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Evidence-Based Complementary and Alternative Medicine
Volume 2017, Article ID 7236436, 15 pages
https://doi.org/10.1155/2017/7236436
Research Article

Systems Pharmacological Approach to the Effect of Bulsu-san Promoting Parturition

Department of Pharmacology, School of Korean Medicine, Pusan National University, Yangsan, Gyeongnam 50612, Republic of Korea

Correspondence should be addressed to Won G. An; rk.ca.nasup@nagw

Received 28 July 2017; Accepted 25 September 2017; Published 29 October 2017

Academic Editor: Gihyun Lee

Copyright © 2017 Su Yeon Suh and Won G. An. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

1. Introduction

The name of Bulsu-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 of Angelicae Sinensis Radix (Danggui, DG) and Cnidium 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 Figure 1.

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:where represents the molecular parameters of herbal compounds and 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 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 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 at https://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).

Although 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 Table 2; 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.
3.3. GO Analysis

397 biological process terms with 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 values were adjusted using the Benjamini-Hochberg method. 30 enriched GO BP terms extracted by 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 by value < 0.01 and gene counts are displayed. The -axis represents enriched biological process (BP) terms for the target genes, and the -axis shows gene counts and ( 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 and value (<0.05), and 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 Figure 5.

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 subunit α-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 that Bulsu-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.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01059994).

