hexyl nitrite

Skin sensitisation

Administrative data

Endpoint:

skin sensitisation: in vivo (LLNA)

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 : QSAR; Statistica 7 StatSoft Ltd.

2. MODEL (incl. version number) : Nonlinear QSAR: ANN for classification of skin sensitization potential, Model version: 23.09.2009

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
[Explain how the model fulfils the OECD principles for (Q)SAR model validation. Consider attaching the QMRF or providing a link]
- Defined endpoint: 4.Human health effects B.40. Human health effects: skin sensitisation, ranking of local lymph node assay (Score index of LLNA) 4.6.Skin sensitisation
- Model or submodel name: Nonlinear QSAR: ANN for classification of skin sensitization potential. QPRF is attached (JRC Q17-10-1-241)
- Unambiguous algorithm: Nonlinear QSAR: Backpropagation Neural Network (Multilayer Perceptron) classification
- Defined domain of applicability: Applicability domain based on training set: diverse set of organic compounds (ketones, esters, carboxylic acids, halogen-derivatives, alcohols, amino-compounds, etc). By descriptor value range (between min and max values): The model is suitable for compounds that have the descriptors in the following range:
Desc ID 1 2 3 4 5 6 7
Min 0 0 0.06572 0 0 0.05874 0
Max 0.0135 0.3333 0.1599 51.99 239.0 1 299.3
- Appropriate measures of goodness-of-fit and robustness and predictivity: 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 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. 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 reaction between the chemical and protein is believed to be covalent in nature. Therefore, skin sensitization is underpinned by mechanisms based on chemical reactivity, where the chemical behaves as an electrophile and the protein behaves as a nucleophile as these are reflected by our descriptors such as Global softness: 1/(LUMO - HOMO) (AM1) and Avg nucleophilic reactivity index (AM1) for H atoms. The descriptors HA dependent HDCA-1 (AM1) (all) reflects transfer of the compounds to a phase characterized by hydrogen bonding and descriptors as well as the interactions between the O and N atoms (Highest n-n repulsion (AM1) for N - O bonds).
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 system 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 7-7-6-1 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.

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 relatively similar to the model compounds. 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 mechanistic domain. All the analogues are very 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.
- Other considerations (as appropriate): 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.

6. ADEQUACY OF THE RESULT
ž.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 ("REACH") 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 429 (Skin Sensitisation: Local Lymph Node Assay)

GLP compliance:

not specified

Test material

Reference

Name:

Unnamed

Type:

Constituent

Test material form:

liquid

In vivo test system

Test animals

Species:

guinea pig

Strain:

CBA

Sex:

female

Details on test animals and environmental conditions:

Young adult (6–12 weeks old) female CBA strain mice are used for regulatory LLNA studies. Animals are maintained under hygienic barriered conditions with free access to food and water. The ambient temperature is maintained between 20 and 24 °C and relative humidity is maintained between 40 and 70% with a 12 h light/dark cycle. Mice are allowed to acclimatize for at least two days after arrival in the facility in cages of four or five animals per group.

Study design: in vivo (LLNA)

Vehicle:

acetone/olive oil (4:1 v/v)

Results and discussion

In vivo (LLNA)

Results

Key result

Parameter:

other: Ss

Value:

ca. -0.45

Remarks on result:

no indication of skin sensitisation based on QSAR/QSPR prediction

Applicant's summary and conclusion

Interpretation of results:

GHS criteria not met

Conclusions:

Non-sensitizer, according to five scale classification: (non-sensitizers, weak sensitizers, moderate sensitizers, strong sensitizers, very-strong sensitizers).

Executive summary:

Non-sensitizer, according to five scale classification: (non-sensitizers, weak sensitizers, moderate sensitizers, strong sensitizers, very-strong sensitizers).

Following EU CLP criteria, if measured experimentally, the predicted value has No category in the CLP classification system.