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Network-based TCM Pharmacology

Network-based TCM Pharmacology. Jing Zhao Modern Research Center for Traditional Chinese Medicine Second Military Medical University Shanghai, China Jan. 14, 2010. Sino-German Workshop on Computational systems biology approaches for cancer research and biomarker discovery.

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Network-based TCM Pharmacology

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  1. Network-based TCM Pharmacology Jing Zhao Modern Research Center for Traditional Chinese Medicine Second Military Medical University Shanghai, China Jan. 14, 2010 Sino-German Workshop on Computational systems biology approaches for cancer research and biomarker discovery

  2. Characteristics of the TCM • Long history • Efficacy and safety for complex chronic diseases • Complicated mechanisms

  3. Complicated mechanisms of TCM • Holistic, complementary, synergic • Multi-component, multi-target, multi-dimensional pharmacology Pharmacology & Therapeutics 2000, 86:191-198

  4. Workflow for network-based TCM pharmacology study Zhao J, Jiang P, Zhang WD. Briefings in Bioinformatics 2009, doi:10.1093/bib/bbp063

  5. TCM databases • The TCM database • The 3D structure database of components from Chinese traditional medicinal herbs • Traditional Chinese Medicine Information Database (TCMID ) In our laboratory: • Large scale Natural Product Library (both virtual and material) • ~ 120 TCM herbs • ~ 5000 compounds , ~ 500 new compounds

  6. Techniques usually applied to isolate active components from TCM • Thin layer chromatography (TLC) • Column chromatography (CC) • Medium Pressure Liquid Chromatography (MPLC) • High Pressure liquid chromatography (HPLC) • High-speed countercurrent chromatography(HSCCC) • Macroporous adsorptive resins (MAR) • Molecular imprinting technique (MIT)

  7. Compounds isolated from TCM materials that are also drugs approved by the FDA.

  8. Collection of TCM formulae and their main ingredients

  9. Disease-associated networks Asthma network Agarwal P, Searls DB. Briefings in Bioinformatics 2008; 9:479-492.

  10. Lee D, Park J, Kay K et al. Proc Natl Acad Sci USA 2008; 105:9880-9885.

  11. Literature search results of disease-associated networks

  12. Chock points Rahman SA, Schomburg D.Bioinformatics 2006; 22:1767-1774.

  13. a. bridging nodes b. High-betweenness nodes Hwang S, Son S-W, Kim SC et al.Journal of Theoretical Biology 2008; 252:722-731. Hwang WC, Zhang A, Ramanathan M. Clin Pharmacol Ther 2008; 84:563-572.

  14. Mathematical models and algorithms to identify potential target combinations: • the minimum knockout problem • the min-interference problem • the OPMET model • the multiple target optimal intervention (MTOI) model • software TIde (Target Identification) Ruths DA, Nakhleh L, Iyengar MS et al. J Comput Biol 2006; 13:1546-1557. Dasika MS, Burgard A, Maranas CD. Biophysical Journal 2006; 91:382-398. Sridhar P, Song B, Kahveciy T et al. Pacific Symposium on Biocomputing 2008; 13:291-302. Yang K, Bai H, Ouyang Q et al. Mol Syst Biol 2008; 4:228. Schulz M, Bakker B, Klipp E. BMC Bioinformatics 2009; 10:344.

  15. Comparison of drug target databases

  16. Case study 1: Antidepressant activity of St.John’s Wort Zhao J, Jiang P, Zhang WD: Molecular networks for the study of TCM pharmacology. Briefings in Bioinformatics 2009, doi:10.1093/bib/bbp063

  17. Main active ingredients of JSW: • hyperforin (HP) • hypericin (HY) • pseudohypericin (PH) • amentoflavone (AF) • flavonoids (FL)

  18. the effects of the SJW active compounds on the system of neuroactive ligand-receptor interaction

  19. Drug-target network of FDA approved antidepressants and SJW compounds.

  20. Case study 2: The effect of Realgar-Indigo naturalis formula(RIF) on acute promyelocytic leukemia(APL) Wang L, Zhou G-B, Liu P et al. Proc Natl Acad Sci USA 2008; 105:4826-4831.

  21. Main active compounds of RIF • tetraarsenic tetrasulfide (As4S4, A) • indirubin (I) • tanshinone IIA (T)

  22. Effects of As4S4（A）、indirubin(I) and tanshinone IIA(T) on different APL-associated proteins

  23. Functional networks of APL disease gene-encoded proteins and RIF-targeted proteins. • Protein interaction network. (B) Protein-pathway association network. • [D]: GO: regulation of cell differentiation; • [P]: GO: regulation of cell proliferation; • [B]: GO: regulation of cell differentiation, and regulation of cell proliferation

  24. Regulations of single RIF compounds on different proteins on AML pathway.

  25. Acknowledgements Zhiwei Cao Tongji University Shanghai Center of Bioinformation and Technology Kailin Tang Shanghai Center of Bioinformation and Technology Lin Tao Shanghai Center of Bioinformation and Technology Weidong Zhang Second Military Medical University Peng Jiang Second Military Medical University Pengyuan Yang Second Military Medical University Yaocheng Rui Second Military Medical University Fan Li Second Military Medical University

  26. Thanks!

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