A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH

Regulatory Toxicology and Pharmacology

Volumn 66, Issue 3, 2013, Pages 301-314

  Nendza, Monika ^a   Gabbert, Silke ^b   Kühne, Ralph ^c   Lombardo, Anna ^d   Roncaglioni, Alessandra ^d   Benfenati, Emilio ^d   Benigni, Romualdo ^e   Bossa, Cecilia ^e   Strempel, Sebastian ^f   Scheringer, Martin ^f   Fernández, Alberto ^g   Rallo, Robert ^g   Giralt, Francesc ^g   Dimitrov, Sabcho ^h   Mekenyan, Ovanes ^h   Bringezu, Frank ⁱ   Schüürmann, Gerrit ^c,j  

^a Analytisches Laboratorium   (Germany)

^b WAGENINGEN UNIVERSITY   (Netherlands)

^c HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

^d MARIO NEGRI INSTITUTE FOR PHARMACOLOGICAL RESEARCH   (Italy)

^e ISTITUTO SUPERIORE DI SANITÀ   (Italy)

^f ETH ZURICH   (Switzerland)

^g UNIVERSITAT ROVIRA I VIRGILI   (Spain)

^h ASSEN ZLATAROV UNIVERSITY   (Bulgaria)

ⁱ MERCK KGAA   (Germany)

^j TU BERGAKADEMIE FREIBERG   (Germany)

more..

Author keywords

Applicability domain; CMR; Consensus; Cost efficiency; Endocrine disruption; PBT; QSARs; Read across; Skin sensitisation; Structural alerts

Indexed keywords

ENDOCRINE DISRUPTOR;

ALGORITHM; AQUATIC ENVIRONMENT; ARTICLE; BIOACCUMULATION; CARCINOGENICITY; COMPARATIVE STUDY; COMPUTER ANALYSIS; COMPUTER PROGRAM; DANGEROUS GOODS; ECOTOXICOLOGY; HEALTH CARE ORGANIZATION; INTERNET; MUTAGENICITY; NONHUMAN; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RELIABILITY; REPRODUCTIVE TOXICITY; SCREENING TEST; SKIN SENSITIZATION; SOCIOECONOMICS; TOXICITY TESTING;

ANIMALS; ENVIRONMENTAL POLICY; ENVIRONMENTAL POLLUTANTS; EUROPE; GOVERNMENT PROGRAMS; GOVERNMENT REGULATION; HAZARDOUS SUBSTANCES; HUMANS; MODELS, THEORETICAL; PREDICTIVE VALUE OF TESTS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; TOXICITY TESTS;

EID: 84877125015     PISSN: 02732300     EISSN: 10960295     Source Type: Journal    
DOI: 10.1016/j.yrtph.2013.05.007     Document Type: Article

References (154)

