|Year : 2022 | Volume
| Issue : 2 | Page : 262-272
Relaxant effect of bioactive component compatibility of San-ao decoction on In vitro guinea pig airway smooth muscle: A dose-response relationship study
Wen-Jie Song1, Yan-Ling Fu2, Sheng-Lou Ni3, Jia-Jia Fan2, Qian Du4, Hao Zheng1
1 Chinese Medical College, School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Jing Hai District, Tianjin, China
2 Chinese Medical College, School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Chaoyang District, Beijing, China
3 Periodicals Publishing Center, Beijing University of Chinese Medicine, Chaoyang District, Beijing, China
4 Wenzhou Traditional Chinese Medicine Hospital, Wenzhou, China
|Date of Submission||04-Dec-2020|
|Date of Acceptance||06-May-2021|
|Date of Web Publication||07-Mar-2022|
Periodicals Publishing Center, Beijing University of Chinese Medicine, 11 Beisanhuan East Road, Chaoyang District, Beijing - 100 029
School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyang Hu Rd., Tuan Bo New Town, Jing Hai District, Tianjin, 301617
Source of Support: None, Conflict of Interest: None
Background: Component compatibility is important to the modernization of traditional Chinese medicine. Studies have shown that San-ao decoction (SAD) can treat respiratory diseases by relaxing airway smooth muscle (ASM) and reducing airway hyper-responsiveness. However, whether its bioactive components and compatibility also present with similar relaxant effects remains unknown. This study aims to explore the potential relaxant property, dose-response relationship, and underlying mechanisms of the bioactive component compatibility in SAD. Methods: Network pharmacology was primarily used to identify the bioactive components of SAD and uncover its underlying mechanisms. ASM tension force measuring technique was utilized to verify the relaxant and dose-response effects on in vitro guinea pig ASM. Results: We postulated pseudoephedrine hydrochloride (PH), amygdalin (AM), and diammonium glycyrrhizate (DG) to be the bioactive components of SAD, which could effectively relax ASM in a dose-dependent manner on both acetylcholine-induced and spontaneous contraction. Both PH and AM could lead to DG dose–response curve shift. The regression equation of these three bioactive components was Y = −2.048 × X1 + 0.411 × X2 + 14.052 × X3 (X1, X2, X3 representing PH, AM, and DG, respectively). The underlying mechanisms of these components might be associated with the regulation of smooth muscle contraction. Conclusions: PH, AM, and DG are the bioactive components of SAD, which can relax ASM in a dose–response manner and exert a synergistic effect. Clinically, compatibility of these three bioactive components may serve as a new complementary and alternative treatment for respiratory diseases.
Keywords: Airway smooth muscle, bioactive component compatibility, dose–response manner, relaxant effect, San-ao decoction
|How to cite this article:|
Song WJ, Fu YL, Ni SL, Fan JJ, Du Q, Zheng H. Relaxant effect of bioactive component compatibility of San-ao decoction on In vitro guinea pig airway smooth muscle: A dose-response relationship study. World J Tradit Chin Med 2022;8:262-72
|How to cite this URL:|
Song WJ, Fu YL, Ni SL, Fan JJ, Du Q, Zheng H. Relaxant effect of bioactive component compatibility of San-ao decoction on In vitro guinea pig airway smooth muscle: A dose-response relationship study. World J Tradit Chin Med [serial online] 2022 [cited 2022 Dec 10];8:262-72. Available from: https://www.wjtcm.net/text.asp?2022/8/2/262/339189
| Background|| |
Respiratory diseases have become a severe health issue worldwide, affecting millions of people and increasing the economic burden on families and the society. Chronic obstructive pulmonary disease (COPD), asthma, and asthma–COPD overlap syndrome are typical inflammatory airway diseases that can lead to a poor quality of life and even death., COPD, for instance, causes over three million deaths around the world annually. In China, COPD is the third leading cause of death after ischemic heart disease and stroke. The morbidity and mortality of respiratory diseases such as COPD and asthma are rising along with increased exposure to risk factors such as smoking and air pollution., Airway smooth muscle (ASM) plays an important role in the airflow obstruction in COPD and asthma, and induces a series of clinical symptoms and pathological changes, such as chronic inflammation, mucus production, and tissue damage., Accordingly, drug therapies such as anti-inflammatories, bronchodilators, and inhaled corticosteroids are commonly used to treat COPD and asthma. However, clinical reports show that they can only control respiratory symptoms and prevent exacerbations but are not suitable for long-term use due to various side effects.,,,, Therefore, there is an urgent need to explore other safe and effective ways to manage respiratory diseases.
