Table of Contents Author Guidelines Submit a Manuscript
International Journal of Medicinal Chemistry
Volume 2013, Article ID 743139, 13 pages
http://dx.doi.org/10.1155/2013/743139
Research Article

Computational Study of Estrogen Receptor-Alpha Antagonist with Three-Dimensional Quantitative Structure-Activity Relationship, Support Vector Regression, and Linear Regression Methods

1Division of Laboratory Medicine, Zuoying Branch of Kaohsiung Armed Forces General Hospital 813, Kaohsiung 81342, Taiwan
2Department of Life Science, National University of Kaohsiung, Kaohsiung 81148, Taiwan
3Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
4Institute of Biotechnology, National University of Kaohsiung, Kaohsiung 81148, Taiwan

Received 14 September 2012; Accepted 29 March 2013

Academic Editor: Graham B. Jones

Copyright © 2013 Ying-Hsin Chang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. E. Christodoulakos, I. V. Lambrinoudaki, and D. C. Botsis, “The cardiovascular effects of selective estrogen receptor modulators,” Annals of the New York Academy of Sciences, vol. 1092, pp. 374–384, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. E. Bonnelye and J. E. Aubin, “Estrogen receptor-related receptor α: a mediator of estrogen response in bone,” Journal of Clinical Endocrinology and Metabolism, vol. 90, no. 5, pp. 3115–3121, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. S. J. McPherson, S. J. Ellem, V. Patchev, K. H. Fritzemeier, and G. P. Risbridger, “The role of Erα and ERβ in the prostate: insights from genetic models and isoform-selective ligands,” Ernst Schering Foundation symposium proceedings, vol. 1, pp. 131–147, 2006. View at Google Scholar · View at Scopus
  4. P. A. Arias-Loza, V. Jazbutyte, K. H. Fritzemeier et al., “Functional effects and molecular mechanisms of subtype-selective ERα and ERβ agonists in the cardiovascular system,” Ernst Schering Foundation symposium proceedings, vol. 1, pp. 87–106, 2006. View at Google Scholar · View at Scopus
  5. M. Lupien, M. Jeyakumar, E. Hébert et al., “Raloxifene and ICI182,780 increase estrogen receptor-α association with a nuclear compartment via overlapping sets of hydrophobic amino acids in activation function 2 helix 12,” Molecular Endocrinology, vol. 21, no. 4, pp. 797–816, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Nichols, P. Cheng, Y. Liu, B. Kanterewicz, P. A. Hershberger, and K. S. McCarty, “Breast cancer-derived M543V mutation in helix 12 of estrogen receptor α inverts response to estrogen and SERMs,” Breast Cancer Research and Treatment, vol. 120, no. 3, pp. 761–768, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Peng, S. Sengupta, and V. C. Jordan, “Potential of selective estrogen receptor modulators as treatments and preventives of breast cancer,” Anti-Cancer Agents in Medicinal Chemistry, vol. 9, no. 5, pp. 481–499, 2009. View at Google Scholar · View at Scopus
  8. R. B. Riggins, A. Zwart, R. Nehra, and R. Clarke, “The nuclear factor κB inhibitor parthenolide restores ICI 182,780 (Faslodex; fulvestrant)-induced apoptosis in antiestrogen-resistant breast cancer cells,” Molecular Cancer Therapeutics, vol. 4, no. 1, pp. 33–41, 2005. View at Google Scholar · View at Scopus
  9. K. Visvanathan, R. T. Chlebowski, P. Hurley et al., “American society of clinical oncology clinical practice guideline update on the use of pharmacologic interventions including tamoxifen, raloxifene, and aromatase inhibition for breast cancer risk reduction,” Journal of Clinical Oncology, vol. 27, no. 19, pp. 3235–3258, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. G. Bertelli, E. Hall, E. Ireland et al., “Long-term endometrial effects in postmenopausal women with early breast cancer participating in the Intergroup Exemestane Study (IES)—a randomised controlled trial of exemestane versus continued tamoxifen after 2-3 years tamoxifen,” Annals of Oncology, vol. 21, no. 3, Article ID mdp358, pp. 498–505, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Biersack and R. Schobert, “Metallodrug conjugates with steroids and selective estrogen receptor modulators (SERM),” Current Medicinal Chemistry, vol. 16, no. 18, pp. 2324–2337, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. FDA: FDA approves new uses for Evista, 2007, http://www.fda.gov/bbs/topics/NEWS/2007/NEW01698.html.
  13. E. Barrett-Connor, L. Mosca, P. Collins et al., “Effects of raloxifene on cardiovascular events and breast cancer in postmenopausal women,” The New England Journal of Medicine, vol. 355, no. 2, pp. 125–137, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. U. Norinder, “Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection,” Neurocomputing, vol. 55, no. 1-2, pp. 337–346, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. H. F. Chen, “Computational study of histamine H3-receptor antagonist with support vector machines and three dimension quantitative structure activity relationship methods,” Analytica Chimica Acta, vol. 624, no. 2, pp. 203–209, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. Cheng, Y. Zhang, and W. Fu, “QSAR study of carboxylic acid derivatives as HIV-1 Integrase inhibitors,” European Journal of Medicinal Chemistry, vol. 45, no. 9, pp. 3970–3980, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Shi, X. Zhang, and