Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March 2009 version

Furuhama, Ayako; Toida, Takashi; Nishikawa, Nozomu; Aoki, Yasunobu; Yoshioka, Yoshitada; Shiraishi, Hiroaki

doi:10.1080/1062936x.2010.501815

Cited by 29 publications

(18 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…two of the outlier chemicals of the QSAAR (see Table 3). The combination QSAAR with the C-judgement structural domain of the KATE system [31] would be helpful to define further structural applicability domains. Furthermore, QSAAR prediction can be used to detect prediction errors in publically available QSAR software, such as ECOSAR [47] and KATE [30,31], at least for chemicals with mw values of ≤364.9.…”

Section: Further Discussionmentioning

confidence: 99%

“…Although the fish and algae toxicities were somewhat correlated with daphnia toxicity (r 2 = 0.55 and 0.51, respectively; Figure 2), the correlations were not high enough to construct interspecies QSARs for toxicity prediction. In the KAshinhou Tool for Ecotoxicity (KATE) system for predicting ecotoxicity [30,31], QSAR equations with r 2 values of >0.7, root mean square error (RMSE) values ≤0.5 and n values >5 gave acceptable robustness values (q 2 , which is the leave-one-out version of the squared correlation coefficient). Therefore, we added some descriptors to the interspecies QSARs to develop QSAARs.…”

Section: Descriptors Analyses and Evaluationsmentioning

confidence: 99%

“…The data for these 10 chemicals were only recently published on the website of the Japanese Ministry of the Environment [19], so they were not collected in the datasets at the time when this study was started and were not used in the reference datasets of the KATE system [31] (the latest version of which was published in March 2011 and is available in Japanese at http://kate.nies.go.jp).…”

Section: Descriptors Analyses and Evaluationsmentioning

confidence: 99%

See 2 more Smart Citations

Interspecies quantitative structure–activity–activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols

Furuhama

Hasunuma²,

Aoki

2015

SAR and QSAR in Environmental Research

Self Cite

View full text Add to dashboard Cite

We propose interspecies quantitative structure-activity-activity relationships (QSAARs), that is, QSARs with descriptors, to estimate species-specific acute aquatic toxicity. Using training datasets consisting of more than 100 aromatic amines and phenols, we found that the descriptors that predicted acute toxicities to fish (Oryzias latipes) and algae were daphnia toxicity, molecular weight (an indicator of molecular size and uptake) and selected indicator variables that discriminated between the absence or presence of various substructures. Molecular weight and the selected indicator variables improved the goodness-of-fit of the fish and algae toxicity prediction models. External validations of the QSAARs proved that algae toxicity could be predicted within 1.0 log unit and revealed structural profiles of outlier chemicals with respect to fish toxicity. In addition, applicability domains based on leverage values provided structural alerts for the predicted fish toxicity of chemicals with more than one hydroxyl or amino group attached to an aromatic ring, but not for fluoroanilines, which were not included in the training dataset. Although these simple QSAARs have limitations, their applicability is defined so clearly that they may be practical for screening chemicals with molecular weights of ≤364.9.

show abstract

Section: Further Discussionmentioning

confidence: 99%

Section: Descriptors Analyses and Evaluationsmentioning

confidence: 99%

Section: Descriptors Analyses and Evaluationsmentioning

confidence: 99%

See 1 more Smart Citation

Interspecies quantitative structure–activity–activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols

Furuhama

Hasunuma²,

Aoki

2015

SAR and QSAR in Environmental Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…TG201 (OECD, 2006), TG202 (OECD, 2004), and TG203 (OECD, 1992)). Countries outside Europe have also adopted similar regulations: in Japan for instance, the Ministry of the Environment is responsible for evaluating toxicity of chemicals on ecosystems, and recommends the use of tests involving aquatic organisms such as Oryzias latipes (fishes) or Daphnia magna (daphnia), in addition to algae data (Furuhama et al, 2010). The choice of these three trophic levels (primary producers, primary and secondary consumers) is considered to be relevant in order to protect aquatic ecosystems (Wei et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Exploring an ecotoxicity database with the OECD (Q)SAR Toolbox and DRAGON descriptors in order to prioritise testing on algae, daphnids, and fish

