|Year : 2018 | Volume
| Issue : 4 | Page : 137-146
Exploring the pathways and targets of Shexiang Baoxin pill for coronary heart disease through a network pharmacology approach
Shou-De Zhang1, Zhan-Hai Su2, Rui-Hui Liu3, Yan-Yan Diao4, Shi-Liang Li4, Ya-Ping-Hua5, Hong-Lin Li4, Wei-Dong Zhang6
1 State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining, Qinghai; Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
2 State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining, Qinghai, China
3 Department of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai, China
4 Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
5 Department of Clinical Science 2, Faculty of Medicine and Dentistry, University of Bergen, 5009, Norway
6 Department of Phytochemistry, School of Pharmacy, Second Military Medical University; Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
|Date of Submission||17-Jul-2018|
|Date of Acceptance||16-Aug-2018|
|Date of Web Publication||06-Nov-2018|
Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237
School of Pharmacy, Second Military Medical University 325#, Guohe Road, Shanghai 200433
Source of Support: None, Conflict of Interest: None
Objective: To investigate the network pharmacology of Shexiang Baoxin pill (SBP) and systematically analyze the mechanisms of SBP. Methods: In this study, we excavated all the targets of 26 constituents of SBP which were identified in rat plasma though literature mining and target calculation (reverse docking and similarity search) and analyzed the multiple pharmacology actions of SBP comprehensively through a network pharmacology approach. Results: In the end, a total of 330 Homo sapiens targets were identified for 26 blood constituents of SBP. Moreover, the pathway enrichment analysis found that these targets mapped into 171 KEGG pathways and 31 of which were more enriched. Among these identified pathways, 3 pathways were selected for analyzing the mechanisms of SBP for treating coronary heart disease. Conclusion: This study systematically illustrated the mechanisms of the SBP by analyzing the corresponding “drug-target-pathway-disease” interaction network.
Keywords: Coronary heart disease, network pharmacology, reverse docking, Shexiang Baoxin pill, similarity search
|How to cite this article:|
Zhang SD, Su ZH, Liu RH, Diao YY, Li SL, YP, Li HL, Zhang WD. Exploring the pathways and targets of Shexiang Baoxin pill for coronary heart disease through a network pharmacology approach. World J Tradit Chin Med 2018;4:137-46
|How to cite this URL:|
Zhang SD, Su ZH, Liu RH, Diao YY, Li SL, YP, Li HL, Zhang WD. Exploring the pathways and targets of Shexiang Baoxin pill for coronary heart disease through a network pharmacology approach. World J Tradit Chin Med [serial online] 2018 [cited 2019 Jan 23];4:137-46. Available from: http://www.wjtcm.net/text.asp?2018/4/4/137/245060
| Introduction|| |
Compared with Western medicine which generally prescribes the treatments for specific diseases on the basis of their physiological cause, traditional Chinese medicine (TCM) focuses on the balance of a whole body involving a complex interaction of physical, spiritual, mental, emotional, genetic, and environmental factors. Moreover, use the herb formula developed from the theories of Jun-Chen-Zuo-Shi to modulate the whole body from imbalance to balance. The Jun (emperor) herbs treat the main cause or primary symptoms of a disease. The Chen (minister) herbs serve to augment or broaden the effects of Jun and relieve secondary symptoms. The Zuo (assistant) herbs are used to modulate the effects of Jun and Chen and to counteract the toxic or side effects of these herbs. The Shi (courier) herbs are included in many formulae to ensure that all components in the prescription are well absorbed and to help deliver or guide them to the target organs. Only those formulae under its synergistic principle can work effectively on the whole body. Therefore, the TCM plays function as a synergy way. From the molecule level, the TCM shows a new “multicomponent, network target” model compared with “one target, one drug” model of Western medicine.
