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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 type of 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:
13.42
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 type of 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:
14.61
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 type of 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:
8.59
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

Explanation of the results

The Log Pow values as evaluated by the VEGA models (Meylan/KOWWIN, ALogP and MLogP) are reported for the target molecule and the two most similar substances identified by VEGA. The similarity degree of the similar substances with the target molecule and their experimental Log Pow values are also reported.

Molecule / CAS

Similarity degree

Experimental Log Pow

Meylan / KOWWIN

ALogP

MLogP

Target

-

n.a.

14.61

13.42

8.59

929-77-1

0.894

10.2

10.195

9.356

6.803

20292-08-4

0.882

8.03

8.648

7.81

6.147

 

Discussion

The applicability domain (AD) evaluation performed by the VEGA software suggests that target molecule may be out of the models’ applicability domain; the main reason relates to the low degree of similarity of the two most similar molecules identified in the models’ dataset. Both similar molecules are esters of fatty acid and alcohol, however with much shorter carbon chains, compared to the target molecule.

Both Meylan and ALogP models gave good estimation of the Log Pow values of the two most similar substances. Moreover, a higher Log Pow is expected for the target molecule, due to its longer chains; such expectation is confirmed by the model’s prediction.

The MLogP model highly underestimated the similar substances’ Log Pow, for this reason, the estimation provided for the target molecule is not taken into account for the final evaluation.

 

Conclusion

Considering the results obtained for target molecule and its similar substances, a value > 10 can be considered as a reasonable estimation of the partition coefficient.