# Systems Pharmacological Approach to the Effect ofBulsu-san Promoting Parturition

Authors: Su Yeon Suh; Won G. An
Journal: Evidence-Based Complementary and Alternative Medicine
 (2017)

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## Abstract

Bulsu-san (BSS) has been commonly used in oriental medicine for pregnant women in East Asia. The purpose of this research was to elucidate the effect of BSS on ease of parturition using a systems-level in silico analytic approach. Research results show that BSS is highly connected to the parturition related pathways, biological processes, and organs. There were numerous interactions between most compounds of BSS and multiple target genes, and this was confirmed using herb-compound-target network, target-pathway network, and gene ontology analysis. Furthermore, the mRNA expression of relevant target genes of BSS was elevated significantly in related organ tissues, such as those of the uterus, placenta, fetus, hypothalamus, and pituitary gland. This study used a network analytical approach to demonstrate that Bulsu-san (BSS) is closely related to the parturition related pathways, biological processes, and organs. It is meaningful that this systems-level network analysis result strengthens the basis of clinical applications of BSS on ease of parturition.


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## Body

## 1. Introduction

The name ofBulsu-san (BSS) originated from its therapeutic effects that help to promote easy labor as if being touched by merciful Buddha’s hand [1]. BSS is composed ofAngelicae Sinensis Radix (Danggui, DG) andCnidium officinale Makino (Cheongung, CG), which is one of the most commonly used herb pairs in Traditional Medicine of East Asia and the usual component ratio is 2 : 3 (CG : DG) or 1 : 1 [2]. BSS is widely used in women’s medicine in East Asia; its recognized therapeutic effects are as follows: removal of impure blood, blood making, easy parturition, acceleration of labor, elimination of dead fetus or placenta, amelioration of pain, nourishing blood, and promoting blood circulation [3].What is more, recent experimental research on the CG-DG herb pair indicated that they affect the nourishment of blood [4], activate blood circulation, and prevent blood stasis [5]. In addition, the CG-DG herb pair showed significant inhibitory effects on the proliferation and protein synthesis of vascular smooth muscle cells [6]. It was suggested BSS could affect the activities of Akt kinase and eNOS by increasing intracellular Ca2+ and reducing ROS levels [7] and regulate menstruation and provide relief from pain by enabling the management of uterine smooth muscle contractions [8]. Although BSS has therapeutic effects on various pathological symptoms in pregnant or childbearing aged women, this research focused on the molecular mechanisms and impact of BSS on easing parturition and the acceleration of labor.In terms of parturition onset, numerous studies have described the complex hormone interactions between estrogen, progesterone, oxytocin, corticosteroid, and prostaglandin. Among these, corticotrophin releasing hormone (CRH) is regarded as a trigger that initiates the labor [9]. The placenta releases substantial amounts of CRH, which stimulates the pituitary glands of both mother and fetus to secrete adrenocorticotropin hormone [10]. This in turn induces the release of estrogen precursor, which is converted into estrogen by the placenta that induces smooth muscle contraction [10]. Additionally, dilatation of cervical connective tissue and smooth muscle is induced by the following changes: a shift from progesterone to estrogen dominance, increased responsiveness to oxytocin via the upregulation of myometrial oxytocin receptor, increased prostaglandins synthesis in uterus, increased myometrial gap junction formation, decreased nitric oxide activity, and increased influx of calcium into myocyte [11].The hypothesis of this study was that BSS may promote the positive-feedback of hormone loops as well as a series of myometrial and cervical changes to ease parturition and safely accelerate labor. A network based in silico approach was used to identify the effect of BSS on parturition related systems and the aim of this study was to elucidate the effect of BSS on the parturition by system-level analysis. The workflow of the network pharmacological study is summarized in Figure1.Figure 1
The workflow: the network pharmacological approach of Bulsu-san (BSS), namely, active compounds screening, target fishing, network analysis, and relevant organ location mapping was performed in this study.


## 2. Material and Methods

### 2.1. Identification of Active Compounds

Compounds in CG and DG were identified using a phytochemical database that is the Traditional Chinese Medicine Systems Pharmacology (TCMSP,http://ibts.hkbu.edu.hk/LSP/tcmsp.php). We applied parameters related to absorption, distribution, metabolism, and excretion (ADME), namely, human drug-likeness (DL) [12], oral bioavailability (OB) [13], and Caco-2 permeability (Caco-2) to screen the Potential active compounds in BSS [14].


#### 2.1.1. Drug-Likeness Evaluation

DL helps filter “drug-like” compounds in oriental herbs, as DL represents a qualitative concept for valuations based on how “drug-like” a prospective compound is [15]. Accordingly, a high DL may lead to a greater possibility of therapeutic success, and compounds with a higher DL value are more likely to possess certain biological properties [16]. The calculations of DL in TCMSP database were based on Tanimoto coefficient formula [17] as follows:(1)FA,B=A×BA2+B2-A×B,where A represents the molecular parameters of herbal compounds and B is the average molecular parameters of all compounds in the Drugbank database (http://www.drugbank.ca/) [18]. In the present study, we excluded compounds with a DL of <0.08. Other previous researches of herbal formulas set a higher threshold in the range of 0.1 to 0.18. However, we found out that most compounds of DG have low DL. In detail, only 36 compounds of 125 in DG show higher or equal DL value than 0.08. For this reason, this study sets a lower threshold of DL than other previous researches to see the most potential targets of BSS.


#### 2.1.2. Oral Bioavailability (OB) Prediction

OB is defined as the ratio of active compounds’ absorption into the systemic circulation, which represents the convergence of the ADME process [13]. OB values are dependent on drug dissolution in the gastrointestinal (GI) tract and hepatic and intestinal first-pass metabolism, as well as on intestinal membrane permeation, which makes it a major pharmacokinetic parameter for drug evaluations [16]. In this study, the OB threshold was set as ≥15%.


#### 2.1.3. Caco-2 Permeability Screening

Caco-2 permeability is used to predict the absorption of an orally administered drug [14]. Surface absorptivity of the small intestine is maximized with the presence of villi and microvilli, for this reason most orally administered drug absorption occurs in the small intestine [19]. Moreover, the movement of orally administered drugs across the intestinal epithelial barrier determines the rate and extent of human absorption and ultimately affects drug bioavailability [20]. In the present study, compounds with OB, DL and Caco-2 values of greater than 15%, 0.08, and >−0.4, respectively, were regarded as active compounds and subjected to further analysis.


#### 2.1.4. Lipinski’s Rule (LR) Screening

In addition, the screening standard used was defined based on Lipinski’s rule (LR), which identifies druggable compounds as having molecular weight (MW) of ≤500 Da (MW ≤ 500), chemical composition with ≤5 hydrogen-bond donors, ≤10 hydrogen-bond acceptors, and an octanol-water partition coefficient, AlogP of ≤ 5 [21]. AlogP can be used to estimate local hydrophobicity, to produce molecular hydrophobicity maps, and to evaluate hydrophobic interactions in protein-ligand complexes [22]. Hdon and Hacc are the number of possible hydrogen-bond donors and acceptors, and the hydrogen-bonding capacity of a drug solute is recognized as a crucial determinant of permeability; moreover high hydrogen-bonding potential is often related to low permeability and absorption [23]. Eventually, in the present study, we selected active compounds satisfying the following criteria: OB ≥ 15%; DL ≥ 0.08; Caco-2 ≥ −0.4; MW ≤ 500; H-bond donors ≤ 5; H-bond acceptors ≤ 10; AlogP ≤ 5.


### 2.2. Target Fishing

Aside from filtering active compounds, we also sought to identify the molecular targets of these active compounds. Compound-target interaction profiles were established based on a systematic prediction of multiple drug-target interactions tool which employs random forest (RF) and support vector machine (SVM) methods and integrates chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale [24]. Compound-target interactions satisfying SVM score ≥ 0.8 and RF score ≥ 0.7 were selected for further study. Additionally, filtered compound-target interaction profile mapping was performed using the UniProt database (http://www.uniprot.org/) [25].


### 2.3. Gene Ontology (GO) Analysis

Biological process (BP) of gene ontology (GO) analysis was employed to determine the biological properties of target genes [26]. GO annotation indicates the possibility of direct statistical analysis on gene function information. In this research, GO BP terms with P values < 0.01 were employed and the data was collected using the DAVID 6.8 Gene Functional Classification Tool (http://david.abcc.ncifcrf.gov/).


### 2.4. Network Construction and Analysis

In order to understand the multiscale interactions between the active compounds of BSS and targets, two types of networks were built: (1) the herb-compound-target network (H-C-T network), in which nodes represent either compounds, target genes, or herbs and edges indicate herb-compound-target connections; and (2) the target-pathway network (T-P network) to extract the pathways from KEGG database (http://www.genome.jp/kegg/), and the terms highly associated with parturition with P values < 0.05 were selected as the related pathways of targets in this work. Related targets were mapped onto relevant pathways, which resulted in the T-P network. Both networks were generated in Cytoscape 3.5.1, an open-source biological network visualization and data integration software package [27].


### 2.5. Target Organ Location Map

Tissue-specific patterns of mRNA expression can indicate important associations with biological events or gene functions [28]. To explore the beneficial effects of BSS during parturition, it is important that the tissue mRNA expression profiles of target genes at the organ level be known [29]. The target organ location map was built according to the Dataset: GeneAtlas U133A, gcrma (http://biogps.org). BioGPS database provides expression data acquired by direct measurements of gene expression obtained by microarrays analysis [30]. First, the mRNA expression patterns of each target gene in 176 parts of organ tissues were obtained. Second, average values were calculated for each gene. Third, frequency of above average mRNA expression tissue organs was inspected. Forth, based on the result from the third step and parturition mechanism theory, mRNA expression data of relevant organ tissues were extracted and categorized into 6 groups, namely, uterus and/or uterus corpus, fetus and/or placenta, hypothalamus and/or pituitary, smooth muscle, and whole blood.


