Etocrilene

Bioaccumulation: aquatic / sediment

Link to relevant study record(s)

Reference

Reference 1

Endpoint:

bioaccumulation: aquatic / sediment

Type of information:

calculation (if not (Q)SAR)

Remarks:

Migrated phrase: estimated by calculation

Adequacy of study:

weight of evidence

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

other: validated, well documented QSAR model

Justification for type of information:

Justification for type of information: In Article 13 of Regulation (EC) No 1907/2006, it is laid down that information on intrinsic properties of substances may be generated by means other than tests, provided that the conditions set out in Annex XI (of the same Regulation) are met. Furthermore according to Article 25 of the same Regulation testing on vertebrate animals shall be undertaken only as a last resort. According to Annex XI of Regulation (EC) No 1907/2006 (Q)SAR results can be used if (1) the scientific validity of the (Q)SAR model has been established, (2) the substance falls within the applicability domain of the (Q)SAR model, (3) the results are adequate for the purpose of classification and labeling and/or risk assessment and (4) adequate and reliable documentation of the applied method is provided. For QMRF and QRPF see ‘Overall remarks, attachments’ (QMRF) and ‘Executive Summary’ (QPRF) of the respective endpoint study records. For the assessment of CAS 5232-99-5 (Q)SAR results were used for aquatic bioaccumulation. The criteria listed in Annex XI of Regulation (EC) No 1907/2006 are considered to be adequately fulfilled and therefore the endpoint(s) sufficiently covered and suitable for risk assessment.

Qualifier:

no guideline followed

Principles of method if other than guideline:

The BCF is estimated based on several molecular descriptors. The applicability domain of predictions is assessed using an Applicability Domain Index (ADI) calculated by grouping several other indices, e.g. by a similarity index that consider molecule's fingerprint and structural aspects (count of atoms, rings and relevant fragments)

Details on estimation of bioconcentration:

Details on estimation of bioconcentration
BASIS INFORMATION- Measured/calculated logPow: calculatedBASIS FOR CALCULATION OF BCF- Estimation software: VEGA CAESAR v2.1.13- Result based on calculated log Pow of: 3.24 (calculated by VEGA)

Type:

BCF

Value:

40 L/kg

Remarks on result:

other: According to the model’s global AD index, the predicted substance could be out of the Applicability Domain of the model.

Executive summary:

The BCF model (CAESAR) v2.1.13 implemented in the VEGA platform v1.0.8:Estimation Domain (QPRF)

 

The applicability domain of predictions is assessed using an Applicability Domain Index (ADI) that has values from 0 (worst case) to 1 (best case). The ADI is calculated by grouping several other indices, each one taking into account a particular issue of the applicability domain. Most of the indices are based on the calculation of the most similar compounds found in the training and test set of the model, calculated by a similarity index that consider molecule's fingerprint and structural aspects (count of atoms, rings and relevant fragments). Note that when the experimental value for the given compound is found, the applicability Domain indices are calculated only considering this value, without taking into account the firstnsimilar compounds.

For each index, including the final ADI, three intervals for its values are defined, such that the first interval corresponds to a positive evaluation, the second one corresponds to a suspicious evaluation and the last one corresponds to a negative evaluation.

Following, all applicability domain components are reported along with their explanation and the

intervals used. Furthermore, the specific index of the substance is given.

 

 

-Similar molecules with known experimental value.

This index takes into account how similar are the first two most similar compounds found. Values near 1 mean that the predicted compound is well represented in the dataset used to build the model, otherwise the prediction could be an extrapolation.

 

Defined intervals are:

1 >= index > 0.9	strongly similar compounds with known experimental value in the training set have been found
0.9 >= index > 0.75	only moderately similar compounds with known experimental value in the training set have been found
index <= 0.75	no similar compounds with known experimental value in the training set have been found

 

The substance has a similarity index of 0.818.

 

 

-Accuracy (average error) of prediction for similar molecules.

This index takes into account the error in prediction for the two most similar compounds found. Values near 0 mean that the predicted compounds falls in an area of the model's space where the model gives reliable predictions, otherwise the greater is the value, the worse the model behaves.

 

Defined intervals are:

index < 0.5	accuracy of prediction for similar molecules found in the training set is good
0.5 <= index <= 1.0	accuracy of prediction for similar molecules found in the training set is not optimal
index > 1.0	accuracy of prediction for similar molecules found in the training set is not adequate

 

The substance has an accuracy index of 0.75.

 

 

-Concordance with similar molecules (average difference between target compound prediction and experimental values of similar molecules).

This index takes into account the difference between the predicted value and the experimental values of the two most similar compounds. Values near 0 mean that the prediction made agrees with the experimental values found in the model's space, thus the prediction is reliable.

 

Defined intervals are:

index < 0.5	similar molecules found in the training set have experimental values that agree with the target compound predicted value
0.5 <= index <= 1.0	similar molecules found in the training set have experimental values that slightly disagree with the target compound predicted value
index > 1.0	similar molecules found in the training set have experimental values that completely disagree with the target compound predicted value

 

The substance has a concordance index of 1.015.

 

 

-Maximum error of prediction among similar molecules.

This index takes into account the maximum error in prediction among the two most similar compounds. Values near 0 means that the predicted compounds falls in an area of the model's space where the model gives reliable predictions without any outlier value.

 

Defined intervals are:

index < 0.5	the maximum error in prediction of similar molecules found in the training set has a low value, considering the experimental variability
0.5 <= index < 1.0	the maximum error in prediction of similar molecules found in the training set has a moderate value, considering the experimental variability
index >= 1.0	the maximum error in prediction of similar molecules found in the training set has a high value, considering the experimental variability

 

The substance has a max error index of 0.77.

 

 

-Atom Centered Fragments similarity check.

