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6TMR Modern Herbal Medicine  2019, Vol. 2 Issue (2): 64-73    DOI: 10.12032/TMRmhm2017A44
    
Mechanism analysis of Traditional Chinese Medicine in treatment of Diabetic Nephropathy based on network pharmacology and Traditional Chinese Medicine inheritance support system
Yu-hui Ma1, Li-na Zhang1, Qian Cui1, Lin-feng Li1, Guo-wei Zhang1,*()
1 College of Chinese Medicine, Hebei University, Baoding, Hebei, China
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Diabetic nephropathy is one of the most serious complications of diabetes mellitus. In the early stage, edema and proteinuria are the main clinical manifestations. In the later stage, glomerulosclerosis and interstitial fibrosis will occur. And the prognosis is poor. Nowadays, traditional Chinese medicine has a remarkable curative effect in the treatment of diabetic nephropathy. There are more and more studies on the treatment of diabetic nephropathy with traditional Chinese medicine, but most of them focus on the syndromes of diabetic nephropathy, which are short of in-depth research and summary on the mechanism of Chinese herbal prescriptions. In this paper, the traditional Chinese medicine inheritance support system and network pharmacology BATMAN-TCM software were used to collect and analyze the relevant literature. It was found that the core compatibility of Shanzhuyu, Fuling and Shuyu in the treatment of diabetic nephropathy is closely related to the signal transduction pathway and target of diabetic nephropathy, and has a positive effect on the improvement of clinical symptoms such as proteinuria, glycometabolism disorder, edema, etc. This paper explores the core compatibility of Shanzhuyu, Fuling and Shuyu on diabetic nephropathy, in order to provide reference for clinical treatment.

Highlights
Diabetic nephropathy is one of the most serious complications of diabetes mellitus, which seriously affects the quality of life of patients for the poor prognosis. Nowadays, Traditional Chinese Medicine has a remarkable curative effect in the treatment of diabetic nephropathy, which has brought some enlightenment to our research ideas. Traditional Chinese Medicine Inheritance Assistant Platform System (TCM Inheritance Assistant Platform) is an organic combination of computer science and TCM. By intellectualized processing of disease prescription, the data of disease information, syndrome information and compatibility of TCM can be more intuitive, which make Chinese herbal medicine and drug pairs for treating diseases more specialized and precise. Network pharmacology is based on system biology to analyze multi-target networks of drugs, and can combine Drug targets with diseases, which make the treatment of diseases more targeted. This paper collects and summarizes the prescriptions of traditional Chinese medicine for diabetic nephropathy in the past 10 years from China Knowledge Net, and uses the system of traditional Chinese Medicine Inheritance assistant platform and BATMAN-TCM database of network pharmacology to analyze the medication rules and action signal pathways. We concluded that Shanzhuyu(Cornus officinalis Sieb. et Zucc), Fuling (Poria cocos (Schw.) Wolf) and Shuyu (Dioscorea opposite) have certain positive effects on the compatibility in the prevention and treatment of diabetic nephropathy and hope to provide new ideas for the research and clinical treatment of diabetic nephropathy with TCM.



Key wordsdiabetic nephropathy      traditional Chinese medicine (TCM)      network pharmacology      TCM inheritance support system      BATMAN-TCM     
Published: 25 April 2019
Fund:  This work was funded by Hebei Culture and Art Project (HB18-YB042).
Corresponding Authors: Zhang Guo-wei     E-mail: xxzgw@126.com
About author: # These authors contributed equally to this work.
Cite this article:

Yu-hui Ma, Li-na Zhang, Qian Cui, Lin-feng Li, Guo-wei Zhang. Mechanism analysis of Traditional Chinese Medicine in treatment of Diabetic Nephropathy based on network pharmacology and Traditional Chinese Medicine inheritance support system. 6TMR Modern Herbal Medicine, 2019, 2(2): 64-73. doi: 10.12032/TMRmhm2017A44

URL:

https://www.tmrjournals.com/mhm/EN/10.12032/TMRmhm2017A44     OR     https://www.tmrjournals.com/mhm/EN/Y2019/V2/I2/64

