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Enoch, S. J.; Roberts, D. W. Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations. Chem. Res. Toxicol. 2013, 26, 5, 767-774.

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Enoch, S. J.; Roberts, D. W. Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations. Chem. Res. Toxicol. 2013, 26, 5, 767-774.

QDB archive DOI: 10.15152/QDB.112   DOWNLOAD

QsarDB content

Property pEC3: Skin sensitisation potency in the local lymph node assay (LLNA) as log(1/EC3) [log(L/mol)]

Compounds: 33 | Models: 4 | Predictions: 4

Eq1: Initial correlation with all data

Regression model (regression)

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Name Type n

R2

σ

Training set training 30 0.018 0.470
Eq2: Statistical outliers (ID: 1,19,20,21) eliminated

Regression model (regression)

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Name Type n

R2

σ

Training set training 26 0.427 0.212
Eq3: Correlation improved with surface area descriptor

Regression model (regression)

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Name Type n

R2

σ

Training set training 26 0.596 0.178
Eq4: Final correlation (eliminated ID: 28)

Regression model (regression)

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Name Type n

R2

σ

Training set training 25 0.794 0.128

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Title: Enoch, S. J.; Roberts, D. W. Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations. Chem. Res. Toxicol. 2013, 26, 5, 767-774.
Abstract: This study outlines the development of a series of quantitative mechanistic models enabling skin sensitization potency in the LLNA to be predicted for direct acting Michael acceptors. These models utilized several computational descriptors based on knowledge of the Michael addition reaction mechanism. The key descriptor was calculated using density functional theory and modeled the stability of the reaction intermediate. A second descriptor relating to the available surface area at the site of the reaction was also found to be important. Several poorly predicted compounds were identified, and in all cases, these could be rationalized mechanistically. The analysis of these compounds allowed a well-defined mechanistically driven applicability domain to be developed. The study showed thatin silico quantitative mechanistic models, with a well-defined applicability domain, can be used to predict skin sensitization potency in the LLNA. The approach presented has the potential to be of use as part of a weight of evidence approach for predicting skin sensitization without the use of animals in risk assessment.
URI: http://hdl.handle.net/10967/112
http://dx.doi.org/10.15152/QDB.112
Date: 2014-03-27


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