Hexane-1,2,6-triol

Endpoint summary

Key value for chemical safety assessment

Genetic toxicity in vitro

Description of key information

In order to fulfill the informatinon requirement on bacterial mutagenicity in REACh Annex VII, QSAR predictions were conducted using Derek Nexus and Sarah Nexus.

Derek Nexus is a structure-based toxicity prediction tool that uses a knowledge base to match parts of the query structure (toxicophores) to alerts in the knowledge base and applies reasoning to assess the likelihood for a prediction. Reasoning considers evidence outlined in a set of rules to provide a logical outcome. On the other hand, Sarah Nexus makes predictions for mutagenicity using fragment-based structural hypotheses derived from a statistically learned self-organising hypothesis network (SOHN) built using bacterial reverse mutation test data.

As a result, the structure of the test item does not match any structural alerts or examples for (bacterial in vitro) mutagenicity in the knowledge base of Derek Nexus. Additionally, the tested structure does not contain any unclassified or misclassified features and is consequently predicted to be negative in the bacterial in vitro (Ames) mutagenicity test. The prediction was classified “inactive” with no misclassified or unclassified features. This kind of negative prediction is used for compounds where all features in the molecule are found in accurately classified compounds from the reference set. The confidence in this kind of negative prediction is therefore high. An analysis by the software provider using public and proprietary data demonstrates the robustness and accuracy of negative predictions are comparable with the Ames test (~85%).

In the Sarah Nexus report, the test item is predicted to be negative with 67% confidence in the prediction for the 'Mutagenicity in vitro' endpoint and supporting hypotheses containing similar examples from the training set have been found. The result is therefore considered to be reliable. Consequently, the test item is predicted to be negative in the bacterial in vitro (Ames) mutagenicity test, also.

Link to relevant study records

Reference

Reference 1

Endpoint:

in vitro gene mutation study in bacteria

Remarks:

in silico toxicity prediction by DEREK Nexus

Type of information:

(Q)SAR

Adequacy of study:

weight of evidence

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
Nexus: 2.1.1
DEREK Nexus: 5.0.2

2. MODEL (incl. version number)
Derek Nexus v5.0 contains 117 active alerts for bacterial mutagenicity, together with reasoning rules and secondary functionality that evaluates potentially misclassified and unclassified features in compounds that do not activate bacterial mutagenicity alerts or examples.

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
Smiles: OCCCCC(O)CO

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:
DEREK Nexus is used for the prediction of the outcome of bacterial mutagenicity testing in the Ames test as required in REACH Annex VII, 8.4.1. The model is able to predict to outcome of bacterial mutagenicity testing (for more details see the attached QMRF).

- Unambiguous algorithm:
DEREK Nexus is a structure-based toxicity prediction tool that uses a knowledge base to match parts of the query structure (toxicophores) to alerts in the knowledge base and applies reasoning to assess the likelihood for a prediction. Reasoning considers evidence outlined in a set of rules to provide a logical outcome (for more details see the attached QMRF).

5. APPLICABILITY DOMAIN
[Explain how the substance falls within the applicability domain of the model]
No unclassified features were found during analysis. Unclassified features are defined as features not found in reference set. So it can be concluded that relevant structural feature (i.e toxicophores) of the test item are included in the reference set (for more details see the attached QMRF).

6. ADEQUACY OF THE RESULT
[Explain how the prediction fits the purpose of classification and labelling and/or risk assessment]
The described prediction represents a high-confidence prediction for negative mutagenicity of the test item and is therefore considered adequate to fulfill the information requirement described in Annex VII section 8.4.1. Thus, no further information on the endpoint mutagenicity for an Annex VII registration is required and according to Regulation (EC) 1272/2008, the data are conclusive but not sufficient for classification.

Qualifier:

equivalent or similar to guideline

Guideline:

other: REACH Guidance on QSARs R.6

Principles of method if other than guideline:

- Software tool(s) used including version:
Nexus: 2.1.1, DEREK Nexus: 5.0.2

- Model(s) used:
Derek Nexus v5.0 contains 117 active alerts for bacterial mutagenicity, together with reasoning rules and secondary functionality that evaluates potentially misclassified and unclassified features in compounds that do not activate bacterial mutagenicity alerts or examples.

- Model description: see field 'Justification for non-standard information'

- Justification of QSAR prediction: see field 'Justification for type of information'

-References:
Judson et al. (2013) ‘Assessing Confidence in Predictions made by Knowledge-Based Systems’, Toxicology Research, vol. 2, no. 1, pp. 70-79. http://pubs.rsc.org/en/content/articlelanding/2013/tx/c2tx20037f
Greene N, Judson P, Langowski J, Marchant CA (1999). Knowledge based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. SAR & QSAR in Environmental Research 10, 299-313.
Sanderson DM & Earnshaw CG (1991). Computer prediction of possible toxic action from chemical structure; The DEREK system. Human and Experimental Toxicology 10, 261-273.

