DECIPHERING THE ACTION MECHANISM OF INDONESIA HERBAL DECOCTION IN THE TREATMENT OF TYPE II DIABETES USING A NETWORK PHARMACOLOGY APPROACH
Objective: The aim of this research was to investigate action mechanism of Indonesia herbal decoctions in the treatment of Type 2 Diabetes (T2D) using network pharmacology approaches.
Methods: Drug target profile analysis via Markov clustering was performed to identify the potent antidiabetic ingredients in the four herbs. Network target base identification of multicomponent synergy was applied to predict the ingredients synergetic effect. The multi-level and integrated target networks were contracted to identify the herbs major ingredients and their presumed targets. Further enrichment analysis and molecular docking were performed to validate network targets.
Results: 278 ingredients from the four herbs were linked to antidiabetic drugs with an overall clustering success rate of 98.58% and 5 ingredient pairs had significant synergetic effects. Enrichment analysis demonstrates herbs candidate presumed targets were frequently involved in the significant biological process and pathways associated with progression of Type 2 diabetes (T2D) diseases. Finally, molecular docking validation revealed there was high binding site similarity between momordicoside F2 (78%), beta-sitosterol (67%) and cis-N-Feruloyltyramine (67%) with miglitol drug. In addition, the four ligands presented the higher binding affinity to Maltase-glucoamylase (MGA) receptor an enzyme responsible for the digestion of dietary starch to glucose.
Conclusion: This study revealed the pharmacological mechanism of action of Indonesia herbal decoctions in the treatment of Type 2 diabetes. The herbs major presumed target played a significant biological role in the progression of Type 2 diabetes (T2D) while major herbal ingredients indicates the potential of curing Type 2 diabetes by inhibiting Maltase-glucoamylase (MGA) activity.
2. Parkes DG, Mace KF, Trautmann ME. Discovery and development of exenatide: the first antidiabetic agent to lever age the multiple benefits of the in cretin hormone, GLP-1. Expert Opin Drug Discov 2014;8:219-44.
3. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 2010;87:4-14.
4. Jeï¬€rey S, Flier. Hormone resistance in diabetes and obesity: insulin, leptin, and FGF21. Yale J Biol Med 2012;85:405-14.
5. Leahy L, Hirsch BI, Peterson KA, Schneider D. Targeting beta-cell function early in the course of therapy for type 2 diabetes mellitus. J Clin Endocrinol Metab 2010;95:4206-16.
6. Ku CR, Lee HJ, Kim SK. Resveratrol prevents streptozotocin induced diabetes by inhibiting the apoptosis of pancreatic beta-cell and the cleavage of poly (ADP-ribose) polymerase. Endocr J 2012;59:103-9.
7. Kannan, Arshad, Kumar S. A study on drug utilization of oral hypoglycemic agents in type-2 diabetic patients. Asian J Pharm Clin Res 2011;4:60-4.
8. Jovanovic L, Hassman DR, Gooch B. Treatment of type 2 diabetes with a combination regimen of repaglinide pluspioglitazone. Diabetes Res Clin Pract 2004;63:127-34.
9. Howlett HCS, Bailey CJ. A risk-benefit assessment of metformin in type 2 diabetes mellitus. Drug Safety 1999;20:489-503.
10. Gastaldelli A, Ferrannini E, Miyazaki Y, Matsuda M, Mari A, DeFronzo RA. tiazolidinediones improve beta cell function in type 2 diabetic patients. Am J Physiol 2007;292:E871-83.
11. Vengurlekar S, Shukla P, Patidar P, Bafna R, Jain S. Prescribing pattern of antidiabetic drugs in indore city hospital. Indian J Pharm Sci 2008;70:637-40.
12. Karunrat T, Walaya J. The pharmacokinetics of 2 doses levothyroxine treatment in athyreotic patients. Int J Appl Pharm 2016;4:66-8.
13. Kumar KS, Sreeramya G, Krishna KM, Nalini K, Kiranmai N, Vasavi P. Drug use pattern study of antidiabetics in type 2 diabetes mellitus at a tertiary care hospital in Tenali, Andhra Pradesh. Int J Invent Pharm Sci 2013;1:162-6.
