Evidence-Based Complementary and Alternative Medicine
Volume 2019 (2019), Article ID 1056708, 17 pages
https://doi.org/10.1155/2019/1056708

Research Article

Systems Pharmacology-Based Approach to Comparatively Study the Independent and Synergistic Mechanisms of Danhong Injection and Naoxintong Capsule in Ischemic Stroke Treatment

Junfeng Zhu,¹ Xiaojiao Yi,² Yiwen Zhang,¹ Zongfu Pan,¹ Like Zhong,¹ and Ping Huang¹

¹Laboratory of Clinical Pharmacy, Zhejiang Cancer Hospital, Hangzhou 310022, China
²College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China

Correspondence should be addressed to Ping Huang; huangping1841@zjcc.org.cn

Received 30 August 2018; Revised 4 November 2018; Accepted 6 January 2019; Published 4 February 2019

Academic Editor: Srinivas Nammi

Copyright © 2019 Junfeng Zhu et al. 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.

To provide evidence for the better clinical use of traditional Chinese medicine preparations (TCMPs), comparison of the pharmacological mechanisms between TCMPs with similar therapeutic effect is necessary. However, methodology for dealing with this issue is still scarce. Danhong injection (DHI) and Naoxintong capsule (NXT) are representative TCMPs for ischemic stroke (IS) treatment, which are also frequently used in combination. Here they were employed as research objects to demonstrate the feasibility of systems pharmacology approach in elucidation of the independent and combined effect of TCMPs. By incorporating chemical screening, target prediction, and network construction, a feasible systems pharmacology model has been established to systematically uncover the underlying action mechanisms of DHI, NXT, or their pair in IS treatment. Systematic analysis of the created TCMP-Compound-Target-Disease network revealed that DHI and NXT shared common targets such as PTGS2, F2, ADRB1, IL6, ALDH2, and CCL2, which were involved in the vasomotor system regulation, blood-brain barrier disruption, redox imbalance, neurotrophin activity, and brain inflammation. In comparative mechanism study, the merged DHI/NXT-IS PPI network and pathway enrichment analysis indicated that DHI and NXT exerted the therapeutic effects mainly through immune system and VEGF signaling pathways. Meanwhile, they had their own unique pathways, e.g., calcium signaling pathway for DHI and gap junction for NXT. While for their synergistic mechanism, DHI and NXT participated in chemokine signaling pathway, T cell receptor signaling pathway, VEGF signaling pathway, gap junction, and so on. Our study provided an optimized strategy for dissecting the different and combined effect of TCMPs with similar actions.

1. Introduction

Ischemic stroke (IS) is a common disease caused by intracranial or neck blood vessel occlusion that impairs blood flow to a portion of the brain and leads to subsequent cerebral necrosis [1]. Due to its high morbidity and mortality, as well as high-risk of permanent disability in surviving individuals, IS represents a substantial economic and social burden throughout the world [2, 3]. Currently, pharmacological or mechanical thrombolytic therapy for restoration of blood flow to ischemic tissue is the main treatment to improve patient outcome [4, 5]. However, their unique benefits are shadowed by limited therapeutic time window and serious complications [6]. Moreover, cellular consequences of ischemic brain injury are very complex and chemical drugs are hardly to achieve favorable outcomes because of their single therapeutic target [7–9]. Thus, novel therapeutic drugs and strategies are urgently needed to improve the efficiency in treating IS.

Traditional Chinese medicine (TCM), characterized as multiple compounds and targets, differs in substance, methodology, and philosophy to chemical drugs [10]. It has been practiced in China for thousands of years and is now becoming more frequently used in the West. In recent years, TCM preparations (TCMPs) have been proposed as important complementary and alternative medicines for IS treatment [11, 12]. Danhong injection (DHI) and Naoxintong capsule (NXT) are both commonly used TCMPs for the prevention and treatment of cardiovascular and cerebrovascular diseases [13–16]. DHI is extracted from Radix Salviae Miltiorrhizae and Flos Carthami, while NXT consists of 16 kinds of herbs including Radix Astragali, Radix Paeoniae Rubra, Radix Salviae Miltiorrhizae, and so on. Their efficiency in protecting against IS has been widely validated, whether by used alone or in combination [17–21]. According to a recent report, DHI-NXT combination therapy could achieve better therapeutic effect than monotherapy in cerebral ischemia-reperfusion model of rats [22]. However, the comparative and synergistic effect of the action mechanisms between DHI and NXT still remain vague and warrant further investigation.

As the multicomponent and multitarget nature of TCMPs, it is very difficult to systematically study their mechanisms using routine methods [23]. Therefore, systems pharmacology method is now widely perceived as an integral and efficient tool to study the complex molecular mechanisms of herbal medicines [24–30]. This approach usually includes three steps, including active compounds screening, drug targets prediction, and network/pathway analysis [31, 32]. It could provide global active compounds-therapeutic targets interactions based on the network pharmacology framework, which matches well with the integrity and systemic conception of TCM. For example, a network of 31 active compounds interacting with 42 disease targets was constructed by Yue et al. to elucidate the polypharmacological mechanisms underlying the efficiency of herb pair Danggui-Honghua for blood stasis syndrome treatment [33]. Still, methodology for comparative study of the mechanisms between TCMPs is scarce.

To provide evidence for the better clinical use of DHI and NXT, here we proposed a feasible systems pharmacology-based method to explore the similarities and differences of the pharmacological mechanisms between DHI and NXT. Their synergistic mechanism in treatment of IS was also investigated. To our knowledge, no work has been reported about the mechanism comparison and synergistic effect of DHI and NXT.

2. Results and Discussion

In this study, a network pharmacology approach based on chemical, pharmacokinetic, and pharmacological data was employed to systematically investigate the comparative and synergistic effects of DHI and NXT against IS. The overall procedure involved five steps (Figure 1): (1) the chemical ingredients of DHI and NXT were collected and evaluated by in silico ADME (absorption, distribution, metabolism, and excretion) system to screen candidate compounds; (2) the putative targets of the candidate compounds and IS were data-mined from various databases and literatures; (3) a basal TCMP-Compound-Target-Disease network was constructed to illustrate the interaction among DHI/NXT, ingredients, targets and IS; (4) for comparative study of the action mechanisms, two protein-protein interaction (PPI) networks named DHI-IS and NXT-IS PPI network were merged and pathway enrichment analysis was separately performed to explore the common and unique mechanisms of DHI and NXT; (5) for the study of their synergistic effect, DHI-NXT-IS PPI network was established among DHI-ingredient targets, NXT-ingredient targets, and IS targets. The enriched pathways were analyzed to explain the synergistic mechanisms of the drug combination.

Figure 1: Workflow for dissecting the comparative and synergistic effects of DHI and NXT against IS.

