentyl)- 0.00 2.19 0.00 26303 42658 7.29 332.58

152-11-4 Benzeneacetonitrile. alpha-[3-[[2-(3.4-dimethoxyphenyl)ethyl] methylamino]propyl]-3.4-dimethoxy-alpha-(1-methylethyl)-. M

1.00 1.62 0.10 2399 15136 5.30 454.61

298-57-7 Piperazine. 1-(diphenylmethyl)-4-(3-phenyl-2-propenyl)- 0.22 1.94 0.00 3090 18621 5.44 368.53

307-34-6 Octane. octadecafluoro- 0.00 -0.07 0.00 3236 26303 7.95 438.06

335-36-4 Furan. 2.2.3.3.4.4.5-heptafluorotetrahydro-5-(nonafluorobutyl)- 0.00 0.27 0.00 1862 12303 5.15 416.06

335-57-9 Heptane. hexadecafluoro- 0.00 0.26 0.00 47863 45709 6.99 388.05

335-67-1 Octanoic acid. pentadecafluoro- 0.00 0.86 0.00 56 39811 6.30 414.07

355-42-0 Hexane. tetradecafluoro- 0.00 0.58 0.00 8511 33884 6.02 338.04

355-43-1 Hexane. 1.1.1.2.2.3.3.4.4.5.5.6.6-tridecafluoro-6-iodo- 0.00 0.64 0.00 36308 45709 6.84 445.95

375-72-4 1-Butanesulfonyl fluoride. 1.1.2.2.3.3.4.4.4-nonafluoro- 0.00 1.38 0.00 5370 26303 5.75 302.09

423-50-7 1-Hexanesulfonyl fluoride. 1.1.2.2.3.3.4.4.5.5.6.6.6-tridecafluoro- 0.00 0.74 0.00 7413 33884 7.68 402.10

507-63-1 Octane. 1.1.1.2.2.3.3.4.4.5.5.6.6.7.7.8.8-heptadecafluoro-8-iodo- 0.00 -0.01 0.00 245 8511 8.77 545.97

SHC/TS 2-3/029


weight


BCF C15

Log Kow16

MW

512-04-9 Spirost-5-en-3-ol. (3beta.25R)- 0.00 1.79 0.00 15136 40738 6.34 414.00

611-75-6 Benzenemethanamine. 2-amino-3.5-dibromo-N-cyclohexyl-N-methyl-. Monohydrochloride

0.00 1.77 0.01 1622 10715 5.07 375.00

626-39-1 Benzene. 1.3.5-tribromo- 0.00 2.10 0.16 589 5129 4.66 314.80

632-79-1 1.3-Isobenzofurandione. 4.5.6.7-tetrabromo- 0.00 1.63 0.01 4266 23442 5.63 463.70

634-66-2 Benzene. 1.2.3.4-tetrachloro- 0.00 1.90 0.01 692 4169 4.57 215.89

678-26-2 Pentane. dodecafluoro- 0.00 0.90 0.00 490 10471 5.05 288.04

732-26-3 Phenol. 2.4.6-tris(1.1-dimethylethyl)- 0.01 2.04 0.05 3311 41687 6.39 262.44

850-92-0 1.3-Cyclopentanedione. 2-[2-(3.4-dihydro-6-methoxy-1(2H)-naphthalenylidene)ethyl]-2-ethyl-

0.23 2.12 0.27 2570 15849 5.33 312.00

903-19-5 1.4-Benzenediol. 2.5-bis(1.1.3.3-tetramethylbutyl)- 0.00 1.72 0.04 170 12023 8.56 334.55

979-02-2 Pregna-5.16-dien-20-one. 3-(acetyloxy)-. (3beta)- 0.13 2.10 0.08 1698 11220 5.10 356.00

1068-27-5 Peroxide. (1.1.4.4-tetramethyl-2-butyne-1.4-diyl)bis[(1.1-dimethylethyl) 0.00 1.72 0.02 6310 28840 5.84 286.42

1173-09-7 Pregn-5-en-20-one. 21-(acetyloxy)-3-hydroxy-16-methyl-. (3beta.16alpha)- 0.23 2.19 0.10 447 2570 4.35 388.00

1478-61-1 Phenol. 4.4'-[2.2.2-trifluoro-1-(trifluoromethyl)ethylidene]bis- 0.00 1.33 0.00 562 3388 4.47 336.24

1582-09-8 Benzenamine. 2.6-dinitro-N.N-dipropyl-4-(trifluoromethyl)- 0.00 1.35 0.00 2570 15488 5.31 335.29

1639-60-7 Benzeneethanol. alpha-[2-(dimethylamino)-1-methylethyl]-alpha-phenyl-. propanoate (ester). hydrochloride. [S-(R*.S*)]-

0.92 2.09 0.03 2239 14791 5.27 339.48

1691-99-2 1-Octanesulfonamide. N-ethyl-1.1.2.2.3.3.4.4.5.5.6.6.7.7.8.8.8-heptadecafluoro-N-(2-hydroxyethyl)-

0.00 0.10 0.00 5495 30903 7.78 571.25

2094-98-6 Cyclohexanecarbonitrile. 1.1'-azobis- 0.06 1.77 0.00 4074 22387 5.60 244.34

2212-81-9 Peroxide. [1.3-phenylenebis(1-methylethylidene)]bis[(1.1-dimethylethyl) 0.00 1.60 0.01 22387 41687 7.34 338.00

2309-94-6 Propanamide. N-[2-[(2-bromo-6-cyano-4-nitrophenyl)azo]-5-(diethylamino)phenyl]-

0.00 1.16 0.00 10 35481 6.10 473.33

2475-31-2 3H-Indol-3-one. 5.7-dibromo-2-(5.7-dibromo-1.3-dihydro-3-oxo-2H-indol-2-ylidene)-1.2-dihydro-

0.00 1.06 0.00 3981 45709 6.67 577.85

SHC/TS 2-3/029


weight


BCF C15

Log Kow16

MW

2781-00-2 Peroxide. [1.4-phenylenebis(1-methylethylidene)]bis[(1.1-dimethylethyl) 0.00 1.60 0.01 22387 41687 7.34 338.49