14. Ciero LT, Yenshou, Lin AP, Yi C, Shao CC, Wen CY. Herbal therapies for type 2 diabetes mellitus: chemistry, biology, and potential application of selected plants and compounds. J Evidence-Based Complementary Altern Med 2013:1-33. Doi:10.1155/2013/378657
15. Zhang XJ, Deng YX, Shi QZ, He MY, Chen B, Qiu XM. Hypolipidemic effect of the Chinese polyherbal Huanglian Jiedu decoction in type 2 diabetic rats and its possible mechanism. J Phymed 2014;21:615â€“23.
16. Yuxin P, Dan W, Zuowang F, Xiaolu C, Fulai Y, Xuan H, et al. Blumea balsamifera phytochemical and pharmacological. Mol Rev 2014;19:9453-77.
17. Dong H, Wang N, Zhao L, Lu F. Berberine in the treatment of type 2 diabetes mellitus: a systemic review and meta-analysis. J Evidence-Based Complementary Altern Med 2012. Doi:10.1155/2012/591654
18. Reddy NM, Rajasekhar R. Tinospora cordifolia chemical constituents and medicinal properties: a review. Scholars Acad J Pharm 2015;4:364-9.
19. Chakraborty D, Mukherjee A, Sikdar S. -gingerol isolated from ginger attenuates sodium arsenite induced oxidative stress and plays a corrective role in improving insulin signaling in mice. Toxicol Lett 2010;210:34-43.
20. Prabhakar PK, Doble M. Mechanism of action of natural products used in the treatment of diabetes mellitus. Chin J Integr Med 2011;17:563-74.
21. Biswal S, Sahoo U, Sethy S, Kumar HKS, Banerjee M. Indole: the molecule of diverse biological activities. Asian J Pharm Clin Res 2012;5:1-6.
22. Thomas N, Zachariah SM. Pharmacological activities of chromene derivatives: an overview. Asian J Pharm Clin Res 2011;6:11-5.
23. Zhang B, Wang X, Li S. An integrative platform of TCM network pharmacology and its application on an herbal formula. J Evidence-Based Complementary Altern Med 2013. http://dx.doi.org/10.1155/2013/456747
24. Li S, Zhang B, Jiang D, Wei Y, Zhang N. Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formulae. BMC Bioinfo 2010;11 Suppl 11:S6.
25. Gustafsson M, Nestor CE, Zhang H, BarabÃ¡si AL, Baranzini S. Modules, networks and systems medicine for understanding disease and aiding diagnosis. Genome Med 2014;6:82-6.
26. Xiujuan W, Natali G, Haiyuan Y. Network-based methods for human disease gene prediction. Briefings Funct Genomics 2011;10:280-93.
27. Wei L, Aiping W, Matteo P, Xiaofan W. Integrative analysis of human protein, function and disease networks. Nat Sci Rept 2015. Doi:10.1038/srep14344
28. Li S, Zhang B, Zhang N. Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Syst Biol 2011;5(Suppl 1):S10.
29. Liang X, Li H, Li SA. Novel network pharmacology approach to analyses traditional herbal formulae: the Liu-Wei-Di-Huang pill as a case study. Mol Biosyst 2014;10:1014-22.
30. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009;37:W623â€“33.
31. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M. NCBI GEO: archive for functional genomics data setsâ€“update. Nucleic Acids Res 2013;41:D991-5.
32. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online mendelian inheritance in man (OMIM), a knowledge base of human genes and genetic disorders. Nucleic Acids Res 2005;33:D514-17.
33. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, et al. DrugBank: a knowledge base for drugs, drug actions and drug targets. Nucleic Acids Res 2008;36:D901-6.
34. Chen JY, Mamidipalli S, Huan T. HAPPI: an online database of comprehensive human annotated and predicted protein interactions. BMC Genomics 2009;34:D140-44.
35. Keshava PS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S. Human protein reference database. Nucleic Acids Res 2009;37:D767-72.