No Name Times No Name Times
1 Astragalus membranaceus (Fisch.) Bunge 61 10 Achyranthes bidentata Blume. 25
2 Salvia miltiorrhiza Bunge 61 11 Ligusticum chuanxiong Hort. 24
3 Poria cocos (Schw.) Wolf 58 12 Atractylodes macrocephala
Koidz.
24
4 Dioscorea opposita 45 13 Schisandra chinensis 23
5 Angelica sinensis (Oliv.) Diels 41 14 Pueraria lobata(Willd.)Ohwi 22
6 Rehmannia glutinosa
(Gaetn.) Libosch. ex Fisch. et Mey.
38 15 Cinnamomum cassia Presl 21
7 Cornus officinalis Sieb. et Zucc. 35 16 Rehmannia glutinosa (Gaert.) Libosch. ex Fisch. et Mey. 21
8 Alisma plantago-aquatica
Linn.
32 17 Aconitum carmichaeli Debx. 20
9 Ophiopogon japonicas (Linn. f.)
Ker-Gawl.
29
Table 1 Drug use frequency≥20 times
No Name Times No Name Times
1 Dioscorea opposita,
Poria cocos (Schw.)
Wolf
33 15 Angelica sinensis
(Oliv.) Diels,
Poria cocos (Schw.)
Wolf
22
2 Cornus officinalis
Sieb. et Zucc,
Dioscorea opposita
31 16 Astragalus membranaceus (Fisch.) Bunge,
Alisma plantago-aquatica Linn.
21
3 Salvia miltiorrhiza
Bunge,
Poria cocos (Schw.)
Wolf
30 17 Rehmannia glutinosa (Gaetn.) Libosch. ex Fisch. et Mey,
Dioscorea opposita
21
4 Astragalus membranaceus (Fisch.) Bunge,
Poria cocos (Schw.)
Wolf
30 18 Cornus officinalis Sieb. et Zucc,
Salvia miltiorrhiza
Bunge
20
5 Salvia miltiorrhiza
Bunge,
Astragalus membranaceus (Fisch.) Bunge
28 19 Rehmannia glutinosa (Gaetn.) Libosch. ex Fisch. et Mey,
Salvia miltiorrhiza Bunge
20
6 Astragalus membranaceus (Fisch.) Bunge,
Dioscorea opposita
27 20 Ophiopogon japonicus (Linn. f.) Ker-Gawl,
Schisandra chinensis
19
7 Cornus officinalis
Sieb. et Zucc,
Dioscorea opposita
26 21 Atractylodes macrocephala Koidz,
Poria cocos (Schw.)
Wolf
19
8 Salvia miltiorrhiza
Bunge,
Dioscorea opposita
26 22 Cornus officinalis
Sieb. et Zucc,
Astragalus membranaceus (Fisch.) Bunge,
Dioscorea opposita
19
9 Salvia miltiorrhiza
Bunge,
Angelica sinensis
(Oliv.) Diels
26 23 Rehmannia glutinosa (Gaert.) Libosch. ex Fisch. et Mey,
Dioscorea opposita
18
10 Poria cocos (Schw.)
Wolf, Alisma plantago-aquatica Linn.
25 24 Rehmannia glutinosa (Gaetn.) Libosch. ex Fisch. et Mey,
Poria cocos (Schw.)
Wolf
18
11 Cornus officinalis
Sieb. et Zucc,
Dioscorea opposita,
Poria cocos (Schw.)
Wolf
25 25 Paeonia suffruticosa
Andrews,
Poria cocos (Schw.)
Wolf
18
12 Rehmannia glutinosa (Gaetn.) Libosch. ex Fisch. et Mey, Astragalus membranaceus (Fisch.) Bunge. 23 26 Cornus officinalis
Sieb. et Zucc,
Salvia miltiorrhiza
Bunge,
Dioscorea opposita
18
13 Cornus officinalis
Sieb. et Zucc, Astragalus membranaceus (Fisch.) Bunge
22 27 Astragalus membranaceus (Fisch.) Bunge,
Dioscorea opposita,
Poria cocos (Schw.)
Wolf
18
14 Astragalus membranaceus (Fisch.) Bunge,
Angelica sinensis
(Oliv.) Diels
22
Table 2 Commonly used drug combinations for treating DN
No Association rules Confidence level
1 Cornus officinalis
Sieb. et Zucc,Poria cocos(Schw.)Wolf—
Dioscorea opposita
0.9615
Table 3 Association rule of drug combinations for treating DN
Figure 1 Commonly used drug combinations network
No Name Targets
Cornus
officinalis
Sieb. et Zucc
Poria cocos (Schw.)
Wolf
Dioscorea opposita
1 Hypertension 13 3 6
2 Chronic dementia 9 5 6
3 Pain 8 5 7
4 Anxiety disorder, unspecified 8 5 6
5 asthma 8 3 7
6 Parkinson’s disease 6 5 7
7 Alzheimer's disease 9 2 5
8 Neuropsychiatric diseases 5 3 5
9 Neurodegenerative diseases 6 2 4
10 Cocaine dependence 3 4 4
11 Migraine 2 7 2
12