2.1. Candidate Ingredients in DHI and NXT

In most systems pharmacology studies, the components in TCMPs were mechanically collected from chemical databases by simple summation of the compounds in each herb. As a matter of fact, the chemical substances and composition ratios of TCMPs differ dramatically due to different preparation processes. For example, tanshinones, as the main components from ethanol extract of Radix Salviae Miltiorrhizae, could not be experimentally detected in DHI because of its water extraction process [34, 35]. Hence, the chemical ingredients in DHI and NXT were collected from the high-resolution LC-MS profile data reported by related literatures to guarantee the accuracy and objectivity of this work [34–38]. Since the glycosides in NXT might be deglycosylated by the glycosidase in the intestinal tract, 14 aglycones were also considered as chemical ingredients labeled by _qt. As a result, a total of 293 ingredients were retrieved for DHI (65) and NXT (228). The detailed information of these ingredients was displayed in Table S1.

Although TCMPs usually contain tens or hundreds of chemical ingredients, only a few of them with favorable pharmacodynamic and pharmacokinetic properties are responsible for their therapeutic effects. Drug-likeness (DL) is an important drug research parameter that used to assess whether a compound functions as a drug. Drug-like compounds are those which ‘contain functional groups and/or have physical properties consistent with the majority of known drugs’ [39]. Therefore, an optimal DL model based on molecules in the DrugBank database (https://www.drugbank.ca/) was employed to prescreen potential active ingredients in DHI and NXT. In this study, DL ≥ 0.18 was used to select candidate compounds for further research. Since NXT is consumed orally, oral bioavailability (OB ≥ 30%) and Caco-2 cell permeability (Caco-2 > –0.4) were also used to screen the candidate ingredients from NXT [31–33]. Further, a few compounds that do not meet these criteria but possessed high amounts and extensive pharmacological activities were also adopted. Consequently, 35 and 82 candidate ingredients were selected from DHI and NXT, respectively. Among them, 10 ingredients were common for both DHI and NXT, such as chlorogenic acid, hydroxysafflor yellow A, ferulic acid, rosmarinic acid, and salvianolic acid B (Table 1).

Table 1: The detailed information of candidate ingredients in DHI and NXT.