1. Abraham M.H., Rafols C. Factors that influence tadpole narcosis. An LFER analysis. J. Chem. Soc. Perkin Trans. 1995, 2:1843-1851.
2. Ahlers J., Stock F., Werschkun B. Integrated testing and intelligent assessment - new challenges under REACH. Environ. Sci. Pollut. Res. 2008, 15:565-572.
3. Aldenberg T., Jaworska J.S. Multiple test in silico weight-of-evidence for toxicological endpoints. In Silico Toxicology: Principles and Applications 2010, Royal Society of Chemistry, Cambridge, UK. M.T.D. Cronin, J.C. Madden (Eds.).
4. Andersen M.E., Al-Zoughool M., Croteau M., Westphal M., Krewski D. The future of toxicity testing. J. Toxicol. Environ. Health Part B 2010, 13:163-196.
5. Ankley, G.T., Bradbury, S.P., Hermens, J.L.M., Mekenyan, O.G., Tollefsen, K.E., 1997. Current approaches to the use of structure activity relationships (SARs) in identifying the hazard of endocrine-disrupting chemicals. SETAC-Europe / OECD / EC Expert Workshop on Endocrine Modulators, Wildlife-Assessment and Testing, (EMWAT) Workshop, 10-13 April 1997, The Netherlands.
6. Antares, 2011. http://www.antares-life.eu/.
7. Arena V.C., Sussman N., Mazumdar S., Yu S., Macina O.T. The utility of structure-activity relationship (SAR) models for prediction and covariate selection in developmental toxicity: comparative analysis of logistic regression and decision tree models. SAR QSAR Environ. Res. 2004, 15:1-18.
8. Arnot J.A., Gobas F.A.P.C. A generic QSAR for assessing the bioaccumulation potential of organic chemicals in aquatic food webs. QSAR Combi. Sci. 2003, 22:337-345.
9. Arnot J.A., Mackay D. Policies for chemical hazard and risk priority setting: can persistence, bioaccumulation, toxicity, and quantity information be combined?. Environ. Sci. Technol. 2008, 42:4648-4654.
10. Atkinson R. Estimation of gas-phase hydroxyl radical rate constants for organic chemicals. Environ. Toxicol. Chem. 1988, 7:435-442.
11. Bailey A.B., Chanderbhan R., Collazo-Braier N., Cheeseman M.A., Twaroski M.L. The use of structure-activity relationship analysis in the food contact notification program. Regul. Toxicol. Pharmacol. 2005, 42:225-235.
12. Basketter D., Darlenski R., Fluhr J.W. Skin irritation and sensitization: mechanisms and new approaches for risk assessment. Skin Pharmacol. Physiol. 2008, 21:191-202.
13. Benigni R., Bossa C. Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology. Mutat. Res. Rev. Mutat. 2008, 659:248-261.
14. Benigni R., Bossa C. Alternative strategies for carcinogenicity assessment: an efficient and simplified approach based on in vitro mutagenicity and cell transformation assays. Mutagenesis 2011, 26:455-460.
15. Benigni R., Bossa C. Mechanisms of chemical carcinogenicity and mutagenicity: a review with implications for predictive toxicology. Chem. Rev. 2011, 111:2507-2536.
16. Bintein S., Devillers J., Karcher W. Nonlinear dependence of fish bioconcentration on n-octanol/water partition coefficient. SAR QSAR Environ. Res. 1993, 1:29-39.
17. Blaschke U., Paschke A., Rensch I., Schüürmann G. Acute and chronic toxicity toward the bacteria Vibrio fischeri of organic narcotics and epoxides: structural alerts for epoxide excess toxicity. Chem. Res. Toxicol. 2010, 23:1936-1945.
18. Blaschke U., Eismann K., Böhme A., Paschke A., Schüürmann G. Structural alerts for the excess toxicity of acrylates, methacrylates and propiolates derived from their short-term and long-term bacterial toxicity. Chem. Res. Toxicol. 2012, 25:170-180.
19. Böhme A., Thaens D., Paschke A., Schüürmann G. Kinetic glutathione chemoassay to quantify thiol reactivity of organic electrophiles - application to α,β-unsaturated ketones, acrylates, and propiolates. Chem. Res. Toxicol. 2009, 22:742-750.
20. Böhme A., Thaens D., Schramm F., Paschke A., Schüürmann G. Thiol reactivity and its impact on the ciliate toxicity of α,β-unsaturated aldehydes, ketones, and esters. Chem. Res. Toxicol. 2010, 23:1905-1912.