References

  1. S. Kim and Y. Lee, “The prescriptions of enriching blood and nourishing vital essence (Fill yin blood prescription),” Journal of Korean Medical Classics, vol. 20, pp. 67–77, 2007. View at Google Scholar
  2. Y. Jin, C. Qu, Y. Tang et al., “Herb pairs containing Angelicae Sinensis Radix (Danggui): a review of bio-active constituents and compatibility effects,” Journal of Ethnopharmacology, vol. 181, pp. 158–171, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Lyu and C. Jeong, “Constitution of prescription and medicinal effect adaptation diseases of 'bullsoosan (berghean)'in korean medical books,” Journal of Korean Medical classics, vol. 29, pp. 17–41, 2016. View at Google Scholar
  4. W. Li, Y. Tang, J. Guo et al., “Enriching blood effect comparison in three kinds of blood deficiency model after oral administration of drug pair of angelicae sinensis radix and chuanxiong rhizoma and each single herb,” China Journal of Chinese Materia Medica, vol. 36, no. 13, pp. 1808–1814, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. W. Li, M. Huang, Y. Tang, J. Guo, E. Shang, and X. Liu, “Establishment and optimization of acute blood stasis rat model,” Chinese Pharmacological Bulletin, vol. 12, p. 032, 2011. View at Google Scholar
  6. Y.-Z. Hou, G.-R. Zhao, Y.-J. Yuan, G.-G. Zhu, and R. Hiltunen, “Inhibition of rat vascular smooth muscle cell proliferation by extract of Ligusticum chuanxiong and Angelica sinensis,” Journal of Ethnopharmacology, vol. 100, no. 1-2, pp. 140–144, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. C. W. C. Bi, L. Xu, X. Y. Tian et al., “Fo Shou San, an ancient chinese herbal decoction, protects endothelial function through increasing endothelial nitric oxide synthase activity,” PLoS ONE, vol. 7, no. 12, Article ID e51670, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. W. Li, Y. Hua, Y. Tang, H. Wang, L. Qian, and M. Gao, “Effects of radix angelicae sinensis and rhizoma chuanxiong on mouse uterine contractions in vitro,” Journal of Nanjing University of Traditional Chinese Medicine, vol. 2, p. 014, 2010. View at Google Scholar
  9. M. McLean and R. Smith, “Corticotrophin-releasing hormone and human parturition,” Reproduction, vol. 121, no. 4, pp. 493–501, 2001. View at Publisher · View at Google Scholar · View at Scopus
  10. S. K. Kota, K. Gayatri, S. Jammula et al., “Endocrinology of parturition,” Indian Journal of Endocrinology and Metabolism, vol. 17, no. 1, pp. 50–59, 2013. View at Publisher · View at Google Scholar
  11. M. Ledingham, A. J. Thomson, I. A. Greer, and J. E. Norman, “Nitric oxide in parturition,” BJOG: An International Journal of Obstetrics & Gynaecology, vol. 107, pp. 581–593, 2000. View at Google Scholar
  12. M. Shen, S. Tian, Y. Li et al., “Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines,” Journal of Cheminformatics, vol. 4, no. 1, article 31, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. X. Xu, W. Zhang, C. Huang et al., “A novel chemometric method for the prediction of human oral bioavailability,” International Journal of Molecular Sciences, vol. 13, no. 6, pp. 6964–6982, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. I. Hubatsch, E. G. E. Ragnarsson, and P. Artursson, “Determination of drug permeability and prediction of drug absorption in Caco-2 monolayers,” Nature Protocols, vol. 2, no. 9, pp. 2111–2119, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Tao, X. Xu, X. Wang et al., “Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease,” Journal of Ethnopharmacology, vol. 145, no. 1, pp. 1–10, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Zhang, Y. Li, S.-S. Chen et al., “Systems pharmacology dissection of the anti-inflammatory mechanism for the medicinal herb Folium eriobotryae,” International Journal of Molecular Sciences, vol. 16, no. 2, pp. 2913–2941, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. P. Willett, J. M. Barnard, and G. M. Downs, “Chemical similarity searching,” Journal of Chemical Information and Computer Sciences, vol. 38, no. 6, pp. 983–996, 1998. View at Publisher · View at Google Scholar · View at Scopus
  18. H. Liu, J. Wang, W. Zhou, Y. Wang, and L. Yang, “Systems approaches and polypharmacology for drug discovery from herbal medicines: an example using licorice,” Journal of Ethnopharmacology, vol. 146, no. 3, pp. 773–793, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. K. S. Pang, “Modeling of intestinal drug absorption: roles of transporters and metabolic enzymes (for the gillette review series),” Drug Metabolism & Disposition, vol. 31, no. 12, pp. 1507–1519, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Pei, C. Zheng, C. Huang et al., “Systematic understanding the mechanisms of vitiligo pathogenesis and its treatment by Qubaibabuqi formula,” Journal of Ethnopharmacology, vol. 190, pp. 272–287, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. C. A. Lipinski, F. Lambardo, B. W. Dominy, and P. J. Feeney, “Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings,” Advanced Drug Delivery Reviews, vol. 64, pp. 4–17, 2012. View at Publisher · View at Google Scholar
