Registration Dossier

Administrative data

Endpoint:
eye irritation
Remarks:
other: in silico prediction
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:
Toxtree (Ideaconsultant, version 2.5.0) is a flexible and user-friendly open-source application that places chemicals into categories and predicts various kinds of toxic effect by applying decision tree approaches. The following decision trees are currently implemented: the Cramer classification scheme, Verhaar scheme for aquatic modes of action, rulebases for skin and eye irritation and corrosion, Benigni-Bossa rulebase for mutagenicity and carcinogenicity, structural alerts for identification of Michael Acceptors, START rulebase for persistance / biodegradation potential.
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 material

Reference
Name:
Unnamed
Type:
Constituent

Test animals / tissue source

Species:
rabbit

Results and discussion

Any other information on results incl. tables

Name

ACD/Percepta

Positive probability

ACD/Percepta

Prediction call

Toxtree

Eye irritation

CONSENSUS

Eye irritation

Methyl 3-α,7-α-diacetoxy-12-oxo-5-β-cholan-24-oate

0.10

NOT

IRRITANT

NOT

IRRITANT

NOT

IRRITANT

Applicant's summary and conclusion

Interpretation of results:
not irritating
Remarks:
Migrated information
Conclusions:
The methyl 3-α,7-α-diacetoxy-12-oxo-5-β-cholan-24-oate is NOT EYE IRRITATION potential.