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EC number: 945-883-1 | CAS number: 1379424-11-9
- Life Cycle description
- Uses advised against
- Endpoint summary
- Appearance / physical state / colour
- Melting point / freezing point
- Boiling point
- Density
- Particle size distribution (Granulometry)
- Vapour pressure
- Partition coefficient
- Water solubility
- Solubility in organic solvents / fat solubility
- Surface tension
- Flash point
- Auto flammability
- Flammability
- Explosiveness
- Oxidising properties
- Oxidation reduction potential
- Stability in organic solvents and identity of relevant degradation products
- Storage stability and reactivity towards container material
- Stability: thermal, sunlight, metals
- pH
- Dissociation constant
- Viscosity
- Additional physico-chemical information
- Additional physico-chemical properties of nanomaterials
- Nanomaterial agglomeration / aggregation
- Nanomaterial crystalline phase
- Nanomaterial crystallite and grain size
- Nanomaterial aspect ratio / shape
- Nanomaterial specific surface area
- Nanomaterial Zeta potential
- Nanomaterial surface chemistry
- Nanomaterial dustiness
- Nanomaterial porosity
- Nanomaterial pour density
- Nanomaterial photocatalytic activity
- Nanomaterial radical formation potential
- Nanomaterial catalytic activity
- Endpoint summary
- Stability
- Biodegradation
- Bioaccumulation
- Transport and distribution
- Environmental data
- Additional information on environmental fate and behaviour
- Ecotoxicological Summary
- Aquatic toxicity
- Endpoint summary
- Short-term toxicity to fish
- Long-term toxicity to fish
- Short-term toxicity to aquatic invertebrates
- Long-term toxicity to aquatic invertebrates
- Toxicity to aquatic algae and cyanobacteria
- Toxicity to aquatic plants other than algae
- Toxicity to microorganisms
- Endocrine disrupter testing in aquatic vertebrates – in vivo
- Toxicity to other aquatic organisms
- Sediment toxicity
- Terrestrial toxicity
- Biological effects monitoring
- Biotransformation and kinetics
- Additional ecotoxological information
- Toxicological Summary
- Toxicokinetics, metabolism and distribution
- Acute Toxicity
- Irritation / corrosion
- Sensitisation
- Repeated dose toxicity
- Genetic toxicity
- Carcinogenicity
- Toxicity to reproduction
- Specific investigations
- Exposure related observations in humans
- Toxic effects on livestock and pets
- Additional toxicological data
Partition coefficient
Administrative data
Link to relevant study record(s)
- Endpoint:
- partition coefficient
- Type of information:
- (Q)SAR
- Adequacy of study:
- weight of evidence
- Study period:
- December 2018
- 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
- Justification for type of information:
- 1. SOFTWARE
: program KOWWIN included in EPISUITE (Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11)
2. MODEL (incl. version number) : KOWWIN v1.68
KOWWIN (the Log Octanol-Water Partition Coefficient Program) estimates the octanol-water partition coefficient (log P) of organic compounds. KOWWIN requires only a chemical structure to estimate a log P. In the method applied, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate.
More complete description of KOWWIN methodology is described in:
Meylan, W.M., and Howard, P.H., Atom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995.
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES : O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COC(=O)CCCCCC
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint:
log Kow (log P) – logarithmic octanol/water partition coefficient. The partition coefficient Kow (P) is the ratio of concentrations of a chemical in n-octanol and in water at equilibrium at a specified temperature (typically 25 °C, although partition coefficient is not usually very temperature dependent and training data for KOWWIN are collected at different temperatures).
- Unambiguous algorithm:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values.
To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom in a structure is a "core" for a fragment; the exact fragment is determined by what is connected to the atom. Several functional groups are treated as core atoms. Connections to each core "atom" are either general or specific. Specific connections take precedence over general connections.
Log P estimates made from atom/fragment values alone could or need to be improved by inclusion of substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method. The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values. They are either factors involving aromatic ring substituent positions, or miscellaneous factors. Correction factors are values for various steric interactions, hydrogen-bondings, and effects from polar functional substructures. Individual correction factors were selected through a tedious process of correlating the differences (between log P estimates from atom/fragments alone and measured log P values) with common substructures.
The general regression equation has the following form:
log P = ∑(f(i)*n(i)) + ∑(c(j)*n(j)) + b
where f(i) is the coefficient of atom/fragment i, n(i) – the number of times the fragment i occurs in the molecule, c(j) is the coefficient for the correction factor j, and n(j) the number of times the factor j occurs (or is applied) in the molecule. b is the linear equation constant; b = 0.229.
Values of f and c coefficients are available.
- Defined domain of applicability:
Currently there is no universally accepted definition of model domain. However, it should be considered that log P estimates may be less accurate for compounds outside the molecular weight range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. Although the training set of the model contains a large number of diverse molecules and can be considered abundant, it is also possible that a compound may be characterised by structural features (e.g. functional groups) not represented in the training set, with no respective fragment/correction coefficient developed. These points should be taken into consideration when interpreting model results.
Training set molecular weights:
Minimum MW: 18.02
Maximum MW: 719.82 (in the validation set: 991.15)
Average in the training set: 199.98.
