1,3,5-Triazin-2-amine, 4-methoxy-6-(trifluoromethyl)-

Skin sensitisation

Administrative data

Endpoint:

skin sensitisation: in vitro

Type of information:

(Q)SAR

Adequacy of study:

key study

Study period:

18 FEB 2022

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model and falling into its applicability domain, with adequate and reliable documentation / justification

Justification for type of information:

1. SOFTWARE
OECD QSAR Toolbox v4.5

2. MODEL (incl. version number)
Allergic Contact Dermatitis, Guinea Pig and Human - Danish QSAR DB Leadscope model (1.0)

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
COc1nc(N)nc(n1)C(F)(F)F

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
The Danish QSAR group applies an overall definition of structures acceptable for QSAR processing which is applicable for all the in‐house QSAR software. According to this definition accepted structures are organic substances with an unambiguous structure, i.e. so‐called discrete organics defined as: organic compounds with a defined two dimensional (2D) structure containing at least two carbon atoms, only certain atoms (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I), and not mixtures with two or more ‘big components’ when analyzed for ionic bonds (for a number of small known organic ions assumed not to affect toxicity the ‘parent molecule’ is accepted). Calculation 2D structures (SMILES and/or SDF) are generated by stripping off ions (of the accepted list given above). Thus, all the training set and prediction set chemicals are used in their non‐ionized form.

5. APPLICABILITY DOMAIN
The definition of the applicability domain consists of two components; the definition of a structural domain in Leadscope and the in‐house further probability refinement algorithm on the output from Leadscope to reach the final applicability domain call. In addition to the general Leadscope structural applicability domain definition the Danish QSAR group has applied a further requirement to the applicability domain of the model. That is only positive predictions with a probability equal to or greater than 0.7 and negative predictions with probability equal to or less than 0.3 are accepted. Predictions within the structural applicability domain but with probability between 0.5 to 0.7 or 0.3 to 0.5 are defined as positives out of applicability domain and negatives out of applicability domain, respectively. When these predictions are wed out the performance of the model in general increases at the expense of reduced model coverage.

Data source

Reference

Reference 1

Reference Type:

other: Danish QSAR model

Title:

Danish QSAR Leadscope model

Author:

Denmark Technical University Food Institute

Year:

2015

Bibliographic source:

Leadscope Predictive Data Miner, a component of Leadscope Enterprise version 3.1.1‐10.

Reference 2

Reference Type:

other: QSAR software

Title:

Unnamed

Year:

2021

Materials and methods

Test guideline

Guideline:

other: ECHA guidance R.6

Version / remarks:

May 2008

Principles of method if other than guideline:

- Software tool(s) used including version: OECD QSAR Toolbox v4.5
- Model(s) used: Danish QSAR Leadscope model(1.0)
- Model description: Leadscope Predictive Data Miner is a software program for systematic sub‐structural analysis of a chemical using predefined structural features stored in a template library, training set‐dependent generated structural features (scaffolds) and calculated molecular descriptors. The feature library contains approximately 27,000 pre‐defined structural features and the structural features chosen for the library are motivated by those typically found in small molecules: aromatics, heterocycles, spacer groups, simple substituents. Leadscope allows for the generation of training set‐dependent structural features (scaffold generation), and these features can be added to the pre‐defined structural features from the library and be included in the descriptor selection process. It is possible in Leadscope to remove redundant structural features before the descriptor selection process and only use the remaining features in the descriptor selection process. Besides the structural features Leadscope also calculates eight molecular descriptors for each training set structure: the octanol/water partition coefficient (alogP), hydrogen bond acceptors (HBA), hydrogen bond donors (HBD), Lipinski score, atom count, parent compound molecular weight, polar surface area (PSA) and rotatable bonds. These eight molecular descriptors are also included in the descriptor selection process.
- Justification of QSAR prediction: see field 'Justification for type of information'

Test material

Test material information

Constituent 1

Reference substance name:

4-methoxy-6-(trifluoromethyl)-1,3,5-triazin-2-amine

EC Number:

610-962-9

Cas Number:

5311-05-7

Molecular formula:

C5H5F3N4O

IUPAC Name:

4-methoxy-6-(trifluoromethyl)-1,3,5-triazin-2-amine

Specific details on test material used for the study:

COc1nc(N)nc(n1)C(F)(F)F

Results and discussion

In vitro / in chemico

Results

Group:

test chemical

Run / experiment:

mean

Remarks on result:

positive indication of skin sensitisation

Applicant's summary and conclusion

Interpretation of results:

study cannot be used for classification

Conclusions:

The skin sensitisation QSAR calculation based on Leadscope model(1.0) in Danish QSAR Database gives a positive result. This prediction falls in the applicability domain of the model.

Executive summary:

The skin sensitisation QSAR calculation based on Leadscope model(1.0) in Danish QSAR Database gives a positive result. This prediction falls in the applicability domain of the model.