Prediction of acute mammalian toxicity from QSARs and interspecies correlations

SAR and QSAR in Environmental Research

Volumn 20, Issue 5-6, 2009, Pages 467-500

  Devillers, J ^a   Devillers, H ^a  

^a CTIS   (France)

Author keywords

Interspecies correlation; Invertebrates; Mouse; QSAR; Rat; Regression analysis

Indexed keywords

BIOCHEMISTRY; COMPUTATIONAL CHEMISTRY; INDICATORS (CHEMICAL); INDUSTRIAL CHEMICALS; RATS; REGRESSION ANALYSIS; TOXICITY;

ACUTE TOXICITY; ANIMAL RIGHTS ACTIVISTS; INTERSPECIES CORRELATIONS; INVERTEBRATE; LABORATORY TEST; MAMMALIAN TOXICITY; MOUSE; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; RELATIVE HAZARDS; TOXICITY TEST;

ORGANIC CHEMICALS;

ANIMALIA; INVERTEBRATA; MAMMALIA; MUS; RATTUS; VERTEBRATA;

EID: 70649105422     PISSN: 1062936X     EISSN: 1029046X     Source Type: Journal    
DOI: 10.1080/10629360903278651     Document Type: Article

References (76)

1. European Community, Fifth report on the statistics on the number of animals used for experimental and other scientific purposes in the member states of the European Union, COMM(2007) 675 final, European Community, Brussels, 2007.
2. A.P. Worth, C.J. van Leeuwen, and T. Hartung, The prospects for using (Q)SARs in a changing political environment. High expectations and a key role for the European Commission's joint research centre, SAR QSAR Environ. Res. 15 (2004), pp. 331-343.
3. G.E. Jensen, J.R. Niemela, E.B. Wedebeye, and N.G. Nikolov, QSAR models for reproductive toxicity and endocrine disruption in regulatory use-a preliminary investigation, SAR QSAR Environ. Res. 19 (2008), pp. 631-641.
4. 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.
5. R. Shrivastava, C. Delomenie, A. Chevalier, G. John, B. Ekwall, E. Walum, and R. Massingham, Comparison of in vivo acute lethal potency and in vitro cytotoxicity of 48 chemicals, Cell Biol. Toxicol. 8 (1992), pp. 157-170.
6. J. Devillers, D. Domine, and S. Bintein, Multivariate analysis of the first MEIC chemicals, SAR QSAR Environ. Res. 2 (1994), pp. 261-270.
7. I. Lessigiarska, A.P. Worth, T.I. Netzeva, J.C. Dearden, and M.T.D. Cronin, Quantitative structure-activity-activity and quantitative structure-activity investigations of human and rodent toxicity, Chemosphere 65 (2006), pp. 1878-1887.
8. G. Wang and N. Bai, Structure-activity relationships for rat and mouse LD50 of miscellaneous alcohols, Chemosphere 36 (1998), pp. 1475-1483.
9. M. Tichy, V. Trcka, Z. Roth, and M. Krivucova, QSAR analysis and data extrapolation among mammals in a series of aliphatic alcohols, Environ. Health Perspect. 61 (1985), pp. 321-328.
10. R.L. Lipnick, C.S. Pritzker, and D.L. Bentley, Application of QSAR to model the toxicology of industrial organic chemicals to aquatic organisms and mammals, in QSAR in Drug Design and Toxicology, D. Hadzi and B. Jerman-Blazic, eds., Elsevier, Amsterdam, 1987, pp. 301-306.
11. M.T.D. Cronin, J.C. Dearden, J.C. Duffy, R. Edwards, N. Manga, A.P. Worth, and A.D.P. Worgan, The importance of hydrophobicity and electrophilicity descriptors in mechanistically-based QSARs for toxicological endpoints, SAR QSAR Environ. Res. 13 (2002), pp. 167-176.
12. H. Tanii, J. Huang, and K. Hashimoto, Structure-acute toxicity relationship of aromatic hydrocarbons in mice, Toxicol. Lett. 76 (1995), pp. 27-31.
13. H. Tanii and K. Hashimoto, Structure-toxicity relationship of acrylates and methacrylates, Toxicol. Lett. 11 (1982), pp. 125-129.
14. H. Tanii and K. Hashimoto, Studies on mechanism of acute toxicity of nitriles in mice, Arch. Toxicol. 55 (1984), pp. 47-54.
15. H. Tanii, H. Tsuji, and K. Hashimoto, Structure-toxicity relationship of monoketones, Toxicol. Lett. 30 (1986), pp. 13-17.