36. Brown KR, Jurisica I. Online predicted human interaction database. Bioinformatics 2005;21:2076-82.
37. Ceol A, Chatr-Aryamontri A, Armstrong J, Woollard P. MINT, The molecular interaction database. Nucleic Acids Res 2010;38:D532-39.
38. Nacher JC, Schwartz JM. A global view of drug-therapy interactions. BMC Pharmacol 2008;8:5-14.
39. KorcsmÃ¡ros T, Szalay MS, BÃ¶de C, KovÃ¡cs IA, Csermely P. How to design multi-target drugs: target search options in cellular networks. Expert Opin Drug Discovery 2007;2:1-10.
40. Arrell DK, Terzic A. Network systems biology for drug discovery. Clin Pharmacol Ther 2010;88:120-5.
41. Hwang WC, Zhang A, Ramanathan M. Identification of information flow modulating drug targets: a novel bridging paradigm for drug discovery. Clin Pharmaco Ther 2008;84:563-72.
42. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2013;3:2498-504.
43. Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chinese J Natur Med 2013;11:110-20.
44. Wuchty S, Almaas E. Evolutionary cores of domain co-occurrence networks. BMC Evol Biol 2005;5:24.
45. Mi H, Tomas P. PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. Methods Mol Biol 2009;563:123-40.
46. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 2009;4:44-57.
47. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreadine. J Comput Chem 2010;31:455-61.
48. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, et al. AutoDock4 and AutoDockTools4:automated docking with selective receptor flexibility. J Comput Chem 2009;16:85-91.
49. Laskowski RA, Swindells MB. LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 2011;51:2778-86.
50. Zhang B, Wang X, Li S. An integrative platform of TCM network pharmacology and its application on herbal formula, Qing-Luo-Yin. J Evidence-Based Complementary Altern Med 2013. http://dx.doi.org/10.1155/2013/456747
51. Xie W, Gu D, Li J, Cui K, Zhang Y. Effects and action mechanisms berberine and Rhizoma coptidis on gut microbes and obesity in high-fat diet-fed C57BL/6J mice. PloS one 2011. http://dx.doi.org/10.1371/journal.pone.0024520
52. Hadimani MB, Purohit MK, Vanampally C. Guaifenesin derivatives promote neurite outgrowth and protect diabetic mice from neuropathy. J Med Chem 2013;56:5071-8.
53. Yin J, Gao Z, Liu D, Liu Z, Ye J. Berberine improves glucose metabolism through induction of glycolysis. Am J Physiol Endocrinol Metab 2008;294:E148â€“56.
54. Bliss CI. The calculation of microbial assays. Bacteriol Rev 1956;20:243-58.
55. Diana S, Kara N, Eleanor P, Ji EC, Hertzel CG. The effect of oral antidiabetic agents on A1C levels. A systematic review and meta-analysis. BMJ Open Diabetes Res Care 2010;33:1859-64.
56. Da Silva Xavier G, Varadi A, Ainscow EK, Rutter GA. Regulation of gene expression by glucose in pancreatic Î²-cells (MIN6) via insulin secretion and activation of phosphatidylinositol 3â€²-kinase. J Biol Chem 2000;275:36269â€“77.
57. O'Neill LA, Bowie AG. Sensing and signaling in antiviral innate immunity. Curr Biol 2010;20:R328-33.
58. Moore PC, Ugas MA, Hagman DK, Parazzoli SD, Poitout V. Evidence against the involvement of oxidative stress in fatty acid inhibition of insulin secretion. Diabetes 2004;53:2610-6.
59. Borden EC, Williams BR. Interferon-stimulated genes and their protein products what and how? J Interferon Cytokine Res 2011;31:1-4.
60. Zhang GB, Li QY, Chen QL, Su SB. Network pharmacology: a new approach for Chinese herbal medicine research. J Evidence Based Complementary Altern Med 2013. http://dx.doi.org/ 10.1155/2013/621423
61. Wang RZ, Lin DQ, Tong HF, Yao SJ. Molecular insights into the binding selectivity of a synthetic ligand DAAG to Fc fragment of IgG. J Mol Recognit 2014;27:250-9.
62. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Delivery Rev 2001;46:3-29.