QSAR study of the acute toxicity to fathead minnow based on a large dataset

References

• OECD, Guidance document on the validation of (Quantitative) structure–activity relationships [(Q)SAR] models, Organisation for Economic Co-Operation and Development, Paris, France, 2007.
   Google Scholar
• C.L. Russom, S.P. Bradbury, S.J. Broderius, R.A. Drummond, and D.E. Hammermeister, Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas), Environ. Toxicol. Chem. 16 (1997), pp. 948–967.
   Web of Science ®Google Scholar
• H.J.M. Verhaar, C.J. Van Leeuwen, and J.L.M. Hermens, Classifying environmental pollutants. 1: Structure-activity relationships for prediction of aquatic toxicity, Chemosphere 25 (1992), pp. 471–491.
   Web of Science ®Google Scholar
• H.J.M. Verhaar, J. Solbe′, J. Speksnijder, C.J. Van Leeuwen, and J.L.M. Hermens, Classifying environmental pollutants: Part 3, External validation of the classification system, Chemosphere 40 (2000), pp. 875–883.
   Google Scholar
• Toxtree Version 2.6.0, software available at http://toxtree.sourceforge.net/
   Google Scholar
• S. Ren, Ecotoxicity prediction using mechanism- and nonmechanism-based QSARs: A preliminary study, Chemosphere 53 (2003), pp. 1053–1065.
   PubMed Web of Science ®Google Scholar
• T.I. Netzeva, M. Pavan, and A.P. Worth, Review of (quantitative) structure-activity relationships for acute aquatic toxicity, QSAR Comb. Sci. 27 (2008), pp. 77–90.
   Google Scholar
• T.M. Martin, D.M. Young, C.R. Lilavois, and M.G. Barron, Comparison of global and mode of action-based models for aquatic toxicity, SAR QSAR Environ. Res. 26 (2015), pp. 245–265.
   PubMed Web of Science ®Google Scholar
• S.C. Basak and V.R. Magnuson, Molecular topology and narcosis. A quantitative structure-activity relationship (QSAR) study of alcohols using complementary information content (CIC), Arzneim. -Forschung, Drug Res. 33 (1983), pp. 501–503.
   PubMed Web of Science ®Google Scholar
• S.C. Basak, D.P. Gieschen, and V.R. Magnuson, A quantitative correlation of the LC50 values of esters in Pimephales promelas using physicochemical and topological parameters, Environ. Toxicol. Chem. 3 (1984), pp. 191–199.
   Web of Science ®Google Scholar
• L.D. Newsome, D.E. Johnson, R.L. Lipnick, S.J. Broderius, and C.L. Russom, A QSAR study of the toxicity of amines to the fathead minnow, Sci. Total Environ. 109 (1991), pp. 537–551.
   PubMed Web of Science ®Google Scholar
• G.D. Veith and S.J. Broderius, Structure-toxicity relationships for industrial chemicals causing type (II) narcosis syndrome, in QSAR in Environmental Toxicology, K.L.E. Kaiser, ed., Springer, The Netherlands, 1987, pp. 385–391.
   Google Scholar
• G.D. Veith, D.J. Call, and L.T. Brooke, Structure-toxicity relationships for the fathead minnow, Pimephales promelas: Narcotic industrial chemicals, Can. J. Fish. Aquat. Sci. 40 (1983), pp. 743–748.
   Web of Science ®Google Scholar
• A.P. Bearden and T.W. Schultz, Structure-activity relationships for Pimephales and Tetrahymena: A mechanism of action approach, Environ. Toxicol. Chem. 16 (1997), pp. 1311–1317.
   Web of Science ®Google Scholar
• M. Nendza and M. Müller, Discriminating toxicant classes by mode of action: 2. Physico-chemical descriptors, Quant. Struct.-Act. Rel, 19 (2000), pp. 581–598.
   Google Scholar
• A.O. Aptula, T.I. Netzeva, I.V. Valkova, M.T.D. Cronin, T.W. Schultz, R. Kühne, and G. Schüürmann, Multivariate discrimination between modes of toxic action of phenols, Quant. Struct.-Act. Rel, 21 (2002), pp. 12–22.