  22. A. K. Ghose, V. N. Viswanadhan, and J. J. Wendoloski, “Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: an analysis of ALOGP and CLOGP methods,” The Journal of Physical Chemistry A, vol. 102, no. 21, pp. 3762–3772, 1998. View at Publisher · View at Google Scholar · View at Scopus
  23. P. Rajasethupathy, S. J. Vayttaden, and U. S. Bhalla, “Systems modeling: a pathway to drug discovery,” Current Opinion in Chemical Biology, vol. 9, no. 4, pp. 400–406, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Yu, J. Chen, X. Xu et al., “A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data,” PLoS ONE, vol. 7, no. 5, Article ID e37608, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. C. H. Wu, R. Apweiler, A. Bairoch, D. A. Natale, W. C. Barker, B. Boeckmann et al., “The universal protein resource (UniProt): an expanding universe of protein information,” Nucleic Acids Research, vol. 34, pp. D187–D191, 2006. View at Google Scholar · View at MathSciNet
  26. G. Bindea, B. Mlecnik, H. Hackl et al., “ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks,” Bioinformatics, vol. 25, no. 8, pp. 1091–1093, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. M. E. Smoot, K. Ono, J. Ruscheinski, P. L. Wang, and T. Ideker, “Cytoscape 2.8: new features for data integration and network visualization,” Bioinformatics, vol. 27, no. 3, pp. 431-432, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. J.-B. Pan, S.-C. Hu, D. Shi et al., “PaGenBase: a pattern gene database for the global and dynamic understanding of gene function,” PLoS ONE, vol. 8, no. 12, Article ID e80747, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. W. Zhang, Q. Tao, Z. Guo et al., “Systems pharmacology dissection of the integrated treatment for cardiovascular and gastrointestinal disorders by traditional chinese medicine,” Scientific Reports, vol. 6, p. 32400, 2016. View at Publisher · View at Google Scholar
  30. Z. Prevoršek, G. Gorjanc, B. Paigen, and S. Horvat, “Congenic and bioinformatics analyses resolved a major-effect Fob3b QTL on mouse Chr 15 into two closely linked loci,” Mammalian Genome, vol. 21, no. 3-4, pp. 172–185, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. J. Du, B. Bai, X. Kuang et al., “Ligustilide inhibits spontaneous and agonists- or K+ depolarization-induced contraction of rat uterus,” Journal of Ethnopharmacology, vol. 108, no. 1, pp. 54–58, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. K. J. Chen and K. Chen, “Ischemic stroke treated with ligusticum chuanxiong,” Chinese Medical Journal, vol. 105, pp. 870–873, 1992. View at Google Scholar
  33. W. Li, J. Guo, Y. Tang et al., “Pharmacokinetic comparison of ferulic acid in normal and blood deficiency rats after oral administration of angelica sinensis, ligusticum chuanxiong and their combination,” International Journal of Molecular Sciences, vol. 13, no. 3, pp. 3583–3597, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. W. Karl, W. Cathy, X. Li, and G. Amy, “Pharmacokinetic analyses of ferulic acid in rat plasma by liquid chromatography?tandem mass spectrometry: a synergistic action of an ancient herbal decoction fo shou san,” Pharmaceutica Analytica Acta, vol. 6, no. 5, p. 2, 2015. View at Publisher · View at Google Scholar
  35. D. Huang, “8 purgative herbs,” Chinese Materia Medica: Chemistry, Pharmacology and Applications, p. 231, 1998. View at Google Scholar
  36. X. Liu, Q. Zhao, C. Li, R. Zhang, X. Wang, and W. Xu, “Synthesis of chuanxiong perlolyrine and its protective effect on injured vascular endothelial cells,” Journal of Shandong University (Health Sciences), vol. 41, pp. 485–487, 2003. View at Google Scholar
  37. J. Ishida, M. Kozuka, H.-K. Wang et al., “Antitumor-promoting effects of cyclic diarylheptanoids on Epstein-Barr virus activation and two-stage mouse skin carcinogenesis,” Cancer Letters, vol. 159, no. 2, pp. 135–140, 2000. View at Publisher · View at Google Scholar · View at Scopus
  38. J. Liu, T. Pei, and J. Mu, “Systems pharmacology uncovers the multiple mechanisms of Xijiao Dihuang decoction for the treatment of viral hemorrhagic fever,” Evidence-Based Complementary and Alternative Medicine, vol. 2016, Article ID 9025036, 17 pages, 2016. View at Publisher · View at Google Scholar
  39. H. Wang and Y. V. Stjernholm, “Plasma membrane receptor mediated MAPK signaling pathways are activated in human uterine cervix at parturition,” Reproductive Biology and Endocrinology, vol. 5, article 3, p. 1, 2007. View at Publisher · View at Google Scholar · View at Scopus
