Qsar investigation of a large data set for fish, algae and <i>Daphnia</i> toxicity

Lessigiarska, Iglika; Worth, Andrew; Sokull-Klüttgen, Birgit; Jeram, Sonja; Dearden, John C.; Netzeva, Tatiana I.; Cronin, Mark T.D.

doi:10.1080/10629360412331297416

Cited by 30 publications

(14 citation statements)

References 9 publications

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“…High-quality data in the training set is an essential requisite for the development of reliable QSARs [29][30][31][32]. This broad uncertainty in the experimental values obviously reduces the possibility of high correlation coefficients for the predicted values, and this partially explains the low statistical values for D. magna.…”

Section: Resultsmentioning

confidence: 99%

Comparison ofin silicomodels for prediction ofDaphnia magnaacute toxicity

Golbamaki

Cassano

Lombardo

et al. 2014

SAR and QSAR in Environmental Research

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Eight in silico modelling packages were evaluated and compared for the prediction of Daphnia magna acute toxicity from the viewpoint of the European legislation on chemicals, REACH. We tested the following models: Discovery Studio (DS) TOPKAT, ACD/Tox Suite, ADMET Predictor, ECOSAR (Ecological Structure Activity Relationships), TerraQSAR, T.E.S.T. (Toxicity Estimation Software Tool) and two models implemented in VEGA on 480 industrial compounds for 48-h median lethal concentrations (LC50) to D. magna, matching them with experimental values. The quality of the estimates was compared using a standard statistical review and an additional classification approach in which the hazard predictions were grouped using well-defined regulatory criteria. The regression parameters, correlation coefficient being the most influential, showed that four models (ADMET Predictor, DS TOPKAT, TerraQSAR and VEGA DEMETRA) had similar reliability. These performed better than the others, but the coefficient of determination was still low (r2 around 0.6), considering that at least half the predicted compounds were inside the training sets. Additionally, we grouped the results in four defined toxicity classes. TerraQSAR™ gave 60% of correct classifications, followed by DS TOPKAT, ADMET Predictor™ and VEGA DEMETRA, with 56%, 54% and 48%, respectively. These results highlight the challenges associated with developing reliable and easily applied acceptability criteria for the regulatory use of QSAR models to D. magna acute toxicity.

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Section: Resultsmentioning

confidence: 99%

Comparison ofin silicomodels for prediction ofDaphnia magnaacute toxicity

Golbamaki

Cassano

Lombardo

et al. 2014

SAR and QSAR in Environmental Research

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“…Table summarizes published QSAR models for aquatic toxicity to D. magna . However, as the majority of these models are based on highly homogeneous data sets, their utility for prediction is severely limited.…”

Section: Introductionmentioning

confidence: 99%

Partial least square and k‐nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity

Stoyanova-Slavova

Slavov

Pearce

et al. 2014

Enviro Toxic and Chemistry

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A diverse set of 154 chemicals that included US Food and Drug Administration-regulated compounds tested for their aquatic toxicity in Daphnia magna were modeled by a 3-dimensional quantitative spectral data-activity relationship (3D-QSDAR). Two distinct algorithms, partial least squares (PLS) and Tanimoto similarity-based k-nearest neighbors (KNN), were used to process bin occupancy descriptor matrices obtained after tessellation of the 3D-QSDAR space into regularly sized bins. The performance of models utilizing bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å was explored. Rigorous quality-control criteria were imposed: 1) 100 randomized 20% hold-out test sets were generated and the average R(2) test of the respective models was used as a measure of their performance, and 2) a Y-scrambling procedure was used to identify chance correlations. A consensus between the best-performing composite PLS model using 0.5 Å × 14 ppm × 14 ppm bins and 10 latent variables (average R(2) test = 0.770) and the best composite KNN model using 0.5 Å × 8 ppm × 8 ppm and 2 neighbors (average R(2) test = 0.801) offered an improvement of about 7.5% (R(2) test consensus = 0.845). Projection of the most frequently occurring bins on the standard coordinate space indicated that the presence of a primary or secondary amino group-substituted aromatic systems-would result in an increased toxic effect in Daphnia. The presence of a second aromatic ring with highly electronegative substituents 5 Å to 7 Å apart from the first ring would lead to a further increase in toxicity.

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“…One of the most important questions discussed in the literature is that of the comparative toxicological sensitivities of different biological objects [5]. One approach addressing this is provided by correlation analysis of toxicity in identical series of compounds [17,18]. Thus, similarities between biological objects are generally evaluated using correlation coefficients.…”

Section: Methodsmentioning

confidence: 99%

Recurrent model of acute toxicity in homologous series of organic compounds

Raevskii

2011

Pharm Chem J

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A simple recurrent equation is proposed as a model describing acute toxicity in homologous series of organic compounds in relation to various biological objects. The recurrent model of toxicity provided better extrapolation than linear and parabolic models. Plotting of The coefficients of recurrent equations plotted on planes defined by these coefficients for types of chemical compounds allowed the nonspecific or specific nature of toxicity in homologous series to be identified. Similar visualization in relation to different types of biological objects allowed toxicological relatedness to be identified on the basis of the distances between them. These results may provide grounds for creating computer programs predicting the acute toxicities of new chemical compounds on the basis of QSAR approaches.Notes. V.f. = Vibrio fisheri (log(1/EC 50 ), mM; T.p. = Tetrahymena pyriformis (log(1/IGC 50 ), mM); P.r. = Poecilia reticulata (log(1/LC 50 ), mM; P.p. = Pimephales promelas (log(1/LC 50 ), mM); M = mice (log(1/LD 50 ), mmol/kg); R = rats (log(1/LD 50 ), mmol/kg). *Interpolated values.

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Qsar investigation of a large data set for fish, algae and Daphnia toxicity

Cited by 30 publications

References 9 publications

Comparison ofin silicomodels for prediction ofDaphnia magnaacute toxicity

Comparison ofin silicomodels for prediction ofDaphnia magnaacute toxicity

Partial least square and k‐nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity

Recurrent model of acute toxicity in homologous series of organic compounds

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