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Evidence-Based Complementary and Alternative Medicine
Volume 2013 (2013), Article ID 658531, 15 pages
http://dx.doi.org/10.1155/2013/658531
Research Article

A Computational Drug-Target Network for Yuanhu Zhitong Prescription

1Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16 Nanxiaojie, Dongzhimennei, Beijing 100700, China
2Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
3Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4Capital Medical University, Beijing 100069, China

Received 20 December 2012; Accepted 10 March 2013

Academic Editor: Shao Li

Copyright © 2013 Haiyu Xu 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. F. Cheung, “TCM: made in China,” Nature, vol. 480, pp. 82–83, 2011. View at Publisher · View at Google Scholar
  2. M. Grayson, “Traditional Asian medicine,” Nature, vol. 480, article 81, 2011.
  3. S. P. Li, J. Zhao, and B. Yang, “Strategies for quality control of Chinese medicines,” Journal of Pharmaceutical and Biomedical Analysis, vol. 55, no. 4, pp. 802–809, 2011. View at Publisher · View at Google Scholar
  4. Y. Tu, “The discovery of artemisinin (QingHaoSu) and gifts from Chinese medicine,” Nature Medicine, vol. 17, pp. 1217–1220, 2011. View at Publisher · View at Google Scholar
  5. K. H. Lee, “Research and future trends in the pharmaceutical development of medicinal herbs from Chinese medicine,” Public Health Nutrition, vol. 3, no. 4, pp. 515–522, 2000. View at Scopus
  6. S. B. Su, A. P. Lu, S. Li, and W. Jia, “Evidence-based ZHENG: a traditional Chinese medicine syndrome,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, Article ID 246538, 2 pages, 2012. View at Publisher · View at Google Scholar
  7. T. Ma, C. Tan, H. Zhang, M. Wang, W. Ding, and S. Li, “Bridging the gap between traditional Chinese medicine and systems biology: the connection of Cold Syndrome and NEI network,” Molecular BioSystems, vol. 6, no. 4, pp. 613–619, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Li, B. Zhang, D. Jiang, Y. Wei, and N. Zhang, “Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formulae,” BMC Bioinformatics, vol. 11, supplement 11, article S6, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Li, B. Zhang, and N. Zhang, “Network target for screening synergistic drug combinations with application to traditional Chinese medicine,” BMC Systems Biology, vol. 5, no. 1, article S10, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Li, X. Xu, J. Wang et al., “A system-level investigation into the mechanisms of chinese traditional medicine: compound danshen formula for cardiovascular disease treatment,” PLoS One, vol. 7, no. 9, pp. 1–16, 2012.
  11. Pharmacopoeia Committee of the Ministry of Health of the People’s Republic of China, Pharmacopoeia of the People’s Republic of China, vol. 1, Chemical Industry Press, Beijing, China, 2010.
  12. K. W. Luo, J. G. Sun, J. Y. W. Chan et al., “Anticancer effects of imperatorin isolated from Angelica dahurica: induction of apoptosis in HepG2 cells through both death-receptor- and mitochondria-mediated pathways,” Chemotherapy, vol. 57, pp. 449–459, 2011. View at Publisher · View at Google Scholar
  13. C. S. Yuan, S. R. Mehendale, C. Z. Wang et al., “Effects of Corydalis yanhusuo and Angelicae dahuricae on cold pressor-induced pain in humans: a controlled trial,” Journal of Clinical Pharmacology, vol. 44, no. 11, pp. 1323–1327, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. C. Zhang, H. Y. Xu, X. M. Chen, et al., “Study on the application of intestinal absorption in vitro coupled with bioactivity assessment in Yuanhu Zhitong preparation,” Journal of Medicinal Plants Research, vol. 6, pp. 1941–1947, 2012.
  15. Y. Zhang, H. Xu, X. Chen et al., “Simultaneous quantification of 17 constituents from Yuanhu Zhitong tablet using rapid resolution liquid chromatography coupled with a triple quadrupole electrospray tandem mass spectrometry,” Journal of Pharmaceutical and Biomedical Analysis, vol. 56, no. 3, pp. 497–504, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Tao, H. Y. Xu, S. S. Wang, et al., “Identification of the absorbed constituents after oral administration of Yuanhu Zhitong prescription extract and its pharmacokinetic study by RRLC-QTOF/MS,” Journal of Chromatography, B. Revised.
