Literature DB >> 12489717

Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis.

Mark T D Cronin1, Aynur O Aptula, Judith C Duffy, Tatiana I Netzeva, Philip H Rowe, Iva V Valkova, T Wayne Schultz.   

Abstract

Quantitative structure-activity relationships (QSARs) for the toxicity of 200 phenols to the ciliated protozoan Tetrahymena pyriformis, and the validation of the QSARs using a test set of a further 50 compounds, are reported. The phenols are structurally heterogeneous and represent a variety of mechanisms of toxic action including polar narcosis, weak acid respiratory uncoupling, electrophilicity, and those compounds capable of being metabolised or oxidised to quinones. For each compound, a total of 108 physico-chemical descriptors have been calculated. A variety of methods were utilised to develop QSARs and are compared. The response-surface, or two parameter, approach was found to be successful, but only following the removal of compounds known to form quinones. Stepwise regression produced a seven parameter QSAR with good statistical fit, but was less interpretable and transparent than the response-surface. Partial least squares produced a good model for phenolic toxicity following supervised selection of parameters, this, however, was the least transparent of all approaches attempted. In all approaches, a large number of outliers were observed, typically these were compounds capable of being metabolised to quinones. The strengths and weaknesses of each of the approaches to predict the toxicity of the validation (test) set of phenols to T. pyriformis are discussed.

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Year:  2002        PMID: 12489717     DOI: 10.1016/s0045-6535(02)00508-8

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  12 in total

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7.  Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis.

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8.  Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse.

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9.  Inside of the Linear Relation between Dependent and Independent Variables.

Authors:  Lorentz Jäntschi; Lavinia L Pruteanu; Alina C Cozma; Sorana D Bolboacă
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10.  A novel approach for a toxicity prediction model of environmental pollutants by using a quantitative structure-activity relationship method based on toxicogenomics.

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