2795-39-3 1-Octanesulfonic acid. 1.1.2.2.3.3.4.4.5.5.6.6.7.7.8.8.8-heptadecafluoro-. potassium salt

0.00 0.29 0.00 56 39811 6.28 500.00

2991-51-7 Glycine. N-ethyl-N-[(heptadecafluorooctyl)sulfonyl]-. potassium salt 0.00 0.27 0.00 10 25119 8.01 585.00

3006-86-8 Peroxide. cyclohexylidenebis[(1.1-dimethylethyl) 0.00 1.99 0.08 13490 39811 6.28 260.38

3147-75-9 Phenol. 2-(2H-benzotriazol-2-yl)-4-(1.1.3.3-tetramethylbutyl)- 0.02 2.12 0.02 12023 38019 6.21 323.44

3278-89-5 Benzene. 1.3.5-tribromo-2-(2-propenyloxy)- 0.01 1.91 0.17 3981 22387 5.59 370.87

3739-67-1 Benzene. 1.1'-(1-methylethylidene)bis[4-(2-propenyloxy)- 0.80 2.19 0.21 18621 42658 6.46 308.42

3810-80-8 4-Piperidinecarboxylic acid. 1-(3-cyano-3.3-diphenylpropyl)-4-phenyl-. Ethyl ester. Monohydrochloride

0.99 1.64 0.02 15136 40738 6.34 452.60

3825-26-1 Octanoic acid. pentadecafluoro-. ammonium salt 0.00 0.86 0.00 56 39811 6.30 414.00

3846-71-7 Phenol. 2-(2H-benzotriazol-2-yl)-4.6-bis(1.1-dimethylethyl)- 0.02 2.12 0.01 4786 39811 6.27 323.44

3851-87-4 Peroxide. bis(3.5.5-trimethyl-1-oxohexyl) 0.01 2.08 0.02 58884 45709 7.03 314.47

3864-99-1 Phenol. 2-(5-chloro-2H-benzotriazol-2-yl)-4.6-bis(1.1-dimethylethyl)- 0.00 1.83 0.00 14791 45709 6.91 357.89

4051-63-2 [1.1'-Bianthracene]-9.9'.10.10'-tetrone. 4.4'-diamino- 0.00 1.86 0.00 5248 45709 6.83 444.45

4162-45-2 Ethanol. 2.2'-[(1-methylethylidene)bis[(2.6-dibromo-4.1-phenylene)oxy]]bis- 0.00 1.25 0.08 7413 45709 6.78 631.98

4378-61-4 Dibenzo[def.mno]chrysene-6.12-dione. 4.10-dibromo- 0.00 1.86 0.01 6166 44668 7.13 464.12

5216-25-1 Benzene. 1-chloro-4-(trichloromethyl)- 0.00 1.75 0.00 631 3981 4.54 229.92

5285-60-9 Benzenamine. 4.4'-methylenebis[N-(1-methylpropyl)- 0.01 2.17 0.00 9550 35481 6.08 310.49

5590-18-1 1H-Isoindol-1-one. 3.3'-(1.4-phenylenediimino)bis[4.5.6.7-tetrachloro- 0.00 0.02 0.00 1905 22387 8.11 641.94

6407-78-9 3H-Pyrazol-3-one. 4-[(2.4-dimethylphenyl)azo]-2.4-dihydro-5-methyl-2-phenyl- 0.00 2.09 0.00 10 23988 5.65 306.37

6410-30-6 2-Naphthalenecarboxamide. N-(4-chlorophenyl)-3-hydroxy-4-[(2-methyl-5-nitrophenyl)azo]-

0.00 1.43 0.00 10 6918 8.88 460.88

SHC/TS 2-3/029


weight


BCF C15

Log Kow16

MW

6410-38-4 2-Naphthalenecarboxamide. 4-[(2.5-dichlorophenyl)azo]-3-hydroxy-N-(2-methoxyphenyl)-

0.00 1.40 0.00 10 22387 8.10 466.33

6731-36-8 Peroxide. (3.3.5-trimethylcyclohexylidene)bis[(1.1-dimethylethyl) 0.00 1.68 0.02 10965 36308 7.56 302.46

7139-02-8 Pyrimido[5.4-d]pyrimidine. 2.6-dichloro-4.8-di-1-piperidinyl- 0.00 1.46 0.00 3090 18621 5.45 367.28

12223-91-5 [1.1'-Biphenyl]-2-ol. 5-[[4-[(2.4-dinitrophenyl)amino]phenyl]azo]- 0.00 1.50 0.00 10 45709 6.91 455.43

13014-24-9 Benzene. 1.2-dichloro-4-(trichloromethyl)- 0.00 1.47 0.00 1950 12882 5.18 264.37

13171-00-1 Ethanone. 1-[6-(1.1-dimethylethyl)-2.3-dihydro-1.1-dimethyl-1H-inden-4-yl]- 0.02 2.14 0.08 1047 31623 5.93 244.38

13417-01-1 1-Octanesulfonamide. N-[3-(dimethylamino)propyl]-1.1.2.2.3.3.4.4.5.5.6.6.7.7.8.8.8-heptadecafluoro-

0.00 -0.34 0.00 1288 19055 8.24 584.29

13680-35-8 Benzenamine. 4.4'-methylenebis[2.6-diethyl- 0.09 1.87 0.00 15136 40738 6.34 310.49

14295-43-3 Benzo[b]thiophen-3(2H)-one. 4.7-dichloro-2-(4.7-dichloro-3-oxobenzo[b]thien-2(3H)-ylidene)-

0.00 1.37 0.00 1445 35481 6.11 434.14

15323-35-0 Ethanone. 1-(2.3-dihydro-1.1.2.3.3.6-hexamethyl-1H-inden-5-yl)- 0.02 2.14 0.09 933 28840 5.85 244.38

15958-61-9 Anthraquinone. 1-[p-(phenylsulfonyl)anilino]- 0.01 2.07 0.00 2239 41687 6.36 439.00

16699-20-0 Piperazine. 1-(diphenylmethyl)-4-(3-phenyl-2-propenyl)-. (E)- 0.22 1.94 0.00 3090 18621 5.44 368.53

17540-75-9 Phenol. 2.6-bis(1.1-dimethylethyl)-4-(1-methylpropyl)- 0.06 2.18 0.06 6310 42658 6.43 262.44

