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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:
other: Values for individual constituents of this natural complex substance (NCS) were calculated using a validated QSAR. All constituents fall within the applicability domain of the QSAR.
Justification for type of information:
1. The software used: see attached QMRF
2. The model(s) used: see attached QPRF
Reason / purpose:
reference to same study
Qualifier:
according to
Guideline:
other: REACH Guidance on QSARs R.6
Deviations:
not applicable
Principles of method if other than guideline:
The partition coefficient of a NCS has little meaning. In all cases, the log Kow can be based on the range of Kow from the calculated or measured values of the individual constituents. Calculated and measured data on the constituents are obtained from Kowwin v1.67. The relevance and reliability of the used QSAR for these constituents is shown in the attached QMRF and QPRF.
GLP compliance:
no
Type of method:
other: Calculation by estimation
Partition coefficient type:
octanol-water
Type:
log Pow
Partition coefficient:
>= 2.06 - <= 6.3
Temp.:
25 °C
pH:
7
Remarks on result:
other: pH is irrelevant. Calculated and measured data on the constituents are obtained from Kowwin v1.67
Details on results:
No data

Substance CAS Estimated log Kow
L-Limonene 5989-54-8 4.83
Neral 106-26-3 3.45
Geranial 141-27-5 3.45
(+)-Citronellal 2385-77-5  3.53
Geraniol 106-24-1 3.47
β-(+)-Citronellol 1117-61-9 3.56
Methyl heptenone 110-93-0 2.06
(-)-Linalol 126-91-0 3.38
beta-Caryophyllene 87-44-5 6.30
alpha-(-)-Pinene 80-56-8 4.27
1,8-Cineol 470-82-6 3.13
Sabinene 3387-41-5 4.69
beta-Myrcene 123-53-3 4.88
Verbenol 473-67-6 2.73
Isoneral 72203-97-5 3.45
Isogeranial 72203-98-6 3.45
Nerol 106-25-2 3.47

Kowwin v1.67a model details

Reference to the type of model used

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.

 

Description of the applicability domain

The applicability domain is based on the maximum number of instances of that a fragment can be used in a chemical and molecular weight. 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

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.

 

Description and results of any possible structural analogues of the substance to assess reliability of the prediction

External validation with a dataset containing 10946 substances resulted in a correlation coefficient (r2) of 0.943, a standard deviation of 0.479 and an absolute deviation of 0.358.

Predictivity assessment of the external validation set:

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%

within <= 1.20 - 97.7%

within <= 1.50 - 99.1%

Uncertainty of the prediction

All constituents for which estimations were made fall within the applicability domain of the model.

Mechanistic domain

KOWWIN (the Log Octanol-Water Partition Coefficient Program) estimates the logarithmic octanol-water partition coefficient (log P) of organic compounds. KOWWIN requires only a chemical structure to estimate a log P. The octanol-water partition coefficient is a physical property used extensively to describe a chemical's lipophilic or hydrophobic properties. It is the ratio of a chemical's concentration in the octanol-phase to its concentration in the aqueous phase of a two phase system at equilibrium. Since measured values range from less than 10^-4 to greater than 10^8 (at least 12 orders of magnitude), the logarithm (log P) is commonly used to characterize its value.

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.

As all regular and common fragments are included in this method, and the constituents for which this method was applied do not contain exotic fragments, there are no limits to the mechanistic domain.

Conclusions:
The log Kow range of the constituents of Litsea cubeba oil is 2.06 - 6.3.
Executive summary:

As Litsea cubeba oil is a naturally complex substance consisting of multiple constituents, a partition coefficient value for this NCS has little meaning. Therefore, in line with the NCS protocol, partition coefficients for the individual known constituents were calculated using the KOWWIN v1.67 QSAR by US-EPA.

The log Kow range for Litsea cubeba oil constituents was found to be 2.06 - 6.3. 16.90% of the constituents has a log Kow >= 4.

Description of key information

The log Kow range for Litsea cubeba oil constituents was found to be 2.06 - 6.3. 16.90% of the constituents has a log Kow >= 4.

Key value for chemical safety assessment

Additional information