- Appropriate measures of goodness-of-fit and robustness and predictivity:
Training set statistics:
N = 2447 compounds
correlation coefficient R2= 0.982
standard deviation = 0.217
absolute deviation = 0.159
Training set estimation error:
within ≤ 0.10 – 45.0%
within ≤ 0.20 – 72.5%
within ≤ 0.40 – 92.4%
within ≤ 0.50 – 96.4%
within ≤ 0.60 – 98.2%
Validation Molecular Weights:
Minimum MW: 27.03
Maximum MW: 991.15
Average MW: 258.98
External validation set statistics:
N = 10946 compounds
correlation coefficient R2= 0.943
standard deviation = 0.479
absolute deviation = 0.356
Validation set estimation error:
within ≤ 0.20 – 39.6%
within ≤ 0.40 – 66.0%
within ≤ 0.50 – 75.6%
within ≤ 0.60 – 82.5%
within ≤ 0.80 – 91.6%
within ≤ 1.00 – 95.6%
- Mechanistic interpretation:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values. KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995). See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.
5. APPLICABILITY DOMAIN
See above, under point 4.
Etimation Outside MW-Fragment Number Domain
The external validation set of 10946 compounds contains 372 compounds that exceed the domain of instances of a given fragment or correction factor maximum for all training set compounds. The estimation accuracy for these compounds is:
Exceed Fragment Instance Domain - Accuracy Statistics:
number in dataset = 372
correlation coef (r2) = 0.939
standard deviation = 0.731
absolute deviation = 0.564
avg Molecular Weight = 460.0
Exceed Molecular Weight Domain - Accuracy Statistics:
number in dataset = 103
correlation coef (r2) = 0.879
standard deviation = 0.815
absolute deviation = 0.619
avg Molecular Weight = 802.16
Exceed BOTH Fragment & MW Domain - Accuracy Statistics:
number in dataset = 75
correlation coef (r2) = 0.879
standard deviation = 0.905
absolute deviation = 0.706
avg Molecular Weight = 812.70
6. ADEQUACY OF THE RESULT
The substance fits in the applicability domain of the model. The prediction is considered valid. - Reason / purpose for cross-reference:
- other: QPRF constituent #1
- Guideline:
- other:
- Version / remarks:
- REACH Guidance on QSARs R.6
- Principles of method if other than guideline:
- Meylan, W.M. and P.H. Howard, 1995 Atom/fragment contribution method for estimating octanol-water partition coefficients. J. Pharm. Sci. 84: 83-92
- GLP compliance:
- no
- Partition coefficient type:
- octanol-water
- Specific details on test material used for the study:
- SMILES : O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COC(=O)CCCCCC
- Type:
- log Pow
- Partition coefficient:
- > 15
- Remarks on result:
- other: QSAR predicted value
Referenceopen allclose all
KOWWIN predicted that the constituent DPE777777 has a log Kow = 15.75.
Due to the fact that this substance contains long chain fatty acid ester which exceeds the applicability domain of KOWWIN the value for log Pow is reported with restrictions.
The applicability domain covers log Pow up to 10 (maximum) so these values should be given as log Pow > 10.
The concrete value is reported to show the high lipophilic nature of the substance.
- Endpoint:
- partition coefficient
- Type of information:
- (Q)SAR
- Adequacy of study:
- weight of evidence
- Study period:
- December 2018
- 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:
- 1. SOFTWARE
: program KOWWIN included in EPISUITE (Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11)
2. MODEL (incl. version number) : KOWWIN v1.68
KOWWIN (the Log Octanol-Water Partition Coefficient Program) estimates the octanol-water partition coefficient (log P) of organic compounds. KOWWIN requires only a chemical structure to estimate a log P. In the method applied, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate.
More complete description of KOWWIN methodology is described in:
Meylan, W.M., and Howard, P.H., Atom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995.
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES : O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COC(=O)CCCCCC
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint:
log Kow (log P) – logarithmic octanol/water partition coefficient. The partition coefficient Kow (P) is the ratio of concentrations of a chemical in n-octanol and in water at equilibrium at a specified temperature (typically 25 °C, although partition coefficient is not usually very temperature dependent and training data for KOWWIN are collected at different temperatures).
- Unambiguous algorithm:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values.
To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom in a structure is a "core" for a fragment; the exact fragment is determined by what is connected to the atom. Several functional groups are treated as core atoms. Connections to each core "atom" are either general or specific. Specific connections take precedence over general connections.
Log P estimates made from atom/fragment values alone could or need to be improved by inclusion of substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method. The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values. They are either factors involving aromatic ring substituent positions, or miscellaneous factors. Correction factors are values for various steric interactions, hydrogen-bondings, and effects from polar functional substructures. Individual correction factors were selected through a tedious process of correlating the differences (between log P estimates from atom/fragments alone and measured log P values) with common substructures.
The general regression equation has the following form:
log P = ∑(f(i)*n(i)) + ∑(c(j)*n(j)) + b
where f(i) is the coefficient of atom/fragment i, n(i) – the number of times the fragment i occurs in the molecule, c(j) is the coefficient for the correction factor j, and n(j) the number of times the factor j occurs (or is applied) in the molecule. b is the linear equation constant; b = 0.229.
Values of f and c coefficients are available.
- Defined domain of applicability:
Currently there is no universally accepted definition of model domain. However, it should be considered that log P estimates may be less accurate for compounds outside the molecular weight range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. Although the training set of the model contains a large number of diverse molecules and can be considered abundant, it is also possible that a compound may be characterised by structural features (e.g. functional groups) not represented in the training set, with no respective fragment/correction coefficient developed. These points should be taken into consideration when interpreting model results.
Training set molecular weights:
Minimum MW: 18.02
Maximum MW: 719.82 (in the validation set: 991.15)
Average in the training set: 199.98.
