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BioMed Research International
Volume 2014 (2014), Article ID 210672, 8 pages
http://dx.doi.org/10.1155/2014/210672
Research Article

Prediction on the Inhibition Ratio of Pyrrolidine Derivatives on Matrix Metalloproteinase Based on Gene Expression Programming

1College of Pharmacy, Taishan Medical University, Taian, Shandong 271016, China
2Institute of Computer Science and Engineering Technology, Qingdao University, Qingdao 266071, China
3College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou 271000, China

Received 27 February 2014; Accepted 29 April 2014; Published 22 May 2014

Academic Editor: Nick V. Grishin

Copyright © 2014 Yuqin Li 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. I. Malanchi, “Tumour cells coerce host tissue to cancer spread,” BoneKEy Reports, vol. 2, p. 371, 2013. View at Google Scholar
  2. C. Coghlin and G. I. Murray, “Current and emerging concepts in tumour metastasis,” Journal of Pathology, vol. 222, no. 1, pp. 1–15, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. E. Hadler-Olsen, J. O. Winberg, and L. Uhlin-Hansen, “Matrix metalloproteinases in cancer: their value as diagnostic and prognostic markers and therapeutic targets,” Tumor Biology, vol. 334, no. 4, pp. 2041–2051, 2013. View at Google Scholar
  4. X. Li, L. Qu, Y. Zhong, Y. Zhao, H. Chen, and L. Daru, “Association between promoters polymorphisms of matrix metalloproteinases and risk of digestive cancers: a meta-analysis,” Journal of Cancer Research and Clinical Oncology, vol. 139, no. 9, pp. 1433–1447, 2013. View at Publisher · View at Google Scholar
  5. M. Verslegers, K. Lemmens, I. van Hove, and L. Moons, “Matrix metalloproteinase-2 and -9 as promising benefactors in development, plasticity and repair of the nervous system,” Progress in Neurobiology, vol. 105, pp. 60–78, 2013. View at Publisher · View at Google Scholar
  6. X. Li and J. Li, “Recent advances in the development of MMPIs and APNIs based on the pyrrolidine platforms,” Mini-Reviews in Medicinal Chemistry, vol. 10, no. 9, pp. 794–805, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. X.-C. Cheng, Q. Wang, H. Fang, and W.-F. Xu, “Advances in matrix metalloproteinase inhibitors based on pyrrolidine scaffold,” Current Medicinal Chemistry, vol. 15, no. 4, pp. 374–385, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Yu, J. Saenz, and J. K. Srirangam, “Facile synthesis of N-aryl pyrroles via Cu(II)-mediated cross coupling of electron deficient pyrroles and arylboronic acids,” Journal of Organic Chemistry, vol. 67, no. 5, pp. 1699–1702, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Guha, “On exploring structure-activity relationships,” Methods in Molecular Biology, vol. 993, pp. 81–93, 2013. View at Publisher · View at Google Scholar
  10. H. González-Díaz, S. Arrasate, N. Sotomayor et al., “MIANN models in medicinal, physical and organic chemistry,” Current Topics in Medicinal Chemistry, vol. 13, no. 5, pp. 619–641, 2013. View at Publisher · View at Google Scholar
  11. M. Wesolowski and B. Suchacz, “Artificial neural networks: theoretical background and pharmaceutical applications: a review,” Journal of AOAC International, vol. 95, no. 3, pp. 652–668, 2012. View at Publisher · View at Google Scholar
  12. J. Gálvez, M. Gálvez-Llompart, and R. García-Domenech, “Introduction to molecular topology: basic concepts and application to drug design,” Current Computer—Aided Drug Design, vol. 8, no. 3, pp. 196–223, 2012. View at Publisher · View at Google Scholar
  13. P. Jasinski, P. Zwolak, R. Isaksson Vogel et al., “MT103 inhibits tumor growth with minimal toxicity in murine model of lung carcinoma via induction of apoptosis,” Investigational New Drugs, vol. 29, no. 5, pp. 846–852, 2011. View at Google Scholar · View at Scopus
  14. D. V. Singh, S. Agarwal, R. K. Kesharwani, and K. Misra, “3D QSAR and pharmacophore study of curcuminoids and curcumin analogs: interaction with thioredoxin reductase,” Interdisciplinary Sciences, vol. 5, no. 4, pp. 286–295, 2013. View at Publisher · View at Google Scholar
  15. Z. Yan, L. Zhang, H. Fu, Z. Wang, and J. J. Lin, “Design of the influenza virus inhibitors targeting the PA endonuclease using 3D-QSAR modeling, side-chain hopping, and docking,” Bioorganic & Medicinal Chemistry Letters, vol. 24, no. 2, pp. 539–547, 2014. View at Publisher · View at Google Scholar
