Abstract
Huatan Tongluo Fang (HTTLF) is a traditional herbal formula that can resolve phlegm and dredge collaterals. HTTLF has also been used to treat rheumatoid arthritis (RA); however, the mechanism underlying the therapeutic effects of HTTLF on RA has not been clearly elucidated at the molecular level. In the present study, an integrated model of system pharmacology containing chemical space analysis, potential active compound prediction and compound-target-disease network was constructed to investigate the molecular mechanisms of HTTLF. The compounds from HTTLF dispersed well in the chemical space. Most of the compounds from HTTLF had similar chemical spaces to drug/drug-like compounds associated with RA, according to the MDL Drug Data Report. A total of 127 potentially active compounds and 17 targets of RA were identified. Among them, 50 compounds interacted with ≥2 targets, while 77 compounds interacted with only one target. In addition, 17 targets were associated with 82 diseases that belonged to 26 categories. These results indicate that HTTLF has diverse chemical spaces and polypharmacology with regards to the treatment of RA. In addition, HTTLF demonstrated therapeutic potential against diverse diseases other than RA, including osteoarthritis, atherosclerosis and brain cancer. This study provides a novel platform for understanding how HTTLF treats RA; this is beneficial for explaining the diverse functions of HTTLF with regards to RA, and may help develop novel compounds with desirable therapeutic targets to treat RA.
Keywords: rheumatoid arthritis, systems pharmacology, Huatan Tongluo Fang, traditional Chinese medicine, diverse functions
Introduction
Rheumatoid arthritis (RA) is an autoimmune disease primarily characterized by arthrosynovitis (1). Its main clinical manifestations are chronic, symmetrical, multi-joint synovitis and articular damage (2). The incidence of RA is ~1% worldwide; RA severely influences quality of life and health as it can result in a high level of disability, and negatively affects individuals, families and society (3). At present, there is no individually recognized drug to control and treat RA. The primary drugs on the market for RA treatment are non-steroidal anti-inflammatory drugs, biological agents, disease-modifying anti-rheumatic drugs and glucocorticoids. However, the pharmacological management of RA has targeted the symptoms of the disease, rather than the underlying causes(4). In addition, the prolonged use of these drugs has numerous side-effects, and they are becoming ineffective as a result of drug resistance (5). Thus, it is important that researchers develop novel anti-rheumatic drugs that delay the progression of RA and reduce disability.
Traditional Chinese medicines (TCMs) have been used to treat RA for >2,000 years. It has been demonstrated that the effect of herbal formulas on RA is an integrated result of various mechanisms of action, including immunity adjustment and inflammatory control (6). Herbal formulae serve a moderate role in the treatment of RA; they have few side-effects and are suitable for long-term use (7,8). The herbal treatment of RA has received increasing attention (9); it is thought that they have great potential to be developed and utilized for the treatment of patients with RA.
Huatan Tongluo Fang (HTTLF) is a traditional herbal formula that has been widely prescribed to treat RA in the Xiamen Hospital of Traditional Chinese Medicine (Xiamen, China). HTTLF is composed of six herbs, including Bile arisaema (BA; Dannanxing), Semen persicae (SP; Taoren), Flos carthami (FC; Honghua), Sinapis alba (SA; Baijiezi), Bombyx batryticatus (BB; Jiangcan) and Paeonia alba (PA; Baishao). Clinical observations have demonstrated that HTTLF can reduce the level of vascular endothelial growth factor (VEGF) in the serum of patients with RA, and significantly alleviate the indexes of erythrocyte rate, C-reactive protein, tenderness and swelling of the joints of patients with RA (10). The results of animal model experiments have demonstrated that HTTLF can relieve inflammation in rats with collagen-induced arthritis, and significantly reduce the expression levels of serum VEGF and matrix metalloproteinase (MMP)-3 (11). However, the underlying molecular mechanisms of HTTLF remain unknown. Fortunately, numerous computer simulation methods have made a significant contribution towards understanding the theory of TCMs and their mechanisms of action at a molecular and systems level (12–14). In the present study, an integrated model of systems pharmacology, developed in a previous study (12,13), that combined molecular database building, chemical space, molecular docking and network pharmacological techniques, was used to investigate the molecular characteristics of HTTLF and map a compound-target-disease network to understand the interaction between HTTLF and therapeutic targets of RA from a systematic point of view. These attempts may offer novel opportunities to investigate the pharmacological basis of HTTLF, and provide an effective method to aid development of treatments for RA using herbal formulae.
