Zinc 3,5,5-trimethylhexanoate

Partition coefficient

Administrative data

Endpoint:

partition coefficient

Type of information:

(Q)SAR

Adequacy of study:

supporting 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
EPI Suite™ Version 4.11 (2012)
2. MODEL (incl. version number)
KOWWIN™ v. 1.68
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
CAS No. 3302-10-1

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL and 5. APPLICABILITY DOMAIN
- Defined endpoint: partition coefficient, Log Kow
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.

To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom (e.g. carbon, nitrogen, oxygen, sulfur, etc.) 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"; these include carbonyl (C=O), thiocarbonyl (C=S), nitro (-NO2), nitrate (ONO2), cyano (-C/N), and isothiocyanate (-N=C=S). Connections to each core "atom" are either general or specific; specific connections take precedence over general connections. For example, aromatic carbon, aromatic oxygen and aromatic sulfur atoms have nothing but general connections; i.e., the fragment is the same no matter what is connected to the atom. In contrast, there are 5 aromatic nitrogen fragments: (a) in a five-member ring, (b) in a six-member ring, (c) if the nitrogen is an oxide-type {i.e. pyridine oxide}, (d) if the nitrogen has a fused ring location {i.e. indolizine}, and (e) if the nitrogen has a +5 valence {i.e. N-methyl pyridinium iodide}; since the oxide-type is most specific, it takes precedence over the other four. The aliphatic carbon atom is another example; it does not matter what is connected to -CH3, -CH2-, or -CH< , the fragment is the same; however, an aliphatic carbon with no hydrogens has two possible fragments: (a) if there are four single bonds with 3 or more carbon connections and (b) any other not meeting the first criteria.

It became apparent, for various types of structures, that log P estimates made from atom/fragment values alone could or needed 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. The correction factors have two main groupings: first, factors involving aromatic ring substituent positions and second, miscellaneous factors. In general, the 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.

Two separate regression analyses were performed. The first regression related log P to atom/fragments of compounds that do not require correction factors (i.e., compounds estimated adequately by fragments alone). The general regression equation has the following form:
log P = Σ(fini ) + b (Equation 1)
where Σ(fini ) is the summation of fi (the coefficient for each atom/fragment) times ni (the number of times the atom/fragment occurs in the structure) and b is the linear equation constant. This initial regression used 1120 compounds of the 2447 compounds in the total training dataset.

The correction factors were then derived from a multiple linear regression that correlated differences between the experimental (expl) log P and the log P estimated by Equation 1 above with the correction factor descriptors. This regression did not utilize an additional equation constant. The equation for the second regression is:

lop P (expl) - log P (eq 1) = Σ(cjnj ) (Equation 1)
where Σ(cjnj ) is the summation of cj (the coefficient for each correction factor) times nj (the number of times the correction factor occurs (or is applied) in the molecule).

Regression Results

Results of the two successive multiple regressions (first for atom/fragments and second for correction factors) yield the following general equation for estimating log P of any organic compound:

log P = Σ(fini ) + Σ(cjnj ) + 0.229 (Equation 3)
(num = 2447, r2 = 0.982, std dev = 0.217, mean error = 0.159)


To be effective an estimation method must be capable of making accurate predictions for chemicals not included in the training set. Currently, KOWWIN has been tested on an external validation dataset of 10,946 compounds (compounds not included in the training set). The validation set includes a diverse selection of chemical structures that rigorously test the predictive accuracy of any model. It contains many chemicals that are similar in structure to chemicals in the training set, but also many chemicals that are different from and structurally more complex than chemicals in the training set. The average molecular weight of compounds in the validation set is 258.98 versus 199.98 for the training set.

Total Validation Set Statistics:
number in dataset = 10946
correlation coef (r2) = 0.943
standard deviation = 0.479
absolute deviation = 0.356
avg Molecular Weight = 258.98

To be effective, an estimation method must be capable of making accurate predictions for chemicals not included in the training set. Currently, KOWWIN has been tested on an external validation dataset of 10,946 compounds (compounds not included in the training set). The validation set includes a diverse selection of chemical structures that rigorously test the predictive accuracy of any model. It contains many chemicals that are similar in structure to chemicals in the training set, but also many chemicals that are different from and structurally more complex than chemicals in the training set. The average molecular weight of compounds in the validation set is 258.98 versus 199.98 for the training set.

Training Set Molecular Weights:
Minimum MW: 18.02
Maximum MW: 719.92
Average MW: 199.98

Validation Molecular Weights:
Minimum MW: 27.03
Maximum MW: 991.15
Average MW: 258.98


Currently there is no universally accepted definition of model domain. However, users may wish to consider the possibility that log P estimates are less accurate for compounds outside the MW range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. It is also possible that a compound may have a functional group(s) or other structural features not represented in the training set, and for which no fragment coefficient was developed. These points should be taken into consideration when interpreting model results.

The KOWWIN training and validation datasets can be downloaded from the Internet at:
http://esc.syrres.com/interkow/KowwinData.htm

6. ADEQUACY OF THE RESULT
The substance 3,5,5-trimethylhexanoic acid has a molecular weight of 158.24 and lies within the range of validated substances and substances of the training set. Additionally, 3,5,5-trimethylhexanoic acid displayed structures that are represented in the structure fragments. In conclusion, the Log Kow estimate for 3,5,5-trimethylhexanoic acid is adequate for the purpose of classification and labelling and/or risk assessment.
Please refer to the field "Overall remarks, attachements" below

Data source

Reference

Title:

EPI Suite Version 4.11

Year:

2012

Bibliographic source:

KOWWIN - Meylan and Howard (1995, J.Pharm. Sci. 84:83-92)

Materials and methods

Test guideline

Guideline:

other: REACH Guidance on QSARs R.6

Principles of method if other than guideline:

Meylan, W.M. and Howard, P.H. 1995. Atom/fragment contribution method for estimating octanol-water partition coefficients. J. Pharm. Sci. 84: 83-92

GLP compliance:

no

Type of method:

calculation method (fragments)

Test material

Test material information

Constituent 1

Reference substance name:

3,5,5-trimethylhexanoic acid

EC Number:

221-975-0

EC Name:

3,5,5-trimethylhexanoic acid

Cas Number:

3302-10-1

Molecular formula:

C9H18O2

IUPAC Name:

3,5,5-trimethylhexanoic acid

Specific details on test material used for the study:

SMILES: O=C(O)CC(CC(C)(C)C)C

Study design

Analytical method:

other: not applicable, estimated value

Results and discussion

Partition coefficient

Type:

log Pow

Partition coefficient:

3.34

Remarks on result:

other: QSAR predicted value

Any other information on results incl. tables

KOWWIN predicted that 3,5,5 -trimethylhexanoic acid has a log Kow = 3.34

Applicant's summary and conclusion

Conclusions:

The estimates for the log Pow of 3,5,5-trimethylhexanoic acid based on an atom/fragment contribution (AFC) method are 3.34 (KOWWIN™ Program Version 1.68; EPI Suite™ v.4.11, 2012).