21. Böhnhardt A., Kühne R., Ebert R.U., Schüürmann G. Indirect photolysis of organic compounds - prediction of OH reaction rate constants through molecular orbital calculations. J. Phys. Chem. A 2008, 112:11399.
22. Böhnhardt A., Kühne R., Ebert R.U., Schüürmann G. Predicting rate constants of OH-mediated indirect photolysis - advances for oxygenated compounds through a molecular orbital HF/6-31G** approach. Theor. Chem. Acc. 2010, 127:355-367.
23. Boethling R.S., Howard P.H., Meylan W., Stiteler W., Beauman J., Tirado N. Group contribution method for predicting probability and rate of aerobic biodegradation. Environ. Sci. Technol. 1994, 28:459-465.
24. Boethling R.S., Costanza J. Domain of EPI suite biotransformation models. SAR QSAR Environ. Res. 2010, 21:415-443.
25. Bowen K.R., Flanagan K.B., Acree W.E.J., Abraham M.H., Rafols C. Correlation of the toxicity of organic compounds to tadpoles using the Abraham model. Sci. Tot. Environ. 2006, 371:99-109.
26. Bradbury S.P., Mekenyan O.G., Anley G.T. The role of ligand flexibility in predicting biological activity: structure-activity relationships for aryl hydrocarbon, estrogen, and androgen receptor binding affinity. Environ. Toxicol. Chem. 1998, 17:15-25.
27. CAESAR, 2011. http://www.caesar-project.eu.
28. Cassano A., Manganaro A., Martin T., Young D., Piclin N., Pintore M., Bigoni D., Benfenati E. CAESAR models for developmental toxicity. Chem. Cent. J. 2010, 4:S4.
29. ChemProp, 2012. http://www.ufz.de/index.php?en=6738.
30. CLP, 2008. Regulation (EC) No 1272/2008 of the European Parliament and of the Council of 16 December 2008 on classification, labelling and packaging of substances and mixtures, amending and repealing Directives 67/548/EEC and 1999/45/EC, and amending Regulation (EC) No 1907/2006. Off. J. Europ. Union L353, 31.12.2008, 1-1355.
31. CLP, 2011. Regulation (EC) No 1272/2008 of the European Parliament and of the Council of 16 December 2008 on classification, labelling and packaging of substances and mixtures, amending and repealing Directives 67/548/EEC and 1999/45/EC, and amending Regulation (EC) No. 1907/2006. Off. J. Europ. Union L353, 31.12.2008, 1-1355. Consolidated version 2008R1272, 19.4.2011, p. 1979.
32. CORAL, 2011. http://www.insilico.eu/coral/.
33. Derek Nexus, 2011. https://www.lhasalimited.org/research/publications/.
34. Devillers J., Marchand-Geneste N., Carpy A., Porcher J.M. SAR and QSAR modeling of endocrine disruptors. SAR QSAR Environ. Res. 2006, 17:393-412.
35. de Wolf W., Comber M., Douben P., Gimeno S., Holt M., Léonard M., Lillicrap A., Sijm D., van Egmond R., Weisbrod A., Whale G. Animal use replacement, reduction, and refinement: development of an integrated testing strategy for bioconcentration of chemicals in fish. Integr. Environ. Assess. Manag. 2007, 3:3-17.
36. Dimitrov S., Breton R., MacDonald D., Walker J.D., Mekenyan O.G. Quantitative prediction of biodegradability, metabolite distribution and toxicity of stable metabolites. SAR QSAR Environ. Res. 2002, 13:445-455.
37. Dimitrov S., Dimitrova G., Pavlov T., Dimitrova N., Patlewicz G.Y., Niemela J., Mekenyan O.G. A stepwise approach for defining the applicability domain of SAR and QSAR models. J. Chem. Inf. Model. 2005, 45:839-849.
38. Dimitrov S., Dimitrova N., Parkerton T., Comber M., Bonnell M., Mekenyan O.G. Base-line model for identifying the bioaccumulation potential of chemicals. SAR QSAR Environ. Res. 2005, 16:531-554.
39. Dragon, 2011. http://www.talete.mi.it.
40. ECHA, 2008. Guidance on information requirements and chemical safety assessment Chapter R.7a: endpoint specific guidance. http://echa.europa.eu/reach_en.asp.
41. ECHA, 2012a. Guidance on information requirements and chemical safety assessment. Chapter R.7a: endpoint-specific guidance. Version 2.0, November 2012.
42. ECHA, 2012b. Guidance on the application of the CLP criteria, ECHA-12-G-14-EN, v 3.0, November 2012. http://echa.europa.eu/documents/10162/13562/clp_en.pdf.
