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Physical & Chemical properties

Partition coefficient

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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. 2.85
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 2.85.
Executive summary:

The partition coefficient (log Kow) value for the test substance, C11-unsatd. 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 2.85. 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 efficient partition alogorith (EPA) 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. 2.83
Remarks on result:
other: predicted for the main constituents
Remarks:
ALOGPS v.2.1
Conclusions:
Using the Efficient Partition Algorithm (EPA) and Associative Neural Networks (ASNN) based regression equations from ALOGPS V.2.1, the partition coefficient (log Kow) value for test substance was predicted to be 2.83.
Executive summary:

The partition coefficient (log Kow) value for the test substance, C11-unsatd. 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 Associative neural network method, the log Kow values for the test substance was predicted to be 2.83 (original estimates). 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.The log Kow predictions for the test substance using ALOGPS v.2.1 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. 2.26
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 2.26.
Executive summary:

The partition coefficient (log Kow) value for the test substance, C11-unsatd. 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 2.26. 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 v1.69 can be considered to be reliable with moderate confidence.

Endpoint:
partition coefficient
Type of information:
experimental study
Adequacy of study:
key study
Study period:
February 22, 2017
Reliability:
1 (reliable without restriction)
Rationale for reliability incl. deficiencies:
guideline study
Qualifier:
according to guideline
Guideline:
other: preliminary studies: OECD Guideline 105, EU Method A6
Deviations:
no
GLP compliance:
not specified
Type of method:
other: preliminary studies: OECD Guideline 105, EU Method A6
Partition coefficient type:
octanol-water
Key result
Type:
log Pow
Partition coefficient:
> 1.58 - < 3.32
Remarks on result:
other: Temperature and pH not specified
Conclusions:
Under the study conditions, the estimated octanol solubility of the test substance is in the range of 200 g/L – 1000 g/L, which results in the partition coefficient within range of: 1.58 and 3.33.
Executive summary:

A study was conducted to determine octanol-water partition coefficient of the test substance C11 DEA (90.6% active). The octanol solubility of the test substance was estimated according to the preliminary test for water solubility (OECD Guideline 105 and EU Method A6. Under the study conditions, the estimated octanol solubility of the test substance is in the range of 200 g/L – 1000 g/L, which results in the partition coefficient within range of: 1.58 and 3.33 (Mund, 2017).

Description of key information

The partition coefficient was determined according to OECD Guideline 117 (HPLC Method) (Torp, 1996). Weighted average partition coefficient values for the substance were 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):
3.33
at the temperature of:
20 °C

Additional information

The experimental partition coefficient was 3.33. Using the fragment constant method of the KOWWIN V.1.68 program of EPI Suite, the weighted average partition coefficient (log Kow) value for test substance was predicted to be 2.26. According to the group contribution method of Molinspiration (miLogP 2.2), the weighted average log Kow was 2.85. According to the efficient partition algorithm and associative neural network-based regression equations from ALOGPS V.2.1, the weighted average log Kow was 2.83.