ID
Source
Compound
CAS
OB ()
Caco-2
DL

D1
DHI
p-Hydroxybenzoic acid-O-glucoside
N/A
42.9
-1.22
0.20

D2
DHI
5-Hydroxymethylfurfural
67-47-0
45.07
0.05
0.02

D3
DHI
Danshensu
76822-21-4
36.91
-0.27
0.06

D4
DHI
Protocatechuic acid
99-50-3
25.37
0.10
0.04

D5
DHI
Neochlorogenic acid
906-33-2
18.05
-1.37
0.33

D6
DHI
Dihydrocaffeic acid
71693-95-3
32.79
0.25
0.05

D7
DHI
Protocatechuic aldehyde
139-85-5
38.35
0.43
0.03

D8
DHI
Syringin
118-34-3
14.64
-1.01
0.32

D9
DHI
Chlorogenic acid
202650-88-2
11.93
-1.03
0.33

D10
DHI
Hydroxysafflor yellow A
78281-02-4
3.53
-3.03
0.68

D11
DHI
Cryptochlorogenic acid
905-99-7
24.5
-1.43
0.33

D12
DHI
Ferulic acid
537-98-4
39.56
0.47
0.06

D13
DHI
Caffeic acid
501-16-6
25.76
0.21
0.05

D14
DHI
Roseoside
54835-70-0
10.81
-1.25
0.36

D15
DHI
Sweroside
14215-86-2
4.96
-1.08
0.38

D16
DHI
Morroniside
25406-64-8
13.86
-2.01
0.50

D17
DHI
p-Coumaric acid
7400-08-0
43.29
0.46
0.04

D18
DHI
Eriocitrin
13463-28-0
4.52
-2.23
0.7

D19
DHI
Rutin
153-18-4
3.2
-1.93
0.68

D20
DHI
Salvianolic acid I
153765-45-8
N/A
N/A
N/A

D21
DHI
Salvianolic acid J
N/A
43.38
-0.82
0.72

D22
DHI
Salviaflaside
178895-25-5
N/A
N/A
N/A

D23
DHI
Kaempferol-3-O-rutinoside
17650-84-9
3.64
-1.77
0.73

D24
DHI
Safflor yellow A
85532-77-0
22.75
-2.52
0.75

D25
DHI
Salvianolic acid D
142998-47-8
1.57
-0.78
0.50

D26
DHI
Salvianolic acid G
N/A
45.56
-0.14
0.61

D27
DHI
Salvianolic acid F
N/A
N/A
N/A
N/A

D28
DHI
Salvianolic acid E
N/A
3.01
-1.52
0.39

D29
DHI
Rosmarinic acid
20283-92-5
1.38
-0.54
0.35

D30
DHI
Lithospermic acid
28831-65-4
2.67
-1.07
0.76

D31
DHI
9′′-Methyl lithospermate B
N/A
3.01
-1.01
0.39

D32
DHI
Salvianolic acid B
115939-25-8
3.01
-1.67
0.41

D33
DHI
Salvianolic acid L
N/A
N/A
N/A
N/A

D34
DHI
Salvianolic acid A
96574-01-5
2.96
-0.56
0.7

D35
DHI
Salvianolic acid C
115841-09-3
2.5
-0.23
0.81

N1
NXT
Gallic acid
149-91-7
31.69
-0.09
0.04

N2
NXT
Mulberroside A
166734-06-1
13.34
-2.43
0.73

N3
NXT
Catechin
154-23-4
54.83
-0.03
0.24

N4
NXT
Chlorogenic acid
202650-88-2
11.93
-1.03
0.33

N5
NXT
Hydroxysafflor yellow A
78281-02-4
3.53
-3.03
0.68

N6
NXT
Vanillic acid
121-34-6
35.47
0.43
0.04

N7
NXT
Epicatechin
35323-91-2
48.96
0.02
0.24

N8
NXT
Albiflorin
39011-90-0
12.09
-1.54
0.77

N9
NXT
Rutin
153-18-4
3.2
-1.93
0.68

N10
NXT
Paeoniflorin
23180-57-6
53.87
-1.47
0.79

N11
NXT
Paeoniflorin_qt
23180-57-6
68.18
-0.34
0.40

N12
NXT
Ferulic acid
537-98-4
39.56
0.47
0.06

N13
NXT
Calycosin-7-O-glucoside
20633-67-4
5.49
-1.13
0.81

N14
NXT
Kaempferol-3-O-rutinoside
17650-84-9
3.64
-1.77
0.73

N15
NXT
Biochanin A
491-80-5
25.21
0.65
0.24

N16
NXT
Salvianolic acid B
115939-25-8
3.01
-1.67
0.41

N17
NXT
Ononin
486-62-4
11.52
-0.74
0.78

N18
NXT
Calycosin
20575-57-9
47.75
0.52
0.24

N19
NXT
Formononetin
485-72-3
69.67
0.78
0.21

N20
NXT
Senkyunolide A
63038-10-8
26.56
1.30
0.07

N21
NXT
Tanshinone II-B
17397-93-2
65.26
0.44
0.45

N22
NXT
Z-Butylidenephthalide
551-08-6
42.44
1.32
0.07

N23
NXT
(E)-ligustilide
4431-01-0
51.3
1.30
0.07

N24
NXT
(Z)-ligustilide
4431-01-0
23.5
1.28
0.07

N25
NXT
Cryptotanshinone
35825-57-1
52.34
0.95
0.40

N26
NXT
Tanshinone IIA
568-72-9
49.89
1.05
0.40

N27
NXT
Caffeic acid
501-16-6
25.76
0.21
0.05

N28
NXT
Amygdalin
29883-15-6
4.42
-1.91
0.61

N29
NXT
Rosmarinic acid
20283-92-5
1.38
-0.54
0.35

N30
NXT
Lithospermic acid
28831-65-4
2.67
-1.07
0.76

N31
NXT
Wogonin
632-85-9
30.68
0.79
0.23

N32
NXT
Senkyunolide H
94596-27-7
34.34
-0.02
0.10

N33
NXT
Neocnidilide
4567-33-3
83.83
1.23
0.07

N34
NXT
Mulberrin
19275-47-9
1.22
0.46
0.59

N35
NXT
Sugiol
511-05-7
36.11
1.14
0.28

N36
NXT
Lindestrene
2221-88-7
36.12
1.75
0.13

N37
NXT
(z)-9-Octadecenamide
301-02-0
31.2
1.26
0.14

N38
NXT
Senkyunolide B
93236-67-0
62.68
1.00
0.08

N39
NXT
Senkyunolide C
91652-78-7
46.8
0.87
0.08

N40
NXT
7-Hydroxycoumarin
93-35-6
27.37
0.74
0.05

N41
NXT
Pratensein
2284-31-3
39.06
0.39
0.28

N42
NXT
Astragaloside IV
83207-58-3
17.74
-2.22
0.15

N43
NXT
Astragaloside II
84676-89-1
46.06
-2.05
0.13

N44
NXT
Methyl tanshinonate
N/A
19.19
0.56
0.55

N45
NXT
Hydroxytanshinone IIA
18887-18-8
44.93
0.53
0.44

N46
NXT
Kumatakenin
3301-49-3
50.83
0.61
0.29

N47
NXT
Trijuganone B
126979-84-8
38.75
0.96
0.36

N48
NXT
Carthamidin
479-54-9
41.15
0.16
0.24

N49
NXT
Acetyl-11-keto-β-boswellic acid
67416-61-9
17.79
0.20
0.67

N50
NXT
Scopolin
531-44-2
25.5
-0.88
0.39

N51
NXT
Daidzoside
552-66-9
14.32
-1.00
0.73

N52
NXT
Ecdysterone
5289-74-7
5.3
-1.32
0.82

N53
NXT
Ethyl gallate
831-61-8
25.61
0.33
0.06

N54
NXT
Luteolin
491-70-3
36.16
0.19
0.25

N55
NXT
Safflor yellow A
85532-77-0
22.75
-2.52
0.75

N56
NXT
Scopoletin
92-61-5
27.77
0.71
0.08

N57
NXT
Luteolin-7-O-β-D-glucopyranoside
26811-41-6
7.29
-1.23
0.78

N58
NXT
Oxyresveratrol
4721/7/7
109.29
0.55
0.13

N59
NXT
Tanshindiol C
96839-30-4
42.85
-0.04
0.45

N60
NXT
Quercetin
117-39-5
46.43
0.05
0.28

N61
NXT
Tanshindiol B
97465-70-8
42.67
0.05
0.45

N62
NXT
Aloe emodin
481-72-1
83.38
-0.12
0.24

N63
NXT
Genistein
446-72-0
17.93
0.43
0.21

N64
NXT
Kaempferol
520-18-3
41.88
0.26
0.24

N65
NXT
Palbinone
139954-00-0
43.56
0.00
0.53

N66
NXT
Farrerol
24211-30-1
42.65
0.59
0.26

N67
NXT
Astragaloside I
84680-75-1
46.79
-2.28
0.11

N68
NXT
Danshenxinkun A
65907-75-7
45.64
0.48
0.30

N69
NXT
E-butylidenephthalide
551-08-6
53.72
1.30
0.07

N70
NXT
Dihydrotanshinone I
87205-99-0
45.04
0.95
0.36

N71
NXT
Furanodiene
19912-61-9
45.11
1.77
0.10

N72
NXT
Tanshinone I
568-73-0
29.27
1.05
0.36

N73
NXT
Cyclomulberrin
19275-51-5
9.84
0.72
0.84

N74
NXT
Miltirone
27210-57-7
38.76
1.23
0.25

N75
NXT
1,2-Dihydrotanshinone
77769-21-2
19.95
1.07
0.36

N76
NXT
Methyl gallate
99-24-1
30.91
0.26
0.05

N77
NXT
Taxifolin
480-18-2
57.84
-0.23
0.27

N78
NXT
Morin
480-16-0
46.23
0.00
0.27

N79
NXT
Apigenin
520-36-5
23.06
0.43
0.21

N80
NXT
Coniferyl ferulate
63644-62-2
4.54
0.71
0.39

N81
NXT
Senkyunolide E
94530-83-3
34.4
0.55
0.08

N82
NXT
Mulberrofuran A
68978-04-1
4.45
1.04
0.53

2.2. Target Collection and Analysis of TCMP-Compound-Target-Disease Network

The putative targets of the candidate ingredients in DHI and NXT were derived from Search Tool for Interactions of Chemicals and Proteins Database (STITCH, http://stitch.embl.de/), Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://lsp.nwu.edu.cn/tcmsp.php) [40] and Binding DB (https://www.bindingdb.org/bind/index.jsp). Finally, 584 putative targets (177 targets were common for both DHI and NXT) were collected from 201 DHI-ingredient targets and 560 NXT-ingredient targets. Meanwhile, IS targets were gathered from Therapeutic Target Database (TTD, http://bidd.nus.edu.sg/group/cjttd/), DrugBank (https://www.drugbank.ca/), PharmGKB (https://www.pharmgkb.org/), MalaCards (http://www.malacards.org/), Comparative Toxicogenomics Database (CTD, http://ctdbase.org/), and then manually supplemented through a wide-scale text-mining method [41, 42]. The detailed information of all the targets was displayed in Table S2. A total of 179 targets were found to be associated with IS. Among them, 71 targets could be targeted by the candidate ingredients of DHI and NXT. Of note, 37 targets were shared by both of them; 40 and 68 targets were regarded as direct anti-IS targets of DHI and NXT, respectively (Figure 2(b)). These results showed that the targets overlapped dramatically in these two TCMPs, which also indicated different ingredients in DHI and NXT shared common or similar targets with synergistic effects.

