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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. 7.14
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 7.14.
Executive summary:

The partition coefficient (log Kow) value for the test substance, IsoC18 MIPA 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 7.14. 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:
experimental study
Adequacy of study:
key study
Study period:
From April 26, 1999 to June 08, 1999
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
Qualifier:
according to guideline
Guideline:
OECD Guideline 117 (Partition Coefficient (n-octanol / water), HPLC Method)
Deviations:
no
Qualifier:
according to guideline
Guideline:
EU Method A.8 (Partition Coefficient - HPLC Method)
Deviations:
no
GLP compliance:
yes (incl. QA statement)
Type of method:
HPLC method
Partition coefficient type:
octanol-water
Analytical method:
high-performance liquid chromatography
Key result
Type:
log Pow
Partition coefficient:
>= 3.3 - <= 7
Temp.:
20 °C
Remarks on result:
other: the log Pow is given for the compounds represented in three main peaks
Remarks:
pH: not specified
Details on results:
The chromatography of the test substance resulted in several peaks each with a reproducible retention time. This was caused by the test substance, which is a mixture of different isomeric compounds and impurities. Therefore, the log Pow (range) was calculated or extrapolated from the calibration equation for the compounds represented in three main peaks. The HPLC method was considered to be suitable for the purpose of the study. Based on the chromatographic data, the test substance was considered to be stable during the test procedure.
The log Pow range of the test substance was calculated with reference to the linear regression (log k’ vs. log Pow) of the reference compounds. The individual values for the three main compounds were log Pow1 = 3.3, log Pow2 = 6.0 and log Pow3 = 7.0. In conclusion, the partition coefficient (n-octanol/water) of the test substance was determined to be in the range log Pow = 3.3 to 7.0 using the HPLC method.
Conclusions:
Under the study conditions, the partition coefficient (log Pow) was determined to be in the range of 3.3 - 7.0 at 20°C.
Executive summary:

A study was conducted to determine the partition coefficient of the test substance, IsoC18 MIPA (94.1% active), according to OECD Guidelines 107 and 117 and EU Method A.8, in compliance with GLP. Under the study conditions, the partition coefficient (log Pow) was determined to be in the range of 3.3 - 7.0 at 20°C (Tognucci, 1999).

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. 6.53
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 6.53.
Executive summary:

The partition coefficient (log Kow) value for the test substance, IsoC18 MIPA 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 6.53. 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:
(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. 6.41
Remarks on result:
other: predicted for the main constituents
Remarks:
ALOGPS v.2.1
Conclusions:
Using the Efficient Partition Alogorithm (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 6.41.
Executive summary:

The partition coefficient (log Kow) value for the test substance, IsoC18 MIPA 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 6.41 (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. Therefore log Kow predictions for the test substance using ALOGPS v.2.1 can be considered to be reliable with moderate confidence.

Description of key information

The partition coefficient was determined according to OECD Guidelines 107 and 117 and EU Method A.8 (HPLC method).

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.

The measured log Kow was retained as key value for risk assessment purposes. 

Key value for chemical safety assessment

Log Kow (Log Pow):
7
at the temperature of:
20 °C

Additional information

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 6.53.

According to the group contribution method of Molinspiration (miLogP 2.2), the weighted average log Kow was 7.14.

According to the efficient partition algorithm and associative neural network-based regression equations from ALOGPS V.2.1, the weighted average log Kow was 6.41.