Transcript 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
Characteristics of the TCM
• Long history
• Efficacy and safety for complex chronic diseases
• Complicated mechanisms
Complicated mechanisms of TCM
•Holistic, complementary, synergic
•Multi-component, multi-target, multi-dimensional
pharmacology
Pharmacology & Therapeutics 2000, 86:191-198
Workflow for network-based TCM
pharmacology study
Zhao J, Jiang P, Zhang WD. Briefings in
Bioinformatics 2009, doi:10.1093/bib/bbp063
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
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)
Compounds isolated from TCM materials that are also drugs
approved by the FDA.
Compound name
CAS
number
DrugBan
k ID
Drug Function
Latin Name of
Source TCM
material
Ajmaline
4360-12-7
DB01426
Antiarrhythmic
Radix Rauvolfiae
Verticillatae
Acylanid
1111-39-3
DB00511
Anti-Arrhythmia
Digitalis lanata
Atropine
51-55-8
DB00572
Anesthesia
Radix Physochlainae
Azelaic Acid
123-99-9
DB00548
Dermatologic
Artemisia scoparia
Benzyl Benzoate
120-51-4
DB00676
Acaricides
Dianthus superbus
Caffeine
5743-12-4
DB00201
Anorexigenic
Agents
Camellia sinensis
Cocaine
50-36-2
DB00907
Anesthetics
pericarpium
papaveris
Codeine
41444-62-6
DB00318
Analgesics
Stephania
cepharantha
Colchicine
64-86-8
DB01394
Gout
Suppressants
Bulbus Lilii
Crystodigin
71-63-6
DB01396
Anti-Arrhythmia
Digitalis purpurea
Dicoumarin
66-76-2
DB00266
Anticoagulants
Daphne genkwa
Dimethyl sulfoxide
67-68-5
DB01093
Analgesics
Phaseolus vulgaris
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Collection of TCM formulae and their main ingredients
TCM formula
Materials
Main bioactive compounds
Shexiang Baoxin Pill
Moschus, Radix rhizoma ginseng,
Calculus bovis, Cortex cinnamomi,
Styrax, Venenum bufonis,
Borneolum syntheticum
Borneol, bufalin, chensodeoxycholic acid,
cinnamaldehyde, cinnamic acid, cinobufagin,
cinnamic acid, deoxycholic acid, ginsenoside Rb1,
ginsenoside Rc, ginsenoside Rd, ginsenoside Re,
isoborneol, muscone, resibufogenin
Yin Chen Hao Tang
Flos Artemisiae,Fructus Gardeniae
Jasminoidis, Radix et Rhizoma Rhei.
6,7-dimethylesculetin, chlorogenic acid, capillarisin,
geniposide and rhein
Danggui Buxue
decoction
Radix Angelica Sinensis, Radix
Astragli
ononoside, calycosin,
3-butylphthalide and ligustilide
Danggui Buxue
Decoction
Radix Angelica Sinensis,Radix
Astragli
Ligustilide, astragaloside IV and formononetin
Si Wu Tang
Radix Angelicae Sinensis,
Raidix Paeoniae Alba,
Rhizoma Chuanxiong,Radix
Rehmanniae preparata
ligustilide, peoniflorin, ferulaic acid and ligustrazine
Huang-Lian-Jie-DuTang
Coptis chinensis, Scutellaria
baicalensis, Phellodendron,
Gardeniae jasminoides
Geniposide, Jatrorrhizine, Palmatine, Berberine,
Baicalin, Baicalein, Wogonin
Zhen Tong Tang
Corydalis tuber, Aconiti sinensis
tuber, Hypecoum chinese (fr),
Semen Ziziphi Spinosae
Fumarine, tetrahydropalmatine, bromcholitin,
tetrahydrocoptisine,
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Disease-associated networks
Asthma network
Agarwal P, Searls DB. Briefings in Bioinformatics 2008; 9:479-492.
Lee D, Park J, Kay K et al. Proc Natl Acad Sci USA 2008;
105:9880-9885.
Literature search results of disease-associated networks
Disease Class
Disease
Metabolic
disorders
Type 2 diabetes, Type 1 diabetes, Obesity
Cancers
Colon cancer, Prostate cancer, Pancreatic cancers, Pancreatic ductal
adenocarcinom(PDAC), glioblastoma, Breast cancer, Gastric cancer,
melanoma, Leukemia
Central
neural system
diseases
Huntington disease, Inherited ataxias, Parkinson’s disease, Alzheimer's
disease, Schizophrenia, Glaucoma
Cardiovascul
ar diseases
Heart failure, Atherosclerosis
Immune
diseases
HIV-1, Autoimmune disease, Asthma, Rheumatoid arthritis
Others
Dupuytren’s disease, Inflammation, Hepatitis C virus, Epstein–Barr virus,
Benign Prostatic Hyperplasia, multiple sclerosis brain-lesion, Autosomal
Dominant Polycystic Kidney Disease (ADPKD), Usher syndrome,
Osteoarthritis
Chock points
Rahman SA, Schomburg D.Bioinformatics 2006; 22:1767-1774.
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.
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.
Comparison of drug target databases
Database
Drug
(chemical)
No.
Target
(protein) No.
e.g. Targets of simvastatin
(anticholesteremic agent)
DrugBank
4765
4566
AGT;BMP2;CASP3;CCL2;CD40;COL13A1;
CYP3A3;F2;HLA-DRB5;HMGCR;ICAM1;
IFNG;IL6;IL8;ITGB2;LTB;MAPK3;MMP3;
MMP9;PPARA;RAC1;RHOA;SERPINE1;TN
F;VEGF
TTD
4198
1675
HMGCR; PPARG
PDTD
-
841
-
Matador
801
2901
Direct interaction: CYP3A4; HMGCR
Indirect interaction: APOE; COG2; LDL;
ENSP00000352471; LDLR; Lipoprotein(a);
LPA
STITCH
>68,000
>1,500,000
LPA; CYP3A4; LDLR; ENSP00000352471;
APOE; HMGCR; COG2
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
Main active ingredients of JSW:
• hyperforin (HP)
• hypericin (HY)
• pseudohypericin (PH)
• amentoflavone (AF)
• flavonoids (FL)
the effects of the
SJW active
compounds on the
system of neuroactive
ligand-receptor
interaction
Drug-target network of FDA approved antidepressants and SJW compounds.
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.
Main active compounds of RIF
• tetraarsenic tetrasulfide (As4S4, A)
• indirubin (I)
• tanshinone IIA (T)
Effects of As4S4（A）、indirubin(I) and tanshinone IIA(T) on
different APL-associated proteins
Functional networks of APL disease gene-encoded proteins and RIF-targeted proteins.
(A) 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
Regulations of single RIF compounds on different proteins
on AML pathway.
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
Thanks!