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02 2023
:17;
105580
doi:
10.1016/j.arabjc.2023.105580

Screening for Q-markers of Antiviral Granules based on neuraminidase inhibitors and Effect-constituent index

Screening for Q-markers of Antiviral Granules
State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
Sichuan Provincial Engineering Research Center for Antiviral Chinese Medicine Industrialization, Sichuan Guangda Pharmaceutical Co., Ltd., Pengzhou 611930, China
Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, China

⁎Corresponding authors at: Chengdu University of Traditional Chinese Medicine, 1166 Liutai Avenue, Wenjiang District, Chengdu, Sichuan 611137, China. zhengchuan@cdutcm.edu.cn (Chuan Zheng), hanliyx@163.com (Li Han), zhangdingkun@cdutcm.edu.cn (Dingkun Zhang)

Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.
These authors contribute equally to this work.

Abstract

Abstract

Background

Antiviral granules (AG) is a classic traditional Chinese medicine commonly employed for influenza treatment. However, there is an urgent need to improve its quality control standards.

Purpose

The quality markers (Q-markers), guided by the Effect-constituent Index (ECI), were screened through in vitro and in vivo component identification, molecular docking, and neuraminidase (NA) inhibition experiments.

Methods

The chemical components in AG underwent analysis using UPLC-Q-Orbitrap HRMS and GC–MS, and serum pharmacochemistry was employed to analyze the components absorbed into the bloodstream. Molecular docking was used to evaluate the affinity of the prototype components to NA, and the inhibitory activity of the candidate components was later confirmed through NA inhibitor experiments. Following this, a UHPLC-MS/MS method was developed to screen and quantify the Q-markers in 20 sample batches. Lastly, the ECI was formulated using a multi-index comprehensive evaluation method that combines composition, content, and activity.

Results

A total of 246 compounds were identified in AG, and 89 prototype components and 19 metabolic components were detected in the serum of the rats. Molecular docking revealed that the binding energy of 35 candidate active ingredients was better than that of the original ligand and the target protein. Among these candidate active ingredients, 12 candidate Q-markers demonstrated significant inhibitory effects in NA inhibition experiments, with IC50 values ranging from 0.954 to 3.408 mM. A UHPLC-MS/MS content determination method was established for the quantification of 11 Q-markers, including Forsythiaside A, Pogostone, Verbascoside, Mangiferin, 5-methoxy-8-hydroxypsoralen, Curcumenol, Luteolin-7-glucuronide, Kaempferol-3-O-rutinoside, Baicalin, Rutin, and Bergaptol, in 20 batches of samples. The ECI was calculated based on activity and content of these Q-markers. Pearson's correlation analysis of the NA inhibitory effect of the samples and the ECI resulted in an R value of 0.859, indicating a good correlation. Furthermore, this correlation coefficient surpassed those observed between the content of each Q-marker and the NA inhibitory effect.

Conclusion

The Q-markers for treating influenza with AG have been successfully screened, and the ECI of AG has been constructed. These findings will provide beneficial strategies for improving the quality control level of AG.

Keywords

Antiviral Granules
Influenza
Q-marker
Effect-constituent Index
NA inhibitor

Abbreviations

AG

Antiviral Granules

NA

Neuraminidase

ECI

Effect-constituent Index

Q-marker

Quality marker

NAIs

Neuraminidase inhibitors

TCM

Traditional Chinese medicine

FTA

Forsythiaside A

PO

Pogostone

VER

Verbascoside

MG

Mangiferin

HMP

5-Hydroxy-8-methoxypsoralen

CUL

Curcumenol

LG

Luteolin-7-glucuronide

KOR

Kaempferol-3-O-rutinoside

BI

Baicalin

RT

Rutin

BER

Bergaptol

BC

Baicalein

OC

Oseltamivir acid

RSD

Relative standard deviation

RMSD

Root mean square deviation

HA

Hemagglutinin

1

1 Introduction

Influenza infection is caused by seasonal and cyclical influenza virus pandemics, impacting approximately 10 % of the world's population annually and resulting in around half a million deaths each year, thereby posing a significant public health challenge (Javanian et al., 2021). Neuraminidase (NA) serves as a pivotal enzyme for viral replication, transmission, and pathogenesis. It is regarded as one of the most promising anti-influenza targets (Grienke et al., 2012). Although neuraminidase inhibitors (NAIs) possess significant medical potential, the emergence of novel drug-resistant strains of influenza virus has recurrently compromised the clinical efficacy of NAIs (such as oseltamivir). In China, traditional Chinese medicine (TCM) has been integral in combating influenza. Many Chinese medicines and prescriptions, with a longstanding history of use, demonstrate ongoing effectiveness and offer potential for the innovation of new anti-influenza medications (Wu et al., 2020). Several studies have illustrated that Chinese herbal extracts, prescriptions, or their isolated bioactive components demonstrate significant inhibitory effects on NA, without inducing drug resistance. This establishes a crucial alternative therapy for influenza (Zhang et al., 2020b).

Antiviral Granules (AG) is based on modifying “Baihu Decoction”, “Qingwen Baidu Decoction”, and “Changpu Yujin Decoction” from the classic prescriptions of TCM. The composition of the prescription includes Radix Isatidis, Gypsum Fibrosum, Rhizoma Phragmitis, Radix Rehmanniae, Radix Curcumae, Rhizoma Anemarrhenae, Rhizoma Acori Tatarinowii, Herba Pogostemonis, and Fructus Forsythiae. It has the therapeutic effects of clearing heat and removing dampness, cooling blood and detoxification(Su et al., 2024). AG is widely utilized in the clinical treatment of viral infections, specifically targeting the influenza virus. A study has illustrated AG's capability to reduce the susceptibility and mortality of mice to influenza viruses. Additionally, AG has shown anti-inflammatory properties and effectively inhibited viral replication in the lungs(Chen et al., 2017a). In a multi-center, randomized, double-blind, double-dummy, positive drug parallel control trial, AG was found to be comparable to oseltamivir phosphate capsules in terms of the duration of main symptom relief, complete antipyretic time, and degree of fever control. Additionally, AG shows faster alleviation of dizziness and chest tightness while maintaining good safety(Nong et al., 2021). A study investigating the inhibitory activity of AG (sugared type) against NA revealed that, at a mass concentration of 0.02 g/mL, the average inhibition rate was 37.24 %. These findings indicate that AG possesses a certain level of NA inhibitory activity(Xie et al., 2023).