This index takes into account the presence of one or more fragments that aren't found in the training set, or that are rare fragments. First order atom centered fragments from all molecules in the training set are calculated, then compared with the first order atom centered fragments from the predicted compound; then the index is calculated as following: a first index RARE takes into account rare fragments (those who occur less than three times in the training set), having value of 1 if no such fragments are found, 0.85 if up to 2 fragments are found, 0.7 if more than 2 fragments are found; a second index NOTFOUND takes into account not found fragments, having value of 1 if no such fragments are found, 0.6 if a fragments is found, 0.4 if more than 1 fragment is found. Then, the final index is given as the product RARE * NOTFOUND.

 

Defined intervals are:

index = 1	all atom centered fragment of the compound have been found in the compounds of the training set
1 > index >= 0.7	some atom centered fragment of the compound have not been found in the compounds of the training set or are rare fragments
index < 0.7	a prominent number of atom centered fragments of the compound have not been found in the compounds of the training set or are rare fragments

 

The substance has an ACF matching index of 0.85.

 

 

- Descriptors noise sensitivity analysis.

This index checks whether the predicted compound falls in a reliable and stable descriptors space or not. A sequence of random scrambling (noise) is applied to the descriptors calculated for the considered compound, and it is checked if the perturbation of descriptors lead to a significant change in the prediction; if the studied descriptors space is stable, these changes should be of little entity. After a large number of such random scrambling, a final index is calculated.

 

Defined intervals are:

1 >= index > 0.8	predictions has a good response to noise scrambling, thus shows a good reliability
0.8 >= index > 0.5	predictions has a not so good response to noise scrambling, thus shows an uncertain reliability
index <= 0.5	predictions has a bad response to noise scrambling, thus shows a low reliability

The substance has a noise sensitivity of 0.948.

 

 

-Model descriptors range check.

This index checks if the descriptors calculated for the predicted compound are inside the range of descriptors of the training and test set. The index has value 1 if all descriptors are inside the range, 0 if at least one descriptor is out of the range.

 

Defined intervals are:

index = 1	descriptors for this compound have values inside the descriptor range of the compounds of the training set
index = 0	descriptors for this compound have values outside the descriptor range of the compounds of the training set

 

The substance’ descriptors range check is true.

 

 

-Global AD Index.

The final global index takes into account all the previous indices, in order to give a general global assessment on the applicability domain for the predicted compound.

 

Defined intervals are:

1 >= index > 0.85	predicted substance is into the Applicability Domain of the model
0.85 >= index > 0.75	predicted substance could be out of the Applicability Domain of the model
index <= 0.75	predicted substance is out of the Applicability Domain of the model

 

The substance has a global AD index of 0.696.

 

According to the VEGA CAESAR model the compound could be out of the applicability domain. The model found only moderately similar molecules with known experimental value in the training set. The model detected a structural alert which should be carefully taken into account and are known to be primarily present in non-bioaccumulative compound. In conclusion, the prediction of the VEGA CEASAR model seems to be adequate for the use in a weight-of-evidence approach. According to the model the target compound is not expected to significalty accumulate in organisms.

Model: The BCF model (CAESAR) v2.1.13 implemented in the VEGA platform v1.0.8
Substance:	etocrilene
CAS-#:	5232-99-5
SMILES:	O=C(OCC)C(C(#N))=C(c(cccc1)c1)c(cccc2)c2
The following structural alerts were detected.
Structural alerts for outliers
Structural alerts related to a special class of chemicals that have a particular BCF bevavior.
SR 02	Carbonyl residue; this SA has been found to be present in a very large (112) number of nonbioaccumulative compounds, even when the logP value was higher than 3.

References:

VEGA Guide to BCF Model version 2.1.13 implemented in the VEGA tool v1.0.8

Reference 2

Endpoint:

bioaccumulation: aquatic / sediment

Type of information:

calculation (if not (Q)SAR)

Remarks:

Migrated phrase: estimated by calculation

Adequacy of study:

weight of evidence

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

other: validated, well documented QSAR model

Justification for type of information:

Justification for type of information: In Article 13 of Regulation (EC) No 1907/2006, it is laid down that information on intrinsic properties of substances may be generated by means other than tests, provided that the conditions set out in Annex XI (of the same Regulation) are met. Furthermore according to Article 25 of the same Regulation testing on vertebrate animals shall be undertaken only as a last resort. According to Annex XI of Regulation (EC) No 1907/2006 (Q)SAR results can be used if (1) the scientific validity of the (Q)SAR model has been established, (2) the substance falls within the applicability domain of the (Q)SAR model, (3) the results are adequate for the purpose of classification and labeling and/or risk assessment and (4) adequate and reliable documentation of the applied method is provided. For QMRF and QRPF see ‘Overall remarks, attachments’ (QMRF) and ‘Executive Summary’ (QPRF) of the respective endpoint study records. For the assessment of CAS 5232-99-5 (Q)SAR results were used for aquatic bioaccumulation. The criteria listed in Annex XI of Regulation (EC) No 1907/2006 are considered to be adequately fulfilled and therefore the endpoint(s) sufficiently covered and suitable for risk assessment.

Qualifier:

no guideline followed

Principles of method if other than guideline:

The BCF is estimated based on several molecular descriptors. The applicability domain of predictions is assessed using an Applicability Domain Index (ADI) calculated by grouping several other indices, e.g. by a similarity index that consider molecule's fingerprint and structural aspects (count of atoms, rings and relevant fragments)

Details on estimation of bioconcentration:

Basis information
- Measured/calculated logPow: calculated
BASIS FOR CALCULATION OF BCF
- Estimation software: VEGA Meylan v1.0.2
- Result based on calculated log Pow of: 10.2 (calculated by VEGA)

Type:

BCF

Value:

693 L/kg

Remarks on result:

other: logBCF 2.84, According to the model’s global AD index, the predicted substance is out of the Applicability Domain of the model.

