Diisononyl adipate

Endpoint summary

Description of key information

The structure of Diisononyl adipate (DINA) did not give any indication for sensitizing properties (for both the parent and metabolites) by using a quantitative structure-activity relationship (QSAR): the validated OASIS-LMC.

Key value for chemical safety assessment

Skin sensitisation

Link to relevant study records

Reference

Endpoint:

skin sensitisation: in chemico

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
OASIS TIMES v.2.31.2.81

2. MODEL (incl. version number)
Skin sensitization with autoxidation v.25.30

3. SMILES IDENTIFIERS USED AS INPUT FOR THE MODEL
CC(C)CCCCCCOC(=O)CCCCC(=O)OCCCCCCC(C)C

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
- Defined endpoint: In vivo: skin sensitization
- Unambiguous algorithm: refer to QMRF

- Defined domain of applicability:
1. General parametric requirements - includes ranges of variation of log KOW and MW. It specifies in the domain only those chemicals that fall in the range of variation of the MW and log Kow defined on the bases of the correctly predicted training set chemicals. This layer of the domain is applied only on parent chemicals.
2. Structural domain - it is represented by list of atom - centered fragments extracted from the chemicals in the training set. The training chemicals were split into two subsets: chemicals correctly predicted by the model and incorrectly predicted chemicals. These two subsets of chemicals were used to extract characteristics determining the "good" and "bad" space of the domain. Extracted characteristics were split into three categories: unique characteristics of correct and incorrect chemicals (presented only in one of the subsets) and fuzzy characteristics presented in both subsets of chemicals. Structural domain is applied on parent chemicals, only.
3. Mechanistic domain - in SS model it includes: Interpolation space: this stage of the applicability domain of the model holds only for chemicals for which an additional COREPA model is required. It estimates the position of the target chemicals in the population density plot built in the parametric space defined by the explanatory variables of the model by making use the training set chemicals. Currently, the accepted threshold of population density is 10%.
The mechanistic domain is applied on the parent structures and on their metabolites.

- Appropriate measures of goodness-of-fit and robustness and predictivity:

External Validation:
For substances in the applicability domain, a predictivity of 100% was found for 100 industrial chemicals for the distinction of non-sensitizers versus sensitizers of GHS Category 1. The evaluation has been published in
W. Teubner, A. Mehling, P.X. Schuster, K.Guth, B. A. Worth, J. Burton, B. van Rawenzwaay, R. Landsiedel: Computer models versus reality: How well do in silico models currently predict the sensitization potential of a substance, Regulatory Toxicology and Pharmacology 67 (2013) 468-485

Statistics for goodness-of-fit:
For 1406 chemicals (39 of which have proprietary data), the TIMES-SS model was able to predict correctly 80% of the strong sensitizers, 60% of the weak sensitizers and 79% of the non-sensitizers, i.e.,
an overall performance of 77 %.
Sensitivity: 73 %
Specificity: 79 %

- Mechanistic interpretation:
The TIMES-SS (Tissue Metabolism Simulator for skin sensitization) model integrates a simulator of skin metabolism together with a number of “local” QSAR models for assessing the reactivity of specific alerts. A skin metabolism simulator was developed based on empirical and theoretical knowledge (not enough reported observed skin metabolism data). The transformation probabilities (defining the priority of their execution) were parameterized to reproduce skin sensitization data. The simulator comprises of about 420 transformations, which can be divided into four main types: abiotic transformations, covalent interaction with proteins, Phase I and Phase II reactions. Autoxidation (AU) of chemical is also accounted for. Interactions with skin proteins are grouped into three types: leading to strong or weak skin
sensitization effect and interactions requiring QSAR models to quantify the potency of sensitization of the alerting groups. The QSAR models were developed by the COmmon PAttern Recognition (COREPA) approach [3]. The skin sensitization model predicts skin sensitization effect in three classes: strong, weak and non-sensitizers.
Reliability of alerts in the TIMES-SS model has been also evaluated to provide transparent mechanistic reasoning for predicting sensitization potential. Alert performance was defined as the ratio between the number of correct (positive and negative) predictions and the total number of chemicals within the local training set that triggered the alert. The alert performance was assessed based on the predictions on parents, autoxidation products simulated by the external AU simulator and metabolites as simulated by the skin metabolism simulator embedded in TIMES-SS model. Four different categories of reliability were defined:
High reliability – alert performance higher than 60% and more than 5 chemical in local (transformation/alert) training set
Low reliability – performance less than 60% and more than 5 chemicals in training set
Undetermined reliability – less than 5 chemicals in training set
Undetermined (theoretical) – there are no chemicals supporting the alert in the local training set

5. APPLICABILITY DOMAIN
- Descriptor domain:
Log(Kow): range = [ -13.7 .. 33.5 ]
calculated: 9.1 (In domain)
MOL._WEIGHT: range = [ 6.93 .. 1350 ]Da
calculated: 399Da (In domain)

- Structural and mechanistic domains:
Fragments in correctly predicted training chemicals – 100.00%
Fragments in non-correctly predicted training chemicals – 0.00%
Fragments not present in the training chemicals – 0.00%

- Similarity with analogues in the training set:
not reported

6. ADEQUACY OF THE RESULT
The substance falls in the applicability domain of the model. The model was found to give reliable predictions for industrial chemicals. It is therefore considered to be acceptable for REACH.

The substance is considered to be non skin seniziting.

