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EC number: 915-384-3 | CAS number: -
Partition coefficient
Administrative data
Link to relevant study record(s)
- 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 group contributions methodology of Molinspiration (miLogP2.2 - November 2005). 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: Group contributions
- Partition coefficient type:
- other: QSAR prediction
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 1.6 - ca. 4.63
- Remarks on result:
- other: predicted for the main constituents
- Remarks:
- Molinspiration (miLogP2.2)
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 3.01
- Remarks on result:
- other: weighted average Log Kow
- Remarks:
- Molinspiration (miLogP2.2)
- Conclusions:
- Using the group contribution method, of Molinspiration (miLogP 2.2), the weighted average partition coefficient (log Kow) value for test substance was predicted to be 3.01.
- Executive summary:
The partition coefficient (log Kow) value for the test substance, C8-10 MIPA was predicted using the group contribution method, of Molinspiration (miLogP 2.2) program. Since the test substance is a UVCB, the log Kow values were predicted for the individual constituents using SMILES codes as the input parameter. Using the group contribution method, the log Kow values for the individual constituents of the test substance ranged from 1.6 to 4.63. All constituents meet the molecular weight and log Kow descriptor domain criteria. Given that the constituents are structurally very similar and vary only in the carbon chain length, a weighted average value, which considers the percentage of the constituent in the substance, was considered to dampen the errors in predictions (if any). Therefore, the weighted average log Kow value was calculated as 3.01. Overall, considering either the individual log Kow predictions for the constituents or the weighted average values, 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:
- experimental study
- Adequacy of study:
- key study
- Study period:
- From April 04, 2017 to April 05, 2017
- Reliability:
- 1 (reliable without restriction)
- Rationale for reliability incl. deficiencies:
- guideline study
- Qualifier:
- according to guideline
- Guideline:
- OECD Guideline 107 (Partition Coefficient (n-octanol / water), Shake Flask Method)
- Deviations:
- no
- GLP compliance:
- not specified
- Type of method:
- flask method
- Partition coefficient type:
- octanol-water
- Type:
- log Pow
- Partition coefficient:
- > 2.6
- Temp.:
- 20 °C
- pH:
- 7
- Conclusions:
- Under the study conditions, the partition coefficient (log Pow) was determined to be greater than 2.6 at 20°C.
- Executive summary:
A study was conducted to determine the partition coefficient of the test substance, C8-10 MIPA, according to OECD Guideline 107. Under the study conditions, the partition coefficient (log Pow) was determined to be greater than 2.6 at 20°C (Schwarzkopf, 2018).
- 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.1.69. program in EPI SuiteTM v4.11. 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: Fragment constant method
- Partition coefficient type:
- other: QSAR prediction
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 0.71 - ca. 3.66
- Remarks on result:
- other: predicted for the main constituents
- Remarks:
- KOWWIN v.1.68. EPI SuiteTM v4.11
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 2.09
- Remarks on result:
- other: weighted average Log Kow
- Remarks:
- KOWWIN v.1.69. EPI SuiteTM v4.11
- Conclusions:
- Using the fragment constant method, of KOWWIN V.1.69 program of EPI SuiteTM, the weighted average partition coefficient (log Kow) value for test substance was predicted to be 2.09.
- Executive summary:
The partition coefficient (log Kow) value for the test substance, C8-10 MIPA was predicted using the fragment constant method, of KOWWIN V.1.69 program. Since the test substance is a UVCB, the log Kow values were predicted for the individual constituents using SMILES codes as the input parameter. Using the fragment constant method, the log Kow values for the individual constituents of the test substance ranged from 0.71 to 3.66. All constituents meet the molecular weight and structural fragment descriptor domain criteria. Given that the constituents are structurally very similar and vary only in the carbon chain length, a weighted average value, which considers the percentage of the constituent in the substance, was considered to dampen the errors in predictions (if any). Therefore, the weighted average log Kow value was calculated as 2.09. Overall, considering either the individual log Kow predictions for the constituents or the weighted average values, 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 v1.69 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 partition 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: Efficient Partition Algorithm (EPA) and Associative Neural Network (ASNN)
- Partition coefficient type:
- other: QSAR
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 1.27 - ca. 4.15
- Remarks on result:
- other: predicted for the main constituents
- Remarks:
- (ALOGPS v.2.1)
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 2.43
- Remarks on result:
- other: weighted average Log Kow
- Remarks:
- (ALOGPS v.2.1)
- Conclusions:
- Using the efficient partition algorithm and associative neural networks based regression equations from ALOGPS V.2.1, the weighted average partition coefficient (log Kow) value for test substance was predicted to be 2.43.
