hexyl nitrite

Acute Toxicity: oral

Administrative data

Endpoint:

acute toxicity: oral

Type of information:

(Q)SAR

Adequacy of study:

key study

Study period:

15/10/2020

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

accepted calculation method

Justification for type of information:

1. SOFTWARE : QSARModel 3.3.8; Statistica 7, StatSoft Ltd. Turu 2, Tartu, 51014, Estonia

2. MODEL (incl. version number) : Nonlinear ANN QSAR model for acute oral toxicity – toxic class method (rat); Model version: 19.12.2010

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
SMILES: CCCCCCON=O, not used for prediction
Other structural representation: 3D Mol file used for prediction

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: Acute Oral Toxicity (ra) - LD50
- Unambiguous algorithm: Nonlinear ANN QSAR model for acute oral toxicity – toxic class method (rat) (QMRF attached separately
- Defined domain of applicability: descriptor domain, structural fragment domain, mechanism domain, metabolic domain
- The uncertainty of predcition:
The source experimental data for the model originate from different labs and different experiment series, adding to uncertainty, however, previous (and present) successful modeling of the set add to the consistence of the data. The significant stati stical 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.
Considering the dataset size, model statistical quality and prediction reliability, a reliability score (Klimisch score) “2” could be assigned to the present prediction.
The prediction reliability in terms of ATE Category is estimated as 86 %
- Mechanistic interpretation:
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.
Artificial Neural networks (ANN) are used in many areas, such as pattern recognition, process analysis and non-linear modelling. An advantage of neural nets is that the neural net model is very flexible in contrast to the classical statistical models. A significant disadvantage is the amount of data needed and the causal ambiguity of the network. The neural net ‘learns’ from examples by one of two different approaches, supervised or unsupervised learning. During supervised learning, the syste m is forced to assign each object in the training set to a specific class, while during unsupervised learning, the clusters are formed without any prior information. One approach commonly used is multi-layer feed-forward (MLF) networks consisting of three or more layers: one input layer, one output layer and one or more intermediate (hidden) layer (Smiths et al., 1994; Xu et al., 1994; De Saint Laumer et al., 1991).
In this model report: nonlinear regression QSAR artificial neural networks model with architecture 9-5-1 trained with back propagation of the error.
ANN is mentioned many times in REACH related official documents ANN is considered as an acceptable algorithm for non-linear correlations.
- Metabolic domain: Hexyl nitrite is considered to be in the same metabolic domain as the molecules in the training set of the model due to the structural similarity.

5. APPLICABILITY DOMAIN
- Descriptor domain: All descriptor values for Hexyl nitrite fall in the applicability domain (training set value ±30%).
- Structural domain: Hexyl nitrite is structurally similar to the training set compounds, the training set contains compounds carbonyl and amide groups, linear and branched alkyl chains. The training set contains compounds of similar size to the studied molecule.
- Mechanistic domain: Hexyl nitrite 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.
- Similarity with analogues in the training set: The structural analogues are relatively similar to the studied compound. The descriptor values of the analogues are close to those of the studied compound. The analogues are considered to be within the same mecha nistic domain. All the analogues are rather well estimated within the model. The following aspects have been considered for the selection and analysis of structural analogues:
Presence and number of common functional groups;
Presence and relevance of non-common functional groups;
Similarity of the ‘core structure’ apart from the (non-)common functional groups;
Potential differences due to reactivity;
Potential differences due to steric hindrance;
Presence of structural alerts;
Position of the double bonds;
Presence of stereoisomers.

6. ADEQUACY OF THE RESULT
6.1 Regulatory purpose:
The present prediction may be used for preparing the REACH Joint Registration Dossier on the Substance(s) for submission to the European Chemicals Agency (“ECHA”) as required by Regulation (EC) N° 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals ("REAC H") and as required by Biocide Product Directive 98/8/EC ("98/8/EC")
6.2 Approach for regulatory interpretation of the model result
The predicted result has been presented in the formats directly usable for the intended regulatory purposes, both the numeric value and the transferred (regulatory) scale values have been presented.
6.3 Outcome
See section 3.2(e) for the classification of the prediction in light of the regulatory purpose described in 6.1.
6.4 Conclusion
Considering the above, the predicted result can be considered adequate for the regulatory conclusion described in 6.1.

Data source

Reference

Reference Type:

study report

Title:

Unnamed

Year:

2020

Report date:

2020

Materials and methods

Test guideline

Qualifier:

according to guideline

Guideline:

OECD Guideline 423 (Acute Oral toxicity - Acute Toxic Class Method)

Deviations:

no

GLP compliance:

no

Test type:

acute toxic class method

Test material

Reference

Name:

Unnamed

Type:

Constituent

Test material form:

liquid

Test animals

Species:

rat

Strain:

not specified

Sex:

not specified

Results and discussion

Effect levels

Key result

Sex:

not specified

Dose descriptor:

LD50

Effect level:

ca. 24 mg/kg bw

Based on:

not specified

Applicant's summary and conclusion

Interpretation of results:

Category 2 based on GHS criteria

Conclusions:

LD50 = 24 mg/kg.
ATE category = “Category 2” (5 < ATE < 50)

Executive summary:

Acute toxicity hazard categories and acute toxicity estimates (ATE) defining the respective categories according to Regulation (EC) No 1272/2008 on the classification, labelling and packaging of substances and mixtures (CLP Regulation):

Exposure route	Category 1	Category 2	Category 3	Category 4
LD50 (oral) (mg/kg)	ATE < 5	5 < ATE < 50	50 < ATE < 300	300 < ATE < 2000

 

On the given scale, Hexyl nitrite has Category 2, therefore “Category 2” is assigned.