Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata

References

• R.P. Schwarzenbach, B.I. Escher, K. Fenner, T.B. Hofstetter, C.A. Johnson, U. von Gunten, and B. Wehrli, The challenge of micropollutants in aquatic systems, Science 313 (2006), pp. 1072–1077.
   Google Scholar
• S.M. Rappaport and M.T. Smith, Epidemiology. Environment and disease risks, Science 330 (2010), pp. 460–461.
   Google Scholar
• A. Prüss-Ustün, C. Vickers, P. Haefliger, and R. Bertollini, Knowns and unknowns on burden of disease due to chemicals: A systematic review, Environ. Health 10 (2011), p. 9.
   Web of Science ®Google Scholar
• Council of the European Union, Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC, 2006.
   Google Scholar
• U.S. Congress, Frank R. Lautenberg Chemical Safety for the 21st Century Act, Public Law No. 114–182, 2016.
   Google Scholar
• European Commission, Technical Guidance Document on Risk Assessment Part II Chapter 3 Environmental Risk Assessment, Ispra: European Commission, 2003
   Google Scholar
• C.J. van Leeuwen‎ and T.G. Vermeire, Risk Assessment of Chemicals: An Introduction 2nd edition, Springer, Dordrecht, 2007.
   Google Scholar
• M.L. Card, V. Gomez-Alvarez, W.H. Lee, D.G. Lynch, N.S. Orentas, M.T. Lee, E.M. Wong, and R.S. Boethling, History of EPI Suite™ and future perspectives on chemical property estimation in US Toxic Substances Control Act new chemical risk assessments, Environ. Sci. Process. Impacts 19 (2017), pp. 203–212.
   PubMed Web of Science ®Google Scholar
• F.S. Collins, G.M. Gray, and J.R. Bucher, Toxicology. Transforming environmental health protection, Science 319 (2008), pp. 906–907.
   Google Scholar
• T. Hartung, Toxicology for the twenty-first century, Nature 460 (2009), pp. 208–212.
   Google Scholar
• R. Kavlock and D. Dix, Computational toxicology as implemented by the U.S. EPA: Providing high throughput decision support tools for screening and assessing chemical exposure, hazard and risk, J. Toxicol. Environ. Health B. Crit. Rev. 13 (2010), pp. 197–217.
   Google Scholar
• G Schüürmann, R.U. Ebert, and R. Kühne, Quantitative read-across for predicting the acute fish toxicity of organic compounds, Environ. Sci. Technol. 45 (2011), pp. 4616–4622.
   PubMed Web of Science ®Google Scholar
• S. Cassani, S. Kovarich, E. Papa, P.P. Roy, L. van der Wal, and P. Gramatica, Daphnia and fish toxicity of (benzo)triazoles: Validated QSAR models, and interspecies quantitative activity–activity modelling, J. Hazard. Mater. 258–259 (2013), pp. 50–60.
   PubMed Web of Science ®Google Scholar
• R. Kühne, R.U. Ebert, P.C. von der Ohe, N. Ulrich, W. Brack, and G. Schüürmann, Read-across prediction of the acute toxicity of organic compounds toward the water flea Daphnia magna, Mol. Inform. 32 (2013), pp. 108–120.
   Google Scholar
• F.S. Lyakurwa, X.H. Yang, X.H. Li, X.L. Qiao, and J.W. Chen, Development and validation of theoretical linear solvation energy relationship models for toxicity prediction to fathead minnow (Pimephales promelas), Chemosphere 96 (2014), pp. 188–194.
   Google Scholar
• J. Villain, S. Lozano, M.P. Halm-Lemeille, G. Durrieu, and R. Bureau, Quantile regression model for a diverse set of chemicals: Application to acute toxicity for green algae, J. Mol. Model. 20 (2014), p. 2508.
   PubMed Web of Science ®Google Scholar
• H. Liu, P. Sun, H.X. Liu, S.G. Yang, L.S. Wang, and Z.Y. Wang, Acute toxicity of benzophenone-type UV filters for Photobacterium phosphoreum and Daphnia magna: QSAR analysis, interspecies relationship and integrated assessment, Chemosphere 135 (2015), pp. 182–188.
   Google Scholar
• R. Aalizadeh, P.C. von der Ohe, and N.S. Thomaidis, Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by ant colony optimization-support vector machine QSTR models. Environ. Sci. Process. Impacts 19 (2017), pp. 438–448.
   PubMed Web of Science ®Google Scholar
• S. Önlü and M.T. Saçan, An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals, Environ. Toxicol. Chem. 36 (2017), pp. 1012–1019.
   PubMed Web of Science ®Google Scholar
• S. Bakire, X.Y. Yang, G.C. Ma, X.X. Wei, H.Y. Yu, J.R. Chen, and H.J. Lin, Developing predictive models for toxicity of organic chemicals to green algae based on mode of action, Chemosphere 190 (2018), pp. 463–470.
   Google Scholar
• J.J. Li, X.J. Zhang, Y. Yang, T. Huang, C. Li, L. Su, Y.H. Zhao, and M.T.D. Cronin, Development of thresholds of excess toxicity for environmental species and their application to identification of modes of acute toxic action, Sci. Total. Environ. 616-617 (2018), pp. 491–499.
   PubMed Web of Science ®Google Scholar
• L. Claeys, F. Iaccino, C.R. Janssen, P. Van Sprang, and F. Verdonck, Development and validation of a quantitative structure-activity relationship for chronic narcosis to fish, Environ. Toxicol. Chem. 32 (2013), pp. 2217–2225.