Shexiang Baoxin pill (SBP) is one of the classical TCMs and has been widely used in the clinic for 30 years and obtains satisfactory therapeutic effects for coronary heart disease. The SBP consists of seven medicinal materials including Moschus, Radix Ginseng, Calculus Bovis, Cortex Cinnamomi, Styrax, Venenum Bufonis, and Borneolum Syntheticum. In which, the Moschus is Jun herb, the Radix Ginseng and Calculus Bovis are Chen herb, the Cortex Cinnamomi, Styrax, and Venenum Bufonis are Zuo herb, and the Borneolum Syntheticum is Shi herb. Some papers revealed certain mechanism-related cardiovascular diseases, such as pharmacological studies showed that SBP can promote the function of endothelial progenitor cell, induce CYP3A, increase the level of NO (nitric oxide), reduce myocardial fibrosis in spontaneously hypertensive rats, and so on. However, a systematical and comprehensive analysis based on the “multicomponent, network target” model is missing.
An herbal formula is a complicated chemical system involving a mixture of many types of chemical compounds which correlated with multiple targets. Thereby, herb formulas are considered to act on the “Network target” of specific disease. Network pharmacology invokes the idea that a drug engages with multiple targets and rarely interacts with a single protein in isolation and updates the research paradigm from the current “one target, one drug” model to a new “multicomponent, network target” model. This approach utilizes principles of systems biology and network analysis to interpret the mechanism of drugs in a complex disease, which is aligned with the theoretical significance of the herbal formula.
The complication of TCM composition makes it hard to analyze the mechanism based on the whole composition. The concept of serum pharmacology based on the hypothesis that active compounds could be absorbed into blood after administration of TCM and only the absorbed components have the chance to show the effects. This strategy has been used to screen the bioactive components from TCM, which contains complicated components and become a very straightforward and helpful method for discovering real bioactive constituents in TCM.,,
Here, to explore the mechanism of action of SBP from a holistic perspective, the network pharmacology method was employed to investigate the molecular behavior of 26 constituents of SBP which were identified in plasma [Figure 1]. Among the 26 constituents of SBP, compound 5 is a metabolite of cholic acid (compound 6). The sources of these compounds have been shown in [Table 1]. We collected all available target information for 26 plasma constituents through literature mining and computational prediction (three-dimensional [3D] similarity search and reverse docking) to construct a network of the “drug–target-pathway-disease” interactions. By integrating these interactions, we found that the SPB exerts the function of the treatment of cardiovascular disease through the modulation of multiple pathways by its plasma constituents, which is in accordance with the “multicomponent, network target” model [Figure 1].
|Figure 1: Twenty-six constituents of Shexiang Baoxin pill which were identified in plasma (Compounds 25 is a metabolite)|
Click here to view
| Methods|| |
Target information collection by literature mining: All available information on the targets of 26 plasma constituents in the literature were collected by searching PubMed using the structure search and comparative toxicogenomics database (CTD, http://www.ctdbase.org/about/) which contains chemical–gene/protein interactions in vertebrates and invertebrates from the published literature. Only the confirmed and active targets were selected from the research results.
Identification of putative protein targets for 26 plasma constituents of SBP: The method of similarity searching and reverse docking was used to predict the putative protein targets for 26 plasma constituents of SBP. For the similarity search, the online web server ChemMapper, which is based on the 3D similarity procedure SHAFTS, used to predict the potent targets according to the similar score. SHAFTS provides a ShapeScore (based on the shape overlap) and a FeatureScore (based on the pharmacophore fit), and the weighted sum of the two scores is considered the hybrid similarity HybridScore. A higher HybridScore implies a better alignment in terms of both shape and chemotype identities between the query and target molecules. In this study, the targets with a HybridScore value higher than 1.500 were selected as potent targets. For the reverse docking, the free web server PharmMapper was used to predict the potent targets according to Fit Score value., In this study, the cutoff Fit Score value was set to 4.000. The targets with a Fit Score value higher than 4.000 were selected as potential targets.
