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EC number: 287-466-0 | CAS number: 85508-41-4
- Life Cycle description
- Uses advised against
- Endpoint summary
- Appearance / physical state / colour
- Melting point / freezing point
- Boiling point
- Density
- Particle size distribution (Granulometry)
- Vapour pressure
- Partition coefficient
- Water solubility
- Solubility in organic solvents / fat solubility
- Surface tension
- Flash point
- Auto flammability
- Flammability
- Explosiveness
- Oxidising properties
- Oxidation reduction potential
- Stability in organic solvents and identity of relevant degradation products
- Storage stability and reactivity towards container material
- Stability: thermal, sunlight, metals
- pH
- Dissociation constant
- Viscosity
- Additional physico-chemical information
- Additional physico-chemical properties of nanomaterials
- Nanomaterial agglomeration / aggregation
- Nanomaterial crystalline phase
- Nanomaterial crystallite and grain size
- Nanomaterial aspect ratio / shape
- Nanomaterial specific surface area
- Nanomaterial Zeta potential
- Nanomaterial surface chemistry
- Nanomaterial dustiness
- Nanomaterial porosity
- Nanomaterial pour density
- Nanomaterial photocatalytic activity
- Nanomaterial radical formation potential
- Nanomaterial catalytic activity
- Endpoint summary
- Stability
- Biodegradation
- Bioaccumulation
- Transport and distribution
- Environmental data
- Additional information on environmental fate and behaviour
- Ecotoxicological Summary
- Aquatic toxicity
- Endpoint summary
- Short-term toxicity to fish
- Long-term toxicity to fish
- Short-term toxicity to aquatic invertebrates
- Long-term toxicity to aquatic invertebrates
- Toxicity to aquatic algae and cyanobacteria
- Toxicity to aquatic plants other than algae
- Toxicity to microorganisms
- Endocrine disrupter testing in aquatic vertebrates – in vivo
- Toxicity to other aquatic organisms
- Sediment toxicity
- Terrestrial toxicity
- Biological effects monitoring
- Biotransformation and kinetics
- Additional ecotoxological information
- Toxicological Summary
- Toxicokinetics, metabolism and distribution
- Acute Toxicity
- Irritation / corrosion
- Sensitisation
- Repeated dose toxicity
- Genetic toxicity
- Carcinogenicity
- Toxicity to reproduction
- Specific investigations
- Exposure related observations in humans
- Toxic effects on livestock and pets
- Additional toxicological data
Partition coefficient
Administrative data
Link to relevant study record(s)
- Endpoint:
- partition coefficient
- Type of information:
- experimental study
- Adequacy of study:
- key study
- Study period:
- February 09, 2017 - February 10, 2017
- Reliability:
- 1 (reliable without restriction)
- Rationale for reliability incl. deficiencies:
- guideline study
- 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:
- no
- Type of method:
- HPLC method
- Partition coefficient type:
- octanol-water
- Analytical method:
- high-performance liquid chromatography
- Type:
- log Pow
- Partition coefficient:
- 6.9
- Temp.:
- 20 °C
- pH:
- 5.8
- Executive summary:
The partition coefficient n-octanol/water of the test item Disperse Blue 291:1 Br was determined according to the HPLC method.
It was found to be:
log POW> 6.57)
log POW=6.98)
7)above the highest log POW value of calibration substances (4,4’-DDT)
8)via extrapolation
- Endpoint:
- partition coefficient
- Type of information:
- calculation (if not (Q)SAR)
- Adequacy of study:
- weight of evidence
- Study period:
- 2020
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- accepted calculation method
- Justification for type of information:
- 1. SOFTWARE
EpiSuite / KOWWIN v1.68
2. MODEL (incl. version number)
EpiSuite 4.1
KOWWIN v1.68
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
See attachment
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See attachment
5. APPLICABILITY DOMAIN
See attachment
6. ADEQUACY OF THE RESULT
See attachment - Principles of method if other than guideline:
- KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, 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. 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. KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995). See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.
