Literature DB >> 14512109

Ecotoxicity prediction using mechanism- and non-mechanism-based QSARs: a preliminary study.

Shijin Ren1.   

Abstract

In ecotoxicology, mechanism-based quantitative structure-activity relationships (QSARs) are usually developed with higher quality than QSARs without regard to toxicity mechanism. Correctly determining the mechanism of a compound, which is not always easy, is required to use mechanism-based QSARs for toxicity prediction. The mechanism determination step may introduce extra errors in addition to the intrinsic prediction errors of mechanism-based QSARs, thus compromising these QSARs' performance compared with QSARs regardless of mechanism. In this study, the mechanism identification-toxicity prediction (MI-TP) approach was compared with the direct toxicity prediction (DTP) approach using a data set containing phenol toxicity to Tetrahymena pyriformis. A statistical mechanism classification model for mechanism prediction, four mechanism-based QSARs and a single QSAR without discriminating between mechanisms were developed for toxicity prediction. Toxicity of phenols in an external data set was predicted following the MI-TP and DTP approaches. Results indicated that the mechanisms of several phenols in the external test set were incorrectly predicted which led to significant over- or under-estimation of their toxicity. Overall, the MI-TP approach did not yield more accurate toxicity prediction than the DTP approach.

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Year:  2003        PMID: 14512109     DOI: 10.1016/S0045-6535(03)00573-3

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


  3 in total

1.  Prediction of toxicity using a novel RBF neural network training methodology.

Authors:  Georgia Melagraki; Antreas Afantitis; Kalliopi Makridima; Haralambos Sarimveis; Olga Igglessi-Markopoulou
Journal:  J Mol Model       Date:  2005-11-08       Impact factor: 1.810

2.  Ecotoxicological modeling and risk assessment using chemometric tools.

Authors:  Kunal Roy
Journal:  Mol Divers       Date:  2006-05       Impact factor: 2.943

3.  Discrimination between modes of toxic action of phenols using rule based methods.

Authors:  Ulf Norinder; Per Lidén; Henrik Boström
Journal:  Mol Divers       Date:  2006-05-24       Impact factor: 2.943

  3 in total

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