Executive summary:

The BCF model (Meylan) v1.0.2 implemented in the VEGA platform v1.0.8:Estimation Domain (QPRF)

 

The applicability domain of predictions is assessed using an Applicability Domain Index (ADI) that has values from 0 (worst case) to 1 (best case). The ADI is calculated by grouping several other indices, each one taking into account a particular issue of the applicability domain. Most of the indices are based on the calculation of the most similar compounds found in the training and test set of the model, calculated by a similarity index that consider molecule's fingerprint and structural aspects (count of atoms, rings and relevant fragments). Note that when the experimental value for the given compound is found, the applicability Domain indices are calculated only considering this value, without taking into account the firstnsimilar compounds.

For each index, including the final ADI, three intervals for its values are defined, such that the first interval corresponds to a positive evaluation, the second one corresponds to a suspicious evaluation and the last one corresponds to a negative evaluation.

Following, all applicability domain components are reported along with their explanation and the

intervals used. Furthermore, the specific index of the substance is given.

 

 

-Similar molecules with known experimental value.

This index takes into account how similar are the first two most similar compounds found. Values near 1 mean that the predicted compound is well represented in the dataset used to build the model, otherwise the prediction could be an extrapolation.

 

Defined intervals are:

1 >= index > 0.9	strongly similar compounds with known experimental value in the training set have been found
0.9 >= index > 0.75	only moderately similar compounds with known experimental value in the training set have been found
index <= 0.75	no similar compounds with known experimental value in the training set have been found

 

The substance has a similarity index of 0.819

 

 

-Accuracy (average error) of prediction for similar molecules.

This index takes into account the error in prediction for the two most similar compounds found. Values near 0 mean that the predicted compounds falls in an area of the model's space where the model gives reliable predictions, otherwise the greater is the value, the worse the model behaves.

 

Defined intervals are:

index < 0.5	accuracy of prediction for similar molecules found in the training set is good
0.5 <= index <= 1.0	accuracy of prediction for similar molecules found in the training set is not optimal
index > 1.0	accuracy of prediction for similar molecules found in the training set is not adequate

 

The substance has an accuracy index of 0.18.

 

 

-Concordance with similar molecules (average difference between target compound prediction and experimental values of similar molecules).

This index takes into account the difference between the predicted value and the experimental values of the two most similar compounds. Values near 0 mean that the prediction made agrees with the experimental values found in the model's space, thus the prediction is reliable.

 

Defined intervals are:

index < 0.5	similar molecules found in the training set have experimental values that agree with the target compound predicted value
0.5 <= index <= 1.0	similar molecules found in the training set have experimental values that slightly disagree with the target compound predicted value
index > 1.0	similar molecules found in the training set have experimental values that completely disagree with the target compound predicted value

 

The substance has a concordance index of 1.51.

 

 

-Maximum error of prediction among similar molecules.

This index takes into account the maximum error in prediction among the two most similar compounds. Values near 0 means that the predicted compounds fall in an area of the model's space where the model gives reliable predictions without any outlier value.

 

Defined intervals are:

index < 0.5	the maximum error in prediction of similar molecules found in the training set has a low value, considering the experimental variability
0.5 <= index < 1.0	the maximum error in prediction of similar molecules found in the training set has a moderate value, considering the experimental variability
index >= 1.0	the maximum error in prediction of similar molecules found in the training set has a high value, considering the experimental variability

 

The substance has a max error index of 0.19.

 

 

- LogP reliability.

This index takes into account the reliability of the logP value used in the model. Note that the Meylan BCF model is strongly based on the logP prediction of the compound, thus this index is highly relevant for the assessment of the final prediction. The reliability of the logP value comes from the assessment of the VEGA LogP model (that provides the used logP value), which is also provided in the “Prediction summary” section of the report.

 

Defined intervals are:

index = 1	the maximum error in prediction of similar molecules found in the training set has a low value, considering the experimental variability
index = 0.7	the maximum error in prediction of similar molecules found in the training set has a moderate value, considering the experimental variability
index = 0	the maximum error in prediction of similar molecules found in the training set has a high value, considering the experimental variability

 

The substance has a LogP reliability index of 0.

 

 

-Model descriptors range check.

This index checks if the descriptors calculated for the predicted compound are inside the range of descriptors of the training and test set. The index has value 1 if all descriptors are inside the range, 0 if at least one descriptor is out of the range.

 

Defined intervals are:

index = 1	descriptors for this compound have values inside the descriptor range of the compounds of the training set
index = 0	descriptors for this compound have values outside the descriptor range of the compounds of the training set

 

The substance’ descriptors range check is true.

 

 

-Global AD Index.

The final global index takes into account all the previous indices, in order to give a general global assessment on the applicability domain for the predicted compound.

 

Defined intervals are:

1 >= index > 0.85	predicted substance is into the Applicability Domain of the model
0.85 >= index > 0.75	predicted substance could be out of the Applicability Domain of the model
index <= 0.75	predicted substance is out of the the Applicability Domain of the model

 

The substance has a global AD index of 0.75.

 

 

Detailed expert analysis

The compound is out of the model's applicability domain which reduces the reliability of the assessment. One of the biggest issues in the assessment of the applicability domain is the reliability of the logP which was predicted as 4.81. Nevertheless, this values seems to adequately describe the substance' partitioning behaviour and therefore, the assessment of the BCF is expected to be reliable in the scope of a weight-of-evidence approach. Moreover, the four most similar compounds have experimental values significantly lower than the predicted values. It is therefore expected, that the experimental value of the target compound is lower than the predicted value as well.

