The authors have declared that no competing interests exist.
By comparing the target proteins (TPs) of classic traditional Chinese medicine (TCM) herbal formulas and modern drugs used for treating coronary artery disease (CAD), this study aimed to identify potential therapeutic TPs for treating CAD. Based on the theory of TCM, the Xuefu-Zhuyu decoction (XZD) and Gualou-Xiebai-Banxia decoction (GXBD), both of which are classic herbal formulas, were selected for treating CAD. Data on the chemical ingredients and corresponding TPs of the herbs in these two formulas and data on modern drugs approved for treating CAD and related TPs were retrieved from professional TCM and bioinformatics databases. Based on the associations between the drugs or ingredients and their TPs, the TP networks of XZD, GXBD, and modern drugs approved for treating CAD were constructed separately and then integrated to create a complex master network in which the vertices represent the TPs and the edges, the ingredients or drugs that are linked to the TPs. The reliability of this master network was validated through statistical tests. The common TPs of the two herbal formulas have a higher possibility of being targeted by modern drugs in comparison with the formula-specific TPs. A total of 114 common XZD and GXBD TPs that are not yet the target of modern drugs used for treating CAD should be experimentally investigated as potential therapeutic targets for treating CAD. Among these TPs, the top 10 are NOS3, PTPN1, GABRA1, PRKACA, CDK2, MAOB, ESR1, ADH1C, ADH1B, and AKR1B1. The results of this study provide a valuable reference for further experimental investigations of therapeutic targets for CAD. The established method shows promise for searching for potential therapeutic TPs based on herbal formulas. It is crucial for this work to select beneficial therapeutic targets of TCM, typical TCM syndromes, and corresponding classic formulas.
Traditional Chinese medicine (TCM) is rooted in thousands of years of history and is one of the forms of alternative medicine endorsed by the World Health Organization [
Chinese herbal medicine (CM) is another important modality of TCM for restoring the body’s balance and preventing or treating illness. In practice, multiple-herb formulas (TCM formulas), instead of a single herb, are more commonly used to achieve optimal therapeutic efficacy [
However, studies on TCM are always controversial in terms of their abstract theory, unclear basis, complex interactions between various ingredients and complex interactive biological systems, and inadequate quality control. With the limited rigorous scientific evidence of its effectiveness, TCM can be difficult for researchers to study because its treatments are often complex and are based on ideas very different from those of modern Western medicine.
Network pharmacology integrates information from bioinformatics, systems biology, and polypharmacology and provides a platform for integrating multiple components and interactions underlying cell, organ, and organism processes in health and disease [
Coinciding with the holistic and systemic characteristics of TCM, network pharmacology is expected to bridge the gap between TCM and modern medicine [
To explore the use of the network-based approach to identify potential therapeutic TPs from TCM formulas, this study focuses on coronary artery disease (CAD), as CAD is now the leading cause of mortality worldwide [
In TCM clinical practice, physicians usually follow the “one classic formula for one typical syndrome” principle. In this case, the Xuefu-Zhuyu decoction (XZD) and Gualou-Xiebai-Banxia decoction (GXBD) are two classic formulas used for treating blood stasis and phlegm turbidity, respectively [
XZD and GXBD are two typical herbal formulas used in the TCM treatment of CAD. XZD comprises 11 CMs: Radix Bupleuri, Radix Angelicae Sinensis, Radix Rehmanniae, Radix Paeoniae Rubra, Flos Carthami, Semen Persicae, Fructus Aurantii, Radix et Rhizoma Glycyrrhizae, Rhizoma Chuanxiong, Radix Achyranthis Bidentatae, and Radix Platycodonis [
A TCM formula comprises different CMs and contains various chemical ingredients. Some of these ingredients can act with different functional proteins
The TP network graph
According to the principle of graph theory, the significance of vertices can be measured and expressed using centrality. Centrality indicators identify the most important vertices within the graph [
On the basis of the above data, target network, and centrality analysis, a network-based approach can be employed to elucidate complex associations between targets and to estimate potential targets after passing statistical validation. For quality control, a random simulation should be performed to see whether the results are significant. In the constructed network of targets, various statistical tests will be conducted to examine the associations between variables, especially variables generated from independent data sources, for instance, target modules from different formulae, centrality indicators, and the novelty relative to existing drug targets. According to the types of variables, this study will adopt various appropriate statistical testing approaches, for example, chi-squared test, t-test, one-way analysis of variance (ANOVA), and Pearson correlation test [
