Literature DB >> 9463544

A quantitative structure-toxicity relationships model for the dermal sensitization guinea pig maximization assay.

K Enslein1, V K Gombar, B W Blake, H I Maibach, J J Hostynek, C C Sigman, D Bagheri.   

Abstract

We have developed quantitative structure-toxicity relationship (QSTR) models for assessing dermal sensitization using guinea pig maximization test (GPMT) results. The models are derived from 315 carefully evaluated chemicals. There are two models, one for aromatics (excluding one-benzene-ring compounds), and the other for aliphatics and one-benzene-ring compounds. For sensitizers, the models can resolve whether they are weak/moderate or severe sensitizers. The statistical methodology, based on linear discriminant analysis, incorporates an optimum prediction space (OPS) algorithm. This algorithm ensures that the QSTR model will be used only to make predictions on query structures which fall within its domain. Calculation of the similarities between a query structure and the database compounds from which the applicable model was developed are used to validate each skin sensitization assessment. The cross-validated specificity of the equations ranges between 81 and 91%, and the sensitivity between 85 and 95%. For an independent test set, specificity is 79%, and sensitivity 82%.

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Year:  1997        PMID: 9463544     DOI: 10.1016/s0278-6915(97)87277-8

Source DB:  PubMed          Journal:  Food Chem Toxicol        ISSN: 0278-6915            Impact factor:   6.023


  3 in total

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Authors:  Yi Li; Yufeng J Tseng; Dahua Pan; Jianzhong Liu; Petra S Kern; G Frank Gerberick; Anton J Hopfinger
Journal:  Chem Res Toxicol       Date:  2007-01       Impact factor: 3.739

Review 2.  In silico prediction of drug toxicity.

Authors:  John C Dearden
Journal:  J Comput Aided Mol Des       Date:  2003 Feb-Apr       Impact factor: 3.686

3.  Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules.

Authors:  Konda Leela Sarath Kumar; Sujit R Tangadpalliwar; Aarti Desai; Vivek K Singh; Abhay Jere
Journal:  PLoS One       Date:  2016-06-07       Impact factor: 3.240

  3 in total

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