Chinese herbal medicine (CHM) is widely recognized to treat various chronic diseases through either compound prescriptions or extracted bioactive components.,, The former has advantages of efficacy improvement, drug dependence prevention, and side effect attenuation as compared to a single component., However, the component of CHM is complex and unclear, which becomes the bottleneck that restricts the modernization and internationalization of CHM. Therefore, in recent years, component compatibility has become a prominent way to study the effects and mechanisms of compound formulas, which may provide a new clinical treatment. Our previous work has shown San-ao decoction (SAD) to be a core formula for respiratory diseases and exerts a favorable therapeutic effect in treating cough, expectoration, wheezing, shortness of breath, and chest tightness., SAD, a well-known formula from Song Dynasty's Beneficial Formulas from the Taiping Imperial Pharmacy (Taiping Huimin Heji Jufang), comprises three Chinese herbal ingredients: Herba Ephedrae, Semen Armeniacae Amarum, and Radix Glycyrrhizae in a 1:1:1 ratio. Earlier researches have demonstrated that SAD could effectively relax ASM and reduce airway hyper-responsiveness through Th1/Th2 modulation, airway inflammation inhibition, as well as activity suppression of H1 and M receptors. In addition, Ma et al. used ultra-performance liquid chromatography-quadrupole tandem time-of-flight mass spectrometry to analyze the chemical compositions of SAD and found 22 compounds, including alkaloids, flavonoids, saponins, such as ephedrine, glycyrrhizic acid, glycyrrhizin, isoglycyrrhizin, formononetin, benzoic acid, and amygdalin, which are related closely to the therapeutic effects of SAD. Researchers often consider ephedrine hydrochloride, pseudoephedrine hydrochloride (PH), amygdalin (AM), and glycyrrhizic acid as the most important components that can be used to determine the content, purification process, effectiveness, and stability of SAD., Other studies have also shown both ephedrine (a component of Herba Ephedrae) and glycyrrhetinic acid (a component of Radix Glycyrrhizae) to have therapeutic effects on ASM., However, ephedrine is a controlled substance, and glycyrrhetinic acid may not be the optimal bioactive component in Radix Glycyrrhizae and thus limits the modernization and internationalization of SAD component compatibility. Hence, we sought to detect the optimal and attainable bioactive components in SAD, on the assumption that this bioactive component compatibility has a better synergistic effect on ASM relaxation compared to single component.
CHM has characteristics of being multi-component, multi-target, and multi-pathway, but not all the components are bioactive. Identification of unique and optimal bioactive components in CHM is therefore necessary. Network pharmacology is a comprehensive method that includes chemo-informatics, bio-informatics, network biology, and traditional pharmacology. It can uncover bioactive components and their underlying mechanisms from a systemic and holistic perspective. In this study, we first applied the method of network pharmacology to identify the bioactive components related with ASM in SAD. Then, combining with the Pharmacopoeia of the People's Republic of China (2010 edition), we selected PH, AM, and DG as our experimental agents, which is closely related with ASM and including found most important bioactive components in SAD, and inferred their underlying mechanisms. Finally, we verified their relaxant effects and explored their dose–response relationships using an in vitro guinea pig ASM model and measuring their tension force.