References:

VEGA Guide to BCF Meylan Model version 1.0.2 implemented in the VEGA tool v1.0.8

Reference 3

Endpoint:

bioaccumulation: aquatic / sediment

Type of information:

calculation (if not (Q)SAR)

Remarks:

Migrated phrase: estimated by calculation

Adequacy of study:

weight of evidence

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

other: validated, well documented QSAR model, Scientifically accepted calculation method

Justification for type of information:

Justification for type of information: In Article 13 of Regulation (EC) No 1907/2006, it is laid down that information on intrinsic properties of substances may be generated by means other than tests, provided that the conditions set out in Annex XI (of the same Regulation) are met. Furthermore according to Article 25 of the same Regulation testing on vertebrate animals shall be undertaken only as a last resort. According to Annex XI of Regulation (EC) No 1907/2006 (Q)SAR results can be used if (1) the scientific validity of the (Q)SAR model has been established, (2) the substance falls within the applicability domain of the (Q)SAR model, (3) the results are adequate for the purpose of classification and labeling and/or risk assessment and (4) adequate and reliable documentation of the applied method is provided. For QMRF and QRPF see ‘Overall remarks, attachments’ (QMRF) and ‘Executive Summary’ (QPRF) of the respective endpoint study records. For the assessment of CAS 5232-99-5 (Q)SAR results were used for aquatic bioaccumulation. The criteria listed in Annex XI of Regulation (EC) No 1907/2006 are considered to be adequately fulfilled and therefore the endpoint(s) sufficiently covered and suitable for risk assessment.

Qualifier:

no guideline followed

Principles of method if other than guideline:

The model performs a read-across and provides a quantitative prediction of bioconcentration factor (BCF) in fish, given in log(L/kg). The read-across is based on the similarity index developed inside the VEGA platform; the index takes into account several structural aspects of the compounds, such as their fingerprint, the number of atoms, of cycles, of heteroatoms, of halogen atoms, and of particular fragments (such as nitro groups). On the basis of this structural similarity index, the three compounds from the dataset resulting most similar to the chemical to be predicted are taken into account: the estimated BCF value is calculated as the weighted average value of the experimental values of the three selected compounds, using their similarity values as weight.

Type:

BCF

Value:

6 L/kg

Remarks on result:

other: logBCF0.75, According to the model’s global AD index, the predicted substance is out of the Applicability Domain of the model.

Executive summary:

The BCF model (Read-Across) v1.0.2 implemented in the VEGA platform v1.0.8:Estimation Domain (QPRF)

 

The applicability domain of predictions is assessed using an Applicability Domain Index (ADI) that has values from 0 (worst case) to 1 (best case). The ADI is calculated by grouping several other indices, each one taking into account a particular issue of the applicability domain. For each index, including the final ADI, two intervals for its values are defined, such that the first interval corresponds to a positive evaluation, and the second one corresponds to a negative evaluation.

 

Following, all applicability domain components are reported along with their explanation.

Furthermore, the specific index of the substance is given.

 

 

-Highest similarity found for similar compounds.

This index takes into account the maximum value of similarity among the three most similar compounds found. Values higher than 0.7 mean that at least one compound with a good structural similarity with the chemical to be predicted has been found. Values lower than 0.7 mean that no remarkably similar compounds have been found, and the read-across could be not reliable.

 

Defined intervals are:

index >= 0.85	the highest similarity value found for similar compounds is adequate for a reliable read-across
index < 0.85	the highest similarity value found for similar compounds is not adequate for a reliable read-across

 

The substance has a maximum value of similarity of 0.819.

 

 

-Lowest similarity found for similar compounds.

This index takes into account the minimum value of similarity among the three most similar compounds found. Values higher than 0.6 mean that also the least similar among the three compounds has an acceptable structural similarity with the chemical to be predicted. Values lower than 0.6 mean that the read-across could be not reliable.

 

Defined intervals are:

index >= 0.7	the lowest similarity value found for similar compounds is adequate for a reliable read-across
index < 0.7	the lowest similarity value found for similar compounds is not adequate for a reliable read-across

 

The substance has a minimum value of similarity of 0.818.

 

 

-Global AD Index.

The final global index takes into account the previous indices, in order to give a general global assessment on the applicability domain for the predicted compound. If at least one of the previous indices has a negative evaluation, the final global index will result in an assessment of unreliability; if all indices have positive evaluation, then the global index will result in an assessment of reliability. In both cases, the global index value is calculated as the average value of the similarity index for the three compounds taken into account for the read-across.

 

The substance has a global AD index of 0.819.

 

Read-across seems to be unreliable due to low similarity in found molecules.

 

 

Detailed expert analysis

However, although the three most similar compounds do not result in a positive assessment in the first index, they comprise important structures for bioaccumulation which are also present in the target compound. Neither of these structures have experimental values which indicate a significant potential to bioaccumulate. Therefore, although the BCF prediction is only moderately reliable it seems to be acceptable in a weight-of-evidence approach.

 

 

References:

VEGA Guide to BCF Read-Across version 1.0.2 implemented in the VEGA tool v1.0.8

 

Reference 4

Endpoint:

bioaccumulation: aquatic / sediment

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

other: Accepted calculation method

Justification for type of information:

Justification for type of information: In Article 13 of Regulation (EC) No 1907/2006, it is laid down that information on intrinsic properties of substances may be generated by means other than tests, provided that the conditions set out in Annex XI (of the same Regulation) are met. Furthermore according to Article 25 of the same Regulation testing on vertebrate animals shall be undertaken only as a last resort. According to Annex XI of Regulation (EC) No 1907/2006 (Q)SAR results can be used if (1) the scientific validity of the (Q)SAR model has been established, (2) the substance falls within the applicability domain of the (Q)SAR model, (3) the results are adequate for the purpose of classification and labeling and/or risk assessment and (4) adequate and reliable documentation of the applied method is provided. For QMRF and QRPF see ‘Overall remarks, attachments’ (QMRF) and ‘Executive Summary’ (QPRF) of the respective endpoint study records. For the assessment of CAS 5232-99-5 (Q)SAR results were used for aquatic bioaccumulation. The criteria listed in Annex XI of Regulation (EC) No 1907/2006 are considered to be adequately fulfilled and therefore the endpoint(s) sufficiently covered and suitable for risk assessment.