- Appropriate measures of goodness-of-fit and robustness and predictivity:
Training set statistics:
N = 2447 compounds
correlation coefficient R2= 0.982
standard deviation = 0.217
absolute deviation = 0.159
Training set estimation error:
within ≤ 0.10 – 45.0%
within ≤ 0.20 – 72.5%
within ≤ 0.40 – 92.4%
within ≤ 0.50 – 96.4%
within ≤ 0.60 – 98.2%
Validation Molecular Weights:
Minimum MW: 27.03
Maximum MW: 991.15
Average MW: 258.98
External validation set statistics:
N = 10946 compounds
correlation coefficient R2= 0.943
standard deviation = 0.479
absolute deviation = 0.356
Validation set estimation error:
within ≤ 0.20 – 39.6%
within ≤ 0.40 – 66.0%
within ≤ 0.50 – 75.6%
within ≤ 0.60 – 82.5%
within ≤ 0.80 – 91.6%
within ≤ 1.00 – 95.6%
- Mechanistic interpretation:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values. KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995). See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.
5. APPLICABILITY DOMAIN
See above, under point 4.
Etimation Outside MW-Fragment Number Domain
The external validation set of 10946 compounds contains 372 compounds that exceed the domain of instances of a given fragment or correction factor maximum for all training set compounds. The estimation accuracy for these compounds is:
Exceed Fragment Instance Domain - Accuracy Statistics:
number in dataset = 372
correlation coef (r2) = 0.939
standard deviation = 0.731
absolute deviation = 0.564
avg Molecular Weight = 460.0
Exceed Molecular Weight Domain - Accuracy Statistics:
number in dataset = 103
correlation coef (r2) = 0.879
standard deviation = 0.815
absolute deviation = 0.619
avg Molecular Weight = 802.16
Exceed BOTH Fragment & MW Domain - Accuracy Statistics:
number in dataset = 75
correlation coef (r2) = 0.879
standard deviation = 0.905
absolute deviation = 0.706
avg Molecular Weight = 812.70
6. ADEQUACY OF THE RESULT
The substance fits in the applicability domain of the model. The prediction is considered valid. - Reason / purpose for cross-reference:
- (Q)SAR model reporting (QMRF)
- Reason / purpose for cross-reference:
- other: QPRF
- Guideline:
- other:
- Version / remarks:
- REACH Guidance on QSARs R.6
- Principles of method if other than guideline:
- Meylan, W.M. and P.H. Howard, 1995 Atom/fragment contribution method for estimating octanol-water partition coefficients. J. Pharm. Sci. 84: 83-92
- GLP compliance:
- no
- Partition coefficient type:
- octanol-water
- Specific details on test material used for the study:
- SMILES : O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CCCCCC)COC(=O)CCCCCC
- Type:
- log Pow
- Partition coefficient:
- > 15
- Remarks on result:
- other: QSAR predicted value
KOWWIN predicted that the constituent DPE777779 has a log Kow = 16.55
Due to the fact that this substance contains long chain fatty acid ester which exceeds the applicability domain of KOWWIN the value for log Pow is reported with restrictions.
The applicability domain covers log Pow up to 10 (maximum) so these values should be given as log Pow > 10.
The concrete value is reported to show the high lipophilic nature of the substance.
- Endpoint:
- partition coefficient
- Type of information:
- (Q)SAR
- Adequacy of study:
- weight of evidence
- Study period:
- December 2018
- 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
- Justification for type of information:
- 1. SOFTWARE
: program KOWWIN included in EPISUITE (Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11)
2. MODEL (incl. version number) : KOWWIN v1.68
KOWWIN (the Log Octanol-Water Partition Coefficient Program) estimates the octanol-water partition coefficient (log P) of organic compounds. KOWWIN requires only a chemical structure to estimate a log P. In the method applied, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate.
More complete description of KOWWIN methodology is described in:
Meylan, W.M., and Howard, P.H., Atom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995.
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES : O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint:
log Kow (log P) – logarithmic octanol/water partition coefficient. The partition coefficient Kow (P) is the ratio of concentrations of a chemical in n-octanol and in water at equilibrium at a specified temperature (typically 25 °C, although partition coefficient is not usually very temperature dependent and training data for KOWWIN are collected at different temperatures).
- Unambiguous algorithm:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values.
To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom in a structure is a "core" for a fragment; the exact fragment is determined by what is connected to the atom. Several functional groups are treated as core atoms. Connections to each core "atom" are either general or specific. Specific connections take precedence over general connections.
Log P estimates made from atom/fragment values alone could or need to be improved by inclusion of substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method. The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values. They are either factors involving aromatic ring substituent positions, or miscellaneous factors. Correction factors are values for various steric interactions, hydrogen-bondings, and effects from polar functional substructures. Individual correction factors were selected through a tedious process of correlating the differences (between log P estimates from atom/fragments alone and measured log P values) with common substructures.
The general regression equation has the following form:
log P = ∑(f(i)*n(i)) + ∑(c(j)*n(j)) + b
where f(i) is the coefficient of atom/fragment i, n(i) – the number of times the fragment i occurs in the molecule, c(j) is the coefficient for the correction factor j, and n(j) the number of times the factor j occurs (or is applied) in the molecule. b is the linear equation constant; b = 0.229.
Values of f and c coefficients are available.
- Defined domain of applicability:
Currently there is no universally accepted definition of model domain. However, it should be considered that log P estimates may be less accurate for compounds outside the molecular weight range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. Although the training set of the model contains a large number of diverse molecules and can be considered abundant, it is also possible that a compound may be characterised by structural features (e.g. functional groups) not represented in the training set, with no respective fragment/correction coefficient developed. These points should be taken into consideration when interpreting model results.
Training set molecular weights:
Minimum MW: 18.02
Maximum MW: 719.82 (in the validation set: 991.15)
Average in the training set: 199.98.
