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EC number: 204-393-1 | CAS number: 120-40-1
Partition coefficient
Administrative data
Link to relevant study record(s)
- Endpoint:
- partition coefficient
- Type of information:
- experimental study
- Adequacy of study:
- supporting study
- Study period:
- 18 June 2012 to 20 August 2012
- Reliability:
- 1 (reliable without restriction)
- Rationale for reliability incl. deficiencies:
- guideline study
- Qualifier:
- according to guideline
- Guideline:
- EU Method A.8 (Partition Coefficient)
- Deviations:
- no
- Qualifier:
- according to guideline
- Guideline:
- EPA OPPTS 830.7570 (Partition Coefficient, n-octanol / H2O, Estimation by Liquid Chromatography)
- Deviations:
- no
- GLP compliance:
- yes
- Type of method:
- estimation method (solubility ratio)
- Key result
- Type:
- Pow
- Partition coefficient:
- 799 000
- Temp.:
- 20 °C
- Key result
- Type:
- log Pow
- Partition coefficient:
- 5.9
- Temp.:
- 20 °C
- Details on results:
- After the stirring period, the test samples of 603, 702 and 799 g/L were clear and no undissolved test substance was observed. In the test sample of 897 g/L was undissolved test substance observed. From this, it was concluded that the n-octanol solubility of amides, C12, N,N-bis(2-hycroxyethlhydroxyethyl) at 20°C was >799 g/L. The Pow, calculated as the quotient of the n-octanol- and the critical micelle concentration, is > 799000 g/L. It corresponds with a log Pow value > 5.90.
- Conclusions:
- Under the study conditions, the partition coefficient (Pow) was > 799000 at 20°C. This corresponds with a log Pow value of > 5.90.
- Executive summary:
A study was conducted to determine the partition coefficient of the test substance C12 DEA (90.2% active), using an estimation method based on n-octanol solubility (>799 g/L) and critical micelle concentration (<1 mg/L). The partition coefficient (Pow) was >799000 at 20°C. This corresponds with a log Pow value of > 5.90 (Baltussen, 2012).
- 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, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification
- Justification for type of information:
- QSAR prediction from a well-known and acknowledged tool. See below under ''attached background material section' for QPRF containing methodology and domain evaluation details.
- Qualifier:
- according to guideline
- Guideline:
- other: REACH guidance on QSARs: Chapter R.6. QSARs and grouping of chemicals
- Principles of method if other than guideline:
- The partition coefficient (log Kow) value for the test substance was estimated using the group contributions methodology of Molinspiration (miLogP2.2 - November 2005). The log Kow value for test substance was predicted using SMILES codes as the input parameter.
- Type of method:
- other: Group contributions
- Partition coefficient type:
- other: QSAR prediction
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 3.88
- Remarks on result:
- other: predicted for the main constituents
- Remarks:
- Molinspiration (miLogP2.2)
- Conclusions:
- Using the group contribution method, of Molinspiration (miLogP 2.2), the partition coefficient (log Kow) value for test substance was predicted to be 3.88.
- Executive summary:
The partition coefficient (log Kow) value for the test substance, C12 DEA was predicted using the group contribution method, of Molinspiration (miLogP 2.2) program. The log Kow value for test substance was predicted using SMILES codes as the input parameter. Using the group contribution method, the log Kow values for the individual constituents of the test substance was predicted to be 3.88. The constituent meets the molecular weight and log Kow descriptor domain criteria. Overall, considering either the log Kow predictions for the constituent, the test substance is expected to be less hydrophobic with a good absorption and low accumulation potential. Therefore the log Kow predictions for the test substance using Molinspiration (miLogP 2.2), can be considered to be reliable with moderate confidence.
- 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, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification
- Justification for type of information:
- QSAR prediction from a well-known and acknowledged tool. See below under ''attached background material section' for QPRF containing methodology and domain evaluation details.
- Qualifier:
- according to guideline
- Guideline:
- other: REACH guidance on QSARs: Chapter R.6. QSARs and grouping of chemicals
- Principles of method if other than guideline:
- The partition coefficient (log Kow) value for the test substance were estimated using the KOWWIN v.1.68. program in EPI SuiteTM v4.11. The log Kow values were predicted for the constituent using SMILES codes as the input parameter.