43. Enoch S.J., Cronin M.T.D. Development of new structural alerts suitable for chemical category formation for assigning covalent and non-covalent mechanisms relevant to DNA binding. Mutat. Res. 2012, 743:10-19.
44. Enoch S.J., Madden J.C., Cronin M.T.D. Identification of mechanisms of toxic action for skin sensitisation using a SMARTS pattern based approach. SAR QSAR Environ. Res. 2008, 19:555-578.
45. Enoch S.J., Ellison C.M., Schultz T.W., Cronin M.T.D. A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxicity. Crit. Rev. Toxicol. 2012, 41:783-802.
46. European Commission Technical Guidance Document in Support of Commission Directive 93/67/EEC on Risk Assessment for New Notified Substances and Commission Regulation (EC) No 1488/94 on Risk Assessment for Existing Substances 1996, European Commission, Brussels, Belgium.
47. European Commission 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, European Commission, Brussels, Belgium.
48. Fang H., Tong W., Shi L., Blair R.M., Perkins R., Branham W.S., Hass B.S., Xie Q., Dial S.L., Moland C.L., Sheehan D.M. Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens. Chem. Res. Toxicol. 2001, 14:280-294.
49. Fang H., Tong W., Welsch W.J., Sheehan D.M. QSAR models in receptor-mediated effects: the nuclear receptor superfamily. J. Mol. Struct. (Theochem.) 2003, 622:113-125.
50. Fernández A., Rallo R., Giralt F. Uncertainty reduction in environmental data with conflicting information. Environ. Sci. Technol. 2009, 43:5001-5006.
51. Fernández A., Lombardo A., Rallo R., Roncaglioni A., Giralt F., Benfenati E. Quantitative consensus of bioaccumulation models for integrated testing strategies. Environ. Int. 2012, 45:51-58.
52. Ferrari T., Gini G. An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts. Chem. Cent. J. 2010, 4:S2.
53. Gabbert S., van Ierland E.C. Cost-effectiveness analysis of chemical testing as decision-support: how to include animal welfare?. Hum. Ecol. Risk Assess. 2010, 16:603-620.
54. Gabbert S., Weikard H.-P. A theory of chemicals testing and regulation. Nat. Resourc. Forum 2010, 34:155-164.
55. Gabbert S., Weikard H.-P. Sequential testing of chemicals when costs matter: a value-of-information approach. Hum. Ecol. Risk Assess. 2013, in press, http://dx.doi.org/10.1080/10807039.2012.685810.
56. Gates K.S. An overview of chemical processes that damage cellular DNA: spontaneous hydrolysis, alkylation, and reactions with radicals. Chem. Res. Toxicol. 2009, 22:1747-1760.
57. Grindon C., Combes R., Cronin M.T.D., Roberts D.W., Garrod J.A. Integrated testing strategies for the use in the EU REACH system. ATLA 2006, 34:407-427.
58. Gunatilleka A.D., Poole C.F. Models for estimating the non-specific aquatic toxicity of organic compounds. Analyt. Commun. 1999, 36:235-242.
59. Gunatilleka A.D., Poole C.F. Models for estimating the non-specific toxicity of organic compounds in short-term bioassays. Analyst 2000, 125:127-132.
60. Hartung T. Toxicology for the twenty-first century. Nature 2010, 460:208-212.
61. Hong H., Tong W., Fang H., Shi L., Xie Q., Wu J., Perkins R., Walker J., Branham W.S., Sheehan D.M. Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts. Environ. Health Persp. 2002, 110:29-36.
62. Hoover K.R., Acree W.E.J., Abraham M.H. Chemical toxicity correlations for several fish species based on the Abraham solvation parameter model. Chem. Res. Toxicol. 2005, 18:1497-1505.
63. Howard P.H., Muir D.C.G. Identifying new persistent and bioaccumulative organics among chemicals in commerce. Environ. Sci. Technol. 2010, 44:2277-2285.
64. Jäckel H., Nendza M. Reactive substructures in the prediction of aquatic toxicity data. Aquat. Toxicol. 1994, 29:305-314.
65. Jaworska J.S., Gabbert S., Aldenberg T. Towards optimization of chemical testing under REACH: a Bayesian network approach to integrated testing strategies. Regul. Toxicol. Pharmacol. 2010, 57:157-167.
66. Jaworska J., Artsiom H., Kern P.S., Gerberick G.F. Integrating non-animal test information into an adaptive testing strategy - skin sensitization proof of concept case. Altex 2011, 28:211-225.