- Executive summary:
The partition coefficient (log Kow) value for the test substance, C8 -10 MIPA was predicted using the EPA and ASNN based regression equations from ALOGPS V.2.1. Since the test substance is a UVCB, the log Kow values were predicted for the individual constituents using SMILES codes as the input parameter. Using the EPA and ASNN-based regression equations, the log Kow values for the individual constituents of the test substance ranged from 1.27 to 4.15. All constituents meet the E-indices, molecular weight and number of non-hydrogen atoms descriptor domain criteria. Further, given that the constituents are structurally very similar and vary only in the carbon chain length, a weighted average value, which considers the percentage of the constituent in the substance, was considered to dampen the errors in predictions (if any). Therefore, the weighted average log Kow value was calculated as 2.43. Overall, considering either the individual log Kow predictions for the constituents or the weighted average values, 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
Predicted value:
Using the group contribution method of molinspiration model, the partition coefficient (Log Kow) values for the different constituents were predicted as follows:
Table 1: Log Kow predictions: Group contribution-basedmethod
Constituents/Carbon chain length* |
Mean/adjusted conc |
Mole fraction Xi = (mi/Mi)/∑ (mi/Mi) |
Log Kow |
Log Kow * xi |
C6 |
5 |
0.05575 |
1.6 |
8.92E-02 |
C8 |
55 |
0.52776 |
2.61 |
1.38E+00 |
C10 |
45 |
0.37898 |
3.62 |
1.37E+00 |
C12 |
5 |
0.03752 |
4.63 |
1.74E-01 |
|
|
|
Log Kow= |
3.01 |
*Glycerol or MEA residues have not been considered for the QSAR prediction
Predicted value:
The predicted log Kow values for the different constituents using the Fragment constant-based equation were as follows:
Table 2: Log Kow predictions: Fragment constant-based method
Constituents/Carbon chain length* |
Mean/ adjusted conc |
Mole fraction Xi = (mi/Mi)/∑ (mi/Mi) |
Log Kow |
Log Kow * xi |
Domain evaluation |
C6 |
5 |
0.05575 |
0.71 |
0.039696269 |
MW, Structural fragment (ID) |
C8 |
55 |
0.52776 |
1.69 |
0.894179408 |
MW, Structural fragment (ID) |
C10 |
45 |
0.37898 |
2.68 |
1.014332077 |
MW, Structural fragment (ID) |
C12 |
5 |
0.03752 |
3.66 |
0.137275025 |
MW, Structural fragment (ID) |
Log Kow= |
2.09 |
|
*Glycerol or MEA residues have not been considered for the QSAR prediction
Log Kow prediction results:
Log Kow(version 1.69 estimate): 0.71
SMILES : O=C(NCC(O)C)CCCCC
CHEM :
MOL FOR: C9 H19 N1 O2
MOL WT : 173.26
-------+-----+--------------------------------------------+---------+--------
TYPE | NUM | LOGKOW FRAGMENT DESCRIPTION | COEFF | VALUE
-------+-----+--------------------------------------------+---------+--------
Frag | 2 | -CH3 [aliphatic carbon] | 0.5473 | 1.0946
Frag | 5 | -CH2- [aliphatic carbon] | 0.4911 | 2.4555
Frag | 1 | -CH [aliphatic carbon] | 0.3614 | 0.3614
Frag | 1 | -OH [hydroxy, aliphatic attach] |-1.4086 | -1.4086
Frag | 1 | -NH- [aliphatic attach] |-1.4962 | -1.4962
Frag | 1 | -C(=O)N [aliphatic attach] |-0.5236 | -0.5236
Const | | Equation Constant | | 0.2290
-------+-----+--------------------------------------------+---------+--------
Log Kow = 0.7121
Log Kow(version 1.69 estimate): 1.69
SMILES : O=C(NCC(O)C)CCCCCCC
CHEM :
MOL FOR: C11 H23 N1 O2
MOL WT : 201.31
-------+-----+--------------------------------------------+---------+--------
TYPE | NUM | LOGKOW FRAGMENT DESCRIPTION | COEFF | VALUE
-------+-----+--------------------------------------------+---------+--------
Frag | 2 | -CH3 [aliphatic carbon] | 0.5473 | 1.0946
Frag | 7 | -CH2- [aliphatic carbon] | 0.4911 | 3.4377
