Literature DB >> 11712907

Parametrization of electrophilicity for the prediction of the toxicity of aromatic compounds.

M T Cronin1, N Manga, J R Seward, G D Sinks, T W Schultz.   

Abstract

The aim of this study was to determine which descriptor best parametrized the electrophilicity of aromatic compounds with regard to their acute toxicity. To achieve this, toxicity data for 203 substituted aromatic compounds containing a nitro- or cyano group were evaluated in the 40-h Tetrahymena pyriformis population growth impairment assay. Quantitative structure-activity relationships (QSARs) were developed relating toxic potency [log(IGC(50)(-1))] with hydrophobicity quantified by the 1-octanol/water partition coefficient (log P) and electrophilic reactivity quantified by the molecular orbital parameters, either the energy of the lowest unoccupied molecular orbital (E(LUMO)) or maximum acceptor superdelocalizability (A(max)) was developed. For the full data set, E(LUMO) and A(max) were collinear (r = 0.87). A comparison of the QSARs [log(IGC(50)(-1)) = 0.40 log P - 0.94E(LUMO) - 1.27; n = 203, r(2) = 0.60, s = 0.49, F = 151] and [log(IGC(50)(-1)) = 0.37 log P + 13.1A(max) - 4.30; n = 203, r(2) = 0.70, s = 0.42, F = 237] reveals A(max) to be the better electrophilic parameter for modeling these data. Analysis of outliers indicates a preponderance of 4-subsituted nitrophenols and nitroanilines. Smaller datasets (51 and 102 compounds) selected in order to reduce the collinearity between A(max) and E(LUMO) were also evaluated. Results indicate A(max) to be the superior descriptor of electrophilicity for the purpose of toxicological QSARs for aromatic compounds. Development of QSARs using partial least-squares yielded similar results.

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Year:  2001        PMID: 11712907     DOI: 10.1021/tx015502k

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  6 in total

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6.  Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.

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  6 in total

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