67. Ji L., Schüürmann G. Computational evidence for α-nitrosamino radical as initial metabolite for both the P450 dealkylation and denitrosation of carcinogenic nitrosamines. J. Phys. Chem. B 2012, 116:903-912.
68. Ji L., Schüürmann G. Model and mechanism: N-hydroxylation of primary aromatic amines by cytochrome P450. Angew. Chem. Int. Ed. 2013, 52:744-748. Angew. Chem. 125, 772-776.
69. Karlberg A.-T., Bergström M.A., Börje A., Luthman K., Nilsson J.L.G. Allergic contact dermatitis - formation, structural requirements, and reactivity of skin sensitizers. Chem. Res. Toxicol. 2008, 21:53-69.
70. Kazius J., McGuire R., Bursi R. Derivation and validation of toxicophores for mutagenicity prediction. J. Med. Chem. 2005, 48:312-320.
71. Klasmeyer Application of multimedia models for screening assessment of long-range transport potential and overall persistence. Environ. Sci. Technol. 2006, 40:53-60.
72. Könemann H. Quantitative structure-activity relationships in fish toxicity studies. Part I: Relationship for 50 industrial pollutants. Toxicology 1981, 19:209-221.
73. Kühne R., Ebert R.U., Schüürmann G. Estimation of compartmental half-lives of organic compounds - structural similarity vs. EPI-Suite. QSAR Combi. Sci. 2007, 26:542-549.
74. Kühne R., Ebert R.U., Schüürmann G. Chemical domain of QSAR models from atom-centered fragments. J. Chem. Inf. Model. 2009, 49:2660-2669.
75. Kühne R., Ebert R.-U., von der Ohe P.C., Ulrich N., Brack W., Schüürmann G. Read-across prediction of the acute toxicity of organic compounds toward the water flea Daphnia magna. Mol. Inf. 2013, 32:108-120.
76. Lave L.B., Omenn G.S. Cost-effectiveness of short-term tests for carcinogenicity. Nature 1986, 324(6):29-34.
77. Lave L.B., Omenn G.S. Screening toxic chemicals: how accurate must tests be?. J. Am. Coll. Toxicol. 1988, 7:565-574.
78. Lapenna S., Gabbert S., Worth A.P. Training needs for toxicity testing in the 21st century: a survey-informed analysis. ATLA 2012, 40:313-320.
79. LAZAR, 2011. http://lazar.in-silico.de/.
80. Leadscope, 2011. http://www.leadscope.com.
81. Legler J., Brouwer A. Are brominated flame retardants endocrine disruptors?. Environ. Int. 2003, 29:879-885.
82. Lilienblum W., Dekant W., Foth H., Gebel T., Hengstler J.G., Kahl R., Kramer P.-J., Schweinfurth H., Wollin K.-M. Alternative methods to safety studies in experimental animals: role in the risk assessment of chemicals under the new European chemicals legislation (REACH). Arch. Toxicol. 2008, 82:211-236.
83. Lipnick R.L. Outliers: their origin and use in the classification of molecular mechanisms of toxicity. Sci. Tot. Environ. 1991, 109(110):131-153.
84. Lipnick R.L., Watson K.R., Strausz A.K. A QSAR study of the acute toxicity of some industrial organic chemicals to goldfish. Narcosis, electrophile and proelectrophile mechanisms. Xenobiotica 1987, 17:1011-1025.
85. Mackay D. Correlation of bioconcentration factors. Environ. Sci. Technol. 1982, 16:274-278.
86. Mackay D., Shiu W.Y., Ma K.C. Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals 1992, Lewis Publishers, Chelsea, MI.
87. Mannhold R., Poda G.I., Ostermann C., Tetko I.V. Calculation of molecular lipophilicity: state-of-the-art and comparison of log P methods on more than 96,000 compounds. J. Pharm. Sci. 2009, 98:861-893.
88. Martin T.M., Young D.M. Prediction of the acute toxicity (96-h LC50) of organic compounds to the fathead minnow (Pimephales promelas) using a group contribution method. Chem. Res. Toxicol. 2001, 14:1378-1385.
89. Mehling A., Eriksson T., Eltze T., Kolle S., Ramirez T., Teubner W., van Ravenzwaay B., Landsiedel R. Non-animal test methods for predicting skin sensitization potentials. Arch. Toxicol. 2012, 86:1273-1295.
90. Mekenyan O.G., Kamenska V., Serafimova R., Poellinger L., Brouwer A., Walker J. Development and validation of an average mammalian estrogen receptor-based QSAR model. SAR QSAR Environ. Res. 2002, 13:579-595.
