Registration Dossier

Data platform availability banner - registered substances factsheets

Please be aware that this old REACH registration data factsheet is no longer maintained; it remains frozen as of 19th May 2023.

The new ECHA CHEM database has been released by ECHA, and it now contains all REACH registration data. There are more details on the transition of ECHA's published data to ECHA CHEM here.

Diss Factsheets

Toxicological information

Genetic toxicity: in vitro

Currently viewing:

Administrative data

genetic toxicity in vitro
Type of genotoxicity: gene mutation
Type of information:
Adequacy of study:
key study
Study period:
2 (reliable with restrictions)
Justification for type of information:
QSAR prediction: migrated from IUCLID 5.6

Data source

Reference Type:
study report
Report date:

Materials and methods

Test guideline
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.
Leadscope Model Applier (Leadscope, Inc.) (version 1.4.6-2) is a chemoinformatic platform that provides QSAR for the prediction of potential toxicity and adverse human clinical effects of pharmaceuticals, cosmetics, food ingredients and other chemicals. The Models are constructed by FDA scientists based on both proprietary and non-proprietary data. Predictions are provided together with several parameters which can be used to assess the prediction in terms of applicability domain. The robustness of the prediction can be further evaluated by examining compounds similar to the target from the training set.
Vega Application (Virtual Models for evaluating the properties of chemicals within a global architecture) (VegaNIC application, Laboratory of Environmental Chemistry and Toxicology of Mario Negri Institute of Pharmacological Research, version 1.0.8) is a platform developed on the basis of contributions from the EU projects CAESAR, ORCHESTRA and ANTARES. It includes CAESAR QSAR model for mutagenicity based on a data set that includes 4225 compounds. It is an integrated model made of two complementary techniques: a machine learning algorithm (SVM), to build an early model with the best statistical accuracy, equipped with an expert facility for false negatives removal based on known structural alerts, to refine its predictions. Thus, the mutagenicity model could classify a compound as mutagen even if it is formally out of the applicability domain. This behaviour is normal for this model and it is related to the use of structural alerts. It also include the CAESAR skin sensitization model, which provides a qualitative prediction of skin sensitisation on mouse (local lymph node assay). The model consists in an Adaptive Fuzzy Partition (AFP) based on 8 descriptors. The AFP produces as output two values positive and negative that represent the belonging degree respectively to the sensitiser and non-sensitise classes. The applicability domain of predictions is assessed using an Applicability Domain Index (ADI) that has values from 0 (worst case) to 1 (best case). The ADI is calculated by grouping several other indices, each one taking into account a particular issue of the applicability domain..
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.
GLP compliance:
Type of assay:
other: in silico prediction

Test material

Constituent 1
Chemical structure
Reference substance name:
Methyl 3-α,7-α-diacetoxy-12-α-hydroxy-5-β-cholan-24-oate
EC Number:
EC Name:
Methyl 3-α,7-α-diacetoxy-12-α-hydroxy-5-β-cholan-24-oate
Cas Number:
Molecular formula:
methyl 3-α,7-α-diacetoxy-12-α-hydroxy-5-β-cholan-24-oate

Results and discussion

Any other information on results incl. tables

Methyl 3-α,7-α-diacetoxy-12-α-hydroxy-5-β-cholan-24-oate is predicted positive, but the prediction is borderline, since its reliability index is equal to 0.41 since only moderately similar compounds with known experimental value in the training set have been found and the similar molecules found in the training set have inconclusive experimental values.

ACD/percepta mutagenicity predictions



Positive probability


Prediction call

Reliability index

Reliability assessment

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





Leadscope mutagenicity predictions







Prediction probability

Prediction reliability parameters

Model Fragment


30% Sim. Training Neighbors Count



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






Vega mutagenicity predictions


Vega prediction call

Vega reliability

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



Applicant's summary and conclusion

Interpretation of results (migrated information):

The Methyl 3-α,7-α-diacetoxy-12-α-hydroxy-5-β-cholan-24-oate is NOT MUTAGEN
Executive summary:

Consensus mutagenicity predictions







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