- Appropriate measures of goodness-of-fit and robustness and predictivity:
Training set statistics:
N = 2447 compounds
correlation coefficient R2= 0.982
standard deviation = 0.217
absolute deviation = 0.159
Training set estimation error:
within ≤ 0.10 – 45.0%
within ≤ 0.20 – 72.5%
within ≤ 0.40 – 92.4%
within ≤ 0.50 – 96.4%
within ≤ 0.60 – 98.2%
Validation Molecular Weights:
Minimum MW: 27.03
Maximum MW: 991.15
Average MW: 258.98
External validation set statistics:
N = 10946 compounds
correlation coefficient R2= 0.943
standard deviation = 0.479
absolute deviation = 0.356
Validation set estimation error:
within ≤ 0.20 – 39.6%
within ≤ 0.40 – 66.0%
within ≤ 0.50 – 75.6%
within ≤ 0.60 – 82.5%
within ≤ 0.80 – 91.6%
within ≤ 1.00 – 95.6%
- Mechanistic interpretation:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values. KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995). See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.
5. APPLICABILITY DOMAIN
See above, under point 4.
Etimation Outside MW-Fragment Number Domain
The external validation set of 10946 compounds contains 372 compounds that exceed the domain of instances of a given fragment or correction factor maximum for all training set compounds. The estimation accuracy for these compounds is:
Exceed Fragment Instance Domain - Accuracy Statistics:
number in dataset = 372
correlation coef (r2) = 0.939
standard deviation = 0.731
absolute deviation = 0.564
avg Molecular Weight = 460.0
Exceed Molecular Weight Domain - Accuracy Statistics:
number in dataset = 103
correlation coef (r2) = 0.879
standard deviation = 0.815
absolute deviation = 0.619
avg Molecular Weight = 802.16
Exceed BOTH Fragment & MW Domain - Accuracy Statistics:
number in dataset = 75
correlation coef (r2) = 0.879
standard deviation = 0.905
absolute deviation = 0.706
avg Molecular Weight = 812.70
6. ADEQUACY OF THE RESULT
The substance fits in the applicability domain of the model. The prediction is considered valid. - Reason / purpose for cross-reference:
- (Q)SAR model reporting (QMRF)
- Reason / purpose for cross-reference:
- other: QPRF constituent #3
- Guideline:
- other:
- Version / remarks:
- REACH Guidance on QSARs R.6
- Principles of method if other than guideline:
- Meylan, W.M. and P.H. Howard, 1995 Atom/fragment contribution method for estimating octanol-water partition coefficients. J. Pharm. Sci. 84: 83-92
- GLP compliance:
- no
- Partition coefficient type:
- octanol-water
- Specific details on test material used for the study:
- SMILES : O=C(CCCCCC)OCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC
- Type:
- log Pow
- Partition coefficient:
- > 15
- Remarks on result:
- other: QSAR predicted value
KOWWIN predicted that the constituent DPE777799 has a log Kow = 17.35
Due to the fact that this substance contains long chain fatty acid ester which exceeds the applicability domain of KOWWIN the value for log Pow is reported with restrictions.
The applicability domain covers log Pow up to 10 (maximum) so these values should be given as log Pow > 10.
The concrete value is reported to show the high lipophilic nature of the substance.
- Endpoint:
- partition coefficient
- Type of information:
- (Q)SAR
- Adequacy of study:
- weight of evidence
- Study period:
- December 2018
- 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
- Justification for type of information:
- 1. SOFTWARE
: program KOWWIN included in EPISUITE (Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11)
2. MODEL (incl. version number) : KOWWIN v1.68
KOWWIN (the Log Octanol-Water Partition Coefficient Program) estimates the octanol-water partition coefficient (log P) of organic compounds. KOWWIN requires only a chemical structure to estimate a log P. In the method applied, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate.
More complete description of KOWWIN methodology is described in:
Meylan, W.M., and Howard, P.H., Atom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995.
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES : O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint:
log Kow (log P) – logarithmic octanol/water partition coefficient. The partition coefficient Kow (P) is the ratio of concentrations of a chemical in n-octanol and in water at equilibrium at a specified temperature (typically 25 °C, although partition coefficient is not usually very temperature dependent and training data for KOWWIN are collected at different temperatures).
- Unambiguous algorithm:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values.
To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom in a structure is a "core" for a fragment; the exact fragment is determined by what is connected to the atom. Several functional groups are treated as core atoms. Connections to each core "atom" are either general or specific. Specific connections take precedence over general connections.
Log P estimates made from atom/fragment values alone could or need to be improved by inclusion of substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method. The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values. They are either factors involving aromatic ring substituent positions, or miscellaneous factors. Correction factors are values for various steric interactions, hydrogen-bondings, and effects from polar functional substructures. Individual correction factors were selected through a tedious process of correlating the differences (between log P estimates from atom/fragments alone and measured log P values) with common substructures.
The general regression equation has the following form:
log P = ∑(f(i)*n(i)) + ∑(c(j)*n(j)) + b
where f(i) is the coefficient of atom/fragment i, n(i) – the number of times the fragment i occurs in the molecule, c(j) is the coefficient for the correction factor j, and n(j) the number of times the factor j occurs (or is applied) in the molecule. b is the linear equation constant; b = 0.229.
Values of f and c coefficients are available.
- Defined domain of applicability:
Currently there is no universally accepted definition of model domain. However, it should be considered that log P estimates may be less accurate for compounds outside the molecular weight range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. Although the training set of the model contains a large number of diverse molecules and can be considered abundant, it is also possible that a compound may be characterised by structural features (e.g. functional groups) not represented in the training set, with no respective fragment/correction coefficient developed. These points should be taken into consideration when interpreting model results.
