Registration Dossier

Administrative data

Endpoint:
skin sensitisation: in vitro
Type of information:
(Q)SAR
Adequacy of study:
key study
Reliability:
1 (reliable without restriction)
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

BT4 has been assessed through computational toxicological chemical modeling (CADRE-SS) in order to ascertain its skin sensitization potential.

CADRE-SS (Kostal, 2016) is a predictive in silico model that has been used extensively in the pharmaceutical/biotechnological industries for predicting skin sensitization potential. The model
employs classical and quantum-mechanical modeling of molecular interactions that are representative of initial biochemical triggers in the adverse outcome pathway for skin sensitization. Specifically, the
model calculates a permeability constant in order to predict whether the chemical will penetrate the skin. Further, it determines the likelihood of the chemical to interact with skin proteins and peptides,
incorporating metabolic potential, i.e. metabolism to electrophilic species, when relevant. A suite of final models uses this information to predict overall skin sensitization potential. Thus, CADRE-SS is a
tiered model system that assesses skin permeability, haptenation mechanisms (including those requiring metabolism of the chemical), and reactivity with skin proteins/peptides. A confidence score is
calculated for the assessment reflecting both computational accuracy and parametrical similarity to training set compounds in the statistical model.

2. MODEL (incl. version number)

CADRE-SS v1.3 (Skin sensitization)

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

Smiles for BT4:
CCCCCC[C@H](OC(=O)C)CCCCCCCCCCC(=O)OC[C@@H](CCCC)CC

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL

Full details can be found in the attached QMRFand the following publication which also identifies successful predictions consistent with skin sensitizers and nonsensitizers based on comparative LLNA study results
In predicting the OECD global harmonized classes (GHS), CADRE-SS achieved 93 and 90% concordance with experimentally determined GHS 1A and 1B categories, respectively. This is due to the model being fine-tuned to predict ECETOC sensitization categories, which are more nuanced than GHS labeling.

Reference:
Kostal, J. a.-K. (2016). CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on
Modeling of Molecular Interactions. Chem. Res. Toxicol. 29, 59-64.

5. APPLICABILITY DOMAIN

CADRE-SS uses a three-class categorical hybrid model consisting of three modules describing different steps in the sensitization process. In the first module, the permeability coefficient (logKp) is calculated by Monte Carlo simulation. The second module uses a set of rules, encoded in a similar way as those implemented in Toxtree, to assign the most likely reaction domain. Compounds for which no reaction domain could be assigned are assumed to be non-sensitizers. Any compounds predicted as sensitizers are passed on to the third module, which calculates the chemical reactivity of compounds based on ground-state, site-specific, or global physicochemical and quantum mechanical descriptors, depending on the reaction domain assigned. For each reaction domain, a linear model was developed that takes the predictions of modules one (skin permeability) and three (chemical reactivity) as input. In addition, a rule-based approach was implemented to account for the qualitative sensitizing potential of metal salts. CADRE-SS was trained on a set of 384 chemicals annotated with LLNA data. Confidence levels for the individual predictions are derived from the range of the descriptors values observed for the training set. Tested on a set of 100 compounds annotated with human data, animal data or both, the model correctly assigned more than 90% of these compounds to one of the three GHS skin sensitization potency categories. The authors emphasize that, in contrast to other in silico tools for the prediction of skin sensitization potential, CADRE-SS was applicable to all compounds of this test set.

6. ADEQUACY OF THE RESULT
CADRE-SS (Kostal, 2016) is a predictive in silico model that has been used extensively in the pharmaceutical/biotechnological industries for predicting skin sensitization potential. The model employs classical and quantum-mechanical modeling of molecular interactions that are representative of initial biochemical triggers in the adverse outcome pathway for skin sensitization. Specifically, the model calculates a permeability constant in order to predict whether the chemical will penetrate the
skin. Further, it determines the likelihood of the chemical to interact with skin proteins and peptides, incorporating metabolic potential, i.e. metabolism to electrophilic species, when relevant. A suite of final models uses this information to predict overall skin sensitization potential. Thus, CADRE-SS is a tiered model system that assesses skin permeability, haptenation mechanisms (including those requiring metabolism of the chemical), and reactivity with skin proteins/peptides. A confidence score is
calculated for the assessment reflecting both computational accuracy and parametrical similarity to training set compounds in the statistical model.

BT4 has been assessed using CADRE-SS. Overall, the model calculations predict, with a very high degree of confidence, that this molecule is a non-sensitizer. The resulting calculations are described below (also see the notes in the attached QPRF prediction results and discussion for BT4).

Protonation State:
The finding of ‘0’ indicates that there are no relevant protonation states for BT4, i.e. the compound exists only in neutral form at the relevant pH.

