Comparison of global and mode of action-based models for aquatic toxicity

SAR and QSAR in Environmental Research

Volumn 26, Issue 3, 2015, Pages 245-262

  Martin, T M ^a   Young, D M ^a   Lilavois, C R ^b   Barron, M G ^b  

^a U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

^b U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

Author keywords

aquatic toxicity; linear discriminant analysis (LDA); mode of action; multiple linear regression; octanol water partition coefficient; quantitative structure activity relationship (QSAR)

Indexed keywords

COMPUTATIONAL CHEMISTRY; DISCRIMINANT ANALYSIS; FORECASTING; RISK ASSESSMENT; RISK PERCEPTION; TOXICITY;

AQUATIC TOXICITY; DISCRIMINANT ANALYSIS MODEL; LINEAR DISCRIMINANT ANALYSE; LINEAR DISCRIMINANT ANALYZE; MODE OF ACTION; MULTIPLE LINEAR REGRESSION MODELS; MULTIPLE LINEAR REGRESSIONS; OCTANOL-WATER PARTITION COEFFICIENT; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIP;

MULTIPLE LINEAR REGRESSION;

WATER POLLUTANT;

ANIMAL; BIOLOGICAL MODEL; COMPARATIVE STUDY; CYPRINIDAE; METABOLISM; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STATISTICAL MODEL; TOXICITY; WATER POLLUTANT;

ANIMALS; CYPRINIDAE; LINEAR MODELS; MODELS, BIOLOGICAL; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; WATER POLLUTANTS, CHEMICAL;

EID: 84925209546     PISSN: 1062936X     EISSN: 1029046X     Source Type: Journal    
DOI: 10.1080/1062936X.2015.1018939     Document Type: Article

References (49)