Training set molecular weights:
Minimum MW: 18.02
Maximum MW: 719.82 (in the validation set: 991.15)
Average in the training set: 199.98.
- Appropriate measures of goodness-of-fit and robustness and predictivity:
Training set statistics:
N = 2447 compounds
correlation coefficient R2= 0.982
standard deviation = 0.217
absolute deviation = 0.159
Training set estimation error:
within ≤ 0.10 – 45.0%
within ≤ 0.20 – 72.5%
within ≤ 0.40 – 92.4%
within ≤ 0.50 – 96.4%
within ≤ 0.60 – 98.2%
Validation Molecular Weights:
Minimum MW: 27.03
Maximum MW: 991.15
Average MW: 258.98
External validation set statistics:
N = 10946 compounds
correlation coefficient R2= 0.943
standard deviation = 0.479
absolute deviation = 0.356
Validation set estimation error:
within ≤ 0.20 – 39.6%
within ≤ 0.40 – 66.0%
within ≤ 0.50 – 75.6%
within ≤ 0.60 – 82.5%
within ≤ 0.80 – 91.6%
within ≤ 1.00 – 95.6%
- Mechanistic interpretation:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values. KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995). See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.
5. APPLICABILITY DOMAIN
See above, under point 4.
Etimation Outside MW-Fragment Number Domain
The external validation set of 10946 compounds contains 372 compounds that exceed the domain of instances of a given fragment or correction factor maximum for all training set compounds. The estimation accuracy for these compounds is:
Exceed Fragment Instance Domain - Accuracy Statistics:
number in dataset = 372
correlation coef (r2) = 0.939
standard deviation = 0.731
absolute deviation = 0.564
avg Molecular Weight = 460.0
Exceed Molecular Weight Domain - Accuracy Statistics:
number in dataset = 103
correlation coef (r2) = 0.879
standard deviation = 0.815
absolute deviation = 0.619
avg Molecular Weight = 802.16
Exceed BOTH Fragment & MW Domain - Accuracy Statistics:
number in dataset = 75
correlation coef (r2) = 0.879
standard deviation = 0.905
absolute deviation = 0.706
avg Molecular Weight = 812.70
6. ADEQUACY OF THE RESULT
The substance fits in the applicability domain of the model. The prediction is considered valid. - Reason / purpose for cross-reference:
- (Q)SAR model reporting (QMRF)
- Reason / purpose for cross-reference:
- other: QPRF constituent #4
- Guideline:
- other:
- Version / remarks:
- REACH Guidance on QSARs R.6
- Principles of method if other than guideline:
- Meylan, W.M. and P.H. Howard, 1995 Atom/fragment contribution method for estimating octanol-water partition coefficients. J. Pharm. Sci. 84: 83-92
- GLP compliance:
- no
- Partition coefficient type:
- octanol-water
- Specific details on test material used for the study:
- SMILES : O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CCCCCC)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC
- Type:
- log Pow
- Partition coefficient:
- > 15
- Remarks on result:
- other: QSAR predicted value
KOWWIN predicted that the constituent DPE777999 has a log Kow = 18.14
Due to the fact that this substance contains long chain fatty acid ester which exceeds the applicability domain of KOWWIN the value for log Pow is reported with restrictions.
The applicability domain covers log Pow up to 10 (maximum) so these values should be given as log Pow > 10.
The concrete value is reported to show the high lipophilic nature of the substance.
- Endpoint:
- partition coefficient
- Type of information:
- (Q)SAR
- Adequacy of study:
- weight of evidence
- Study period:
- December 2018
- 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
- Justification for type of information:
- 1. SOFTWARE
: program KOWWIN included in EPISUITE (Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11)
2. MODEL (incl. version number) : KOWWIN v1.68
KOWWIN (the Log Octanol-Water Partition Coefficient Program) estimates the octanol-water partition coefficient (log P) of organic compounds. KOWWIN requires only a chemical structure to estimate a log P. In the method applied, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate.
More complete description of KOWWIN methodology is described in:
Meylan, W.M., and Howard, P.H., Atom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995.
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint:
log Kow (log P) – logarithmic octanol/water partition coefficient. The partition coefficient Kow (P) is the ratio of concentrations of a chemical in n-octanol and in water at equilibrium at a specified temperature (typically 25 °C, although partition coefficient is not usually very temperature dependent and training data for KOWWIN are collected at different temperatures).
- Unambiguous algorithm:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values.
To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom in a structure is a "core" for a fragment; the exact fragment is determined by what is connected to the atom. Several functional groups are treated as core atoms. Connections to each core "atom" are either general or specific. Specific connections take precedence over general connections.
Log P estimates made from atom/fragment values alone could or need to be improved by inclusion of substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method. The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values. They are either factors involving aromatic ring substituent positions, or miscellaneous factors. Correction factors are values for various steric interactions, hydrogen-bondings, and effects from polar functional substructures. Individual correction factors were selected through a tedious process of correlating the differences (between log P estimates from atom/fragments alone and measured log P values) with common substructures.
The general regression equation has the following form:
log P = ∑(f(i)*n(i)) + ∑(c(j)*n(j)) + b
where f(i) is the coefficient of atom/fragment i, n(i) – the number of times the fragment i occurs in the molecule, c(j) is the coefficient for the correction factor j, and n(j) the number of times the factor j occurs (or is applied) in the molecule. b is the linear equation constant; b = 0.229.
Values of f and c coefficients are available.