However, despite the partial exploration of the pharmacological effects and clinical efficacy of AG, its quality assessment and control are significantly limited. Presently, the quality standard for AG depends solely on forsythin as a quantitative index, which does not offer an objective reflection of its overall quality. Furthermore, the measured component cannot be correlated with its clinical efficacy, hence restraining the quality control of AG. To achieve precise quality control of TCM, Professor Liu (China) introduced the concept of quality marker (Q-marker) in 2016(Liu et al., 2017). This concept has gained extensive popularity in the research on assessing quality and controlling processes of TCM (Xiong et al., 2018a, Gao et al., 2019). Hence, the discovery of ideal Q-markers holds significant importance in enhancing the level of quality control for AG.

Multiple Q-markers can provide a more comprehensive reflection of the therapeutic effects of TCM compared to a single component. However, without considering the varying contributions of different components to the pharmacological effects, the specific role played by these active components in treatment remains unclear, thus hindering the comparison of quality among different samples (Zhang et al., 2016). Effect-constituent Index (ECI) is a comprehensive quality control measure that combines chemical composition analysis and biological effect detection (Zhang et al., 2019). It is defined as the sum of effective component contents, adjusted by the relative biological activity coefficient (Xiong et al., 2018b). Building upon the existing quality control model of “finding components and measuring content”, the concept of “joint effect” is incorporated into ECI. This inclusion facilitates an effective characterization of the individual efficacies of different components, thereby rendering the quality evaluation and control model of TCM more scientific and comprehensive (Xiong et al., 2014). Li et al. (Li et al., 2023) successfully developed an ECI model to evaluate the quality of Chuanxiong Rhizoma by integrating the bioassay method with chemical fingerprint analysis and employing correlation analysis and spectral effect characteristic relationships. This study provides a valuable example for improving the quality control of other TCMs.

This study initially characterizes the chemical composition, followed by analyzing the blood components of rats after the oral administration of AG. The prototype components were subsequently molecularly docked with NA, and compounds with binding energy lower than the original protein ligand were identified as potential active components. Further experiments utilizing NA inhibitors further confirmed the inhibitory activity of potential active components and identified potential Q-markers. Ultimately, the UHPLC-MS/MS method for content determination was developed, verified, and the Q-markers were identified. The content of 11 Q-markers in 20 sample batches was quantitatively determined, and the effectiveness of AG was evaluated by calculating ECI along with biological evaluation. This study holds practical significance in establishing and enhancing the overall quality control system of AG, as well as improving clinical services. Additionally, the strategy for discovering Q-markers, based on component analysis and biological effects, offers novel perspectives for assessing and controlling the quality of other TCMs.

2

2 Materials and methods

2.1

2.1 Materials and chemicals

20 batches of samples of AG (sugar-free) were purchased from Sichuan Guangda Pharmaceutical Co., Ltd. (No. 2305444 ∼ 2305463). Radix Isatidis, Gypsum Fibrosum, Rhizoma Phragmitis, Radix Rehmanniae, Radix Curcumae, Rhizoma Anemarrhenae, Rhizoma Acori Tatarinowii, Herba Pogostemonis, and Fructus Forsythiae were offered from Sichuan Guangda Pharmaceutical Co., Ltd.. The chemical standards, including Baicalin (BI), TimosaponinA-III, FTA, Baicalein (BC), Gitogenin, Luteolin-7-glucuronide (LG), Kaempferol-3-O-rutinoside (KOR), Sarsasapogenin, Rutin (RT), VER, Quercetin, Lariciresinol-4-O-D-glucoside, Pinoresinol-4-O-glucoside, Arctiin, Pinoresinol, Imperatorin, Mangiferin (MG), Phillygenin, Oxypeucedanin, Matairesinol, 5-Hydroxy-8-methoxypsoralen (HMP), Osthol, Salidroside, Bergaptol (BER), Deoxyvasicinone, Eremanthin, Adenosine, alpha-Curcumene, PO, Cyclo(-Leu-Pro), Curcumenol (CUL), beta-Asarone, Nootkatone, Protocatechuic acid, Arginine, Leucine, Adenosine, Indigotin, and Indirubin, were purchased from Letianmei Pharmaceutical Technology Co., Ltd. (Chengdu, China). Forsythoside E, Forsythin, and Salicylic acid were purchased from Chengdu Efa Biotechnology Co., Ltd. (Chengdu, China). Ursolic acid was purchased from Chengdu Klomar Biotechnology Co., Ltd. (Chengdu, China). The purity of the aforementioned standards is not less than 98 %. The NA solution, NA detection buffer, and fluorescent substrate were purchased from Shanghai Biyuntian Biotechnology Co., Ltd. (Shanghai, China). Methanol and acetonitrile (chromatographic grade) were purchased from Merck (Darmstadt, Germany). Formic acid (chromatographic grade) was purchased from Chengdu Kelong Chemical Reagent Factory (Chengdu, China).

2.2

2.2 Animal

Healthy female SD rats (220 ± 20 g) were purchased from Chengdu Dashuo Experimental Animal Co., Ltd. (Chengdu, China), with license number SCXK (Sichuan) 2020–030. The animals were housed in the experimental animal center at Chengdu University of TCM under controlled conditions: room temperature maintained at 25 ± 2 °C, relative humidity at 50 %-60 %, and a light/dark cycle of 12 h/12 h. The rats were randomly assigned to two groups (n = 6): the administration group (treated with AG) and the control group. They underwent a 7-day adaptive feeding period with ad libitum access to water and food. Prior to the experiment, a 12 h fasting period was implemented, allowing free access to water. All animal experimental protocols followed the guidelines and were approved by the Medical Ethics Committee of the Affiliated Hospital of Chengdu University of TCM (Approval No.: 2021–49).

2.3

2.3 Analysis of the chemical compositions in AG

2.3.1

2.3.1 Sample and standard preparation

Take 2 bags from each of the 5 batches of samples and mix them thoroughly. The volatile oil was extracted according to the method outlined in the 2020 edition of the Chinese Pharmacopoeia (general rule 2204 A method). The sample was weighed to 80.0 g and placed in a round-bottom flask containing 400 mL of water. Then, 2 mL of cyclohexane was added to the water surface in the volatile oil detector. The mixture was slowly heated to boiling and maintained at a gentle boil for 3 h before being cooled. The cyclohexane layer was transferred to a 2 mL volumetric flask. It was then diluted with additional cyclohexane up to the calibration mark, thoroughly shaken, and dehydrated using an appropriate amount of anhydrous sodium sulfate. The sample solution for GC–MS analysis was obtained by filtering the sample through a 0.22 μm microporous membrane.

