Determination and prediction of xenoestrogens by recombinant yeast-based assay and QSAR

Chemosphere

Volumn 74, Issue 9, 2009, Pages 1152-1157

  Li, Fei ^a   Chen, Jingwen ^a   Wang, Zijian ^b   Li, Jian ^b   Qiao, Xianliang ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b RESEARCH CENTER FOR ECO ENVIRONMENTAL SCIENCES   (China)

Author keywords

Applicability domain; External validation; Partial least squares; QSAR; Xenoestrogens; Yeast based assay

Indexed keywords

APPLICABILITY DOMAIN; EXTERNAL VALIDATION; PARTIAL LEAST SQUARES; QSAR; XENOESTROGENS;

ALGEBRA; CURVE FITTING; LEAST SQUARES APPROXIMATIONS; MOLECULAR GRAPHICS; SULFUR COMPOUNDS;

YEAST;

BIOASSAY; ESTROGENIC COMPOUND; MOLECULAR ANALYSIS; RECOMBINATION; REGRESSION ANALYSIS; SOFTWARE; YEAST;

BIOLOGICAL ASSAY; ESTRADIOL CONGENERS; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REGRESSION ANALYSIS; XENOBIOTICS; YEASTS;

EID: 61349184332     PISSN: 00456535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemosphere.2008.11.081     Document Type: Article

References (38)