   Web of Science ®Google Scholar
• S.C. Basak, G.D. Grunwald, G.E. Host, G.J. Niemi, and S.P. Bradbury, A comparative study of molecular similarity, statistical, and neural methods for predicting toxic modes of action, Environ. Toxicol. Chem. 17 (1998), pp. 1056–1064.
   Web of Science ®Google Scholar
• U. Norinder, P. Lidén, and H. Boström, Discrimination between modes of toxic action of phenols using rule based methods, Mol. Divers. 10 (2006), pp. 207–212.
   PubMed Web of Science ®Google Scholar
• G. Schüürmann, A.O. Aptula, R. Kühne, and R.-U. Ebert, Stepwise discrimination between four modes of toxic action of phenols in the Tetrahymena pyriformis assay, Chem Res. Toxicol. 16 (2003), pp. 974–987.
   PubMed Web of Science ®Google Scholar
• T.M. Martin, D.M. Young, C.R. Lilavois, and M.G. Barron, Comparison of global and mode of action-based models for aquatic toxicity, SAR QSAR Environ. Res. 26 (2015), pp. 245–262.
   PubMed Web of Science ®Google Scholar
• D.V. Eldred, C.L. Weikel, P.C. Jurs, and K.L.E. Kaiser, Prediction of fathead minnow acute toxicity of organic compounds from molecular structure, Chem. Res. Toxicol. 12 (1999), pp. 670–678.
   PubMed Web of Science ®Google Scholar
• M. Pavan, T.I. Netzeva, and A.P. Worth, Validation of a QSAR model for acute toxicity, SAR QSAR Environ. Res. 17 (2006), pp. 147–171.
   PubMed Web of Science ®Google Scholar
• Y. Wang, M. Zheng, J. Xiao, Y. Lu, F. Wang, J. Lu, X. Luo, W. Zhu, H. Jiang, and K. Chen, Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow, SAR QSAR Environ. Res. 21 (2010), pp. 559–570.
   PubMed Web of Science ®Google Scholar
• P. Mazzatorta, M. Vracko, A. Jezierska, and E. Benfenati, Modeling toxicity by using supervised Kohonen neural networks, J. Chem. Inf. Model. 43 (2003), pp. 485–492.
   Web of Science ®Google Scholar
• G. Gini, M.V. Craciun, C. Konig, and E. Benfenati, Combining unsupervised and supervised artificial neural networks to predict aquatic toxicity, J. Chem. Inf. Model. 44 (2004), pp. 1897–1902.
   Web of Science ®Google Scholar
• B. Hemmateenejad, M.A. Safarpour, R. Miri, and F. Taghavi, Application of ab initio theory to QSAR study of 1, 4-dihydropyridine-based calcium channel blockers using GA-MLR and PC-GA-ANN procedures, J. Comput. Chem. 25 (2004), pp. 1495–1503.
   PubMed Web of Science ®Google Scholar
• J. Li, B. Lei, H. Liu, S. Li, X. Yao, M. Liu, and P. Gramatica, QSAR study of malonyl-CoA decarboxylase inhibitors using GA-MLR and a new strategy of consensus modeling, J. Comput. Chem. 29 (2008), pp. 2636–2647.
   PubMed Web of Science ®Google Scholar
• J.B. Ghasemi, A. Abdolmaleki, and N. Mandoumi, A quantitative structure property relationship for prediction of solubilization of hazardous compounds using GA-based MLR in CTAB micellar media, J. Hazard. Mater. 161 (2009), pp. 74–80.
   PubMed Web of Science ®Google Scholar
• E. Papa, F. Villa, and P. Gramatica, Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow), J. Chem. Inf. Model. 45 (2005), pp. 1256–1266.
   PubMed Web of Science ®Google Scholar
• F. Lyakurwa, X. Yang, X. Li, X. Qiao, and J. 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.
   PubMed Web of Science ®Google Scholar
• A. Roncaglioni, E. Benfenati, E. Boriani, and M. Clook, A protocol to select high quality datasets of ecotoxicity values for pesticides, J. Environ. Sci. Heal. B. B39 (2004), pp. 641–652.