Qualifier:

according to guideline

Guideline:

other: REACH guidance on QSARs R.6, May/July 2008

Principles of method if other than guideline:

TIMES-SS v.2.32.2.81 - Skin sensitization with autoxidation v.25.30 (structure-toxicity and structure-metabolism relationships)

GLP compliance:

no

Specific details on test material used for the study:

SMILES: CC(C)CCCCCCOC(=O)CCCCC(=O)OCCCCCCC(C)C

The prediction was made for a substance of high purity (as determined)

Remarks on result:

no indication of skin sensitisation

Outcome of the prediction model:

no or minimal reactivity [in chemico]

TIMES-SS model aims to encode structure toxicity and structure metabolism relationships through a number of transformations simulating skin metabolism and interaction of the generated reactive metabolites with skin proteins. The skin metabolism simulator mimics metabolism using 2D structural information. The autoxidation (abiotic oxidation) of chemicals is also accounted for. A training set of diverse chemicals was compiled and their skin sensitization potency assigned to one of three classes. These three classes were Strong, Weak or Non sensitizing. The skin sensitization model was built as a composite of the following submodels: 1. Skin metabolism Simulator: This mimics the metabolic fate of parent chemical controlled by skin enzymes and thus the potential formation of protein adducts with reactive agents. 2D structural information of parent chemicals is used to model metabolism. Metabolic pathways are generated based on a set of hierarchically ordered principal transformations including spontaneous reactions, enzyme-catalyzed Phase I and Phase II drug metabolism reactions, and reactions with protein nucleophiles. The formation of macromolecular immunogens was used to identify probable structural alerts in parent chemicals or their metabolites. 2. COREPA (COmmon Pattern Recognition approach) 3D QSARs for intrinsic reactivity of compounds having substructures associated with activity. These models depend on both the structural alert and the rate of skin sensitization. Steric effects around the active site, molecular size, shape, solubility, lipophilicity and electronic properties are taken into account. These models generally may involve combinations of molecular parameters or descriptors, which trigger (“fire”) the alerting group. A quantitative structure-activity relationship (QSAR) system for estimating skin sensitization potency has been developed which incorporates skin metabolism and considers the potential of parent chemicals and/or their activated metabolites to react with skin proteins. The autoxidation (abiotic oxidation) of chemicals is also accounted for. A training set of diverse chemicals was compiled and their skin sensitization potency assigned to one of three classes. These three classes were Strong, Weak or Non sensitizing.

The applicability domain of TIMES-SS model consists of the following layers:

1. General parametric requirements - includes ranges of variation of log KOW and MW. It specifies in the domain only those chemicals that fall in the range of variation of the MW (from 30 to 737 Da, in this study) and log Kow (from -13.2 to 15.4, in this study) defined on the bases of the correctly predicted training set chemicals. This layer of the domain is applied only on parent chemicals.

2. Structural domain - it is represented by the list of atom - centered fragments extracted from the chemicals in the training set. The training chemicals were split into two subsets: chemicals correctly predicted by the model and incorrectly predicted chemicals. These two subsets of chemicals were used to extract characteristics determining the "good" and "bad" space of the domain. Extracted characteristics were split into three categories: unique characteristics of correct and incorrect chemicals (presented only in one of the subsets) and fuzzy characteristics presented in both subsets of chemicals. The target structure is also partitioned into atom-centered

fragments and when they present in the list of extracted atom-centered fragments from the training set chemicals and satisfy the accepted thresholds the chemical is categorized as belonging to the structural domain. The default thresholds for classifying of chemicals to the structural domain of the current skin sensitization model are:

· All extracted fragments to belong to the "good" domain ("Correct" = 100%)

· All fuzzy fragments are considered as part of the "good" domain

· No fragments belonging to "bad" domain ("Incorrect" = 0%)

· No unique fragment ("Unknown" = 0%)

Structural domain is applied on parent chemicals, only.

3. Mechanistic domain - in SS model it includes:

· Interpolation space: this stage of the applicability domain of the model holds only for chemicals for which an additional COREPA model is required. It estimates the position of the target chemicals in the population density plot built in the parametric space defined by the explanatory variables of the model by making use the training set chemicals. The accepted threshold of population density in the

current study is 10%. Chemicals with values below 10% are "Out of domain". "N/A" is assigned when this type of sub-domain is not relevant to the structure and will be not accounted in the total domain. "Unknown" is referred for the cases when some parameters could not be calculated by any reason or for chemicals with equivocal predictions (not reaching the probability threshold of the COREPA model and reported in TIMES as Can't predict). The mechanistic domain is applied on the parent structures and on their metabolites.

In order to belong to the model domain a target structure must meet the requirements of all the domain layers.

The registrant considers this predication as valid because TIMES-SS was validated with 100 substances from the registrant's portfolio (Teubner et al., Regulatory Toxicology and Pharmacology 67 (2013) 468–485). All predictions that fullfilled all domain requirements were correct (Specificity 100%).

Interpretation of results:

GHS criteria not met

Conclusions:

The QSAR program calculated a negative sensitization potential of the test substance. The substance is in domain of the system.

Endpoint conclusion

Endpoint conclusion:

no adverse effect observed (not sensitising)

Additional information:

The skin sensitizing properties of the structure of diisononyl adipate (DINA) were considered by expert judgement, and indications for an alert were considered not present. This was confirmed by using a validated quantitative structure-activity relationship (QSAR), OASIS-LMC, that predicted both the parent compound and its metabolites as non-sensitiser.

Furthermore, the available animal studies on the structural analogue Di-(2 -ethylhexyl) adipate DEHA), a patch test with rabbits and a Draize test with guinea pigs, did not indicate a skin sensitising effect (Mallette 1952; CFTA,1967).

Respiratory sensitisation

Endpoint conclusion

Endpoint conclusion:

no study available

Justification for classification or non-classification

Based on the available data, DINA is considered to be not sensitising. In accordance with EU Classification, Labelling and Packaging of Substances and Mixtures (CLP) Regulation (EC) No. 1272/2008, classification is not necessary for sensitisation.