Tris(trimethylsilyl) phosphate

Partition coefficient

Link to relevant study record(s)

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
QSAR Toolbox 2.3.0.1132

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

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
O=P(O[Si](C)(C)C)(O[Si](C)(C)C)O[Si](C)(C)C

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
[[Explain how the model fulfils the OECD principles for (Q)SAR model validation. Consider attaching the QMRF and/or QPRF or providing a link]
- Defined endpoint:
Physical Chemical Properties#Partition Coefficient:#N-Octanol/Water
- Unambiguous algorithm:
The first regression related log P to atom/fragments of compounds that do not require correction factors:
log P = Σ(fini) + b (Equation 1)
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 above equation with the correction factor descriptors:
lop P (expl) - log P (eq 1) = Σ(cjnj) (Equation 2)
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)

- Defined domain of applicability:
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.
The minimum and maximum values for molecular weight are the following:
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

- Appropriate measures of goodness-of-fit and robustness and predictivity:

- Mechanistic interpretation:
A posteriori mechanistic interpretation, consistent with published scientific interpretations of experiments.

5. APPLICABILITY DOMAIN
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.
The correction factors were then derived from a multiple linear regression that correlated differences between the experimental (expl) log P and the log P. The first regression related log P to atom/fragments of compounds that do not require correction factors. 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. Results of the two successive multiple regressions (first for atom/fragments and second for correction factors) yield the estimating log P of any organic compound (data from 2447 compounds).
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.
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.

6. ADEQUACY OF THE RESULT
[Explain how the prediction fits the purpose of classification and labelling and/or risk assessment]
The molecular weight of the substance is 314.54, within the scope of the model (18.02~719.92).Therefore, the predicted value is considered to be reliable.

Guideline:

other: REACH guideline on QSARs R.6

Principles of method if other than guideline:

general model

Key result

Type:

log Pow

Partition coefficient:

4.72

Remarks on result:

not measured/tested

Details on results:

KOWWIN Fragments, Correction Factors, Coefficients and Frequency
Fragment Descriptor Coef Number
-CH3- [aliphatic carbon] 0.5473 9
-O-P- [aliphatic carbon] -0.0162 3
O=P -2.4239 1
-Si- [silicon, aromatic or oxygen attach] 0.6800 3

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.

The minimum and maximum values for molecular weight are the following:

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

Conclusions:

The log Pow of substance is predicted to be 4.72.

Description of key information

The log Pow of substance is predicted to be 4.72.

Key value for chemical safety assessment

Log Kow (Log Pow):

4.72

at the temperature of:

20 °C