Coronary heart disease drugs and therapeutic targets: The file “drugbank.xml” that contains all the FDA-approved drugs and targets were downloaded from the DrugBank database and searched by the keywords “cardiovascular,” “heart,” “coronary heart disease,” “myocardial.” All related drugs and targets were exported and listed to a new file. This process was performed by the Python script.
Network construction and analysis
Protein–protein interaction network: The individual interaction networks for each protein were built by the use of the STRING database which is the integration of known and predicted protein interactions. The network interactions were selected according to STRING-computed confidence scores (medium confidence 0.4000). The Cytoscape is an open source software platform for visualizing molecular interaction networks and developed by the program Cytoscape (Version 2.8.3, developed by Cytoscape Consortium, http://www.cytoscape.org/) and the Network Analyzer plugin (version:3.3.2, developed by Cytoscape Consortium, The National Institute of General Medical Sciences is the sponser, http://apps.cytoscape.org/apps/networkanalyzer) were used to visualize the network and calculate the basic network parameters including the degree of distribution, degree exponent, shortest path length distribution, and clustering coefficient.
Drug–target interaction network: For the coronary heart disease-related drugs and targets, a protein node and a drug node are linked if the protein is targeted by that specific drug according to the DrugBank information. For the 26 plasma constituents of SBP, a link was built between compounds and protein according to the literature mining and computational prediction results.
Target-pathway network: The interactions in the “target-pathway” network were selected from the pathway enrichment results.
Degree distribution of the network: The sizes of the nodes correspond to the node degree. In a network, the degree k of a node is the number of edges linked to that node. The degree distribution P (k) of a network is the frequency of occurrence of nodes with degree k, (k = 1, 2,…).
Pathway enrichment: P values were used to determine if a specific pathway in the KEGG database was more enriched with the related proteins than by chance. Assuming that a total of K proteins related to the 25 constituents of SBP were mapped into KEGG, which contains N distinct proteins and k proteins from a pathway of size n are related to the GTPs, P value is given by
The two-sided hypergeometric test and the Bonferroni correction were used.
| Results|| |
To collect targets information for 25 constituents of SBP comprehensively, we did not only collect confirmed targets through searching PubMed and CTD but collected the most potent targets by similarity searching and reverse docking. Similar Property Principle suggested that structurally similar molecules should exhibit the same (or similar) bioactivities. Based on this entrenched assumption, the top-ranked similar molecules in a biological structure query are likely to possess the same activity profiles and may be regarded as the prime candidates for pharmacological tests. This method has been regarded as one of the most powerful tools in the medicinal chemists' toolkit. Reverse docking is a novel technology that allows the docking of a compound with a known biological activity into the binding sites of all of the 3D structures in a given protein database. The PharmMapper server is a freely accessed web server designed to identify potential target candidates for a specific small molecule probe (drugs, natural products, or other newly discovered compounds with unidentified binding targets) using the pharmacophore mapping approach. It is backed up by a database with a large repertoire of pharmacophores extracted from all of the targets in TargetBank, DrugBank, BindingDB, and PDTD. More than 7000 receptor-based pharmacophore models (covering 1627 drug targets, 459 of which are human protein targets) are stored and accessed by PharmMapper. This program finds the best mapping poses of the user-uploaded molecules against all of the targets in PharmTargetDB, and the top N potential drug targets, as well as the respective molecules' aligned poses, are outputted.