Materials and methods
Molecular database building
All chemical ingredients from the six herbs of HTTLF were collected from the Chinese Herbal Drug Database, the Handbook of the Constituents in Chinese Herb Original Plants and other literature (15–19). A total of 692 compounds were obtained, of which 144 were obtained from Dannanxing, 68 from Taoren, 163 from Honghua, 119 from Baijiezi, 93 from Jiangcan and 105 from Baoshao. The chemical structures were drawn using ISIS Draw version 2.5 (MDL Information Systems, Inc., San Leandro, CA, USA) and further optimized by Discovery Studio version 2.0 (DS 2.0; Accelrys, Ltd., San Diego, CA, USA) with a Merck molecular force field (MMFF). In addition, 1,362 RA-associated drug/drug-like compounds were collected from the MDL Drug Data Report (20); these were optimized with the MMFF and saved to files in standard definition format in preparation for the subsequent analyses (21).
Chemical space analysis
In the current study, a total of 150 physicochemical properties were calculated by the quantitative structure-activity relationship (QSAR) module of DS 2.0 (13), and principal components analysis was used to map the distributions of HTTLF and drug/drug-like compounds in the chemical space in two dimensions. According to Lipinski's rule of five (22), four important pharmacology-associated descriptors, including molecular weight (MW), the number of hydrogen bond donors (nHDon), the number of hydrogen bond acceptors (nHAcc) and octanol-water partition coefficients (AlogP), were calculated in order to evaluate the drug-likeness of HTTLF compounds.
Molecular docking
To determine whether HTTLF can interact with 17 key targets associated with RA (23,24), molecular docking simulations were performed between HTTLF compounds and these targets by the LigandFit module of DS 2.0. Their protein crystal structures were retrieved from the Protein Data Bank (PDB; Table I) (25). All crystallographic waters were removed from the file and the hydrogen atoms were added. An inhibitor from the PDB file was used to define the active site. HTTLF compounds were docked onto the protein models. The interactions between these were evaluated using DockScore (26). The compounds with the top 20 DockScores were selected as potentially active compounds of HTTLF (13).
Table I.
17 key protein targets associated with rheumatoid arthritis.
| Protein name | PDB code |
|---|---|
| Dihydroorotate dehydrogenase, mitochondrial | 3O8A |
| Cyclooxygenase-2 | 3MQE |
| Tyrosine-protein kinase Janus kinase 3 | 3LXL |
| Tumor necrosis factor-α | 2AZ5 |
| Interleukin 1 receptor | 1IRA |
| Integrin α-4 | 3V4V |
| Thioredoxin reductase, cytoplasmic | 4B1B |
| Interleukin-2 | 1M48 |
| Cathepsin K | 1AU0 |
| Janus kinase 1 | 4IVD |
| Tyrosine-protein kinase SYK | 4FZ6 |
| Mitogen-activated kinase p38 | 1CM8 |
| Metalloproteinase domain-17 | 2A8H |
| Inhibitor of nuclear factor κB kinase | 3RZF |
| Matrix metalloproteinase-9 | 1GKC |
| Vascular endothelial growth factor receptor 2 | 1Y6A |
| Macrophage migration inhibitory factor | 4F2K |
PDB, Protein Data Bank; SYK, spleen tyrosine kinase.