Reason / purpose for cross-reference:

reference to same study

Qualifier:

no guideline followed

Principles of method if other than guideline:

Calculated with Catalogic v5.11.16 BCF base-line model v02.08.

Details on estimation of bioconcentration:

BASIS INFORMATION
- Measured/calculated logPow: calculated

BASIS FOR CALCULATION OF BCF
- Estimation software: BCF base-line model v02.08 of OASIS CATALOGIC v5.11.16

Type:

BCF

Value:

218

Remarks on result:

other: logBCF corrected 2.347 +- 0.22; all mitigating factors applied

Type:

BCF

Value:

984

Remarks on result:

other: logBCFmax 2.993, no mitigation factors applied

Model domain similarity:

- Parametric domain: The chemical fulfils the general properties requirements

- Structural domain: 4.76% correct fragments, 0% incorrect fragments (95.24% unknown)

- Mechanistic domain: The expected uptake mechanism of the target chemical is passive diffusion across biological

membranes. The chemical is in the mechanistic domain of the model.

Effects of mitigating factors on BCF:

Acids	0.0000
Metabolism	0.544
Phenols	0.0000
Size	0.45
Water solubility	0.00321

The BCF base-line model estimates the log BCF for the test substance to be 2.34 (BCF 218).

logBCFmax =2.993 (BCF 984)

Diameter information (values are given in Angstrom):

DiamMax-Average: 13.188

DiamMax-Min: 11.542

DiamMax-Max: 14.056

Molecular weight: 277.33

Reference 5

Endpoint:

bioaccumulation: aquatic / sediment

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

other: Scientifically accepted calculation method, substance is within the applicability domain of the model.

Justification for type of information:

Justification for type of information: In Article 13 of Regulation (EC) No 1907/2006, it is laid down that information on intrinsic properties of substances may be generated by means other than tests, provided that the conditions set out in Annex XI (of the same Regulation) are met. Furthermore according to Article 25 of the same Regulation testing on vertebrate animals shall be undertaken only as a last resort. According to Annex XI of Regulation (EC) No 1907/2006 (Q)SAR results can be used if (1) the scientific validity of the (Q)SAR model has been established, (2) the substance falls within the applicability domain of the (Q)SAR model, (3) the results are adequate for the purpose of classification and labeling and/or risk assessment and (4) adequate and reliable documentation of the applied method is provided. For QMRF and QRPF see ‘Overall remarks, attachments’ (QMRF) and ‘Executive Summary’ (QPRF) of the respective endpoint study records. For the assessment of CAS 5232-99-5 (Q)SAR results were used for aquatic bioaccumulation. The criteria listed in Annex XI of Regulation (EC) No 1907/2006 are considered to be adequately fulfilled and therefore the endpoint(s) sufficiently covered and suitable for risk assessment.

Qualifier:

no guideline followed

Principles of method if other than guideline:

Estimation of BCF, BAF and biotransformation rate using BCFBAF v 3.01

GLP compliance:

no

Test organisms (species):

other: fish

Details on estimation of bioconcentration:

BASIS INFORMATION
- Results from toxicokinetic study: not applicable
- Results from residue study: not applicable
- Monitoring data: not applicable

BASIS FOR CALCULATION OF BCF
- Estimation software: EPISUITE 4.11, BCFBAF v3.01
- Result based on calculated log Pow of 4.01

Type:

BCF

Value:

205 L/kg

Basis:

not specified

Remarks on result:

other: The substance is within the applicability domain of the BCFBAF submodel: Bioconcentration factor (BCF; Meylan et al., 1997/1999).

Type:

BCF

Value:

70.88

Basis:

not specified

Calculation basis:

steady state

Remarks on result:

other: Upper trophic, incl. biotransformation estimates; The substance is within the applicability domain of the BCFBAF submodel: Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003).

Type:

BCF

Value:

1 054

Calculation basis:

steady state

Remarks on result:

other: Upper trophic, incl. biotransformation rate of zero; The substance is within the applicability domain of the BCFBAF submodel: Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003).

Type:

BAF

Value:

70.89 L/kg

Basis:

not specified

Remarks on result:

other: Upper trophic, incl. biotransformation estimates; The substance is within the applicability domain of the BCFBAF submodel: Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003).

Type:

BAF

Value:

2 431 L/kg

Basis:

not specified

Remarks on result:

other: Upper trophic, incl. biotransformation rate of zero; The substance is within the applicability domain of the BCFBAF submodel: Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003).

Details on kinetic parameters:

log Biotransformation half-life (days): -0.7514 (normalised to 10 g fish)
Biotransformation rate (kM, normalised to 10 g fish at 15 °C): 0.177
The substance is within the applicability domain of the BCFBAF submodel: Biotransformation rate in fish (kM; Arnot et al., 2008a/b).

