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Articles

Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering

Pages 17-30 | Received 19 Nov 2015, Accepted 24 Nov 2015, Published online: 19 Jan 2016

References

  • National Research Council. A framework to guide selection of chemical alternatives, 2014. Available at http://www.nap.edu/catalog/18872/a-framework-to-guide-selection-of-chemical-alternatives.
  • F.S. Collins, G.M. Gray, and J.R. Bucher, Transforming environmental health protection, Science 319 (2008), pp. 906–907.
  • US EPA. ToxCast, 2015. Available at http://www2.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data.
  • D.M. Rotroff, D.J. Dix, K.A. Houck, T.B. Knudsen, M.T. Martin, K.W. McLaurin, D.M. Reif, K.M. Crofton, A.V. Singh, M. Xia, R. Huang, and R.S. Judson, Using in vitro high throughput screening assays to identify potential endocrine-disrupting chemicals, Environ. Health Perspect. 121 (2013), pp. 7–14.
  • US EPA. Use of high throughput assays and computational tools; endocrine disruptor screening program; notice of availability and opportunity for comment, 2011. Available at https://www.federalregister.gov/articles/2015/06/19/2015-15182/use-of-high-throughput-assays-and-computational-tools-endocrine-disruptor-screening-program-notice.
  • A. Sedykh, H. Zhu, H. Tang, L. Zhang, A. Richard, I. Rusyn, and A. Tropsha, Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity, Environ. Health Perspect. 119 (2011), pp. 367–370.
  • H. Hong, W. Tong, H. Fang, L. Shi, Q. Xie, J. Wu, R. Perkins, J.D. Walker, W. Branham, and D.M. Sheehan, Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts, Environ. Health Perspect. 110 (2002), pp. 29–36.
  • A. Asikainen, M. Kolehmainen, J. Ruuskanen, and K. Tuppurainen, Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods, Chemosphere 62 (2006), pp. 658–673.
  • Y. Chen, F. Cheng, L. Sun, W. Li, G. Liu, and Y. Tang, Computational models to predict endocrine-disrupting chemical binding with androgen or oestrogen receptors, Ecotoxicol. Environ. Safety 110 (2014), pp. 280–287.
  • J. Li and P. Gramatica, Classification and virtual screening of androgen receptor antagonists, J. Chem. Inf. Model. 50 (2010), pp. 861–874.
  • J. Li and P. Gramatica, QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs, SAR QSAR Environ. Res. 21 (2010), pp. 657–669.
  • H. Li, C.Y. Ung, C.W. Yap, Y. Xue, Z.R. Li, and Y.Z. Chen, Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods, J. Molec. Graph. Model. 25 (2006), pp. 313–323.
  • H. Liu, E. Papa, J.D. Walker, and P. Gramatica, In silico screening of estrogen-like chemicals based on different nonlinear classification models, J. Molec. Graph. Model. 26 (2007), pp. 135–144.
  • 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. 129–151.
  • G.E. Jensen, N.G. Nikolov, E.B. Wedebye, T. Ringsted, and J.R. Niemelä, QSAR models for anti-androgenic effect –A preliminary study, SAR QSAR Environ. Res. 22 (2011), pp. 35–49.
  • G.E. Jensen, N.G. Nikolov, K.D. Sørensen, A.M. Vinggaard, and J.R. Niemelä, QSAR model for androgen receptor antagonism - Data from CHO cell reporter gene assays, Steroids Horm. Sci. (2012)doi:10.4172/2157-7536.S2-006.
  • W. Tong, H. Fang, H. Hong, Q. Xie, R. Perkins, and D.M. Sheehan, Receptor-mediated toxicity: QSARs for estrogen receptor binding and priority setting of potential estrogenic endocrine disruptors, in Predicting Chemical Toxicity and Fate, M.T.D. Cronin and D.J. Livingstone, eds., 2004, pp. 285–314. CRC Press, Boca Raton.
  • L.M. Shi, H. Fang, W. Tong, J. Wu, R. Perkins, R.M. Blair, W.S. Branham, S.L. Dial, C.L. Moland, and D.M. Sheehan, QSAR models using a large diverse set of estrogens, J. Chem. Inform. Comput. Sci. 41 (2001), pp. 186–195.
  • N. Stojić, S. Erić, and I. Kuzmanovski, Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counter-propagation artificial neural networks, J. Molec. Graph. Model. 29 (2010), pp. 450–460.
  • J. Shen, L. Xu, H. Fang, A.M. Richard, J.D. Bray, R.S. Judson, G. Zhou, T.J. Colatsky, J.L. Aungst, C. Teng, S.C. Harris, W. Ge, S.Y. Dai, Z. Su, A.C. Jacobs, W. Harrouk, R. Perkins, W. Tong, and H. Hong, EADB: An estrogenic activity database for assessing potential endocrine activity, Toxicol. Sci. 135 (2013), pp. 277–291.