Take 1 bag from each of the 5 batches of samples, remove the packaging, grind them finely, and mix thoroughly. Approximately 2.0 g of the sample powder was accurately weighed and placed into a stoppered 100 mL conical flask. Then, 100 mL of 70 % methanol solution was added. Subject the mixture to ultrasonic extraction for 30 min. After cooling, reweigh the flask and compensate for any weight loss by adding 70 % methanol solution. The sample was filtered using a 0.22 μm microporous membrane to obtain the filtrate, which was then utilized as the test solution for UPLC-Q-Orbitrap HRMS analysis.

A solution of mixed standard substances was created by dissolving an appropriate amount of each standard substance in methanol, resulting in a mass concentration of about 60 μg/mL. The solution was subsequently filtered through a 0.22 μm microporous membrane to obtain the filtrate, which served as the standard solution for UPLC-Q-Orbitrap HRMS analysis.

2.3.2

2.3.2 GC–MS analysis

The volatile components were analyzed by 7890A-5975C series gas chromatography-mass spectrometry (Agilent, USA).

An Agilent 19091S-433 capillary chromatographic column (30 m × 250 μm × 0.25 μm) was utilized for the analysis. The column temperature box initially held a temperature of 110 °C for 7 min, followed by a ramp to 170 °C at a rate of 12 °C/min and held at this temperature for 2 min. Subsequently, the temperature was raised to 230 °C at a rate of 20 °C/min and held for 2 min. Helium was employed as the carrier gas at a flow rate of 1 mL/min with an inlet temperature set at 230 °C. The split ratio was 20: 1, and the injection volume was 1 μL. Ionization was conducted using the EI mode, with an electron energy of 70 eV, an ion source temperature of 200 °C, and a scanning mass range of 30–400. The analysis utilized the NIST mass spectrometry retrieval standard library.

2.3.3

2.3.3 UPLC-Q-Orbitrap HRMS analysis

The samples were analyzed using ultra-high performance liquid chromatography-quadrupole-electrostatic field orbitrap high-resolution mass spectrometry (Thermo Fisher Scientific, USA). The chromatographic separation was carried out using a Thermo Scientific AccucoreTM C18 column (3 mm × 100 mm, 2.6 μm). The mobile phase consisted of a 0.1 % formic acid aqueous solution (A) and methanol (B). The gradient elution method was as follows: 0–11 min, 5 %-35 % B; 11–18 min, 35 %-55 % B; 18–22 min, 55 %-75 % B; 22–24 min, 75 %-90 % B; 24–25 min, 90 %-100 % B; 25–30 min, 100 % B. The column temperature was maintained at 50 °C. The flow rate was set at 0.3 mL/min, and the injection volume was 5 μL.

The mass spectrometry conditions for the analysis involved electrospray ion source (ESI) in both positive and negative ion modes of detection. The spray voltage was set to 3.5 kV (+) and 3.0 kV (-). The auxiliary gas heating temperature was maintained at 350 °C, with a sheath gas flow rate of 35 arb and an auxiliary gas flow rate of 10 arb. The ion transport tube temperature was set at 320 °C. The scanning mode employed was a full scan of first-order mass spectrometry combined with an automatic trigger second-order mass spectrometry scanning mode (Full MS/dd-MS2). The first-order resolution was 35,000, while the second-order resolution was 17, 500. The ion scanning range extended from m/z 50 to 1,500, with collision energy gradients of 20, 40, and 60 eV being employed.

The raw data collected were imported into Compound Discoverer 3.2 software. Using the software's wizard setting and method template, an identification process for unknown compounds was established, and peak alignment and extraction were performed on the original data. To determine possible molecular formulas, the extracted molecular ion chromatographic peaks and isotope peaks were fitted. The measured spectra of secondary fragments were then compared with the mz Cloud network database and the local Chinese medicine component database, OTCML. The filtering parameters for the matching results were set as follows: first-order and second-order quality deviations of 5 ppm, and a matching score greater than 80. The filtered ions were compared to the compound information and reference substances in the database. Subsequently, the chemical constituents were further analyzed and identified using relevant literature and online databases such as PubChem, Human Metabolome Database, PubMed, and CNKI. Finally, the source of each component was attributed (Fu et al., 2021, Xing et al., 2023).

2.4

2.4 Serum pharmacochemistry study of AG

2.4.1

2.4.1 Gavage sample preparation

According to the drug standard issued by the State Food and Drug Administration of China, the recommended clinical oral dosage of AG is 12 g/d, while the intragastric dose for rats is determined based on the conversion coefficient between adult and rat dosages. Appropriate samples of AG were collected and dissolved in distilled water to obtain a concentration of 1 g/mL gastric juice. The gavage dose for rats was set at 10 times the equivalent daily dose of AG, corresponding to 10 g/kg. The control group received an equal volume of distilled water.

2.4.2

2.4.2 Preparation of the sample and standard solutions

The rats received the prescribed dose of liquid medicine or water orally twice a day over 3 consecutive days. Fasting was enforced for 12 h before the last administration, with access to drinking water was permitted. Subsequently, At 5, 15, 30, 60, 120, 240, and 360 min after the last administration, 0.5 mL of blood was collected from the orbital region and transferred into an EP tube containing heparin sodium. The blood samples were then centrifuged at 4 °C and 4000 rpm for 15 min. The resulting supernatant was collected, aliquoted, and stored at −80 °C. Prior to analysis, take 200 μL of serum from each rat at 7 time points and mix them together. Additionally, three times the amount of acetonitrile was added, followed by vortexing for 3 min to ensure thorough mixing. Centrifuge the mixture at 4 °C and 12000 rpm for 15 min. Transfer the supernatant to a clean EP tube, evaporate it with nitrogen gas at room temperature, and reconstitute the residue with 200 μL of 70 % methanol. Following vortexing for 3 min and sonication for 5 min, the mixture was again centrifuged at 4 °C and 12000 rpm for 15 min. The obtained supernatant was transferred to a glass insert in the liquid phase to yield the serum pharmacochemical test solution. The detection and analysis were performed using the UPLC-Q-Orbitrap HRMS analysis method detailed in section 2.3.3.

2.5

2.5 Molecular docking

Molecular docking technology is a commonly employed research method in drug discovery and screening, particularly in the investigation of active components of TCM (Li et al., 2022c). Combined with the existing literature research (Zhang et al., 2020a; Huang et al., 2023; Xie et al., 2023), crystals closely associated with human infection were retrieved from the PDB database to serve as receptors for molecular docking(https://www.rcsb.org/). In this study, AutoDock Vina was used to assess the prototype components of AG and NA (PDB ID: 3CL0, N1 type; 4MWQ, N9 type; 4GZP, N2 type; 3TI8, N5 type; 3CKZ, N1 type; 2HU0, N1 type) and compared with the original ligand of the protein.