16. H. Tanii, S. Saito, and K. Hashimoto, Structure-toxicity relationship of ethylene glycol ether, Arch. Toxicol. 66 (1992), pp. 368-371.
17. J.A. Durden, Acute oral toxicity of 2-alkyl and 2,6-dialkyl anilines. Correlation with lipophilicity, J. Med. Chem. 16 (1973), p. 1316.
18. H. Jackel and W. Klein, Prediction of mammalian toxicity by quantitative structure activity relationships: Aliphatic amines and anilines, Quant. Struct.-Act. Relat. 10 (1991), pp. 198-204.
19. S.R. Johnson and P.C. Jurs, Prediction of acute mammalian toxicity from molecular structure for a diverse set of substituted anilines using regression analysis and computational neural networks, in Computer-Assisted Lead Finding and Optimization. Current Tools for Medicinal Chemistry, H. van de Waterbeemd, B. Testa, and G. Folkers, eds., Wiley-VCH, Basel, 1997, pp. 31-48.
20. J. Devillers, Neural Networks in QSAR and Drug Design, Academic Press, London, 1996.
21. M. Nendza, B. Dittrich, A. Wenzel, and W. Klein, Predictive QSAR models for estimating ecotoxic hazard of plant-protecting agents: Target and non-target toxicity, in QSAR in Environmental Toxicology-IV, J.L.P. Hermens and A. Opperhuizen, eds., Elsevier, Amsterdam, 1991, pp. 527-535.
22. 50) in a large series of herbicidal benzimidazoles, Pestic. Sci. 15 (1984), pp. 31-39.
23. L.B. Kier and L.H. Hall, The electrotopological state: Structure modeling for QSAR and database analysis, in Topological Indices and Related Descriptors in QSAR and QSPR, J. Devillers and A.T. Balaban, eds., Gordon and Breach, Amsterdam, 1999, pp. 491-562.
24. J.D. Gough and L.H. Hall, Modeling the toxicity of amide herbicides using the electrotopological state, Environ. Toxicol. Chem. 18 (1999), pp. 1069-1075.
25. D. Zakarya, E.M. Larfaoui, A. Boulaamail, and T. Lakhlifi, Analysis of structure-toxicity relationships for a series of amide herbicides using statistical methods and neural network, SAR QSAR Environ. Res. 5 (1996), pp. 269-279.
26. J. Devillers, Prediction of mammalian toxicity of organophosphorus pesticides from QSTR modeling, SAR QSAR Environ. Res. 15 (2004), pp. 501-510.
27. T.B. Gaines, The acute toxicity of pesticides to rats, Toxicol. Appl. Pharmacol. 2 (1960), pp. 88-99.
28. T.B. Gaines, Acute toxicity of pesticides, Toxicol. Appl. Pharmacol. 14 (1969), pp. 515-534.
29. T.B. Gaines and R.E. Linder, Acute toxicity of pesticides in adult and weanling rats, Fundam. Appl. Toxicol. 7 (1986), pp. 299-308.
30. J. Devillers, Autocorrelation descriptors for modeling (eco)toxicological endpoints, in Topological Indices and Related Descriptors in QSAR and QSPR, J. Devillers and A.T. Balaban, eds., Gordon and Breach, Amsterdam, 1999, pp. 595-612.
31. R.F. Rekker and R. Mannhold, Calculation of Drug Lipophilicity. The Hydrophobic Fragmental Constant Approach, VCH, Weinheim, 1992.
32. C. Hansch and A. Leo, Substituent Constants for Correlation Analysis in Chemistry and Biology, J. Wiley & Sons, New York, 1979.
33. R. Kato, Sex-related differences in drug metabolism, Drug Metab. Rev. 3 (1974), pp. 1-32.
34. D.V. Eldred and P.C. Jurs, Prediction of acute mammalian toxicity of organophosphorus pesticide compounds from molecular structure, SAR QSAR Environ. Res. 10 (1999), pp. 75-99.
35. M. Zahouily, A. Rhihil, H. Bazoui, S. Sebti, and D. Zakarya, Structure-toxicity relationships study of a series of organophosphorus insecticides, J. Mol. Model. 8 (2002), pp. 168-172.
36. R. Garcia-Domenech, P. Alarcon-Elbal, G. Bolas, R. Bueno-Mari, F.A. Chorda-Olmos, S.A. Delcour, M.C. Mourino, A. Vidal, and J. Galvez, Prediction of acute toxicity of organophosphorus pesticides using topological indices, SAR QSAR Environ. Res. 18 (2007), pp. 745-755.