  17. Y. F. Chen, H. Y. Tsai, and T. S. Wu, “Anti-inflammatory and analgesic activities from roots of Angelica pubescens,” Planta Medica, vol. 61, no. 1, pp. 2–8, 1995. View at Publisher · View at Google Scholar · View at Scopus
  18. W. C. Leung, H. Zheng, M. Huen, S. L. Law, and H. Xue, “Anxiolytic-like action of orally administered dl-tetrahydropalmatine in elevated plus-maze,” Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 27, no. 5, pp. 775–779, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. K. O. Hiller, M. Ghorbani, and H. Schlicher, “Antispasmodic and relaxant activity of chelidonine, protopine, coptisine, and Chelidonium majus extracts on isolated guinea-pig ileum,” Planta Medica, vol. 64, no. 8, pp. 758–760, 1998. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Nie, L. Z. Meng, J. Y. Zhou, X. F. Fan, Y. Luo, and G. W. Zhang, “Imperatorin is responsible for the vasodilatation activity of Angelica dahurica var. formosana regulated by nitric oxide in an endothelium-dependent manner,” Chinese Journal of Integrative Medicine, vol. 15, no. 6, pp. 442–447, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Lin, “An information-theoretic definition of similarity,” in Proceedings of the 15th International Conference on Machine Learning, vol. 25, pp. 296–304, Morgan Kaufmann, 1998.
  22. S. Zhao and S. Li, “Network-based relating pharmacological and genomic spaces for drug target identification,” PLoS ONE, vol. 5, no. 7, Article ID e11764, 10 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. G. Bindea, B. Mlecnik, H. Hackl et al., “ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks,” Bioinformatics, vol. 25, no. 8, pp. 1091–1093, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. Z. Zsoldos, D. Reid, A. Simon, S. B. Sadjad, and A. P. Johnson, “eHiTS: a new fast, exhaustive flexible ligand docking system,” Journal of Molecular Graphics and Modelling, vol. 26, no. 1, pp. 198–212, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. E. Linney, L. Upchurch, and S. Donerly, “Zebrafish as a neurotoxicological model,” Neurotoxicology and Teratology, vol. 26, no. 6, pp. 709–718, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. H. Teraoka, W. Dong, and T. Hiraga, “Zebrafish as a novel experimental model for developmental toxicology,” Congenital anomalies, vol. 43, no. 2, pp. 123–132, 2003. View at Scopus
  27. R. D. Porsolt, M. Le Pichon, and M. Jalfre, “Depression: a new animal model sensitive to antidepressant treatments,” Nature, vol. 266, no. 5604, pp. 730–732, 1977. View at Scopus
  28. I. Lucki, A. Dalvi, and A. J. Mayorga, “Sensitivity to the effects of pharmacologically selective antidepressants in different strains of mice,” Psychopharmacology, vol. 155, no. 3, pp. 315–322, 2001. View at Publisher · View at Google Scholar · View at Scopus
  29. L. Steru, R. Chermat, B. Thierry, and P. Simon, “The tail suspension test: a new method for screening antidepressants in mice,” Psychopharmacology, vol. 85, no. 3, pp. 367–370, 1985. View at Scopus
  30. S. S. de Buck, V. K. Sinha, L. A. Fenu, R. A. Gilissen, C. E. Mackie, and M. J. Nijsen, “The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools,” Drug Metabolism and Disposition, vol. 35, no. 4, pp. 649–659, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. D. D. Stranz, S. Miao, S. Campbell, G. Maydwell, and S. Ekins, “Combined computational metabolite prediction and automated structure-based analysis of mass spectrometric data,” Toxicology Mechanisms and Methods, vol. 18, no. 2-3, pp. 243–250, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. H. Han, L. Yang, and Y. Xu, “Identification of metabolites of geniposide in rat urine using ultra-performance liquid chromatography combined with electrospray ionization quadrupole time-of-flight tandem mass spectrometry,” Rapid Communications in Mass Spectrometry, vol. 25, no. 21, pp. 3339–3350, 2011. View at Publisher · View at Google Scholar
  33. M. Kuhn, M. Campillos, P. González, L. J. Jensen, and P. Bork, “Large-scale prediction of drug-target relationships,” The FEBS Letters, vol. 582, no. 8, pp. 1283–1290, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. M. J. Keiser, V. Setola, J. J. Irwin et al., “Predicting new molecular targets for known drugs,” Nature, vol. 462, no. 7270, pp. 175–181, 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. Y. C. Wang, Z. X. Yang, Y. Wang, and N. Y. Deng, “Computationally probing drug-protein interactions via support vector machine,” Letters in Drug Design & Discovery, vol. 7, no. 5, pp. 370–378, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. Y. C. Wang, C. H. Zhang, N. Y. Deng, and Y. Wang, “Kernel based data fusion improves the drug-protein interaction prediction,” Computational Biology and Chemistry, vol. 35, no. 6, pp. 353–362, 2011. View at Publisher · View at Google Scholar
  37. Y. Y. Wang, J. C. Nacher, and X. M. Zhao, “Predicting drug targets based on protein domains,” Molecular BioSystems, vol. 8, no. 5, pp. 1528–1534, 2012. View at Publisher · View at Google Scholar
  38. F. X. Cheng, C. Liu, J. Jiang et al., “Prediction of drug-target interactions and drug repositioning via network-based inference,” PLOS Computational Biology, vol. 8, no. 5, pp. 1–12, 2012.