   Google Scholar
• P.C. von der Ohe, R. Kuhne, R. Ebert, R. Altenburger, M. Liess, and G. Schuurmann, Structural alerts - A new classification model to discriminate excess toxicity from narcotic effect levels of organic compounds in the acute daphnid assay, Chem. Res. Toxicol. 18 (2005), pp. 536–555.
   PubMed Web of Science ®Google Scholar
• G.D. Veith, D.J. Call, and L.T. Brooke, Structure-toxicity relationships for the fathead minnow, Pimephales promelas: Narcotic industrial chemicals, Can. J. Fish. Aquat. Sci. 40 (1983), pp. 743–748.
   Web of Science ®Google Scholar
• M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G.A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H.P. Hratchian, A.F. Izmaylov, J. Bloino, G. Zheng, J.L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J.A. Montgomery Jr, J.E. Peralta, F. Ogliaro, M.J. Bearpark, J. Heyd, E.N. Brothers, K.N. Kudin, V.N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A.P. Rendell, J.C. Burant, S.S. Iyengar, J. Tomasi, M. Cossi, N. Rega, N.J. Millam, M. Klene, J.E. Knox, J.B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J.W. Ochterski, R.L. Martin, K. Morokuma, V.G. Zakrzewski, G.A. Voth, P. Salvador, J.J. Dannenberg, S. Dapprich, A.D. Daniels, A. Farkas, J.B. Foresman, J.V. Ortiz, J. Cioslowski, and D.J. Fox, Gaussian 09, Gaussian Inc., Wallingford, CT, USA, 2009.
   Google Scholar
• Y. Zhao and D.G. Truhlar, Hybrid Meta density functional theory methods for thermochemistry, thermochemical kinetics, and noncovalent interactions: The MPW1B95 and MPWB1K models and comparative assessments for hydrogen bonding and van der Waals interactions, J. Phys. Chem. A 108 (2004), pp. 6908–6918.
   Web of Science ®Google Scholar
• X. Wu, X. Sun, C. Zhang, C. Gong, and J. Hu, Micro-mechanism and rate constants for OH-initiated degradation of methomyl in atmosphere, Chemosphere 107 (2014), pp. 331–335.
   PubMed Web of Science ®Google Scholar
• Talete srl, Dragon (Software for Molecular Descriptor Calculation) Version 6.0 – 2014, software available at http://www.talete.mi.it/
   Google Scholar
• US Enviromental Protection Agency (US EPA), Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11., US EPA, Washington DC, 2014; software available at http://www.epa.gov/opptintr/exposure/pubs/episuitedl.htm.
   Google Scholar
• A.K. Ghose, V.N. Viswanadhan, and J.J. Wendoloski, Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: An analysis of ALOGP and CLOGP methods, J. Phys. Chem. A 102 (1998), pp. 3762–3772.
   Web of Science ®Google Scholar
• J. Li, B. Lei, H. Liu, S. Li, X. Yao, M. Liu, and P. Gramatica, QSAR study of malonyl-CoA decarboxylase inhibitors using GA-MLR and a new strategy of consensus modeling, J. Comput. Chem. 29 (2008), pp. 2636–2647.
   PubMed Web of Science ®Google Scholar
• R. Todeschini, V. Consonni, and A. Maiocchi, The K correlation index: Theory development and its application in chemometrics, Chemom Int. Lab. Syst. 46 (1999), pp. 13–29.
   Web of Science ®Google Scholar
• T.I. Netzeva, A.P. Worth, T. Aldenberg, R. Benigni, M.T.D. Cronin, P. Gramatica, J.S. Jaworska, S. Kahn, G. Klopman, C.A. Marchant, G. Myatt, N. Nikolova-Jeliazkova, G.Y. Patlewicz, R. Perkins, D.W. Roberts, T.W. Schultz, D.T. Stanton, J.J.M. van de Sandt, W. Tong, G. Veith, and C. Yang, Current status of methods for defining the applicability domain of (Quantitative) Structure-Activity Relationships. The report and recommendations of ECVAM Workshop 52, Altern. Lab. Anim. 33 (2005), pp. 155–173.