91. Mekenyan O., Patlewicz G., Dimitrova G., Kuseva C., Todorov M., Stoeva S., Kotov S., Donner E.M. Use of genotoxicity information in the development of integrated testing strategies (ITS) for skin sensitization. Chem. Res. Toxicol. 2010, 23:1519-1540.
92. Mekenyan O.G., Dimitrov S., Pavlov T., Dimitrova G., Todorov M., Petkov P., Kotov S. Simulation of chemical metabolism for fate and hazard assessment. V. Mammalian hazard assessment. SAR QSAR Environ. Res. 2012, 23:553-606.
93. Meylan W.M., Howard P.H., Boethling R.S., Aronson D. Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partition coefficient. Environ. Toxicol. Chem. 1999, 18:664-672.
94. Mizutani M.Y., Tomioka N., Itai A. Rational automatic search method for stable docking methods of protein and ligand. J. Mol. Biol. 1994, 243:310-326.
95. Müller, M., Wenzel, A., Nendza, M., Lewin, G., 2009. Assessment and regulation of environmental hormones (Sub-project 5): development of structure- and risk-based methods for the identification of chemicals with suspected endocrine disrupting activities for the prioritisation of chemicals as part of the authorisation procedure under REACH. Final report FKZ 206 67 448/05 (in German).
96. Mulliner D., Schüürmann G. Model suite for predicting the aquatic toxicity of α,β-unsaturated esters triggered by their chemoavailability. Mol. Inf. 2013, 32:98-107.
97. Mulliner D., Wondrousch D., Schüürmann G. Predicting Michael-acceptor reactivity and toxicity through quantum chemical transition-state calculations. Org. Biomol. Chem. 2011, 9:8400-8412.
98. Multicase, 2011. http://www.multicase.com.
99. ow/log BCF correlations. Bioaccumulation in Aquatic Systems 1991, VCH, Weinheim. R. Nagel, R. Loskill (Eds.).
100. Nendza M., Aldenberg T., Benfenati E., Benigni R., Cronin M.T.D., Escher S., Fernandez A., Gabbert S., Giralt F., Hewitt M., Hrovat M., Jeram S., Kroese D., Madden J.C., Mangelsdorf I., Rallo R., Roncaglioni A., Rorije E., Segner H., Simon-Hettich B., Vermeire T.G. Data quality assessment for in silico methods: a survey of approaches and needs. In Silico Toxicology: Principles and Applications 2010, Royal Society of Chemistry, Cambridge, UK. M.T.D. Cronin, J.C. Madden (Eds.).
101. Nendza M., Herbst T. Screening for low aquatic bioaccumulation (2): physico-chemical constraints. SAR QSAR Environ. Res. 2011, 22:351-364.
102. Nendza M., Müller M. Discriminating toxicant classes by mode of action. 2: Physico-chemical descriptors. Quant. Struct. Act. Relat. 2000, 19:581-598.
103. Nendza M., Wenzel A. Discriminating toxicant classes by mode of action: 1. (Eco)toxicity profiles. Environ. Sci. Pollut. Res. 2006, 13:192-203.
104. OECD Guidance Document on the Validation of (Quantitative) Structure-Activity Relationships [(Q)SAR] Models 2007, OECD Environment Health and Safety Publications OECD, Paris.
105. OECD, 2011. Guidelines for Testing of Chemicals 305. Bioaccumulation in Fish: Aqueous and Dietary Exposure. Paris: OECD.
106. OECD, 2012. (Q)SAR Application Toolbox (version 2.3). http://www.oecd.org/env/existingchemicals/qsar.
107. OSIRIS, 2011. Optimized Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-Test and Test Information. EU Project, contract no. GOCE-CT-2007-037017, 2007-2011. OSIRIS project website:. OSIRIS webtool website:. http://osiris.simpple.com/OSIRIS-ITS/welcome.do.
108. Patlewicz G., Roberts D.W., Uriarte E. A comparison of reactivity schemes for the prediction of skin sensitization potential. Chem. Res. Toxicol. 2008, 21:521-541.
109. Petkov, P., Dimitrov, S., Mekenyan, O.G., Software implementation of mechanistic domain, and user manual. OSIRIS Deliverab, D1.3.12, 2011.
110. Roncaglioni A., Piclin N., Pintore M., Benfenati E. Binary classification models for endocrine disrupter effects mediated through the estrogen receptor. SAR QSAR Environ. Res. 2008, 19:697-733.
111. Rorije E., Aldenberg T., Buist H., Kroese D., Schüürmann G. The OSIRIS weight of evidence approach: ITS for skin sensitisation. Regulat. Toxicol. Pharmacol. 2013, in revision.
