Printing Date 2013-08-05 13:48:37 CEST Restriction of specific regulatory purposes EU: REACH

Owner 2- B utenedioic acid (E)-, di-C8-18-alkyl esters, CAS 68610-90-2 /
Legal entity owner 

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05.08.2013 This document is directed to USEPA pursuant the Toxic Substances Control Act (TSCA). This document is the property of the sponsor and may be used only for that purpose intended by the sponsor and as authorized under applicable law. Every other or additional use, exploitation, reproduction, publication or submission to other parties requires the written permission of the sponsor, except as otherwise required by applicable law (e.g., TSCA). The use of these studies/documents for regulatory, registration or other commercial purposes [including but not limited to Registration, Evaluation, Authorization and Restriction of Chemical Substances (REACH)], without the express written permission of the sponsor is unauthorized and strictly prohibited.

## Endpoint study record: WoE, RA - A C8 FA component, 2011, KOWWIN v1.68, RL2

Administrative Data

Data source Reference

Data access


### Materials and methods Partition coefficient type
Principles of method if other than guideline

GLP compliance

Test materials Identity of test material same as for substance defined in section 1 (if not read-across)

Details on test material

Any other information on materials and methods incl. tables UUID IUC5-6e41f4b5-deb7-4c45-be1d-c0dc0466ee3e Dossier UUID 0 Author Date 2011-09-09 14:31:43 CEST Remarks

EU: REACH Purpose flag wei ght of ev idence; robust study summary Study result type (Q)SAR Reliability 2 (reliab l e with restrictions) Rationale for reliability incl. deficiencies Accepted calculation method

Reference type Author Year Title Bibliographic source Testing laboratory Report no. Owner company Company study no. Report date study report 2011 Episuite 4.10 calculation with 2- Butenedioic acid (E)-, di-C8-18- alkyl esters 20110609- Hop-2 Fatty acid ester umbrella consortium (FEUC), PFAE linear subgroup 2011- 06-09

data submitter is data owner

octanol-water

Calculation based on KOWWIN v1.68, Estimation Programs Interface SuiteTM for Microsoft® Windows v4.10. US EPA, United States Environmental Protection Agency, Washington, DC, USA.

no

no

- Name of test material (as cited in study report): 2-Butenedioic acid (E)-, di-C8-alkyl ester - Molecular formula (if other than submission substance): C20 H36 O4 - Molecular weight (if other than submission substance): 340.51 g/mol - Smiles notation: CCCCCCCCOC(=O)C=CC(=O)OCCCCCCCC - Analytical purity: not applicable 

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05.08.2013Input data: SMILES: CCCCCCCCOC(=O)C=CC(=O)OCCCCCCCC KOWWIN (the Log Octanol-Water Partition Coefficient Program) estimates the octanol-water partition coefficient (log P) of organic compounds. KOWWIN requires only a chemical structure to estimate a log P.In the method applied, 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. Validity of the model according to the OECD principles: 1. 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). 2. 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, hydrogenbondings, 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. 3. Applicability domain: 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 

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### Results and discussions 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.
4. Appropriate measures of goodness of fit, robustness and predictivity: Training set statistics: N = 2447 compounds correlation coefficient R 2 = 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 R 2 = 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%
5. Mechanistic inte rpretation, if possible: 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., A tom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995. 

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05.08.2013Partition coefficient

Any other information on results incl. tables



### Overall remarks, attachments Remarks on results including tables and figures

Type Partition coefficient Temp. pH Remarks log Pow 8.09

QSAR result; pH and temperature are not reported. For detailed description of the model and its applicability, see "Any other information on materials and methods incl. tables".

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

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