Pred. Kp:
Pred Kp is a predicted rate of skin permeation. In the case of BT4, the result of -6.2 (logarithmic scale in cm/h) indicates that BT4 is predicted to have a moderate ability to permeate the skin. (For comparison, -3 - -4 represents a “High” rate of permeability.)

Haptenation Mechanism:
In that the formation of a stable hapten-protein conjugate has been shown to be one of the key steps in the adverse outcome pathway for skin sensitization (and appears to be key in determining potency of skin sensitization for industrial chemicals), the prediction of whether a chemical is likely to form a hapten-protein conjugate is critical to an accurate prediction.
In the case of BT4, a finding of “NONE” indicates that there was no mechanism identified in the model by which the molecule could form a hapten-protein conjugate. The model identifies areas of the molecule where haptenation could occur, and in the case of BT4, there are none.

Metabolism:
If a haptenation mechanism is identified in the model, the model determines whether metabolic activation is required in order for the chemical to interact with skin proteins/peptides. In the case of BT4, no haptenation mechanism was identified, thus a result of “N/A” is reported for metabolism.

Dichotomous and Potency Predictions:
Dichotomous prediction is the overall prediction as a sensitizer or a non-sensitizer. For sensitizers, an indication of potency is predicted by the model. In the case of BT4, the model predicts that there is no potential for skin sensitization, thus the finding of “NON-SENS.”

Confidence Score:
The confidence score provides a measure of certainty in the prediction. For this chemical, the score of 2+3 is the highest possible score, i.e. the highest confidence in the prediction. The individual scores represent a computational accuracy score (2) plus a metric that assesses parametrical similarity to a training set of compounds (both sensitizers and nonsensitizers) (3).

CONCLUSION
CADRE-SS, a computational toxicological method that employs quantum-mechanical modeling of molecular interactions that are representative of initial biochemical triggers in the adverse outcome pathway for skin sensitization, including likelihood of skin penetration, haptenation potential (with or without metabolic activation), and potential for binding to skin proteins/peptides, predicts with high confidence that BT4 is a non-sensitizer.

References:
Kostal, J. a.-K. (2016). CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions. Chem. Res. Toxicol. 29, 59-64.

Melnikov, F. K.-K. (2016). Assessment of predictive models for estimating the acute aquatic toxicity of organic chemicals. Green Chem. 18, 4432-4445.

Teubner, W. M. (2013). Computer models versus reality: How well do in silico models currently predict the sensitization potential of a substance. Regulatory Toxicology and Pharmacology, 67: 468-485.

Data source

Reference
Reference Type:
publication
Title:
Unnamed
Year:
2015
Report Date:
2015

Materials and methods

Test guideline
Qualifier:
no guideline available
Principles of method if other than guideline:
BT4 has been assessed through computational toxicological chemical modeling (CADRE-SS) in order to ascertain its skin sensitization potential.

CADRE-SS (Kostal, 2016) is a predictive in silico model that has been used extensively in the pharmaceutical/biotechnological industries for predicting skin sensitization potential. The model employs classical and quantum-mechanical modeling of molecular interactions that are representative of initial biochemical triggers in the adverse outcome pathway for skin sensitization. Specifically, the model calculates a permeability constant in order to predict whether the chemical will penetrate the
skin. Further, it determines the likelihood of the chemical to interact with skin proteins and peptides, incorporating metabolic potential, i.e. metabolism to electrophilic species, when relevant. A suite of final models uses this information to predict overall skin sensitization potential. Thus, CADRE-SS is a tiered model system that assesses skin permeability, haptenation mechanisms (including those requiring metabolism of the chemical), and reactivity with skin proteins/peptides. A confidence score is
calculated for the assessment reflecting both computational accuracy and parametrical similarity to training set compounds in the statistical model.

Reference:
Kostal, J. a.-K. (2016). CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on
Modeling of Molecular Interactions. Chem. Res. Toxicol. 29, 59-64.
Justification for non-LLNA method:
BT4 has been assessed through computational toxicological chemical modeling (CADRE-SS) in order to ascertain its skin sensitization potential. Per the resulting report, the chemical is predicted to have negligible ability to cause skin sensitization; confidence in the prediction is extremely high.

Test material

Reference
Name:
Unnamed
Type:
Constituent
Test material form:
liquid

Results and discussion

In vitro / in chemico

Results
Key result
Remarks on result:
no indication of skin sensitisation
Remarks:
CADRE-SS, a computational toxicological method that employs quantum-mechanical modeling of molecular interactions that are representative of initial biochemical triggers in the adverse outcome pathway for skin sensitization, including likelihood of skin penetration, haptenation potential (with or without metabolic activation), and potential for binding to skin proteins/peptides, predicts with high confidence that BT4 is a non-sensitizer.