   PubMed Web of Science ®Google Scholar
• T.J. Austin and C.V. Eadsforth, Development of a chronic fish toxicity model for predicting sub-lethal NOEC values for non-polar narcotics, SAR QSAR Environ. Res. 25 (2014), pp. 147–160.
   Google Scholar
• D. Fan, J.N. Liu, L. Wang, X.H. Yang, S.H. Zhang, Y. Zhang, and L.L. Shi, Development of quantitative structure-activity relationship models for predicting chronic toxicity of substituted benzenes to Daphnia magna, Bull. Environ. Contam. Toxicol. 96 (2016), pp. 664–670.
   PubMed Web of Science ®Google Scholar
• M.T. Cronin, (Q)SARs to predict environmental toxicities: Current status and future needs, Environ. Sci. Process. Impacts 19 (2017), pp. 213–220.
   PubMed Web of Science ®Google Scholar
• OECD, Guidance Document On The Validation Of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models. OECD Environment Health and Safety Publications Series on Testing and Assessment No. 69, Paris: OECD, 2007.
   Google Scholar
• United Nations, Globally Harmonized System of Classification and Labelling of Chemicals (GHS), Seventh revised edition GHS (Rev.7), New York and Geneva: United Nations, 2017.
   Google Scholar
• MOPAC, James J. P. Stewart, Stewart Computational Chemistry, Colorado Springs, CO, USA, http://OpenMOPAC.net, 2016.
   Google Scholar
• Talete srl, Dragon (Software for Molecular Descriptor Calculation) Version 6.0. http://www.Talete.Mi.It/, 2012.
   Google Scholar
• H.H. Liu, X.H. Yang, and R. Lu, Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin, Chemosphere 156 (2016), pp. 1–7.
   Google Scholar
• U.S. EPA, Estimation Programs Interface Suite™ for Microsoft Windows, v 4.10. United States Environmental Protection Agency, Washington, DC, USA, 2012.
   Google Scholar
• H.H. Liu, X.H. Yang, C. Yin, M.B. Wei, X. He, Development of predictive models for predicting binding affinity of endocrine disrupting chemicals to fish sex hormone-binding globulin. Ecotoxicol. Environ. Saf. 136 (2017), pp. 46–54.
   PubMed Web of Science ®Google Scholar
• C. Yin, X.H. Yang, M.B. Wei, and H.H. Liu, Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor, Environ. Sci. Pollut. Res. 24 (2017), pp. 20063–20071.
   PubMed Web of Science ®Google Scholar
• J.Y. He, T. Peng, X.H. Yang, and H.H. Liu, Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor, Ecotoxicol. Environ. Saf. 148 (2018), pp. 211–219.
   PubMed Web of Science ®Google Scholar
• F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, Scikit-learn: Machine learning in Python, J. Mach. Learn. Res. 12 (2011), pp. 2825–2830.
   Web of Science ®Google Scholar
• S. Kovarich, E. Papa, and P. Gramatica, QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants, J. Hazard. Mater. 190 (2011), pp. 106–112.
   PubMed Web of Science ®Google Scholar
• L. Sun, C. Zhang, Y.J. Chen, S.L. Zhuang, W.H. Li, P.W. Lee, and Y. Tang, In silico prediction of chemical aquatic toxicity with chemical category approaches and structural alerts, Toxicol. Res. 4 (2015), pp. 452–463.
   Google Scholar
• P. Dao, K. Wang, C. Collins, M. Ester, A. Lapuk, and S.C. Sahinalp, Optimally discriminative subnetwork markers predict response to chemotherapy, Bioinformatics 27 (2011), pp. i205–i213.
   Google Scholar
• F. Como, E. Carnesecchi, S. Volani, J.L. Dorne, J. Richardson, A. Bassan, M. Pavan, and E. Benfenati, Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model, Chemosphere 166 (2017), pp. 438–444.
   Google Scholar
• T. Fawcett, An introduction to ROC analysis, Pattern Recogn. Lett. 27 (2006), pp. 861–874.
   Google Scholar
• P. Gramatica, Principles of QSAR models validation: Internal and external, QSAR Comb. Sci. 26 (2007), pp. 694–701.
   Web of Science ®Google Scholar
• R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics, 2nd ed, Wiley-VCH, Weinheim, Germany, 2009, pp. 460−461.
   Google Scholar
• K. Khan and K. Roy, Ecotoxicological modelling of cosmetics for aquatic organisms: A QSTR approach, SAR QSAR Environ. Res. 28 (2017), pp. 567–594.
   Google Scholar
• G. Tugcu, M.T. Saçan, M. Vračko, M. Novič, and N. Minovski, QSTR modelling of the acute toxicity of pharmaceuticals to fish, SAR QSAR Environ. Res. 23 (2012), pp. 297–310.
   Google Scholar
• O. Devinyak, D. Havrylyuk, and R. Lesyk, 3D-MoRSE descriptors explained, J. Mol. Graph. Model. 54 (2014), pp. 194–203.
   PubMed Web of Science ®Google Scholar
• M.C. Hemmer, V. Steinhauer, and J. Gasteiger, Deriving the 3D structure of organic molecules from their infrared spectra, Vib. Spectrosc. 19 (1999), pp. 151–164.
   Web of Science ®Google Scholar
• M.P. González, J. Caballero, A.M. Helguera, M. Garriga, G. González, and M. Fernández, 2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors, Bull. Math. Biol. 68 (2006), pp. 735–751.
   Google Scholar
• P. Yang, J.W. Chen, S. Chen, X. Yuan, K.W. Schramm, and A. Kettrup, QSPR models for physicochemical properties of polychlorinated diphenyl ethers, Sci. Total. Environ. 305 (2003), pp. 65–76.
   PubMed Web of Science ®Google Scholar