- Defined domain of applicability:
Currently there is no universally accepted definition of model domain. However, it should be considered that log P estimates may be less accurate for compounds outside the molecular weight range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. Although the training set of the model contains a large number of diverse molecules and can be considered abundant, it is also possible that a compound may be characterised by structural features (e.g. functional groups) not represented in the training set, with no respective fragment/correction coefficient developed. These points should be taken into consideration when interpreting model results.
Training set molecular weights:
Minimum MW: 18.02
Maximum MW: 719.82 (in the validation set: 991.15)
Average in the training set: 199.98.
- Appropriate measures of goodness-of-fit and robustness and predictivity:
Training set statistics:
N = 2447 compounds
correlation coefficient R2= 0.982
standard deviation = 0.217
absolute deviation = 0.159
Training set estimation error:
within ≤ 0.10 – 45.0%
within ≤ 0.20 – 72.5%
within ≤ 0.40 – 92.4%
within ≤ 0.50 – 96.4%
within ≤ 0.60 – 98.2%
Validation Molecular Weights:
Minimum MW: 27.03
Maximum MW: 991.15
Average MW: 258.98
External validation set statistics:
N = 10946 compounds
correlation coefficient R2= 0.943
standard deviation = 0.479
absolute deviation = 0.356
Validation set estimation error:
within ≤ 0.20 – 39.6%
within ≤ 0.40 – 66.0%
within ≤ 0.50 – 75.6%
within ≤ 0.60 – 82.5%
within ≤ 0.80 – 91.6%
within ≤ 1.00 – 95.6%
- Mechanistic interpretation:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values. KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995). See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.
5. APPLICABILITY DOMAIN
See above, under point 4.
Etimation Outside MW-Fragment Number Domain
The external validation set of 10946 compounds contains 372 compounds that exceed the domain of instances of a given fragment or correction factor maximum for all training set compounds. The estimation accuracy for these compounds is:
Exceed Fragment Instance Domain - Accuracy Statistics:
number in dataset = 372
correlation coef (r2) = 0.939
standard deviation = 0.731
absolute deviation = 0.564
avg Molecular Weight = 460.0
Exceed Molecular Weight Domain - Accuracy Statistics:
number in dataset = 103
correlation coef (r2) = 0.879
standard deviation = 0.815
absolute deviation = 0.619
avg Molecular Weight = 802.16
Exceed BOTH Fragment & MW Domain - Accuracy Statistics:
number in dataset = 75
correlation coef (r2) = 0.879
standard deviation = 0.905
absolute deviation = 0.706
avg Molecular Weight = 812.70
6. ADEQUACY OF THE RESULT
The substance fits in the applicability domain of the model. The prediction is considered valid. - Reason / purpose for cross-reference:
- (Q)SAR model reporting (QMRF)
- Reason / purpose for cross-reference:
- other: QPRF constituent #5
- Guideline:
- other:
- Version / remarks:
- REACH Guidance on QSARs R.6
- Principles of method if other than guideline:
- Meylan, W.M. and P.H. Howard, 1995 Atom/fragment contribution method for estimating octanol-water partition coefficients. J. Pharm. Sci. 84: 83-92
- GLP compliance:
- no
- Partition coefficient type:
- octanol-water
- Specific details on test material used for the study:
- SMILES : O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CCCCCC
- Type:
- log Pow
- Partition coefficient:
- > 15
- Remarks on result:
- other: QSAR predicted value
KOWWIN predicted that the constituent DPE779999 has a log Kow = 18.94
Due to the fact that this substance contains long chain fatty acid ester which exceeds the applicability domain of KOWWIN the value for log Pow is reported with restrictions.
The applicability domain covers log Pow up to 10 (maximum) so these values should be given as log Pow > 10.
The concrete value is reported to show the high lipophilic nature of the substance.
- Endpoint:
- partition coefficient
- Type of information:
- (Q)SAR
- Adequacy of study:
- weight of evidence
- Study period:
- December 2018
- 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
- Justification for type of information:
- 1. SOFTWARE
: program KOWWIN included in EPISUITE (Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11)
2. MODEL (incl. version number) : KOWWIN v1.68
KOWWIN (the Log Octanol-Water Partition Coefficient Program) estimates the octanol-water partition coefficient (log P) of organic compounds. KOWWIN requires only a chemical structure to estimate a log P. In the method applied, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate.
More complete description of KOWWIN methodology is described in:
Meylan, W.M., and Howard, P.H., Atom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995.
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES : O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CC(C)CC(C)(C)C
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint:
log Kow (log P) – logarithmic octanol/water partition coefficient. The partition coefficient Kow (P) is the ratio of concentrations of a chemical in n-octanol and in water at equilibrium at a specified temperature (typically 25 °C, although partition coefficient is not usually very temperature dependent and training data for KOWWIN are collected at different temperatures).
- Unambiguous algorithm:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values.
To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom in a structure is a "core" for a fragment; the exact fragment is determined by what is connected to the atom. Several functional groups are treated as core atoms. Connections to each core "atom" are either general or specific. Specific connections take precedence over general connections.
Log P estimates made from atom/fragment values alone could or need to be improved by inclusion of substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method. The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values. They are either factors involving aromatic ring substituent positions, or miscellaneous factors. Correction factors are values for various steric interactions, hydrogen-bondings, and effects from polar functional substructures. Individual correction factors were selected through a tedious process of correlating the differences (between log P estimates from atom/fragments alone and measured log P values) with common substructures.
The general regression equation has the following form:
log P = ∑(f(i)*n(i)) + ∑(c(j)*n(j)) + b
where f(i) is the coefficient of atom/fragment i, n(i) – the number of times the fragment i occurs in the molecule, c(j) is the coefficient for the correction factor j, and n(j) the number of times the factor j occurs (or is applied) in the molecule. b is the linear equation constant; b = 0.229.