  16. G. Subramanian and S. N. Rao, “Comprehending renin inhibitor's binding affinity using structure-based approaches,” Bioorganic & Medicinal Chemistry Letters, vol. 23, no. 24, pp. 6667–6672, 2013. View at Publisher · View at Google Scholar
  17. A. Manvar, V. Khedkar, J. Patel et al., “Synthesis and binary QSAR study of antitubercular quinolylhydrazides,” Bioorganic & Medicinal Chemistry Letters, vol. 23, no. 17, pp. 4896–4902, 2013. View at Publisher · View at Google Scholar
  18. S. Marchetti, D. Pluim, M. V. Eijndhoven et al., “Effect of the drug transporters ABCG2, Abcg2, ABCB1 and ABCC2 on the disposition, brain accumulation and myelotoxicity of the aurora kinase B inhibitor barasertib and its more active form barasertib-hydroxy-QPA,” Investigational New Drugs, vol. 31, no. 5, pp. 1125–1135, 2013. View at Publisher · View at Google Scholar
  19. C. Ventura, D. A. Latino, and F. Martins, “Comparison of multiple linear regressions and neural networks based QSAR models for the design of new antitubercular compounds,” European Journal of Medicinal Chemistry, vol. 70, pp. 831–845, 2013. View at Publisher · View at Google Scholar
  20. A. Worachartcheewan, C. Nantasenamat, W. Owasirikul et al., “Insights into antioxidant activity of 1-adamantylthiopyridine analogs using multiple linear regression,” European Journal of Medicinal Chemistry, vol. 73, pp. 258–264, 2013. View at Google Scholar
  21. P. Liu and W. Long, “Current mathematical methods used in QSAR/QSPR studies,” International Journal of Molecular Sciences, vol. 10, no. 5, pp. 1978–1998, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. H. Si, J. Zhao, L. Cui et al., “Study of human dopamine sulfotransferases based on gene expression programming,” Chemical Biology and Drug Design, vol. 78, no. 3, pp. 370–377, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. 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
  24. Y.-Q. Li, H.-Z. Si, Y.-L. Xiao et al., “Quantitative structure activity relationship models based on heuristic method and gene expression programming for the prediction of the pKa values of sulfa drugs,” Chin Acta Pharm Sinica, vol. 44, no. 5, pp. 486–490, 2009. View at Google Scholar · View at Scopus
  25. H. Si, N. Lian, S. Yuan et al., “Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds,” European Journal of Medicinal Chemistry, vol. 44, no. 10, pp. 4044–4050, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. L. Zhang, W.-F. Xu, and J. Zhang, “Protein catabolic enzymes inhibitory activities of pyrrolidine derivatives,” Chinese Pharmaceutical Journal, vol. 43, no. 6, pp. 472–474, 2008. View at Google Scholar · View at Scopus
  27. D. F. Qiu, Q. Zhao, K. C. Liu, Y. C. Guo, and Y. Q. Feng, “Synthesis, crystal structure and biological activity of a planar copper complex derived from s-benzyldithiocarbazate,” Chinese Journal of Structural Chemistry, vol. 29, no. 10, pp. 1513–1518, 2010. View at Google Scholar
  28. K. Tuppurainen, “Frontier orbital energies, hydrophobicity and steric factors as physical QSAR descriptors of molecular mutagenicity. A review with a case study: MX compounds,” Chemosphere, vol. 38, no. 13, pp. 3015–3030, 1999. View at Publisher · View at Google Scholar · View at Scopus
  29. A. G. Mercader, P. R. Duchowicz, F. M. Fernández, and E. A. Castro, “Modified and enhanced replacement method for the selection of molecular descriptors in QSAR and QSPR theories,” Chemometrics and Intelligent Laboratory Systems, vol. 92, no. 2, pp. 138–145, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. L. C. Porto, É. S. Souza, B. da Silva Junkes, R. A. Yunes, and V. E. F. Heinzen, “Semi-empirical topological index: development of QSPR/QSRR and optimization for alkylbenzenes,” Talanta, vol. 76, no. 2, pp. 407–412, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. P. Silakari, S. D. Shrivastava, G. Silakari et al., “QSAR analysis of 1,3-diaryl-4,5,6,7-tetrahydro-2H-isoindole derivatives as selective COX-2 inhibitors,” European Journal of Medicinal Chemistry, vol. 43, no. 7, pp. 1559–1569, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. H. Z. Si, T. Wang, K. J. Zhang, Z. D. Hu, and B. T. Fan, “QSAR study of 1,4-dihydropyridine calcium channel antagonists based on gene expression programming,” Bioorganic and Medicinal Chemistry, vol. 14, no. 14, pp. 4834–4841, 2006. View at Publisher · View at Google Scholar · View at Scopus