The 3D structure of proteins associated with NA were obtained from the Protein Data Bank (PDB) (https://www.rcsb.org/). AutoDock Tools 1.5.6 was utilized to e remove the bound proligands and crystal water molecules from the active site of the protein, and then to add polar hydrogen atoms and charges to the macromolecules. In AutoDock 1.5.6, the Gasteiger charge for each atom in the receptor molecule was calculated, and the resulting structures were saved as PDBQT files for the protein receptors (Berman et al., 2000). Chemical structures of the components to be docking were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The 3D structures of the compounds were then imported into Open Babel 3.1.1 software and transformed into PDB format. In AutoDock Tools 1.5.6, the obtained 3D structure is modified by adding hydrogen and performing protonation. Specifically, non-polar hydrogens are appropriately attached to their respective carbon atoms, and the necessary torsions are automatically set within the software. Subsequently, the modified structure is saved as a PDBQT ligand file. The PDBQT structures of the receptor and ligand were imported into AutoDock Tools 1.5.6 to create a docking binding site. During the construction of the binding site, the spacing (e) is set to 1. The DeepSite protein prediction website (https://playmolecule.com/deepsite/) was utilized to predict the centroid of each protein's binding site. Subsequently, the grid points are adjusted in the x, y, and z dimensions to fully cover the protein within the binding site. The docking calculations are performed using AutoDock Vina, with all other parameters set to their default values. Upon completion of the Vina operation, the binding affinity score between the target protein receptor and the small molecule ligand was determined based on the calculated docking binding free energy. Potential active ingredients were selected by comparing their binding energies with those of the original ligand. It is widely accepted that a lower energy indicates a higher likelihood of stable binding between the ligand and receptor. A binding energy ≤ -5.0 kJ/mol suggests good binding activity, while a binding energy ≤ -7.0 kJ/mol indicates strong binding activity (Yi et al., 2023). The schematic diagram of ligand interactions, including hydrogen bonds and hydrophobic interactions, was visualized using PyMOL 2.5.0 (Li et al., 2022b).

2.6

2.6 In vitro NA inhibitory assay

The experiment for NA inhibition was conducted using a 96-well SpectraMax iD5 multi-function microplate reader (Molecular, Inc., US). The experimental design followed the instructions provided by the kit manufacturer. Experimental reports have been published regarding the NA inhibitor screening test of active ingredients found in TCM (Luo et al., 2020, Tian et al., 2022). A reaction mixture consisting of 70 μL of reaction buffer, 10 μL of NA, and 10 μL of the sample was added to each well of a black 96-well plate. The sample, dissolved in DMSO, had a standard stock solution concentration of 10 mM. The stock solution was then diluted 2, 10, 100, and 1000 times. The concentration of the AG solution was 0.04 g/mL. The reaction mixture was vigorously mixed and incubated at 37 °C for 30 min to ensure complete interaction between NA and samples. Subsequently, 10 μL of fluorescent substrate was added to the reaction mixture, resulting in a total volume of 100 μL. The entire mixture was then vigorously mixed for approximately 1 min and incubated at 37 °C for 30 min. The fluorescence of the sample was measured using a multifunctional microplate reader. The excitation and emission wavelengths were 322 nm and 450 nm. The blank control well contained the same reaction mixture and an equal volume of DMSO as the sample. For each experiment, three wells were prepared, and the average of the results was used to determine the inhibition rate of the sample on NA. The data were analyzed using GraphPad Prism 9.3 software by GraphPad Software Inc. to determine the half inhibitory concentration (IC50) of NA activity. The inhibition rate was calculated using formula (1).

(1)
N A i n h i b i t i o n r a t e ( % ) = [ 1 - ( F s - F 0 ) / ( F m - F 0 ) ] × 100 %

Fs: the fluorescence intensity of the sample; f0: fluorescence intensity of background; fm: Blank control fluorescence intensity.

2.7

2.7 Simultaneous quantification of the Q-markers in AG by UHPLC-MS/MS

2.7.1

2.7.1 Sample and standard preparation

Each batch of AG was finely ground, and the resulting powder was accurately weighed to 2.0 g. The weighed powder was then transferred into a stoppered conical bottle. Next, 50 mL of a 50 % methanol solution was added to the conical bottle. The conical bottle was then weighed again before being subjected to ultrasonic extraction for 30 min. After cooling, any weight loss was compensated by adding additional 50 % methanol solution. The resulting solution was filtered using a 0.22 μm microporous membrane. The filtered solution served as the test solution for UHPLC-MS/MS analysis. The amounts of FTA, PO, VER, MG, HMP, CUL, LG, KOR, BI, RT and BER were accurately weighed and individually dissolved in methanol to prepare a single reference stock solution with a concentration of 0.197, 0.417, 0.426, 0.441, 0.075, 0.259, 0.102, 0.101, 0.122, 0.139 and 0.234 mg/mL, respectively.

2.7.2

2.7.2 Negative sample solution preparation

Negative control samples lacking Fructus Forsythiae, Rhizoma Anemarrhenae, Rhizoma Acori Tatarinowii, Radix Curcumae, Radix Rehmanniae, Herba Pogostemonis and Radix Rehmanniae, Herba Pogostemonis, Radix Isatidis, Angelica dahurica Tincture were prepared according to the preparation method of AG. The preparation method is as follows: The Rhizoma Anemarrhenae was extracted with 80 % ethanol by heating reflux for 3 times, 3 h each time, resulting in a concentrated paste. The remaining 8 herbs were extracted with water by heating reflux for 3 times (with collection of volatile oil), the first 1 h, the second and third 0.5 h each, resulting in a concentrated paste. The above two kinds of paste were mixed, and appropriate amount of dextrin and sodium cyclohexyl sulfamate were added to make granules. The volatile oil, patchouli oil (0.8 mL), peppermint oil (0.4 mL), and angelica dahurica Tincture (8 mL) were then mixed in. Finally, Negative control test solution were prepared according to 2.7.1.

2.7.3

2.7.3 Chromatographic conditions for UHPLC-MS/MS analysis

UHPLC-MS/MS analysis was performed using a Nexera UHPLC LC-30A/QTRAP 5500 + MS/MS system (Kyoto, Japan), equipped with an electrospray ionization (ESI) source (AB Sciex).