37. I.O. Juranic, B.J. Drakulic, S.D. Petrovic, D.Z. Mijin, and M.V. Stankovic, A QSAR study of acute toxicity of N-substituted fluoroacetamides to rats, Chemosphere 62 (2006), pp. 641-649.
38. C. Hansch and A. Kurup, QSAR of chemical polarizability and nerve toxicity. 2, J. Chem. Inf. Comput. Sci. 43 (2003), pp. 1647-1651.
39. M.W. Williams, J.D. Hoeschele, J.E. Turner, K.B. Jacobson, N.T. Christie, C.L. Paton, L.H. Smith, H.R. Witschi, and E.H. Lee, Chemical softness and acute toxicity in mice and Drosophila, Toxicol. Appl. Pharmacol. 63 (1982), pp. 461-469.
40. K.L.E. Kaiser and S.R. Esterby, Regression and cluster analysis of the acute toxicity of 267 chemicals to six species of biota and the octanol/water partition coefficient, in QSAR in Environmental Toxicology-IV, J.L.P. Hermens and A. Opperhuizen, eds., Elsevier, Amsterdam, 1991, pp. 499-514.
41. K.L.E. Kaiser, S.P. Niculescu, and T.W. Schultz, Probabilistic neural network modelling of the toxicity of chemicals to Tetrahymena pyriformis with molecular fragment descriptors, SAR QSAR Environ. Res. 13 (2002), pp. 57-67.
42. J.P. Bigus, Data Mining with Neural Networks, McGraw-Hill, New York, 1996.
43. J. Wang, L. Lai, and Y. Tang, Data mining of toxic chemicals: Structure patterns and QSAR, J. Mol. Model. 5 (1999), pp. 252-262.
44. G. Klopman, Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules, J. Am. Chem. Soc. 106 (1984), pp. 7315-7320.
45. J. Pangrekar, G. Klopman, and H.S. Rosenkranz, Expert-system comparison of structural determinants of chemical toxicity to environmental bacteria, Environ. Toxicol. Chem. 13 (1994), pp. 979-1001.
46. G. Klopman, S.K. Chakravarti, N. Harris, J. Ivanov, and R.D. Saiakhov, In-silico screening of high production volume chemicals for mutagenicity using the MCASE QSAR expert system, SAR QSAR Environ. Res. 14 (2003), pp. 165-180.
47. J. Devillers, QSAR modeling of large heterogeneous sets of molecules, SAR QSAR Environ. Res.12 (2001), pp. 515-528.
48. V.K. Gombar, K. Enslein, and D.A. Reid, Computer-assisted toxicity assessment: criteria for acceptance, http://www.netsci.org/Science/Special/feature05.html (accessed 28 March 2009).
49. V.K. Gombar, Reliable assessment of log P of compounds of pharmaceutical relevance, SAR QSAR Environ. Res. 10 (1999), pp. 371-380.
50. K. Enslein, T.R. Lander, M.E. Tomb, and P.N. Craig, A Predictive Model for Estimating Rat Oral LD50 Values, Princeton Scientific Publishers, Princeton, NJ, 1983.
51. I. Tsakovska, I. Lessigiarska, T. Netzeva, and A. Worth, Review of (Q)SARs for Mammalian Toxicity, EUR 22486 EN, JRC, 2006.
52. 50, In Vitro Toxicol. 5 (1992), pp. 73-82.
53. K.L.E. Kaiser, M.B. McKinnon, and F.L. Fort, Interspecies toxicity correlations of rat, mouse and Photobacterium phosphoreum, Environ. Toxicol. Chem. 13 (1994), pp. 1599-1606.
54. J. Devillers, P. Pandard, E. Thybaud, and A. Merle, Interspecies correlations for predicting the acute toxicity of xenobiotics, in Ecotoxicology Modeling, J. Devillers, ed., Springer, New York, 2009, pp. 85-115.
55. J. Devillers, P. Pandard, and E. Thybaud, Re ́alisation du Bilan des Connaissances Scientifiques: Recensement et Analyse Critique des Donne ́es Existantes sur les Corre ́lations entre les Re ́sultats des Tests Toxicologiques et Ecotoxicologiques, Rapport final 04 94 C 0023, ADEME, 2006.
56. J.W. Biggar and R.L. Riggs, Apparent solubility of organochlorine insecticides in water at various temperatures, Hilgardia 42 (1974), pp. 383-391.
57. I.E. Yates, Differential sensitivity to mutagens by Photobacterium phosphoreum, J. Microbiol. Meth. 3 (1985), pp. 171-180.
58. M.P. Sauvant, D. Pepin, J. Bohatier, C.A. Groliere, and J. Guillot, Toxicity assessment of 16 inorganic environmental pollutants by six bioassays, Ecotoxicol. Environ. Safety 37 (1997), pp. 131-140.
