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

Science of the Total Environment

Volumn 409, Issue 18, 2011, Pages 3334-3343

  Tebby, Cleo ^a   Mombelli, Enrico ^a   Pandard, Pascal ^a   Péry, Alexandre R R ^a  

^a INSTITUT NATIONAL DE L ENVIRONNEMENT INDUSTRIEL ET DES RISQUES INERIS   (France)

Author keywords

Acute aquatic toxicity; Cross species comparison; OECD (Q)SAR Toolbox; Quantitative structure activity relationships (QSAR); Sensitivity

Indexed keywords

AQUATIC TOXICITY; CROSS-SPECIES COMPARISON; OECD (Q)SAR TOOLBOX; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSAR); SENSITIVITY;

BIOCHEMISTRY; COMPUTATIONAL CHEMISTRY; FORECASTING; INDICATORS (CHEMICAL);

TOXICITY;

ANILINE; CARBENE; ETHER; HALIDE; PHENOL DERIVATIVE; POLYCYCLIC AROMATIC HYDROCARBON;

ALGA; BIOTRANSFORMATION; CRUSTACEAN; DATABASE; ECOTOXICOLOGY; PHYSICOCHEMICAL PROPERTY; TOXICITY;

ACUTE TOXICITY; ALGA; ARTICLE; BIOACCUMULATION; CHEMICAL STRUCTURE; COMPUTER PROGRAM; DAPHNIA; DATA BASE; ECOTOXICITY; NONHUMAN; ORGANIZATION FOR ECONOMIC COOPERATION AND DEVELOPMENT; ORGANIZATIONAL DEVELOPMENT; ORYZIAS; PHYSICAL CHEMISTRY; PREDICTION; PRIORITY JOURNAL; PSEUDOKIRCHNERIELLA SUBCAPITATA; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SENSITIVITY ANALYSIS; TOXICITY TESTING;

ANIMALS; CHLOROPHYTA; DAPHNIA; DATABASES AS TOPIC; ENVIRONMENTAL MONITORING; FISHES; FOOD CHAIN; HYDROPHOBIC AND HYDROPHILIC INTERACTIONS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; TOXICITY TESTS; WATER POLLUTANTS, CHEMICAL;

ALGAE; DAPHNIA MAGNA; DAPHNIIDAE; ORYZIAS LATIPES; SELENASTRUM CAPRICORNUTUM;

EID: 79959688079     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2011.05.029     Document Type: Article

References (40)