The chromatographic column used in the study was ACQUITY UPLC BEH C18 (2.1 × 100 mm, 1.7 μm; Waters, Massachusetts, USA), with a temperature of 35 °C. The mobile phase consisted of 0.1 % acetic acid in water (A) and acetonitrile (B). The gradient elution conditions were as follows: 0–1 min, 5 % B; 1–2 min, 50–15 % B; 2–4 min, 15–20 % B; 4–12 min, 20–80 % B; 12–13 min, 80 % B; 13–15 min, 80–5 % B; 15–16 min, 5 % B. The injection volume and flow rate were 5 μL and 0.4 mL/min, respectively.

The samples underwent positive and negative ion analysis in sMRM mode. Nitrogen served as the curtain, nebulizer, and heater gas, with pressures set at 35, 40, and 40 psi, respectively. The ion spray voltage was optimized to 5500 V, and the source temperature was set at 450 °C. Table 1 displays the optimal MRM parameters for the four analytes and IS. The data collection and analysis were performed using Analyst™ 1.6.3 software.

Table 1 Analysis Parameters of AG in Q-Markers Multi-Reaction Monitoring Mode.
No. Compounds mode Transition DP/V CE/eV tR/min
1 FTA [M + H]- 623.2/161.2 −160 −45 6.75
2 PO [M + H]+ 225.1/139.2 80 25 13.35
3 VER [M + H]- 623.2/161.2 −160 −45 6.91
4 MG [M + H]- 421.2/301.1 −150 –32 5.49
5 HMP [M + H]+ 233.0/218.1 180 40 8.79
6 CUL [M + H]+ 235.1/217.0 100 14 12.12
7 LG [M + H]+ 463.1/287.1 140 30 6.83
8 KOR [M + H]- 593.1/285.1 −200 −67 7.16
9 BI [M + H]- 445.1/113.0 −200 −40 7.92
10 RT [M + H]- 609.2/300.1 −200 −53 6.73
11 BER [M + H]+ 203.1/147.0 180 30 8.86

2.7.4

2.7.4 Method validation

The analysis was conducted in accordance with the guidelines outlined in the Chinese Pharmacopoeia 2020 and previous studies that specifically addressed the quantification of multi-component content in TCM (Commission, 2020, Song et al., 2023).

The mixed reference solution, test sample solution and each negative control sample solution were injected under the specified chromatographic and mass spectrometry conditions. The specificity was verified by comparing the chromatograms of mixed reference solution, test sample solution and negative control sample. The reference solutions were evaluated using the specified UHPLC-MS/MS conditions, which involved measuring concentration gradients. A standard curve was constructed using the mass concentration of the target compound (ng/mL) as the X-axis and the corresponding peak area as the Y-axis. The regression equation, correlation coefficient, and linear range were determined. The limit of quantification (S/N = 10) and the limit of detection (S/N = 3) for each analyte were also determined. The accuracy was assessed by performing six consecutive analyses of a sample solution. The reproducibility was evaluated through the analysis of six parallel sample solutions. The stability was estimated by analyzing repeated points (0, 2, 4, 8, 12, 24 h) from the same sample solution at different time intervals. The recovery rate was determined by weighing six parallel samples from the same batch with known content of each component and adding a reference solution with a similar mass concentration. The identical batch of sample solutions were injected and analyzed using various chromatographic columns (ACQUITY UPLC BEH C18; UltiMate UHPLC AQ C18; ACQUITY UPLC HSS C18), column temperatures (31.5; 35; 38.5 °C), and acetic acid concentrations (0.09 %; 0.1 %; 0.11 %). The robustness was investigated by calculating the RSD value for each component's content tested under varied conditions.

2.8

2.8 Generation of ECI

2.8.1

2.8.1 Calculation of ECI

Based on the results of the NA inhibitor screening experiment, the reciprocals of the IC50 values of each component were normalized to calculate the pharmacological weights of each component. The method for calculation is outlined in Formula (2).

(2)
W i = 1 / I C 50 i i = 1 n 1 / I C 50 i

In the formula, W i is the contribution of each component, IC 50 i is the IC50 value of the active component, i is the name of the component, and n is the number of components.

The ECI is assessed using the comprehensive index evaluation method, as per the calculation method outlined in Formula (3).

(3)
Z m = i = 1 n W i × X i

In the formula, Z m is ECI of different batches of samples, W i is the efficacy weight of each component, and X i is the amount of each component in the sample.

2.8.2

2.8.2 Verification of ECI of AG

The NA inhibitory activity of 20 sample batches was assessed. The relative inhibition rate for each batch of AG samples were determined by comparing them to the reference inhibitory activity of FTA. To investigate the correlation between ECI and efficacy, Pearson's correlation coefficient (R) was used to calculate the correlation between ECI and the content of each component with the relative inhibition rate.

3

3 Results and discussion

3.1

3.1 In vivo and in vitro component analysis of AG

UPLC-Q-Orbitrap HRMS and GC–MS techniques were used to analyze the components of AG. Fig. 1 displays the corresponding chromatograms. A total of 246 components were identified by comparing their molecular weight, retention time, mass spectrometry data, and standard substance information. Among these, 207 were identified using UPLC-Q-Orbitrap HRMS, while 39 were identified using GC–MS (supplementary material 1). A total of 89 prototype compounds and 19 metabolites were identified from the serum of the administration group rats using UPLC-Q-Orbitrap HRMS (Table 2). The prototype compounds include organic acids, alkaloids, nucleosides, coumarins, terpenes, and phenylethanoid glycosides. The metabolites mainly consist of products resulting from hydrolysis, dehydrogenation, methylation, hydroxylation, and other reactions of in vitro components. The mass spectrometry behavior of the prototype components, and possible metabolic pathways of the metabolic components were shown in Table 2. The related chromatograms of serum pharmacochemistry were shown in Fig. 1.

