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Toxicological information

Acute Toxicity: oral

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Administrative data

Endpoint:
acute toxicity: oral
Type of information:
(Q)SAR
Adequacy of study:
key study
Study period:
2013
Reliability:
2 (reliable with restrictions)
Justification for type of information:
QSAR prediction: migrated from IUCLID 5.6

Data source

Reference
Reference Type:
study report
Title:
Unnamed
Year:
2013
Report Date:
2013

Materials and methods

Test guideline
Qualifier:
no guideline available
Principles of method if other than guideline:
QSAR approach: Different tools were used, when possible, in order to apply a consensus approach and thus enhance the reliability of the predictions. In fact, a single in silico prediction model may provide acceptable results. However, by definition all models are simulation of reality, and therefore they will never be completely accurate; sometimes a single model will not work. When multiple models and multiple approaches are combined in a single consensus score, more accurate predictions can be achieved.
If two prediction methods that use data and different approaches are consistent, the reliability of prediction is better. The errors of a model/approach should be different from another, and therefore compensate.

Several computational tools are nowadays available for applying in silico approaches. Among them, for QSAR predictions the following was selected and used for the endpoint:
ACD/Percepta (Advanced Chemistry Development, Inc., Pharma Algorithms, Inc.) (release 2012) is a suite of comprehensive tools for the prediction of basic toxicity endpoints, including hERG Inhibition, CYP3A4 Inhibition, Genotoxicity, Acute Toxicity, Aquatic Toxicity, Eye/Skin Irritation, Endocrine System Disruption, and Health Effects. Predictions are made from chemical structure and based upon large validated databases and QSAR models, in combination with expert knowledge of organic chemistry and toxicology. It also allows to evaluate the robustness of the prediction by examining compounds similar to the target from the training set, together with literature data and reference. The models also provide an estimation of the reliability of the prediction, by a reliability index (RI). This index provides values in a range from 0 to 1 and gives an evaluation of whether a submitted compound falls within the Model Applicability Domain. Estimation of the RI takes into account the following two aspects: similarity of the tested compound to the training set and the consistency of experimental values for similar compounds. If the RI is less than 0.3 the prediction has to be considered not reliable while if RI is more than 0.5 the prediction results are considered reliable.
GLP compliance:
no
Test type:
other: in silico prediction

Test material

Reference
Name:
Unnamed
Type:
Constituent

Test animals

Species:
rat

Results and discussion

Effect levels
Sex:
not specified
Dose descriptor:
LD50
Effect level:
1 600 mg/kg bw
Remarks on result:
other: Reliability index: 0.57

Applicant's summary and conclusion

Interpretation of results:
harmful
Remarks:
Migrated information Criteria used for interpretation of results: EU
Conclusions:
The LD50 prediction of test itme is 1600 mg7kg.