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Physical & Chemical properties

Partition coefficient

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Endpoint:
partition coefficient
Type of information:
(Q)SAR
Adequacy of study:
weight of evidence
Reliability:
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification
Remarks:
The substance is not fully compliant with the applicability domain of the model. However, this calculation is used in a weight of evidence approach, in accordance to the REACh Regulation (EC) No 1907/2006, Annex XI General rules for adaptation of the standard testing regime set out in Annexes VII to X, 1.2. It is adequately documented and justified: the overall internal quality check in VEGA v1.1.3 indicates that the prediction is reliable with a Klimisch score of 2.
Justification for type of information:
1. SOFTWARE
VEGA version 1.1.3

2. MODEL (incl. version number)
ALogP Model v. 1.0.0

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
See “Test material information”

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See attached information on the model provided by the developer. Further information on the OECD criteria as outlined by the applicant is provided below under "Any other information of materials and methods incl. tables"

5. APPLICABILITY DOMAIN
See attached information and information as provided in "Any other information on results incl. tables".

6. ADEQUACY OF THE RESULT
See assessment of adequacy as outlined in the "Overall remarks, attachments" section.
Qualifier:
according to guideline
Guideline:
other: REACH Guidance on QSARs R.6
Principles of method if other than guideline:
- Software tool(s) used including version: VEGA v1.1.3
- Model(s) used: ALogP Model version 1.0.0
The model is based on the Ghose-Crippen-Viswanadhan LogP (ALogP) and consists of a regression equation based on the hydrophobicity contribution of 120 atom types as described in: A.K. Ghose and G.M. Crippen, J. Comput. Chem. 1986, 7, 565-577; V.N. Viswanadhan et al., J. Comput. Chem. 1993, 14, 1019-1026; A.K. Ghose, V.N. Viswanadhan, J.J. Wendoloski, J. Phys. Chem. A 1998, 102, 3762-3772. For the purpose of applicability domain assessment, the training set of the Meylan LogP model (9,961 compounds) has been considered, setting all molecules as belonging to the test set.
- Model description: see field 'Justification for non-standard information', 'Attached justification' and 'any other information on Material and methods'
- Justification of QSAR prediction: see field 'Justification for type of information', 'Attached justification' and/or 'overall remarks'
GLP compliance:
no
Type of method:
other: QSAR
Partition coefficient type:
octanol-water
Type:
log Pow
Partition coefficient:
16.16
Remarks on result:
other: QSAR result, no information on temperature and pH available.

For detailed information on the results please refer to the attached report.

Endpoint:
partition coefficient
Type of information:
(Q)SAR
Adequacy of study:
weight of evidence
Reliability:
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification
Remarks:
The substance is not fully compliant with the applicability domain of the model. However, this calculation is used in a weight of evidence approach, in accordance to the REACh Regulation (EC) No 1907/2006, Annex XI General rules for adaptation of the standard testing regime set out in Annexes VII to X, 1.2. It is adequately documented and justified: the overall internal quality check in VEGA v1.1.3 indicates that the prediction is reliable with a Klimisch score of 2.
Justification for type of information:
1. SOFTWARE
VEGA version 1.1.3

2. MODEL (incl. version number)
Meylan/Kowwin v. 1.1.4

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
See “Test material information”

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See attached information on the model provided by the developer. Further information on the OECD criteria as outlined by the applicant is provided below under "Any other information of materials and methods incl. tables"

5. APPLICABILITY DOMAIN
See attached information and information as provided in "Any other information on results incl. tables".

6. ADEQUACY OF THE RESULT
See assessment of adequacy as outlined in the "Overall remarks, attachments" section.
Qualifier:
according to guideline
Guideline:
other: REACH Guidance on QSARs R.6
Principles of method if other than guideline:
- Software tool(s) used including version: VEGA v1.1.3
- Model(s) used: Meylan/Kowwin LogP Model version 1.1.4
The model is based on the Atom/Fragment Contribution (AFC) method from the work of Meylan and Howard (W.M. Meylan and P.H. Howard, Atom/fragment contribution method for estimating octanol-water partition coefficients, 1995, J. Pharm. Sci. 84: 83-92.), as implemented in the EPI Suite KOWWIN module (http://www.epa.gov/oppt/exposure/pubs/episuite.htm). The calculated model has a lower bound of -5.0 log units (all predictions lower than this value are set to -5.0). A dataset of compounds with experimental logP values has been built starting from the original dataset provided in EPI suite. The set has been processed and cleared from compounds that were replicated or that had problems with the provided molecule structure. The final dataset has 9,961 compounds.
- Model description: see field 'Justification for non-standard information', 'Attached justification' and 'any other information on Material and methods'
- Justification of QSAR prediction: see field 'Justification for type of information', 'Attached justification' and/or 'overall remarks'
GLP compliance:
no
Type of method:
other: QSAR
Partition coefficient type:
octanol-water
Type:
log Pow
Partition coefficient:
17.56
Remarks on result:
other: QSAR result, no information on temperature and pH available.