Network construction and analysis
Potentially active compounds and corresponding targets were analyzed using compound-target network (CTN) and target-disease network (TDN) (18). The CTN was constructed by linking the potential active compounds and their corresponding targets, and the TDN was constructed by linking the potential targets and their corresponding diseases. The associations between the targets and diseases were retrieved from the Therapeutic Targets Database (23). The above networks were generated and analyzed by Cytoscape version 2.8.3 (University of California, San Diego, CA, USA) (27).
Results
Molecular physicochemical property analysis of HTTLF
The distribution of the physicochemical properties of compounds from HTTLF was diverse (Table II). The majority of the compounds were observed to have clustered on the left side of the chemical space (Fig. 1). There was a large overlap between HTTLF compounds and drug/drug-like compounds in the chemical space. Fig. 2 presents the percentages of MW (<500), AlogP (<5), nHDon (<5) and nHAcc (<10) were 95.59, 67.92, 90.89 and 89.31%, respectively. According to the structure-activity relationship theory and Lipinski's rule of five (22,28), the compounds from HTTLF possessed molecular diversity and drug-likeness.
Table II.
Statistics of key molecular properties of compounds in Huatan Tongluo Fang.
| Name | Mean | Standard Deviation | Minimum | Maximum |
|---|---|---|---|---|
| Carbon count | 15.91 | 10.77 | 1.00 | 75.00 |
| Nitrogen count | 0.38 | 0.92 | 0.00 | 5.00 |
| Oxygen count | 3.50 | 5.53 | 0.00 | 47.00 |
| Octanol-water partition coefficients | 3.36 | 4.16 | −4.62 | 18.62 |
| Molecular weight | 278.85 | 203.10 | 53.06 | 1707.20 |
| Number of rotatable bonds | 6.97 | 7.18 | 0.00 | 37.00 |
| Number of hydrogen bond acceptors | 3.74 | 5.50 | 0.00 | 47.00 |
| Number of hydrogen bond donors | 1.97 | 3.40 | 0.00 | 28.00 |
| Molecular volume | 199.67 | 133.85 | 19.20 | 985.43 |
| Molecular surface area | 292.18 | 187.51 | 72.33 | 1479.01 |
| Molecular polar surface area | 66.23 | 94.03 | 0.00 | 812.37 |
| JX | 2.54 | 0.68 | 0.97 | 4.70 |
| JY | 2.62 | 0.70 | 1.02 | 4.72 |
| Wiener | 2301.02 | 7270.50 | 9.00 | 88225.00 |
| Zagreb | 94.71 | 82.67 | 10.00 | 682.00 |
Figure 1.
Chemical space distributions of compounds from HTTLF and the MDDR. (A) Chemical space distributions of compounds from HTTLF. (B) Chemical space distributions of compounds from MDDR. (C) Black circles represent compounds from HTTLF, whereas white circles represent drug/drug-like compounds from the MDDR. HTTLF, Huatan Tongluo Fang; PC, principal component; MDDR, MDL Drug Data Report.
Figure 2.
The distributions of four important molecular properties of compounds from Huatan Tongluo Fang. Molecular properties consist of (A) MW, (B) AlogP, (C) nHDon and (D) nHAcc. MW, molecular weight; AlogP, octanol-water partition coefficients; nHDon, number of hydrogen bond donors; nHAcc, number of hydrogen bond acceptors.
Diverse functions of HTTLF
Docking results demonstrated that there were 127 potentially active compounds in HTTLF. The interactions between compounds and targets are presented in the compound-target network (CTN) (Fig. 3). The CNT showed that 50 compounds can interact with ≥2 targets, while 77 compounds can interact with only one target (Fig. 4). The general network properties and key compounds in the CTN are listed in Tables III and IV, respectively. The values of network heterogeneity and network centralization were 1.336 and 0.108, respectively. In addition, a TDN was constructed (Fig. 5). The TDN demonstrated that 17 targets were associated with 82 diseases (Table V) that belonged to 26 categories, such as musculoskeletal, immune system and cardiovascular diseases, and neoplasms. This suggests that HTTLF may demonstrate efficacy in targeting these diseases.