## 2.1. Identification of Active Compounds

Compounds in CG and DG were identified using a phytochemical database that is the Traditional Chinese Medicine Systems Pharmacology (TCMSP,http://ibts.hkbu.edu.hk/LSP/tcmsp.php). We applied parameters related to absorption, distribution, metabolism, and excretion (ADME), namely, human drug-likeness (DL) [12], oral bioavailability (OB) [13], and Caco-2 permeability (Caco-2) to screen the Potential active compounds in BSS [14].


### 2.1.1. Drug-Likeness Evaluation

DL helps filter “drug-like” compounds in oriental herbs, as DL represents a qualitative concept for valuations based on how “drug-like” a prospective compound is [15]. Accordingly, a high DL may lead to a greater possibility of therapeutic success, and compounds with a higher DL value are more likely to possess certain biological properties [16]. The calculations of DL in TCMSP database were based on Tanimoto coefficient formula [17] as follows:(1)FA,B=A×BA2+B2-A×B,where A represents the molecular parameters of herbal compounds and B is the average molecular parameters of all compounds in the Drugbank database (http://www.drugbank.ca/) [18]. In the present study, we excluded compounds with a DL of <0.08. Other previous researches of herbal formulas set a higher threshold in the range of 0.1 to 0.18. However, we found out that most compounds of DG have low DL. In detail, only 36 compounds of 125 in DG show higher or equal DL value than 0.08. For this reason, this study sets a lower threshold of DL than other previous researches to see the most potential targets of BSS.


### 2.1.2. Oral Bioavailability (OB) Prediction

OB is defined as the ratio of active compounds’ absorption into the systemic circulation, which represents the convergence of the ADME process [13]. OB values are dependent on drug dissolution in the gastrointestinal (GI) tract and hepatic and intestinal first-pass metabolism, as well as on intestinal membrane permeation, which makes it a major pharmacokinetic parameter for drug evaluations [16]. In this study, the OB threshold was set as ≥15%.


### 2.1.3. Caco-2 Permeability Screening

Caco-2 permeability is used to predict the absorption of an orally administered drug [14]. Surface absorptivity of the small intestine is maximized with the presence of villi and microvilli, for this reason most orally administered drug absorption occurs in the small intestine [19]. Moreover, the movement of orally administered drugs across the intestinal epithelial barrier determines the rate and extent of human absorption and ultimately affects drug bioavailability [20]. In the present study, compounds with OB, DL and Caco-2 values of greater than 15%, 0.08, and >−0.4, respectively, were regarded as active compounds and subjected to further analysis.


### 2.1.4. Lipinski’s Rule (LR) Screening

In addition, the screening standard used was defined based on Lipinski’s rule (LR), which identifies druggable compounds as having molecular weight (MW) of ≤500 Da (MW ≤ 500), chemical composition with ≤5 hydrogen-bond donors, ≤10 hydrogen-bond acceptors, and an octanol-water partition coefficient, AlogP of ≤ 5 [21]. AlogP can be used to estimate local hydrophobicity, to produce molecular hydrophobicity maps, and to evaluate hydrophobic interactions in protein-ligand complexes [22]. Hdon and Hacc are the number of possible hydrogen-bond donors and acceptors, and the hydrogen-bonding capacity of a drug solute is recognized as a crucial determinant of permeability; moreover high hydrogen-bonding potential is often related to low permeability and absorption [23]. Eventually, in the present study, we selected active compounds satisfying the following criteria: OB ≥ 15%; DL ≥ 0.08; Caco-2 ≥ −0.4; MW ≤ 500; H-bond donors ≤ 5; H-bond acceptors ≤ 10; AlogP ≤ 5.


## 2.1.1. Drug-Likeness Evaluation

DL helps filter “drug-like” compounds in oriental herbs, as DL represents a qualitative concept for valuations based on how “drug-like” a prospective compound is [15]. Accordingly, a high DL may lead to a greater possibility of therapeutic success, and compounds with a higher DL value are more likely to possess certain biological properties [16]. The calculations of DL in TCMSP database were based on Tanimoto coefficient formula [17] as follows:(1)FA,B=A×BA2+B2-A×B,where A represents the molecular parameters of herbal compounds and B is the average molecular parameters of all compounds in the Drugbank database (http://www.drugbank.ca/) [18]. In the present study, we excluded compounds with a DL of <0.08. Other previous researches of herbal formulas set a higher threshold in the range of 0.1 to 0.18. However, we found out that most compounds of DG have low DL. In detail, only 36 compounds of 125 in DG show higher or equal DL value than 0.08. For this reason, this study sets a lower threshold of DL than other previous researches to see the most potential targets of BSS.


## 2.1.2. Oral Bioavailability (OB) Prediction

OB is defined as the ratio of active compounds’ absorption into the systemic circulation, which represents the convergence of the ADME process [13]. OB values are dependent on drug dissolution in the gastrointestinal (GI) tract and hepatic and intestinal first-pass metabolism, as well as on intestinal membrane permeation, which makes it a major pharmacokinetic parameter for drug evaluations [16]. In this study, the OB threshold was set as ≥15%.


## 2.1.3. Caco-2 Permeability Screening

Caco-2 permeability is used to predict the absorption of an orally administered drug [14]. Surface absorptivity of the small intestine is maximized with the presence of villi and microvilli, for this reason most orally administered drug absorption occurs in the small intestine [19]. Moreover, the movement of orally administered drugs across the intestinal epithelial barrier determines the rate and extent of human absorption and ultimately affects drug bioavailability [20]. In the present study, compounds with OB, DL and Caco-2 values of greater than 15%, 0.08, and >−0.4, respectively, were regarded as active compounds and subjected to further analysis.


## 2.1.4. Lipinski’s Rule (LR) Screening

In addition, the screening standard used was defined based on Lipinski’s rule (LR), which identifies druggable compounds as having molecular weight (MW) of ≤500 Da (MW ≤ 500), chemical composition with ≤5 hydrogen-bond donors, ≤10 hydrogen-bond acceptors, and an octanol-water partition coefficient, AlogP of ≤ 5 [21]. AlogP can be used to estimate local hydrophobicity, to produce molecular hydrophobicity maps, and to evaluate hydrophobic interactions in protein-ligand complexes [22]. Hdon and Hacc are the number of possible hydrogen-bond donors and acceptors, and the hydrogen-bonding capacity of a drug solute is recognized as a crucial determinant of permeability; moreover high hydrogen-bonding potential is often related to low permeability and absorption [23]. Eventually, in the present study, we selected active compounds satisfying the following criteria: OB ≥ 15%; DL ≥ 0.08; Caco-2 ≥ −0.4; MW ≤ 500; H-bond donors ≤ 5; H-bond acceptors ≤ 10; AlogP ≤ 5.


## 2.2. Target Fishing

Aside from filtering active compounds, we also sought to identify the molecular targets of these active compounds. Compound-target interaction profiles were established based on a systematic prediction of multiple drug-target interactions tool which employs random forest (RF) and support vector machine (SVM) methods and integrates chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale [24]. Compound-target interactions satisfying SVM score ≥ 0.8 and RF score ≥ 0.7 were selected for further study. Additionally, filtered compound-target interaction profile mapping was performed using the UniProt database (http://www.uniprot.org/) [25].


## 2.3. Gene Ontology (GO) Analysis

Biological process (BP) of gene ontology (GO) analysis was employed to determine the biological properties of target genes [26]. GO annotation indicates the possibility of direct statistical analysis on gene function information. In this research, GO BP terms with P values < 0.01 were employed and the data was collected using the DAVID 6.8 Gene Functional Classification Tool (http://david.abcc.ncifcrf.gov/).


## 2.4. Network Construction and Analysis

In order to understand the multiscale interactions between the active compounds of BSS and targets, two types of networks were built: (1) the herb-compound-target network (H-C-T network), in which nodes represent either compounds, target genes, or herbs and edges indicate herb-compound-target connections; and (2) the target-pathway network (T-P network) to extract the pathways from KEGG database (http://www.genome.jp/kegg/), and the terms highly associated with parturition with P values < 0.05 were selected as the related pathways of targets in this work. Related targets were mapped onto relevant pathways, which resulted in the T-P network. Both networks were generated in Cytoscape 3.5.1, an open-source biological network visualization and data integration software package [27].


## 2.5. Target Organ Location Map

Tissue-specific patterns of mRNA expression can indicate important associations with biological events or gene functions [28]. To explore the beneficial effects of BSS during parturition, it is important that the tissue mRNA expression profiles of target genes at the organ level be known [29]. The target organ location map was built according to the Dataset: GeneAtlas U133A, gcrma (http://biogps.org). BioGPS database provides expression data acquired by direct measurements of gene expression obtained by microarrays analysis [30]. First, the mRNA expression patterns of each target gene in 176 parts of organ tissues were obtained. Second, average values were calculated for each gene. Third, frequency of above average mRNA expression tissue organs was inspected. Forth, based on the result from the third step and parturition mechanism theory, mRNA expression data of relevant organ tissues were extracted and categorized into 6 groups, namely, uterus and/or uterus corpus, fetus and/or placenta, hypothalamus and/or pituitary, smooth muscle, and whole blood.