| Methods|| |
Network pharmacology prediction
Bioactive components of the three herbs in SAD were obtained mainly from the Traditional Chinese Medicine (TCM) System Pharmacology Database (TCMSP, http://lsp.nwu.edu.cn/tcmsp.php, updated on May 31, 2014), one of the largest pharmacology platforms for TCM, containing all herbs, chemical components, and pharmacokinetic properties (absorption, distribution, metabolism, and excretion, namely ADME) in the Pharmacopoeia of the People's Republic of China (2010 Edition). In addition, the databases of the China National Knowledge Infrastructure, Wanfang, and PubMed were used to supplement any omitted components. In this study, 595 components were retrieved, including 230, 85, and 280 from Herba Ephedrae, Semen Armeniacae Amarum, and Radix Glycyrrhizae, respectively.
According to a previous pharmacokinetic study, we selected two ADME-related models and preliminarily set the screening criteria for bioactive components: OB ≥20% and DL ≥0.18.,, Other components such as O-benzoyl-L-(+)-pseudoephedrine, ()-N-methylpseudoephedrine, amygdalin, and glycyrrhizin were initially excluded based on this screening criteria but later re-retrieved manually for further analyses due to their reported properties. Finally, 136 bioactive components were included, 11 from Herba Ephedrae, 10 from Semen Armeniacae Amarum, and 115 from Radix Glycyrrhizae.
ASM plays a vital role in the pathological manifestations of the respiratory system when there is airway obstruction. Based on research by Wang et al.,, we considered ASM relaxation as the key pharmacodynamic indicator and identified the main bioactive components using computer-assisted network pharmacology. Through this method, we identified potential targets of 136 bioactive components in SAD based on three databases of components and targets – TCMSP, PubChem Project, and ZINC and performed biological processes (BPs) enrichment analysis to select ASM-related BP, which we could infer the target genes and bioactive components related with ASM in SAD. Finally, we found 95 bioactive components that are associated with ASM relaxation. Among them, the bioactive components of Herba Ephedrae are mainly concentrated in O-benzoyl-L-(+)-pseudoephedrine, ()-N-methylpseudoephedrine, and ()-N-methylephedrine; the bioactive components of Semen Armeniacae Amarum are mainly concentrated in amygdalin, estrone, and stigmasterol; while the bioactive components of Radix Glycyrrhizae are scattered, such as glycyrrhizin and licoisoflavanone.
Combining information from the Pharmacopoeia of the People's Republic of China (2010 edition) – the recognized bioactive components isolated from CHM, their pharmacological effects, and quality inspection standard – with their respective therapeutic effects on ASM, we finally determined PH, AM, and diammonium glycyrrhizate (DG) to be the bioactive components in SAD.
Bioactive component-target network construction
With the systematic drug targeting approach developed by Wang et al., we utilized three databases of components and targets – TCMSP, PubChem Project (PubChem, https://pubchem.ncbi.nlm.nih.gov/, updated on October 26, 2017), and ZINC (http://zinc15.docking.org, updated on March 14, 2016) – to identify the potential targets of these four most important bioactive components (O-benzoyl-L-(+)-pseudoephedrine, ()-N-methylpseudoephedrine, amygdalin, and glycyrrhizin), and converted them into gene names with species limited to “Homo sapiens” using UniProt Knowledgebase (http://www.uniprot.org).
Gene ontology and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Analysis
Gene ontology (GO) mainly includes BPs, cell component (CC), molecular function (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis are the common methods used to describe the characteristics of candidate targets. In this study, we performed GO and KEGG pathway enrichment analysis on the potential targets using the Database for Annotation Visualization and Integrated Discovery (DAVID, https://david.nicifcrf.gov/, updated in March 2017), an online platform for the high-throughput functional annotation bioinformatics. The enriched GO terms and KEGG pathways with P < 0.05 were considered as significant. We then drew the top two KEGG pathways and marked each potential target with a different color using the KEGG Mapper (https://www.genome.jp/kegg/mapper.html, updated in October 2017).