Figure 3.
Compound-target network. Potentially active compounds are connected with associated diseases; the circles and rectangles represent the candidate compounds and target proteins, respectively. D, disease; BA, bile arisaema; SP, Semen persicae; FC, Flos carthami; SA, Sinapis alba; BB, Bombyx batryticatus; PA, Paeonia alba.
Figure 4.
Degree distributions of compound nodes.
Table III.
General network properties of the compound-target network.
| Parameter | Compound-target network |
|---|---|
| Number of nodes | 144 |
| Number of edges | 340 |
| Network density | 0.033 |
| Network heterogeneity | 1.336 |
| Isolated nodes | 0 |
| Number of self-loops | 0 |
| Multi-edge node pairs | 0 |
| Network centralization | 0.108 |
| Shortest paths | 20,592 (100%) |
| Characteristic path length | 3.891 |
| Average number of neighbors | 4.722 |
Table IV.
Key compounds with top-10 degree in the compound-target network.
| Index | Degree | Chemical name | Known |
|---|---|---|---|
| SP-27 | 12 | Naringenin 7-O-beta-D-glucoside | No |
| BA-75 | 12 | Isoschaftoside | No |
| BA-74 | 12 | Schaftoside | No |
| BA-143 | 12 | Apigenin 6, 8-di-C-glucoside | No |
| SP-68 | 11 | Chlorogenic acid | Yes |
| SP-31 | 10 | Quercitrin | No |
| SP-30 | 10 | Quercetin | Yes |
| SP-1 | 10 | Amygdalin | Yes |
| BA-54 | 10 | Deacetylcentapicrin | No |
| BA-144 | 10 | Apioside | No |
Figure 5.
Target-disease network. In the target-disease network, 17 targets (rectangles) were associated with 82 diseases (circles) which belonged to 26 categories (triangles).D, disease.
Table V.
Diseases associated with potential targets.
| Index | Disease |
|---|---|
| D1 | Abdominal aortic aneurysm |
| D2 | Acute pain |
| D3 | Adenomatous polyposis |
| D4 | Advanced lung cancer |
| D5 | Allergic rhinitis, unspecified |
| D6 | Allergy, unspecified |
| D7 | Alzheimer's disease |
| D8 | Amebiasis |
| D9 | Arthritis |
| D10 | Asthma |
| D11 | Atherosclerosis |
| D12 | Autoimmune diseases |
| D13 | Bacterial infections |
| D14 | Behcet's disease |
| D15 | Bladder cancer |
| D16 | Brain cancer |
| D17 | Breast cancer |
| D18 | Cancer, unspecific |
| D19 | Cancers |
| D20 | Carcinoma in situ, unspecified |
| D21 | Carpal tunnel syndrome |
| D22 | Chronic pain |
| D23 | Colorectal cancer |
| D24 | Congestive heart failure |
| D25 | Coronary artery disease |
| D26 | Diabetes mellitus |
| D27 | Diabetic nephropathy |
| D28 | Dysmenorrhea, unspecified |
| D29 | Endometriosis |
| D30 | Facial Pain |
| D31 | Fibrosing alveolitis |
| D32 | Genitourinary tumors |
| D33 | Gestational hypertension |
| D34 | Glomerulonephritis |
| D35 | Graft-versus-host disease |
| D36 | Guillain-barre syndrome |
| D37 | Heart failure |
| D38 | Hepatocellular carcinoma |
| D39 | Hormone-refractory prostate cancer |
| D40 | Human immunodeficiency virus disease |
| D41 | Hyperimmunoglobulinemia D |
| D42 | Inflammation |
| D43 | Inflammatory bowel disease |
| D44 | Kaposi's sarcoma |
| D45 | Leprosy, unspecified |
| D46 | Lung cancer |
| D47 | Lymphoma, non-hodgkin |
| D48 | Malaria |
| D49 | Malignancies |
| D50 | Malignant mesothelioma |
| D51 | Meningioma |
| D52 | Multiple sclerosis |
| D53 | Myocardial infarction |
| D54 | Neurologic disorders |
| D55 | Non-insulin-dependent diabetes mellitus |
| D56 | Non-small cell lung cancer |
| D57 | Obstructive airway disease |
| D58 | Oropharyngeal squamous cell carcinoma |
| D59 | Osteoarthritis |
| D60 | Osteoporosis |
| D61 | Pain, unspecified |
| D62 | Pancreatic cancer |
| D63 | Parasitic diseases |
| D64 | Pathological angiogenesis |