In this study, 787 components of XZD and 179 components of GXBD were generated from the TCMSP and TCMID databases (
As summarized in
No. of CMs | No. of retrieved ingredients | No. of therapeutic target proteins | |||
---|---|---|---|---|---|
Targets of FDA-approved drugs for CAD | Others | Total | |||
XZD | 11 | 787 | 62 | 152 | 214 |
GXBD | 3 | 179 | 50 | 128 | 178 |
JI | 0 | 0.0662 | 0.8065 | 0.6867 | 0.7193 |
The XZD and GXBD TP networks were constructed separately and then integrated. As illustrated in
(A) Formula-based target network in the context of drug targets. The vertex represents the target protein. The blue vertices indicate formula-based target proteins that have not been targeted by drugs used for treating CAD, and the red vertices indicate the drug targets and are labeled when they overlap with herbal formula target proteins. A colored edge indicates a drug or compound linked to two target proteins (blue: XZD-specific edges; orange: GXBD-specific edges; yellow: overlapped edges between XZD and GXBD; and purple: edges associated with drugs approved for use for treating CAD). (B) The distribution of different target proteins in the network (yellow: common XZD and GXBD target proteins that have not been targeted by modern drugs used for treating CAD; white: common XZD and GXBD target proteins targeted by modern drugs used for treating CAD; orange: GXBD-specific target proteins; blue: XZD-specific target proteins that have not been targeted by drugs used for treating CAD; dark purple: XZD-specific proteins targeted by drugs used for treating CAD; and light purple: target proteins specific to drugs used for treating CAD). The numbers in parentheses represent the number of target proteins in each specific set.
As shown in
According to the types of variables, various statistical tests were conducted to examine the associations between the variables (i.e., network modules, three centralities, and drug target for treating CAD). The chi-square test analyzed the association between the dummy variable “drug target proteins” and the categorical variable “modules”; the independent samples
First, in terms of the aim of this study, whether the GXBD-specific target proteins, XZD-specific target proteins, and common XZD and GXBD target proteins are significantly different from the TPs of modern drugs used for treating CAD should be validated. In other words, the percentages of the TPs of modern drugs used for treating CAD should be tested among the three samples generated from the combinations of the five modules. The differences in the percentages of the TPs of modern drugs used for treating CAD among specific and common parts of two formulas were statistically significant at the 0.05 level with a chi-square value of 6.386 and
Target proteins of modern drugs | Total | |||
---|---|---|---|---|
No | Yes | |||
Target proteins in different modules | GXBD-specific | 14 (10.2) | 0 (3.8) | 14 (14.0) |
XZD-specific | 38 (36.4) | 12 (13.6) | 50 (50.0) | |
Overlapping | 114 (119.4) | 50 (44.6) | 164 (164.0) | |
Total | 62 (62.0) | 166 (166.0) | 228 (228.0) |
Note: (1) Cell values denote observed counts, and numbers in parentheses indicate expected counts; (2) 1 cell (16.7%) has expected count less than 5 and the minimum expected count is 3.8.
Furthermore, the TPs of the FDA-approved drugs for treating CAD in the master network played more crucial roles than the others with
After the above quality control of the master network, the network analysis results are discussed as below. In the complex master network shown in
With this approach, the top 10 mutual targets worthy of further investigation in the context of new drug discovery are generated and summarized in
Proteins | Degree | Proteins | Betweenness | Proteins | Closeness |
---|---|---|---|---|---|
NOS3 | 192 | NOS3 | 1585.3080 | NOS3 | 1.5324 |
PTPN1 | 181 | CDK2 | 983.2459 | PTPN1 | 1.5892 |
GABRA1 | 176 | PTPN1 | 836.1325 | CDK2 | 1.5919 |
PRKACA | 173 | ESR1 | 810.6359 | GABRA1 | 1.6189 |
CDK2 | 168 | ADH1C | 709.8504 | MAOB | 1.6216 |
MAOB | 162 | MAOB | 636.5054 | PRKACA | 1.6324 |
ESR1 | 159 | ADH1B | 614.3183 | ESR1 | 1.6486 |
ADH1C | 151 | GABRA1 | 607.0142 | ADH1C | 1.6622 |
AKR1B1 | 148 | PRKACA | 590.1397 | ADH1B | 1.6865 |
TNF | 145 | ALOX5 | 413.5665 | AKR1B1 | 1.6946 |
Note: The centrality indicators identify the important vertices within the network. Higher degree centrality and betweenness centrality indicate greater importance, whereas lower closeness centrality indicates greater importance.