   PubMed Web of Science ®Google Scholar
• L. Eriksson, J. Jaworska, A.P. Worth, M.T.D. Cronin, R.M. McDowell, and P. Gramatica, Methods for reliability and uncertainty assessment and for applicability evaluations of classification and regression-based QSARs, Environ. Health. Perspect. 111 (2003), pp. 1361–1375.
   PubMed Web of Science ®Google Scholar
• R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, 2000.
   Google Scholar
• R.G. Pearson, Hard and soft acids and bases, J. Am. Chem. Soc. 85 (1963), pp. 3533–3539.
   Web of Science ®Google Scholar
• K. Fukui, T. Yonezawa, and H. Shingu, A molecular orbital theory of reactivity in aromatic hydrocarbons, J. Chem. Phys. 20 (1952), pp. 722–725.
   Web of Science ®Google Scholar
• G. Klopman, Chemical reactivity and the concept of charge-and frontier-controlled reactions, J. Am. Chem. Soc. 90 (1968), pp. 223–234.
   Web of Science ®Google Scholar
• L. Salem, Intermolecular orbital theory of the interaction between conjugated systems. I. General theory, J. Am. Chem. Soc. 90 (1968), pp. 543–552.
   Web of Science ®Google Scholar
• M. Cassotti, D. Ballabio, V. Consonni, A. Mauri, I.V. Tetko, and R. Todeschini, Prediction of acute aquatic toxicity toward Daphnia magna by using the GA-kNN Method, ATLA 42 (2014), pp. 31–41.
   Google Scholar
• M. Casalegno, E. Benfenati, and G. Sello, An automated group contribution method in predicting aquatic toxicity: The diatomic fragment approach, Chem. Res. Toxicol. 18 (2005), pp. 740–746.
   PubMed Web of Science ®Google Scholar
• S.P. Niculescu, A. Atkinson, G. Hammond, and M. Lewis, Using fragment chemistry data mining and probabilistic neural networks in screening chemicals for acute toxicity to the fathead minnow, SAR QSAR Environ. Res. 15 (2004), pp. 293–309.
   PubMed Web of Science ®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
• T. Martin, P. Harten, R. Venkatapathy, and D. Young, T.E.S.T. (Toxicity Estimation Software Tool). U.S. E.P.A., 2012; available at http://www.epa.gov/nrmrl/std/qsar/qsar.html.
   Google Scholar
• A. Colombo, E. Benfenati, M. Karelson, and U. Maran, The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity, Chemosphere 72 (2008), pp. 772–780.
   PubMed Web of Science ®Google Scholar
• G. Klopman, R. Saiakhov, and H.S. Rosenkranz, Multiple computer-automated structure evaluation study of aquatic toxicity II. Fathead minnow, Environ. Toxicol. Chem. 19 (2000), pp. 441–447.
   Web of Science ®Google Scholar
• T.I. Netzeva, A.O. Aptula, E. Benfenati, M.T.D. Cronin, G. Gini, I. Lessigiarska, and U. Maran, M. Vračko, and G. Schüürmann, Description of the electronic structure of organic chemicals using semiempirical and ab initio methods for development of toxicological QSARs, J. Chem. Inf. Model. 45 (2005), pp. 106–114.
   PubMed Web of Science ®Google Scholar
• S. Lozano, M.-P. Halm-Lemeille, A. Lepailleur, S. Rault, and R. Bureau, Consensus QSAR related to global or MOA models: Application to acute toxicity for fish, Mol. Inform. 29 (2010), pp. 803–813.
   Web of Science ®Google Scholar
• M. Nendza and C.L. Russom, QSAR modelling of the ERL-D fathead minnow acute toxicity database, Xenobiotica 21 (1991), pp. 147–170.
   PubMed Web of Science ®Google Scholar
• VEGA Non-Interactive Client. Milano, Italy: Istituto di Ricerche Farmacologiche Mario Negri; available at http://www.vega-qsar.eu/index.php.
   Google Scholar
• M. Cassotti, D. Ballabio, R. Todeschini, and V. Consonni, A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas), SAR QSAR Environ. Res. 26 (2015), pp. 217–243.
   PubMed Web of Science ®Google Scholar