Summary Results:

Log BCF (regression-based estimate): 2.31 (BCF = 205 L/kg wet-wt)

Biotransformation Half-Life (days) : 0.177 (normalized to 10 g fish)

Log BAF (Arnot-Gobas upper trophic): 1.85 (BAF = 70.9 L/kg wet-wt)

 

Log Kow (experimental): not available from database

Log Kow used by BCF estimates: 4.01

 

Equation Used to Make BCF estimate:

Log BCF = 0.6598 log Kow - 0.333 + Correction

 

Correction(s):Value

No Applicable Correction Factors

 

Estimated Log BCF = 2.312 (BCF = 205 L/kg wet-wt)

 

Whole Body Primary Biotransformation Rate Estimate for Fish:

TYPE	NUM	LOG BIOTRANSFORMATION FRAGMENT DESCRIPTION	COEFF	VALUE
Frag	1	Ester [-C(=O)-O-C]	-0.7605	-0.7605
Frag	1	Cyanide / Nitriles [-C#N]	0.1542	0.1542
Frag	2	Unsubstituted phenyl group (C6H5-)	-0.6032	-1.2064
Frag	10	Aromatic-H	0.2664	2.6638
Frag	1	Methyl [-CH3]	0.2451	0.2451
Frag	1	-CH2- [linear]	0.0242	0.0242
Frag	2	Benzene	-0.4277	-0.8555
L Kow	*	Log Kow = 4.01 (KowWin estimate)	0.3073	1.2320
MolWt	*	Molecular Weight Parameter		-0.7112
Const	*	Equation Constant		-1.5371
RESULT	LOG Bio Half-Life (days)			-0.7514
RESULT	Bio Half-Life (days)			0.1773
NOTE	Bio Half-Life Normalized to 10 g fish at 15 deg C

 

Biotransformation Rate Constant:

kM (Rate Constant): 3.91 /day (10 gram fish)

kM (Rate Constant): 2.199 /day (100 gram fish)

kM (Rate Constant): 1.236 /day (1 kg fish)

kM (Rate Constant): 0.6953 /day (10 kg fish)

 

Arnot-Gobas BCF & BAF Methods (including biotransformation rate estimates):

Estimated Log BCF (upper trophic) = 1.851 (BCF = 266.2 L/kg wet-wt)

Estimated Log BAF (upper trophic) = 1.851 (BAF = 288 L/kg wet-wt)

Estimated Log BCF (mid trophic)  = 1.957 (BCF = 365.4 L/kg wet-wt)

Estimated Log BAF (mid trophic)  = 1.957 (BAF = 782.2 L/kg wet-wt)

Estimated Log BCF (lower trophic) = 1.985 (BCF = 402.6 L/kg wet-wt)

Estimated Log BAF (lower trophic) = 1.990 (BAF = 2499 L/kg wet-wt)

 

Arnot-Gobas BCF & BAF Methods (assuming a biotransformation rate of zero):

Estimated Log BCF (upper trophic) =3.023 (BCF = 1054 L/kg wet-wt)

Estimated Log BAF (upper trophic) = 3.386 (BAF = 2431 L/kg wet-wt)

Executive summary:

QPRF: BCFBAF v3.01

 

1.	Substance	See “Test material identity”
2.	General information
2.1	Date of QPRF	See “Data Source (Reference)”
2.2	QPRF author and contact details	See “Data Source (Reference)”
3.	Prediction
3.1	Endpoint (OECD Principle 1)	Endpoint	Bioaccumulation (aquatic)
		Dependent variable	- Bioconcentration factor (BCF) - Bioaccumulation factor (BAF; 15 °C) - Biotransformation rate (kM) and half-life
3.2	Algorithm (OECD Principle 2)	Model or submodel name	BCFBAF Submodels: 1) Bioconcentration factor (BCF; Meylan et al., 1997/1999) 2) Biotransformation rate in fish (kM; Arnot et al., 2008a/b) 3) Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003)
		Model version	v. 3.01
		Reference to QMRF	Estimation of Bioconcentration, bioaccumulation and biotransformation in fish using BCFBAF v3.01 (EPI Suite v4.11)
		Predicted value (model result)	See “Results and discussion”
		Input for prediction	Chemical structure via CAS number or SMILES; log Kow (optional)
		Descriptor values	- SMILES: structure of the compound as SMILES notation - log Kow - Molecular weight
3.3	Applicability domain (OECD principle 3)	Domains:
		1) Bioconcentration factor (BCF; Meylan et al., 1997/1999)
		a) Ionic/non-Ionic	The substance is non-ionic.
		b) Molecular weight (range of test data set): - Ionic: 68.08 to 991.80 - Non-ionic: 68.08 to 959.17 (On-Line BCFBAF Help File, Ch. 7.1.3 Estimation Domain and Appendix G)	The substance is within range (277.33 g/mol).
		c) log Kow (range of test data set): - Ionic: -6.50 to 11.26 - Non-ionic: -1.37 to 11.26 (On-Line BCFBAF Help File, Ch. 7.1.3 Estimation Domain and Appendix G)	The substance is within range (4.01).
		d) Maximum number of instances of correction factor in any of the training set compounds (On-Line BCFBAF Help File, Appendix E)	Not applicable as correction factors were not used.
		2) Biotransformation rate in fish (kM; Arnot et al., 2008a/b)
		a) The substance does not appreciably ionize at physiological pH. (On-Line BCFBAF Help File, Ch. 7.2.3)	fulfilled
		b) Molecular weight (range of test data set): 68.08 to 959.17 (On-Line BCFBAF Help File, Ch. 7.2.3)	The substance is within range (277.33 g/mol).
		c) The molecular weight is ≤ 600 g/mol. (On-Line BCFBAF Help File, Ch. 7.2.3)	fulfilled
		d) Log Kow: 0.31 to 8.70 (On-Line BCFBAF Help File, Ch. 7.2.3)	The substance is within range (4.01).
		e) The substance is no metal or organometal, pigment or dye, or a perfluorinated substance. (On-Line BCFBAF Help File, Ch. 7.2.3)	fulfilled
		f) Maximum number of instances of biotransformation fragments in any of the training set compounds (On-Line BCFBAF Help File, Appendix F)	Not exceeded.
		3) Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003)
		a) Log Kow ≤ 9 (On-Line BCFBAF Help File, Ch. 7.3.1)	fulfilled
		b) The substance does not appreciably ionize. (On-Line BCFBAF Help File, Ch. 7.3.1)	fulfilled
		c) The substance is no pigment, dye, or perfluorinated substance. (On-Line BCFBAF Help File, Ch. 7.3.1)	fulfilled
3.4	The uncertainty of the prediction (OECD principle 4)	1. Bioconcentration factor (BCF; Meylan et al., 1997/1999) Statistical accuracy of the training data set (non-ionic plus ionic data): - Correlation coefficient (r^2) = 0.833 - Standard deviation = 0.502 log units - Absolute mean error = 0.382 log units   2. Biotransformation Rate in Fish (kM) Statistical accuracy (training set): - Correlation coefficient (r^2) = 0.821 - Correlation coefficient (Q^2) = 0.753 - Standard deviation = 0.494 log units - Absolute mean error = 0.383 log units   3. Arnot-Gobas BAF/BCF model No information on the statistical accuracy given in the documentation.
3.5	The chemical mechanisms according to the model underpinning the predicted result (OECD principle 5)	1. The BCF model is mainly based on the relationship between bioconcentration and hydrophobicity. The model also takes into account the chemical structure and the ionic/non-ionic character of the substance.   2. Bioaccumulation is the net result of relative rates of chemical inputs to an organism from multimedia exposures (e.g., air, food, and water) and chemical outputs (or elimination) from the organism.   3. The model includes mechanistic processes for bioconcentration and bioaccumulation such as chemical uptake from the water at the gill surface (BCFs and BAFs) and the diet (BAFs only), and chemical elimination at the gill surface, fecal egestion, growth dilution and metabolic biotransformation (Arnot and Gobas 2003). Other processes included in the calculations are bioavailability in the water column (only the freely dissolved fraction can bioconcentrate) and absorption efficiencies at the gill and in the gastrointestinal tract.