  39. T. Cheng, Q. Li, Y. Wang, and S. H. Bryant, “Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining,” Journal of Chemical Information and Modeling, vol. 51, no. 9, pp. 2440–2448, 2011. View at Publisher · View at Google Scholar
  40. M. J. Keiser, B. L. Roth, B. N. Armbruster, P. Ernsberger, J. J. Irwin, and B. K. Shoichet, “Relating protein pharmacology by ligand chemistry,” Nature Biotechnology, vol. 25, no. 2, pp. 197–206, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. A. Schuffenhauer, P. Floersheim, P. Acklin, and E. Jacoby, “Similarity metrics for ligands reflecting the similarity of the target proteins,” Journal of Chemical Information and Computer Sciences, vol. 43, no. 2, pp. 391–405, 2003. View at Publisher · View at Google Scholar · View at Scopus
  42. “The Anatomical Therapeutic Chemical (ATC) Classification,” http://www.whocc.no/atcddd/.
  43. J. C. Nacher and J. M. Schwartz, “A global view of drug-therapy interactions,” BMC Pharmacology, vol. 8, article 5, 2008. View at Publisher · View at Google Scholar · View at Scopus
  44. R. J. Gatchel, Y. B. Peng, M. L. Peters, P. N. Fuchs, and D. C. Turk, “The biopsychosocial approach to chronic pain: scientific advances and future directions,” Psychological Bulletin, vol. 133, no. 4, pp. 581–624, 2007. View at Publisher · View at Google Scholar · View at Scopus
  45. M. D. Sullivan and J. P. Robinson, “Antidepressant and anticonvulsant medication for chronic pain,” Physical Medicine and Rehabilitation Clinics of North America, vol. 17, no. 2, pp. 381–400, 2006. View at Publisher · View at Google Scholar · View at Scopus
  46. R. Benyamin, A. M. Trescot, S. Datta et al., “Opioid complications and side effects,” Pain Physician, vol. 11, no. 2, pp. S105–S120, 2008. View at Scopus
  47. Z. Yang, Y. C. Shao, S. J. Li et al., “Medication of l-tetrahydropalmatine significantly ameliorates opiate craving and increases the abstinence rate in heroin users: a pilot study,” Acta Pharmacologica Sinica, vol. 29, no. 7, pp. 781–788, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. M. T. Lin, J. J. Wu, A. Chandra, and B. L. Tsay, “Activation of striatal dopamine receptors induces pain inhibition in rats,” Journal of Neural Transmission, vol. 51, no. 3-4, pp. 213–222, 1981. View at Scopus
  49. I. Carroll, S. Mackey, and R. Gaeta, “The role of adrenergic receptors and pain: the good, the bad, and the unknown,” Seminars in Anesthesia, Perioperative Medicine and Pain, vol. 26, no. 1, pp. 17–21, 2007. View at Publisher · View at Google Scholar · View at Scopus
  50. P. Sharma and A. J. Halayko, “Emerging molecular targets for the treatment of asthma,” Indian Journal of Biochemistry and Biophysics, vol. 46, no. 6, pp. 447–460, 2009. View at Scopus
  51. D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists,” Nucleic Acids Research, vol. 37, no. 1, pp. 1–13, 2009. View at Publisher · View at Google Scholar · View at Scopus
  52. G. Bindea, B. Mlecnik, H. Hackl et al., “ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks,” Bioinformatics, vol. 25, no. 8, pp. 1091–1093, 2009. View at Publisher · View at Google Scholar · View at Scopus
  53. Z. G. Liao, X. L. Liang, J. Y. Zhu et al., “Correlation between synergistic action of Radix Angelica dahurica extracts on analgesic effects of Corydalis alkaloid and plasma concentration of dl-THP,” Journal of Ethnopharmacology, vol. 129, no. 1, pp. 115–120, 2010. View at Publisher · View at Google Scholar · View at Scopus
  54. Y. Y. Zhu and B. Y. Yu, “Comparative studies on chemistry and pharmacodynamics of the compatibility of Yuanhu Zhitong Prescription,” Journal of China Pharmaceutical University, vol. 34, no. 5, pp. 461–464, 2003. View at Scopus
  55. M. Narita and L. F. Tseng, “Evidence for the existence of the β-endorphin-sensitive 'ε-opioid receptor' in the brain: the mechanisms of ε-mediated antinociception,” Japanese Journal of Pharmacology, vol. 76, no. 3, pp. 233–253, 1998. View at Publisher · View at Google Scholar · View at Scopus
