Isotridecyl isononanoate

Partition coefficient

Link to relevant study record(s)

Reference

Reference 1

Endpoint:

partition coefficient

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Reliability:

4 (not assignable)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, and documentation / justification is limited

Justification for type of information:

Isotridecyl isononanoate – LogP model (ALogP) 1.0.0 (VEGA)
1 Substance
1.1 CAS number 42131-27-1
1.2 EC number 255-673-5
1.3 Chemical name
IUPAC 11-Methyldodecyl 7-methyloctanoate
Other (ISO) Octanoic acid, 7-methyl-, 11-methyldodecyl ester
Other Isotridecyl isononanoate
1.4 Structural formula

1.5 Structure codes
SMILES O=C(OCCCCCCCCCCC(C)C)CCCCCC(C)C
InChI InChI=1S/C22H44O2/c1-20(2)16-12-9-7-5-6-8-10-15-19-24-22(23)18-14-11-13-17-21(3)4/h20-21H,5-19H2,1-4H3
Other
Stereochemical features N/A

2 General Information
2.1 Date of QPRF 20 April 2018
2.2 Author and contact details Envigo, Shardlow Business Park, London Road, Shardlow, Derbyshire, DE72 2GD

3 Prediction
3.1 Endpoint (OECD Principle 1)
Endpoint Partition coefficient (Log KOW)
Dependent variable KOW (unitless)
Log KOW (unitless)
3.2 Algorithm (OECD Principle 2)
Model or sub model name LogP model (ALogP) 1.0.0 within VEGA 1.1.4
Model version 1.0.0
Reference to QMRF There is no QMRF available. Information to the VEGA models can be found at vega-qsar.eu
Predicted values (model result) 8.45
Predicted values (comments) This is identical to the value predicted by the KOWWIN model
Input for prediction Smiles
Calculated descriptor values Not provided by the software
3.3 Applicability domain (OECD Principle 3)
Domains i. The predicted compound is outside the Applicability Domain of the model
ii. accuracy of prediction for similar molecules found in the training set is not optimal.
iii. similar molecules found in the training set have experimental values that disagree with the predicted value.
iv the maximum error in prediction of similar molecules found in the training set has a moderate value, considering the experimental variability.
Structural analogues i. 2-ethylhexyl laurate
ii. Butyl laurate
iii. Methyl icosanoate
iv. Methyl docosanoate

Consideration on structural analogues With 95% the average similarity of the four most similar analogues in the training set to the query structure is considered high. Predicted and experimental values of similar structures vary by a factor of up to 7.2 which is below a default factor of 10 often used in traditional risk assessment of environmental chemicals to compensate for uncertainties*. Hence concordance between predicted and actual value is high.
3.4 The uncertainty of the prediction (OECD principle 4)
The prediction is flawed by its failure to fulfil the domain criteria set out by the model, which brings the results into dispute. Also the model reports some variance in the measured results for the nearest structures. However the similarity for the dataset molecules to the target is high, and furthermore, there would appear to be concordance with the results vs. the predicted values as observed above. In short the results should not be considered reliable on their own, but some reassurance can be taken from the positive statistics reported above.
3.5 The chemical and biological mechanisms according to the model underpinning the predicted result (OECD principle 5)
LogP prediction based on Ghose-Crippen-Viswanadhan octanol-water partition coefficient (ALogP), calculated from the AlogP model consisting of a regression equation based on the hydrophobicity contribution of 115 atom types

4 Adequacy (Optional)
4.1 Regulatory purpose Partition coefficient endpoint for REACh registration.

4.2 Approach for regulatory interpretation of the model result
No unit conversion necessary (unitless).

4.3 Outcome Uncertainty is indicated by the substance falling outside of the applicability domain for the model as described above. The model identifies that similarity of nearest structures is high but that concordance of results with measured data for those structures is moderate. An assessment of these values shows some confidence can be taken from the consideration of structural analogues section in 3.3 above. As such the result here, while of poor reliability, should not be used alone but could contribute to a weight of evidence.

