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

Shijin Ren
Author Information
  1. Shijin Ren: Center for Environmental Biotechnology, 676 Dabney Hall, University of Tennessee, Knoxville, Tennessee 37996-1605, USA. sjren@utk.edu

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.

MeSH Term

Animals
Models, Statistical
Phenol
Predictive Value of Tests
Quantitative Structure-Activity Relationship
Tetrahymena pyriformis
Toxicology

Chemicals

Phenol

Word Cloud

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