  56. J. G. R. de Mey, P. M. Schiffers, R. H. P. Hilgers, and M. M. W. Sanders, “Toward functional genomics of flow-induced outward remodeling of resistance arteries,” American Journal of Physiology—Heart and Circulatory Physiology, vol. 288, no. 3, pp. H1022–H1027, 2005. View at Publisher · View at Google Scholar · View at Scopus
  57. E. Schneider, M. Rolli-Derkinderen, M. Arock, and M. Dy, “Trends in histamine research: new functions during immune responses and hematopoiesis,” Trends in Immunology, vol. 23, no. 5, pp. 255–263, 2002. View at Publisher · View at Google Scholar · View at Scopus
  58. M. Wienrich, D. Meier, H. A. Ensinger et al., “Pharmacodynamic profile of the M1 agonist talsaclidine in animals and man,” Life Sciences, vol. 68, no. 22-23, pp. 2593–2600, 2001. View at Publisher · View at Google Scholar · View at Scopus
  59. L. Wan, X. P. Qian, and B. R. Liu, “Progress of research on the chemical constituents and anti-tumor activity of alkaloid fraction of Corydalis,” Modern Oncology, vol. 20, pp. 1042–1044, 2012.
  60. W. H. Erik, F. Marc, K. R. Eva, et al., “Dietary α-linolenic acid inhibits arterial thrombus formation, tissue factor expression, and platelet activation,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 31, pp. 1772–1780, 2011. View at Publisher · View at Google Scholar
  61. W. C. Leung, H. Zheng, M. Huen, S. L. Law, and H. Xue, “Anxiolytic-like action of orally administered dl-tetrahydropalmatine in elevated plus-maze,” Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 27, no. 5, pp. 775–779, 2003. View at Publisher · View at Google Scholar · View at Scopus
  62. K. Schmerbach and A. Patzak, “The renin-angiotensin system-a functional jack-of-all-trades,” Acta Physiologica, vol. 205, no. 4, pp. 453–455, 2012. View at Publisher · View at Google Scholar
  63. J. Shen, X. Xu, F. Cheng et al., “Virtual screening on natural products for discovering active compounds and target information,” Current Medicinal Chemistry, vol. 10, no. 21, pp. 2327–2342, 2003. View at Publisher · View at Google Scholar · View at Scopus
  64. Z. Bencan, D. Sledge, and E. D. Levin, “Buspirone, chlordiazepoxide and diazepam effects in a zebrafish model of anxiety,” Pharmacology Biochemistry and Behavior, vol. 94, no. 1, pp. 75–80, 2009. View at Publisher · View at Google Scholar · View at Scopus
  65. N. Peitsaro, J. Kaslin, O. V. Anichtchik, and P. Panula, “Modulation of the histaminergic system and behaviour by α-fluoromethylhistidine in zebrafish,” Journal of Neurochemistry, vol. 86, no. 2, pp. 432–441, 2003. View at Publisher · View at Google Scholar · View at Scopus
  66. E. D. Levin, Z. Bencan, and D. T. Cerutti, “Anxiolytic effects of nicotine in zebrafish,” Physiology and Behavior, vol. 90, no. 1, pp. 54–58, 2007. View at Publisher · View at Google Scholar · View at Scopus
  67. B. Karolewicz and I. A. Paul, “Group housing of mice increases immobility and antidepressant sensitivity in the forced swim and tail suspension tests,” European Journal of Pharmacology, vol. 415, no. 2-3, pp. 197–201, 2001. View at Publisher · View at Google Scholar · View at Scopus
  68. P. Willner, “Validity, reliability and utility of the chronic mild stress model of depression: a 10-year review and evaluation,” Psychopharmacology, vol. 134, no. 4, pp. 319–329, 1997. View at Publisher · View at Google Scholar · View at Scopus
  69. D. Abdulla and K. W. Renton, “β-adrenergic receptor modulation of the LPS-mediated depression in CYP1A activity in astrocytes,” Biochemical Pharmacology, vol. 69, no. 5, pp. 741–750, 2005. View at Publisher · View at Google Scholar · View at Scopus
  70. R. J. Tynan, S. Naicker, and M. Hinwood, “Chronic stress alters the density and morphology of microglia in a subset of stress-responsive brain regions,” Brain, Behavior, and Immunity, vol. 24, pp. 1058–1068, 2010. View at Publisher · View at Google Scholar