59. M.C. Calleja and G. Persoone, Cyst-based toxicity tests. IV. The Potential of ecotoxicological tests for the prediction of acute toxicity in man as evaluated on the first ten chemicals of the MEIC programme, ATLA 20 (1992), pp. 396-405.
60. M. Tichy, V. Borek-Dohalsky, and M. Rucki, Worms (Tubifex tubifex) and partition coefficients for estimation of acute toxicity, in Progress in the Reduction, Refinement and Replacement of Animal Experimentations, M. Balls, A.M. van Zeller, and M.E. Halder, eds., Elsevier, Amsterdam, 2000, pp. 513-520.
61. B.S. Khangarot and P.K. Ray, The confirmation of a mammalian poison classification using a water flea (Daphnia magna) screening method, Arch. Hydrobiol. 113 (1988), pp. 447-455.
62. S.K. Janardan, C.S. Olson, and D.J. Schaeffer, Quantitative comparisons of acute toxicity of organic chemicals to rat and fish, Ecotoxicol. Environ. Safety 8 (1984), pp. 531-539.
63. W.W. Johnson and M.T. Finley, Handbook of Acute Toxicity of Chemicals to Fish and Aquatic Invertebrates, Resource Publication 135, US Department of the Interior, Fish and Wildlife Service, Washington, DC, 1980.
64. P.V. Hodson, A comparison of the acute toxicity of chemicals to fish, rats and mice, J. Appl. Toxicol. 5 (1985), pp. 220-226.
65. D. Delistraty, B. Taylor, and R. Anderson, Comparisons of acute toxicity of selected chemicals to rainbow trout and rats, Ecotoxicol. Environ. Safety 39 (1998), pp. 195-200.
66. D. Delistraty, Acute toxicity to rats and trout with a focus on inhalation and aquatic exposures, Ecotoxicol. Environ. Safety 46 (2000), pp. 225-233.
67. K. Enslein, T.M. Tuzzeo, H.H. Borgstedt, B.W. Blake, and J.B. Hart, Prediction of rat oral LD50 from Daphnia magna LC50 and chemical structure, in QSAR in 500 J. Devillers and H. Devillers Environmental Toxicology-II, K.L.E. Kaiser, ed., Reidel Publishing Company, Dordrecht, 1987, pp. 91-106.
68. K. Enslein, T.M. Tuzzeo, B.W. Blake, J.B. Hart, and W.G. Landis, Prediction of Daphnia magna EC50 values from rat oral LD50 and structural parameters, in Aquatic Toxicology and Environmental Fate, G.W. Suter and M.A. Lewis, eds., Vol. 11, ASTM STP 1007, Philadelphia, PA, 1989, pp. 397-409.
69. S. Kacew, Role of rat strain in the differential sensitivity to pharmaceutical agents and naturally occurring substances, J. Toxicol. Environ. Health, Part A 47 (1996), pp. 1-30.
70. T.W. Schultz, TETRATOX: Tetrahymena pyriformis population growth impairment endpoint-a surrogate for fish lethality, Toxicol. Methods 7 (1997), pp. 289-309.
71. P. Pandard, J. Devillers, A.M. Charissou, V. Poulsen, M.J. Jourdain, J.F. Ferard, C. Grand, and A. Bispo, Selecting a battery of bioassays for ecotoxicological characterization of wastes, Sci. Total Environ. 363 (2006), pp. 114-125.
72. V. Vapnik, The Nature of Statistical Learning Theory, Springer, Berlin, 1995.
73. N.X. Tan, H.B. Rao, Z.R. Li, and X.Y. Li, Prediction of chemical carcinogenicity by machine learning approaches, SAR QSAR Environ. Res. 20 (2009), pp. 27-75.
74. A. Panaye, J.P. Doucet, J. Devillers, N. Marchand-Geneste, and J.M. Porcher, Decision trees versus support vector machine for classification of androgen receptor ligands, SAR QSAR Environ. Res. 19 (2008), pp. 128-151.
75. S. Li, A. Fedorowicz, and M.E. Andrew, A new descriptor selection scheme for SVM in unbalanced class problem: A case study using skin sensitisation dataset, SAR QSAR Environ. Res. 18 (2007), pp. 423-441.
76. S. Yuan, M. Xiao, G. Zheng, M. Tian, and X. Lu, Quantitative structure-property relationship studies on electrochemical degradation of substituted phenols using a support vector machine, SAR QSAR Environ. Res. 17 (2006), pp. 473-481.