18181-80-1 Benzeneacetic acid. 4-bromo-alpha-(4-bromophenyl)-alpha-hydroxy-. 1-methylethyl ester

0.02 1.91 0.04 2884 7943 4.90 428.12

18254-13-2 Phenol. 2.4.6-tris(1-phenylethyl)- 0.99 2.20 0.00 42658 44668 7.13 406.57

20241-76-3 9.10-Anthracenedione. 1.8-dihydroxy-4-nitro-5-(phenylamino)- 0.02 2.15 0.00 2344 41687 6.38 376.33

23593-75-1 1H-Imidazole. 1-[(2-chlorophenyl)diphenylmethyl]- 0.12 2.06 0.01 13183 38905 6.26 344.85

25155-25-3 Peroxide. [1.3(or 1.4)-phenylenebis(1-methylethylidene)]bis[(1.1-dimethylethyl)

0.00 1.60 0.01 22387 41687 7.34 338.49

25268-77-3 2-Propenoic acid. 2-[[(heptadecafluorooctyl)sulfonyl]methylamino]ethyl ester 0.00 -0.01 0.00 355 10471 8.65 611.27

25973-55-1 Phenol. 2-(2H-benzotriazol-2-yl)-4.6-bis(1.1-dimethylpropyl)- 0.01 2.05 0.01 10233 42658 7.25 351.50

SHC/TS 2-3/029


weight


BCF C15

Log Kow16

MW

26748-47-0 Neodecaneperoxoic acid. 1-methyl-1-phenylethyl ester 0.01 1.91 0.03 15849 41687 6.36 306.45

27137-85-5 Silane. trichloro(dichlorophenyl)- 0.01 2.17 0.00 1175 7943 4.89 280.44

29312-59-2 Benzenamine. 4-(2.6-diphenyl-4-pyridinyl)-N.N-dimethyl- 0.10 2.00 0.00 13490 39811 6.27 350.47

29398-96-7 [1.1'-Biphenyl]-4.4'-diamine. N.N'-bis(2.4-dinitrophenyl)-3.3'-dimethoxy- 0.00 0.86 0.00 44668 45709 6.94 576.48

30707-68-7 3H-Pyrazol-3-one. 5-[(2-chloro-5-nitrophenyl)amino]-2.4-dihydro-2-(2.4.6-trichlorophenyl)-

0.00 1.11 0.00 2239 14454 5.26 434.07

31188-91-7 Benzamide. 3-[[[2.4-bis(1.1-dimethylpropyl)phenoxy]acetyl]amino]-N-[4.5-dihydro-5-oxo-1-(2.4.6-trichlorophenyl)-1H-pyraz

0.00 0.56 0.00 117 5495 9.00 672.06

35578-47-3 Ethanedione. bis(4-bromophenyl)- 0.00 2.11 0.02 2570 16218 5.34 368.03

36861-47-9 Bicyclo[2.2.1]heptan-2-one. 1.7.7-trimethyl-3-[(4-methylphenyl)methylene]-. (±)-

0.02 2.12 0.12 2089 13490 5.22 254.38

37853-59-1 Benzene. 1.1'-[1.2-ethanediylbis(oxy)]bis[2.4.6-tribromo- 0.00 0.75 0.01 74 4074 9.15 687.64

38521-51-6 Benzene. pentabromo(bromomethyl)- 0.00 1.30 0.00 22387 41687 7.33 565.00

38850-60-1 1-Propanesulfonic acid. 3-[[3-(dimethylamino)propyl][(tridecafluorohexyl)sulfonyl]amino]-

0.00 0.22 0.00 3 2239 4.29 606.00

39489-75-3 Phenol. 2.4-dichloro-5-nitro-. carbonate (2:1) (ester) 0.00 1.06 0.00 2951 18197 5.42 441.00

40567-16-6 Butanoyl chloride. 2-[2.4-bis(1.1-dimethylpropyl)phenoxy]- 0.05 1.97 0.03 19498 43652 6.48 338.92

41604-19-7 1.1'-Biphenyl. 4-bromo-2-fluoro- 0.00 2.12 0.00 4467 7244 4.85 251.10

41999-84-2 Benzene. 1.4-dichloro-2.5-bis(dichloromethyl)- 0.00 1.40 0.00 2188 14125 5.25 312.00

42074-68-0 Benzene. 1-chloro-2-(chlorodiphenylmethyl)- 0.03 1.96 0.01 12589 38905 6.23 313.23

43076-30-8 1-Butanone. 1-[4-(1.1-dimethylethyl)phenyl]-4-[4-(hydroxydiphenylmethyl)-1-piperidinyl]-

0.00 1.50 0.00 2754 40738 7.39 469.67

50679-08-8 1-Piperidinebutanol. alpha-[4-(1.1-dimethylethyl)phenyl]-4-(hydroxydiphenylmethyl)-

0.01 1.68 0.00 2089 35481 7.62 471.69

50772-29-7 Butanoyl chloride. 4-[2.4-bis(1.1-dimethylpropyl)phenoxy]- 0.05 1.97 0.07 22387 44668 6.55 338.00

51630-58-1 Benzeneacetic acid. 4-chloro-alpha-(1-methylethyl)-. cyano(3-phenoxyphenyl)methyl ester

1.00 2.01 0.02 11749 45709 6.76 419.91

SHC/TS 2-3/029


weight


BCF C15

Log Kow16

MW

52179-28-9 Propanoic acid. 2-[4-(2.2-dichlorocyclopropyl)phenoxy]-2-methyl-. ethyl ester 0.15 1.73 0.13 871 5754 4.72 317.00

52434-90-9 1.3.5-Triazine-2.4.6(1H.3H.5H)-trione. 1.3.5-tris(2.3-dibromopropyl)- 0.00 1.76 0.00 19953 40738 7.37 728.00

52740-90-6 2-Anthracenecarboxamide. 1-amino-N-(3-bromo-9.10-dihydro-9.10-dioxo-2-anthracenyl)-9.10-dihydro-9.10-dioxo-

0.00 1.57 0.00 5248 45709 6.83 551.36

53184-75-1 Phosphorous acid. (1-methylethylidene)di-4.1-phenylene tetrakis[(3-ethyl-3-oxetanyl)methyl] ester