For detailed information on the results please refer to the attached report.

Endpoint:
partition coefficient
Type of information:
(Q)SAR
Adequacy of study:
weight of evidence
Reliability:
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification
Remarks:
The substance is not fully compliant with the applicability domain of the model. However, this calculation is used in a weight of evidence approach, in accordance to the REACh Regulation (EC) No 1907/2006, Annex XI General rules for adaptation of the standard testing regime set out in Annexes VII to X, 1.2. It is adequately documented and justified: the overall internal quality check in VEGA v1.1.3 indicates that the prediction is reliable with a Klimisch score of 2.
Justification for type of information:
1. SOFTWARE
VEGA version 1.1.3

2. MODEL (incl. version number)
MLogP Model v. 1.0.0

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
See “Test material information”

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See attached information on the model provided by the developer. Further information on the OECD criteria as outlined by the applicant is provided below under "Any other information of materials and methods incl. tables"

5. APPLICABILITY DOMAIN
See attached information and information as provided in "Any other information on results incl. tables".

6. ADEQUACY OF THE RESULT
See assessment of adequacy as outlined in the "Overall remarks, attachments" section.
Qualifier:
according to guideline
Guideline:
other: REACH Guidance on QSARs R.6
Principles of method if other than guideline:
- Software tool(s) used including version: VEGA v1.1.3
- Model(s) used: MLogP Model version 1.0.0
The model is based on the the Moriguchi LogP (MLogP) and consists of a regression equation based on 13 structural parameters as described in: I. Moriguchi, S. Hirono, Q. Liu, I. Nakagome, and Y. Matsushita, Chem. Pharm. Bull. 1992, 40, 127-130; I. Moriguchi, S. Hirono, I. Nakagome, H. Hirano, Chem. Pharm. Bull. 1994, 42, 976-978. For the purpose of applicability domain assessment, the training set of the Meylan LogP model (9,961 compounds) has been considered, setting all molecules as belonging to the test set.
- Model description: see field 'Justification for non-standard information', 'Attached justification' and 'any other information on Material and methods'
- Justification of QSAR prediction: see field 'Justification for type of information', 'Attached justification' and/or 'overall remarks'
GLP compliance:
no
Type of method:
other: QSAR
Partition coefficient type:
octanol-water
Type:
log Pow
Partition coefficient:
9.67
Remarks on result:
other: QSAR result, no information on temperature and pH available.

For detailed information on the results please refer to the attached report.

Description of key information

Log Pow > 10 (QSAR, Vega version 1.1.3 - three models: Meylan/Kowwin version 1.1.4, MLogP version 1.0.0, ALogP version 1.0.0)

Key value for chemical safety assessment

Additional information

Overall results

For the evaluated component of the substance, the Log Pow values as estimated by the three models present in VEGA v1.1.3 are reported in the following table. The two most similar substances identified by VEGA are also included (using their CAS number), together with their similarity degree with the target component, experimental Log Pow values and the predictions obtained by the QSAR models.

Evaluation of

Molecule / CAS

Similarity degree

Experimental Log Pow

VEGA / ALogP

VEGA / Meylan-KOWWIN

VEGA / MLogP

C16ester

Target

-

n.a.

16.6

17.56

9.67

929-77-1

0.854

10.2

9.356

10.195

6.803

20292-08-4

0.841

8.03

7.81

8.648

6.147

 

Discussion

The performance of the ALogP and Meylan-KOWWIN models on similar molecules are good to moderate. A trend between the experimental values of the similar substances and their carbon chain length can be observed: the longer the chains, the higher the Log Pow. Such trend is reflected also by the predicted values. The target substance is characterized by much longer alkyl chains compared to the similar substances, a higher Log Pow could be therefore expected and is also predicted by the two models.

The MLogP model’s performances on similar molecules are poor, due to high underestimation of their Log Pow values. The predictions (including the target molecule) however follow the same trend as for the other two models, supporting a higher Log Pow for the target molecule compared to its identified analogues.

 

The substance is a UVCB, which can be mainly represented by three molecules (including the one here assessed):

- C22 Fatty acid ester with C16 Alcohol

- C20 Fatty acid ester with C18 Alcohol

- C22 Fatty acid ester with C18 Alcohol

 

Ester of C22 fatty acid with C16 alcohol was chosen for the assessment. The C22-C18 ester will have slightly higher partition coefficient (supported by the aforementioned trend), whereas the C20-C18 will have comparable value. C22-C16 was chosen according to the substance’s composition.

 

Conclusion

Considering the above discussion, a Log Pow value > 10 can be considered as a reasonable estimation for the target substance.