The 26 constituents of SBP shown in [Figure 1] were searched by PubMed, CTD, ChemMapper, and PharmMapper. After removing any redundant and other sapiens targets, 330 Homo sapiens targets were selected for the subsequent analysis [see [Table S1] in the supplementary material]. From the “drug–target” interaction network [Figure 2], we found that the components 2, 3, 7, 18, and 21 have more abundant targets than other components. There were 21 targets related to compound 2, 21 targets related with compounds 3, 27 targets related with compound 7, 31 targets related with compound 18, and 28 targets related with compound 21. However, there were only <3 targets related to compounds 9, 11, 12, 13, 15, 16, 20, 23, and 25. This phenomenon can be attributed to the research level of the compounds and the structural complexity. Higher research level should result in more target information. In addition, the structural complexity of compounds increases the difficulty of reverse docking and similarity searching. Simultaneously, most of the targets can be targeted by more than one constituents [Figure 2].
|Figure 2: The “drug–target” interaction network.” The circles represent 26 constituents of Shexiang Baoxin pill and the triangle represents targets related with constituents|
Click here to view
Shexiang Baoxin pill-regulated coronary heart disease network
After searching the DrugBank database, 322 drugs and 490 protein targets [Table S2] that related to coronary heart disease were extracted. The coronary heart disease network was constructed by mapping the 490 targets into the String database. After excluding isolated nodes, the protein interaction network induced by SPB components was composed of 488 nodes (proteins) and 3919 edges (interactions) [Figure 3]a. The topological properties of the network were analyzed with the network analyzer plugin. Among these properties, the node degree distribution was in accordance with a power law, indicating that the constructed network is scale free and does not present a random topology [Figure S1]. The SBP-regulated coronary heart disease network was constructed by mapping the 330 protein targets from literature mining and computational prediction into the coronary heart disease network [Figure 3]. Finally, we found that 52 proteins of 330 protein targets presented in coronary heart disease network [Figure 3]c and [Figure 3]d. Moreover, 20 constituents of SBP could modulate this coronary heart disease network [Figure 3].
|Figure 3: Shexiang Baoxin pill-regulated coronary heart disease network. (a) Coronary heart disease network. (b) Shexiang Baoxin pill-related protein targets presented in coronary heart disease network. (c) Part of coronary heart disease network that can be modulated by Shexiang Baoxin pill. (d) Twenty-six Shexiang Baoxin pill constituents mapped into coronary heart disease network. The circles represent protein targets, the triangle represents constituents of Shexiang Baoxin pill, blue represents coronary heart disease targets, and yellow represents coronary heart disease targets that can be modulated by Shexiang Baoxin pill|
Click here to view
TCM usually exhibits diverse bioactivities by mediating multiple pathways, so it is reasonable to analyze the potential pathways. In this study, pathway enrichment based on the hypergeometric test was utilized to analyze the potential pathways mediated by the SBP. Ultimately, 171 KEGG pathways were identified and 31 of these were found to be more enriched (P < 0.01) [Table 2] and [Table S3]. Three pathways (hsa04976, hsa04960, and hsa04370) of them caught our attention because they related with coronary heart disease. Hsa04976 pathway controls the bile secretion and related with hyperbilirubinemia. Previous research has proved that coronary heart disease is closely related to the level of bilirubin in the blood. Low bilirubin content can exacerbate the incidence of coronary heart disease and increasing of bilirubin content could decrease the incidence of coronary heart disease. Bilirubin and bile acids can compensate for each other in the body. While Cholic acid regulate the synthesis of bile acids through negative feedback. The cholic acid-type compounds [5-7,19] from SBP may mimic the role of bilirubin to cure coronary heart disease, but the mechanism need confirm. As showed in [Figure 4], 15 proteins (NTCP, OCT1, CYP7A1, SHP, FXR, RXRa, UGT2B4, CYP3A4, ABCG5, ABCG8, BSEP, MRP2, MDR1, MDR3, and CA) in the bile secretion pathway can be modulated by the constituents of SBP [Figure 4].