## 3. Results

### 3.1. Identification of Active Compounds

314 compounds of BSS were identified, including 189 molecules in CG and 125 in DG (as shown in Supplementary Material Table S1 in Supplementary Material available online athttps://doi.org/10.1155/2017/7236436) and active compounds met the criteria OB ≥ 15%, Caco-2 ≥ −0.4, and DL ≥ 0.08, as well as the standards of Lipinski’s rule (LR) (as shown in Table 1). In detail, 60 active compounds were initially chosen, but 8 compounds were present in both herbs, namely, 3-butylidene-7-hydroxyphthalide, adenine, BdPh, beta-selinene, palmitic acid, senkyunolide-C, senkyunolide-D, and senkyunolide-E, and 14 had no target protein information and were thus excluded from the list of active compounds, whereas 27 compounds with lower ADME properties than above thresholds were included, which were reported to be related to oxytocin. In total, 65 active compounds were filtered.Table 1
65 Potential active compounds of BSS (compound with∗ was present in both herbs).
ID
Active compounds
OB (%)
Caco-2
DL
Herb
C1
()-alpha-Terpineol
46.3
1.28
0.03
DG
C2
()-Aromadendrene
55.74
1.81
0.1
CG
C3
()-Terpinen-4-ol
81.41
1.36
0.03
CG
C4
(+)-alpha-Funebrene
52.87
1.79
0.1
CG
C5
(+)-Ledol
16.96
1.43
0.12
DG
C6
(1R,5R,7S)-4,7-Dimethyl-7-(4-methylpent-3-enyl)bicyclo[3.1.1]hept-3-ene
16.23
1.86
0.09
CG
C7
(1S,4aR,8aR)-1-Isopropyl-7-methyl-4-methylene-2,3,4a,5,6,8a-hexahydro-1H-naphthalene
19.8
1.86
0.08
DG
C8
(1S,4E,8E,10R)-4,8,11,11-tetramethylbicyclo[8.1.0]undeca-4,8-diene
21.69
1.86
0.08
CG
C9
(3E)-3-butylidene-7-hydroxy-2-benzofuran-1-one
42.17
1.03
0.08
DG
C10
(L)-alpha-Terpineol
48.8
1.39
0.03
CG
C11
(R)-Linalool
39.8
1.33
0.02
CG
C12
(Z)-Ligustilide
53.72
1.3
0.07
CG
C13
1-Acetyl-beta-carboline
67.12
1.18
0.13
CG
C14
1-beta-Ethylacrylate-7-aldehyde-beta-carboline
28.53
0.45
0.31
CG
C15
1H-Cycloprop(e)azulen-7-ol, decahydro-1,1,7-trimethyl-4-methylene-, (1aR-(1aalpha,4aalpha,7beta,7abeta,7balpha))-
82.33
1.37
0.12
CG
C16
1-Terpineol
49.83
1.24
0.03
CG
C17
2,6-Di(phenyl)thiopyran-4-thione
69.13
1.74
0.15
DG
C18
2-[(2S,5S,6S)-6,10-Dimethylspiro[4.5]dec-9-en-2-yl]propan-2-ol
37.62
1.44
0.09
CG
C
19
∗
3-Butylidene-7-hydroxyphthalide
62.68
1
0.08
CG&DG
C20
4,7-Dihydroxy-3-butylphthalide
106.09
0.69
0.1
CG
C21
49070_FLUKA
85.51
1.29
0.12
CG
C22
4-Hydroxy-3-butylphthalide
70.31
0.9
0.08
CG
C23
58870_FLUKA
49.01
1.82
0.1
CG
C
24
∗
Adenine
62.81
-
0.3
0.03
CG&DG
C25
ADO
15.98
-
1.56
0.18
CG
C26
alpha-Cubebene
16.73
1.83
0.11
CG
C27
alpha-Selinene
31.81
1.82
0.1
CG
C28
Aromadendrene oxide 2
65.1
1.56
0.14
CG
C
29
∗
BdPh
42.44
1.32
0.07
CG&DG
C30
beta-Chamigrene
31.99
1.82
0.08
DG
C
31
∗
beta-Selinene
24.39
1.83
0.08
CG&DG
C32
beta-Cubebene
32.16
1.82
0.11
CG
C33
Cadinene
17.12
1.88
0.08
DG
C34
Caffeic acid
25.76
0.21
0.05
CG
C35
Carotol
149.03
1.46
0.09
CG
C36
Cedrene
51.14
1.82
0.11
CG
C37
Chuanxiongol
22.19
0.94
0.1
CG
C38
cis-Thujopsene
56.43
1.84
0.12
DG
C39
Coniferyl ferulate
4.54
0.71
0.39
DG
C40
Crysophanol
18.64
0.62
0.21
CG
C41
FA
68.96
-
1.5
0.71
CG
C42
Ferulic acid (CIS)
54.97
0.53
0.06
DG
C43
InChI=1/C15H24/c1-10-7-8-15-9-12(10)14(3,4)13(15)6-5-11(15)2/h7,11-13H,5-6,8-9H2,1-4H
55.56
1.79
0.1
DG
C44
L-Bornyl acetate
65.52
1.29
0.08
CG
C45
Methyl palmitate
18.09
1.37
0.12
CG
C46
Myricanone
40.6
0.67
0.51
CG
C47
Nicotinic acid
47.65
0.34
0.02
DG
C48
Oleic acid
33.13
1.17
0.14
CG
C
49
∗
Palmitic acid
19.3
1.09
0.1
CG&DG
C50
Perlolyrine
65.95
0.88
0.27
CG
C51
PLO
14.07
0.69
0.43
CG
C52
Scopoletol
27.77
0.71
0.08
DG
C53
Senkyunolide A
26.56
1.3
0.07
CG
C54
Senkyunolide G
39.52
0.63
0.08
CG
C
55
∗
Senkyunolide-C
46.8
0.87
0.08
CG&DG
C
56
∗
Senkyunolide-D
79.13
0.12
0.1
CG&DG
C
57
∗
Senkyunolide-E
34.4
0.55
0.08
CG&DG
C58
Senkyunolide-K
61.75
0.52
0.08
CG
C59
Sinapic acid
64.15
0.48
0.08
CG
C60
Sphingomyelin
0.31
-
0.46
0.51
DG
C61
Stearic acid
17.83
1.15
0.14
CG
C62
Stigmasterol
43.83
1.44
0.76
DG
C63
Succinic acid
29.62
-
0.44
0.01
DG
C64
Sucrose
7.17
-
2.89
0.23
CG
C65
Wallichilide
42.31
0.82
0.71
CGAlthough ligustilide and ferulic acid have a DL of <0.08, both were included in this study. Since ligustilide (C12, DL = 0.07, OB = 53.72, Caco-2 = 1.3) was reported to be the main compound of DG in uterine contraction [31], and ferulic acid (C42, DL = 0.06, OB = 54.97, Caco-2 = 0.53) has been reported to be useful for the treatment of vascular diseases [6, 32] and blood deficiency syndrome [33] in China and to suppress inflammatory responses and tumor progression [34]. Some other compounds also have been shown experimentally to have various biological activities; for example, crysophanol (C42, DL = 0.21, OB = 18.64, Caco-2 = 0.62) can be used to treat menorrhagia and thrombocytopenia [35]. Perlolyrine (C52, DL = 0.27, OB = 65.95, Caco-2 = 0.88) was confirmed to have a protective effect on injured human umbilical vein endothelial cells [36], and myricanone (C48, DL = 0.51, OB = 57.61, Caco-2 = 0.67) was found to best inhibit mouse skin tumor progression [37].