  40. S. Mesiano and T. N. Welsh, “Steroid hormone control of myometrial contractility and parturition,” Seminars in Cell & Developmental Biology, vol. 18, no. 3, pp. 321–331, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. P. W. Nathanielsz, S. L. Jenkins, J. D. Tame, J. A. Winter, S. Guller, and D. A. Giussani, “Local paracrine effects of estradiol are central to parturition in the rhesus monkey,” Nature Medicine, vol. 4, no. 4, pp. 456–459, 1998. View at Publisher · View at Google Scholar · View at Scopus
  42. Y. Li, H. Je, S. Malek, and K. G. Morgan, “ERK1/2-mediated phosphorylation of myometrial caldesmon during pregnancy and labor,” American Journal of Physiology—Regulatory, Integrative and Comparative Physiology, vol. 284, no. 1, pp. R192–R199, 2003. View at Publisher · View at Google Scholar
  43. Y. Li, H.-D. Je, S. Malek, and K. G. Morgan, “Role of ERK1/2 in uterine contractility and preterm labor in rats,” American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, vol. 287, no. 2, pp. R328–R335, 2004. View at Publisher · View at Google Scholar · View at Scopus
  44. S. I. Berger and R. Iyengar, “Network analyses in systems pharmacology,” Bioinformatics, vol. 25, no. 19, pp. 2466–2472, 2009. View at Publisher · View at Google Scholar · View at Scopus
  45. P. Li, L.-W. Qi, E.-H. Liu, J.-L. Zhou, and X.-D. Wen, “Analysis of Chinese herbal medicines with holistic approaches and integrated evaluation models,” TrAC Trends in Analytical Chemistry, vol. 27, no. 1, pp. 66–77, 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. L. M. Espinoza-Fonseca, “The benefits of the multi-target approach in drug design and discovery,” Bioorganic & Medicinal Chemistry, vol. 14, no. 4, pp. 896-897, 2006. View at Publisher · View at Google Scholar · View at Scopus
  47. W. Zhang, Y. Bai, Y. Wang, and W. Xiao, “Polypharmacology in drug discovery: a review from systems pharmacology perspective,” Current Pharmaceutical Design, vol. 22, no. 21, pp. 3171–3181, 2016. View at Publisher · View at Google Scholar · View at Scopus
  48. S. Wray, K. Jones, and S. Kupittayanant, “Calcium signaling and uterine contractility,” Journal of the Society for Gynecologic Investigation, vol. 10, no. 5, pp. 252–264, 2003. View at Publisher · View at Google Scholar · View at Scopus
  49. G. Y. Oudit and Z. Kassiri, “Role of PI3 kinase gamma in excitation-contraction coupling and heart disease,” Cardiovascular and Hematological Disorders, vol. 7, no. 4, pp. 295–304, 2007. View at Publisher · View at Google Scholar · View at Scopus
  50. R. Smith, “Parturition,” The New England Journal of Medicine, vol. 356, no. 3, pp. 271–283, 2007. View at Publisher · View at Google Scholar
  51. S. Y. Suh and W. G. An, “Systems pharmacological approach of pulsatillae radix on treating crohn’s disease,” Evidence-Based Complementary and Alternative Medicine, vol. 2017, Article ID 4198035, 21 pages, 2017. View at Publisher · View at Google Scholar
  52. C. l. Chen and J. Y. Chen, “Analysis on the effect of foshousan combined with ethacridine lactate in mid-term pregnancy,” Journal of Practical Traditional Chinese Medicine, vol. 30, pp. 511-512, 2014. View at Publisher · View at Google Scholar
  53. Y. Sun, “Modified Xiong Gui Tang with auricular acupoint press for 70 cases of late pregnancy,” Guide of China Medicine, vol. 7, no. 20, pp. 92-93, 2009. View at Google Scholar
  54. J.-J. Koksma, J.-M. Fritschy, V. MacK, R. E. Van Kesteren, and A. B. Brussaard, “Differential GABA A receptor clustering determines GABA synapse plasticity in rat oxytocin neurons around parturition and the onset of lactation,” Molecular and Cellular Neuroscience, vol. 28, no. 1, pp. 128–140, 2005. View at Publisher · View at Google Scholar · View at Scopus
  55. H. Maul, M. Longo, G. R. Saade, and R. E. Garfield, “Nitric oxide and its role during pregnancy: From ovulation to delivery,” Current Pharmaceutical Design, vol. 9, no. 5, pp. 359–380, 2003. View at Publisher · View at Google Scholar · View at Scopus
  56. M.-J. Ahn, M. K. Lee, Y. C. Kim, and S. H. Sung, “The simultaneous determination of coumarins in Angelica gigas root by high performance liquid chromatography-diode array detector coupled with electrospray ionization/mass spectrometry,” Journal of Pharmaceutical and Biomedical Analysis, vol. 46, no. 2, pp. 258–266, 2008. View at Publisher · View at Google Scholar · View at Scopus
  57. S. Kim, H. Oh, J. Kim, J. Hong, and S. Cho, “A review of pharmacological effects of angelica gigas, angelica sinensis, angelica acutiloba and their bioactive compounds,” The Journal of Korean Oriental Medicine, vol. 32, pp. 1–24, 2011. View at Publisher · View at Google Scholar · View at Scopus
  58. J. E. Norman, S. Bollapragada, M. Yuan, and S. M. Nelson, “Inflammatory pathways in the mechanism of parturition,” BMC Pregnancy and Childbirth, vol. 7, supplement 1, article S7, 2007. View at Publisher · View at Google Scholar · View at Scopus