To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom (e.g. carbon, nitrogen, oxygen, sulfur, etc.) 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"; these include carbonyl (C=O), thiocarbonyl (C=S), nitro (-NO2), nitrate (ONO2), cyano (-C/N), and isothiocyanate (-N=C=S). Connections to each core "atom" are either general or specific; specific connections take precedence over general connections. For example, aromatic carbon, aromatic oxygen and aromatic sulfur atoms have nothing but general connections; i.e., the fragment is the same no matter what is connected to the atom. In contrast, there are 5 aromatic nitrogen fragments: (a) in a five-member ring, (b) in a six-member ring, (c) if the nitrogen is an oxide-type {i.e. pyridine oxide}, (d) if the nitrogen has a fused ring location {i.e. indolizine}, and (e) if the nitrogen has a +5 valence {i.e. N-methyl pyridinium iodide}; since the oxide-type is most specific, it takes precedence over the other four. The aliphatic carbon atom is another example; it does not matter what is connected to -CH3, -CH2-, or -CH< , the fragment is the same; however, an aliphatic carbon with no hydrogens has two possible fragments: (a) if there are four single bonds with 3 or more carbon connections and (b) any other not meeting the first criteria.
It became apparent, for various types of structures, that log P estimates made from atom/fragment values alone could or needed to be improved by inclusion of substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method. The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values. The correction factors have two main groupings: first, factors involving aromatic ring substituent positions and second, miscellaneous factors. In general, the correction factors are values for various steric interactions, hydrogen-bondings, 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.
Two separate regression analyses were performed. The first regression related log P to atom/fragments of compounds that do not require correction factors (i.e., compounds estimated adequately by fragments alone). The general regression equation has the following form:
log P = Σ(fini ) + b (Equation 1)
where Σ(fini ) is the summation of fi (the coefficient for each atom/fragment) times ni (the number of times the atom/fragment occurs in the structure) and b is the linear equation constant. This initial regression used 1120 compounds of the 2447 compounds in the total training dataset.
The correction factors were then derived from a multiple linear regression that correlated differences between the experimental (expl) log P and the log P estimated by Equation 1 above with the correction factor descriptors. This regression did not utilize an additional equation constant. The equation for the second regression is:
lop P (expl) - log P (eq 1) = Σ(cjnj ) (Equation 1)
where Σ(cjnj ) is the summation of cj (the coefficient for each correction factor) times nj (the number of times the correction factor occurs (or is applied) in the molecule).
Regression Results
Results of the two successive multiple regressions (first for atom/fragments and second for correction factors) yield the following general equation for estimating log P of any organic compound:
log P = Σ(fini ) + Σ(cjnj ) + 0.229 (Equation 3)
(num = 2447, r2 = 0.982, std dev = 0.217, mean error = 0.159) - Type of method:
- calculation method (fragments)
- Partition coefficient type:
- octanol-water
- Key result
- Type:
- log Pow
- Partition coefficient:
- 6.67
- Temp.:
- 25 °C
- pH:
- 7.4
- Remarks on result:
- other: assumed values for modelling
- Conclusions:
- Log Kow = 6.67
- Executive summary:
Log Kow (version 1.68 estimate): 6.67
- Endpoint:
- partition coefficient
- Type of information:
- calculation (if not (Q)SAR)
- Adequacy of study:
- weight of evidence
- Study period:
- 2020
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- accepted calculation method
- Justification for type of information:
- 1. SOFTWARE
EpiSuite / KOWWIN v1.68
2. MODEL (incl. version number)
EpiSuite 4.1
KOWWIN v1.68
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
See attachment
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See attachment
5. APPLICABILITY DOMAIN
See attachment
6. ADEQUACY OF THE RESULT
See attachment - Principles of method if other than guideline:
- KOWWIN uses a "fragment constant" methodology to predict log P. In a "fragment constant" method, 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. 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. KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (1979) that is available in the CLOGP Program (Daylight, 1995). See the Meylan and Howard (1995) journal article for a more complete description of KOWWIN’s methodology.