0.00 0.45 0.00 3090 26303 7.96 748.84

53928-30-6 alpha-D-Glucofuranose. 1.2-O-(1-methylethylidene)-3.5.6-tris-O-(phenylmethyl)-

0.00 1.70 0.00 3890 21878 5.57 490.60

54079-53-7 Propanedinitrile. [[4-[[2-(4-cyclohexylphenoxy)ethyl]ethylamino]-2-methylphenyl]methylene]-

1.00 1.66 0.01 8913 34674 7.63 413.57

54914-37-3 Cyclohexanemethanamine. 1.3.3-trimethyl-N-(2-methylpropylidene)-5-[(2-methylpropylidene)amino]-

0.01 2.16 0.02 38019 44668 7.16 278.49

55525-54-7 Urea. N.N'-bis[(5-isocyanato-1.3.3-trimethylcyclohexyl)methyl]- 0.00 1.43 0.00 24547 41687 7.31 418.58

58997-88-9 Benzoic acid. 3.4.5-trimethoxy-. 2-(dimethylamino)-2-phenylbutyl ester. (±)- 1.00 2.00 0.24 7943 32359 5.98 387.48

59447-55-1 2-Propenoic acid. (pentabromophenyl)methyl ester 0.00 1.43 0.03 39811 45709 6.89 556.00

61167-58-6 2-Propenoic acid. 2-(1.1-dimethylethyl)-6-[[3-(1.1-dimethylethyl)-2-hydroxy-5-methylphenyl]methyl]-4-methylphenyl ester

0.66 1.88 0.02 275 15136 8.40 394.56

64131-85-7 Phosphorothioic acid. O.O.O-tris(4-nitrophenyl) ester 1.00 1.79 0.00 7244 30903 5.92 477.34

65294-17-9 Methylium. tris[4-(dimethylamino)phenyl]-. salt with 3-[[4-(phenylamino)phenyl]azo]benzenesulfonic acid (1:1)

0.00 1.53 0.00 6918 30200 5.90 373.55

67564-91-4 Morpholine. 4-[3-[4-(1.1-dimethylethyl)phenyl]-2-methylpropyl]-2.6-dimethyl-. cis-

0.00 1.98 0.00 1259 19953 5.50 303.49

68937-41-7 Phenol. isopropylated. phosphate (3:1) 1.00 2.13 0.00 16 4786 9.07 452.54

70321-86-7 Phenol. 2-(2H-benzotriazol-2-yl)-4.6-bis(1-methyl-1-phenylethyl)- 0.09 1.89 0.00 2754 33884 7.67 447.58

72968-71-9 2-Thiophenecarboxylic acid. 4-cyano-5-[[5-cyano-2.6-bis[(3-methoxypropyl)amino]-4-methyl-3-pyridinyl]azo]-3-methyl-. met

0.00 1.12 0.00 10 5495 4.70 499.59

89347-09-1 1.3.4-Thiadiazole. 2.5-bis(tert-nonyldithio)- 0.00 1.74 0.00 3 5248 9.02 466.84

SHC/TS 2-3/029

Table 2 Ecotoxicity (mg/l)

CAS

TGD QSAR acute toxicity Multicase acute Lethal BB NOEC

T 17

Fish np

18 Fish p

19

Daph npFejl!

Ukendt argument

for parameter.

Daph pFejl!

Ukendt argument

for parameter.

Algae Fish Daph LBNO2S20

LBNO2C21

50-29-3 NA22

NA NA NA NA NA 3.0E-03 2.0E-03 2.0E-03 T

58-89-9 2.8E+00 1.6E+00 1.2E+00 1.9E+00 9.4E-01 5.3E-01 1.6E+00 1.9E-01 2.8E-02 T

77-47-4 1.3E+00 7.9E-01 5.2E-01 1.1E+00 3.8E-01 NA 2.3E+00 3.6E-02 1.1E-02 T

78-63-7 NA NA NA NA NA NA NA 3.0E-03 1.0E-03 T




87-83-2 NA NA NA NA NA NA 1.0E-02 2.0E-03 2.0E-03 T


115-27-5 1.5E+00 9.4E-01 6.0E-01 1.4E+00 4.3E-01 NA NA 8.7E-02 1.3E-02 T

115-32-2 1.7E-01 1.5E-01 5.4E-02 3.4E-01 3.4E-02 3.4E-01 1.4E-01 5.0E-02 3.0E-03 T

116-29-0 5.7E-01 4.1E-01 2.0E-01 7.3E-01 1.4E-01 NA 5.0E-02 1.0E-01 6.0E-03 T

117-08-8 1.3E+00 8.0E-01 5.2E-01 1.2E+00 3.8E-01 NA NA 7.5E-02 1.1E-02

117-18-0 2.0E+00 1.1E+00 8.4E-01 1.5E+00 6.3E-01 2.9E-01 8.0E-01 1.1E-01 1.9E-02 T

118-74-1 1.2E-01 1.0E-01 3.7E-02 2.4E-01 2.3E-02 1.0E-01 6.9E-01 1.1E-02 2.0E-03 T

118-82-1 NA NA NA NA NA NA NA 2.0E+00 1.5E-02





129-73-7 1.8E-01 1.5E-01 5.8E-02 3.4E-01 3.7E-02 1.0E-02 4.1E-01 1.3E-02 3.0E-03 T


133-49-3 1.1E-01 9.5E-02 3.3E-02 2.2E-01 2.0E-02 NA NA 8.0E-03 2.0E-03 T



152-11-4 5.8E-01 4.3E-01 2.0E-01 7.9E-01 1.3E-01 NA NA 3.8E-02 6.0E-03 T

298-57-7 3.6E-01 2.7E-01 1.2E-01 5.4E-01 7.9E-02 NA NA 2.4E-02 4.0E-03 T


17

Fulfilling T criteria for ecotoxicity or mammalian toxicity independent of vPvB or PB assignment. 18

np = non polar 19

p = polar 20

Lethal body burden NOEC based on Syracuse BCFWIN BCF, 2 mmol/kg lethal body burden and an acute-to-chronic ratio of 10 21

Lethal body burden NOEC based on Connell BCF, 2 mmol/kg lethal body burden and an acute-to-chronic ratio of 10 22

NA = Not Applicable if log Kow > 6 except for Multicase Daphnia where NA for log Kow > 7. For Multicase models also NA if outside model domain, unknown

fragments etc.