UPLC-Q-Orbirap HRMS positive (a), negative (b) ion mode of AG containing serum (A), extract (B), mixed reference solution (C) total ion flow diagram. GC–MS total ion chromatogram (D) of volatile oil of AG. Peaks 1–17 are Arginine, Adenosine, DL-Norleucine, Protocatechuic acid, Forsythoside E, MG, FTA, VER, RT, Salicylic acid, Forsythin, Indigo, CUL, Indirubin, PO, TimosaponinA-Ⅲ, Ursolic Acid, respectively.
Fig. 1
UPLC-Q-Orbirap HRMS positive (a), negative (b) ion mode of AG containing serum (A), extract (B), mixed reference solution (C) total ion flow diagram. GC–MS total ion chromatogram (D) of volatile oil of AG. Peaks 1–17 are Arginine, Adenosine, DL-Norleucine, Protocatechuic acid, Forsythoside E, MG, FTA, VER, RT, Salicylic acid, Forsythin, Indigo, CUL, Indirubin, PO, TimosaponinA-Ⅲ, Ursolic Acid, respectively.
Table 2 The components of AG that are absorbed into the bloodstream.
NO. Name tR
(min)
Formula Adduct Observed
m/z
Mass
Error(ppm)
Fragment ions Absorption
form
1 Malic acid 1.37 C4H6O5 [M−H]- 133.0137 0.00 133.0135,89.0234,71.0128 Prototype
2 Citric acid 1.40 C6H8O7 [M−H]- 191.0196 0.21 191.0194,147.0291,111.0079 Prototype
3 2-Furoic acid 1.85 C5H4O3 [M−H]- 111.0080 −0.18 111.0079,83.0130,67.0179 Prototype
5 Succinic acid 2.06 C4H6O4 [M−H]- 117.0186 −0.17 116.9277,99.0078,73.0285 Prototype
6 Proline 2.15 C5H9NO2 [M + H]+ 116.0710 −0.09 116.0709,70.0658 Prototype
7 Adenosine 2.45 C10H13N5O4 [M−H]- 113.0350 0.35 136.0620,57.0340 Prototype
8 2′-Deoxyadenosine 2.60 C10H13N5O3 [M + H]+ 252.1097 0.00 252.1090,136.0620,117.0552 Prototype
9 Tyrosol 3.50 C8H10O2 [M + H]+ 139.0757 0.29 121.0288,97.0653 Prototype
10 5-methoxy-8-hydroxy-psoralen 3.59 C12H8O4 [M−H]- 215.0327 −0.79 215.0098,89.0236,71.0327 Prototype
11 Epigoitrin 4.22 C5H7NOS [M + H]+ 130.0325 −0.15 130.0323,96.0450,70.0658 Prototype
12 3,4-Dihydroxyphenylethanol 4.23 C8H10O3 [M−H]- 153.0500 −0.13 123.0444,109.0287,81.0336 Prototype
13 Protocatechuic acid 4.76 C7H6O4 [M + H]+ 155.0343 −0.06 153.0194,109.0286,91.0189 Prototype
14 Eremanthin 4.88 C15H18O2 [M + Na]+ 253.1301 3.79 253.1299,236.1032 Prototype
15 Indole-3-carboxaldehyde 5.52 C9H7NO [M + H]+ 146.0602 −0.27 146.0603,118.0655,91.0548 Prototype
16 D-Tryptophan 5.63 C11H12N2O2 [M + H]+ 205.0974 0.15 132.0810,188.0708 Prototype
17 DL-4-Hydroxyphenyllactic acid 5.73 C9H10O4 [M−H]- 181.0502 −0.22 163.0392,135.0441 Prototype
18 Indole-3-acrylic acid 5.82 C11H9NO2 [M + H]+ 188.0709 −0.16 188.0709,170.0602 Prototype
19 Mussaenosidic acid 5.93 C16H24O10 [M−Cl]- 411.1065 0.17 375.1300,213.0767,169.0864 Prototype
20 Salidroside 6.53 C14H20O7 [M−Cl]- 335.0909 0.33 299.1137,179.0565,119.0493 Prototype
21 Vanillin 7.08 C8H8O3 [M + H]+ 153.0530 −1.05 125.0599,111.0446 Prototype
22 Forsythoside E 7.11 C20H30O12 [M−H]- 461.1674 0.33 315.1093,205.0715,135.0444 Prototype
23 Chlorogenic acid 7.45 C16H18O9 [M−H]- 353.0886 0.37 191.0557,179.0345,161.0245 Prototype
24 2,3-Dihydroxybenzoic acid 8.10 C7H6O4 [M−H]- 153.0189 0.07 153.0187,109.0286 Prototype
25 Caffeic acid 8.36 C9H8O4 [M−H]- 179.0347 0.17 179.0346,135.0445,107.0493 Prototype
26 Mangiferin 10.16 C19H18O11 [M + H]+ 423.0928 0.02 405.0822,387.0717,273.0398 Prototype
27 Cyclo(leucylprolyl) 10.46 C11H18N2O2 [M + H]+ 211.1445 −0.09 211.1446,183.1495,70.0659 Prototype
28 Kaempferol-3-O-rutinoside 10.64 C27H30O15 [M−H]- 593.1525 2.53 593.1510,363.0672 Prototype
29 p-Coumaric acid 10.74 C9H8O3 [M−H]- 163.0396 0.06 119.0495,117.0340,91.0543 Prototype
30 coumarin 11.05 C9H6O2 [M + H]+ 147.0442 0.07 119.0494,91.0548 Prototype
31 2,4,5-trimethoxybenzoic acid 11.14 C10H12O5 [M + H]+ 213.0761 −0.09 195.0654,151.0391,137.0235 Prototype
32 Quercetin 11.36 C15H10O7 [M−H]- 301.0309 −1.30 301.0709,121.0286 Prototype
33 Forsythoside A 11.50 C29H36O15 [M−H]- 623.1995 0.30 461.1662,179.0643,133.0287 Prototype
34 Deoxyvasicinone 11.57 C11H10N2O [M + H]+ 187.0869 −0.11 187.0868,144.0812 Prototype
35 6-Hydroxynicotinic acid 11.61 C6H5NO3 [M−H]- 138.0191 −0.07 138.0190,108.0208,92.0388 Prototype
36 Apigenin-7-O-Glucoside 12.11 C21H20O10 [M−H]- 431.0993 0.35 341.0667,311.0565,283.0615 Prototype
37 Lariciresinol-4-O-β-D-glucoside 12.13 C26H34O11 [M−H]- 521.2011 −0.23 329.1394,175.0761,160.0523 Prototype
38 Apigenin7-O-glucuronide 12.42 C21H18O11 [M + H]+ 447.0933 0.11 271.0603,243.0656,153.0184 Prototype
39 Bergaptol 12.93 C11H6O4 [M−H]- 201.0250 3.08 201.0224,89.0236 Prototype
40 pinoresinol 13.11 C20H22O6 [M−H]- 357.1346 0.22 342.1112,151.0394,136.0159 Prototype
41 Pinoresinol4-O-glucoside 13.14 C26H32O11 [M−H]- 519.1877 0.21 314.1385,291.1018,175.0755 Prototype
42 Indole-3-acetic acid 13.41 C10H9NO2 [M + H]+ 176.0710 0.23 130.0653,117.9802 Prototype
43 Verbascoside 13.57 C29H36O15 [M−H]- 623.1995 0.30 461.1671,161.0239,1330288 Prototype
44 Umbelliferone 13.61 C9H6O3 [M + H]+ 163.0393 0.18 145.0288,135.0442 Prototype
45 Luteolin-7-glucuronide 13.65 C21H18O12 [M−H]- 461.0740 0.43 285.0407,151.0029,133.0287 Prototype
46 Matairesinol 13.96 C20H22O6 [M + H]+ 359.1496 0.03 341.1388,323.1284,137.0599 Prototype
47 Cinnamaldehyde 14.01 C9H8O [M + H]+ 133.0651 −0.15 133.0650,105.0703,79.0549 Prototype
48 Ferulaldehyde 14.03 C10H10O3 [M + H]+ 179.0706 −0.11 161.0599,133.0650,105.0703 Prototype
49 Rutin 14.09 C27H30O16 [M−H]- 609.1472 0.26 300.0277,271.0251,243.0298 Prototype
50 Salicylic acid 14.18 C7H6O3 [M−H]- 137.0238 −0.07 137.0237,93.0337 Prototype
51 2,4,5-Trimethoxybenzaldehyde 14.34 C10H12O4 [M + Na]+ 197.0797 −0.56 169.0861,138.0677,154.0626 Prototype