Values of f and c coefficients are available.
- Defined domain of applicability:
Currently there is no universally accepted definition of model domain. However, it should be considered that log P estimates may be less accurate for compounds outside the molecular weight range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. Although the training set of the model contains a large number of diverse molecules and can be considered abundant, it is also possible that a compound may be characterised by structural features (e.g. functional groups) not represented in the training set, with no respective fragment/correction coefficient developed. These points should be taken into consideration when interpreting model results.
Training set molecular weights:
Minimum MW: 18.02
Maximum MW: 719.82 (in the validation set: 991.15)
Average in the training set: 199.98.
- Appropriate measures of goodness-of-fit and robustness and predictivity:
Training set statistics:
N = 2447 compounds
correlation coefficient R2= 0.982
standard deviation = 0.217
absolute deviation = 0.159
Training set estimation error:
within ≤ 0.10 – 45.0%
within ≤ 0.20 – 72.5%
within ≤ 0.40 – 92.4%
within ≤ 0.50 – 96.4%
within ≤ 0.60 – 98.2%
Validation Molecular Weights:
Minimum MW: 27.03
Maximum MW: 991.15
Average MW: 258.98
External validation set statistics:
N = 10946 compounds
correlation coefficient R2= 0.943
standard deviation = 0.479
absolute deviation = 0.356
Validation set estimation error:
within ≤ 0.20 – 39.6%
within ≤ 0.40 – 66.0%
within ≤ 0.50 – 75.6%
within ≤ 0.60 – 82.5%
within ≤ 0.80 – 91.6%
within ≤ 1.00 – 95.6%
- Mechanistic interpretation:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values. KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995). See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.
5. APPLICABILITY DOMAIN
See above, under point 4.
Etimation Outside MW-Fragment Number Domain
The external validation set of 10946 compounds contains 372 compounds that exceed the domain of instances of a given fragment or correction factor maximum for all training set compounds. The estimation accuracy for these compounds is:
Exceed Fragment Instance Domain - Accuracy Statistics:
number in dataset = 372
correlation coef (r2) = 0.939
standard deviation = 0.731
absolute deviation = 0.564
avg Molecular Weight = 460.0
Exceed Molecular Weight Domain - Accuracy Statistics:
number in dataset = 103
correlation coef (r2) = 0.879
standard deviation = 0.815
absolute deviation = 0.619
avg Molecular Weight = 802.16
Exceed BOTH Fragment & MW Domain - Accuracy Statistics:
number in dataset = 75
correlation coef (r2) = 0.879
standard deviation = 0.905
absolute deviation = 0.706
avg Molecular Weight = 812.70
6. ADEQUACY OF THE RESULT
The substance fits in the applicability domain of the model. The prediction is considered valid. - Reason / purpose for cross-reference:
- (Q)SAR model reporting (QMRF)
- Reason / purpose for cross-reference:
- other: QPRF constituent #6
- Guideline:
- other:
- Version / remarks:
- REACH Guidance on QSARs R.6
- Principles of method if other than guideline:
- Meylan, W.M. and P.H. Howard, 1995 Atom/fragment contribution method for estimating octanol-water partition coefficients. J. Pharm. Sci. 84: 83-92
- GLP compliance:
- no
- Partition coefficient type:
- octanol-water
- Specific details on test material used for the study:
- SMILES : O=C(CCCCCC)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CC(C)CC(C)(C)C
- Type:
- log Pow
- Partition coefficient:
- > 15
- Remarks on result:
- other: QSAR predicted value
KOWWIN predicted that the constituent DPE799999 has a log Kow = 19.74
Due to the fact that this substance contains long chain fatty acid ester which exceeds the applicability domain of KOWWIN the value for log Pow is reported with restrictions.
The applicability domain covers log Pow up to 10 (maximum) so these values should be given as log Pow > 10.
The concrete value is reported to show the high lipophilic nature of the substance.
- Endpoint:
- partition coefficient
- Type of information:
- (Q)SAR
- Adequacy of study:
- weight of evidence
- Study period:
- December 2018
- 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
- Justification for type of information:
- 1. SOFTWARE
: program KOWWIN included in EPISUITE (Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11)
2. MODEL (incl. version number) : KOWWIN v1.68
KOWWIN (the Log Octanol-Water Partition Coefficient Program) estimates the octanol-water partition coefficient (log P) of organic compounds. KOWWIN requires only a chemical structure to estimate a log P. In the method applied, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate.
More complete description of KOWWIN methodology is described in:
Meylan, W.M., and Howard, P.H., Atom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995.
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES : O=C(CC(C)CC(C)(C)C)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CC(C)CC(C)(C)(C)
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint:
log Kow (log P) – logarithmic octanol/water partition coefficient. The partition coefficient Kow (P) is the ratio of concentrations of a chemical in n-octanol and in water at equilibrium at a specified temperature (typically 25 °C, although partition coefficient is not usually very temperature dependent and training data for KOWWIN are collected at different temperatures).
- Unambiguous algorithm:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values.
To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom in a structure is a "core" for a fragment; the exact fragment is determined by what is connected to the atom. Several functional groups are treated as core atoms. Connections to each core "atom" are either general or specific. Specific connections take precedence over general connections.
Log P estimates made from atom/fragment values alone could or need to be improved by inclusion of substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method. The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values. They are either factors involving aromatic ring substituent positions, or miscellaneous factors. Correction factors are values for various steric interactions, hydrogen-bondings, and effects from polar functional substructures. Individual correction factors were selected through a tedious process of correlating the differences (between log P estimates from atom/fragments alone and measured log P values) with common substructures.