SHC/TS 2-3/029

CAS


T 17

Fish np

18 Fish p

19

Daph npFejl!

Ukendt argument

for parameter.

Daph pFejl!

Ukendt argument

for parameter.


LBNO2C21

335-36-4 7.1E-01 5.0E-01 2.6E-01 8.8E-01 1.7E-01 NA 1.0E+03 4.5E-02 7.0E-03 T

335-57-9 NA NA NA NA NA NA 1.0E+03 2.0E-03 2.0E-03 T

335-67-1 NA NA NA NA NA NA NA 1.5E+00 2.0E-03 T



375-72-4 1.6E-01 1.3E-01 5.0E-02 3.0E-01 3.2E-02 NA 1.0E+03 1.1E-02 2.0E-03 T


507-63-1 NA NA NA NA NA NA NA 4.5E-01 1.3E-02


611-75-6 7.5E-01 5.2E-01 2.7E-01 8.8E-01 1.9E-01 NA NA 4.6E-02 7.0E-03 T

626-39-1 1.4E+00 8.6E-01 5.6E-01 1.3E+00 4.1E-01 3.5E-01 5.6E-01 1.1E-01 1.2E-02

632-79-1 3.1E-01 2.5E-01 9.9E-02 5.3E-01 6.4E-02 NA NA 2.2E-02 4.0E-03 T

634-66-2 1.1E+00 6.9E-01 4.7E-01 9.7E-01 3.4E-01 3.9E-01 4.7E-01 6.2E-02 1.0E-02 T

678-26-2 6.0E-01 4.1E-01 2.2E-01 6.9E-01 1.5E-01 1.0E+03 1.0E+03 1.2E-01 6.0E-03 T


850-92-0 3.7E-01 2.8E-01 1.3E-01 5.2E-01 8.6E-02 9.5E-01 NA 2.4E-02 4.0E-03 T


979-02-2 6.7E-01 4.7E-01 2.4E-01 8.0E-01 1.7E-01 NA 2.4E-01 4.2E-02 6.0E-03 T

1068-27-5 1.3E-01 1.1E-01 3.9E-02 2.5E-01 2.4E-02 NA NA 9.0E-03 2.0E-03 T

1173-09-7 3.2E+00 1.8E+00 1.4E+00 2.3E+00 1.0E+00 NA 2.0E-04 1.7E-01 3.0E-02 T

1478-61-1 2.2E+00 1.3E+00 9.1E-01 1.7E+00 6.7E-01 5.5E-01 NA 1.2E-01 2.0E-02

1582-09-8 4.2E-01 3.1E-01 1.4E-01 5.8E-01 9.7E-02 NA 2.4E-01 2.6E-02 4.0E-03 T

1639-60-7 4.6E-01 3.3E-01 1.6E-01 6.2E-01 1.1E-01 NA NA 3.0E-02 5.0E-03 T


2094-98-6 1.7E-01 1.4E-01 5.6E-02 2.9E-01 3.6E-02 NA NA 1.2E-02 2.0E-03 T





2795-39-3 NA NA NA NA NA NA 1.0E+03 1.8E+00 3.0E-03 T




3278-89-5 2.7E-01 2.1E-01 8.7E-02 4.5E-01 5.6E-02 1.6E-01 9.0E-02 1.9E-02 3.0E-03 T






SHC/TS 2-3/029

CAS


T 17

Fish np

18 Fish p

19

Daph npFejl!

Ukendt argument

for parameter.

Daph pFejl!

Ukendt argument

for parameter.


LBNO2C21





5216-25-1 1.3E+00 7.7E-01 5.4E-01 1.1E+00 3.9E-01 NA NA 7.3E-02 1.2E-02



6407-78-9 2.0E-01 1.6E-01 6.3E-02 3.4E-01 4.0E-02 NA NA 6.1E+00 3.0E-03 T




7139-02-8 3.5E-01 2.7E-01 1.2E-01 5.3E-01 7.7E-02 NA NA 2.4E-02 4.0E-03 T


13014-24-9 4.3E-01 3.0E-01 1.5E-01 5.4E-01 1.0E-01 NA NA 2.7E-02 4.0E-03 T

13171-00-1 9.1E-02 7.9E-02 2.7E-02 1.9E-01 1.7E-02 NA NA 4.7E-02 2.0E-03 T




15323-35-0 1.1E-01 9.1E-02 3.2E-02 2.1E-01 2.0E-02 4.6E-01 3.0E-02 5.2E-02 2.0E-03 T


16699-20-0 3.6E-01 2.7E-01 1.2E-01 5.4E-01 7.9E-02 NA NA 2.4E-02 4.0E-03 T


18181-80-1 1.2E+00 7.8E-01 4.5E-01 1.3E+00 3.2E-01 NA NA 3.0E-02 1.1E-02





25268-77-3 NA NA NA NA NA NA NA 3.5E-01 1.2E-02



27137-85-5 8.0E-01 5.2E-01 3.0E-01 8.3E-01 2.1E-01 NA NA 4.8E-02 7.0E-03 T



30707-68-7 6.0E-01 4.3E-01 2.1E-01 8.0E-01 1.4E-01 NA NA 3.9E-02 6.0E-03 T


35578-47-3 4.3E-01 3.2E-01 1.5E-01 6.1E-01 9.9E-02 NA NA 2.9E-02 5.0E-03 T

36861-47-9 3.8E-01 2.7E-01 1.3E-01 4.9E-01 9.0E-02 NA 2.0E-02 2.4E-02 4.0E-03 T



SHC/TS 2-3/029

CAS


T 17

Fish np

18 Fish p

19

Daph npFejl!

Ukendt argument

for parameter.

Daph pFejl!

Ukendt argument

for parameter.