Chemicals and Reagents
PH (Lot No. 171237-201510, purity: HPLC ≥98%, M: 201.69 g/mol) was purchased from National Institute for Food and Drug Control (Beijing, China). AM (Lot No. Z28A6 L2815, purity: HPLC ≥98%, M: 457.43 g/mol) and DG (Lot No. FO2S6J2990, purity: HPLC ≥98%, M: 857 g/mol) were obtained from Shanghai Yuanye Bio-Technology Co. (Shanghai, China). Acetylcholine chlorides (Ach, Lot No. 20150323, purity: HPLC ≥98%, M: 181.66 g/mol) and other chemical agents were from Sinopharm Chemical Reagent Co. (Beijing, China).
Male specific-pathogen-free guinea pigs (weight 250 ± 50 g) were purchased from Vital River Laboratory Animal Technology Co. (SCXX (Jing) 2012-0001, Beijing, China) and housed at room temperature (20°C‒25°C) and humidity (50%‒60%) under a 12-h light/dark cycle with free access to food and water for no more than 3 days. The animals were assigned to each test using a random number table (n ≥6). All procedures on animals in this study were conducted in conformity with the Laboratory Animal–Requirements of Environment and Housing Facilities (GB14925‒2001) and the ethical guidelines for researchers by the International Council for Laboratory Animal Science (ICLAS). The animal protocol was approved by the Animal Care and Use Committee of the Beijing University of Chinese Medicine (BUCM-4-2017032824 -1024).
Guinea pig airway smooth muscle ring preparation
Guinea pigs were sacrificed by a blow and exsanguination. ASM rings were then isolated cleanly and divided into three to four smaller sections (3‒5 mm length). Each section was quickly placed into a 5 ml organ bath containing normal Krebs–Henseleit solution (KHS: NaCl 118.96 mM, KCl 4.73 mM, KH2PO4 1.17 mM, MgSO4·7H2O 1.17 mM, NaHCO3 25.00 mM, CaCl2 2.54 mM, and glucose 11.10 mM). The rings were maintained at (37°C ± 0.5°C and bubbled with 95% O2‒5% CO2 constantly [Figure 1]a. After acclimatization, the ASM rings were equilibrated under a baseline tension of 2.0 g for at least 1 h. After that, they were stimulated with 60 mM KCl for 1–3 times (when necessary) to obtain a reproducible contraction and then washed repeatedly until they returned to baseline tension. During the preparation, ASM rings were washed with fresh KHS at 15-min intervals [Figure 1]b.
|Figure 1: Device and image data schematics. (a) Equipment diagram used to measure ASM tension force. (b) Time–contraction force curve diagram for experiment agents. Each ASM ring was placed in Radnoti four-channel tissue organ bath system (Radnoti LLC., Covina, CA, USA) and threaded over the triangular-shaped metal and L-shaped metal. The former was connected with sixteen-channel Biopack System polygraph MP-150 through a TSD 125C force–displacement transducer (Biopac System Inc., Goleta, CA, USA) to constantly record the isometric tension, and the latter was connected with micrometer. Dose–response curve for chemical agents was presented in computer. ASM: Airway smooth muscle|
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Tension force measurement
After preparation, ASM rings were stimulated continuously with different experimental agents by dose accumulation on both Ach-induced and spontaneous contractions. The dose-relaxant effect curve, 50% effective concentration (EC50), and regression equation were then calculated. After the calculation of EC50, the doses of AM and PH for prestimulation were obtained.
To further explore the interactions in different bioactive component compatibility, tension force was measured using DG + AM and DG + AM + PH as per the the abovementioned protocol [Table 1].
|Table 1: Experimental design of chemical dose in each combination (mg/mL)|
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All statistical analysis was performed using SPSS, version 24.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism, version 7.0 (GraphPad Software Inc., San Diego, CA, USA). The results were expressed as mean ± standard deviation (SD).