|Figure 4: The Shexiang Baoxin pill-modulated aldosterone-regulated sodium reabsorption pathway (Hsa04960). All of the targets are shown as box and the Shexiang Baoxin pill-regulated targets are filled with red|
Click here to view
Aldosterone-regulated sodium reabsorption pathway (hsa04960) can influence human blood pressure by regulating epithelial sodium ion channels. Studies have shown that inhibition of sodium ion channel can lower blood pressure. Coincidently, hypertension is a major cause of coronary heart disease. Five proteins (IRS1, ATPase, ERK1/2, MR, and 11β-HSD2) in this pathway can be affected by constituents of SBP [Figure 5]. Among them, the ATPase regulating sodium and potassium exchange can be targeted by the compounds bufalin (1), 3-epi-bufalin (3), 1-hydroxybufalin (17), and gamabufotalin (23) [Figure 5].
|Figure 5: The Shexiang Baoxin pill-modulated bile secretion pathway (Hsa04976). All of the targets are shown as box and the Shexiang Baoxin pill-regulated targets are filled with red|
Click here to view
Vascular endothelial growth factor signaling pathway (Hsa04370) can promote the survival, migration, and proliferation of vascular endothelial cells through activating the Akt signaling pathway, and thereby regulating the formation of vascular endothelium and endocardium, which are all associated with coronary heart disease. Eleven proteins in this pathway can be targeted by SBP constituents [Figure 6].
|Figure 6: The Shexiang Baoxin pill-modulated vascular endothelial growth factor signaling pathway (Hsa04370). All of the targets are shown as box, and the Shexiang Baoxin pill-regulated targets are filled with red|
Click here to view
| Discussion|| |
TCM is a type of holistic, natural health-care system because it stimulates the body's own healing mechanisms and takes into account all aspects of a patient's life, rather than just several obvious signs or symptoms. TCM practitioners view the body as a complex network of interconnected parts, rather than separate systems or organs. Chinese herbal medicine, especially Chinese herbal formula, is one of the most important therapies in TCM. Formulas allow you to blend herbs to enhance their positive effects and reduce or eliminate any negative side effects they may have. The formulas take years and years of practice to master and many are kept within families and/or generations of teacher–student transmissions. Therefore, they are a very valuable medical gift from the previous generations. However, some, particularly in the West, doubt the effect and synergy of TCM as its mechanisms are less definitive than Western-style scientific medicine. Understanding the scientific basis of herbal formulae at the molecular level and from a systems perspective is still one of the great challenges for evidence-based TCM. Network pharmacology links the multiple components that play principal, complementary, and assistant therapeutic roles in TCM formulae to the principal, complementary, and assistant targets in a disease network., This approach offers a novel philosophical guide and technological route to designing and understanding mechanisms of action of TCM drugs through projecting a TCM drug component network onto a disease network.
Coronary heart disease has many causes. SBP is a classic TCM formula that can effectively treat the coronary heart disease. Therefore, it is reasonable that to analyze the mechanism of SBP for treating coronary heart disease from a holistic view. In this study, the method of network pharmacology was utilized to analyze the mechanism of SBP systematically. First, we focused on the 26 blood constituents of SBP and collected all available targets of them. Second, “drugs-targets” and “targets-targets” networks were constructed. Third, the disease-target network was constructed. Finally, we analyzed the mechanism of SBP by mapping the component network onto the disease network and pathway enrichment. After this work, the SPB-modulated coronary heart disease network was constructed, in which 20 constituents of SBP could modulate the coronary heart disease network. Moreover, three key pathways that related to the coronary heart disease network were identified. In this process, the network pharmacology method is very suitable for a comprehensive analysis of the mechanism of SBP in treating coronary heart disease. However, our work still has some drawbacks. First, target information is not enough just through literature mining and computational prediction. In the future work, the experimental targets should be identified and added to this work. Second, the targets information identified by literature mining and computational prediction has a bias. Small molecules that are more studied tend to have more target information. In addition, the computational prediction will generate more target information for simpler small molecules than molecules with more complicated structure. In a brief, the target information should not only come from literature mining and computational prediction but also need to collect more experimental targets information.
| Conclusion|| |
In summary, the mechanism of SBP treating for coronary heart disease was analyzed systematically using network pharmacology in this study. Finally, we constructed the disease network of coronary heart disease and identified 20 blood constituents of SBP that can modulate this network. Moreover, three pathways relating to coronary heart disease were identified. In addition, this research study provides comprehensive and useful data for more in-depth studies of mechanisms of SBP.