| D65 | Periodic fever syndrome |
| D66 | Peutz-jeghers syndrome |
| D67 | Pneumocystis infections |
| D68 | Prostate cancer |
| D69 | Psoriasis |
| D70 | Renal cell carcinoma |
| D71 | Restenosis |
| D72 | Rheumatic diseases |
| D73 | Rheumatoid arthritis |
| D74 | Rheumatoid arthritis, unspecified |
| D75 | Sepsis |
| D76 | Septic shock |
| D77 | Sjogren's syndrome |
| D78 | Stroke |
| D79 | Systemic lupus erythematosus |
| D80 | Thrombocytopenia |
| D81 | Tumors |
| D82 | Ulcerative colitis |
Discussion
RA is a common chronic inflammatory disease that results in a considerable burden for the patient and society. The cause of RA is not a single effect; it is caused by multiple molecular abnormalities (29). Numerous clinical studies have demonstrated that a number of Chinese herbal monomers, such as triptolide and sinomenine, have efficient therapeutic effects in treating RA (30,31). It has been demonstrated that a combination of Tripterygium wilfordii polyglycoside and methotrexate can improve the therapeutic effects; in addition, the combination treatment can reduce side-effects and drug resistance (32). Furthermore, Tripterginum wilfordii polyglycoside in combination with glycyrrhizic acid has been demonstrated to reduce liver injury (33). Considering complex diseases, the model of drug discovery has been changed from identifying a single target to identifying multi-targets based on systems biology (34). Notably, herbal remedies with the characteristics of multi-component and multi-target compounds are most prevalent and effective in the treatment of chronic illnesses in a number of Asian countries (35);thus, chemical components can be used effectively in Chinese and Western medicines. (36). Thus, it may be useful to study compounds, targets and networks to investigate how Chinese herbal ingredients are effective against RA.
HTTLF is a traditional herbal formula that has been widely used in the Xiamen Hospital of Traditional Chinese Medicine. In the present study, the results demonstrated that the chemical space distributions of compounds from HTTLF were diverse. The data also demonstrated that there is a large overlap between HTTLF compounds and drug/drug-like compounds in the chemical space. According to the QSAR, the compounds with similar chemical space have similar active properties (28). Thus, the compounds from HTTLF may have diverse properties; the majority of compounds possessed drug-like properties, which aids in identifying anti-RA compounds from HTTLF.
Docking results in the current study demonstrated that 127 compounds from HTTLF could interact with 17 targets associated with RA. Among them, 50 compounds had potentially a large number of drug properties, while 77 compounds had the potential to be used in combination therapy. These results demonstrate that HTTLF is a broad-spectrum herbal treatment. To further understand the association between potential compounds and their targets, a CTN was constructed. The network consisted of 144 nodes (127 compounds and 17 targets) and 340 edges. The compounds in the outer CTN displayed fewer interactions with targets than those in the inner CTN. The values of network heterogeneity and network centralization were 1.336 and 0.108, respectively. This indicated that a number of compounds were more central than others (37). For example, isoschaftoside (BA-75) had the largest number of target interactions, whereas β-carotene had one target interaction. Thus, HTTLF exhibited diverse therapeutic effects via interacting with the same or different targets.
