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Partition coefficient

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partition coefficient
Type of information:
Adequacy of study:
key study
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:
EPI Suite™-Estimation Program Interface

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

Smiles code: O=C(C1CC1)N1CCN(CC1)Cc1ccccc1

- 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.

- Descriptor domain:
The substance has a molecular weight of 244.34 and is therefore in the molecular weight range of the compounds in the training set (between 18 and 720)

- Structural and mechanistic domains:
The number of each fragment found in the substance does not exceed the maximum number of this fragment that occurs in any individual compound of the training set.

- Similarity with analogues in the training set:
The prediction for a similar substance 1,4-Benzodiazpin-2-one,5(2FPh)7CL (CAS 75349-23-4) results in a logKow of 0.31. The experimental result for this substance indicates a logKow of 0.35 (Hansch,C & Leo,A, 1985). This supports the use of the model for the substance.

The substance fits in the applicability domain of the model. The prediction is valid and can be used for classification and risk assessment.

Data source

Reference Type:
study report
Report Date:

Materials and methods

Principles of method if other than guideline:
- Justification of QSAR prediction: see field 'Justification for type of information'
GLP compliance:
Type of method:
calculation method (fragments)
Partition coefficient type:

Test material


Results and discussion

Partition coefficient
log Pow
Partition coefficient:
Remarks on result:
other: QSAR

Applicant's summary and conclusion