Applicant's summary and conclusion

Interpretation of results:
GHS criteria not met
Conclusions:
CADRE-SS, a computational toxicological method that employs quantum-mechanical modeling of molecular interactions that are representative of initial biochemical triggers in the adverse outcome pathway for skin sensitization, including likelihood of skin penetration, haptenation potential (with or without metabolic activation), and potential for binding to skin proteins/peptides, predicts with high confidence that BT4 is a non-sensitizer.

Further, our knowledge of the chemical behavior of the functional groups in BT4, along with a large body of negative data on similar molecules allows for the conclusion that BT4 is highly unlikely to cause skin sensitization.
Executive summary:

SUMMARY
BT4 has been assessed through computational toxicological chemical modeling (CADRE-SS) in order to ascertain its skin sensitization potential. Per the resulting report, the chemical is predicted to have
negligible ability to cause skin sensitization; confidence in the prediction is extremely high.


BACKGROUND
CADRE-SS (Kostal, 2016) is a predictive in silico model that has been used extensively in the pharmaceutical/biotechnological industries for predicting skin sensitization potential. The model employs classical and quantum-mechanical modeling of molecular interactions that are representative of initial biochemical triggers in the adverse outcome pathway for skin sensitization. Specifically, the model calculates a permeability constant in order to predict whether the chemical will penetrate the skin. Further, it determines the likelihood of the chemical to interact with skin proteins and peptides, incorporating metabolic potential, i.e. metabolism to electrophilic species, when relevant. A suite of final models uses this information to predict overall skin sensitization potential. Thus, CADRE-SS is a tiered model system that assesses skin permeability, haptenation mechanisms (including those requiring metabolism of the chemical), and reactivity with skin proteins/peptides. A confidence score is calculated for the assessment reflecting both computational accuracy and parametrical similarity to training set compounds in the statistical model.
As opposed to CADRE’s use of toxicologically relevant calculations, QSAR methods employ physicochemical or structural descriptors and statistics in order to obtain a prediction of skin sensitization potential. [See (Kostal, 2016) for comparison with other in silico tests.] Advantages of CADRE over strictly QSAR methods includes the ability to assess multiple protonation states, and the incorporation of the effect of possible metabolic activation, i.e. CADRE-SS is a mechanistic model that incorporates current knowledge about the adverse outcome pathway for skin sensitization. In addition, the model is not dependent upon a body of data on similar structures as is the case with strict QSAR methods.
The CADRE-SS computational model was trained on 384 chemicals (182 sensitizers, 202 nonsensitizers based on data from the Local Lymph Node Assay (LLNA)). The actual validation set was adopted from
Teuber et al. and consisted of 45 sensitizers and 55 nonsensitizers, identified from human and animal studies (Teubner, 2013). The chemicals consisted of organic chemicals and metal salts with a wide range
of functional groups. Molecular weights are below 500 Da and skin permeability is expected to be significant in the selected chemicals. CASDRE-SS correctly assigned sensitization potential for 95 of 100
chemicals. All potent sensitizers (Category 1A in animal testing) were correctly identified by CADRE-SS. The five chemicals that were not identified correctly by CADRE-SS were all Category 1B sensitizers in
LLNA.
In further support of the CADRE approach, in general, it is noteworthy that a similar approach has been widely used in the CADRE-AT model for acute aquatic toxicity prediction, where it has shown excellent
predictive performance (Melnikov, 2016). Toxicity estimation tools such as CADRE-AT that incorporate mechanistically relevant information, e.g. chemical bioavailability and reactivity, have performed comparatively better than purely statistical methods based on structural properties with unspecified toxicological relevance.


RESULTS
BT4 has been assessed using CADRE-SS. Overall, the model calculations predict, with a very high degree of confidence, that this molecule is a non-sensitizer. The resulting calculations are detailed in the attached discussion documents (QPRFs).

A large body of evidence accumulated over many years has shown that the alkyl esters as a group are non-sensitizing. Many of these studies are summarized in the CIR for alkyl esters as used in cosmetics.
BT4 is a large alkyl ester molecule, quite similar in structure to some of the alkyl esters reviewed in the CIR, and does not contain any additional chemical function groups not seen in these same molecules.
Knowledge of the chemical behavior of the functional groups in BT4, along with a large body of negative data on similar molecules allows for the conclusion that BT4 is highly unlikely to cause skin sensitization.

Further discussion is provide in teh attached document "Skin Sens Structure-Activity Review of BT4 & Related Esters.pdf"