(a)

(b)

Figure 2: (a) TCMP-Compound-Target-Disease network. The IS targets targeted by the ingredients of DHI and NXT were labeled by yellow circle. (b) Venn diagram showing the number of common and unique targets of the candidate ingredients from DHI and NXT in treating IS.

For the purpose of interpreting the complex relationships among TCMPs, candidate ingredients, putative targets, and IS, a TCMP-Compound-Target-Disease network (Figure 2(a)) was established using Cytoscape (version 3.6.0). This network consisted of 693 nodes (2 TCMPs, 107 candidate ingredients and 584 targets) and 2561 compound-target interactions. The IS targets targeted by the ingredients of DHI and NXT were labeled by yellow circle. The degree of a node, defined the number of edges connected to it, was analyzed by Network Analysis plugin. The average degree of candidate ingredients was 25.03 and 29 ingredients possessed degrees greater than 25, indicating that these ingredients were more likely to exert therapeutic effects by acting on multiple targets. Among them, the one with a higher degree was more important in the network [43], especially quercetin (degree = 291), apigenin (degree = 143), kaempferol (degree = 128), luteolin (degree = 126), rutin (degree = 116), genistein (degree = 116), caffeic acid (degree = 108), rosmarinic acid (degree = 90), ferulic acid (degree = 76), and biochanin A (degree = 67). For example, in focal cerebral ischemia rats, quercetin can decrease cell apoptosis, prevent free radicals associated with oxidative damage, and reduce the elevated MMP-9 activity, suggesting that quercetin might have a potential role in the treatment of IS [44–46]. Rosmarinic acid, a representative compound in both DHI and NXT, showed protective effects against IS induced brain injury by synaptogenic activity and anti-inflammatory action [47, 48].

As shown in the TCMP-Compound-Target-Disease network (Figure 2(a)), the ingredients of DHI and NXT could not only modulate the crucial targets involved in the regulation of the vasomotor system and the disruption of blood-brain barrier (PTGS2, F2, ADRB1, and EGFR), but also could concentrate on the redox imbalance, neurotrophin activity, and brain inflammation (IL6, ALDH2, CCL2, STAT3, and AKT1). These processes were closely associated with IS pathogenesis. For example, increased F2 generation was associated with the risk of IS in the elderly [49, 50]. The protein F2 was found to be connected with 33 ingredients, among which danshensu, salvianolic acid C and kaempferol were reported to inhibit the activity of F2 [51–53]. In addition, the neurotoxic proinflammatory mediator PTGS2 also had interactions with multiple ingredients. Specifically, ferulic acid, tanshinone IIA, and (Z)-ligustilide could exert anti-inflammatory effects by attenuating the PTGS2 expression [54–56].

2.3. Comparative Study of the Anti-IS Mechanisms of DHI and NXT

The interaction between putative targets and IS targets were derived by mapping them onto a PPI network. In this section, the IS targets and ingredient targets from DHI or NXT were integrated into STRING database (https://string-db.org/) to acquire PPI data, respectively. The species was limited to “Homo sapiens” and the confidence score was set as 0.7. Irrelevant proteins, including the putative targets and IS targets that could not directly or indirectly interact with each other, were removed. Two PPI networks, named DHI-IS and NXT-IS PPI network, were generated by Cytoscape and then merged to identify common and unique targets of DHI and NXT (Figure 3). A hub node should be two times greater than the average node degree in the network [57]; 47 and 75 hub nodes were identified for DHI-IS and NXT-IS PPI network, respectively. In the DHI-IS PPI network, 29 hub nodes were IS targets and 29 were DHI-ingredient targets. Besides the 11 overlapping targets, the remaining 18 DHI-ingredient targets were regarded as indirect anti-IS targets of DHI, illustrating the important indirect interactions with the IS targets. While in NXT-IS PPI network, 23 out of 75 hub nodes were IS targets, 67 were NXT-ingredient targets, and 15 were overlapping targets. From them, 52 targets were considered as indirect anti-IS targets of NXT.

Figure 3: The merged DHI-IS and NXT-IS PPI network. The map node size gets larger with increased degree.

To comparatively dissect the underlying mechanisms of DHI and NXT, 58 putative targets of DHI (combining 40 direct anti-IS targets with 18 indirect anti-IS targets) and 120 putative targets of NXT (combining 68 direct anti-IS targets with 52 indirect anti-IS targets) were employed to conduct pathway enrichment analysis, respectively. The pathway information was achieved from Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/). The top 20 pathways (Benjamini Hochberg corrected p value < 0.05) for DHI and NXT were shown in Figure 4. DHI and NXT shared 11 common pathways, including 4 immune system pathways (T cell receptor signaling pathway, toll-like receptor signaling pathway, chemokine signaling pathway, and NOD-like receptor signaling pathway), 2 signal transduction pathways (ErbB signaling pathway and VEGF signaling pathway), and 5 cancer-related pathways (colorectal cancer, pancreatic cancer, bladder cancer, prostate cancer, and pathways in cancer). Among them, T cell receptor signaling pathway, toll-like receptor signaling pathway, chemokine signaling pathway, NOD-like receptor signaling pathway, and VEGF signaling pathway were closely associated with cardiocerebrovascular diseases [58]. For example, angiogenesis was observed in stroke patients, which contained extracellular matrix degradation, endothelial cell proliferation, and new vessel formation [41]. Activation of VEGF signaling pathway played a vital role in angiogenesis [59], which had also been regarded as a promising strategy for IS treatment [60]. Consistent with Wan’s report [22], DHI and NXT could both activate VEGF signaling pathway and promote angiogenesis.

Figure 4: The merged T-P network for exploration of the comparative effect of DHI and NXT.

Besides these common pathways, DHI was also involved in 5 IS-related pathways, including cytokine-cytokine receptor interaction, calcium signaling pathway, complement and coagulation cascades, neuroactive ligand-receptor interaction, and MAPK signaling pathway. Among them, the calcium signaling pathway participated in the regulation of agonist-stimulated contraction and myogenic tone in vascular smooth muscle cells [61]. Previous study suggested that modification of calcium activity may be a novel therapeutic strategy for cardiocerebrovascular diseases, such as IS [62]. While for NXT, the 120 putative targets were also enriched in gap junction, endometrial cancer, glioma, progesterone-mediated oocyte maturation, acute myeloid leukemia, chronic myeloid leukemia, melanoma, Fc epsilon RI signaling pathway, and non-small-cell lung cancer. Besides the 6 cancer-related pathways, gap junction may play a crucial role during the development of IS due to its vital function in neuroprotection [63, 64].

Overall, it could be concluded that DHI and NXT exerted the therapeutic effects in the treatment of IS mainly through immune system pathways and VEGF signaling pathway. Furthermore, DHI could also focus on some signaling pathways, while NXT involved in gap junction. These results demonstrated that the anti-IS mechanisms of DHI and NXT had similarities and differences, which may provide important evidence for clinical choice of these two TCMPs.