52 Baicalin 15.19 C21H18O11 [M−H]- 445.0785 0.31 269.0459,175.0244,113.0236 Prototype
53 Arctiin 15.43 C27H34O11 [M + H]+ 557.2011 0.22 371.1505,356.1270,121.0286 Prototype
54 D-(+)-Camphor 15.61 C10H16O [M + H]+ 153.1275 0.07 153.1275,135.1170 Prototype
55 Thymol 15.83 C10H14O [M + H]+ 151.1120 0.20 133.1014,104.9638,93.0704 Prototype
56 Ferulic acid 15.85 C10H10O4 [M + Na]+ 217.0476 0.00 199.1158,129.0700 Prototype
57 Forsythin 16.04 C27H34O11 [M + H]+ 557.2005 0.11 371.1503,121.0287,71.0127 Prototype
58 Phillygenin 16.08 C21H24O6 [M + H]+ 373.1651 −0.04 337.1437,284.1044,137.0599 Prototype
59 Eugenol 17.59 C10H12O2 [M + H]+ 165.0916 0.36 165.0548,137.0298,104.9638 Prototype
60 Baicalein 18.70 C15H10O5 [M−H]- 269.0459 0.33 241.0498,197.0506 Prototype
61 β-Asarone 19.04 C12H16O3 [M + H]+ 209.1176 −0.10 181.0861,121.0651 Prototype
62 Osthol 20.41 C15H16O3 [M + H]+ 245.1176 0.16 227.1077,69.0343 Prototype
63 Oxypeucedanin 20.57 C16H14O5 [M + H]+ 287.0919 −0.03 203.0340,147.0442,131.0494 Prototype
64 Abscisic acid 20.90 C15H20O4 [M + H]+ 265.1438 −0.08 265.1419,247.1329,229.1228 Prototype
65 (+)-alpha-Curcumene 21.60 C15H22 [M + H]+ 203.1802 0.39 203.0528,147.1170 Prototype
66 Corchorifattyacid F 21.71 C18H32O5 [M−H]- 327.2181 0.28 291.1966,229.1444,183.1391 Prototype
67 (15Z)-9,12,13-Trihydroxy-15-octadecenoic acid 22.38 C18H34O5 [M−H]- 329.2339 0.33 293.2123,211.1337,139.1121 Prototype
68 Imperatorin 22.44 C16H14O4 [M + Na]+ 293.0790 0.00 293.2097,224.0782 Prototype
69 TimosaponinA-III 23.15 C39H64O13 [M + H]+ 741.4433 0.11 579.3903,417.3383,255.2111 Prototype
70 Sarsasapogenin 23.21 C27H44O3 [M + H]+ 417.3367 −0.05 273.2215,255.2109,159.1169 Prototype
71 Nootkatone 24.13 C15H22O [M + H]+ 219.1747 0.18 201.1317,121.1014,95.0497 Prototype
72 Pogostone 24.28 C12H16O4 [M + H]+ 225.1122 −0.22 207.1018,165.0549,139.0391 Prototype
73 Curcumenol 24.47 C15H22O2 [M + H]+ 235.1693 −0.21 189.1640,95.0861 Prototype
74 Cuminaldehyde 24.58 C10H12O [M + H]+ 149.0963 0.13 149.0961,133.0652,79.0549 Prototype
75 Diisobutylphthalate 25.03 C16H22O4 [M + Na]+ 301.1417 0.03 223.0963,149.0235,57.0707 Prototype
76 Dibutylphthalate 25.22 C16H22O4 [M + Na]+ 301.1417 0.03 149.0235,121.0298 Prototype
77 Palmitoleic acid 25.58 C16H30O2 [M + H]+ 255.2321 −0.12 253.2373,207.7657 Prototype
78 Gitogenin 25.85 C27H44O4 [M + Na]+ 455.3141 0.09 455.3139,336.1871 Prototype
79 Oleanolic acid 26.70 C30H48O3 [M−H]- 455.3536 0.24 439.3580,393.3514,249.1859 Prototype
80 α-Linolenic acid 26.88 C18H30O2 [M−H]- 277.2175 0.25 149.0235,109.1016,81.0706 Prototype
81 Myristic acid 26.95 C14H28O2 [M−H]- 227.2016 0.22 227.2016,165.1912 Prototype
82 Hexadecanamide 26.97 C16H33NO [M + H]+ 256.2640 0.00 130.1231,116.1072,88.0763 Prototype
83 Oleamide 27.02 C18H35NO [M + H]+ 282.2798 0.04 247.2420,97.1017,83.0862 Prototype
84 Linoleic acid 27.16 C18H32O2 [M−H]- 279.2331 0.25 279.2331,261.4989,57.0045 Prototype
85 Elaidic acid 27.53 C18H34O2 [M−H]- 281.2488 0.28 281.2486,56.2365 Prototype
86 Diisooctyl phthalate 27.56 C24H38O4 [M + H]+ 391.2850 0.18 149.0234,121.0287,71.0862 Prototype
87 Erucamide 28.20 C22H43NO [M + Na]+ 360.3239 −0.11 321.3153,303.3047,135.1168 Prototype
88 Stearic acid 28.28 C18H36O2 [M−H]- 283.2644 0.04 283.2644,111.4894 Prototype
89 Arachidic acid 29.13 C20H40O2 [M−H]- 311.2959 0.29 311.2960,183.0118 Prototype
90 Pogostone dehydrogenated metabolite 0.40 C12H14O4 [M + Na]+ 245.0790 −0.32 91.0581 Metabolites
91 Caffeic acid deoxy metabolite 1.83 C9H8O3 [M + H]+ 165.0548 −2.24 147.0441,137.0596,123.0443 Metabolites
92 Methyl protocatechuic acid sulfate metabolite 4.49 C8H8O7S [M−H]- 246.9921 3.43 167.0344 Metabolites
93 Caffeic acid glucuronid 5.71 C15H16O10 [M−H]- 355.0676 3.03 355.0676 Metabolites
94 Methylated caffeic acid sulfate conjugate 7.57 C10H10O7S [M−H]- 273.0081 4.39 193.0502 Metabolites
95 Methylcaffeic acid glucuronide metabolite 7.89 C16H18O10 [M−H]- 369.0835 3.59 193.0505 Metabolites
96 Hydrated oxidizing metabolite of oxypeucedanin 14.12 C16H16O7 [M + H]+ 321.0959 −4.76 321.0958 Metabolites
97 Neohesperidin methylated dehydrogenated
metabolite
14.14 C29H34O15 [M−H]- 621.1834 2.33 621.1834 Metabolites
98 Kaempferol-3-O-rutinoside deglucose-hydrogenated metabolite 16.94 C21H22O10 [M−H]- 433.1347 2.83 257.0819,175.0243,113.0236 Metabolites
99 Hydroxylated pogostone 18.57 C12H16O5 [M−H]- 239.0924 1.88 221.0815,195.1022,81.0334 Metabolites
100 Quercetin deoxygenating metabolite 18.62 C15H10O5 [M−H]- 269.0459 3.35 224.0482,133.0287,107.0132 Metabolites
101 Dehydration-reduction oxypeucedanin hydrate
gluconuronic acid binding
20.35 C22H26O11 [M−H]- 465.1409 2.60 289.1084,245.1183 Metabolites
102 Dehydrated oxypeucedanin hydrate 20.57 C16H14O5 [M + Na]+ 287.0918 −0.52 203.0340,175.0389,159.0441 Metabolites
103 Hydrolyzed metabolites of pogostone 21.78 C12H14O5 [M−H]- 237.0769 2.53 193.1022,179.0705,99.0078 Metabolites
104 α-linolenic acid hydrogenated reducing metabolite 24.97 C18H32O2 [M−H]- 279.2323 −0.38 261.1858,135.1172,71.0128 Metabolites
105 α-linolenic acid hydroxylated metabolite 25.52 C18H30O3 [M−H]- 293.2127 3.51 275.9474 Metabolites
106 α-linolenic acid hydrolytic metabolite 25.99 C18H32O3 [M−H]- 295.2280 2.30 277.2176 Metabolites
107 α-linolenic acid dehydrogenated metabolite 26.59 C18H28O2 [M−H]- 275.2019 2.89 231.2117,59.0126 Metabolites
108 Timosaponin BII-3 × C6H10O5 27.16 C27H46O4 [M−H]- 433.3317 −0.20 433.3317 Metabolites

