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Diss Factsheets
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EC number: 202-299-5 | CAS number: 94-05-3
- 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
Acute Toxicity: inhalation
Administrative data
- Endpoint:
- acute toxicity: inhalation
- Type of information:
- (Q)SAR
- Adequacy of study:
- key study
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- other: see 'Remark'
- Remarks:
- The source experimental data for the model originate from different labs and different experiment series, adding to uncertainty, however, previous (and present) successful modeling add to the consistence of the data. The significant statistical quality (RMS, correlation coefficients etc.) of the model supports reliable predictions within the margins of the experimental error. The similarity of the analogues together with the correct estimates supports potential prediction consistency.
- Justification for type of information:
- QSAR prediction: migrated from IUCLID 5.6
Data source
Reference
- Reference Type:
- study report
- Title:
- Unnamed
- Year:
- 2 012
- Report date:
- 2012
Materials and methods
Test guideline
- Qualifier:
- according to guideline
- Guideline:
- OECD Guideline 403 (Acute Inhalation Toxicity)
- Principles of method if other than guideline:
- Type of model: Nonlinear QSAR: Backpropagation Neural Network (Multilayer Perceptron) regression Model for acute toxicity - inhalation - rat male; female)
The source experimental data for the model originate from different labs and different experiment series, adding to uncertainty, however, previous (and present) successful modeling add to the consistence of the data. The significant statistical quality (RMS, correlation coefficients etc.) of the model supports reliable predictions within the margins of the experimental error. The similarity of the analogues together with the correct estimates supports potential prediction consistency. - GLP compliance:
- no
Test material
- Reference substance name:
- Ethyl 2-cyano-3-ethoxyacrylate
- EC Number:
- 202-299-5
- EC Name:
- Ethyl 2-cyano-3-ethoxyacrylate
- Cas Number:
- 94-05-3
- Molecular formula:
- C8H11NO3
- IUPAC Name:
- ethyl 2-cyano-3-ethoxyacrylate
- Details on test material:
- - Name of test material (as cited in study report): ethyl-2-cyano-3-ethoxyprop-2-enoate
Constituent 1
Test animals
- Species:
- rat
- Strain:
- Sprague-Dawley
- Sex:
- male/female
Results and discussion
Effect levels
- Sex:
- male/female
- Dose descriptor:
- LC50
- Effect level:
- 122.2 mg/L air
Any other information on results incl. tables
log(LC50) = 2.86
Applicability domain
- descriptor domain : All descriptor values for ethyl 2-cyano-3-ethoxyacrylate fall in the applicability domain (training set value ±30%).
- structural fragment domain : Ethyl 2-cyano-3-ethoxyacrylate is structurally relatively similar to the model compounds, the model contains compounds featuring short alkyl chains; unsaturated CN (including cyano groups) and CC bonds, ether and ester functionalities. The training set contains compounds of similar size to the studied molecule.
- mechanism domain Ethyl 2-cyano-3-ethoxyacrylate is considered to be in the same mechanistic domain as the molecules in the training set as it is structurally similar to the model compounds.
- metabolic domain, if relevant : Ethyl 2-cyano-3-ethoxyacrylate is considered to be in the same metabolic domain as the molecules in the training set of the model due to the structural similarity.
Structural analogues:
CAS | smiles | source | log (LD50) exp /pred | |
140 -88-5 | C(OCC)(C=C)=O | training | 1.95 | 1.694 |
107 -13 -1 | C(C#N)=C | training | 1.23 | 1.507 |
107 -02 -8 | C(C=O)=C | training | - 0.52 | - 0.558 |
The mechanistic picture of the model is complicated due to the nature of the endpoint and the nonlinear modeling technique - ANN (artificial neural network). The nature of ANN does not show direct quantitative relations between descriptor and endpoint values, rather the combinations of descriptor values are important. This makes analysis based on the trends of the descriptor values difficult, as most descriptors will have very diverse values for both highly toxic and less toxic compounds.
Overall, there is strong qualitative agreement with the generally accepted scientific understanding. The acute inhalation toxicity is known to be correlated to the chemical stability and reactivity of a compound. The presence of three reactivity and stability related quantum chemical descriptors in the model agrees well with generally accepted understanding. Regarding the first three reactivity descriptors, it can be noted that they have slight negative correlation with the property. It might suggest that with the increase of these descriptors, the property would decrease. It appears also that the oxygen atoms in the molecules contribute to lower LC50 values according to the 4th descriptor. The general trend for the model shows that higher reactivity is an indicator of increased toxicity.
The prediction reliability in terms of ATE Category is estimated as 84 %
Applicant's summary and conclusion
- Interpretation of results:
- not classified
- Remarks:
- Migrated information Criteria used for interpretation of results: EU
- Conclusions:
- On the basis of the predicted LC50 value of 122.2 mg/l, according to Regulation (EC) No 1272/2008 on the classification, labelling and packaging of substances and mixtures (CLP Regulation), the substance is not classified .
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