  • K. Mansouri, A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha, A. Varnek, A. Zakharov, A. Worth, A.M. Richard, C.M. Grulke, D. Trisciuzzi, D. Fourches, D. Horvath, E. Benfenati, E. Muratov, E.B. Wedebye, F. Grisoni, G.F. Mangiatordi, G.M. Incisivo, H. Hong, H.W. Ng, I.V. Tetko, I. Balabin, J. Kancherla, J. Shen, J. Burton, M. Nicklaus, M. Cassotti, N.G. Nikolov, O. Nicolotti, P.L. Andersson, Q. Zang, R. Politi, R.D. Beger, R. Todeschini, R. Huang, S. Farag, S.A. Rosenberg, S. Slavov, X. Hu, and R.S. Judson, US EPA. CERAPP - Collaborative Estrogen Receptor Activity Prediction Project, Envion. Health Perspect. (2015), Available at http://www.epa.gov/sites/production/files/2015-10/documents/cerapp_consensus.pdf. in review.
  • H. Zhu, I. Rusyn, A. Richard, and A. Tropsha, Use of Cell Viability Assay Data Improves the prediction accuracy of conventional quantitative structure–activity relationship models of animal carcinogenicity, Environ. Health Perspect. 116 (2008), pp. 506–513.
  • OECD. OECD principles for the validation, for regulatory purposes, of (Quantitative) Structure-Activity Relationship Models, 2004. Available at http://www.oecd.org/chemicalsafety/risk-assessment/37849783.pdf.
  • R.S. Judson, F.M. Magpantay, V. Chickarmane, C. Haskell, N. Tania, J. Taylor, M. Xia, R. Huang, D.M. Rotroff, D.L. Filer, K.A. Houck, M.T. Martin, N. Sipes, A.M. Richard, K. Mansouri, R.W. Setzer, T.B. Knudsen, K.M. Crofton, and R.S. Thomas, Integrated model of chemical perturbations of a biological pathway using 18 in vitro high-throughput screening assays for the estrogen receptor, Toxicol. Sci. 148 (2015), pp. 137–154.
  • T.M. Martin, P. Harten, R. Venkatapathy, S. Das, and D.M. Young, A hierarchical clustering methodology for the estimation of toxicity, Toxicol. Mechan. Meth. 18 (2008), pp. 251–266.
  • L. Breiman, J. Friedman, C.J. Stone, and R.A. Olshen, Classification and Regression Trees, Chapman and Hall/CRC, Boca Raton, FL, 1984.
  • US EPA. User’s guide for T.E.S.T. (version 4.1) (Toxicity Estimation Software Tool), 2012. Available at http://www2.epa.gov/chemical-research/toxicity-estimation-software-tool-test.
  • A.V. Zakharov, M.L. Peach, M. Sitzmann, and M.C. Nicklaus, QSAR modeling of imbalanced high-throughput screening data in PubChem, J. Chem. Inf. Model. 54 (2014), pp. 705–712.
  • N.V. Chawla, Data mining for imbalanced datasets: An overview, in The Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, eds., Springer, New York, NY, 2005, pp. 853–867.
  • D. Newby, A.A. Freitas, and T. Ghafourian, Coping with unbalanced class data sets in oral absorption models, J. Chem. Inf. Model. 53 (2013), pp. 461–474.
  • D. Dix, K. Houck, M. Martin, A. Richard, R. Setzer, and R. Kavlock, The ToxCast program for prioritizing toxicity testing of environmental chemicals, Toxicol. Sci. Off. J. Soc. Toxicol. 95 (2007), pp. 5–12.
  • METI Ministry of Economy Trade and Industry, current status of testing methods development for endocrine disrupters, 6th Meeting of the Task Force on Endocrine Disrupters Testing and Assessment (EDTA), Tokyo, Japan, 2002.
  • A. Gaulton, L.J. Bellis, A.P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani, and J.P. Overington, ChEMBL: A large-scale bioactivity database for drug discovery, Nucleic Acids Res. 40(Database issue) (2012), pp. D1100–D1107.
  • US EPA. CERAPP – Collaborative estrogen receptor activity prediction project, 2015. Available at http://www2.epa.gov/sites/production/files/2015-10/documents/cerapp_consensus.pdf.
  • H.C. Romesburg, Cluster Analysis for Researchers, Lifetime Learning Publications, Belmont, CA, 1984.
  • 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.
  • D.C. Montgomery, Introduction to Linear Regression Analysis, John Wiley and Sons, New York, NY, 1982, p. 143.
  • US EPA. T.E.S.T. Version 4.1., 2012; software available at http://www2.epa.gov/chemical-research/toxicity-estimation-software-tool-test.
  • 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.

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