Tebby

Mombelli

Pandard

et al. 2011

Science of The Total Environment

View full text Add to dashboard Cite

The European regulation on chemicals (REACh) places emphasis on reduction of systematic toxicity testing, thus fostering development of alternative methods. Consequently, we analysed acute toxicity data gathered by the Japanese Ministry of Environment for three species belonging to three different trophic levels (i.e., Pseudokirchneriella subcapitata 72-hour EC50, Daphnia magna 48-hour EC50 and Oryzias latipes 96-hour LC50). This paper investigates the relationships between the chemical structure and both the toxicity of the chemicals and the cross-species differences in sensitivity. The physicochemical properties of the chemicals were represented by the categories they belonged to in several widely-used categorisation schemes implemented by the freely available OECD (Q)SAR Toolbox and by quantitative molecular descriptors using DRAGON software. The outputs of these software products were analysed and compared in terms of quality of prediction and biological interpretation. Amongst the categorisations implemented by the OECD Toolbox, those focussing on bioaccumulation or biotransformation appeared to be the most interesting in terms of environmental prediction on a whole set of chemicals, in particular as the predicted biotransformation half-life is strongly dependent on hydrophobicity. In predicting toxicity towards each species, simple linear regression on logP performed better than PLS regression of toxicity on a very large set of molecular descriptors. However, the predictions based on the interspecies correlations performed better than the QSAR predictions. The results in terms of cross-species comparisons encourage the use of test strategies focussing on reducing the number of tests on fish.

show abstract

“…However, this tool suffers from the lack of external validations and model performance data of the test sets.KAshinhou Tool for Ecotoxicity (KATE) on PAS 2011 estimates acute aquatic toxicity via the Mayer-Overton relationship for chemicals within a total of 40 structural chemical classes[32]. KATE is trained on the US EPA fathead minnow (Pimephales promelas) and the Japanese Ministry of Environment Oryzias latipes datasets[33]. The tool is available as a standalone application or as a web plug-in.…”

mentioning

confidence: 99%

Comparison of Seven in Silico methods for Evaluating of Ecotoxicological Acute Toxicity of Daphnia magna and Pimephales promelas: Case study on Chinese Priority Controlled Chemicals

Zhou¹,

Fan²,

Yin³

et al. 2019

Preprint

View full text Add to dashboard Cite

Background: The acute toxicity on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). Currently, a number of predictive models for aquatic toxicity are available, however, the accuracy of in silico tools in priority assessment and risk assessment still remains to be further studied. Herein, this study evaluated the performance of seven Quantitative Structure–Activity Relationship (QSAR) in silico methods (Danish QSAR Database, Ecological Structure Activity Relationships, KAshinhou Tool for Ecotoxicity on PAS, Toxicity Estimation Software Tool, QSAR Toolbox, Read Across, and Virtual models for property Evaluation of chemicals within a Global Architecture) for assessing acute aquatic toxicity to Daphnia magna and Pimephales promelas using the first batch list of Priority Controlled Chemicals in China. Results: Based on the values for the median lethal dose and the US Environmental Protection Agency’s acute aquatic toxicity categories of concern, the acute toxicity grade was classified into six categories. According to the comparative prediction results, the accuracy of the Daphnia magna toxicity categories prediction was 25%–56%, the correlation coefficient ranged from 0.1236 to 0.6349, and the correlation coefficients of the applicability domain were 0.040 and 0.5148. The corresponding values for the Pimephales promelas toxicity categories prediction were 22%–44%, 0.1495–0.4144, 0.2156 and 0.6793. Conclusion: As the structure of chemicals of first batch list of Priority Controlled Chemicals in China are complex, the accuracy of model prediction is low, which depends on the quality of the constructed model and application domain. Although in silico methods can be used to preliminarily estimate aquatic toxicity, experimental data validation is still required for prioritizing environmental hazards assessments and risk assessments.

show abstract

Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March 2009 version

Cited by 29 publications

References 12 publications

Interspecies quantitative structure–activity–activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols

Interspecies quantitative structure–activity–activity relationships (QSAARs) for prediction of acute aquatic toxicity of aromatic amines and phenols

Exploring an ecotoxicity database with the OECD (Q)SAR Toolbox and DRAGON descriptors in order to prioritise testing on algae, daphnids, and fish

Comparison of Seven in Silico methods for Evaluating of Ecotoxicological Acute Toxicity of Daphnia magna and Pimephales promelas: Case study on Chinese Priority Controlled Chemicals

Contact Info

Product

Resources

About