Meanwhile, the common proteins which have been validated as targets of approved anti-CAD drugs, i.e., the 50 mutual targets covered by the two formulas and anti-CAD drugs in
The drug network can be seen as a measure to explore the synergy between drugs because drugs targeting the same target are connected in the network. However, the interpretation of the TP network seems difficult. Targets intervened by the same compound (not targets of intrinsic connections in biological functions) are linked together. On the one hand, it may be implied that herbal formulas generate a multicomponent and multitarget therapeutic mechanism under the precondition of the safety and efficacy of formulas. Thus, the TP network can be applied to explore potential “new” therapeutic targets, as discussed in results. On the other hand, there exists another possibility if the precondition about the TCM formulas is insufficient, namely, the TP network may imply side effects in that targets functioning in different biological functions are always interfered together by the formulas. Thus, it is still necessary to examine the biological meaning of the targets found in the network analysis sufficiently, although we comprehensively reviewed XZD and GXBD in terms of clinical utilization in TCM therapy for treating CAD and established an experimental basis before conducting this study.
Generally, the association of top mutual TPs with CAD and relevant biological significance have been widely discussed in existing literature. For instance, NOS3 (endothelial nitric oxide synthase) is the nitric oxide synthase isoform responsible for maintaining systemic blood pressure, vascular remodeling and angiogenesis, and vascular smooth muscle relaxation through directly regulating NO production [
As stated in the introduction, TCM follows the principle of “one classic formula for one typical syndrome,” and XZD and GXBD are intended for two different syndromes in the TCM domain. TCM is well known and considered attractive for its synergistic effects that are observable at the physiological level. On the other hand, modern drugs follow the reductionism approach, which identifies the single most potent compound for one objective. In TCM and modern medicine theory, the definition of syndromes is quite different. The modern CAD concept covers the TCM blood stasis and phlegm turbidity concepts. Such differences might explain the high frequency of modern CAD drugs targeting overlapping targets.
However, there is no reason to consider the formulae-specific targets unimportant. Actually, this is an area not yet well explored by the modern reductionism drug discovery research. Although XZD and GXBD are two classic formulas for treating CAD, they are used differently in TCM clinical practice for two different syndromes, i.e., blood stasis and phlegm turbidity, respectively. The scientific mechanism of TCM syndromes is not yet clear; however, blood stasis and phlegm turbidity provide a valuable basis for studying CAD subtypes, especially under the background of the emerging medical model of precision medicine for customizing healthcare. Thus, formula-specific targets are still worthy of further experimental investigation based on formula-specific clinical applications of TCM and precision medicine, with the aim of exploring therapeutic targets for CAD.
As illustrated in
Several limitations of this study should be noted. First, as a
The therapeutic effects of herbal formulas in disease management have been demonstrated by clinical practice over thousands of years. Numerous TPs of chemical ingredients combined in herbal formulas have been identified by modern pharmacological studies on TCM, although the overall mechanism of TCM has not been elucidated. By comparing the similarities and differences in TPs between herbal formulas and modern pharmaceutical agents, potential TPs for further experimental investigation can be identified. This study examined two herbal formulas used for treating CAD as an example for exploring a new methodology based on finding therapeutic TPs.
The beneficial therapeutic areas of TCM should be clarified in the context of modern medicine. Classic formulas corresponding to typical syndromes in these therapeutic areas should be selected. Based on the approaches used in this study, a series of potential TPs of the herbal formulas are promising for future experimental study.
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We thank Prof. Mingli Zheng for his valuable help and suggestion in the study and academic editor, senior staff editor, and five anonymous referees for their thoughtful comments and suggestions.