2.4. Elucidation of the Synergistic Mechanism of DHI and NXT

To reveal the synergistic mechanism of DHI and NXT, in this section, the 179 IS targets and 584 ingredient targets (including 201 DHI-ingredient targets and 560 NXT-ingredient targets) were entirely integrated into STRING database to acquire PPI data. The DHI-NXT-IS PPI network was shown in Figure 5. The average degree of all the nodes was 23.25 and 76 nodes had degree bigger than 46. Among these hub nodes, 24 were IS targets and 68 were ingredient targets. Besides the 16 overlapping targets, the remaining 52 targets were considered as indirect anti-IS targets of this TCMP pair.

Figure 5: The DHI-NXT-IS PPI network. The map node size gets larger with increased degree.

The 123 putative targets of DHI and NXT (combining 71 direct anti-IS targets with 52 indirect anti-IS targets) were employed to elucidate the synergistic mechanism of DHI and NXT against IS using DAVID database. The information of top 20 pathways (Benjamini Hochberg corrected p value < 0.05) was summarized in Figure 6. Six pathways had high correlations with cardiocerebrovascular diseases, including four immune system pathways (chemokine signaling pathway, T cell receptor signaling pathway, toll-like receptor signaling pathway, and NOD-like receptor signaling pathway), one signaling pathways (VEGF signaling pathway), and one cell-related pathway (gap junction) [58]. Studies have reported that focal cerebral ischemia could result in the activation of inflammatory cytokines, chemokines, and chemokine receptors in the immune system. These processes may be caused by the formation of thrombi in blood vessels, particularly inside microvessels [65, 66]. Meanwhile, NOD-like receptor signaling pathway was also closely associated with the putative targets of DHI and NXT. Recent studies have reported that NOD-like receptor played an important role in central nervous diseases, such as IS. The abnormal changes of NOD-like receptors can cause redox imbalance in the brain, which had a marked impact in the pathogenesis of IS [67]. Of note, DHI and NXT showed synergistic effect in activation of the VEGF signaling pathway, as proved experimentally by Wan’s study [22]. Fortuitously, 11 cancer-related pathways were also discerned in Figure 6. Danshensu from DHI was validated to exhibit antitumor activity by affecting on tumor angiogenesis and tumor cell invasion [68]. Additionally, butylidenephthalide, senkyunolide A, and (Z)-ligustilide from NXT showed selective cytotoxic and antiproliferative effects in human colon cancer cells [69].

Figure 6: The T-P network for elucidation of the synergistic mechanism of DHI and NXT. The lower 11 pathways were associated with cancers and the upper 9 pathways were related to other processes.

In summary, DHI and NXT exerted the synergistic effect against IS by regulating 6 meaningful pathways, which indicated that the combination of DHI and NXT may provide better protective effect than monotherapy in immune system, angiogenesis, and neuroprotection.

3. Materials and Methods

3.1. Chemical Ingredients Collection and ADME Evaluation

The high-resolution LC-MS profile data of DHI and NXT were manually gathered from relevant researches. In order to explore the potential bioactive compounds from DHI and NXT, three important parameters were employed using in silico ADME models, including OB, Caco-2, and DL. Those ingredients with OB ≥30%, Caco-2 >−0.4 and DL ≥0.18 were preserved and adopted as candidate ingredients for further analysis. The details of all the ingredients and candidate ingredients were described in Tables S1 and 1, respectively.

3.2. Target Identification

With names and/or chemical structures (denoted by the canonical SMILES format) as key words, the putative targets of the candidate ingredients were derived from STITCH, TCMSP, and Binding DB. The obtained targets were normalized to the official gene symbols using UniProt database (https://www.uniprot.org/) with the species limited to “Homo sapiens”.

The targets related to IS were derived from five public databases, including TTD, DrugBank, PharmGKB, MalaCards, CTD with keywords “ischemic stroke”, “cerebral infarction”, “cerebral ischemia”, “brain ischemia”, and “cerebrovascular ischaemia”. Then the targets were manually supplemented through a wide-scale text-mining method and annotated using the official gene symbols as described above.

3.3. Network Construction and Analysis

To further obtain a global perspective of the complex relationships among compounds, targets, diseases, and pathways, several networks were constructed.

(1) TCMP-Compound-Target-Disease network. TCMPs, candidate ingredients of DHI and NXT and their putative targets, were employed to build this network. The IS targets targeted by the ingredients of DHI and NXT were labeled by yellow circle.

(2) Merged DHI-IS and NXT-IS PPI network and corresponding Target-Pathway (T-P) network. Based on the PPI data derived from STRING, with the species limited to “Homo sapiens” and a confidence score >0.7, two PPI networks named DHI-IS and NXT-IS PPI network were constructed for the ingredient targets of DHI and NXT interacting with IS targets, respectively. These two networks were merged to identify overlapping and distinct targets of DHI and NXT. Then the anti-IS targets of DHI and NXT were employed to conduct pathway enrichment analysis, respectively. The enriched pathway information of these targets was obtained from DAVID (used for pathway analysis according to Kyoto Encyclopedia of Genes and Genomes, KEGG), and then T-P networks composed of anti-IS targets of DHI or NXT and their corresponding pathways were created, respectively. These two T-P networks were merged to explore common and unique mechanisms of DHI and NXT for treating IS.

(3) DHI-NXT-IS PPI network and corresponding T-P network. A DHI-NXT-IS PPI network was established among DHI-ingredient targets, NXT-ingredient targets, and IS targets. Then the anti-IS targets were used to performed pathway enrichment analysis using DAVID database. The T-P network was generated to elucidate the synergistic mechanism of DHI and NXT in the treatment of IS.

All these networks were created by Cytoscape 3.6.0, open software for visualizing, integrating, modeling, and analyzing the biological networks.

4. Conclusions

To provide evidence for the better clinical use of TCMPs, comparison of the pharmacological mechanisms between TCMPs with similar therapeutic effect is necessary. DHI and NXT are representative TCMPs for IS treatment, which are also frequently used in combination. However, their independent and combined mechanisms have not been clearly clarified. In the present study, a feasible network pharmacology strategy based on chemical, pharmacokinetic, and pharmacological data was employed to elucidate the comparative and synergistic effects of DHI and NXT against IS. By systematic analysis of the constructed networks, we could learn that (1) the candidate ingredients of DHI and NXT not only could target the vasomotor system-related and blood-brain barrier-related proteins (PTGS2, F2 ADRB1, and ADRB2), but also could involve in the redox imbalance, neurotrophin activity, and brain inflammation (IL6, ALDH2, CCL2, STAT3, and AKT1). (2) DHI and NXT exerted the protective effects mainly through immune system and VEGF signaling pathways. But DHI and NXT also had their own unique pathways for treating IS, e.g., calcium signaling pathway for DHI and gap junction for NXT. (3) The synergistic mechanism of DHI and NXT was mainly associated with chemokine signaling pathway, T cell receptor signaling pathway, VEGF signaling pathway, gap junction, and so on. Overall, this study provided an effective approach for dissecting the comparative and synergistic effects of DHI and NXT in the treatment of IS.