1. 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.
2. R. Benigni and A.M. Richard, QSARS of mutagens and carcinogens: Two case studies illustrating problems in the construction of models for noncongeneric chemicals, Mutat. Res.-Genet. Tox. 371 (1996), pp. 29–46.
3. T.W. Schultz, G.D. Sinks, and A.P. Bearden, QSAR in aquatic toxicology: A mechanism of action approach comparing toxic potency to Pimephales promelas, Tetrahymena pyriformis, and Vibrio fischeri, in Comparative QSAR, J. Devillers, ed., Taylor & Francis, New York, 1998, pp. 51–109.
4. S.G. Abernethy, D. MacKay, and L.S. McCarty, ‘Volume fraction’ correlation for narcosis in aquatic organisms: The key role of partitioning, Environ. Toxicol. Chem. 7 (1988), pp. 469–481.
5. M.T.D. Cronin and T.W. Schultz, Structure-toxicity relationships for phenols to Tetrahymena pyriformis, Chemosphere 32 (1996), pp. 1453–1468.
6. O.G. Mekenyan and G.D. Veith, Relationships between descriptors for hydrophobicity and soft electrophilicity in predicting toxicity, SAR QSAR Environ. Res. 1 (1993), pp. 335–344.
7. H. Yuan, Y.-Y. Wang, and Y.-Y. Cheng, Mode of action-based local QSAR modeling for the prediction of acute toxicity in the fathead minnow, J. Molec. Graph. Model. 26 (2007), pp. 327–335.
8. W.F. Zheng and A. Tropsha, Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle, J. Chem. Inf. Comp. Sci. 40 (2000), pp. 185–194.
9. 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, Altern. Lab. Anim. 42 (2014), pp. 31–41.
10. T.M. Martin, P. Harten, R. Venkatapathy, S. Das, and D.M. Young, A hierarchical clustering methodology for the estimation of toxicity, Toxicol. Mech. Method. 18 (2008), pp. 251–266.
11. C.X. Xue, R.S. Zhang, H.X. Liu, X.J. Yao, M.C. Liu, Z.D. Hu, and B.T. Fan, An Accurate QSPR study of O–H bond dissociation energy in substituted phenols based on support vector machines, J. Chem. Inf. Comp. Sci. 44 (2004), pp. 669–677.
12. 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.
13. 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.
14. J.A. Castillo-Garit, Y. Marrero-Ponce, J. Escobar, F. Torrens, and R. Rotondo, A novel approach to predict aquatic toxicity from molecular structure, Chemosphere 73 (2008), pp. 415–427.
15. J.F. Contrera, E.J. Matthews, and R.D. Benz, Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices, Regul. Toxicol. Pharm. 38 (2003), pp. 243–259.
16. K.L.E. Kaiser and S.P. Niculescu, Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales promelas): A study based on 865 chemicals, Chemosphere 38 (1999), pp. 3237–3245.
17. 50) of organic compounds tothe fathead minnow (Pimephales promelas) using a group contribution method, Chem Res. Toxicol. 14 (2001), pp. 1378–1385.
18. T. Öberg, A general structure-property relationship to predict the enthalpy of vaporisation at ambient temperatures, SAR QSAR Environ. Res. 18 (2007), pp. 127–139.
19. T. Öberg, A QSAR for baseline toxicity: Validation, domain of application, and prediction, Chem Res. Toxicol. 17 (2004), pp. 1630–1637.
20. A. Cherkasov, Inductive QSAR descriptors. Distinguishing compounds with antibacterial activity by artificial neural networks, Int. J. Mol. Sci. 6 (2005), pp. 63–86.
21. Virtual Computational Chemistry Laboratory (VCCLAB), Associative Neural Network, VCCLAB, Munich, 2007; software available at http://www.vcclab.org/lab/asnn/.
22. L. He and P.C. Jurs, Assessing the reliability of QSAR model's predictions, J. Mol. Graph. Model. 23 (2005), pp. 503–523.
23. H. Könemann, Quantitative structure-activity relationships in fish toxicity studies Part 1: Relationship for 50 industrial pollutants, Toxicology 19 (1981), pp. 209–221.
24. US Enviromental Protection Agency (US EPA), Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11. (2014), US EPA, Washington DC; software available at http://www.epa.gov/opptintr/exposure/pubs/episuitedl.htm.
25. C.L. Russom, S.P. Bradbury, S.J. Broderius, D.E. Hammermeister, and R.A. Drummond, Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales Promelas), Environ. Toxicol. Chem. 16 (1997), pp. 948–967.
26. M.T.D. Cronin, A.O. Aptula, J.C. Duffy, T.I. Netzeva, P.H. Rowe, I.V. Valkova, and T. Wayne Schultz, Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis, Chemosphere 49 (2002), pp. 1201–1221.
27. 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.
28. 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.
29. 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.
30. G. Schüürmann, A.O. Aptula, R. Kühne, and R.-U. Ebert, Stepwisediscrimination between four modes of toxic action of phenols in the Tetrahymena pyriformis assay, Chem Res. Toxicol. 16 (2003), pp. 974–987.
31. B. Niu, Y. Jin, W. Lu, and G. Li, Predicting toxic action mechanisms of phenols using AdaBoost Learner, Chemom. Intell. Lab. Syst. 96 (2009), pp. 43–48.
32. S. Spycher, M. Nendza, and J. Gasteiger, Comparison of different classification methods applied to a mode of toxic action data set, QSAR Comb. Sci. 23 (2004), pp. 77–791.
33. 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.
34. X.J. Yao, A. Panaye, J.P. Doucet, H.F. Chen, R.S. Zhang, B.T. Fan, M.C. Liu, and Z.D. Hu, Comparative classification study of toxicity mechanisms using support vector machines and radial basis function neural networks, Anal. Chim. Acta 535 (2005), pp. 259–273.
35. S. Ren, Predicting three narcosis mechanisms of aquatic toxicity, Toxicol. Lett. 133 (2002), pp. 127–139.
36. L. Michielan, L. Pireddu, M. Floris, and S. Moro, Support vector machine (SVM) as alternative tool to assign acute aquatic toxicity warning labels to chemicals, Mol. Inform. 29 (2010), pp. 51–64.
37. 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.
38. T.M. Martin, C.M. Grulke, D.M. Young, C.L. Russom, N.Y. Wang, C.R. Jackson, and M.G. Barron, Prediction of aquatic toxicity mode of action using linear discriminant and random forest models, J. Chem. Inf. Model. 53 (2013), pp. 2229–2239.
39. S. Spycher, E. Pellegrini, and J. Gasteiger, Use of structure descriptors to discriminate between modes of toxic action of phenols, J. Chem. Inf. Model. 45 (2005), pp. 200–208.
40. Un Environmental Protection Agency (US EPA), ASTER (Assessment Tools for the Evaluation of Risk), US EPA, Washington DC, 2012; software available at http://www.epa.gov/med/Prods_Pubs/aster.htm.
41. Insecticide Resistance Action Committee (IRAC), IRAC MoA Classification Scheme, IRAC International MoA Working Group, 2012; software available at http://www.irac-online.org/eClassification/.
42. L. Eriksson, J.S. 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 Persp. 111 (2003), pp. 1361–1375.
43. D.C. Montgomery, Introduction to Linear Regression Analysis, John Wiley & Sons, New York, 1982.
44. R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, Weinheim, Germany, 2000.
45. L.B. Kier and L.H. Hall, Molecule Structure Description: The Electrotopological State, Academic Press, New York, 1999.
46. Talete, Dragon Version 6, Talete SRL, Milan, Italy, 2006; software available at http://www.talete.mi.it/products/dragon_description.htm.
47. 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.
48. A.K. Ghose, V.N. Viswanadhan, and J.J. Wendoloski, Prediction of hydrophilic (lipophilic) properties of small organic molecules using fragmental methods: An analysis of ALOGP and CLOGP methods, J. Phys. Chem. 102 (1998), pp. 3762–3772.
49. H. Zhu, T.M. Martin, L. Ye, A. Sedykh, D.M. Young, and A. Tropsha, Quantitative structure−activity relationship modeling of rat acute toxicity by oral exposure, Chem Res. Toxicol. 22 (2009), pp. 1913–1921.