For two-group comparisons, the independent sample Student's t-test was used. For multi-group comparisons, one-way analysis of variance (ANOVA) followed with Student–Newman–Keuls test and Fisher's least significant difference procedure test was used. For data comparisons among different influencing factors, univariate ANOVA was used. Multiple linear regression was used for the description of bioactive component compatibility. GraphPad Prism was used to calculate the nonlinear regression of Hill equation and EC50. In all analyses, P < 0.05 was considered statistically significant.
| Results|| |
Identification of bioactive component and its mechanism in San-ao decoction
In this study, 136 bioactive components were retrieved for the three herbs, including 11, 10, and 115 from Herba Ephedrae, Semen Armeniacae Amarum, and Radix Glycyrrhizae, respectively [Figure 2]a. Through bioactive components - potential targets identification and BP enrichment analysis, ASM-related BP were selected [Figure 2]b. Among these, four bioactive components (O-benzoyl-L-(+)-pseudoephedrine, ()-N-methylpseudoephedrine, amygdalin, andglycyrrhizin) in SAD were closely related with our experiment, and 47 potential targets were obtained for these 4 bioactive components [Figure 2]c.
|Figure 2: Network pharmacology prediction results. (a) Identification of all the components in SAD. (b) BP network diagram related with ASM relaxation in SAD. (c) Bioactive component-target network of experimental agents. Combining the bioactive components related with ASM and the Pharmacopoeia of the People's Republic of China (2010 edition), we finally selected PH, AM, and DG as our experimental agents, which is closely related with ASM and including four most important bioactive components (O-benzoyl-L-(+)-pseudoephedrine, ()-N-methylpseudoephedrine, amygdalin, and glycyrrhizin) in SAD. Then we constructed these four bioactive components-target network by Cytoscape 3.2.1 software. (d) GO and (e) KEGG pathway enrichment analysis. (P < 0.05). (f) and (g) Network diagram of the top 2 KEGG pathways in the new bioactive component compatibility (including PH, AM and DG). ASM: Airway smooth muscle, PH: Pseudoephedrine hydrochloride, AM: Amygdalin, DG: Diammonium glycyrrhizate, SAD: San-ao decoction, GO: Gene ontology, KEGG: Kyoto Encyclopedia of Genes and Genomes, BP: Biological process|
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To further explore the underlying mechanisms of bioactive component compatibility in SAD, we performed GO and KEGG pathway enrichment analysis with ClueGO and identified 15 BPs, 5 CC, 11 MFs, and 48 KEGG pathways. After a comprehensive analysis of the GO and KEGG pathway results, neuroactive ligand–receptor, G-protein-coupled receptors, adrenergic receptor, blood circulation, and ASM were recognized as the main action sites [Figure 2]d and [Figure 2]e. Based on these data, we hypothesized that this new bioactive component compatibility of SAD could relax ASM through the regulations of nervous system, inflammatory response, and blood circulation. Its underlying mechanisms might be related with G-protein-coupled receptors, neuroactive ligand–receptor interaction, calcium signaling pathway, cGMP-PKG signaling pathway, adenylate cyclase-activating adrenergic receptor signaling pathway, regulation of vasoconstriction, and regulation of smooth muscle contraction.
To further elucidate the potential molecular mechanisms of these four bioactive components in relaxing ASM and their inter-relations, we selected the top two KEGG pathways for observation and found that the three experimental reagents (PH, AM and DG) all participated in the same pathway and played synergistic roles [Figure 2]f and [Figure 2]g.
To verify the hypothesis and investigate its dose–response relationship, we adopted tension force measurement technique on guinea pig ASM in vitro.