LBNO2C21

38850-60-1 5.6E+00 3.1E+00 2.4E+00 3.9E+00 1.8E+00 NA 1.0E+03 3.8E+01 5.4E-02

39489-75-3 4.4E-01 3.4E-01 1.5E-01 6.6E-01 9.9E-02 NA NA 3.0E-02 5.0E-03 T


41604-19-7 7.7E-01 5.0E-01 3.0E-01 7.8E-01 2.1E-01 2.6E-01 2.0E-01 1.1E-02 7.0E-03 T

41999-84-2 4.4E-01 3.2E-01 1.5E-01 5.8E-01 1.0E-01 NA NA 2.9E-02 4.0E-03 T






52179-28-9 1.3E+00 7.9E-01 5.0E-01 1.2E+00 3.6E-01 NA NA 7.3E-02 1.1E-02 T




53928-30-6 3.7E-01 2.9E-01 1.2E-01 6.0E-01 7.8E-02 NA NA 2.5E-02 4.0E-03 T




58997-88-9 1.3E-01 1.2E-01 3.9E-02 2.8E-01 2.4E-02 NA NA 1.0E-02 2.0E-03 T



64131-85-7 1.8E-01 1.6E-01 5.4E-02 3.7E-01 3.4E-02 1.0E-01 5.0E-02 1.3E-02 3.0E-03 T

65294-17-9 1.5E-01 1.3E-01 4.4E-02 3.0E-01 2.8E-02 1.1E-01 1.5E+00 1.1E-02 2.0E-03 T

67564-91-4 2.6E-01 2.0E-01 8.7E-02 4.1E-01 5.7E-02 1.0E+00 2.4E+00 4.8E-02 3.0E-03 T



72968-71-9 2.1E+00 1.3E+00 8.2E-01 1.9E+00 5.9E-01 NA NA 1.0E+01 1.8E-02


Table 3 Toxicity

CAS Self classification list

23 Reprotox

Sub chronic

24

Annex 1 T25

MUT CARC Reprotox Teratogenicity NOAEL Risk Phrases

50-29-3 POS pred. NA26

NA 25-40-48/25-50/53

T

58-89-9 NEG test NEG test 2,25

Fejl! Ukendt

argument for parameter. 23/24/25-36/38-50/53

T

77-47-4 NA NA 0,01 22-24-26-34-50/53

T

78-63-7 NA NA NA T

79-94-7 NA NA 96,94 T

81-98-1 POS pred. NA 303,32 T

85-22-3 POS pred. NA 24,80 T

87-83-2 POS pred. NA 22,71 T

93-46-9 MUT NA NA NA T

115-27-5 POS pred. NA 0,04 36/37/38 T

115-32-2 NA NA 14,89 21/22-38-43 T

116-29-0 POS pred. NA 91,94 T

117-08-8 NA NA 20,24

117-18-0 NA NA 8,58 43 T

118-74-1 NA NA 2,9227

45-48/25-50/53 T

118-82-1 NA NA NA

119-47-1 NA NA NA T

128-69-8 POS pred. NA NA T

128-83-6 NA NA 175,34 T


129-73-7 NA NA NA T

133-14-2 NA NA NA T

133-49-3 POS pred. NA 7,72 T

135-91-1 CARC NA NA NA T

139-60-6 NA NA NA T

152-11-4 NA NA NA T


307-34-6 NA NA NA T

335-36-4 NA NA NA T

335-57-9 NA NA NA T

335-67-1 NEG test NA 172,22 T

355-42-0 NA NA NA T

355-43-1 NA NA 275,60 T

375-72-4 NA NA NA T

423-50-7 NA NA NA T

507-63-1 NA NA 308,44

512-04-9 NA NA 7,00 T

611-75-6 NA NA 4,05 T

626-39-1 NA NA NA

632-79-1 NA NA 32,83 T

634-66-2 POS test NA 34,93 T

678-26-2 NA NA NA T

732-26-3 NA NA NA T

850-92-0 NA POS pred. NA T

903-19-5 NA NA NA T

979-02-2 NA NA NA T

1068-27-5 NA NA NA T

Danish EPA (2001): Report on the Advisory list for selfclassification of dangerous substances. 24

1 year rat sub chronic prediction. Bold figures indicate that R48 is warranted where the criterion used is that NOAEL

< 15 mg/kg BW/d c.f. definition of PBT in the TGD. 25

Fulfilling T criteria for ecotoxicity or mammalian toxicity independent of vPvB or PB assignment. 26

NA Not Applicable i.e. not available or outside model domain. 27

Test confirms R48.

CAS

Self classification list23

Reprotox Sub chronic

24

Annex 1 T25


1173-09-7 NA NA NA T

1478-61-1 NA NA NA

1582-09-8 POS pred. NA 6,1028

36-43 T

1639-60-7 NA NEG test 23,11 T

1691-99-2 NA NA NA T

2094-98-6 NA NA NA T

2212-81-9 NA NA NA T

2309-94-6 NA NA NA T

2475-31-2 NA NA 154,82 T

2781-00-2 NA NA NA T

2795-39-3 NA NA NA T

2991-51-7 NA NA NA T

3006-86-8 NA NA NA T

3147-75-9 NA NA NA T

3278-89-5 NA NA 280,04 T

3739-67-1 NA NA NA T

3810-80-8 NA NA NA T

3825-26-1 NEG test NA 172,19 T

3846-71-7 NA NA NA T

3851-87-4 NA NA NA T

3864-99-1 NA NA NA T


4162-45-2 NA NA 0,94 T

4378-61-4 POS pred. NA 483,76 T

5216-25-1 NA NA 351,22

5285-60-9 NA NA NA T

5590-18-1 NA NA NA T

6407-78-9 NA NA NA T

6410-30-6 NA NA NA

6410-38-4 NA NA NA T

6731-36-8 NA NA NA T

7139-02-8 NA NA 5,62 T

12223-91-5 MUT CARC NA NA NA T

13014-24-9 NA NA 563,91 T

13171-00-1 NA NA NA T

13417-01-1 NA NA NA T

13680-35-8 NA NA NA T

14295-43-3 NA NA 122,36 T

15323-35-0 NA POS pred. NA T

15958-61-9 MUT CARC NA NA 594,93 T


17540-75-9 NA NA NA T

18181-80-1 NA NA 29,35

18254-13-2 NA NA NA T

20241-76-3 NA NA 262,16 T

23593-75-1 POS pred. NEG test 9,01

Fejl! Ukendt

argument for parameter. T

25155-25-3 NA NA NA T

25268-77-3 NA NA NA

25973-55-1 NA NA NA T

26748-47-0 NA NA NA T

27137-85-5 NA NA NA T

29312-59-2 NA NA 43,02 T

29398-96-7 NA NA NA T

30707-68-7 NA NA 121,22 T

31188-91-7 NA NA NA

28

Test do not confirm R48.