112. Russom C.L., Bradbury S.P., Broderius S.J., Hammermeister D.E., Drummond R.A. Predicting modes of toxic action from chemical structure: acute toxicity in the fathead minnow (Pimephales promelas). Environ. Toxicol. Chem. 1997, 16:948-967.
113. Saliner A.G., Netzeva T.I., Worth A.P. Prediction of estrogenicity: validation of a classification model. SAR QSAR Environ. Res. 2006, 17:195-223.
114. Scheringer M. Characterization of the environmental distribution behavior of organic chemicals by means of persistence and spatial range. Environ. Sci. Technol. 1997, 31:2891-2897.
115. Scheringer, M., MacLeod, M., Matthies, M., Klasmeier, J., Persistence criteria in the REACH legislation: critical evaluation and recommendations, Final report. Umweltbundesamt, 21.12.2006.
116. Schmieder P., Koleva Y., Mekenyan O.G. A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes. SAR QSAR Environ. Res. 2002, 13:353-364.
117. Schmieder P., Mekenyan O.G., Bradbury S.P., Veith G.D. QSAR prioritization of chemical inventories for endocrine disruptor testing. Pure Appl. Chem. 2003, 75:2389-2396.
118. Schramm F., Müller A., Hammer H., Paschke A., Schüürmann G. Epoxide and thiirane toxicity in vitro with the ciliates Tetrahymena pyriformis: structural alerts indicating excess toxicity. Environ. Sci. Technol. 2011, 45:5812-5819.
119. Schüürmann G., Ebert R.U., Kühne R. Prediction of physicochemical properties of organic compounds from 2D molecular structure - fragment methods vs. LFER models. Chimia 2006, 60:691-698.
120. Schüürmann G., Ebert R.U., Nendza M., Dearden J.C., Paschke A., Kühne R. Predicting fate-related physicochemical properties. Risk Assessment of Chemicals: An Introduction 2007, Springer, Dordrecht, The Netherlands. Second ed. C.J. van Leeuwen, T.G. Vermeire (Eds.).
121. 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. Model. 2008, 48:2140-2145.
122. Schüürmann G., Ebert R.U., Kühne R. Quantitative read-across for predicting the acute fish toxicity of organic compounds. Environ. Sci. Technol. 2011, 45:4616-4622.
123. Schwöbel J.A.H., Wondrousch D., Koleva Y.K., Madden J.C., Cronin M.T.D., Schüürmann G. Prediction of Michael-type acceptor reactivity toward glutathione. Chem. Res. Toxicol. 2010, 23:1576-1585.
124. Serafimova, R., Fuart Gatnik, M., Worth, A., 2010. Review of the QSAR models and software tools for predicting genotoxicity and carcinogenicity. JRC Technical Report EUR 24427 EN. Publications Office of the European Union, Luxenbourg.
125. Sijm D.T.H.M., Rikken M.G.J., Rorije E., Traas T.P., McLachlan M.S., Peijnenburg W.J.G.M. Transport, accumulation and transformation processes. Risk Assessment of Chemicals: An Introduction 2007, Springer, Dordrecht, The Netherlands. Second ed. C.J. van Leeuwen, T.G. Vermeire (Eds.).
126. Small M.J. Methods for assessing uncertainty in fundamental assumptions and associated models for cancer risk assessment. Risk Anal. 2008, 28:1289-1308.
127. Strempel S., Scheringer M., Ng C.A., Hungerbühler K. Screening for PBT chemicals among the 'existing' and 'new' chemicals of the EU. Environ. Sci. Technol. 2012, 46:5680-5687.
128. Strempel S., Nendza M., Scheringer M., Hungerbühler Using conditional inference trees and random forests to predict the bioaccumulation potential of organic chemicals. Environ. Toxicol. Chem. 2013, in press, http://dx.doi.org/10.1002/etc.2150.
129. Thaens D., Heinzelmann D., Böhme A., Paschke A., Schüürmann G. Chemoassay screening of DNA-reactive mutagenicity with 4-(4-nitrobenzyl)pyridine - application to epoxides, oxetanes and sulfur heterocycles. Chem. Res. Toxicol. 2012, 25:2092-2102.
130. Tong W., Fang H., Hong H., Xie Q., Perkins R., Sheehan D.M. Receptor-mediated toxicity: QSARs for estrogen receptor binding and priority setting of potential estrogenic endocrine disruptors. Predicting Chemical Toxicity and Fate 2004, Taylor and Francis, London, UK. M.T.D. Cronin, D.J. Livingstone (Eds.).