Data Availability

The data of our research can be acquired from the Supplementary Materials uploaded with this article.

Conflicts of Interest

The authors declare no conflicts of interest.

Authors’ Contributions

Junfeng Zhu, Yiwen Zhang, and Ping Huang conceived and designed the study; Junfeng Zhu and Xiaojiao Yi performed the network pharmacology analysis and wrote the paper; Yiwen Zhang, Zongfu Pan, and Like Zhong participated in the analysis and improved the English language of the manuscript.

Acknowledgments

This study was supported by Zhejiang Provincial Natural Science Foundation of China (no. LQ19H280002), Hospital Pharmacy Research Project from Zhejiang Pharmaceutical Association (no. 2018ZYY08), Projects of Medical and Health Technology Program in Zhejiang Province (nos. 2018KY286 and 2018KY148), Zhejiang Provincial Program for the Cultivation of 151 Talents (to Ping Huang), and Zhejiang Provincial Program for the Cultivation of High-Level Innovative Heath Talents (to Ping Huang).

Supplementary Materials

Table S1: the detailed information of all compounds in DHI and NXT. Table S2: the detailed information of DHI-ingredient targets, NXT-ingredient targets, and IS targets. (Supplementary Materials)

References

L. Catanese, J. Tarsia, and M. Fisher, “Acute Ischemic Stroke Therapy Overview,” Circulation Research, vol. 120, no. 3, pp. 541–558, 2017.

E. J. Benjamin, M. J. Blaha, S. E. Chiuve et al., “Heart disease and stroke statistics2017 update: a report from the American Heart Association,” Circulation, vol. 135, no. 10, pp. e146–e603, 2017.

E. J. Benjamin, S. S. Virani, C. W. Callaway et al., “Heart disease and stroke statistics2018 update: a report from the American Heart Association,” Circulation, vol. 137, no. 12, pp. e67–e492, 2018.

H. Asadi, D. Williams, and J. Thornton, “Changing management of acute ischaemic stroke: The new treatments and emerging role of endovascular therapy,” Current Treatment Options in Neurology, vol. 18, no. 5, 2016.

B. R. Christophe, S. H. Mehta, A. L. A. Garton, J. Sisti, and E. S. Connolly, “Current and future perspectives on the treatment of cerebral ischemia,” Expert Opinion on Pharmacotherapy, vol. 18, no. 6, pp. 573–580, 2017.

A. Meretoja, M. Keshtkaran, J. L. Saver et al., “Stroke thrombolysis: save a minute, save a day,” Stroke, vol. 45, no. 4, pp. 1053–1058, 2014.

F.-T. Zuo, H. Liu, H.-J. Wu, N. Su, J.-Q. Liu, and A.-Q. Dong, “The effectiveness and safety of dual antiplatelet therapy in ischemic cerebrovascular disease with intracranial and extracranial arteriostenosis in Chinese patients,” Medicine (United States), vol. 96, no. 1, Article ID e5497, 2017.

M. D. Mijajlovic, O. Shulga, S. Bloch, N. Covickovic-Sternic, V. Aleksic, and N. M. Bornstein, “Clinical consequences of aspirin and clopidogrel resistance: An overview,” Acta Neurologica Scandinavica, vol. 128, no. 4, pp. 213–219, 2013.

N. Kapil, Y. H. Datta, N. Alakbarova et al., “Antiplatelet and Anticoagulant Therapies for Prevention of Ischemic Stroke,” Clinical and Applied Thrombosis/Hemostasis, vol. 23, no. 4, pp. 301–318, 2017.

F. Cheung, “TCM: made in China,” Nature, vol. 480, no. 7378, pp. S82–S83, 2011.

K. Sun, J. Fan, and J. Han, “Ameliorating effects of traditional Chinese medicine preparation, Chinese materia medica and active compounds on ischemia/reperfusion-induced cerebral microcirculatory disturbances and neuron damage,” Acta Pharmaceutica Sinica B (APSB), vol. 5, no. 1, pp. 8–24, 2015.

J.-Y. Han, Q. Li, Z.-Z. Ma, and J.-Y. Fan, “Effects and mechanisms of compound Chinese medicine and major ingredients on microcirculatory dysfunction and organ injury induced by ischemia/reperfusion,” Pharmacology & Therapeutics, vol. 177, pp. 146–173, 2017.

B. Li, Y. Wang, J. Lu et al., “Evaluating the effects of Danhong injection in treatment of acute ischemic stroke: Study protocol for a multicenter randomized controlled trial,” Trials, vol. 16, no. 1, 2015.

Y. Guan, Y. Yin, Y.-R. Zhu et al., “Dissection of Mechanisms of a Chinese Medicinal Formula: Danhong Injection Therapy for Myocardial Ischemia/Reperfusion Injury In Vivo and In Vitro,” Evidence-Based Complementary and Alternative Medicine, vol. 2013, Article ID 972370, 12 pages, 2013.

L. Su, Y. Li, B. Lv et al., “Clinical study on Naoxintong capsule for stroke recovery of Qi-deficiency and blood-stasis syndrome,” Zhongguo Zhongyao Zazhi, vol. 36, no. 11, pp. 1530–1533, 2011.

L.-X. Li, L. Chen, and H.-J. Zhao, “Effect of naoxintong capsule on the vascular endothelial function and the infarct size of patients with acute myocardial infarction,” Chinese Journal of Integrated Traditional and Western Medicine, vol. 31, no. 12, pp. 1615–1618, 2011.

Y. He, H. Wan, Y. Du et al., “Protective effect of Danhong injection on cerebral ischemia-reperfusion injury in rats,” Journal of Ethnopharmacology, vol. 144, no. 2, pp. 387–394, 2012.

J. Wei, Y. Zhang, Q. Jia et al., “Systematic investigation of transcription factors critical in the protection against cerebral ischemia by Danhong injection,” Scientific Reports, vol. 6, no. 1, 2016.

M. Liu, X. Liu, H. Wang et al., “Metabolomics study on the effects of Buchang Naoxintong capsules for treating cerebral ischemia in rats using UPLC-Q/TOF-MS,” Journal of Ethnopharmacology, vol. 180, pp. 1–11, 2016.

J. Xue, X. Zhang, C. Zhang et al., “Protective effect of Naoxintong against cerebral ischemia reperfusion injury in mice,” Journal of Ethnopharmacology, vol. 182, pp. 181–189, 2016.