Specific details on test material used for the study:

Smiles: OCCCCC(O)CO

Genotoxicity:

negative

Remarks on result:

no mutagenic potential (based on QSAR/QSPR prediction)

The structure of the test item does not match any structural alerts or examples for (bacterial in vitro) mutagenicity in DEREK. Additionally, the tested structure does not contain any unclassified or misclassified features and is consequently predicted to be inactive (i.e. negative) in the bacterial in vitro (Ames) mutagenicity test.

Conclusions:

The structure of the test item does not match any structural alerts or examples for (bacterial in vitro) mutagenicity in the knowledge base of DEREK Nexus (Version 5.0.2). Additionally, the tested structure does not contain any unclassified or misclassified features and is consequently predicted to be negative in the bacterial in vitro (Ames) mutagenicity test. Thus, according to Regulation (EC) 1272/2008, the data are conclusive but not sufficient for classification.

Reference 2

Endpoint:

in vitro gene mutation study in bacteria

Remarks:

in silico toxicity prediction by Sarah Nexus

Type of information:

(Q)SAR

Adequacy of study:

supporting study

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
Nexus: 2.1.1
Sarah Nexus: 2.0.1

2. MODEL (incl. version number)
Sarah Model 1.1.19

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
Smiles: OCCCCC(O)CO

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:
Sarah Nexus is used for the prediction of the outcome of bacterial mutagenicity testing in the Ames test as required in REACH Annex VII, 8.4.1. The model is able to predict to outcome of bacterial mutagenicity testing (for more details see the attached QMRF).

- Unambiguous algorithm:
Sarah Nexus 2.0.1 makes predictions for mutagenicity using fragment-based structural hypotheses derived from a statistically learned self-organising hypothesis network (SOHN) built using bacterial reverse mutation test data for 9507 compounds (for more details see the attached QMRF).

5. APPLICABILITY DOMAIN
[Explain how the substance falls within the applicability domain of the model]
The applicability domain of Sarah Nexus is defined by comparing the structural fragments present in the training set with those present in the query compound. If all of the atoms in the query compound are covered by structural fragments found in the Sarah Nexus training set, as it is the case with the test item (see the attached Sarah Report), then the query compound is considered inside the applicability domain of the model (for more details see the attached QMRF).

6. ADEQUACY OF THE RESULT
[Explain how the prediction fits the purpose of classification and labelling and/or risk assessment]
The described prediction represents a reliable prediction for negative mutagenicity of the test item and is therefore considered adequate supportive to fulfill the information requirement described in Annex VII section 8.4.1.

Qualifier:

equivalent or similar to guideline

Guideline:

other: REACH Guidance on QSARs R.6

Principles of method if other than guideline:

- Software tool(s) used including version:
Nexus: 2.1.1, Sarah Nexus: 2.0.1

- Model(s) used:
Sarah Model 1.1.19

- Model description: see field 'Justification for non-standard information'

- Justification of QSAR prediction: see field 'Justification for type of information'

-References:
Hanser T, Barber C, Rosser E, Vessey JD, Webb SJ & Werner S (2014). Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge. Journal of Cheminformatics 6:21.

Barber C, Cayley A, Hanser T, Harding A, Heghes C, Vessey JD, Werner S, Weiner SK, Wichard J, Giddings A, Glowienke S, Parenty A, Brigo A, Spirkl HP, Amberg A, Kemper R & Greene N (2016). Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained. Regulatory Toxicology and Pharmacology 76, 7-20.

Specific details on test material used for the study:

Smiles: OCCCCC(O)CO

Genotoxicity:

negative

Remarks on result:

no mutagenic potential (based on QSAR/QSPR prediction)

The compound is predicted to be negative with 67% confidence for the 'Mutagenicity in vitro' endpoint in the model: 'Sarah Model - 1.1.19'. Supporting hypotheses containing similar examples from the training set have been found.

Conclusions:

The described Sarah Nexus prediction represents a reliable prediction for negative mutagenicity of the test item and is therefore considered adequate supportive to fulfill the information requirement for bacterial mutagenicity.

Endpoint conclusion

Endpoint conclusion:

no adverse effect observed (negative)

Justification for classification or non-classification

The Derek Nexus prediction represents a high-confidence prediction for negative mutagenicity of the test item and is therefore considered adequate to fulfill the information requirement described in Annex VII section 8.4.1. It is supported by the Sarah Nexus prediction, which also represents a reliable prediction for negative mutagenicity of the test item. Thus, no further information on the endpoint mutagenicity for an Annex VII registration is required and according to Regulation (EC) 1272/2008, the data are conclusive but not sufficient for classification.