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EC number: 237-307-6 | CAS number: 13734-41-3
- 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: oral
Administrative data
- Endpoint:
- acute toxicity: oral
- 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 guidelineopen allclose all
- Qualifier:
- equivalent or similar to guideline
- Guideline:
- OECD Guideline 423 (Acute Oral toxicity - Acute Toxic Class Method)
- Qualifier:
- equivalent or similar to guideline
- Guideline:
- EU Method B.1 tris (Acute Oral Toxicity - Acute Toxic Class Method)
- Principles of method if other than guideline:
- Type of model: Nonlinear ANN QSAR Model for acute oral toxicity - toxic class method (rat) Version 2010-12-19 .Backpropagation Neural Network (Multilayer Perceptron) regression.
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 type:
- other: QSAR
Test material
- Reference substance name:
- N-[(tert-butoxy)carbonyl]-L-valine
- EC Number:
- 237-307-6
- EC Name:
- N-[(tert-butoxy)carbonyl]-L-valine
- Cas Number:
- 13734-41-3
- Molecular formula:
- C10H19NO4
- IUPAC Name:
- N-(tert-butoxycarbonyl)-L-valine
- Test material form:
- solid: particulate/powder
- Remarks:
- migrated information: powder
- Details on test material:
- - Name of test material (as cited in study report): N-(tert-butoxy)carbonyl-L-valine
Constituent 1
Test animals
- Species:
- rat
- Strain:
- not specified
- Sex:
- not specified
Administration / exposure
- Route of administration:
- oral: unspecified
- Vehicle:
- not specified
Results and discussion
Effect levels
- Sex:
- not specified
- Dose descriptor:
- LD50
- Effect level:
- 2 870 mg/kg bw
Any other information on results incl. tables
Log(LD50) = 1.12
Domains:
i. descriptor domain All descriptor values for N-(tert-butoxy)carbonyl-L-valine fall in the applicability domain (training set value ±30%).
ii. structural fragment domain : N-(tert-butoxy)carbonyl-L-valine is structurally relatively similar to the model compounds, the model contains compounds featuring short branched alkyl chains; carbonyl compounds, amides, carboxylic acids, other nitrogencontaining
compounds. The training set contains compounds of similar size to the studied molecule.
iii. mechanism domain : N-(tert-butoxy)carbonyl-L-valine 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.
iv. metabolic domain, if relevant : N-(tert-butoxy)carbonyl-L-valine 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(LC5) exp. / pred | ||
39236-46-9 | C1(N(C(C(N1)=O)NC(NCNC(NC1C(NC(N1CO)=O)=O)=O)=O)CO)=O | training | 0.97 | 0.79 | |
139-13-9 | N(CC(O)=O)(CC(O)=O)CC(O)=O | testing | 0.89 | 1.22 | |
4530-20-5 | O=C(O)CNC(OC(C)(C)C)=O | * | >1.06 | 0.95 | |
57294-38-9 | O=C(O)CCCNC(OC(C)(C)C)=O | ** | 0.68 | 1.31 |
* Leonid Chemicals, MSDS of BOC-Glycine, 99+%, 2002
** Clearsynth Labs Pvt. Ltd., MSDS of Boc-gamma-Abu-OH,
The experimental acute oral toxicity values for compounds of similar functionalities fall mostly to near or above the Category 4 region, with obvious dependence on the particular structural features. The structural analogues are relatively similar to the studied compound, covering all the chemical features. In addition to model compounds, two external analogues have been considered. The descriptor values of the analogues are close to those of the studied compound. The analogues are considered to be within the same mechanistic domain. All the analogues are relatively well estimated within the model.
The mechanistic picture of the model is complicated due to the nature of the endpoint and the nonlinear modeling technique - ANN (artificial neural network). Nevertheless, it can be concluded that model descriptors are related to the overall polarity -polarizability, reactivity, electrostatic and hydrogen donor/acceptor ability of the compounds stressing the role of heteroatoms.
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. According to that and the present model, the acute oral toxicity is strongly dependent on the stability and reactivity of chemicals, in particular the presence of heteroatoms like oxygen, nitrogen, phosphorus, and halogenides in the structures, represented by the charge distribution descriptors in the model (LUMO+1 energy (AM1), Tot molecular 1-center E-N attraction (AM1), RNCG Relative negative charge (QMNEG/QTMINUS) (Zefirov)) – describing the availability of electrons and orbitals for binding. Several descriptors take into account also the size, shape, and overall stability of the molecule, influencing the numeric outcome. In general, more (and more reactive) heteroatoms lead to increased toxicity, while oxygen atoms (especially in hydrogen bonding forms) tend to lower toxicity.
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 LD50 value of 2870 mg/kg, according to Regulation (EC) No 1272/2008 on the classification, labelling and packaging of substances and mixtures (CLP Regulation), the substance does not required classification for acute oral toxicity.
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