CAS

Self classification list23

Reprotox Sub chronic

24

Annex 1 T25


35578-47-3 NA NA 516,28 T

36861-47-9 NA NA NA T


38521-51-6 NA NA 18,24 T

38850-60-1 NA NA NA


40567-16-6 NA NA NA T

41604-19-7 NA NA 89,09 T


42074-68-0 NA NA 81,82 T

43076-30-8 NA NA NA T

50679-08-8 NA NA NA T

50772-29-7 NA NA NA T

51630-58-1 NA NA 14,16 T


52434-90-9 MUT CARC NA NA NA T

52740-90-6 NA NA NA T

53184-75-1 NA NA NA T

53928-30-6 NA NA NA T

54079-53-7 NA NA NA T



58997-88-9 NA NA 1,31 T

59447-55-1 POS pred. NA 39,09 T

61167-58-6 NA NA NA T


65294-17-9 NA NA NA T

67564-91-4 NA NA NA T

68937-41-7 NA NA NA

70321-86-7 NA NA NA T

72968-71-9 NA NA NA

89347-09-1 NA NA NA

Table 4 Potential vPvBs/PBTs with significant environmental release and exposure

potential according to Nordic Product Register information.

Chemical ID High or medium

volume score

CAS Chemical and

widespread use

or widespread

use

and not widespread

use

50-29-3 Benzene, 1,1'-(2,2,2-trichloroethylidene)bis[4-chloro- 1

58-89-9 Cyclohexane, 1,2,3,4,5,6-hexachloro-, (1alpha,2alpha,3beta,4alpha,5alpha,6beta)-

1

77-47-4 1,3-Cyclopentadiene, 1,2,3,4,5,5-hexachloro- 1

78-63-7 Peroxide, (1,1,4,4-tetramethyl-1,4-butanediyl)bis[(1,1-dimethylethyl)* 1 1

79-94-7 Phenol, 4,4'-(1-methylethylidene)bis[2,6-dibromo- 1 1

115-27-5 4,7-Methanoisobenzofuran-1,3-dione, 4,5,6,7,8,8-hexachloro-3a,4,7,7a-tetrahydro-

1 1

116-29-0 Benzene, 1,2,4-trichloro-5-[(4-chlorophenyl)sulfonyl]- 1

118-74-1 Benzene, hexachloro- 1

118-82-1 Phenol, 4,4'-methylenebis[2,6-bis(1,1-dimethylethyl)- 1 1

119-47-1 Phenol, 2,2'-methylenebis[6-(1,1-dimethylethyl)-4-methyl- 1 1

128-69-8 Perylo[3,4-cd:9,10-c'd']dipyran-1,3,8,10-tetrone 1 1

133-14-2 Peroxide, bis(2,4-dichlorobenzoyl)* 1

133-49-3 Benzenethiol, pentachloro- 1

152-11-4 Benzeneacetonitrile, alpha-[3-[[2-(3,4-dimethoxyphenyl)ethyl]methylamino] propyl]-3,4-dimethoxy-alpha-(1-methylethyl)-, monohydrochloride

1

307-34-6 Octane, octadecafluoro- 1

335-36-4 Furan, 2,2,3,3,4,4,5-heptafluorotetrahydro-5-(nonafluorobutyl)- 1

355-42-0 Hexane, tetradecafluoro- 1

611-75-6 Benzenemethanamine, 2-amino-3,5-dibromo-N-cyclohexyl-N-methyl-, monohydrochloride

1

732-26-3 Phenol, 2,4,6-tris(1,1-dimethylethyl)- 1 1

1068-27-5 Peroxide, (1,1,4,4-tetramethyl-2-butyne-1,4-diyl)bis[(1,1-dimethylethyl)* 1 1

1478-61-1 Phenol, 4,4'-[2,2,2-trifluoro-1-(trifluoromethyl)ethylidene]bis- 1

1582-09-8 Benzenamine, 2,6-dinitro-N,N-dipropyl-4-(trifluoromethyl)- 1

1639-60-7 Benzeneethanol, alpha-[2-(dimethylamino)-1-methylethyl]-alpha-phenyl-, propanoate (ester), hydrochloride, [S-(R*,S*)]-

1

1691-99-2 1-Octanesulfonamide, N-ethyl-1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptadecafluoro-N-(2-hydroxyethyl)-

1

2094-98-6 Cyclohexanecarbonitrile, 1,1'-azobis- 1

2212-81-9 Peroxide, [1,3-phenylenebis(1-methylethylidene)]bis[(1,1-dimethylethyl)* 1

2309-94-6 Propanamide, N-[2-[(2-bromo-6-cyano-4-nitrophenyl)azo]-5-(diethylamino)phenyl]-

1

2475-31-2 3H-Indol-3-one, 5,7-dibromo-2-(5,7-dibromo-1,3-dihydro-3-oxo-2H-indol-2-ylidene)-1,2-dihydro-

1

2781-00-2 Peroxide, [1,4-phenylenebis(1-methylethylidene)]bis[(1,1-dimethylethyl)* 1

2795-39-3 1-Octanesulfonic acid, 1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptadecafluoro-, potassium salt

1

Chemical ID

High or medium

volume score

CAS Chemical and

widespread use

or widespread

use

and not widespread

use

2991-51-7 Glycine, N-ethyl-N-[(heptadecafluorooctyl)sulfonyl]-, potassium salt 1

3006-86-8 Peroxide, cyclohexylidenebis[(1,1-dimethylethyl)* 1

3147-75-9 Phenol, 2-(2H-benzotriazol-2-yl)-4-(1,1,3,3-tetramethylbutyl)- 1

3825-26-1 Octanoic acid, pentadecafluoro-, ammonium salt 1

3846-71-7 Phenol, 2-(2H-benzotriazol-2-yl)-4,6-bis(1,1-dimethylethyl)- 1 1

3851-87-4 Peroxide, bis(3,5,5-trimethyl-1-oxohexyl)* 1

3864-99-1 Phenol, 2-(5-chloro-2H-benzotriazol-2-yl)-4,6-bis(1,1-dimethylethyl)- 1 1