To estimate log P, KOWWIN initially separates a molecule into distinct atom/fragments. In general, each non-hydrogen atom (e.g. carbon, nitrogen, oxygen, sulfur, etc.) 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"; these include carbonyl (C=O), thiocarbonyl (C=S), nitro (-NO2), nitrate (ONO2), cyano (-C/N), and isothiocyanate (-N=C=S). Connections to each core "atom" are either general or specific; specific connections take precedence over general connections. For example, aromatic carbon, aromatic oxygen and aromatic sulfur atoms have nothing but general connections; i.e., the fragment is the same no matter what is connected to the atom. In contrast, there are 5 aromatic nitrogen fragments: (a) in a five-member ring, (b) in a six-member ring, (c) if the nitrogen is an oxide-type {i.e. pyridine oxide}, (d) if the nitrogen has a fused ring location {i.e. indolizine}, and (e) if the nitrogen has a +5 valence {i.e. N-methyl pyridinium iodide}; since the oxide-type is most specific, it takes precedence over the other four. The aliphatic carbon atom is another example; it does not matter what is connected to -CH3, -CH2-, or -CH< , the fragment is the same; however, an aliphatic carbon with no hydrogens has two possible fragments: (a) if there are four single bonds with 3 or more carbon connections and (b) any other not meeting the first criteria.
It became apparent, for various types of structures, that log P estimates made from atom/fragment values alone could or needed to be improved by inclusion of substructures larger or more complex than "atoms"; hence, correction factors were added to the AFC method. The term "correction factor" is appropriate because their values are derived from the differences between the log P estimates from atoms alone and the measured log P values. The correction factors have two main groupings: first, factors involving aromatic ring substituent positions and second, miscellaneous factors. In general, the correction factors are values for various steric interactions, hydrogen-bondings, 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.
Two separate regression analyses were performed. The first regression related log P to atom/fragments of compounds that do not require correction factors (i.e., compounds estimated adequately by fragments alone). The general regression equation has the following form:
log P = Σ(fini ) + b (Equation 1)
where Σ(fini ) is the summation of fi (the coefficient for each atom/fragment) times ni (the number of times the atom/fragment occurs in the structure) and b is the linear equation constant. This initial regression used 1120 compounds of the 2447 compounds in the total training dataset.
The correction factors were then derived from a multiple linear regression that correlated differences between the experimental (expl) log P and the log P estimated by Equation 1 above with the correction factor descriptors. This regression did not utilize an additional equation constant. The equation for the second regression is:
lop P (expl) - log P (eq 1) = Σ(cjnj ) (Equation 1)
where Σ(cjnj ) is the summation of cj (the coefficient for each correction factor) times nj (the number of times the correction factor occurs (or is applied) in the molecule).
Regression Results
Results of the two successive multiple regressions (first for atom/fragments and second for correction factors) yield the following general equation for estimating log P of any organic compound:
log P = Σ(fini ) + Σ(cjnj ) + 0.229 (Equation 3)
(num = 2447, r2 = 0.982, std dev = 0.217, mean error = 0.159) - Type of method:
- calculation method (fragments)
- Partition coefficient type:
- octanol-water
- Key result
- Type:
- log Pow
- Partition coefficient:
- 6.43
- Temp.:
- 25 °C
- pH:
- 7.4
- Remarks on result:
- other: assumed values for modelling
- Conclusions:
- Log Kow = 6.43
- Executive summary:
Log Kow (version 1.68 estimate): 6.43
Referenceopen allclose all
Individual results
Calculation of the partition coefficient log POW from the preliminary test
The program KOWWIN (part of EPI Suite) was used for calculating the log POW of the test item. The calculated value was 6.7.
Therefore according to the guidelines the partition coefficient n-octanol/water of the test item at room temperature was determined by the HPLC method.
HPLC method
The results from the HPLC method are summarized in Table2 to Table6.