Financial support and sponsorship
The work was financially supported by the Professor of Chang Jiang Scholars Program, NSFC (81520108030, 21472238), Shanghai Engineering Research Center for the Preparation of Bioactive Natural Products (16DZ2280200), the Scientific Foundation of Shanghai China (13401900103, 13401900101), the National Key Research and Development Program of China (2017YFC1700200), and the Project of Qinghai Science and Technology Department (2016-ZJ-Y01, 2018-ZJ-948Q).
Conflicts of interest
There are no conflicts of interest.
| References|| |
Zhou X, Seto SW, Chang D, Kiat H, Razmovski-Naumovski V, Chan K, et al.
Synergistic effects of Chinese herbal medicine: A comprehensive review of methodology and current research. Front Pharmacol 2016;7:201.
Qiu J. Traditional medicine: A culture in the balance. Nature 2007;448:126-8.
Zhang S, Shan L, Li Q, Wang X, Li S, Zhang Y, et al.
Systematic analysis of the multiple bioactivities of green tea through a network pharmacology approach. Evid Based Complement Alternat Med 2014;2014:512081.
Zhang KJ, Zhu JZ, Bao XY, Zheng Q, Zheng GQ, Wang Y, et al.
Shexiang baoxin pills for coronary heart disease in animal models: Preclinical evidence and promoting angiogenesis mechanism. Front Pharmacol 2017;8:404.
Jiang P, Fu P, Xiang L, Wang S, Liu X, Yang L, et al.
The effectiveness of borneol on pharmacokinetics changes of four ginsenosides in Shexiang Baoxin pill in vivo
. Biomed Chromatogr 2014;28:419-27.
Li G, Chen Y, Wu J. Promotion of function of endothelial progenitor cells with Shexiang Baoxin pill treatment under shear stress. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2015;32:847-53.
Jiang B, Cai F, Gao S, Meng L, Liang F, Dai X, et al.
Induction of cytochrome P450 3A by Shexiang Baoxin pill and its main components. Chem Biol Interact 2012;195:105-13.
Wu JX, Liang C, Ren YS. Effects of shexiang baoxin pill on function and nitric oxide secretion of endothelial progenitor cells. Zhongguo Zhong Xi Yi Jie He Za Zhi 2009;29:511-3.
Wu DJ, Hong HS, Jiang Q. Effect of Shexiang Baoxin pill in alleviating myocardial fibrosis in spontaneous hypertensive rats. Zhongguo Zhong Xi Yi Jie He Za Zhi 2005;25:350-3.
Li S, Zhang B. Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chin J Nat Med 2013;11:110-20.
Liu AL, Du GH. Network pharmacology: New guidelines for drug discovery. Yao Xue Xue Bao 2010;45:1472-7.
Azmi AS. Adopting network pharmacology for cancer drug discovery. Curr Drug Discov Technol 2013;10:95-105.
Homma M, Oka K, Yamada T, Niitsuma T, Ihto H, Takahashi N, et al.
A strategy for discovering biologically active compounds with high probability in traditional Chinese herb remedies: An application of saiboku-to in bronchial asthma. Anal Biochem 1992;202:179-87.
Wang P, Liang Y, Zhou N, Chen B, Yi L, Yu Y, et al.
Screening and analysis of the multiple absorbed bioactive components and metabolites of dangguibuxue decoction by the metabolic fingerprinting technique and liquid chromatography/diode-array detection mass spectrometry. Rapid Commun Mass Spectrom 2007;21:99-106.
Wang X, Sun W, Sun H, Lv H, Wu Z, Wang P, et al.