### 3.2. Target Fishing

The 65 active compounds interact with 185 target proteins, as shown in Table2; in other words, on average, each compound on average interacts with 2.85 target proteins. This result confirms the polypharmacological character of oriental medicine and demonstrates the synergistic effects of multiple compounds on multiple targets [38]. Different compounds in CG and DG can directly affect common targets, for example, the target protein “calmodulin (CALM1)” interacts with crysophanol from CG and coniferyl ferulate from DG at the same time, which implies the synergetic or cumulative effects of herbal medicine.Table 2
Related targets of potential compounds in BSS.
UniProt ID
Target name
Gene Name
P80404
4-aminobutyrate aminotransferase, mitochondrial
ABAT
P33121
Long-chain-fatty-acid--CoA ligase 1
ACSL1
O60488
Long-chain-fatty-acid--CoA ligase 4
ACSL4
P00813
Adenosine deaminase
ADA
P07327
Alcohol dehydrogenase 1A
ADH1A
P00325
Alcohol dehydrogenase 1B
ADH1B
P00326
Alcohol dehydrogenase 1C
ADH1C
P29274
Adenosine A2a receptor
ADORA2A
P35348
Alpha-1A adrenergic receptor
ADRA1A
P35368
Alpha-1B adrenergic receptor
ADRA1B
P25100
Alpha-1D adrenergic receptor
ADRA1D
P08913
Alpha-2A adrenergic receptor
ADRA2A
P18089
Alpha-2B adrenergic receptor
ADRA2B
P18825
Alpha-2C adrenergic receptor
ADRA2C
P08588
Beta-1 adrenergic receptor
ADRB1
P07550
Beta-2 adrenergic receptor
ADRB2
Q5SY84
Adenylosuccinate synthetase
ADSS
P21549
Serine--pyruvate aminotransferase
AGXT
O43865
Putative adenosylhomocysteinase 2
AHCYL1
P15121
Aldose reductase
AKR1B1
P13716
Delta-aminolevulinic acid dehydratase
ALAD
P51649
Succinate semialdehyde dehydrogenase, mitochondrial
ALDH5A1
P04745
Alpha-amylase 1
AMY1A
P04746
Pancreatic alpha-amylase
AMY2A
P04114
Apolipoprotein B-100
APOB
P10275
Androgen receptor
AR
P06576
ATP synthase subunit beta, mitochondrial
ATP5B
P06276
Cholinesterase
BCHE
P10415
Apoptosis regulator Bcl-2
BCL2
Q06187
Tyrosine-protein kinase BTK
BTK
P00915
Carbonic anhydrase I
CA1
P62158
Calmodulin
CALM1
P42574
Caspase-3
CASP3
P04040
Catalase
CAT
P06307
Cholecystokinin
CCK
P20248
Cyclin-A2
CCNA2
P30305
M-phase inducer phosphatase 2
CDC25B
P24941
Cell division protein kinase 2
CDK2
P11597
Cholesteryl ester transfer protein
CETP
P28329
Choline O-acetyltransferase
CHAT
O14757
Serine/threonine-protein kinase Chk1
CHEK1
P36222
Chitinase-3-like protein 1
CHI3L1
P11229
Muscarinic acetylcholine receptor M1
CHRM1
P08172
Muscarinic acetylcholine receptor M2
CHRM2
P20309
Muscarinic acetylcholine receptor M3
CHRM3
Q15822
Neuronal acetylcholine receptor subunit alpha-2
CHRNA2
P36544
Neuronal acetylcholine receptor protein, alpha-7 chain
CHRNA7
Q99966
Cbp/p300-interacting transactivator 1
CITED1
P02452
Collagen alpha-1(I) chain
COL1A1
Q02388
Collagen alpha-1(VII) chain
COL7A1
P17538
Chymotrypsinogen B
CTRB1
P07339
Cathepsin D
CTSD
P04798
Cytochrome P450 1A1
CYP1A1
P05177
Cytochrome P450 1A2
CYP1A2
Q9ULA0
Aspartyl aminopeptidase
DNPEP
P27487
Dipeptidyl peptidase IV
DPP4
P21728
Dopamine D1 receptor
DRD1
P14416
D(2) dopamine receptor
DRD2
P25101
Endothelin-1
EDNRA
Q07075
Glutamyl aminopeptidase
ENPEP
Q6UWV6
Ectonucleotide pyrophosphatase/phosphodiesterase family member 7
ENPP7
P04626
Receptor tyrosine-protein kinase erbB-2
ERBB2
P03372
Estrogen receptor
ESR1
Q92731
Estrogen receptor beta
ESR2
P00742
Coagulation factor Xa
F10
P00734
Thrombin
F2
P08709
Coagulation factor VII
F7
P07148
Fatty acid-binding protein, liver
FABP1
P01100
Proto-oncogene c-Fos
FOS
P15408
Fos-related antigen 2
FOSL2
P35575
Glucose-6-phosphatase
G6PC
P14867
Gamma-aminobutyric acid receptor subunit alpha-1
GABRA1
P47869
Gamma-aminobutyric-acid receptor alpha-2 subunit
GABRA2
P34903
Gamma-aminobutyric-acid receptor alpha-3 subunit
GABRA3
P48169
Gamma-aminobutyric-acid receptor subunit alpha-4
GABRA4
P31644
Gamma-aminobutyric-acid receptor alpha-5 subunit
GABRA5
Q16445
Gamma-aminobutyric-acid receptor subunit alpha-6
GABRA6
P17677
Neuromodulin
GAP43
P47871
Glucagon
GCGR
P14136
Glial fibrillary acidic protein
GFAP
Q2TU84
Growth-inhibiting protein 18
GIG18
P23415
Glycine receptor alpha-1 chain
GLRA1
P00367
Glutamate dehydrogenase 1, mitochondrial
GLUD1
P17174
Aspartate aminotransferase, cytoplasmic
GOT1
P00505
Aspartate aminotransferase, mitochondrial
GOT2
P42262
Glutamate receptor 2
GRIA2
P49841
Glycogen synthase kinase-3 beta
GSK3B
Q15486
Putative beta-glucuronidase-like protein SMA3
GUSBP1
P19367
Hexokinase-1
HK1
P04035
3-hydroxy-3-methylglutaryl-coenzyme A reductase
HMGCR
P00738
Haptoglobin
HP
O14756
Oxidoreductase
HSD17B6
P08238
Heat shock protein HSP 90
HSP90AB1
P28223
5-hydroxytryptamine 2A receptor
HTR2A
P01344
Insulin-like growth factor II
IGF2
P01857
Ig gamma-1 chain C region
IGHG1
P22301
Interleukin-10
IL10
P05231
Interleukin-6
IL6
P01308
Insulin
INS
Q12809
Potassium voltage-gated channel subfamily H member 2
KCNH2
Q12791
Calcium-activated potassium channel subunit alpha 1
KCNMA1
P35968
Vascular endothelial growth factor receptor 2
KDR
P09848
Lactase-phlorizin hydrolase
LCT
Q32P28
Prolyl 3-hydroxylase 1
LEPRE1
Q8IVL6
Prolyl 3-hydroxylase 3
LEPREL2
P06858
Lipoprotein lipase
LPL
P09960
Leukotriene A-4 hydrolase
LTA4H
P21397
Amine oxidase [flavin-containing] A
MAOA
P27338
Amine oxidase [flavin-containing] B
MAOB
Q16539
Mitogen-activated protein kinase 14
MAPK14
Q00266
S-adenosylmethionine synthetase isoform type-1
MAT1A
P31153
S-adenosylmethionine synthetase isoform type-2
MAT2A
P23368
NAD-dependent malic enzyme, mitochondrial
ME2
Q16798
NADP-dependent malic enzyme, mitochondrial
ME3
Q3SYC2
2-acylglycerol O-acyltransferase 2
MOGAT2
P05164
Myeloperoxidase
MPO
Q15788
Nuclear receptor coactivator 1
NCOA1
Q15596
Nuclear receptor coactivator 2
NCOA2
P29475
Nitric-oxide synthase, brain
NOS1
P35228
Nitric oxide synthase, inducible
NOS2
P29474
Nitric oxide synthase, endothelial
NOS3
P16083
NRH dehydrogenase [quinone] 2
NQO2
Q14994
Nuclear receptor subfamily 1 group I member 3
NR1I3
P04150
Glucocorticoid receptor
NR3C1
P08235
Mineralocorticoid receptor
NR3C2
Q16620
BDNF/NT-3 growth factors receptor
NTRK2
P04181
Ornithine aminotransferase, mitochondrial
OAT
P00480
Ornithine carbamoyltransferase, mitochondrial
OTC
Q9BYC2
Succinyl-CoA:3-ketoacid-coenzyme A transferase 2, mitochondrial
OXCT2
O15460
Prolyl 4-hydroxylase subunit alpha-2
P4HA2
P49585
Choline-phosphate cytidylyltransferase A
PCYT1A
Q14432
CGMP-inhibited 3′,5′-cyclic phosphodiesterase A
PDE3A
O00330
Pyruvate dehydrogenase protein X component, mitochondrial
PDHX
P52945
Pancreas/duodenum homeobox protein 1
PDX1
P06401
Progesterone receptor
PGR
P48736
Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoform
PIK3CG
P11309
Proto-oncogene serine/threonine-protein kinase Pim-1
PIM1
P61925
cAMP-dependent protein kinase inhibitor alpha
PKIA
P14618
Pyruvate kinase isozymes M1/M2
PKM2
P04054
Phospholipase A2
PLA2G1B
P00749
Urokinase-type plasminogen activator
PLAU
P00747
Plasminogen
PLG
P00491
Purine nucleoside phosphorylase
PNP
P27169
Serum paraoxonase/arylesterase 1
PON1
Q07869
Peroxisome proliferator-activated receptor alpha
PPARA
Q03181
Peroxisome proliferator-activated receptor delta
PPARD
P37231
Peroxisome proliferator activated receptor gamma
PPARG
Q9UBK2
Peroxisome proliferator-activated receptor gamma coactivator 1-alpha
PPARGC1A
P17612
mRNA of PKA Catalytic Subunit C-alpha
PRKACA
P05771
Protein kinase C beta type
PRKCB
P35030
Trypsin-3
PRSS3
P60484
Phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN
PTEN
P43115
Prostaglandin E2 receptor EP3 subtype
PTGER3
P23219
Prostaglandin G/H synthase 1
PTGS1
P35354
Prostaglandin G/H synthase 2
PTGS2
P18031
mRNA of Protein-tyrosine phosphatase, non-receptor type 1
PTPN1
P10082
Peptide YY
PYY
P63000
Ras-related C3 botulinum toxin substrate 1
RAC1
P50120
Retinol-binding protein 2
RBP2
P08100
Rhodopsin
RHO
P19793
Retinoic acid receptor RXR-alpha
RXRA
O00767
Acyl-CoA desaturase
SCD
Q14524
Sodium channel protein type 5 subunit alpha
SCN5A
P31040
Succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrial
SDHA
P16109
P-selectin
SELP
P05121
Plasminogen activator inhibitor 1
SERPINE1
P14410
Sucrase-isomaltase, intestinal
SI
O76082
Solute carrier family 22 member 5
SLC22A5
Q9UBX3
Mitochondrial dicarboxylate carrier
SLC25A10
P11168
Solute carrier family 2, facilitated glucose transporter member 2
SLC2A2
P23975
Sodium-dependent noradrenaline transporter
SLC6A2
Q01959
Sodium-dependent dopamine transporter
SLC6A3
P31645
Sodium-dependent serotonin transporter
SLC6A4
P35610
Sterol O-acyltransferase 1
SOAT1
P00441
Superoxide dismutase[Cu-Zn]
SOD1
P08047
Transcription factor Sp1
SP1
P12931
Proto-oncogene tyrosine-protein kinase SRC
SRC
P36956
Sterol regulatory element-binding protein 1
SREBF1
Q12772
Sterol regulatory element-binding protein 2
SREBF2
Q9P2R7
Succinyl-CoA ligase [ADP-forming] beta-chain, mitochondrial
SUCLA2
Q99973
Telomerase protein component 1
TEP1
P01375
Tumor necrosis factor
TNF
Q16881
Thioredoxin reductase, cytoplasmic
TXNRD1
P55851
Mitochondrial uncoupling protein 2
UCP2
P55916
Mitochondrial uncoupling protein 3
UCP3


