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EC number: 671-273-7 | CAS number: 22245-83-6
- 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
- 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
EPISuite v4.11
2. MODEL (incl. version number)
KOWWIN v1.68
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
Smiles: FC(F)(F)C1=CC=CNC1=O
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: Partion coefficient octanol/water (log Kow)
- Unambiguous algorithm: 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.
- Defined domain of applicability: Currently there is no universally accepted definition of model domain. However, it should be considered that log P estimates may be less accurate for compounds outside the molecular weight range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. Although the training set of the model contains a large number of diverse molecules and can be considered abundant, it is also possible that a compound may be characterised by structural features (e.g. functional groups) not represented in the training set, with no respective fragment/correction coefficient developed. These points should be taken into consideration when interpreting model results.
- Appropriate measures of goodness-of-fit and robustness and predictivity: Please refer to 'attached justification' for more detailed information.
- Mechanistic interpretation: KOWWIN’s "reductionist" fragment constant methodology (i.e. derivation via multiple regression) differs from the "constructionist" fragment constant methodology of Hansch and Leo (Hansch, C. and Leo, A.J., Substituent Constants for Correlation Analysis in Chemistry and Biology, Wiley, New York, 1979). More complete description of KOWWIN methodology is described in: Meylan, W.M., and Howard, P.H., Atom/Fragment Contribution Method for Estimating Octanol-Water Partition Coefficients, J. Pharm. Sci 84: 83-92, 1995.
5. APPLICABILITY DOMAIN
- Descriptor domain: Molecular weight, type of „fragment“
- Similarity with analogues in the training set:
APPENDIX D of the HELP section in KOWWIN v1.66 contains the fragments used in the training set. The substance consists of fragments which are part of the training set. Moreover, as depicted above also the logKow´s of chemicals exceeding the molecular weight of the training set and/or exceeding the complexicity of the training set fragments are predicted with sufficient accuracy.
Further, the very similar compound Trifluoromethyluracil (CAS 54-20-6) is in the training set and experimental data is available. The predicted logKow for Trifluoromethyluracil is 0.1 whereas the experimental value for the logKow is 0.04. Therefore, the model works very well for Trifluoromethyluracil. The very similar structure of 2-Hydroxy-3-(trifluoromethyl)pyridine (JAP-1) and Trifluoromethyluracil supports the use of the model also for the substance 2-Hydroxy-3-(trifluoromethyl)pyridine (JAP-1).
6. ADEQUACY OF THE RESULT
As explained in detail in the sections above the substance falls within the range of reliable predictivity. The substance falls within the molecular weight range of the model. furthermore, the substance consists of common functional groups, thus, the results of the estimation are considered to be sufficient to fulfil the information requirements for registration.
Data source
Reference
- Reference Type:
- study report
- Title:
- Unnamed
- Year:
- 2 019
- Report date:
- 2019
Materials and methods
Test guideline
- Qualifier:
- no guideline followed
- Principles of method if other than guideline:
- - Software tool(s) used including version:
EPISuitev 4.11
- Model(s) used:KOWWIN v1.68
- Model description: see field 'Attached justification'
- Justification of QSAR prediction: see field 'Attached justification' - GLP compliance:
- no
- Type of method:
- other: QSAR estimation
- Partition coefficient type:
- octanol-water
Test material
- Reference substance name:
- 3-(trifluoromethyl)pyridin-2(1H)-one
- EC Number:
- 671-273-7
- Cas Number:
- 22245-83-6
- Molecular formula:
- C6H4F3NO
- IUPAC Name:
- 3-(trifluoromethyl)pyridin-2(1H)-one
Constituent 1
Results and discussion
Partition coefficient
- Key result
- Type:
- log Pow
- Partition coefficient:
- 0.71
- Temp.:
- 25 °C
- Remarks on result:
- other: pH value not reported
Applicant's summary and conclusion
- Conclusions:
- In this study report the partition coefficient of 2-Hydroxy-3-(trifluoromethyl)pyridine (JAP-1; CAS No 22245-83-6) was estimated using EPISuite/KOWWIN v1.68. Based on the results the logKow is considered to be 0.71
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