4.4 Conclusion The prediction is considered to be of poor reliability, but will be used as part of a weight of evidence,

Qualifier:

no guideline followed

Principles of method if other than guideline:

- Principle of test: QSAR for Octanol Water Partition Coefficient
- Short description of test conditions: n/a
- Parameters analysed / observed: Octanol Water Partition Coefficient

Key result

Type:

log Pow

Partition coefficient:

8.45

Remarks on result:

other: value predicted from QSAR

Isotridecyl isononanoate – LogP model (ALogP) 1.0.0 (VEGA)
1	Substance
	1.1	CAS number			42131-27-1
	1.2	EC number			255-673-5
	1.3	Chemical name
			IUPAC		11-Methyldodecyl 7-methyloctanoate
			Other (ISO)		Octanoic acid, 7-methyl-, 11-methyldodecyl ester
			Other		Isotridecyl isononanoate
	1.4	Structural formula
	1.5	Structure codes
			SMILES		O=C(OCCCCCCCCCCC(C)C)CCCCCC(C)C
			InChI		InChI=1S/C22H44O2/c1-20(2)16-12-9-7-5-6-8-10-15-19-24-22(23)18-14-11-13-17-21(3)4/h20-21H,5-19H2,1-4H3
			Other
			Stereochemical features		N/A
2	General Information
	2.1	Date of QPRF			20 April 2018
	2.2	Author and contact details			Envigo, Shardlow Business Park, London Road, Shardlow, Derbyshire, DE72 2GD
3	Prediction
	3.1	Endpoint (OECD Principle 1)
			Endpoint		Partition coefficient (Log KOW)
			Dependent variable		KOW                                  (unitless) Log KOW                      (unitless)
	3.2	Algorithm (OECD Principle 2)
			Model or sub model name		LogP model (ALogP) 1.0.0 within VEGA 1.1.4
			Model version		1.0.0
			Reference to QMRF		There is no QMRF available. Information to the VEGA models can be found at vega-qsar.eu
			Predicted values (model result)		8.45
			Predicted values (comments)		This is identical to the value predicted by the KOWWIN model
			Input for prediction		Smiles
			Calculated descriptor values		Not provided by the software
	3.3	Applicability domain (OECD Principle 3)
			Domains		i.	The predicted compound is outside the Applicability Domain of the model
					ii.	accuracy of prediction for similar molecules found in the training set is not optimal.
					iii.	similar molecules found in the training set have experimental values that disagree with the predicted value.
					iv	the maximum error in prediction of similar molecules found in the training set has a moderate value, considering the experimental variability.
			Structural analogues		i. 2-ethylhexyl laurate ii. Butyl laurate iii. Methyl icosanoate iv. Methyl docosanoate
			Consideration on structural analogues		With 95% the average similarity of the four most similar analogues in the training set to the query structure is considered high. Predicted and experimental values of similar structures vary by a factor of up to 7.2 which is below a default factor of 10 often used in traditional risk assessment of environmental chemicals to compensate for uncertainties*. Hence concordance between predicted and actual value is high.
	3.4	The uncertainty of the prediction (OECD principle 4)
					The prediction is flawed by its failure to fulfil the domain criteria set out by the model, which brings the results into dispute. Also the model reports some variance in the measured results for the nearest structures. However the similarity for the dataset molecules to the target is high, and furthermore, there would appear to be concordance with the results vs. the predicted values as observed above. In short the results should not be considered reliable on their own, but some reassurance can be taken from the positive statistics reported above.
	3.5	The chemical and biological mechanisms according to the model underpinning the predicted result (OECD principle 5)
					LogP prediction based on Ghose-Crippen-Viswanadhan octanol-water partition coefficient (ALogP), calculated from the AlogP model consisting of a regression equation based on the hydrophobicity contribution of 115 atom types
4	Adequacy (Optional)
	4.1	Regulatory purpose			Partition coefficient endpoint for REACh registration.
	4.2	Approach for regulatory interpretation of the model result
					No unit conversion necessary (unitless).
	4.3	Outcome			Uncertainty is indicated by the substance falling outside of the applicability domain for the model as described above. The model identifies that similarity of nearest structures is high but that concordance of results with measured data for those structures is moderate. An assessment of these values shows some confidence can be taken from the consideration of structural analogues section in 3.3 above. As such the result here, while of poor reliability, should not be used alone but could contribute to a weight of evidence.
	4.4	Conclusion			The prediction is considered to be of poor reliability, but will be used as part of a weight of evidence,