### 3.3. GO Analysis

397 biological process terms withP values of <0.01 were sorted using the functional annotation chart of the DAVID 6.8 Gene Functional Classification Tool, based on 185 filtered target genes, and P values were adjusted using the Benjamini-Hochberg method. 30 enriched GO BP terms extracted by P value and gene counts are displayed in Figure 2. It is meaningful that most of the target genes are significantly related to the various BP involved in parturition. For instance, 30 extracted GO BP terms include “MAPK signaling pathways,” “steroid hormone mediated signaling pathway,” “response to glucocorticoid,” “response to estradiol,” and “positive regulation of ERK1 and ERK2 cascade.” “MAPK signaling pathways” were reported to be activated in human uterine cervical ripening during parturition [39]. “Steroid hormone mediated signaling pathway” is highly related to parturition process as estrogen and progesterone play important roles in pregnancy and parturition, and estrogen induceS the principal stimulatory myometrial contractility [40]. Also, estradiol takes key place in parturition process [41]. It was identified that increased ERK activation is observed at the onset of labor, and it promotes myometrial contractility and development of parturition [42, 43]. To sum up, the target genes of BSS are highly associated with the biological process (BP) of parturition.Figure 2
GO analysis: 30 enriched biological process (BP) of gene ontology (GO) terms sorted byP value < 0.01 and gene counts are displayed. The y-axis represents enriched biological process (BP) terms for the target genes, and the x-axis shows gene counts and -log⁡10 (P value).


### 3.4. Network Construction and Analysis

Network analysis is an efficient tool for visualizing and understanding multiple targeted drug actions and demonstrates drug actions within the context of the whole genome [44, 45]. For a better insight of therapeutic impacts, H-C-T and T-P networks were constructed and displayed in Figures 3 and 4, respectively. In the H-C-T network, nodes represent herb names, compounds, and targets. Also in the T-P network, circular nodes represent targets and triangle nodes represent pathways. Besides node size is relative to the degree and edges show interactions between nodes.Figure 3
H-C-T network: herb-compound-target (H-C-T) network demonstrated multicompound, multitarget property of BSS. In this network, red and blue nodes represent herbs, green nodes show compounds, and pink nodes indicate targets and node size is relative to the degree and edges demonstrate interactions between nodes.Figure 4
T-P network: in target-pathway (T-P) network, circular nodes represent compounds and triangles indicate pathways. Node size is relative to the degree and edges demonstrate interactions between nodes.H-C-T network confirmed that there were 739 interactions between 185 targets and 65 active compounds of CG and DG: oleic acid (C48, degree = 42) with the highest number of interactions with targets, followed by succinic acid (C63, degree = 40) and stigmasterol (C62, degree = 37). It shows that single molecules target multiple receptors [46]. Also, some compounds from CG and DG were found to share common targets. Likewise, prostaglandin G/H synthase 2 (PTGS2, degree = 56) displayed the most affinitive connections with compounds, followed by gamma-aminobutyric acid receptor subunit alpha-1 (GABRA1, degree = 48), prostaglandin G/H synthase 1 (PTGS1, degree = 37), and muscarinic acetylcholine receptor M1 (CHRM1, degree = 37). Except for C60 (PLA2G1B, degree = 1), the rest of the 64 active compounds are connected with more than one target; likewise, 73 (39.5%) target genes out of 185 interacted with more than one compound. This result demonstrates the multicompounds and multitarget properties of herbal compounds and there was a report that compounds with multiple targets could have greater therapeutic efficacy [47].In addition, the top 40 pathways were extracted based on gene counts andP value (<0.05), and P value was adjusted by Benjamini-Hochberg method. T-P network using relevant targets of herbal compounds is demonstrated in Figure 4. There were 485 interactions between the top 40 pathways and 135 of 185 target genes. “Metabolic pathways” (degree = 49) and “neuroactive ligand-receptor interaction pathway” (degree = 32) had the highest and the second highest numbers of connections with the targets, followed by “calcium signaling” (degree = 21), “cAMP signaling pathway” (degree = 17), and “cGMP PKG signaling pathway” (degree = 15). These are compelling results that parturition processes are the complex hormone interactions and it is well known that calcium signals within the myometrium are pivotal for uterine contractions [48]. In the same manner, some target genes demonstrated higher degree centrality with top 40 pathways, namely, PI3-kinase subunit gamma (PIK3CG, degree = 23), cAMP-dependent protein kinase catalytic subunit alpha (PRKACA, degree = 20), protein kinase C beta type (PRKCB, degree = 18), and calmodulin (CALM1, degree = 11). We can confirm the same result in the previous researches. For instance, PI3-kinase subunit gamma plays the key role in regulating cAMP, calcium cycling, and beta-adrenergic signaling [49]. Moreover, during the labor, calmodulin-calcium complex activates myosin light-chain kinase, which causes the generation of ATPase activity; eventually, uterine contraction is promoted [50].H-C-T network explains the multitarget, multicompounds properties and accumulates effect of herbal medicines and T-P network shows that target genes of BSS are highly related to the pathway associated with parturition process.


### 3.5. Target Organ Location Map

It is important to confirm the tissue mRNA expression profiles of the target genes at the organ level to identify the effects of BSS on parturition. Since there was no mRNA expression information in BioGPS of muscarinic acetylcholine receptor M1 (CHRM1), putative beta-glucuronidase-like protein SMA3 (GUSBP1), and retinol-binding protein 2 (RBP2), excluding these 3 targets from 185 filtered targets, totally 182 genes mRNA expression profiles were analyzed in this study. There were 519 interactions between target genes and organ locations. The networks of target genes tissue mRNA expression profiles and compounds of BSS are shown in Figure5.Figure 5
Target organ location map: it shows that tissue-specific patterns of mRNA expression are highly active in relative organs of parturition process such as uterus, fetus, placenta, hypothalamus, pituitary, and smooth muscle. Yellow nodes show compounds and pink nodes indicate targets and node size is relative to the degree and edges demonstrate interactions between nodes.As a result, 159 of 182 target genes displayed beyond average mRNA expression in relevant organ tissues, such as uterus and/or uterus corpus, fetus and/or placenta, hypothalamus and/or pituitary, smooth muscle, and whole blood. The rest of 23 genes of 182 targets did not display above average mRNA expression in above organ tissues, for example, gamma-aminobutyric acid receptor subunit alpha-6 (GABRA6) and coagulation factor X (F10).Nevertheless, most genes of 159 demonstrated high expression patterns in several organs of parturition related tissues at the same time. In detail, 60 genes showed most significant mRNA expression in the uterus and/or uterus corpus group, 130 for placenta and/or fetus, 86 for hypothalamus and/or pituitary, 82 for smooth muscle, 80 for pituitary, and 81 for whole blood. Besides, 30 of 159 genes showed expression in all of 6 groups. For instance, muscarinic acetylcholine receptor M2 (CHRM2), neuronal acetylcholine receptor subunitα-2 (CHRNA2), gamma-aminobutyric acid receptor subunit alpha-3 (GABRA3), NO synthase, inducible (NOS2), cGMP-inhibited 3′,5′-cyclic phosphodiesterase A (PDE3A), and sodium-dependent dopamine transporter (SLC6A3) recorded beyond average mRNA expression in all six groups. Furthermore, 79% of targets were expressed in two or more organ tissues, which suggests that those organs and target genes of BSS are closely correlated.