4051-63-2 [1,1'-Bianthracene]-9,9',10,10'-tetrone, 4,4'-diamino- 1 1

4378-61-4 Dibenzo[def,mno]chrysene-6,12-dione, 4,10-dibromo- 1 1

5285-60-9 Benzenamine, 4,4'-methylenebis[N-(1-methylpropyl)- 1

5590-18-1 1H-Isoindol-1-one, 3,3'-(1,4-phenylenediimino)bis[4,5,6,7-tetrachloro- 1 1

6407-78-9 3H-Pyrazol-3-one, 4-[(2,4-dimethylphenyl)azo]-2,4-dihydro-5-methyl-2-phenyl- 1

6410-30-6 2-Naphthalenecarboxamide, N-(4-chlorophenyl)-3-hydroxy-4-[(2-methyl-5-nitrophenyl)azo]-

1 1

6731-36-8 Peroxide, (3,3,5-trimethylcyclohexylidene)bis[(1,1-dimethylethyl)* 1 1

13171-00-1 Ethanone, 1-[6-(1,1-dimethylethyl)-2,3-dihydro-1,1-dimethyl-1H-inden-4-yl]- 1

13680-35-8 Benzenamine, 4,4'-methylenebis[2,6-diethyl- 1

14295-43-3 Benzo[b]thiophen-3(2H)-one, 4,7-dichloro-2-(4,7-dichloro-3-oxobenzo[b]thien-2(3H)-ylidene)-

1 1

15323-35-0 Ethanone, 1-(2,3-dihydro-1,1,2,3,3,6-hexamethyl-1H-inden-5-yl)- 1

17540-75-9 Phenol, 2,6-bis(1,1-dimethylethyl)-4-(1-methylpropyl)- 1

18181-80-1 Benzeneacetic acid, 4-bromo-alpha-(4-bromophenyl)-alpha-hydroxy-, 1-methylethyl ester

1

20241-76-3 9,10-Anthracenedione, 1,8-dihydroxy-4-nitro-5-(phenylamino)- 1 1

23593-75-1 1H-Imidazole, 1-[(2-chlorophenyl)diphenylmethyl]- 1

25155-25-3 Peroxide, [1,3(or 1,4)-phenylenebis(1-methylethylidene)]bis[(1,1-dimethylethyl)*

1 1

25268-77-3 2-Propenoic acid, 2-[[(heptadecafluorooctyl)sulfonyl]methylamino]ethyl ester 1

25973-55-1 Phenol, 2-(2H-benzotriazol-2-yl)-4,6-bis(1,1-dimethylpropyl)- 1 1

29398-96-7 [1,1'-Biphenyl]-4,4'-diamine, N,N'-bis(2,4-dinitrophenyl)-3,3'-dimethoxy- 1

36861-47-9 Bicyclo[2.2.1]heptan-2-one, 1,7,7-trimethyl-3-[(4-methylphenyl)methylene]-, (±)-

1

37853-59-1 Benzene, 1,1'-[1,2-ethanediylbis(oxy)]bis[2,4,6-tribromo- 1

51630-58-1 Benzeneacetic acid, 4-chloro-alpha-(1-methylethyl)-, cyano(3-phenoxyphenyl)methyl ester

1

54079-53-7 Propanedinitrile, [[4-[[2-(4-cyclohexylphenoxy)ethyl]ethylamino]-2-methylphenyl]methylene]-

1

54914-37-3 Cyclohexanemethanamine, 1,3,3-trimethyl-N-(2-methylpropylidene)-5-[(2-methylpropylidene)amino]-

1

67564-91-4 Morpholine, 4-[3-[4-(1,1-dimethylethyl)phenyl]-2-methylpropyl]-2,6-dimethyl-, cis-

1

68937-41-7 Phenol, isopropylated, phosphate (3:1) 1 1

Chemical ID

High or medium

volume score

CAS Chemical and

widespread use

or widespread

use

and not widespread

use

70321-86-7 Phenol, 2-(2H-benzotriazol-2-yl)-4,6-bis(1-methyl-1-phenylethyl)- 1 1

72968-71-9 2-Thiophenecarboxylic acid, 4-cyano-5-[[5-cyano-2,6-bis[(3-methoxypropyl)amino]-4-methyl-3-pyridinyl]azo]-3-methyl-, methyl ester

1

89347-09-1 1,3,4-Thiadiazole, 2,5-bis(tert-nonyldithio)- 1 1

*: excluded because of likely hydrolysis to degradation products that are not vPvBs/PBTs (expert judgement)

Annex 2 Overview of the QSAR identification of discrete organic "PBs" on EINECS, The EU list on MPVC and HPVC.

Abbreviations:

HPV: High Production Volume (> 1000 tpa/EU-manufacturer or -importer)

MPV: Medium Production Volume (10-1000 tpa/EU-manufacturer or -importer

P: persistent according to (BIOWIN2<0.5 or MITIDEG 2< 0.5)and BIOWIN 3< 2.2

Bc: BCF(Fish) according to the BCF-Connell model

Bs: BCF(Fish) according to the BCF-Syracuse model

46706 EINECS Substances

1,2%

5,5%

84%

7%

10%

EINECS P P+Bc 2-5000 P+Bc >5000

46706 EINECS Substances

0,7%

1,3%

14%

2%

84%

EINECS P P+Bs 2-5000 P+Bs >5000

4165 MPV Substances

1,4%

6,1%

83%

8%

10%

MPV P P+Bc 2-5000 P+Bc >5000

4165 MPV Substances

0,7%

1,3%

16%

2%

82%

MPV P P+Bs 2-5000 P+Bs >5000

1351 HPV Substances

0,7%

1,4%91%

2%

7%

HPV P P+Bc 2-5000 P+Bc >5000

1351 HPV Substances

0,1%

0,4%

7%

1%

93%

HPV P P+Bs 2-5000 P+Bs >5000

danish epa copenhagen 2002-06-27 - miljøstyrelseneng.mst.dk/media/mst/69087/qsar pbt final...

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