- Type of method:
- other: Fragment constant method
- Partition coefficient type:
- other: QSAR prediction
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 2.89
- Remarks on result:
- other: predicted for the main constituents
- Remarks:
- KOWWIN v.1.68. EPI SuiteTM v4.11
- Conclusions:
- Using the fragment constant method, of KOWWIN V.1.68 program of EPI SuiteTM, the partition coefficient (log Kow) value for test substance was predicted to be 2.89.
- Executive summary:
The partition coefficient (log Kow) value for the test substance, C12 DEA was predicted using the fragment constant method, of KOWWIN V.1.68 program. The log Kow values for the constituent were predicted using SMILES codes as the input parameter. Using the fragment constant method, the log Kow value for the test substance was predicted to be 2.89. The constituent meets the molecular weight and structural fragment descriptor domain criteria. Overall, considering either the individual log Kow predictions for the constituent, the test substance is expected to be less hydrophobic with a good absorption and low accumulation potential. Therefore the log Kow predictions for the test substance using KOWWIN v1.68 can be considered to be reliable with moderate confidence.
- 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, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification
- Justification for type of information:
- QSAR prediction from a well-known and acknowledged tool. See below under ''attached background material section" for QPRF containing methodology and domain evaluation details.
- Qualifier:
- according to guideline
- Guideline:
- other: REACH guidance on QSARs: Chapter R.6. QSARs and grouping of chemicals
- Principles of method if other than guideline:
- The Partition Coefficient (Log Kow) value for the test substance were estimated using the efficient parttion algorithm (EPA) and associative neural network (ASNN) method of the ALOGPS v.2.1 program from the virtual computational chemistry laboratory. Since the test substance is a UVCB, the Log Kow values were predicted for the individual constituents using SMILES codes as the input parameter.
- Type of method:
- other: Associative neural network method (ALOGPS v.2.1)
- Partition coefficient type:
- other: QSAR
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 3.67
- Remarks on result:
- other: predicted for the main constituents
- Remarks:
- ALOGPS v.2.1
- Conclusions:
- Using the Efficient Partition Algorithm (EPA) and Associative Neural Network (ASNN) based regression equations from ALOGPS V.2.1, the partition coefficient (log Kow) value for test substance was predicted to be 3.67.
- Executive summary:
The partition coefficient (log Kow) value for the test substance, C12 DEA was predicted using the EPA and ASNN based regression equations from ALOGPS V.2.1. The log Kow values for test substance were predicted using SMILES codes as the input parameter. Using the EPA and ASNN method, the log Kow values for the test substance was predicted to be 2.83. The constituent meets the E-indices, molecular weight and number of non-hydrogen atoms descriptor domain criteria. Overall, considering either the log Kow predictions for the constituent, the test substance is expected to be less hydrophobic with a good absorption and low accumulation potential. Therefore the log Kow predictions for the test substance using ALOGPS v.2.1 can be considered to be reliable with moderate confidence.
Referenceopen allclose all
After the stirring period, the test samples of 603, 702 and 799 g/L were clear and no undissolved test substance was observed. In the test sample of 897 g/L was undissolved test substance observed. From this, it was concluded that the n-octanol solubility of amides, C12, N,N-bis(2-hycroxyethlhydroxyethyl) at 20°C was >799 g/L. The Pow, calculated as the quotient of the n-octanol- and the critical micelle concentration, is > 799000 g/L. It corresponds with a log Pow value > 5.90.
Description of key information
The partition coefficient was determined using a calculation method based on the n-octanol solubility and the critical micelle concentration according to EU Method A.8 and US EPA OPPTS 830.7570 Guidelines (Baltussen, 2012).
A partition coefficient value for the substance was also modelled using the fragment constant method of the KOWWIN V.1.68 program of EPI Suite, the group contribution method of Molinspiration (miLogP 2.2) and the efficient partition algorithm and associative neural network-based regression equations from ALOGPS V.2.1.
Key value for chemical safety assessment
- Log Kow (Log Pow):
- 3.48
Additional information
The calculated Log Kow was >5.9.
Using the fragment constant method of the KOWWIN V.1.68 program of EPI Suite, the weighted average partition coefficient (log Kow) for test substance was predicted to be 2.89. According to the group contribution method of Molinspiration (miLogP 2.2), the weighted average log Kow was 3.88. According to the efficient partition algorithm and associative neural network-based regression equations from ALOGPS V.2.1, the weighted average log Kow was 3.67.
The mean modeled log Kow of 3.48 was retained as a key value for risk assessment purposes.
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