Conclusions:

Uncertainty is indicated by the substance falling outside of the applicability domain for the model as described above. The model identifies that similarity of nearest structures is high but that concordance of results with measured data for those structures is moderate. An assessment of these values shows some confidence can be taken from the consideration of structural analogues section in 3.3 above. As such the result here, while of poor reliability, should not be used alone but could contribute to a weight of evidence.

The prediction is considered to be of poor reliability, but will be used as part of a weight of evidence,

Log Kow = 8.45

Reference 2

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 and falling into its applicability domain, with limited documentation / justification

Justification for type of information:

Partition coefficient of Isotridecyl isononanoate – KOWWIN (EPI Suite)-
1 Substance
1.1 CAS number 42131-27-1
1.2 EC number 255-673-5
1.3 Chemical name
IUPAC 11-Methyldodecyl 7-methyloctanoate
Other (ISO) Octanoic acid, 7-methyl-, 11-methyldodecyl ester
Other Isotridecyl isononanoate
1.4 Structural formula

1.5 Structure codes
SMILES O=C(OCCCCCCCCCCC(C)C)CCCCCC(C)C
InChI InChI=1S/C22H44O2/c1-20(2)16-12-9-7-5-6-8-10-15-19-24-22(23)18-14-11-13-17-21(3)4/h20-21H,5-19H2,1-4H3
Other
Stereochemical features N/A

2 General Information
2.1 Date of QPRF 19 April 2018
2.2 Author and contact details Envigo, Shardlow Business Park, London Road, Shardlow, Derbyshire, DE72 2GD

3 Prediction
3.1 Endpoint (OECD Principle 1)
Endpoint Partition coefficient (Log KOW)
Dependent variable KOW (unitless)
Log KOW (unitless)
3.2 Algorithm (OECD Principle 2)
Model or sub model name KOWWIN™
Model version KOWWIN™ v1.68
part of EPI Suite™ 4.11
Reference to QMRF There is no QMRF available. Information to EPI Suite™ models can be found in the Help files for the models provided by the EPA. Further information can also be found at the EPI Suite website https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-program-interface.
For information similar to those provided in the QMRF it is also referred to section below KOWWIN Estimation Methodology, Accuracy, & Domain.
Predicted values (model result) Log KOW = 9.56
Predicted values (comments) Predicted value, while high, is expected due to the nature of the material.
Input for prediction Smiles
Calculated descriptor values n/a
3.3 Applicability domain (OECD Principle 3)
Domains i. The predicted compound is in the Applicability Domain of the Training set: 18.01 ≤ Mr ≤ 719,92
ii. The predicted compound is in the Applicability Domain of the Validation set: 27.03 ≤ Mr ≤ 991.15

Structural analogues EPISUITE (KOWWIN) does not provide information on structural analogues due to the nature of the regression algorithm.

Consideration on structural analogues Not applicable, see above.
3.4 The uncertainty of the prediction (OECD principle 4)
The uncertainty is not measurable for individual predictions where the molecular eight lies within the applicability domain, however a detailed explanation of the mean error is reported in the Estimation Methodology, Accuracy, & Domain section below.
Appendix D of the KOWWIN documentation lists maximum numbers for fragments used in the prediction. These are all above those in the prediction, with the highest being CH2 Fragments, of which 15 are identified in the prediction, the limits for the trainig set and validation set are 18 and 28 respectively. Therefore no further consideration or uncertainty can be derived from the Appendix D criteria.
3.5 The chemical and biological mechanisms according to the model underpinning the predicted result (OECD principle 5)
EPISUITE (KOWWIN) based on regression algorithm