## 3.1. Identification of Active Compounds

314 compounds of BSS were identified, including 189 molecules in CG and 125 in DG (as shown in Supplementary Material Table S1 in Supplementary Material available online athttps://doi.org/10.1155/2017/7236436) and active compounds met the criteria OB ≥ 15%, Caco-2 ≥ −0.4, and DL ≥ 0.08, as well as the standards of Lipinski’s rule (LR) (as shown in Table 1). In detail, 60 active compounds were initially chosen, but 8 compounds were present in both herbs, namely, 3-butylidene-7-hydroxyphthalide, adenine, BdPh, beta-selinene, palmitic acid, senkyunolide-C, senkyunolide-D, and senkyunolide-E, and 14 had no target protein information and were thus excluded from the list of active compounds, whereas 27 compounds with lower ADME properties than above thresholds were included, which were reported to be related to oxytocin. In total, 65 active compounds were filtered.Table 1
65 Potential active compounds of BSS (compound with∗ was present in both herbs).
ID
Active compounds
OB (%)
Caco-2
DL
Herb
C1
()-alpha-Terpineol
46.3
1.28
0.03
DG
C2
()-Aromadendrene
55.74
1.81
0.1
CG
C3
()-Terpinen-4-ol
81.41
1.36
0.03
CG
C4
(+)-alpha-Funebrene
52.87
1.79
0.1
CG
C5
(+)-Ledol
16.96
1.43
0.12
DG
C6
(1R,5R,7S)-4,7-Dimethyl-7-(4-methylpent-3-enyl)bicyclo[3.1.1]hept-3-ene
16.23
1.86
0.09
CG
C7
(1S,4aR,8aR)-1-Isopropyl-7-methyl-4-methylene-2,3,4a,5,6,8a-hexahydro-1H-naphthalene
19.8
1.86
0.08
DG
C8
(1S,4E,8E,10R)-4,8,11,11-tetramethylbicyclo[8.1.0]undeca-4,8-diene
21.69
1.86
0.08
CG
C9
(3E)-3-butylidene-7-hydroxy-2-benzofuran-1-one
42.17
1.03
0.08
DG
C10
(L)-alpha-Terpineol
48.8
1.39
0.03
CG
C11
(R)-Linalool
39.8
1.33
0.02
CG
C12
(Z)-Ligustilide
53.72
1.3
0.07
CG
C13
1-Acetyl-beta-carboline
67.12
1.18
0.13
CG
C14
1-beta-Ethylacrylate-7-aldehyde-beta-carboline
28.53
0.45
0.31
CG
C15
1H-Cycloprop(e)azulen-7-ol, decahydro-1,1,7-trimethyl-4-methylene-, (1aR-(1aalpha,4aalpha,7beta,7abeta,7balpha))-
82.33
1.37
0.12
CG
C16
1-Terpineol
49.83
1.24
0.03
CG
C17
2,6-Di(phenyl)thiopyran-4-thione
69.13
1.74
0.15
DG
C18
2-[(2S,5S,6S)-6,10-Dimethylspiro[4.5]dec-9-en-2-yl]propan-2-ol
37.62
1.44
0.09
CG
C
19
∗
3-Butylidene-7-hydroxyphthalide
62.68
1
0.08
CG&DG
C20
4,7-Dihydroxy-3-butylphthalide
106.09
0.69
0.1
CG
C21
49070_FLUKA
85.51
1.29
0.12
CG
C22
4-Hydroxy-3-butylphthalide
70.31
0.9
0.08
CG
C23
58870_FLUKA
49.01
1.82
0.1
CG
C
24
∗
Adenine
62.81
-
0.3
0.03
CG&DG
C25
ADO
15.98
-
1.56
0.18
CG
C26
alpha-Cubebene
16.73
1.83
0.11
CG
C27
alpha-Selinene
31.81
1.82
0.1
CG
C28
Aromadendrene oxide 2
65.1
1.56
0.14
CG
C
29
∗
BdPh
42.44
1.32
0.07
CG&DG
C30
beta-Chamigrene
31.99
1.82
0.08
DG
C
31
∗
beta-Selinene
24.39
1.83
0.08
CG&DG
C32
beta-Cubebene
32.16
1.82
0.11
CG
C33
Cadinene
17.12
1.88
0.08
DG
C34
Caffeic acid
25.76
0.21
0.05
CG
C35
Carotol
149.03
1.46
0.09
CG
C36
Cedrene
51.14
1.82
0.11
CG
C37
Chuanxiongol
22.19
0.94
0.1
CG
C38
cis-Thujopsene
56.43
1.84
0.12
DG
C39
Coniferyl ferulate
4.54
0.71
0.39
DG
C40
Crysophanol
18.64
0.62
0.21
CG
C41
FA
68.96
-
1.5
0.71
CG
C42
Ferulic acid (CIS)
54.97
0.53
0.06
DG
C43
InChI=1/C15H24/c1-10-7-8-15-9-12(10)14(3,4)13(15)6-5-11(15)2/h7,11-13H,5-6,8-9H2,1-4H
55.56
1.79
0.1
DG
C44
L-Bornyl acetate
65.52
1.29
0.08
CG
C45
Methyl palmitate
18.09
1.37
0.12
CG
C46
Myricanone
40.6
0.67
0.51
CG
C47
Nicotinic acid
47.65
0.34
0.02
DG
C48
Oleic acid
33.13
1.17
0.14
CG
C
49
∗
Palmitic acid
19.3
1.09
0.1
CG&DG
C50
Perlolyrine
65.95
0.88
0.27
CG
C51
PLO
14.07
0.69
0.43
CG
C52
Scopoletol
27.77
0.71
0.08
DG
C53
Senkyunolide A
26.56
1.3
0.07
CG
C54
Senkyunolide G
39.52
0.63
0.08
CG
C
55
∗
Senkyunolide-C
46.8
0.87
0.08
CG&DG
C
56
∗
Senkyunolide-D
79.13
0.12
0.1
CG&DG
C
57
∗
Senkyunolide-E
34.4
0.55
0.08
CG&DG
C58
Senkyunolide-K
61.75
0.52
0.08
CG
C59
Sinapic acid
64.15
0.48
0.08
CG
C60
Sphingomyelin
0.31
-
0.46
0.51
DG
C61
Stearic acid
17.83
1.15
0.14
CG
C62
Stigmasterol
43.83
1.44
0.76
DG
C63
Succinic acid
29.62
-
0.44
0.01
DG
C64
Sucrose
7.17
-
2.89
0.23
CG
C65
Wallichilide
42.31
0.82
0.71
CGAlthough ligustilide and ferulic acid have a DL of <0.08, both were included in this study. Since ligustilide (C12, DL = 0.07, OB = 53.72, Caco-2 = 1.3) was reported to be the main compound of DG in uterine contraction [31], and ferulic acid (C42, DL = 0.06, OB = 54.97, Caco-2 = 0.53) has been reported to be useful for the treatment of vascular diseases [6, 32] and blood deficiency syndrome [33] in China and to suppress inflammatory responses and tumor progression [34]. Some other compounds also have been shown experimentally to have various biological activities; for example, crysophanol (C42, DL = 0.21, OB = 18.64, Caco-2 = 0.62) can be used to treat menorrhagia and thrombocytopenia [35]. Perlolyrine (C52, DL = 0.27, OB = 65.95, Caco-2 = 0.88) was confirmed to have a protective effect on injured human umbilical vein endothelial cells [36], and myricanone (C48, DL = 0.51, OB = 57.61, Caco-2 = 0.67) was found to best inhibit mouse skin tumor progression [37].


