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Please be aware that this old REACH registration data factsheet is no longer maintained; it remains frozen as of 19th May 2023.

The new ECHA CHEM database has been released by ECHA, and it now contains all REACH registration data. There are more details on the transition of ECHA's published data to ECHA CHEM here.

Diss Factsheets

Administrative data

Endpoint:
partition coefficient
Type of information:
(Q)SAR
Adequacy of study:
weight of evidence
Study period:
2018
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: KOWWIN v1.69; integrated within the Estimation Programme Interface (EPI) Suite programme for Microsoft Windows v4.11; September 2010 (model development); November 2012 (model publication)
2. MODEL (incl. version number): KOWWIN v1.69
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL: CAS RN
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL: There is no overt mechanistic basis for the model.
The model correlates thermodynamic relationships of surrogates to chemical activity. The KOWWIN v1.69 run in standalone mode allows Log Kow to be estimated based on measured values of analogues within the training set (if available). Then the model applies by adding/subtracting fragment constants and correction factors from the measured value. This therefore improves prediction since calculations are based on structural differences between target and analogue. The model domain ideally has at least one or more structurally similar substances to target substances on which to then apply ACF methodology.

Whilst there appears to be no direct analogues within the training set. The model has been has been extensively validated externally (using > 10,000) substances with a correlation coefficient (r2) = 0.943.

The model is non-proprietary and the training sets and validation sets can be downloaded from the internet. Model predictivity could be improved by the assignment of additional substances into the training set. Inclusion of additional structural fragments and expansion of sub-structure correction factors and related rules. In addition, rules for stereochemical effects could feasibly improve modelling.

5. APPLICABILITY DOMAIN:
(i) All constituents fall within the Molecular Weight ranges domain.
(ii) No substances have functional groups or features not in the training set of the model and/or for which no fragment constants and correction factors available. No constituents contain multiple fragment instances than the maximum of the training set.

6. ADEQUACY OF THE RESULT:
a) QSAR model is scientifically valid.
b) The substance falls within the applicability domain of the QSAR model.
c) The prediction is fit for regulatory purpose.

The prediction is adequate as supporting information for the Classification and Labelling or risk assessment of the substance as indicated in REACH Regulation (EC) 1907/2006: Annex XI Section 1.3.

Data source

Reference
Reference Type:
other: QSAR predicted value
Title:
KOWWIN v. 1.69 (Epi Suite) - Partition coefficient
Year:
2012
Bibliographic source:
Meylan, W.M. and P.H. Howard. 1995. Atom/fragment contribution method for estimating octanolwater pa rtition coefficients. J. Pharm. Sci. 84: 83-92.

Materials and methods

Principles of method if other than guideline:
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.
GLP compliance:
no
Type of method:
other: QSAR predicted value
Partition coefficient type:
octanol-water

Test material

Constituent 1
Chemical structure
Reference substance name:
2,4,6-trimethylpyridine
EC Number:
203-613-3
EC Name:
2,4,6-trimethylpyridine
Cas Number:
108-75-8
Molecular formula:
C8H11N
IUPAC Name:
2,4,6-trimethylpyridine
Specific details on test material used for the study:
No specific details on test maretial used for the study was provided. QSAR predicted value.

Study design

Analytical method:
other: QSAR predicted value

Results and discussion

Partition coefficient
Key result
Type:
log Pow
Partition coefficient:
ca. 2.45
Remarks on result:
other: QSAR predicted value

Applicant's summary and conclusion

Conclusions:
KOWWIN v.1.69 (Epi Suite) predicted that 2,4,6-collidine has a partition coefficient of 2.45.
Executive summary:

KOWWIN v.1.69 (Epi Suite) predicted that 2,4,6-collidine has a partition coefficient of 2.45. The training dataset of KOWWIN also includes the experimental partition coefficient data of 2,4,6–collidine which is 1.88. Further, this is a valid model for this substance which falls into its applicability domain as explained in the attached reports.