1. Arnold S.F., Collins B.M., Robinson M.K., Guillette L.J., and McLachlan J.A. Differential interaction of natural and synthetic estrogens with extracellular binding proteins in a yeast estrogen screen. Steroids 61 (1996) 642-646
2. Asikainen A.H., Ruuskanen J., and Tuppurainen K.A. Consensus kNN QSAR: A versatile method for predicting the estrogenic activity of organic compounds in silico. A comparative study with five estrogen receptors and a large, diverse set of ligands. Environ. Sci. Technol. 38 (2004) 6724-6729
3. Asikainen A., Kolehmainen M., Ruuskanen J., and Tuppurainen K. Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods. Chemosphere 62 (2006) 658-673
4. Chen C.W., Hurd C., Vorojeikina D.P., Arnold S.F., and Notides A.C. Transcriptional activation of the human estrogen receptor by DDT isomers and metabolites in yeast and MCF-7 cells. Biochem. Pharmacol. 53 (1997) 1161-1172
5. Coldham N.G., Dave M., Sivapathasundaram S., McDonnell D.P., Connor C., and Sauer M.J. Evaluation of a recombinant yeast cell estrogen screening assay. Environ. Health Persp. 105 (1997) 734-742
6. Consonni V., Todeschini R., and Pavan M. Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J. Chem. Inf. Comput. Sci. 42 (2002) 682-692
7. Couse J.F., and Korach K.S. Estrogen receptor null mice: what have we learned and where will they lead us?. Endocr. Rev. 20 (1999) 459
8. Eriksson L., Jaworska J., Worth A.P., Cronin M.T.D., McDowell R.M., and Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ. Health Persp. 111 (2003) 1361-1375
9. Fang H., Tong W.D., Perkins R., Soto A.M., Prechtl N.V., and Sheehan D.M. Quantitative comparisons of in vitro assays for estrogenic activities. Environ. Health Persp. 108 (2000) 723-729
10. Gaido K.W., Leonard L.S., Lovell S., Gould J.C., Babai D., Portier C.J., and McDonnell D.P. Evaluation of chemicals with endocrine modulating activity in a yeast-based steroid hormone receptor gene transcription assay. Toxicol. Appl. Pharm. 143 (1997) 205-212
11. Gierthy J.F., Arcaro K.F., Li A., Silber P., Lloyd S., and Yang Y. Optimization and validation of the MCF-7 focus assay for estrogen modulators. Toxicol. Sci. 72 (2003) 154-155
12. Golbraikh A., Shen M., Xiao Z.Y., Xiao Y.D., Lee K.H., and Tropsha A. Rational selection of training and test sets for the development of validated QSAR models. J. Comput. Aid. Mol. Des. 17 (2003) 241-253
13. Huang X.Y., Chen J.W., Gao L.N., Ding G.H., Zhao Y.Z., and Schramm K.W. Data evaluations and quantitative predictive models for vapor pressures of polycyclic aromatic hydrocarbons at different temperatures. SAR QSAR Environ. Res. 15 (2004) 115-125
14. Iafrati M.D., Karas R.H., Aronovitz M., Kim S., Sullivan T.R., Lubahn D.B., ODonnell T.F., Korach K.S., and Mendelsohn M.E. Estrogen inhibits the vascular injury response in estrogen receptor alpha-deficient mice. Nat. Med. 3 (1997) 545-548
15. Kanno J., Onyon L., Haseman J., Fenner-Crisp P., Ashby J., and Owens W. The OECD program to validate the rat uterotrophic bioassay to screen compounds for in vivo estrogenic responses: Phase 1. Environ. Health Persp. 109 (2001) 785-794
16. Kavlock R.J., Daston G.P., DeRosa C., FennerCrisp P., Gray L.E., Kaattari S., Lucier G., Luster M., Mac M.J., Maczka C., Miller R., Moore J., Rolland R., Scott G., Sheehan D.M., Sinks T., and Tilson H.A. Research needs for the risk assessment of health and environmental effects of endocrine disruptors: A report of the US EPA-sponsored workshop. Environ. Health Persp. 104 (1996) 715-740
17. Liu H.X., Papa E., and Gramatica P. QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles. Chem. Res. Toxicol. 19 (2006) 1540-1548
18. Ma M., Li J., and Wang Z.J. Assessing the detoxication efficiencies of wastewater treatment processes using a battery of bioassays/biomarkers. Arch. Environ. Con. Toxicol. 49 (2005) 480-487
19. Maier C.G.A., Chapman K.D., and Smith D.W. Differential estrogenic activities of male and female plant-extracts from 2 dioecious species. Plant Sci. 109 (1995) 31-43
20. Marini F., Roncaglioni A., and Novic M. Variable selection and interpretation in structure-affinity correlation modeling of estrogen receptor binders. J. Chem. Inf. Model. 45 (2005) 1507-1519
21. Migliaccio S., Newbold R.R., Mclachlan J.A., and Korach K.S. Alterations in estrogen-levels during development affects the skeleton - use of an animal-model. Environ. Health Persp. 103 (1995) 95-97
22. Morales A.H., Duchowicz P.R., Perez M.A.C., Castro E.A., Cordeiro M.N.D.S., and Gonzalez M.P. Application of the replacement method as a novel variable selection strategy in QSAR1. Carcinogenic potential. Chemometr. Intell. Lab. 81 (2006) 180-187
23. Nendza M., and Wenzel A. Discriminating toxicant classes by mode of action - 1. (Eco)toxicity profiles. Environ. Sci. Pollut. 13 (2006) 192-203
24. Routledge E.J., and Sumpter J.P. Structural features of alkylphenolic chemicals associated with estrogenic activity. J. Biol. Chem. 272 (1997) 3280-3288
25. Shi L.M., Fang H., Tong W.D., Wu J., Perkins R., Blair R.M., Branham W.S., Dial S.L., Moland C.I., and Sheehan D.M. QSAR models using a large diverse set of estrogens. J. Chem. Inf. Comput. Sci. 41 (2001) 186-195
26. Soto A.M., Sonnenschein C., Chung K.L., Fernandez M.F., Olea N., and Serrano F.O. The e-screen assay as a tool to identify estrogens - an update on estrogenic environmental-pollutants. Environ. Health Persp. 103 (1995) 113-122
27. Todeschini R., and Consonni V. Handbook of Molecular Descriptors (2000), Wiley-VCH, Weinheim, Germany
28. Tong W.D., Perkins R., Strelitz R., Collantes E.R., Keenan S., Welsh W.J., Branham W.S., and Sheehan D.M. Quantitative structure-activin relationships (QSARs) for estrogen binding to the estrogen receptor: Predictions across species. Environ. Health Persp. 105 (1997) 1116-1124
29. Tong W.D., Perkins R., Xing L., Welsh W.J., and Sheehan D.M. QSAR models for binding of estrogenic compounds to estrogen receptor alpha and beta subtypes. Endocrinology 138 (1997) 4022-4025
30. Tong W., Lowis D.R., Perkins R., Chen Y., Welsh W.J., Goddette D.W., Heritage T.W., and Sheehan D.M. Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor. J. Chem. Inf. Comput. Sci. 38 (1998) 669-677
31. Tropsha A., Gramatica P., and Gombar V.K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QASR Comb. Sci. 22 (2003) 69-77
32. Tunkel J., Mayo K., Austin C., Hickerson A., and Howard P. Practical considerations on the use of predictive models for regulatory purposes. Environ. Sci. Technol. 39 (2005) 2188-2199
33. Waller C.L., Oprea T.I., Chae K., Park H.K., Korach K.S., Laws S.C., Wiese T.E., Kelce W.R., and Gray L.E. Ligand-based identification of environmental estrogens. Chem. Res. Toxicol. 9 (1996) 1240-1248
34. Waller C.L. A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds. J. Chem. Inf. Comput. Sci. 44 (2004) 758-765
35. Wold S., Sjostrom M., and Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometr. Intell. Lab. 58 (2001) 109-130
36. Yamasaki K., Sawaki M., Noda S., Muroi T., Takakura S., Mitoma H., Sakamoto S., Nakai M., and Yakabe Y. Comparison of the Hershberger assay and androgen receptor binding assay of twelve chemicals. Toxicology 195 (2004) 177-186
37. Yu S.J., Keenan S.M., Tong W., and Welsh W.J. Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative-activity relationship (3D-QSAR) models: Predicting the estrogenic activity of xenoestrogens. Chem. Res. Toxicol. 15 (2002) 1229-1234
38. Zefirov N.S., and Palyulin V.A. QSAR for boiling points of "small" sulfides Are the "high-quality structure-property-activity regressions" the real high quality QSAR models?. J. Chem. Inf. Comput. Sci. 41 (2001) 1022-1027