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Diss Factsheets
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EC number: 803-919-2 | CAS number: 409071-16-5
- 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:
- (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 and falling into its applicability domain, with adequate and reliable documentation / justification
- Justification for type of information:
- 1. SOFTWARE
QSAR Toolbox 2.3.0.1132
2. MODEL (incl. version number)
KOWWIN (EPISUITE) v1.68
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
O=C1OB(F)(F)([Li])OC1=O
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
[[Explain how the model fulfils the OECD principles for (Q)SAR model validation. Consider attaching the QMRF and/or QPRF or providing a link]
- Defined endpoint:
Physical Chemical Properties#Partition Coefficient:#N-Octanol/Water
- Unambiguous algorithm:
The first regression related log P to atom/fragments of compounds that do not require correction factors:
log P = Σ(fini) + b (Equation 1)
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 above equation with the correction factor descriptors:
lop P (expl) - log P (eq 1) = Σ(cjnj) (Equation 2)
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)
- Defined domain of applicability:
Currently, KOWWIN has been tested on an external validation dataset of 10,946 compounds (compounds not included in the training set). The validation set includes a diverse selection of chemical structures that rigorously test the predictive accuracy of any model. It contains many chemicals that are similar in structure to chemicals in the training set, but also many chemicals that are different from and structurally more complex than chemicals in the training set. The average molecular weight of compounds in the validation set is 258.98 versus 199.98 for the training set.
The minimum and maximum values for molecular weight are the following:
Training Set Molecular Weights:
Minimum MW: 18.02
Maximum MW: 719.92
Average MW: 199.98
Validation Molecular Weights:
Minimum MW: 27.03
Maximum MW: 991.15
Average MW: 258.98
KOWWIN Fragments, Correction Factors, Coefficients and Frequency
Fragment Descriptor Coef Number
-F [fluorine, aliphatic attach] -0.0031 2
-C(=O)O [ester, aliphatic attach] -0.9505 2
Boron 0.0000 1
Cyclic ester [di-carbonyl type] 0.7500 2
{Na, K, Li} [not oxy attach] -1.0000 1
- Appropriate measures of goodness-of-fit and robustness and predictivity:
yes
5. APPLICABILITY DOMAIN
[Explain how the substance falls within the applicability domain of the model]
- Descriptor domain:
LogKow is only descriptor in the model. No further requirements for logKow are required.
6. ADEQUACY OF THE RESULT
[Explain how the prediction fits the purpose of classification and labelling and/or risk assessment]
The molecular weight of the substance is 143.77, within the scope of the model (18.02~719.92).Therefore, the predicted value is considered to be reliable. - Guideline:
- other: REACH guideline on QSARs R.6
- Principles of method if other than guideline:
- general model
- Key result
- Type:
- log Pow
- Partition coefficient:
- -1.18
- Remarks on result:
- not measured/tested
- Details on results:
- The first regression related log P to atom/fragments of compounds that do not require correction factors:
log P = Σ(fini) + b (Equation 1)
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 above equation with the correction factor descriptors:
lop P (expl) - log P (eq 1) = Σ(cjnj) (Equation 2)
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)
KOWWIN Fragments, Correction Factors, Coefficients and Frequency
Fragment Descriptor Coef Number
-CH3- [aliphatic carbon] 0.5473 9
-O-P- [aliphatic carbon] -0.0162 3
O=P -2.4239 1
-Si- [silicon, aromatic or oxygen attach] 0.6800 3 - Conclusions:
- The log Pow of substance is predicted to be -1.18
Reference
Currently, KOWWIN has been tested on an external validation dataset of 10,946 compounds (compounds not included in the training set). The validation set includes a diverse selection of chemical structures that rigorously test the predictive accuracy of any model. It contains many chemicals that are similar in structure to chemicals in the training set, but also many chemicals that are different from and structurally more complex than chemicals in the training set. The average molecular weight of compounds in the validation set is 258.98 versus 199.98 for the training set.
The minimum and maximum values for molecular weight are the following:
Training Set Molecular Weights:
Minimum MW: 18.02
Maximum MW: 719.92
Average MW: 199.98
Validation Molecular Weights:
Minimum MW: 27.03
Maximum MW: 991.15
Average MW: 258.98
Total Training Set Statistics:
number in dataset = 2447
correlation coef (r2) = 0.982
standard deviation = 0.217
absolute deviation = 0.159
avg Molecular Weight = 199.98
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.
Description of key information
The log Pow of substance is predicted to be -1.18.
Key value for chemical safety assessment
- Log Kow (Log Pow):
- -1.18
- at the temperature of:
- 20 °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.