4 Adequacy (Optional)
4.1 Regulatory purpose Partition coefficient endpoint for REACh registration.

4.2 Approach for regulatory interpretation of the model result
No unit conversion necessary (unitless)

4.3 Outcome The substance falls into the boundary criteria for molecular weight both the training set and the validation set. Furthermore, the molecule falls within the maximum number of types of fragaments defined by the software. As all conditions explained by the software are met then confidence can be taken with the result. Some uncertainty could be derived from the estimated value being in the upper limits of the regression line shown in the training set. The Validation set however better covers these higher values. Therefore, moderate confidence can be placed in the prediction, but further models should be consulted to confirm the predicted value.

4.4 Conclusion The prediction is considered to be of moderate reliability, to be used as a weight of evidence.

Qualifier:

no guideline followed

Principles of method if other than guideline:

- Principle of test: QSAR for Octanol Water Partition Coefficient
- Short description of test conditions: n/a
- Parameters analysed / observed: Octanol Water Partition Coefficient

Key result

Type:

log Pow

Partition coefficient:

9.56

Remarks on result:

other: value predicted from QSAR

Conclusions:

Uncertainty is indicated by the substance falling outside of the required number of certain fragments identified by the training set. However this value is within the allowance for the validation set. Furthermore, the molecular weight is within the applicability domain of the model, affording some confidence in the prediction. Therefore moderate confidence can be placed in the prediction and further models should be consulted to confirm the predicted value.

The prediction is considered to be of moderate reliability, to be used as a weight of evidence.
Log Kow = 9.56

Reference 3

Endpoint:

partition coefficient

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Reliability:

4 (not assignable)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, and documentation / justification is limited

Justification for type of information:

Isotridecyl isononanoate – LogP model (Meylan/Kowwin) 1.1.4 (VEGA)
1 Substance
1.1 CAS number 42131-27-1
1.2 EC number 255-673-5
1.3 Chemical name
IUPAC 11-Methyldodecyl 7-methyloctanoate
Other (ISO) Octanoic acid, 7-methyl-, 11-methyldodecyl ester
Other Isotridecyl isononanoate
1.4 Structural formula

1.5 Structure codes
SMILES O=C(OCCCCCCCCCCC(C)C)CCCCCC(C)C
InChI InChI=1S/C22H44O2/c1-20(2)16-12-9-7-5-6-8-10-15-19-24-22(23)18-14-11-13-17-21(3)4/h20-21H,5-19H2,1-4H3
Other
Stereochemical features N/A

2 General Information
2.1 Date of QPRF 20 April 2018
2.2 Author and contact details Envigo, Shardlow Business Park, London Road, Shardlow, Derbyshire, DE72 2GD

3 Prediction
3.1 Endpoint (OECD Principle 1)
Endpoint Partition coefficient (Log KOW)
Dependent variable KOW (unitless)
Log KOW (unitless)
3.2 Algorithm (OECD Principle 2)
Model or sub model name LogP model (Meylan/Kowwin) 1.1.4 within VEGA 1.2.4
Model version 1.1.4
Reference to QMRF There is no QMRF available. Information to the VEGA models can be found at vega-qsar.eu
Predicted values (model result) 9.56
Predicted values (comments) This is identical to the value predicted by the KOWWIN model
Input for prediction Smiles
Calculated descriptor values Not provided by the software
3.3 Applicability domain (OECD Principle 3)
Domains i. The predicted compound is outside the Applicability Domain of the model
ii. Similar molecules found in the training set have experimental values that disagree with the predicted value.
iii. the maximum error in prediction of similar molecules found in the training set has a moderate value, considering the experimental variability.
Structural analogues i. 2-ethylhexyl laurate
ii. Butyl laurate
iii. Methyl icosanoate
iv. Methyl docosanoate