Relaxant effect of bioactive component in San-ao decoction
To gain an in-depth understanding of the degree of relaxation in different bioactive components (including PH, AM, and DG) under different initial conditions, we investigated the dose–response relationship on both Ach-induced and spontaneous contractions. Our study found that Ach-contracted ASM was present in a dose-dependent manner. The regression equation was: Y =19.01 + 43.93/(1 + 10^ ((−5.195 − X) × 0.7049)), R2 = 0.8992, and the logEC50 was −5.195 mol/l [Figure 3]a. Meanwhile, we detected an excessive dose of Ach that led to the inactivation of ASM, so the appropriate dose of Ach was crucial for the experiment and was set at 2 × 10−5 mol/l for the later study.
|Figure 3: Dose–response curves of each agent. In this part, we applied GraphPad Prism to draw the dose–response curves of each agent and calculate the nonlinear regression of Hill equation. Besides, we used SPSS to compare the statistical significance of different groups. (a) Dose–response curve of Ach. Dose–response curve of (b) DG, (c) AM, and (d) PH on Ach-induced and spontaneous contractions. Values were expressed as mean ± SD (n ≥ 6, number of aortic rings). *P < 0.05, **P < 0.01 vs. spontaneous contraction. PH: Pseudoephedrine hydrochloride, AM: Amygdalin, DG: Diammonium glycyrrhizate, SAD: San-ao decoction, Ach: Acetylcholine chlorides, SD: Standard deviation|
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Our results showed that each bioactive component had a dose-dependent relaxant effect on ASM. The regression equations of DG on Ach-induced and spontaneous contractions were: Y =6.059 + 274.659/(1 + 10^ ((0.9248 − X) ×1.18)) (R2 = 0.8918) and Y = −3.567 + 158.567/(1 + 10^ ((0.8798 − X) × 1.002)) (R2 = 0.6492), respectively. A higher dose of DG (4 mg/mL) led to a significant difference between Ach-induced and spontaneous contractions (P < 0.05) [Figure 3]b. Similarly, the regression equations of AM were: Y =9.188 + 46.652/(1 + 10^ ((1.516 − X) ×3.055)) (R2 = 0.9203) and Y = 0.19 + 9.378/(1 + 10^ ((1.561 − X) ×4.751)) (R2 = 0.6956), respectively. Results exhibited significant differences in AM doses under the two different initial conditions (P < 0.01) [Figure 3]c. Unlike DG and AM, PH had a relaxant effect at a higher dose but a contraction effect at a lower one (threshold doses: 11.8032 mM and 15.2405 mM under Ach-induced and spontaneous conditions respectively). The regression equations were: Y = −23.26 + 164.76/(1 + 10^ ((1.279 − X) × 3.786)) (R2 = 0.8626), and Y = −46.55 + 96.89/(1 + 10^ ((1.217 − X) × 14.95)) (R2 = 0.8593). The PH doses were significantly different under the initial conditions (P < 0.05 or P < 0.01) [Figure 3]d. Then, EC50 and logEC50 of each agent were calculated by GraphPad Prism, which indicate the varying therapeutic intensity [Table 2]. From these results, we inferred that DG exerted the most relaxation on in vitro ASM, followed by PH and AM.
|Table 2: Effective concentration50 and log effective concentration50 of each chemical (mM)|
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Influencing factors of relaxant effect
For in-depth understanding the influencing factors of relaxant effect in different component compatibilities, we observed the change of DG dose–response curve under different component compatibilities. The results found that different component compatibilities could change the DG dose–response curve under the two initial conditions. Specifically, under the prestimulation of AM (20 mg/mL), the regression equations of DG under Ach-induced and spontaneous contractions were: Y =8.159 + 610.241/(1 + 10^ ((1.372 − X) × 1.113)) (R2 = 0.8883), and Y = 35.92 + 39.47/(1 + 10^ ((0.2157 − X) × 2.629)) (R2 = 0.8788). If prestimulated with AM (20 mg/mL) + PH (3 mg/mL) under the two initial conditions, the regression equations of DG were: Y = −18.84 + 43349.84/(1 + 10^ ((2.88 − X) × 1.212)) (R2 = 0.9464), and Y = 11.15 + 117.05/(1 + 10^ ((1.358 − X) × 0.8202)) (R2 = 0.5738). Moreover, the relaxant effects of different bioactive component compatibilities are shown in [Figure 4]a and [Figure 4]b.
