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Partition coefficient

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Reference
Endpoint:
partition coefficient
Type of information:
(Q)SAR
Adequacy of study:
key study
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 adequate and reliable documentation / justification
Justification for type of information:
1. SOFTWARE
Estimation Programs Interface Suite™ for Microsoft® Windows v4.11. US EPA, United States Environmental Protection Agency, Washington, DC, USA.

2. MODEL (incl. version number)
KOWWIN v1.68

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
CAS-no.: 328-50-7; C(CC(=O)[O-])C(=O)C(=O)[O-]


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. 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. 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%
 
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’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (Hansch, C. and Leo, A.J., Substituent Constants for Correlation Analysis in Chemistry and Biology, Wiley, New York, 1979). 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.

5. APPLICABILITY DOMAIN
- Descriptor domain: molecular weight, structure fragments
- Structural and mechanistic domains: KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (Hansch, C. and Leo, A.J., Substituent Constants for Correlation Analysis in Chemistry and Biology, Wiley, New
York, 1979). 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.
- Similarity with analogues in the training set: Since KOWWIN uses a "fragment constant" methodology to predict log P, the functional groups within arginine, i.e. the amino- and the carboxy-group can be found in the training set provided by the KOWWIN help section.


6. ADEQUACY OF THE RESULT
2-oxoglutaric acid contains a very common structure with two terminal carboxy-groups and one carbonyl group. Since these functional groups are part of the training sets for the fragment constant methodology, the substance falls within the applicability domain and therefore, the results are reliable and is considered to be adequate for classification and labelling as well as for risk assessment.

For a more detailed explanation please refer to the "attached justification" section.
Qualifier:
no guideline followed
Version / remarks:
accepted calculation method
Principles of method if other than guideline:
- Software tool(s) used including version: EPISuite v4.11; KOWWIN v1.68
- Model(s) used: KOWWIN v1.68
- Model description: see field 'Justification for non-standard information', 'Attached justification' and/or 'Cross-reference'
- Justification of QSAR prediction: see field 'Justification for type of information', 'Attached justification' and/or 'Cross-reference'
GLP compliance:
no
Partition coefficient type:
octanol-water
Key result
Type:
log Pow
Partition coefficient:
-2.08
Temp.:
25 °C
Remarks on result:
other: pH was not estimated
Conclusions:
According to KOWWIN v.1.68 the logKow of 2-oxoglutaric acid is -2.08. Therefore the substance is considered to be hydrophilic.

Description of key information

QSAR calculation with EpiSuite TM; KOWWIN v.1.68, RL2, substance falls within the applicability domain of the model

Key value for chemical safety assessment

Log Kow (Log Pow):
-2.08
at the temperature of:
25 °C

Additional information