## 3.2. Target Fishing

The 65 active compounds interact with 185 target proteins, as shown in Table2; in other words, on average, each compound on average interacts with 2.85 target proteins. This result confirms the polypharmacological character of oriental medicine and demonstrates the synergistic effects of multiple compounds on multiple targets [38]. Different compounds in CG and DG can directly affect common targets, for example, the target protein “calmodulin (CALM1)” interacts with crysophanol from CG and coniferyl ferulate from DG at the same time, which implies the synergetic or cumulative effects of herbal medicine.Table 2
Related targets of potential compounds in BSS.
UniProt ID
Target name
Gene Name
P80404
4-aminobutyrate aminotransferase, mitochondrial
ABAT
P33121
Long-chain-fatty-acid--CoA ligase 1
ACSL1
O60488
Long-chain-fatty-acid--CoA ligase 4
ACSL4
P00813
Adenosine deaminase
ADA
P07327
Alcohol dehydrogenase 1A
ADH1A
P00325
Alcohol dehydrogenase 1B
ADH1B
P00326
Alcohol dehydrogenase 1C
ADH1C
P29274
Adenosine A2a receptor
ADORA2A
P35348
Alpha-1A adrenergic receptor
ADRA1A
P35368
Alpha-1B adrenergic receptor
ADRA1B
P25100
Alpha-1D adrenergic receptor
ADRA1D
P08913
Alpha-2A adrenergic receptor
ADRA2A
P18089
Alpha-2B adrenergic receptor
ADRA2B
P18825
Alpha-2C adrenergic receptor
ADRA2C
P08588
Beta-1 adrenergic receptor
ADRB1
P07550
Beta-2 adrenergic receptor
ADRB2
Q5SY84
Adenylosuccinate synthetase
ADSS
P21549
Serine--pyruvate aminotransferase
AGXT
O43865
Putative adenosylhomocysteinase 2
AHCYL1
P15121
Aldose reductase
AKR1B1
P13716
Delta-aminolevulinic acid dehydratase
ALAD
P51649
Succinate semialdehyde dehydrogenase, mitochondrial
ALDH5A1
P04745
Alpha-amylase 1
AMY1A
P04746
Pancreatic alpha-amylase
AMY2A
P04114
Apolipoprotein B-100
APOB
P10275
Androgen receptor
AR
P06576
ATP synthase subunit beta, mitochondrial
ATP5B
P06276
Cholinesterase
BCHE
P10415
Apoptosis regulator Bcl-2
BCL2
Q06187
Tyrosine-protein kinase BTK
BTK
P00915
Carbonic anhydrase I
CA1
P62158
Calmodulin
CALM1
P42574
Caspase-3
CASP3
P04040
Catalase
CAT
P06307
Cholecystokinin
CCK
P20248
Cyclin-A2
CCNA2
P30305
M-phase inducer phosphatase 2
CDC25B
P24941
Cell division protein kinase 2
CDK2
P11597
Cholesteryl ester transfer protein
CETP
P28329
Choline O-acetyltransferase
CHAT
O14757
Serine/threonine-protein kinase Chk1
CHEK1
P36222
Chitinase-3-like protein 1
CHI3L1
P11229
Muscarinic acetylcholine receptor M1
CHRM1
P08172
Muscarinic acetylcholine receptor M2
CHRM2
P20309
Muscarinic acetylcholine receptor M3
CHRM3
Q15822
Neuronal acetylcholine receptor subunit alpha-2
CHRNA2
P36544
Neuronal acetylcholine receptor protein, alpha-7 chain
CHRNA7
Q99966
Cbp/p300-interacting transactivator 1
CITED1
P02452
Collagen alpha-1(I) chain
COL1A1
Q02388
Collagen alpha-1(VII) chain
COL7A1
P17538
Chymotrypsinogen B
CTRB1
P07339
Cathepsin D
CTSD
P04798
Cytochrome P450 1A1
CYP1A1
P05177
Cytochrome P450 1A2
CYP1A2
Q9ULA0
Aspartyl aminopeptidase
DNPEP
P27487
Dipeptidyl peptidase IV
DPP4
P21728
Dopamine D1 receptor
DRD1
P14416
D(2) dopamine receptor
DRD2
P25101
Endothelin-1
EDNRA
Q07075
Glutamyl aminopeptidase
ENPEP
Q6UWV6
Ectonucleotide pyrophosphatase/phosphodiesterase family member 7
ENPP7
P04626
Receptor tyrosine-protein kinase erbB-2
ERBB2
P03372
Estrogen receptor
ESR1
Q92731
Estrogen receptor beta
ESR2
P00742
Coagulation factor Xa
F10
P00734
Thrombin
F2
P08709
Coagulation factor VII
F7
P07148
Fatty acid-binding protein, liver
FABP1
P01100
Proto-oncogene c-Fos
FOS
P15408
Fos-related antigen 2
FOSL2
P35575
Glucose-6-phosphatase
G6PC
P14867
Gamma-aminobutyric acid receptor subunit alpha-1
GABRA1
P47869
Gamma-aminobutyric-acid receptor alpha-2 subunit
GABRA2
P34903
Gamma-aminobutyric-acid receptor alpha-3 subunit
GABRA3
P48169
Gamma-aminobutyric-acid receptor subunit alpha-4
GABRA4
P31644
Gamma-aminobutyric-acid receptor alpha-5 subunit
GABRA5
Q16445
Gamma-aminobutyric-acid receptor subunit alpha-6
GABRA6
P17677
Neuromodulin
GAP43
P47871
Glucagon
GCGR
P14136
Glial fibrillary acidic protein
GFAP
Q2TU84
Growth-inhibiting protein 18
GIG18
P23415
Glycine receptor alpha-1 chain
GLRA1
P00367
Glutamate dehydrogenase 1, mitochondrial
GLUD1
P17174
Aspartate aminotransferase, cytoplasmic
GOT1
P00505
Aspartate aminotransferase, mitochondrial
GOT2
P42262
Glutamate receptor 2
GRIA2
P49841
Glycogen synthase kinase-3 beta
GSK3B
Q15486
Putative beta-glucuronidase-like protein SMA3
GUSBP1
P19367
Hexokinase-1
HK1
P04035
3-hydroxy-3-methylglutaryl-coenzyme A reductase
HMGCR
P00738
Haptoglobin
HP
O14756
Oxidoreductase
HSD17B6
P08238
Heat shock protein HSP 90
HSP90AB1
P28223
5-hydroxytryptamine 2A receptor
HTR2A
P01344
Insulin-like growth factor II
IGF2
P01857
Ig gamma-1 chain C region
IGHG1
P22301
Interleukin-10
IL10
P05231
Interleukin-6
IL6
P01308
Insulin
INS
Q12809
Potassium voltage-gated channel subfamily H member 2
KCNH2
Q12791
Calcium-activated potassium channel subunit alpha 1
KCNMA1
P35968
Vascular endothelial growth factor receptor 2
KDR
P09848
Lactase-phlorizin hydrolase
LCT
Q32P28
Prolyl 3-hydroxylase 1
LEPRE1
Q8IVL6
Prolyl 3-hydroxylase 3
LEPREL2
P06858
Lipoprotein lipase
LPL
P09960
Leukotriene A-4 hydrolase
LTA4H
P21397
Amine oxidase [flavin-containing] A
MAOA
P27338
Amine oxidase [flavin-containing] B
MAOB
Q16539
Mitogen-activated protein kinase 14
MAPK14
Q00266
S-adenosylmethionine synthetase isoform type-1
MAT1A
P31153
S-adenosylmethionine synthetase isoform type-2
MAT2A
P23368
NAD-dependent malic enzyme, mitochondrial
ME2
Q16798
NADP-dependent malic enzyme, mitochondrial
ME3
Q3SYC2
2-acylglycerol O-acyltransferase 2
MOGAT2
P05164
Myeloperoxidase
MPO
Q15788
Nuclear receptor coactivator 1
NCOA1
Q15596
Nuclear receptor coactivator 2
NCOA2
P29475
Nitric-oxide synthase, brain
NOS1
P35228
Nitric oxide synthase, inducible
NOS2
P29474
Nitric oxide synthase, endothelial
NOS3
P16083
NRH dehydrogenase [quinone] 2
NQO2
Q14994
Nuclear receptor subfamily 1 group I member 3
NR1I3
P04150
Glucocorticoid receptor
NR3C1
P08235
Mineralocorticoid receptor
NR3C2
Q16620
BDNF/NT-3 growth factors receptor
NTRK2
P04181
Ornithine aminotransferase, mitochondrial
OAT
P00480
Ornithine carbamoyltransferase, mitochondrial
OTC
Q9BYC2
Succinyl-CoA:3-ketoacid-coenzyme A transferase 2, mitochondrial
OXCT2
O15460
Prolyl 4-hydroxylase subunit alpha-2
P4HA2
P49585
Choline-phosphate cytidylyltransferase A
PCYT1A
Q14432
CGMP-inhibited 3′,5′-cyclic phosphodiesterase A
PDE3A
O00330
Pyruvate dehydrogenase protein X component, mitochondrial
PDHX
P52945
Pancreas/duodenum homeobox protein 1
PDX1
P06401
Progesterone receptor
PGR
P48736
Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoform
PIK3CG
P11309
Proto-oncogene serine/threonine-protein kinase Pim-1
PIM1
P61925
cAMP-dependent protein kinase inhibitor alpha
PKIA
P14618
Pyruvate kinase isozymes M1/M2
PKM2
P04054
Phospholipase A2
PLA2G1B
P00749
Urokinase-type plasminogen activator
PLAU
P00747
Plasminogen
PLG
P00491
Purine nucleoside phosphorylase
PNP
P27169
Serum paraoxonase/arylesterase 1
PON1
Q07869
Peroxisome proliferator-activated receptor alpha
PPARA
Q03181
Peroxisome proliferator-activated receptor delta
PPARD
P37231
Peroxisome proliferator activated receptor gamma
PPARG
Q9UBK2
Peroxisome proliferator-activated receptor gamma coactivator 1-alpha
PPARGC1A
P17612
mRNA of PKA Catalytic Subunit C-alpha
PRKACA
P05771
Protein kinase C beta type
PRKCB
P35030
Trypsin-3
PRSS3
P60484
Phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN
PTEN
P43115
Prostaglandin E2 receptor EP3 subtype
PTGER3
P23219
Prostaglandin G/H synthase 1
PTGS1
P35354
Prostaglandin G/H synthase 2
PTGS2
P18031
mRNA of Protein-tyrosine phosphatase, non-receptor type 1
PTPN1
P10082
Peptide YY
PYY
P63000
Ras-related C3 botulinum toxin substrate 1
RAC1
P50120
Retinol-binding protein 2
RBP2
P08100
Rhodopsin
RHO
P19793
Retinoic acid receptor RXR-alpha
RXRA
O00767
Acyl-CoA desaturase
SCD
Q14524
Sodium channel protein type 5 subunit alpha
SCN5A
P31040
Succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrial
SDHA
P16109
P-selectin
SELP
P05121
Plasminogen activator inhibitor 1
SERPINE1
P14410
Sucrase-isomaltase, intestinal
SI
O76082
Solute carrier family 22 member 5
SLC22A5
Q9UBX3
Mitochondrial dicarboxylate carrier
SLC25A10
P11168
Solute carrier family 2, facilitated glucose transporter member 2
SLC2A2
P23975
Sodium-dependent noradrenaline transporter
SLC6A2
Q01959
Sodium-dependent dopamine transporter
SLC6A3
P31645
Sodium-dependent serotonin transporter
SLC6A4
P35610
Sterol O-acyltransferase 1
SOAT1
P00441
Superoxide dismutase[Cu-Zn]
SOD1
P08047
Transcription factor Sp1
SP1
P12931
Proto-oncogene tyrosine-protein kinase SRC
SRC
P36956
Sterol regulatory element-binding protein 1
SREBF1
Q12772
Sterol regulatory element-binding protein 2
SREBF2
Q9P2R7
Succinyl-CoA ligase [ADP-forming] beta-chain, mitochondrial
SUCLA2
Q99973
Telomerase protein component 1
TEP1
P01375
Tumor necrosis factor
TNF
Q16881
Thioredoxin reductase, cytoplasmic
TXNRD1
P55851
Mitochondrial uncoupling protein 2
UCP2
P55916
Mitochondrial uncoupling protein 3
UCP3


## 3.3. GO Analysis

397 biological process terms withP values of <0.01 were sorted using the functional annotation chart of the DAVID 6.8 Gene Functional Classification Tool, based on 185 filtered target genes, and P values were adjusted using the Benjamini-Hochberg method. 30 enriched GO BP terms extracted by P value and gene counts are displayed in Figure 2. It is meaningful that most of the target genes are significantly related to the various BP involved in parturition. For instance, 30 extracted GO BP terms include “MAPK signaling pathways,” “steroid hormone mediated signaling pathway,” “response to glucocorticoid,” “response to estradiol,” and “positive regulation of ERK1 and ERK2 cascade.” “MAPK signaling pathways” were reported to be activated in human uterine cervical ripening during parturition [39]. “Steroid hormone mediated signaling pathway” is highly related to parturition process as estrogen and progesterone play important roles in pregnancy and parturition, and estrogen induceS the principal stimulatory myometrial contractility [40]. Also, estradiol takes key place in parturition process [41]. It was identified that increased ERK activation is observed at the onset of labor, and it promotes myometrial contractility and development of parturition [42, 43]. To sum up, the target genes of BSS are highly associated with the biological process (BP) of parturition.Figure 2
GO analysis: 30 enriched biological process (BP) of gene ontology (GO) terms sorted byP value < 0.01 and gene counts are displayed. The y-axis represents enriched biological process (BP) terms for the target genes, and the x-axis shows gene counts and -log⁡10 (P value).