|Figure 4: Dose–response curves of different bioactive component compatibilities. In this part, we applied GraphPad Prism to draw the dose-response curves of each agent and calculate the nonlinear regression of Hill equation. Besides, we used SPSS to compare the statistical significance of different groups. The dose-response curve of DG prestimulated with AM and PH + AM on (a) Ach-induced and (b) spontaneous contractions. Values were expressed as mean ± SD (n ≥ 6, number of aortic rings). *P < 0.05, **P < 0.01 versus DG groups. ΔP < 0.05, ΔΔP < 0.01 versus AM + DG groups. PH: Pseudoephedrine hydrochloride, AM: Amygdalin, DG: Diammonium glycyrrhizate, SAD: San-ao decoction, Ach: Acetylcholine chlorides, SD: Standard deviation|
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To further explore the influencing factors on therapeutic intensity, we performed univariate ANOVA with SPSS [Table 3] and multiple linear regression with GraphPad Prism [Table 4]. Results showed that component compatibility and dose displayed statistical significance (P < 0.01), but not in the initial condition (P > 0.05). The regression equation without the interference of initial condition was Y = −2.048 × X1 + 0.411 × X2 + 14.052 × X3, R2 = 0.767 (X1, X2, X3 stands for PH, AM, and DG respectively).
|Table 4: Coefficients of the multiple linear regression equation for bioactive component compatibility|
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| Discussion|| |
Respiratory diseases manifest as breathlessness, chest tightness, cough, expectoration, and wheezing that usually influence patient quality of life to varying degrees and can even lead to death. Research has shown excessive and/or inappropriate contraction of ASM caused by inflammation to be a main cause of these symptoms., However, for the corresponding anti-inflammatory, bronchodilator, and glucocorticoid therapies, the effects are not favorable in part due to their side effects. The CHM formula SAD has been proven to be an effective therapy for treating respiratory diseases reliably and safely by a large body of clinical and experimental evidence. However, the efficacy of its bioactive component compatibility on ASM-dysfunction-induced respiratory diseases has been less studied. Therefore, we applied network pharmacology to identify the bioactive components along with its underlying mechanisms and adopted ASM tension force measurement technique to verify the relaxant effects as well as dose–response relationships. The schematic diagram of the research methodology and the proposed model of SAD bioactive component compatibility on in vitro guinea pig ASM are shown in [Figure 5].
|Figure 5: Schematic diagram of research methodology and proposed model of SAD bioactive component compatibility on in vitro guinea pig ASM. SAD: San-ao decoction, ASM: Airway smooth muscle|
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In this study, 136 bioactive components of SAD were obtained in total. PH, AM, and DG were considered as the representative ones in SAD, which were closely related to 47 potential targets. The component compatibility composed of PH, AM, and GD could effectively relax ASM in a dose-dependent manner in both Ach-induced and spontaneous conditions. The underlying mechanisms might be related to G-protein-coupled receptors, neuroactive ligand–receptor interaction, IL-17 signaling pathway, and blood circulation, which is in concordance with other research.,,,,
In this study, we have also demonstrated the significance of the TCM principle of “chief, deputy, assistant, and envoy” by analyzing these component compatibilities. The principle is used to explain the relationships between/among different herbs and the role of different ingredients in a compound formula. Usually, there are four main roles, namely synergistic effect, toxicity (side effect) attenuation, new effect emerging, and concurrent treatment for complications, which is the main difference between TCM compound formulas versus a single ingredient. Some research has applied the principle of “chief, deputy, assistant, and envoy” to investigate the relationships among bioactive components., We had originally planned to choose Maxing Ganshi Decoction, another formula effective on ASM, from the Treatise on Febrile and Miscellaneous Diseases (Shang Han Za Bing Lun). The formula contains four ingredients corresponding to the principle of “chief, deputy, assistant, and envoy,” but the main bioactive component of Gypsum Fibrosum, calcium sulfate, is insoluble in water. Finally, we chose the remaining three herbs, which are the constituents of SAD, as the research subject. Based on the analyses of relaxant effect and the EC50 of each component, we assigned DG as the chief, AM as the deputy, and PH as the assistant component. In this study, we focused on the influence of deputy or/and assistant component(s) on the chief component, that is, the AM-or/and-PH-on-DG dose–response curve. By measuring ASM tension force, with the additional stimulation of AM, there was a leftward shift of the DG dose–response curve, suggesting that relaxation was intensified at the same DG concentration and that AM + DG results in a synergistic effect. We also found that at 3 mg/mL PH, 20 mg/mL AM, and different doses of DG all displayed relaxant effects on ASM, but with the stimulation of AM + PH, there was in rightward shift of the DG dose–response curve, suggesting that relaxation was attenuated at the same DG concentration. This would indicate a decrease in relaxant effect or even a contraction at a lower dose of DG (threshold dose of DG was 1.2882 mM) on Ach-induced contraction. The underlying reason requires further exploration.