Analysis of the constituents in the rat plasma after oral administration of Yin Chen Hao Tang by UPLC/Q-TOF-MS/MS. J Pharm Biomed Anal 2008;46:477-90.
Wang S, Peng C, Jiang P, Fu P, Tao J, Han L, et al.
Simultaneous determination of seven bufadienolides in rat plasma after oral administration of Shexiang Baoxin pill by liquid chromatography- electrospray ionization-tandem mass spectrometry: Application to a pharmacokinetic study. J Chromatogr B Analyt Technol Biomed Life Sci 2014;967:255-63.
Jiang P, Liu R, Dou S, Liu L, Zhang W, Chen Z, et al.
Analysis of the constituents in rat plasma after oral administration of Shexiang Baoxin pill by HPLC-ESI-MS/MS. Biomed Chromatogr 2009;23:1333-43.
Gong J, Cai C, Liu X, Ku X, Jiang H, Gao D, et al.
ChemMapper: A versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method. Bioinformatics 2013;29:1827-9.
Lu W, Liu X, Cao X, Xue M, Liu K, Zhao Z, et al.
SHAFTS: A hybrid approach for 3D molecular similarity calculation 2. Prospective case study in the discovery of diverse p90 ribosomal S6 protein kinase 2 inhibitors to suppress cell migration. J Med Chem 2011;54:3564-74.
Liu X, Jiang H, Li H. SHAFTS: A hybrid approach for 3D molecular similarity calculation 1. Method and assessment of virtual screening. J Chem Inf Model 2011;51:2372-85.
Liu X, Ouyang S, Yu B, Liu Y, Huang K, Gong J, et al.
PharmMapper server: A web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res 2010;38:W609-14.
Wang X, Shen Y, Wang S, Li S, Zhang W, Liu X, et al.
PharmMapper 2017 update: A web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res 2017;45:W356-60.
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al.
The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 2017;45:D362-8.
Bajorath J. Molecular similarity concepts for informatics applications. Methods Mol Biol 2017;1526:231-45.
Maldonado AG, Doucet JP, Petitjean M, Fan BT. Molecular similarity and diversity in chemoinformatics: From theory to applications. Mol Divers 2006;10:39-79.
Muchmore SW, Edmunds JJ, Stewart KD, Hajduk PJ. Cheminformatic tools for medicinal chemists. J Med Chem 2010;53:4830-41.
Schwertner HA, Jackson WG, Tolan G. Association of low serum concentration of bilirubin with increased risk of coronary artery disease. Clin Chem 1994;40:18-23.
Li YQ, Prentice DA, Howard ML, Mashford ML, Desmond PV. Bilirubin and bile acids may modulate their own metabolism via regulating uridine diphosphate-glucuronosyltransferase expression in the rat. J Gastroenterol Hepatol 2000;15:865-70.
Li-Hawkins J, Gåfvels M, Olin M, Lund EG, Andersson U, Schuster G, et al.
Cholic acid mediates negative feedback regulation of bile acid synthesis in mice. J Clin Invest 2002;110:1191-200.
Tayo BO, Tong L, Cooper RS. Association of polymorphisms in the aldosterone-regulated sodium reabsorption pathway with blood pressure among hispanics. BMC Proc 2016;10:343-8.
Geraldes V, Laranjo S, Rocha I. Hypothalamic ion channels in hypertension. Curr Hypertens Rep 2018;20:14.
Schoenberger JA. Antihypertensive drug therapy and coronary heart disease risk. J Fam Pract 1993;36:70-3, 77-84.
Best B, Moran P, Ren B. VEGF/PKD-1 signaling mediates arteriogenic gene expression and angiogenic responses in reversible human microvascular endothelial cells with extended lifespan. Mol Cell Biochem 2018;446:199-207.
Yang M, Chen JL, Xu LW, Ji G. Navigating traditional Chinese medicine network pharmacology and computational tools. Evid Based Complement Alternat Med 2013;2013:731969.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
[Table 1], [Table 2]