References

- Arnot JA, Gobas FAPC. 2003. A generic QSAR for assessing the bioaccumulation potential of organic chemicals in aquatic food webs. QSAR and Combinatorial Science 22: 337-345.

- Arnot JA, Mackay D, Parkerton TF, Bonnell M. 2008a. A database of fish biotransformation rates for organic chemicals. Environmental Toxicology and Chemistry 27(11), 2263-2270.

- Arnot JA, Mackay D, Bonnell M. 2008b.Estimating metabolic biotransformation rates in fish from laboratory data. Environmental Toxicology and Chemistry 27: 341-351.

- Meylan, W.M., Howard, P.H, Aronson, D., Printup, H. and S. Gouchie. 1997. "Improved Method for Estimating Bioconcentration Factor (BCF) from Octanol-Water Partition Coefficient", SRC TR-97-006 (2nd Update), July 22, 1997; prepared for: Robert S. Boethling, EPA-OPPT, Washington, DC; Contract No. 68-D5-0012; prepared by: ; Syracuse Research Corp., Environmental Science Center, 6225 Running Ridge Road, North Syracuse, NY 13212.

- Meylan, WM, Howard, PH, Boethling, RS et al. 1999. Improved Method for Estimating Bioconcentration / Bioaccumulation Factor from Octanol/Water Partition Coefficient. Environ. Toxicol. Chem. 18(4): 664-672 (1999). 

- US EPA (2012). On-Line BCFBAF Help File.

 

 

Identified Correction Factors (Appendix E), Biotransformation Fragments and Coefficient values (Appendix F)

Model:	BCFBAF v3.01
Substance:	Etocrilene
CAS:	5232-99-5
SMILES:	O=C(OCC)C(C(#N))=C(c(cccc1)c1)c(cccc2)c2
Ionic/Non-ionic	non-ionic
Ionisation at physiological relevant pH	Substance ionises appreciably at physiological relevant pH range.
Molecular Weight:	277.33
Log Kow:	4.01

 

Appendix E: BCF Non-Ionic Correction Factors Used by BCFBAF
The Training Set used to derive the BCF Correction Factors listed below contained a total of 431 compounds (see Appendix G for the compound list).  The number of compounds in the training set with logKow values of 1.0 to 7.0 total 396 compounds ... 35 training set compounds have a logKow value greater than 7.0 ... Compounds with logKow less than 1.0 were not used to derive correction factors.
Correction Factor	BCFBAF	No. compounds containing factor in training set		Maximum number of each fragment in any individual compound	No. of instances of each fragment for the current substance
Not applicable
Appendix F: kM Biotransformation Fragments & Coefficient Values					.
The Training Set used to derive the Coefficient Values listed below contained a total of 421 compounds (see Appendix I for the compound list).					.
Fragment Description	Coefficient value		No. compounds containing fragment in total training set	Maximum number of each fragment in any individual compound	No. of instances of each fragment for the current substance
Ester [-C(=O)-O-C]	-0.76052851		15	2	1
Cyanide / Nitriles [-C#N]	0.1542211		8	2	1
Unsubstituted phenyl group (C6H5-)	-0.60319946		47	3	2
Aromatic-H	0,26637806		305	15	10
Methyl [-CH3]	0.24510529		170	12	1
-CH2-  [linear]	0.02418707		109	28	1
Benzene	-0.427728		197	3	2

 

Assessment of Applicability Domain Based on Molecular Weight and log Kow

 

Assessment of applicability domain based on molecular weight and log Kow
1. Bioconcentration Factor (BCF; Meylan et al., 1997/1999)
Training set: Molecular weights	Ionic	Non-ionic
Minimum	68.08	68.08
Maximum	991.80	959.17
Average	244.00	244.00
Assessment of molecular weight	Molecular weight within range of training set.
Training set: Log Kow	Ionic	Non-ionic
Minimum	-6.50	-1.37
Maximum	11.26	11.26
Assessment of log Kow	Log Kow within range of training set.
2. Biotransformation Rate in Fish (kM; Arnot et al., 2008a/b)
Training set: Molecular weights
Minimum	68.08
Maximum	959.17
Average	259.75
Assessment of molecular weight	Molecular weight within range of training set.
Training set: Log Kow
Minimum	0.31
Maximum	8.70
Assessment of log Kow	Log Kow within range of training set.

