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Please be aware that this old REACH registration data factsheet is no longer maintained; it remains frozen as of 19th May 2023.

The new ECHA CHEM database has been released by ECHA, and it now contains all REACH registration data. There are more details on the transition of ECHA's published data to ECHA CHEM here.

Diss Factsheets

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:
2012
Report date:
2012

Materials and methods

Test guideline
Qualifier:
equivalent or similar 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), Version 12.04.2010

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

Constituent 1
Chemical structure
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): ethyl-2-cyano-3-ethoxyprop-2-enoate

Test animals

Species:
rat
Strain:
Sprague-Dawley
Sex:
male/female

Results and discussion

Effect levels
Sex:
male/female
Dose descriptor:
LC50
Effect level:
6.22 mg/L air

Any other information on results incl. tables

log(LC50) = 1.46

Applicability domain

- descriptor domain : All descriptor values for ethyl N-(tert-butoxy)carbonyl-L-valine fall in the applicability domain (training set value ±30%).

- 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, carboxylic acids, nitrogen-containing compounds. The training set contains compounds of similar size to the studied molecule.

- 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.

- 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
 123-86-4  O(CCCC)C(C)=O  testing  2.54  2.49
 79-41-4  C(C(=O)O)(C)=C testing  1.91  1.61
   O=C(OC1=C2OC(C)(C)CC2=CC=C1)NC  * 1.61  1.07

*Bayer CropScience, MSDS of Furadan 480 SC, 2008

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 80 %

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 6.22 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 (DL50 powder > 5.0) .