Mammary cancer 6 2 3
13 Cardiovascular disease, unspecified 7 1 3
14 Heart failure 4 2 4
15 Drug dependence 4 1 5
16 erectile dysfunction 7 1 2
17 Arrhythmia 4 1 4
18 prostatic cancer 4 3 2
19 Brain damage 4 2 3
20 Cognitive deficits 4 2 3
21 Respiratory diseases 1 4 4
22 Non-insulin dependent diabetes mellitus 3 2 4
23 Mental illness 1 3 3
24 Cancer, nonspecific 4 2 1
25 ischemic heart disease 2 2 2
26 Chronic ileitis 1 2 2
27 Excessive drinking 2 1 2
28 Nausea and vomiting 3 1 1
29 Inflammatory bowel disease 1 2 2
30 Neurological diseases 1 2 2
31 Pain, nonspecific 3 1 1
32 Alcohol dependence 3 1 1
33 benign prostatic hyperplasia 2 1 1
34 Vomit 1 2 1
35 Paranoia 1 1 1
36 Multiple sclerosis 1 1 1
37 Insulin resistance (obesity related) 1 1 1
38 Hypoxic-ischemic encephalopathy 1 1 1
39 Dependence on opioids 1 1 1
40 Ischemia-reperfusion injury 1 1 1
41 Autoimmune diseases 1 1 1
42 Attention deficit hyperactivity disorder 1 1 1
43 Coronary heart disease 1 1 1
44 Gastric emptying disorder 1 1 1
45 Oxygen-induced retinopathy 1 1 1
46 Vitamin D deficiency 1 1 1
47 Dyskinesia 1 1 1
48 Raynaud syndrome 1 1 1
49 Behcet's disease 1 1 1
50 Hypertrophic vascular disease 1 1 1
51 Chaos in Gilles de la Tourette 1 1 1
52 progressive autonomic failure 1 1 1
53 kidney failure 1 1 1
54 nephropathy 1 1 1
Table 4 Analysis results of Shanzhuyu, Fuling and Shuyu single drug and compatibility for disease enrichment
No KEGG pathway ID KEGG pathway Name
1 hsa04010 MAPK signaling pathway
2 hsa04014 Ras signaling pathway
3 hsa04015 Rap1 signaling pathway
4 hsa04020 Calcium signaling pathway
5 hsa04022 cGMP - PKG signaling pathway
6 hsa04064 NF-kappa B signaling pathway
7 hsa04068 Fox-O signaling pathway
8 hsa04070 Phosphatidylinositol signaling system
9 hsa04150 mTOR signaling pathway
10 hsa04151 PI3K-Akt signaling pathway
11 hsa04152 AMPK signaling pathway
12 hsa04330 Notch signaling pathway
13 hsa04350 TGF-beta signaling pathway
14 hsa04370 VEGF signaling pathway
15 hsa04390 Hippo signaling pathway
16 hsa04668 TNF signaling pathway
Table 5 Analysis result of KEGG pathway
Figure 2 Visualization of potential targets, signaling pathways and disease networks
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