131. Toropov A.A., Toropova A.P., Lombardo A., Roncaglioni A., Benfenati E., Gini G. CORAL: building up the model for bioconcentration factor and defining it's applicability domain. Eur. J. Med. Chem. 2011, 46:1400-1403.
132. Toxtree, 2011. http://toxtree.sourceforge.net/.
133. US EPA, 2009. PBT Profiler. http://www.pbtprofiler.net/.
134. US EPA, 2010. T.E.S.T. v3.3. http://www.epa.gov/nrmrl/std/cppb/qsar/index.html.
135. US EPA, 2011a. EPI Suite v4.1. http://www.epa.gov/oppt/exposure/pubs/episuitedl.htm.
136. US EPA, 2011b. Toxcast. Screening chemicals to predict chemicals faster and better. http://www.epa.gov/ncct/toxcast/.
137. US EPA, 2012. ECOSAR v1.11. http://www.epa.gov/oppt/newchems/tools/21ecosar.htm.
138. van Leeuwen C.J., van der Zandt P.T.J., Aldenberg T., Verhaar H.J.M., Hermens J.L.M. Application of QSARs, extrapolation and equilibrium partitioning in aquatic effects assessment. 1. Narcotic industrial pollutants. Environ. Toxicol. Chem. 1992, 11:267-282.
139. van Leeuwen C.J., Patlewicz G.Y., Worth A.P. Intelligent testing strategies. Risk Assessment of Chemicals: An Introduction 2007, Springer, Dordrecht, The Netherlands. Second ed. C.J. van Leeuwen, T.G. Vermeire (Eds.).
140. Vega, 2011. http://www.vega-qsar.eu/.
141. Veith G.D., Broderius S.J. Rules for distinguishing toxicants that cause type I and type II narcosis syndromes. Environ. Health Persp. 1990, 87:207-211.
142. Veith G.D., Defoe D.L., Bergstedt B.V. Measuring and estimating the bioconcentration factor of chemicals in fish. J. Fish. Board Can. 1979, 36:1040-1048.
143. Veith G.D., Call D.J., Brooke L.T. Structure-toxicity relationships for the fathead minnow, Pimephales promelas: narcotic industrial chemicals. Can. J. Fish. Aquat. Sci. 1983, 40:743-748.
144. Verhaar H.J.M., van Leeuwen C.J., Hermens J.L.M. Classifying environmental pollutants. 1: structure-activity relationships for prediction of aquatic toxicity. Chemosphere 1992, 25:471-491.
145. Verhaar, H.J.M., Mulder, W., Hermens, J.L.M., 1995. Overview of structure-activity relationships for environmental endpoints. Part 1: General outline and procedure. II QSAR for ecotoxicity. Report of the EU-DG-XII project QSAR for predicting fate and effects of chemicals in the environment. Contract # EV5V-CT92-0211.
146. Vighi M., Migliorati S., Monti G.S. Toxicity on the luminescent bacterium Vibrio fischeri (Beijerinck). I: QSAR equation for narcotics and polar narcotics. Ecotox. Environ. Saf. 2009, 72:154-161.
147. Vinggaard A.M., Niemela J., Wedeby E.B., Jensen G.E. Screening of 397 chemicals and development of a quantitative structure-activity relationship model for androgen receptor antagonism. Chem. Res. Toxicol. 2008, 21:813-823.
148. von der Ohe P.C., Kühne R., Ebert R.U., Altenburger R., Liess M., Schüürmann G. 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. 2005, 18:536-555.
149. Wania F., Mackay D. Tracking the distribution of persistent organic pollutants. Environ. Sci. Technol. 1996, 30:390A-396A.
150. Wondrousch D., Böhme A., Thaens D., Ost N., Schüürmann G. Local electrophilicity predicts toxicity-relevant reactivity of Michael acceptors. J. Phys. Chem. Lett. 2010, 1:1605-1610.
151. Yokota F., Gray G., Hammitt J.K., Thompson K. Tiered chemical testing: a value of information approach. Risk Anal. 2004, 24:1625-1639.
152. Yumi, A., 2004. Development of a 3D-QSAR (3-dimensional quantitative structure-activity relationships) model for predicting binding potency to human estrogen receptor α. Presentation: 2nd meeting of the validation management group for non-animal testing (VMG-NA) of the task force on endocrine disruptors testing and assessment (EDTA) 4-5 November 2004, OECD, Paris.
153. Zeiger E. Identification of rodent carcinogens and noncarcinogens using genetic toxicity tests: premises, promises, and performance. Regulat. Pharmacol. Toxicol. 1998, 28:85-95.
154. Zhao C., Boriani E., Chana A., Roncaglioni A., Benfenati E. A new hybrid system of QSAR models for predicting bioconcentration factor (BCF). Chemosphere 2008, 73:1701-1707.