 

Description of key information

No experimental data on bioaccumulation is expected.
Based on a weight of evidence approach it can be concluded that the bioaccumulation potential is below a BCF of 500. 

Additional information

In Article 13 of Regulation (EC) No 1907/2006, it is laid down that information on intrinsic properties of substances may be generated by means other than tests, provided that the conditions set out in Annex XI (of the same Regulation) are met. Furthermore according to Article 25 of the same Regulation testing on vertebrate animals shall be undertaken only as a last resort.

According to Annex XI of Regulation (EC) No 1907/2006 (Q)SAR results can be used if (1) the scientific validity of the (Q)SAR model has been established, (2) the substance falls within the applicability domain of the (Q)SAR model, (3) the results are adequate for the purpose of classification and labeling and/or risk assessment and (4) adequate and reliable documentation of the applied method is provided.

For the assessment of CAS 5232-99-5 (Q)SAR results were used for aquatic bioaccumulation. The criteria listed in Annex XI of Regulation (EC) No 1907/2006 are considered to be adequately fulfilled and therefore the endpoint(s) sufficiently covered and suitable for risk assessment.

Therefore, and for reasons of animal welfare, further experimental studies on bioaccumulation are not provided.

 

The bioaccumulative potential of the substance was assessed in a weight of evidence approach including several QSAR estimations and data on the molecular size and log Kow.The single QSAR models and their results are summarized in the table below.

Model		BCF	logBCF	Remarks
Catalogic v5.11.15		218 (corrected)	2.34	all mitigating factors applied (main factors metabolism and size); substance is mechanistic and parametric domain but only 4.76% in the structural domain (95.24% unknown)
		984 (max)	2.99	No mitigating factors applied;substance is mechanistic and parametric domain but only 4.76% in the structural domain (95.24% unknown)
EPISuite v4.11	Regression-based estimate	205	2.31	Within the applicability domain
	Arnot-Gobas upper trophic level; incl. biotransformation estimates	70.88	1.851	Within the applicability domain
	Arnot-Gobas upper trophic level; incl. biotransformation rate of zero	1054	3.023	Within the applicability domain
VEGA CAESAR v2.1.13		40	1.6	According to the model’s global AD index, the predicted substance could be out of the Applicability Domain of the model.
VEGA Meylan v1.0.2		693	2.84	According to the model’s global AD index, the predicted substance is out of the applicability domain of the model.
VEGA Read across v1.0.2		6	0.75	According to the model’s global AD index, the predicted substance is out of the applicability domain of the model.
Other evidence Toxicological studies	No indication for bioaccumulation could be found in the available studies			(see IUCLID section 7.1)
Other evidence Log Pow 4.01 Screening criteria for a BCF >= 2000 are not fulfilled	< 2000			(see IUCLID section 4.7)
Other evidence Size (Catalogic v5.11.15)	DiamMax average 1.2 nm (MW 277.3)

 

According to the results of the models the compound does not have a high bioaccumulative potential. 

The BCF base-line model integrated in Catalogic is a sophisticated model which takes into account different mitigating factors, i.e. acids, metabolism, phenols, size and water solubility. The compound was inside the parametric and the mechanistic domains of the compound but only 4.76% of the fragments of the target chemical are present in correctly predicted training chemicals. Nevertheless, the result is regarded as reliable and suitable to be used in a weight of evidence approach. With all mitigating factors applied the BCF is determined as 218. The biggest influence on the bioaccumulative potential has the metabolism and the size both limiting the uptake. Therefore, the BCF corrected is assessed to be more realistic.

 

 

US EPA’s EPISuite includes the regression-based estimation and the Arnot-Gobas model which takes biotransformation processes into account. The present chemical is within the applicability domain of the regression-based estimation, molecular weight range and in the log Kow range of both the regression-based estimation and within the molecular weight range but outside the logKow range of the Arnot-Gobas model. The regression-based model predicted a BCF of 205 L/kg. The Arnot-Gobas model predicted BCF values of 70.88 and 1054 L/kg for the upper trophic level including biotransformation rate estimates and biotransformation rates of zero. For a reliable estimation of the BCF the value including biotransformation rate estimates were used. The EPISuite results were regarded as suitable in the weight of evidence approach.

 

The VEGA package includes three different estimations tools with each of them providing detailed information on the applicability domain. Each model was taken into account in the weight-of-evidence approach although the compound could be out of the applicability domain of the Caesar model and is out of the domain of the Meylan and the Read-across model. Nevertheless, the different compounds detected by the single models for the assessment comprise key structures which are relevant for bioaccumulation and which are also present in the target chemical. None of these compounds has experimentally significant BCF values and it was concluded that the target chemical is therefore not bioaccumulative. Moreover, structural alerts usually present in non-bioaccumulative compounds have been detected which are also a sign that the target chemical has a low bioaccumulative potential. Furthermore, for the most similar substances the predicted BCF values are higher than the experimental values. The BCF values of the CEASAR, Meylan and Read-across models were 40, 693 and 6, respectively.

 

According to ECHA’s Guidance on Information Requirements and Chemical Safety Assessment chapter R.11 – PBT Assessment, compounds with an average maximum diameter of >1.7 nm together with molecular weight of greater than 1100 are unlikely to have a BCF of >2000. The present compound has a DiamMax-average of 1.2. Although the molecular weight is only 277 it can be concluded that the compound may reduce the potential to cross biological membranes.Furthermore, the toxicological studies show no evidence for bioaccumulation.

 

All QSAR calculations result in BCF values < 2000. This is supported by the logPow of 4.01. Only three QSAR calculations reveal BCF values > 500 (693-1054). However, in these calculations no mitigating factor as size and metabolism were considered.

 

In conclusion, all available estimations are extremely well in line and the BCF of the target compound is expected to be below 500.