The derivation of quantitative correlations between skin sensitisation and physio-chemical parameters for alkylating agents, and their application to experimental data for sultones

Roberts, David W.; Williams, D. L.

doi:10.1016/0022-5193(82)90199-0

Cited by 146 publications

(118 citation statements)

References 5 publications

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“…1) (Andersen 1986). Previously reported guinea data for picryl chloride and dinitrochlorobenzene (Macher & Chase 1969), sultones (Roberts & Williams 1982), and p-nitrobenzyl compounds (Roberts et al 1983) showed similar results. Stadler & Karol (1985) found a non-linear dose-response relationship for dicyclohexylmethane-4,4'-disocyanate and picryl chloride in BALB/cBy mice.…”

Section: Dosage and Concentrationsupporting

confidence: 73%

Testing for Contact Allergy in Experimental Animals

Andersen

1987

Pharmacology & Toxicology

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Section: Dosage and Concentrationsupporting

confidence: 73%

Testing for Contact Allergy in Experimental Animals

Andersen

1987

Pharmacology & Toxicology

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“…This model has been used to evaluate data on various sets of skin sensitizing chemicals, including alkylating agents 8 , sulfonate esters and acrylates 9 , α,β-diketones 10 , aldehydes 11 and other classes of chemicals. 12 Predictive studies of skin sensitization can be divided into two different major categories: mechanism-based and data correlation-based. Most of work carried out until the early 1990's belong to the former category.…”

Section: Introductionmentioning

confidence: 99%

4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on the Classification of Local Lymph Node Assay Measures

Tseng

Pan

et al. 2006

Chem. Res. Toxicol.

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Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, , are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms.

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“…An example of both is described here. The first physicochemical mathematical model for skin sensitisation was the RAI (Relative Alkylation Index) model (Roberts and Williams, 1982). This index quantifies the relative extent of sensitiser binding to the skin protein as a function of the dose given, the chemical reactivity (which could be expressed in the terms of the measured rate constants for reaction with a model nucleophile, in terms of Taft or Hammett substituent constants or in terms of computed molecular orbital indices) and hydrophobicity expressed as the octanol/water partition coefficient.…”

mentioning

confidence: 99%

Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models

2014

OECD Series on Testing and Assessment

163

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The derivation of quantitative correlations between skin sensitisation and physio-chemical parameters for alkylating agents, and their application to experimental data for sultones

Cited by 146 publications

References 5 publications

Testing for Contact Allergy in Experimental Animals

Testing for Contact Allergy in Experimental Animals

4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on the Classification of Local Lymph Node Assay Measures

Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models

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