## 3.4. Network Construction and Analysis

Network analysis is an efficient tool for visualizing and understanding multiple targeted drug actions and demonstrates drug actions within the context of the whole genome [44, 45]. For a better insight of therapeutic impacts, H-C-T and T-P networks were constructed and displayed in Figures 3 and 4, respectively. In the H-C-T network, nodes represent herb names, compounds, and targets. Also in the T-P network, circular nodes represent targets and triangle nodes represent pathways. Besides node size is relative to the degree and edges show interactions between nodes.Figure 3
H-C-T network: herb-compound-target (H-C-T) network demonstrated multicompound, multitarget property of BSS. In this network, red and blue nodes represent herbs, green nodes show compounds, and pink nodes indicate targets and node size is relative to the degree and edges demonstrate interactions between nodes.Figure 4
T-P network: in target-pathway (T-P) network, circular nodes represent compounds and triangles indicate pathways. Node size is relative to the degree and edges demonstrate interactions between nodes.H-C-T network confirmed that there were 739 interactions between 185 targets and 65 active compounds of CG and DG: oleic acid (C48, degree = 42) with the highest number of interactions with targets, followed by succinic acid (C63, degree = 40) and stigmasterol (C62, degree = 37). It shows that single molecules target multiple receptors [46]. Also, some compounds from CG and DG were found to share common targets. Likewise, prostaglandin G/H synthase 2 (PTGS2, degree = 56) displayed the most affinitive connections with compounds, followed by gamma-aminobutyric acid receptor subunit alpha-1 (GABRA1, degree = 48), prostaglandin G/H synthase 1 (PTGS1, degree = 37), and muscarinic acetylcholine receptor M1 (CHRM1, degree = 37). Except for C60 (PLA2G1B, degree = 1), the rest of the 64 active compounds are connected with more than one target; likewise, 73 (39.5%) target genes out of 185 interacted with more than one compound. This result demonstrates the multicompounds and multitarget properties of herbal compounds and there was a report that compounds with multiple targets could have greater therapeutic efficacy [47].In addition, the top 40 pathways were extracted based on gene counts andP value (<0.05), and P value was adjusted by Benjamini-Hochberg method. T-P network using relevant targets of herbal compounds is demonstrated in Figure 4. There were 485 interactions between the top 40 pathways and 135 of 185 target genes. “Metabolic pathways” (degree = 49) and “neuroactive ligand-receptor interaction pathway” (degree = 32) had the highest and the second highest numbers of connections with the targets, followed by “calcium signaling” (degree = 21), “cAMP signaling pathway” (degree = 17), and “cGMP PKG signaling pathway” (degree = 15). These are compelling results that parturition processes are the complex hormone interactions and it is well known that calcium signals within the myometrium are pivotal for uterine contractions [48]. In the same manner, some target genes demonstrated higher degree centrality with top 40 pathways, namely, PI3-kinase subunit gamma (PIK3CG, degree = 23), cAMP-dependent protein kinase catalytic subunit alpha (PRKACA, degree = 20), protein kinase C beta type (PRKCB, degree = 18), and calmodulin (CALM1, degree = 11). We can confirm the same result in the previous researches. For instance, PI3-kinase subunit gamma plays the key role in regulating cAMP, calcium cycling, and beta-adrenergic signaling [49]. Moreover, during the labor, calmodulin-calcium complex activates myosin light-chain kinase, which causes the generation of ATPase activity; eventually, uterine contraction is promoted [50].H-C-T network explains the multitarget, multicompounds properties and accumulates effect of herbal medicines and T-P network shows that target genes of BSS are highly related to the pathway associated with parturition process.


## 3.5. Target Organ Location Map

It is important to confirm the tissue mRNA expression profiles of the target genes at the organ level to identify the effects of BSS on parturition. Since there was no mRNA expression information in BioGPS of muscarinic acetylcholine receptor M1 (CHRM1), putative beta-glucuronidase-like protein SMA3 (GUSBP1), and retinol-binding protein 2 (RBP2), excluding these 3 targets from 185 filtered targets, totally 182 genes mRNA expression profiles were analyzed in this study. There were 519 interactions between target genes and organ locations. The networks of target genes tissue mRNA expression profiles and compounds of BSS are shown in Figure5.Figure 5
Target organ location map: it shows that tissue-specific patterns of mRNA expression are highly active in relative organs of parturition process such as uterus, fetus, placenta, hypothalamus, pituitary, and smooth muscle. Yellow nodes show compounds and pink nodes indicate targets and node size is relative to the degree and edges demonstrate interactions between nodes.As a result, 159 of 182 target genes displayed beyond average mRNA expression in relevant organ tissues, such as uterus and/or uterus corpus, fetus and/or placenta, hypothalamus and/or pituitary, smooth muscle, and whole blood. The rest of 23 genes of 182 targets did not display above average mRNA expression in above organ tissues, for example, gamma-aminobutyric acid receptor subunit alpha-6 (GABRA6) and coagulation factor X (F10).Nevertheless, most genes of 159 demonstrated high expression patterns in several organs of parturition related tissues at the same time. In detail, 60 genes showed most significant mRNA expression in the uterus and/or uterus corpus group, 130 for placenta and/or fetus, 86 for hypothalamus and/or pituitary, 82 for smooth muscle, 80 for pituitary, and 81 for whole blood. Besides, 30 of 159 genes showed expression in all of 6 groups. For instance, muscarinic acetylcholine receptor M2 (CHRM2), neuronal acetylcholine receptor subunitα-2 (CHRNA2), gamma-aminobutyric acid receptor subunit alpha-3 (GABRA3), NO synthase, inducible (NOS2), cGMP-inhibited 3′,5′-cyclic phosphodiesterase A (PDE3A), and sodium-dependent dopamine transporter (SLC6A3) recorded beyond average mRNA expression in all six groups. Furthermore, 79% of targets were expressed in two or more organ tissues, which suggests that those organs and target genes of BSS are closely correlated.


## 4. Discussion

In this study, network pharmacology method with DL, OB, Caco-2, and LR evaluation, multiple drug-target prediction, network analysis, and relevant organ location mapping was used to explain the targets of BSS in relation to the parturition process. There is no denying that network based analysis is powerful approach for identifying the actions of multitargeting herbal medicines at the systems level and our study shows target genes of BSS are strongly connected to parturition related pathways, biological processes, and organs. It was confirmed that 98% of the active compounds of BSS were interacted with more than two targets and 39.5% of the targets related to more than one compound. The synergetic multitarget properties of BSS were visualized, but further discussion about differentiated drug action based on degree centrality and simultaneous targeting effect of more than one compound is required [51]. Also, detailed potential pathways of BSS should be explored deeply in the future.Similar findings were identified in a few RCT researches in China that using BSS in induction of labor can reduce the delivery time, the amount of bleeding, and the residual rate of placenta [52, 53]. In addition, BSS targets six genes of GABA receptor and NOS, which was reported to be related oxytocin neurons at the time of parturition in rats [54]. Also, BSS targets NOS and NO (nitric oxide) which are involved in the regulation of uterine contractility during pregnancy and is a key factor for the onset of labor [55], and iNOS (inducible nitric oxide synthase) can be upregulated accordantly by similar inflammatory mediators during ripening [11].In fact, rather than DG,Angelicae Gigantis Radix (Danggwi, AGR) grows naturally in Korea; for that reason, the combination of AGR and CG is commonly used as BSS in Korea. Instead, DG is named as Chinese Danggwi for accurate classification in Korea. Several studies have shown AGR is differs from DG in terms of its main active constituents and genetic form. AGR is mainly composed of water soluble polysaccharide but coumarin, which is liposoluble including nodakenin (1), peucedanone (2), marmesin (3), decursinol (4), 7-hydroxy-6-(2R-hydroxy-3-methylbut-3-enyl) coumarin (5), demethylsuberosin (6), decursin (7), decursinol angelate (8), and isoimperatorin (9) [56]. Of these, decursin and its isomer decursinol angelate have been reported to be the active compounds in AGR [57]. It was identified in the experimental studies that AGR and DG act via different mechanisms in the cardiovascular, central nervous system, and anticancer activity but both have similar pharmacological effects [57]. Since the compositions of DG and AGR differ, further study on BSS with AGR is required. Currently, BSS is commonly prescribed to treat cerebra vascular and cardiovascular diseases in China [33], but, in Korea, BSS is widely applied in obstetrics.The similarity between cervical ripening during parturition and inflammatory reaction has been pointed out in earlier studies; this has been attributed to the induction of leukocyte migration into tissue, thus promoting cervical remodeling and parturition by estrogen [58]. Further study is needed in terms of the effect of BSS on inflammatory reactions and parturition.Furthermore, the CG-DG herb pair has other names, such as, Gunggui-tang (weight ratios of 2 : 3 or 1 : 1), Ogeum-san (1 : 1), Iphyo-san (1 : 1), and Sinmyo Bulsu-san (1 : 2), those are prepared at different weight ratios [3]. Accordingly, weight ratio should be determined based on considerations of targeted symptoms for relevant clinical applications.


## 5. Conclusion

This study results show thatBulsu-san (BSS) is highly connected to the parturition related pathways, biological processes, and organs. Most compounds in BSS work together with multiple target genes in a synergetic way, and this was confirmed using herb-compound-target network and target-pathway network analysis. The mRNA expression of relevant target genes of BSS was elevated significantly in parturition related organ tissues, such as those of the uterus, placenta, fetus, hypothalamus, and pituitary gland.This study employed the network analytical methods to show the multicompound, multitarget properties of BSS. The results not only support clinical applications of BSS on easing childbirth but also suggest the related target genes and pathways of BSS on promoting parturition according to a systems-level in silico analytic approach. However, detailed mechanisms and other functions of BSS should be discussed further.



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*Source: 7236436-2017-10-29.xml*