Y. Lv, Y. Pan, Y. Gao et al., “Effect of Danhong injection combined with Naoxintong tablets on prognosis and inflammatory factor expression in acute coronary syndrome patients undergoing percutaneous coronary intervention,” Acta Cardiologica Sinica, vol. 31, no. 4, pp. 301–307, 2015.

J. Wan, H. Wan, R. Yang et al., “Protective effect of Danhong Injection combined with Naoxintong Capsule on cerebral ischemia-reperfusion injury in rats,” Journal of Ethnopharmacology, vol. 211, pp. 348–357, 2018.

X. Yi, J. Zhu, J. Zhang et al., “Investigation of the reverse effect of Danhong injection on doxorubicin-induced cardiotoxicity in H9c2 cells: Insight by LC–MS based non-targeted metabolomic analysis,” Journal of Pharmaceutical and Biomedical Analysis, vol. 152, pp. 264–270, 2018.

J. Huang, L. Li, F. Cheung et al., “Network Pharmacology-Based Approach to Investigate the Analgesic Efficacy and Molecular Targets of Xuangui Dropping Pill for Treating Primary Dysmenorrhea,” Evidence-Based Complementary and Alternative Medicine, vol. 2017, Article ID 7525179, 12 pages, 2017.

J. Zhang, Q. Zhang, X. Chen et al., “Revealing Synergistic Mechanism of Multiple Components in Gandi Capsule for Diabetic Nephropathy Therapeutics by Network Pharmacology,” Evidence-Based Complementary and Alternative Medicine, vol. 2018, Article ID 6503126, 11 pages, 2018.

J. Huang, H. Tang, S. Cao et al., “Molecular Targets and Associated Potential Pathways of Danlu Capsules in Hyperplasia of Mammary Glands Based on Systems Pharmacology,” Evidence-Based Complementary and Alternative Medicine, vol. 2017, Article ID 1930598, 10 pages, 2017.

S. Y. Suh and W. G. An, “Systems Pharmacological Approach to the Effect of Bulsu-san Promoting Parturition,” Evidence-Based Complementary and Alternative Medicine, vol. 2017, Article ID 7236436, 15 pages, 2017.

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.

Z. Song, F. Yin, B. Xiang, B. Lan, and S. Cheng, “Systems Pharmacological Approach to Investigate the Mechanism of Acori Tatarinowii Rhizoma for Alzheimer’s Disease,” Evidence-Based Complementary and Alternative Medicine, vol. 2018, Article ID 5194016, 20 pages, 2018.

M. Lyu, C. Yan, H. Liu et al., “Network pharmacology exploration reveals endothelial inflammation as a common mechanism for stroke and coronary artery disease treatment of Danhong injection,” Scientific Reports, vol. 7, no. 1, 2017.

W. Yu, Z. Li, F. Long et al., “A Systems Pharmacology Approach to Determine Active Compounds and Action Mechanisms of Xipayi KuiJie'an enema for Treatment of Ulcerative colitis,” Scientific Reports, vol. 7, no. 1, 2017.

J. Li, P. Zhao, Y. Li, Y. Tian, and Y. Wang, “Systems pharmacology-based dissection of mechanisms of Chinese medicinal formula Bufei Yishen as an effective treatment for chronic obstructive pulmonary disease,” Scientific Reports, vol. 5, Article ID 15290, 2015.

S. Yue, L. Xin, Y. Fan et al., “Herb pair Danggui-Honghua: mechanisms underlying blood stasis syndrome by system pharmacology approach,” Scientific Reports, vol. 7, no. 1, article no. 40318, 2017.

Q.-Q. Zhang, X. Dong, X.-G. Liu, W. Gao, P. Li, and H. Yang, “Rapid separation and identification of multiple constituents in Danhong Injection by ultra-high performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight tandem mass spectrometry,” Chinese Journal of Natural Medicines, vol. 14, no. 2, pp. 147–160, 2016.

J. Zhu, X. Yi, P. Huang, S. Chen, and Y. Wu, “Drug-protein binding of Danhong injection and the potential influence of drug combination with aspirin: Insight by ultrafiltration LC–MS and molecular modeling,” Journal of Pharmaceutical and Biomedical Analysis, vol. 134, pp. 100–107, 2017.

X. Ma, B. Lv, P. Li et al., “Identification of ‘multiple components-multiple targets-multiple pathways’ associated with naoxintong capsule in the treatment of heart diseases using UPLC/Q-TOF-MS and network pharmacology,” Evidence-Based Complementary and Alternative Medicine, vol. 2016, Article ID 9468087, 15 pages, 2016.

H. Wang, Y. Jiang, M. Ding et al., “Simultaneous determination and qualitative analysis of six types of components in Naoxintong capsule by miniaturized matrix solid-phase dispersion extraction coupled with ultra high-performance liquid chromatography with photodiode array detection and quadrupole time-of-flight mass spectrometry,” Journal of Separation Science, vol. 41, no. 9, pp. 2064–2084, 2018.

W. Songsong, X. Haiyu, M. Yan et al., “Characterization and rapid identification of chemical constituents of NaoXinTong capsules by UHPLC-linear ion trap/Orbitrap mass spectrometry,” Journal of Pharmaceutical and Biomedical Analysis, vol. 111, pp. 104–118, 2015.

W. P. Walters and M. A. Murcko, “Prediction of ‘drug-likeness’,” Advanced Drug Delivery Reviews, vol. 54, no. 3, pp. 255–271, 2002.

J. Ru, P. Li, J. Wang et al., “TCMSP: a database of systems pharmacology for drug discovery from herbal medicines,” Journal of Cheminformatics, vol. 6, no. 1, article 13, 2014.

Q. Guo, M. Zhong, H. Xu, X. Mao, Y. Zhang, and N. Lin, “A systems biology perspective on the molecular mechanisms underlying the therapeutic effects of Buyang Huanwu decoction on ischemic stroke,” Rejuvenation Research, vol. 18, no. 4, pp. 313–325, 2015.

Y.-Q. Zhang, S.-S. Wang, W.-L. Zhu et al., “Deciphering the pharmacological mechanism of the Chinese formula Huanglian-Jie-Du decoction in the treatment of ischemic stroke using a systems biology-based strategy,” Acta Pharmacologica Sinica, vol. 36, no. 6, pp. 724–733, 2015.

Y. Zhang, X. Guo, M. Yang, L. Yu, Z. Li, and N. Lin, “Identification of AKT kinases as unfavorable prognostic factors for hepatocellular carcinoma by a combination of expression profile, interaction network analysis and clinical validation,” Molecular BioSystems, vol. 10, no. 2, pp. 215–222, 2014.

A. Ahmad, M. M. Khan, M. N. Hoda et al., “Quercetin protects against oxidative stress associated damages in a rat model of transient focal cerebral ischemia and reperfusion,” Neurochemical Research, vol. 36, no. 8, pp. 1360–1371, 2011.