The general regression equation has the following form:
log P = ∑(f(i)*n(i)) + ∑(c(j)*n(j)) + b
where f(i) is the coefficient of atom/fragment i, n(i) – the number of times the fragment i occurs in the molecule, c(j) is the coefficient for the correction factor j, and n(j) the number of times the factor j occurs (or is applied) in the molecule. b is the linear equation constant; b = 0.229.
Values of f and c coefficients are available.
- Defined domain of applicability:
Currently there is no universally accepted definition of model domain. However, it should be considered that log P estimates may be less accurate for compounds outside the molecular weight range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. Although the training set of the model contains a large number of diverse molecules and can be considered abundant, it is also possible that a compound may be characterised by structural features (e.g. functional groups) not represented in the training set, with no respective fragment/correction coefficient developed. These points should be taken into consideration when interpreting model results.
Training set molecular weights:
Minimum MW: 18.02
Maximum MW: 719.82 (in the validation set: 991.15)
Average in the training set: 199.98.
- Appropriate measures of goodness-of-fit and robustness and predictivity:
Training set statistics:
N = 2447 compounds
correlation coefficient R2= 0.982
standard deviation = 0.217
absolute deviation = 0.159
Training set estimation error:
within ≤ 0.10 – 45.0%
within ≤ 0.20 – 72.5%
within ≤ 0.40 – 92.4%
within ≤ 0.50 – 96.4%
within ≤ 0.60 – 98.2%
Validation Molecular Weights:
Minimum MW: 27.03
Maximum MW: 991.15
Average MW: 258.98
External validation set statistics:
N = 10946 compounds
correlation coefficient R2= 0.943
standard deviation = 0.479
absolute deviation = 0.356
Validation set estimation error:
within ≤ 0.20 – 39.6%
within ≤ 0.40 – 66.0%
within ≤ 0.50 – 75.6%
within ≤ 0.60 – 82.5%
within ≤ 0.80 – 91.6%
within ≤ 1.00 – 95.6%
- Mechanistic interpretation:
KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, a structure is divided into fragments (atom or larger functional groups) and coefficient values of each fragment or group are summed together to yield the log P estimate. KOWWIN’s methodology is known as an Atom/Fragment Contribution (AFC) method. Coefficients for individual fragments and groups were derived by multiple regression of 2447 reliably measured log P values. KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995). See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.
5. APPLICABILITY DOMAIN
See above, under point 4.
Etimation Outside MW-Fragment Number Domain
The external validation set of 10946 compounds contains 372 compounds that exceed the domain of instances of a given fragment or correction factor maximum for all training set compounds. The estimation accuracy for these compounds is:
Exceed Fragment Instance Domain - Accuracy Statistics:
number in dataset = 372
correlation coef (r2) = 0.939
standard deviation = 0.731
absolute deviation = 0.564
avg Molecular Weight = 460.0
Exceed Molecular Weight Domain - Accuracy Statistics:
number in dataset = 103
correlation coef (r2) = 0.879
standard deviation = 0.815
absolute deviation = 0.619
avg Molecular Weight = 802.16
Exceed BOTH Fragment & MW Domain - Accuracy Statistics:
number in dataset = 75
correlation coef (r2) = 0.879
standard deviation = 0.905
absolute deviation = 0.706
avg Molecular Weight = 812.70
6. ADEQUACY OF THE RESULT
The substance fits in the applicability domain of the model. The prediction is considered valid. - Reason / purpose for cross-reference:
- (Q)SAR model reporting (QMRF)
- Reason / purpose for cross-reference:
- other: QPRF constituent #7
- Guideline:
- other:
- Version / remarks:
- REACH Guidance on QSARs R.6
- Principles of method if other than guideline:
- Meylan, W.M. and P.H. Howard, 1995 Atom/fragment contribution method for estimating octanol-water partition coefficients. J. Pharm. Sci. 84: 83-92
- GLP compliance:
- no
- Partition coefficient type:
- octanol-water
- Specific details on test material used for the study:
- SMILES :
O=C(CC(C)CC(C)(C)C)OCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COCC(COC(=O)CC(C)CC(C)(C)C)(COC(=O)CC(C)CC(C)(C)C)COC(=O)CC(C)CC(C)(C)(C) - Type:
- log Pow
- Partition coefficient:
- > 15
- Remarks on result:
- other: QSAR predicted value
KOWWIN predicted that the constituent DPE999999 has a log Kow = 20.54
Due to the fact that this substance contains long chain fatty acid ester which exceeds the applicability domain of KOWWIN the value for log Pow is reported with restrictions.
The applicability domain covers log Pow up to 10 (maximum) so these values should be given as log Pow > 10.
The concrete value is reported to show the high lipophilic nature of the substance.
Description of key information
log Kow > 10
Key value for chemical safety assessment
- Log Kow (Log Pow):
- 10
- at the temperature of:
- 20 °C
Additional information
The partition coefficient of the substance Dipentaerythritol hexaesters of 3,5,5 -trimethylhexanoic and n-heptanoic acids (CAS 1379424 -11 -9, EC 945 -883 -1) was determined by QSAR calculation with EPISUITE v.4.11 (MPBWIN v.1.43) for the single components. The value of log Kow of the constituents ranges from 15.75 to 20.54.
Due to the fact that this substance is long chain fatty acid ester which exceeds the applicability domain of KOWWIN the value for log Pow is reported with restrictions. Therefore the partition coefficient of the substance is deduced to be > 10.
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