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EC number: 412-570-1 | CAS number: 119462-56-5
Applicability domain (OECD principle 3)
i. descriptor domain: all descriptor values for the substance fall in the applicability domain.
ii. Structural fragment domain: the substance is structurally relatively similar to the model compounds, the model contains compounds featuring benzene rings, carbonyl compounds including amides (also cyclic amides), and short alkyl chains. The model training set contains compounds of similar size to the studied molecule.
iii. Mechanism domain: the substance 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/a
b. Structural analogues:
CAS No. 94-62-2, CAS No. 732-11-6
c. Considerations on structural analogues :
The experimental algal inhibition values for compounds of similar functionalities (benzene rings, (cyclic) amides) are mostly positioned in the low to moderate toxicity region of the scales, depending on the molecular size and other functionalities present in the structures.
The analogues are only relatively similar to the substance, although, important chemical features are represented in the analogues. The experimental and predicted toxicity values of the analogues are also close to the predicted value of the studied compound.
The descriptor values of the analogues are closed to those of the studied compound. The analogues are considered to be within the same mechanistic domain. All the analogues are rather well estimated within the model.
The uncertainty of the prediction (OECD principle 4)
The source experimental data for the model originate form different labs and different experiment series. However, previous (and present) successful modeling add to the consistency of the data. The high statistical quality (correlation coefficients, T-test 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.
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