Consideration on structural analogues With 95% the average similarity of the four most similar analogues in the training set to the query structure is considered high. Predicted and experimental values of similar structures vary by a factor of up to 4.1 which is well below a default factor of 10 often used in traditional risk assessment of environmental chemicals to compensate for uncertainties*. Hence concordance between predicted and actual value is high.
3.4 The uncertainty of the prediction (OECD principle 4)
The prediction is flawed by its failure to fulfil the domain criteria set out by the model, which brings the results into dispute. Nevertheless, the similarity for the dataset molecules to the target are high, and furthermore, there would appear to be concordance with the results vs. the predicted values for the dataset as reported by the model (despite its claim that there are values that disagree with the conclusion). In short the results should not be considered reliable on their own, but some reassurance can be taken from the positive statistics reported above.
3.5 The chemical and biological mechanisms according to the model underpinning the predicted result (OECD principle 5)
LogP prediction based on Meylan work (W.M. Meylan, P.H. Howard, "Atom/fragment contribution method for estimating octanol-water partition coefficients", 1995, J. Pharm. Sci. 84:83-92) and implemented in EPI Suite software as KOWIN. Model developed inside the VEGA platform.

4 Adequacy (Optional)
4.1 Regulatory purpose Partition coefficient endpoint for REACh registration.

4.2 Approach for regulatory interpretation of the model result
No unit conversion necessary (unitless).

4.3 Outcome Uncertainty is indicated by the substance falling outside of the applicability domain for the model as described above. The model identifies that similarity of nearest structures and concordance of results with those structures is high, this was also confirmed in the assessment made in the consideration of structural analogues section in 3.3 above. It is also recognised that this result is identical to the KOWWIN value which has been deemed to be within the domain of that model. As such the result here, while of poor reliability, should not be used alone but could contribute to a weight of evidence.

4.4 Conclusion The prediction is considered to be of poor reliability, but will be used as part of a weight of evidence,
*Stedeford, T.; Zhao, Q.J.; Dourson, M.L.; Banasik, M.; Hsu, C.H. The application of non-default uncertainty factors in the US EPA’s Integrated Risk Information System (IRIS). Part I: UFL, UFS, and “Other uncertainty factors”. J. Environ. Sci. Heal. C 2007, 25, 245–279.

Qualifier:

no guideline followed

Principles of method if other than guideline:

- Principle of test: QSAR for Octanol Water Partition Coefficient
- Short description of test conditions: n/a
- Parameters analysed / observed: Octanol Water Partition Coefficient

Key result

Type:

log Pow

Partition coefficient:

9.56

Remarks on result:

