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EC number: 609-737-8 | CAS number: 3967-32-6
- Life Cycle description
- Uses advised against
- Endpoint summary
- Appearance / physical state / colour
- Melting point / freezing point
- Boiling point
- Density
- Particle size distribution (Granulometry)
- Vapour pressure
- Partition coefficient
- Water solubility
- Solubility in organic solvents / fat solubility
- Surface tension
- Flash point
- Auto flammability
- Flammability
- Explosiveness
- Oxidising properties
- Oxidation reduction potential
- Stability in organic solvents and identity of relevant degradation products
- Storage stability and reactivity towards container material
- Stability: thermal, sunlight, metals
- pH
- Dissociation constant
- Viscosity
- Additional physico-chemical information
- Additional physico-chemical properties of nanomaterials
- Nanomaterial agglomeration / aggregation
- Nanomaterial crystalline phase
- Nanomaterial crystallite and grain size
- Nanomaterial aspect ratio / shape
- Nanomaterial specific surface area
- Nanomaterial Zeta potential
- Nanomaterial surface chemistry
- Nanomaterial dustiness
- Nanomaterial porosity
- Nanomaterial pour density
- Nanomaterial photocatalytic activity
- Nanomaterial radical formation potential
- Nanomaterial catalytic activity
- Endpoint summary
- Stability
- Biodegradation
- Bioaccumulation
- Transport and distribution
- Environmental data
- Additional information on environmental fate and behaviour
- Ecotoxicological Summary
- Aquatic toxicity
- Endpoint summary
- Short-term toxicity to fish
- Long-term toxicity to fish
- Short-term toxicity to aquatic invertebrates
- Long-term toxicity to aquatic invertebrates
- Toxicity to aquatic algae and cyanobacteria
- Toxicity to aquatic plants other than algae
- Toxicity to microorganisms
- Endocrine disrupter testing in aquatic vertebrates – in vivo
- Toxicity to other aquatic organisms
- Sediment toxicity
- Terrestrial toxicity
- Biological effects monitoring
- Biotransformation and kinetics
- Additional ecotoxological information
- Toxicological Summary
- Toxicokinetics, metabolism and distribution
- Acute Toxicity
- Irritation / corrosion
- Sensitisation
- Repeated dose toxicity
- Genetic toxicity
- Carcinogenicity
- Toxicity to reproduction
- Specific investigations
- Exposure related observations in humans
- Toxic effects on livestock and pets
- Additional toxicological data
![](https://echa.europa.eu/o/diss-blank-theme/images/factsheets/A-REACH/factsheet/print_toxicological-information.png)
Genetic toxicity: in vivo
Administrative data
- Endpoint:
- in vivo mammalian somatic cell study: cytogenicity / erythrocyte micronucleus
- Remarks:
- Type of genotoxicity: chromosome aberration
- Type of information:
- (Q)SAR
- Adequacy of study:
- key study
- 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
- Report date:
- 2014
Materials and methods
- Principles of method if other than guideline:
- Micronucleus in vivo on mouse predictions were generated employing Leadscope Model Applier.
Micronucleus in vivo on rodent predictions were generated employing three predictors: Leadscope Model Applier, ACD/Percepta and Toxtree decision rule system. - GLP compliance:
- no
- Type of assay:
- micronucleus assay
Test material
- Reference substance name:
- 2-(1-methylpiperidin-4-yl)tricyclo[9.4.0.0^{3,8}]pentadeca-1(11),3,5,7,9,12,14-heptaen-2-ol
- EC Number:
- 609-737-8
- Cas Number:
- 3967-32-6
- Molecular formula:
- C21H23NO
- IUPAC Name:
- 2-(1-methylpiperidin-4-yl)tricyclo[9.4.0.0^{3,8}]pentadeca-1(11),3,5,7,9,12,14-heptaen-2-ol
Constituent 1
Test animals
- Species:
- other: mouse and rodent
Results and discussion
Any other information on results incl. tables
Micronucleus in vivo on mouse predictions:
Leadscope prediction call (RI) | LeadscopePositivePrediction probability | Model FeaturesCount | 30% Sim. Training Neighbors Count |
NEGATIVE (Little reliable) | 0.11 | 18 | 3 |
Model Features Count. Parameter used to verify that the target compound, i.e. 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol, contains a significant number of features that are present in the prediction model. The structural features used to make the prediction provide information on the reliability of the prediction: a prediction provided by a low number of features means that the model is not able to fully describe the test compound, while a prediction supported by a high number of features reveals that the test compound is well described by the model. Since 18 features were found, it was concluded that 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol is represented by the model.