J.-K. Lee, H.-J. Kwak, M.-S. Piao, J.-W. Jang, S.-H. Kim, and H.-S. Kim, “Quercetin reduces the elevated matrix metalloproteinases-9 level and improves functional outcome after cerebral focal ischemia in rats,” Acta Neurochirurgica, vol. 153, no. 6, pp. 1321–1329, 2011.

R.-Q. Yao, D.-S. Qi, H.-L. Yu, J. Liu, L.-H. Yang, and X.-X. Wu, “Quercetin attenuates cell apoptosis in focal cerebral ischemia rat brain via activation of BDNF-TrkB-PI3K/Akt signaling pathway,” Neurochemical Research, vol. 37, no. 12, pp. 2777–2786, 2012.

A. A. Fonteles, C. M. de Souza, J. C. de Sousa Neves et al., “Rosmarinic acid prevents against memory deficits in ischemic mice,” Behavioural Brain Research, vol. 297, pp. 91–103, 2016.

H. Luan, Z. Kan, Y. Xu, C. Lv, and W. Jiang, “Rosmarinic acid protects against experimental diabetes with cerebral ischemia: relation to inflammation response,” Journal of Neuroinflammation, vol. 10, no. 1, article 28, 2013.

L. Carcaillon, M. Alhenc-Gelas, Y. Bejot et al., “Increased thrombin generation is associated with acute ischemic stroke but not with coronary heart disease in the elderly: the three-city cohort study,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 31, no. 6, pp. 1445–1451, 2011.

J. P. Casas, A. D. Hingorani, L. E. Bautista, and P. Sharma, “Meta-analysis of genetic studies in ischemic stroke: Thirty-two genes involving approximately 18 000 cases and 58 000 controls,” JAMA Neurology, vol. 61, no. 11, pp. 1652–1661, 2004.

Y. Shen, Y.-L. Hu, Y. Zhang, and J.-M. Wang, “Effects of danshensu on maternal syndrome in phosphatidyleserine/ phosphatidylcholine microvesicle induced-mouse model: Is it a candidate for preeclampsia remedy?” Chinese Medical Journal, vol. 123, no. 7, pp. 895–900, 2010.

J. Cao, J.-J. Xu, X.-G. Liu, S.-L. Wang, and L.-Q. Peng, “Screening of thrombin inhibitors from phenolic acids using enzyme-immobilized magnetic beads through direct covalent binding by ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry,” Journal of Chromatography A, vol. 1468, pp. 86–94, 2016.

L. Liu, H. Ma, N. Yang et al., “A series of natural flavonoids as thrombin inhibitors: Structure-activity relationships,” Thrombosis Research, vol. 126, no. 5, pp. e365–e378, 2010.

A. Hirata, Y. Murakami, T. Atsumi et al., “Ferulic acid dimer inhibits lipopolysaccharide-stimulated cyclooxygenase-2 expression in macrophages,” In Vivo, vol. 19, no. 5, pp. 849–854, 2005.

G. Fan, X. Gao, H. Wang et al., “The anti-inflammatory activities of Tanshinone IIA, an active component of TCM, are mediated by estrogen receptor activation and inhibition of iNOS,” The Journal of Steroid Biochemistry and Molecular Biology, vol. 113, no. 3–5, pp. 275–280, 2009.

J. W. Chung, R. J. Choi, E.-K. Seo et al., “Anti-inflammatory effects of (Z)-ligustilide through suppression of Mitogen-activated protein kinases and nuclear factor-κB activation pathways,” Archives of Pharmacal Research, vol. 35, no. 4, pp. 723–732, 2012.

S. Li, Z. Q. Zhang, L. J. Wu, X. G. Zhang, Y. D. Li, and Y. Y. Wang, “Understanding ZHENG in traditional Chinese medicine in the context of neuro-endocrine-immune network,” IET Systems Biology, vol. 1, no. 1, pp. 51–60, 2007.

Y. Yang, Y. Li, J. Wang et al., “Systematic Investigation of Ginkgo biloba leaves for treating cardio-cerebrovascular diseases in an animal model,” ACS Chemical Biology, vol. 12, no. 5, pp. 1363–1372, 2017.

A.-L. Yang, Q.-H. Liang, H.-J. Cui, H.-J. Zhou, J.-K. Luo, and T. Tang, “Angiogenesis opens a way for Chinese medicine to treat stroke,” Chinese Journal of Integrative Medicine, vol. 19, no. 11, pp. 815–819, 2013.

J. M. Adamczak, G. Schneider, M. Nelles et al., “In vivo bioluminescence imaging of vascular remodeling after stroke,” Frontiers in Cellular Neuroscience, vol. 8, 2014.

A. Moretti, F. Ferrari, and R. F. Villa, “Neuroprotection for ischaemic stroke: Current status and challenges,” Pharmacology & Therapeutics, vol. 146, pp. 23–34, 2015.

M. Wang, H. Yang, L.-Y. Zheng et al., “Downregulation of TMEM16A calcium-activated chloride channel contributes to cerebrovascular remodeling during hypertension by promoting basilar smooth muscle cell proliferation,” Circulation, vol. 125, no. 5, pp. 697–707, 2012.

C. Zhang, Y. Li, J. Chen et al., “Bone marrow stromal cells upregulate expression of bone morphogenetic proteins 2 and 4, gap junction protein connexin-43 and synaptophysin after stroke in rats,” Neuroscience, vol. 141, no. 2, pp. 687–695, 2006.

M. G. Kozoriz, J. F. Bechberger, G. R. Bechberger et al., “The connexin43 C-terminal region mediates neuroprotection during stroke,” Journal of Neuropathology & Experimental Neurology, vol. 69, no. 2, pp. 196–206, 2010.

H. Offner, S. Subramanian, S. M. Parker, M. E. Afentoulis, A. A. Vandenbark, and P. D. Hurn, “Experimental stroke induces massive, rapid activation of the peripheral immune system,” Journal of Cerebral Blood Flow & Metabolism, vol. 26, no. 5, pp. 654–665, 2006.

B. Engelmann and S. Massberg, “Thrombosis as an intravascular effector of innate immunity,” Nature Reviews Immunology, vol. 13, no. 1, pp. 34–45, 2013.

G. Trendelenburg, “Molecular regulation of cell fate in cerebral ischemia: role of the inflammasome and connected pathways,” Journal of Cerebral Blood Flow & Metabolism, vol. 34, no. 12, pp. 1857–1867, 2014.

L.-J. Zhang, L. Chen, Y. Lu et al., “Danshensu has anti-tumor activity in B16F10 melanoma by inhibiting angiogenesis and tumor cell invasion,” European Journal of Pharmacology, vol. 643, no. 2-3, pp. 195–201, 2010.

W. L. T. Kan, C. H. Cho, J. A. Rudd, and G. Lin, “Study of the anti-proliferative effects and synergy of phthalides from Angelica sinensis on colon cancer cells,” Journal of Ethnopharmacology, vol. 120, no. 1, pp. 36–43, 2008.