Ruscus aculeatus, ext.

Genetic toxicity: in vitro

Administrative data

Endpoint:

in vitro gene mutation study in bacteria

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

Study period:

2015

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model and falling into its applicability domain, with adequate and reliable documentation / justification

Justification for type of information:

1. SOFTWARE

2. MODEL (incl. version number)

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
[Explain how the model fulfils the OECD principles for (Q)SAR model validation. Consider attaching the QMRF or providing a link]
- Defined endpoint:
- Unambiguous algorithm:
- Defined domain of applicability:
- Appropriate measures of goodness-of-fit and robustness and predictivity:
- Mechanistic interpretation:

5. APPLICABILITY DOMAIN
[Explain how the substance falls within the applicability domain of the model]
- Descriptor domain:
- Structural and mechanistic domains:
- Similarity with analogues in the training set:
- Other considerations (as appropriate):

6. ADEQUACY OF THE RESULT
[Explain how the prediction fits the purpose of classification and labelling and/or risk assessment]1. SOFTWARE

2. MODEL (incl. version number)

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
[Explain how the model fulfils the OECD principles for (Q)SAR model validation. Consider attaching the QMRF or providing a link]
- Defined endpoint:
- Unambiguous algorithm:
- Defined domain of applicability:
- Appropriate measures of goodness-of-fit and robustness and predictivity:
- Mechanistic interpretation:

5. APPLICABILITY DOMAIN
[Explain how the substance falls within the applicability domain of the model]
- Descriptor domain:
- Structural and mechanistic domains:
- Similarity with analogues in the training set:
- Other considerations (as appropriate):

6. ADEQUACY OF THE RESULT
[Explain how the prediction fits the purpose of classification and labelling and/or risk assessment]1. SOFTWARE

2. MODEL (incl. version number)

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
[Explain how the model fulfils the OECD principles for (Q)SAR model validation. Consider attaching the QMRF or providing a link]
- Defined endpoint:
- Unambiguous algorithm:
- Defined domain of applicability:
- Appropriate measures of goodness-of-fit and robustness and predictivity:
- Mechanistic interpretation:

5. APPLICABILITY DOMAIN
[Explain how the substance falls within the applicability domain of the model]
- Descriptor domain:
- Structural and mechanistic domains:
- Similarity with analogues in the training set:
- Other considerations (as appropriate):

6. ADEQUACY OF THE RESULT
[Explain how the prediction fits the purpose of classification and labelling and/or risk assessment]

Data source

Reference

Reference Type:

study report

Title:

Unnamed

Year:

2015

Report date:

2015

Materials and methods

Test guideline

Guideline:

other: REACH Guidance on QSARs R.6

Deviations:

no

Principles of method if other than guideline:

Organisation for Economic Co-operation and Development Report on the Regulatory Uses and Applications in OECD Member Countries of (Quantitative) Models in the Assessment of New and Existing Chemicals. ENV/JM/MONO(2006)25, OECD, Paris France 2007
Document on the Validation of (Quantitative) Structure Activity Relationship (QSAR) Models. OECD Series on Testing and Assessment N. 69.ENV/JM/MONO(2007)2.

Test material

Test material information

Constituent 1

Reference substance name:

Spirosta-5,25(27)-diene-1β,3β-diol

EC Number:

241-660-1

EC Name:

Spirosta-5,25(27)-diene-1β,3β-diol

Cas Number:

17676-33-4

Molecular formula:

C27H40O4

IUPAC Name:

spirosta-5,25(27)-dien-1,3-diol

Specific details on test material used for the study:

Neoruscogenin is one of the most important component of the Ruscus aculeatus extr.

Results and discussion

Remarks on result:

mutagenic potential (based on QSAR/QSPR prediction)

Any other information on results incl. tables

Name	QSAR statistical-based weight of evidence prediction	QSAR expert rule-based weight of evidence prediction	QSAR weight of evidence prediction
Neoruscogenin	NEGATIVE (moderate reliable)	NEGATIVE (High reliable)	NEGATIVE (High reliable)

Applicant's summary and conclusion

Conclusions:

The computational toxicology assessment based on two different QSAR methodologies, i.e. statistical-besed and expert-rule based, predicted the target compound Neoruscogenin as NOT MUTAGENIC, being predicted negative for microbial in vitro Salmonella (Ames test). The prediction was assessment as high reliable.