Table2: Determination of the dead time t0
Dead time marker |
tR / min 1st injection |
tR / min 2st injection |
tR / min |
Formamide |
1.085 |
1.086 |
1.086 |
Dead time: t0 = 1.086 min
Table3: Calibration data 1st injection
Calibration substance |
tR / min |
k |
log k |
log POW 1) |
2-Butanone |
1.282 |
0.18 |
-0.74 |
0.3 |
Acetanilide |
1.355 |
0.25 |
-0.61 |
1.0 |
Cinnamyl alcohol |
1.611 |
0.48 |
-0.32 |
1.9 |
2,6-Dichlorobenzonitrile |
2.029 |
0.87 |
-0.06 |
2.6 |
Allyl phenyl ether |
2.856 |
1.63 |
0.21 |
2.9 |
Benzophenone |
2.592 |
1.39 |
0.14 |
3.2 |
Cumene |
5.138 |
3.73 |
0.57 |
3.7 |
Diphenyl ether |
4.706 |
3.34 |
0.52 |
4.2 |
Fluoranthene |
9.370 |
7.63 |
0.88 |
5.1 |
4,4’-DDT |
22.464 |
19.69 |
1.29 |
6.5 |
1)OECD guideline 117, adopted 2004
Regression:
Parameters: a = 2.599
b = 2.842
r2= 0.9811
Table4: log POW of the test item – 1st injection
Test item |
tR/ min |
k |
log k |
log POW |
Disperse Blue 291/1 Br |
36.468 |
32.60 |
1.51 |
> 6.52) (6.9)3) |
2)above the highest log POW value of calibration substances (4,4’-DDT)
3)via extrapolation
Table5: Calibration data 2nd injection
Calibration substance |
tR / min |
k |
log k |
log POW 4) |
2-Butanone |
1.280 |
0.18 |
-0.75 |
0.3 |
Acetanilide |
1.354 |
0.25 |
-0.61 |
1.0 |
Cinnamyl alcohol |
1.609 |
0.48 |
-0.32 |
1.9 |
2,6-Dichlorobenzonitrile |
2.025 |
0.87 |
-0.06 |
2.6 |
Allyl phenyl ether |
2.853 |
1.63 |
0.21 |
2.9 |
Benzophenone |
2.587 |
1.38 |
0.14 |
3.2 |
Cumene |
5.124 |
3.72 |
0.57 |
3.7 |
Diphenyl ether |
4.693 |
3.32 |
0.52 |
4.2 |
Fluoranthene |
9.358 |
7.62 |
0.88 |
5.1 |
4,4’-DDT |
22.358 |
19.60 |
1.29 |
6.5 |
4)OECD guideline 117, adopted 2004
Regression:
Parameters: a = 2.604
b = 2.840
r2= 0.9811
Table6: log POW of the test item – 2nd injection
Test item |
tR/ min |
k |
log k |
log POW |
Disperse Blue 291/1 Br |
36.415 |
32.55 |
1.51 |
> 6.55) (6.9)6) |
5)above the highest log POW value of calibration substances (4,4’-DDT)
6)via extrapolation
The results of both measurements were averaged. The partition coefficient POW of the test item was determined to be:
log POW > 6.57)
log POW = 6.98)
7)above the highest log POWvalue of calibration substances (4,4’-DDT)
8)via extrapolation
Description of key information
The partition coefficient n-octanol/water of the test item Disperse Blue 291:1 Br was determined according to the HPLC method.
It was found to be above the highest log Kow value of calibration substances (4,4’-DDT; log Kow= 6.5) and was hence extrapolated to be: log Kow= 6.9.
The study to determine the partition coefficient n-octanol/water of the test item Disperse Blue 291.1 Cl was using an incorrect testing method, i.e. the shake flask method (not a suitable method for poorly water soluble substances) instead of the HPLC method, and lead to a log Kow of 1.9.
As this value is clearly wrong for a poorly water soluble substance, the n-octanol/water partition coefficient was calculated using KOWWIN v1.68 in EpiSuite 4.1 for Disperse Blue 291:1 Br and Disperse Blue 291.1 Cl, to compare both calculated values, as a valid experimental study exists for Disperse Blue 291:1 Br.
The calculated log Kow for Disperse Blue 291:1 Br is 6.67, the calculated log Kow for Disperse Blue 291:1 Cl is 6.43. Based on the results of the experimental study with Disperse Blue 291:1 Br, these values seem both to be reasonable and are hence taken for further assessment of the test substances.
Key value for chemical safety assessment
- Log Kow (Log Pow):
- 6.4
- at the temperature of:
- 25 °C
Additional information
Information on Registered Substances comes from registration dossiers which have been assigned a registration number. The assignment of a registration number does however not guarantee that the information in the dossier is correct or that the dossier is compliant with Regulation (EC) No 1907/2006 (the REACH Regulation). This information has not been reviewed or verified by the Agency or any other authority. The content is subject to change without prior notice.
Reproduction or further distribution of this information may be subject to copyright protection. Use of the information without obtaining the permission from the owner(s) of the respective information might violate the rights of the owner.