other: value predicted from QSAR

Isotridecyl isononanoate – LogP model (Meylan/Kowwin) 1.1.4(VEGA)
1	Substance
	1.1	CAS number			42131-27-1
	1.2	EC number			255-673-5
	1.3	Chemical name
			IUPAC		11-Methyldodecyl 7-methyloctanoate
			Other (ISO)		Octanoic acid, 7-methyl-, 11-methyldodecyl ester
			Other		Isotridecyl isononanoate
	1.4	Structural formula
	1.5	Structure codes
			SMILES		O=C(OCCCCCCCCCCC(C)C)CCCCCC(C)C
			InChI		InChI=1S/C22H44O2/c1-20(2)16-12-9-7-5-6-8-10-15-19-24-22(23)18-14-11-13-17-21(3)4/h20-21H,5-19H2,1-4H3
			Other
			Stereochemical features		N/A
2	General Information
	2.1	Date of QPRF			20 April 2018
	2.2	Author and contact details			Envigo, Shardlow Business Park, London Road, Shardlow, Derbyshire, DE72 2GD
3	Prediction
	3.1	Endpoint (OECD Principle 1)
			Endpoint		Partition coefficient (Log KOW)
			Dependent variable		KOW                                  (unitless) Log KOW                      (unitless)
	3.2	Algorithm (OECD Principle 2)
			Model or sub model name		LogP model (Meylan/Kowwin) 1.1.4 within VEGA 1.2.4
			Model version		1.1.4
			Reference to QMRF		There is no QMRF available. Information to the VEGA models can be found at vega-qsar.eu
			Predicted values (model result)		9.56
			Predicted values (comments)		This is identical to the value predicted by the KOWWIN model
			Input for prediction		Smiles
			Calculated descriptor values		Not provided by the software
	3.3	Applicability domain (OECD Principle 3)
			Domains		i.	The predicted compound is outside the Applicability Domain of the model
					ii.	Similar molecules found in the training set have experimental values that disagree with the predicted value.
					iii.	the maximum error in prediction of similar molecules found in the training set has a moderate value, considering the experimental variability.
			Structural analogues		i. 2-ethylhexyl laurate ii. Butyl laurate iii. Methyl icosanoate iv. Methyl docosanoate
			Consideration on structural analogues		With 95% the average similarity of the four most similar analogues in the training set to the query structure is considered high. Predicted and experimental values of similar structures vary by a factor of up to 4.1 which is well below a default factor of 10 often used in traditional risk assessment of environmental chemicals to compensate for uncertainties*. Hence concordance between predicted and actual value is high.
	3.4	The uncertainty of the prediction (OECD principle 4)
					The prediction is flawed by its failure to fulfil the domain criteria set out by the model, which brings the results into dispute. Nevertheless, the similarity for the dataset molecules to the target are high, and furthermore, there would appear to be concordance with the results vs. the predicted values for the dataset as reported by the model (despite its claim that there are values that disagree with the conclusion). In short the results should not be considered reliable on their own, but some reassurance can be taken from the positive statistics reported above.
	3.5	The chemical and biological mechanisms according to the model underpinning the predicted result (OECD principle 5)
					LogP prediction based on Meylan work (W.M. Meylan, P.H. Howard, "Atom/fragment contribution method for estimating octanol-water partition coefficients", 1995, J. Pharm. Sci. 84:83-92) and implemented in EPI Suite software as KOWIN. Model developed inside the VEGA platform.
4	Adequacy (Optional)
	4.1	Regulatory purpose			Partition coefficient endpoint for REACh registration.
	4.2	Approach for regulatory interpretation of the model result
					No unit conversion necessary (unitless).
	4.3	Outcome			Uncertainty is indicated by the substance falling outside of the applicability domain for the model as described above. The model identifies that similarity of nearest structures and concordance of results with those structures is high, this was also confirmed in the assessment made in the consideration of structural analogues section in 3.3 above. It is also recognised that this result is identical to the KOWWIN value which has been deemed to be within the domain of that model. As such the result here, while of poor reliability, should not be used alone but could contribute to a weight of evidence.
	4.4	Conclusion			The prediction is considered to be of poor reliability, but will be used as part of a weight of evidence,
*Stedeford, T.; Zhao, Q.J.; Dourson, M.L.; Banasik, M.; Hsu, C.H. The application of non-default uncertainty factors in the US EPA’s Integrated Risk Information System (IRIS). Part I: UFL, UFS, and “Other uncertainty factors”. J. Environ. Sci. Heal. C 2007, 25, 245–279.

Conclusions:

Uncertainty is indicated by the substance falling outside of the applicability domain for the model as described above. The model identifies that similarity of nearest structures and concordance of results with those structures is high, this was also confirmed in the assessment made in the consideration of structural analogues section in 3.3 above. It is also recognised that this result is identical to the KOWWIN value which has been deemed to be within the domain of that model. As such the result here, while of poor reliability, should not be used alone but could contribute to a weight of evidence.

The prediction is considered to be of poor reliability, but will be used as part of a weight of evidence,
Log Kow = 9.56

Description of key information

Several QSAR models were used to determine an octanol water partition coefficient for Isotridecyl isononanoate.

The models were roughly in agreement, other than the one outlier, however with the exception of the KOWWIN prediction, the predictions failed to meet the validation criteria of their respective models.

Given that the VEGA models all showed a lack of reliability the value predicted by KOWWIN has been used in this summary, two of the three VEGA models concur with this result.

Key value for chemical safety assessment

Log Kow (Log Pow):

9.56