Previous studies have demonstrated that particular compounds exhibit biological activities against targets associated with RA (38–41). For example, chlorogenic acid can inhibit the expression of cyclooxygenase-2 and attenuate pro-inflammatory cytokines (including interleukin-1β and tumor necrosis factor-α), which may be beneficial for the prevention and treatment of inflammatory diseases. Quercetin and amygdalin have also been reported to possess anti-inflammation properties (39,41). The combination of these three compounds may have synergistic actions on anti-inflammation. In addition, the CTN demonstrated that the three compounds shared 6 common targets. Thus, the synergistic actions of compounds in HTTLF may be responsible for the therapeutic efficacy of RA.
In order to further verify the diverse functions of HTTLF, a TDN was constructed. The network contained 17 targets and 82 diseases. For example, MMP-9 connected with 15 diseases, including advanced lung cancer, atherosclerosis, brain cancer, osteoarthritis and rheumatoid arthritis. Clinical studies have demonstrated that the expression of MMP-9 can reflect the progression of knee osteoarthritis, atherosclerotic coronary artery disease and RA (42–44). In addition, according to the Medical Subject Headings (http://www.nlm.nih.gov/mesh/MBrowser.html) the 82 diseases were classified into 26 groups, including skin and connective tissue diseases, neoplasms, immune system diseases, cardiovascular diseases and musculoskeletal diseases. For example, osteoarthritis, osteoporosis and RA belong to musculoskeletal diseases; Sjögrkn's syndrome, asthma and allergic rhinitis are immune system diseases. This suggests that compounds from HTTLF have potential therapeutic effects on diverse diseases (45) in addition to RA; for example, amygdalin can be used to treat diseases such as asthma, tumors and diabetes (46). Thus, a TDN provides a visual representation of the association between potential active compounds of HTTLF and diseases through the targets associated with RA.
In conclusion, a novel platform of system pharmacology integrating physicochemical property analysis, active compound prediction, and compound-target and target-disease associate networks was created in the present study, in order to investigate the mechanisms underlying the therapeutic effects of HTTLF on RA. The results demonstrated that: i) Compounds from HTTLF exhibit diverse chemical space and drug-like properties; ii) a total of 127 compounds from HTTLF are regarded as potentially active compounds, interacting with 17 targets associated with RA; iii) compounds from HTTLF have diverse and synergistic actions in the treatment of RA, and exert therapeutic effects for other diseases, such as osteoarthritis, osteoporosis and neoplasms. This is consistent with the hypothesis of the ‘same treatment for different diseases’ in TCM. The present investigation provides a visual understanding of the chemical and pharmacological basis of TCM; this is beneficial to the discovery of anti-rheumatoid drugs from TCM. Based upon the present study, future research will include the extraction of effective constituents from HTTLF, which will be examined in vivo and in vitro for the discovery of anti-rheumatoid drugs from TCM.
Acknowledgements
The present study was supported by the National Natural Science Foundation of China (grant no. 81473574) and the Developmental Fund of ChenKeji Integrative Medicine (grant no. CKJ2015010).
Glossary
Abbreviations
- RA
rheumatoid arthritis
- TCMs
traditional Chinese medicines
- HTTLF
Huatan Tongluo Fang
- BA
Bile arisaema
- SP
Semen persicae
- FC
Flos carthami
- SA
Sinapis alba
- BB
Bombyx batryticatus
- PA
Paeonia alba
- CIA
collagen-induced arthritis
- VEGF
vascular endothelial growth factor
- MMP
matrix metalloproteinase
- MMFF
Merck molecular force field
- MW
molecular weight
- nHDon
number of hydrogen bond donors
- nHAcc
number of hydrogen bond acceptors
- AlogP
octanol-water partition coefficients
- nRB
number of rotatable bonds
- MPSA
molecular polar surface area
- PDB
Protein Data Bank
- COX-2
cyclooxygenase-2
- JAKs
janus kinases
- TNF-α
tumor necrosis factor α
- Syk
spleen tyrosine kinase
- MAP
mitogen-activated protein
- Std Dev
standard deviation
- TTD
Therapeutic Targets Database
- QSAR
quantitative structure-activity relationship
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