30% Similarity Training Neighbours Count. Number of compounds structurally similar to the target, i.e. 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol, in the model's training set of compounds. Another way to assess the reliability of the prediction is looking at the analogues, i.e. the compounds structurally similar to the target in the model's training set of compounds. While this information does not take part to the prediction, it provides the user a complementary means to see how similar compounds were predicted and what the experimental values of similar compounds are. Look at analogues is also an initial, less-sophisticated easy way to understand estimate of toxicity. Three compounds, illustrated in Table 18, were identified in the training set as analogue to 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol characterized by little similarity with respect to the target 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol. Therefore, the prediction was considered of little reliability.
TerfenadineResult: negativeSimilarity: 0.42 |
AzatadineResult: negativeSimilarity: 0.37 |
FexofenadineResult: negativeSimilarity: 0.32 |
Micronucleus in vivo on rodent predictions:
Leadscope | ACD/Percepta | Toxtree | Consensus prediction |
NEGATIVELittle reliable | - | NEGATIVE | NEGATIVELittle reliable |
Leadscope Model Applier
Leadscope prediction call (RI) | LeadscopePositivePrediction probability | Model FeaturesCount | 30% Sim. Training Neighbors Count |
NEGATIVE (Little reliable) | 0.08 | 9 | 3 |
Model Features Count. Parameter used to verify that the target compound, i.e. 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol, contains a significant number of features that are present in the prediction model. The structural features used to make the prediction provide information on the reliability of the prediction: a prediction provided by a low number of features means that the model is not able to fully describe the test compound, while a prediction supported by a high number of features reveals that the test compound is well described by the model. Since 9 features were found, it was concluded that 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol is represented by the model.
30% Similarity Training Neighbours Count. Number of compounds structurally similar to the target, i.e. 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol, in the model's training set of compounds. Another way to assess the reliability of the prediction is looking at the analogues, i.e. the compounds structurally similar to the target in the model's training set of compounds. While this information does not take part to the prediction, it provides the user a complementary means to see how similar compounds were predicted and what the experimental values of similar compounds are. Look at analogues is also an initial, less-sophisticated easy way to understand estimate of toxicity. Three compounds, illustrated in Table 21, were identified in the training set as analogue to 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol characterized by little similarity with respect to the target 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol. Therefore, the prediction was considered of little reliability.
TerfenadineResult: negativeSimilarity: 0.42 |
AzatadineResult: negativeSimilarity: 0.37 |
FexofenadineResult: negativeSimilarity: 0.32 |
ACD/Percepta did not provide any prediction for the target 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol since it resulted to be out of the model applicability domain.
Toxtree predicts the positive or negative micronucleus activity according to decision rules based on the identification of Structural Alerts (SA) for based on the rules, published in the document “Development of structural alerts for the in vivo micronucleus assay in rodents”, by Romualdo Benigni, Cecilia Bossa, Olga Tcheremenskaia and Andrew Worth, European Commission report EUR 23844 EN. As one or more SAs embedded in a molecular structure are recognised, the system flags the potential micronucleus activity of the chemical. Toxtree did not identify any structural alert in the target 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol. An assessment of the reliability of the prediction is not provided.
Applicant's summary and conclusion
- Conclusions:
- Interpretation of results (migrated information): negative
The consensus assessment was based only on Leadscope (for mouse) and on Leadscope and Toxtree (for rodent) predictions which were in agreement concluding that the target 5-(1-methylpiperidin-4-yl)-5H-dibenzo[a,d][7]annulen-5-ol is NEGATIVE, although the prediction is of little reliability since Leadscope prediction is of little reliability and Toxtree does not provide an assessment of its reliability. - Executive summary:
Micronucleus in vivo on rodent of the target was estimated by using three predictors: Leadscope Model
Applier, ACD/Percepta and Toxtree decision rule system. The three predictors were employed in order to
apply a consensus approach to enhance the reliability of the prediction. In the consensus assessment only
reliable predictions are to be taken into account. In the case of the target, the consensus assessment was
based only on ACD/Percepta prediction, concluding that the target 2-Thiophenecarboxylic acid, 3-[[(2-
methoxy-2-oxoethyl)amino]sulfonyl]-, methyl ester is LIKELY NEGATIVE, although the prediction is of
borderline reliability.
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