REPOSITORY ABOUT GUIDELINES CITING BLOG

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.

QsarDB Repository

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)]

Eq1: Initial correlation with all data

Regression model (regression)

Open in:QDB Explorer QDB Predictor

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)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

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

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

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

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 25 0.794 0.128

Citing

When using this QDB archive, please cite (see details) it together with the original article:

  • Piir, G. Data for: Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations. QsarDB repository, QDB.112. 2014. http://dx.doi.org/10.15152/QDB.112

  • 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, 767-774. http://dx.doi.org/10.1021/tx4000655

Metadata

Show simple item record

dc.date.accessioned 2014-03-27T18:29:26Z
dc.date.available 2014-03-27T18:29:26Z
dc.date.issued 2014-03-27
dc.identifier.uri http://hdl.handle.net/10967/112
dc.identifier.uri http://dx.doi.org/10.15152/QDB.112
dc.description.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.
dc.publisher Geven Piir
dc.rights Attribution 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.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.
qdb.property.endpoint 4. Human health effects 4.6. Skin sensitisation en_US
qdb.property.species Mus musculus (Mouse)
qdb.descriptor.application Gaussian 09 en_US
qdb.descriptor.application Chimera en_US
qdb.prediction.application Minitab 16 en_US
bibtex.entry article en_US
bibtex.entry.author Enoch, S. J.
bibtex.entry.author Roberts, D. W.
bibtex.entry.doi 10.1021/tx4000655 en_US
bibtex.entry.journal Chem. Res. Toxicol. en_US
bibtex.entry.number 5 en_US
bibtex.entry.pages 767-774 en_US
bibtex.entry.title Predicting Skin Sensitization Potency for Michael Acceptors in the LLNA Using Quantum Mechanics Calculations en_US
bibtex.entry.volume 26 en_US
bibtex.entry.year 2013
qdb.model.type Regression model (regression) en_US


Files in this item

Name Description Format Size View
2013CRT767.qdb.zip Linear model for Michael acceptors application/zip 29.04Kb View/Open
Files associated with this item are distributed
under Creative Commons license.

This item appears in the following Collection(s)

Show simple item record