Research has demonstrated DG to increase risk of edema due to pseudo-hyperaldosteronism-induced sodium retention, which can in turn be alleviated through the combination with other components.,,, As observed in clinical practice, PH is a sympathomimetic α- and β-adrenergic receptor agonist and effectively reduces eyelid and nasal edema., Therefore, we inferred that PH may be able to reduce the side effects of DG to some extent.
With regard to the synergistic effect in component compatibility, another possible mechanism could be that different components may have the same or similar targets. For instance, NOS2 and PTGS2 are also present in these three components and can regulate pathological changes by mediating the same or similar pathways. Furthermore, although different components have different targets, they could still mediate the same or similar pathways. For instance, PTGS2 (a target of PH), HSP90AB1 (a target of AM), and TNF (a target of DG) together participate in the IL-17 signaling pathway. Hence, the TCM principle of “chief, deputy, assistant, and envoy” can be applied well to explain component compatibility.
Besides, due to the complexity and uncertainty of CHM, bioactive component compatibility approach has been a future development trend for modern Chinese Medicine. Compared to traditional CHM formulary, this approach has advantages of a simpler composition, clearer mechanism, greater quality control, economy in material requirement, and efficacy improvement. Compared to traditional single-target pharmacology, this approach also has advantages of being multi-component, multi-target and having a comprehensive, macro-level regulation effect. Therefore, the bioactive component compatibility of SAD may provide a new complementary and alternative treatment for respiratory diseases.
However, there are a few limitations in this study. First, the screening criteria of bioactive components were not comprehensive and the targets and pathways to some bioactive components have not been detected. Second, we substituted ephedrine with PH because the former is a controlled substance, but it might be an important bioactive component of Herba Ephedrae in influencing of ASM contraction. Third, we only focused on in vitro experiments to verify the therapeutic effect and dose–response relationship but lacked in vivo testing and clinical confirmation. Therefore, further studies on the underlying mechanisms in vivo and clinic testing will be expected.
| Conclusions|| |
We have primarily identified PH, AM, and DG as the main bioactive components of SAD. The bioactive component compatibility, especially AM + DG, can effectively relax ASM with synergistic effects. Its underlying mechanisms may be associated with G-protein-coupled receptors, neuroactive ligand–receptor interaction, calcium signaling pathway, cGMP-PKG signaling pathway, adenylate cyclase-activating adrenergic receptor signaling pathway, regulation of vasoconstriction, and regulation of smooth muscle contraction. It may be a novel therapeutic method for respiratory diseases in the future, and the optimal component compatibility and dosage are crucial to improving its safety and efficacy for the clinic.
We are grateful for the Classical Prescription Basic Research Team of the Beijing University of Chinese Medicine. In addition, we thank Medical Experimental Center, China Academy of Chinese Medical Sciences for providing the necessary facilities (Beijing, China). Finally, the authors thank Elsevier Language Services for providing language assistance and for proofreading the manuscript.
Financial support and sponsorship
This work was supported by the Research and Development Foundation of the Beijing University of Chinese Medicine (No. 2016-ZXFZJJ-116).
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 1], [Table 2], [Table 3], [Table 4]