Q. Shen, “Quantitative structure-activity relationships studies of CCR5 inhibitors and toxicity of aromatic compounds using gene expression programming,” European Journal of Medicinal Chemistry, vol. 45, no. 1, pp. 49–54, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Yan, Y. Chong, L. Wang, X. Hu, and K. Wang, “Prediction of biological activity of Aurora-A kinase inhibitors by multilinear regression analysis and support vector machine,” Bioorganic and Medicinal Chemistry Letters, vol. 21, no. 8, pp. 2238–2243, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. T. A. Grese, S. Cho, D. R. Finley et al., “Structure-activity relationships of selective estrogen receptor modulators: modifications to the 2-arylbenzothiophene core of raloxifene,” Journal of Medicinal Chemistry, vol. 40, no. 2, pp. 146–167, 1997. View at Publisher · View at Google Scholar · View at Scopus
  20. R. D. Cramer, D. E. Patterson, and J. D. Bunce, “Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins,” Journal of the American Chemical Society, vol. 110, no. 18, pp. 5959–5967, 1988. View at Google Scholar · View at Scopus
  21. H. Kubinyi, Comparative Molecular Field Analysis (CoMFA), The Encyclopedia of Computational Chemistry, 1998.
  22. G. Klebe, U. Abraham, and T. Mietzner, “Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity,” Journal of Medicinal Chemistry, vol. 37, no. 24, pp. 4130–4146, 1994. View at Publisher · View at Google Scholar · View at Scopus
  23. A. C. Wold S, W. J. Dunn III, U. Edlund et al., “Multivariate data analysis in chemistry,” in Chemometrics: Mathematics and Statistics in Chemistry, B. Kowalski, Ed., Reidel, Dordrecht, The Netherlands, 1984. View at Google Scholar
  24. R. D. Cramer III, J. D. Bunce, D. E. Patterson et al., “Crossvalidation, bootstrapping, and partial least squares compared with multiple regression in conventional QSAR studies,” Quantitative Structure-Activity Relationships, vol. 7, no. 1, pp. 18–25, 1988. View at Publisher · View at Google Scholar
  25. T. G. Gantchev, H. Ali, and J. E. Van Lier, “Quantitative structure-activity relationships/comparative molecular field analysis (QSAR/CoMFA) for receptor-binding properties of halogenated estradiol derivatives,” Journal of Medicinal Chemistry, vol. 37, no. 24, pp. 4164–4176, 1994. View at Google Scholar · View at Scopus
  26. P. Wolohan and D. E. Reichert, “CoMFA and docking study of novel estrogen receptor subtype selective ligands,” Journal of Computer-Aided Molecular Design, vol. 17, no. 5-6, pp. 313–328, 2003. View at Publisher · View at Google Scholar · View at Scopus
  27. T. G. Gantchev, H. Ali, and J. E. V. Lier, “Quantitative structure-activity relationships/comparative molecular field analysis (QSAR/CoMFA) for receptor-binding properties of halogenated estradiol derivatives,” Journal of Medicinal Chemistry, vol. 37, no. 24, pp. 4164–4176, 1994. View at Google Scholar · View at Scopus
  28. H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, NY, USA, 2006.
  30. A. M. Brzozowski, A. C. W. Pike, Z. Dauter et al., “Molecular basis of agonism and antagonism in the oestrogen receptor,” Nature, vol. 389, no. 6652, pp. 753–758, 1997. View at Publisher · View at Google Scholar · View at Scopus
  31. L. B. Salum, I. Polikarpov, and A. D. Andricopulo, “Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity,” Journal of Chemical Information and Modeling, vol. 48, no. 11, pp. 2243–2253, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Sun, J. Baudry, J. A. Katzenellenbogen, and B. S. Katzenellenbogen, “Molecular basis for the subtype discrimination of the estrogen receptor-β-selective ligand, diarylpropionitrile,” Molecular Endocrinology, vol. 17, no. 2, pp. 247–258, 2003. View at Publisher · View at Google Scholar · View at Scopus
  33. R. W. Hsieh, S. S. Rajan, S. K. Sharma et al., “Identification of ligands with bicyclic scaffolds provides insights into mechanisms of estrogen receptor subtype selectivity,” The Journal of Biological Chemistry, vol. 281, no. 26, pp. 17909–17919, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. C. C. Chang and C. J. Lin, LIBSVM: A library for Support Vector Machines, 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
  35. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
  36. T. Z. Bao, G. Z. Han, J. Y. Shim, Y. Wen, and X. R. Jiang, “Quantitative structure-activity relationship of various endogenous estrogen metabolites for human estrogen receptor α and β subtypes: insights into the structural determinants favoring a differential subtype binding,” Endocrinology, vol. 147, no. 9, pp. 4132–4150, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. R. K. Dubey, S. P. Tofovic, and E. K. Jackson, “Cardiovascular Pharmacology of estradiol metabolites,” Journal of Pharmacology and Experimental Therapeutics, vol. 308, no. 2, pp. 403–409, 2004. View at Publisher · View at Google Scholar · View at Scopus
  38. M. Chang, K. W. Peng, I. Kastrati et al., “Activation of estrogen receptor-mediated gene transcription by the equine estrogen metabolite, 4-methoxyequilenin, in human breast cancer cells,” Endocrinology, vol. 148, no. 10, pp. 4793–4802, 2007. View at Publisher · View at Google Scholar · View at Scopus