1. Arnot J.A., Meylan W., Tunkel J., Howard P.H., Mackay D., Bonnell M., et al. A quantitative structure-activity relationship for predicting metabolic biotransformation rates for organic chemicals in fish. Environ Toxicol Chem 2009, 28:1168-1177.
2. Chen C.-Y., Ko C.-W., Lee P.-I. Toxicity of substituted anilines to Pseudokirchneriella subcapitata and quantitative structure-activity relationship analysis for polar narcotics. Environ Toxicol Chem 2007, 26:1158-1164.
3. de Roode D., Hoekzema C., de Vries-Buitenweg S., van de Waart B., van der Hoeven J. QSARs in ecotoxicological risk assessment. Regul Toxicol Pharmacol 2006, 45:24-35.
4. Dom N., Knapen D., Benoot D., Nobels I., Blust R. Aquatic multi-species acute toxicity of (chlorinated) anilines: experimental versus predicted data. Chemosphere 2010, 81:177-186.
5. Duchowicz P.R., Ocsachoque M.A. Quantitative structure-toxicity models for heterogeneous aliphatic compounds. QSAR Comb Sci 2009, 28:281-295.
6. Furuhama A., Toidaa T., Nishikawaa N., Aokia Y., Yoshiokab Y., Shiraishi H. Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March 2009 version. SAR QSAR Environ Res 2010, 21:403-413.
7. 2!. J Mol Graph Model 2002, 20:269-276.
8. Henegar A., Mombelli E., Pandard P., Péry A.R.R. What can be learnt from an ecotoxicity database in the framework of the REACh regulation?. Sci Total Environ 2011, 409:489-494.
9. Hoekzema C.C., Murk A.J., van de Waart B.J., van der Hoeven J.C.M., de Roode D.F. Alternative approaches can greatly reduce the number of fish used for acute toxicity testing. Environ Toxicol Chem 2006, 25:1322-1325.
10. Hsieh S.-H., Hsu C.-H., Tsai D.-Y., Chen C.-Y. Quantitative structure-activity relationships for toxicity of nonpolar narcotic chemicals to Pseudokirchneriella subcapitata. Environ Toxicol Chem 2006, 2006:2920-2926.
11. Huang C.-P., Wang Y.-J., Chen C.-Y. Toxicity and quantitative structure-activity relationships of nitriles based on Pseudokirchneriella subcapitata. Ecotoxicol Environ Saf 2007, 67:439-446.
12. Hutchinson T.H., Barrett S., Buzby M., Constable D., Hartmann A., Hayes E., et al. A strategy to reduce the numbers of fish used in acute ecotoxicity testing of pharmaceuticals. Environ Toxicol Chem 2003, 22:3031-3036.
13. Japanese Ministry of Environment Japan ecotoxicity tests data 2010, March 2010.
14. Jeram S., Riego Sintes J.M., Halder M., Baraibar Fentanes J., Sokull-Klüttgen B., Hutchinson T.H. A strategy to reduce the use of fish in acute ecotoxicity testing of new chemical substances notiWed in the European Union. Regul Toxicol Pharmacol 2005, 42:218-224.
15. Lessigiarska I., Worth A.P., Sokull-Klüttgen B., Jeram S., Dearden J.C., Netzeva T.I., et al. QSAR investigation of a large data set for fish, algae and Daphnia toxicity. SAR QSAR Environ Res 2004, 15:413-431.
16. Moriguchi I., Hirono S., Liu Q., Nakagome I., Matsushita Y. Simple method of calculating octanol/water partition coefficient. Chem Pharm Bull 1992, 40:127-130.
17. Neuwoehner J., Zilberman T., Fenner K., Escher B.I. QSAR-analysis and mixture toxicity as diagnostic tools: influence of degradation on the toxicity and mode of action of diuron in algae and daphnids. Aquat Toxicol 2010, 97:58-67.
18. OECD Organisation for Economic Cooperation and Development. Guidelines for testing of chemicals 203, fish acute toxicity test 1992.
19. OECD Organisation for Economic Cooperation and Development. OECD guidelines for testing of chemicals, guideline 202. Daphnia sp., acute immobilisation test 2004.
20. OECD Organisation for Economic Cooperation and Development. OECD guidelines for testing of chemicals, guideline 201. Freshwater alga and cyanobacteria, growth inhibition test 2006.
21. OECD Organisation for Economic Cooperation and Development. QSAR Toolbox user manual and installation notes 2010.
22. Patrick GL. Quantitative structure-activity relationships. In: Oxford University Press, editor. An introduction to medicinal chemistry. 2001. pp. 622.
23. Pedretti A., Villa L., Vistoli G. VEGA - an open platform to develop chemo-bio-informatics applications, using plug-in architecture and script programming. J Comput Aided Mol Des 2004, 18:167-173.
24. R: a language and environment for statistical computing 2010, R Development Core Team.
25. Schüürmann G., Ebert R.-U., Chen J., Wang B., Kühne R. External validation and prediction employing the predictive squared correlation coefficient - test set activity mean vs training set activity mean. J Chem Inf Comput Sci 2008, 48:2140-2145.
26. Shigeoka T., Sato Y., Takeda Y., Yoshida K., Yamauchi F. Acute toxicity of chlorophenols to green algae, Selenastrum capricornutum and Chlorella vulgaris, and quantitative structure-activity relationships. Environ Toxicol Chem 1988, 7:847-854.
27. Tetko I., Tanchuk V., Kasheva T., Villa A. Estimation of aqueous solubility of chemical compounds using E-state indices. J Chem Inf Comput Sci 2001, 41:1488-1493.
28. Tetko I.V., Gasteiger J., Todeschini R., Mauri A., Livingstone D., Ertl P., et al. Virtual computational chemistry laboratory - design and description. J Comput Aided Mol Des 2005, 19:453-463.
29. Tremolada P., Finizio A., Villa S., Gaggi C., Vighi M. Quantitative inter-specific chemical activity relationships of pesticides in the aquatic environment. Aquat Toxicol 2004, 67:87-103.
30. Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform 2010, 29:476-488.
31. Tunkel J., Howard P.H., Boethling R.S., Stiteler W., Loonen H. Predicting ready biodegradability in the Japanese Ministry of International Trade and Industry Test. Environ Toxicol Chem 2000, 19:2478-2485.
32. Vaal M., van der Wall J.T., Hoekstra J. Pattern analysis of the variation in the sensitivity of aquatic species to toxicants. Chemosphere 1997, 35:1291-1309.
33. Vaal M., van der Wall J.T., Hoekstra J., Hermens J. Variation in the sensitivity of aquatic species in relation to the classification of environmental pollutants. Chemosphere 1997, 35:1311-1327.
34. VCCLAB Virtual computational chemistry laboratory 2005.
35. Viswanadhan V.N., Ghose A.K., Revankar G.R., Robins R.K. Atomic physicochemical parameters for 3 dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally-occurring nucleoside antibiotics. J Chem Inf Comput Sci 1989, 29:163-172.
36. Wei D., Kisuno A., Kameya T., Urano K. A new method for evaluating biological safety of environmental water with algae, daphnia and fish toxicity ranks. Sci Total Environ 2006, 371:383-390.
37. Weiniger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 1988, 28:31-36.
38. Weiniger D., Weiniger A., Weiniger J.L. SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inf Comput Sci 1989, 29:97-101.
39. Weyers A., Sokull-Klüttgen B., Baraibar-Fentanes J., Vollmer G. Acute toxicity data: a comprehensive comparison of results of fish, daphnia, and algae tests with new substances notified in the European Union. Environ Toxicol Chem 2000, 19:1931-1933.
40. Zhang X.J., Qin H.W., Su L.M., Qin W.C., Zou M.Y., Sheng L.X., et al. Interspecies correlations of toxicity to eight aquatic organisms: theoretical considerations. Sci Total Environ 2010, 408:4549-4555.