Frag | 1 | -CH [aliphatic carbon] | 0.3614 | 0.3614
Frag | 1 | -OH [hydroxy, aliphatic attach] |-1.4086 | -1.4086
Frag | 1 | -NH- [aliphatic attach] |-1.4962 | -1.4962
Frag | 1 | -C(=O)N [aliphatic attach] |-0.5236 | -0.5236
Const | | Equation Constant | | 0.2290
-------+-----+--------------------------------------------+---------+--------
Log Kow = 1.6943
Log Kow(version 1.69 estimate): 2.68
SMILES : O=C(NCC(O)C)CCCCCCCCC
CHEM :
MOL FOR: C13 H27 N1 O2
MOL WT : 229.37
-------+-----+--------------------------------------------+---------+--------
TYPE | NUM | LOGKOW FRAGMENT DESCRIPTION | COEFF | VALUE
-------+-----+--------------------------------------------+---------+--------
Frag | 2 | -CH3 [aliphatic carbon] | 0.5473 | 1.0946
Frag | 9 | -CH2- [aliphatic carbon] | 0.4911 | 4.4199
Frag | 1 | -CH [aliphatic carbon] | 0.3614 | 0.3614
Frag | 1 | -OH [hydroxy, aliphatic attach] |-1.4086 | -1.4086
Frag | 1 | -NH- [aliphatic attach] |-1.4962 | -1.4962
Frag | 1 | -C(=O)N [aliphatic attach] |-0.5236 | -0.5236
Const | | Equation Constant | | 0.2290
-------+-----+--------------------------------------------+---------+--------
Log Kow = 2.6765
Log Kow(version 1.69 estimate): 3.66
SMILES : O=C(NCC(O)C)CCCCCCCCCCC
CHEM :
MOL FOR: C15 H31 N1 O2
MOL WT : 257.42
-------+-----+--------------------------------------------+---------+--------
TYPE | NUM | LOGKOW FRAGMENT DESCRIPTION | COEFF | VALUE
-------+-----+--------------------------------------------+---------+--------
Frag | 2 | -CH3 [aliphatic carbon] | 0.5473 | 1.0946
Frag | 11 | -CH2- [aliphatic carbon] | 0.4911 | 5.4021
Frag | 1 | -CH [aliphatic carbon] | 0.3614 | 0.3614
Frag | 1 | -OH [hydroxy, aliphatic attach] |-1.4086 | -1.4086
Frag | 1 | -NH- [aliphatic attach] |-1.4962 | -1.4962
Frag | 1 | -C(=O)N [aliphatic attach] |-0.5236 | -0.5236
Const | | Equation Constant | | 0.2290
-------+-----+--------------------------------------------+---------+--------
Log Kow = 3.6587
Predicted value:
The predicted Partition coefficient (Log Kow) values for the different constituents using the Efficient Partition Algorithm (EPA) and Associative Neural Network (ASNN) method were as follows:
Table 1: Log Kow predictions: EPA and ASNN basedmethod
Constituents/Carbon chain length* | Mean/adjusted conc | Mole fraction Xi = (mi/Mi)/∑ (mi/Mi) | Log Kow | Log Kow * xi |
C6 | 5 | 0.05575 | 1.27 | 0.070796604 |
C8 | 55 | 0.52776 | 1.93 | 1.018571834 |
C10 | 45 | 0.37898 | 3.13 | 1.18619817 |
C12 | 5 | 0.03752 | 4.15 | 0.155708682 |
|
|
| Log Kow= | 2.43 |
*Glycerol or DEA residues have not been considered for the QSAR prediction
Description of key information
The partition coefficient was determined according to OECD Guideline 107.
Weighted average partition coefficient values for the substance were also modelled using the fragment constant method of the KOWWIN V.1.69 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):
- 2.6
- at the temperature of:
- 20 °C
Additional information
The partition coefficient measured according to OECD Guideline 107 was > 2.6.
Using the fragment constant method of the KOWWIN V.1.69 program of EPI Suite, the weighted average partition coefficient (log Kow) value for test substance was predicted to be 2.09.
According to the group contribution method of Molinspiration (miLogP 2.2), the weighted average log Kow was 3.01.
According to the efficient partition algorithm and associative neural network-based regression equations from ALOGPS V.2.1, the weighted average log Kow was 2.43.
The measured log Kow was retained as a key value for risk assessment purposes.
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