3.2

3.2 Molecular docking

A redocking process was carried out to validate the used docking protocol, the original ligand of each NA protein was redocked to the active sites of these proteins(PDB ID 3CL0; 4MWQ; 4GZP; 3TI8; 3CKZ; 2HU0). The verification results showed that the structures of the original ligand and the docked ligand were well superimposed, and the root mean square deviation (RMSD) values were 0.82, 0.931, 0.258, 0.976, 0.069 and 0.701 ≤ 2 Å, indicating that the docking scheme is reliable(Bell and Zhang, 2019).

To examine the interaction between AG and NA, molecular docking modeling was employed to evaluate the binding affinity of the prototype components to the corresponding proteins. Potential active components were screened based on their binding activity, comparing them to the original ligand of the protein, evaluating those that displayed equal or superior binding activity. The findings revealed that 35 compounds, including timosaponin A-III, BI, and FTA, exhibited superior binding activity compared to the original ligands of at least one of the paired proteins. Moreover, the binding activity of these active components was observed to be less than −5.0 kJ/mol (Fig. 2).

Heat map of molecular docking results of potential active ingredients with the NA. The ligands in the map represent the protein's original ligand and its respective docking results. The intensity of the blue color on the map indicates the strength of the binding activity.
Fig. 2
Heat map of molecular docking results of potential active ingredients with the NA. The ligands in the map represent the protein's original ligand and its respective docking results. The intensity of the blue color on the map indicates the strength of the binding activity.

The molecular docking analysis revealed a consistent trend in the binding activity between the compounds in the bloodstream and various proteins. Most of the potential bioactive compounds exhibited good binding affinity with different proteins. The PyMOL software was employed to visually represent select docking results. It is evident that BI interacts via hydrogen bonding with amino acid residues ASN-296, ARG-294, ASN-348, and G39-513 of the 4MWQ protein. Timosaponin A-III interacts through hydrogen bonding with amino acid residues TYR-100, VAL-136, ARG-172, GLU-174, and LYS-206 in the 2HU0 protein. FTA forms hydrogen bonds with amino acid residues TYR-169A, ASN-170, ARG-130, ARG-172, GLU-128, and LYS-206 in the same protein. KOR is capable of hydrogen bonding with amino acid residues GLU-276, TYR-374, ARG-224, ARG-292, ASN-294, GLU-119, THR-439, ARG-156, and TRP-178 in the 2HU0 protein. Lastly, Gitogenin forms hydrogen bonds with amino acid residues LYS-431, ARG-430, and ARG-118 in the 4GZP protein. Fig. 3 illustrated the predicted binding mode of the potential active ingredients that exhibit significant binding activity towards five NA proteins.

The predicted binding mode of active components to NA. Green represents active compounds, translucent blue represents proteins, and dark blue represents amino acid residues.
Fig. 3
The predicted binding mode of active components to NA. Green represents active compounds, translucent blue represents proteins, and dark blue represents amino acid residues.

3.3

3.3 Experiment for screening NA inhibitors

The inhibitory activity of 35 potential active compounds and the positive control OC against NA was assessed using a NA inhibitor screening kit. The results revealed that the IC50 value of the positive control was 1.461 mM. FTA, BC, PO, VER, MG, HMP, CUL, LG, KOR, BI, RT, and BER exhibited IC50 values of 0.594, 0.802, 1.063, 1.215, 1.331, 1.487, 1.515, 1.526, 1.599, 1.773, 2.657, and 3.408 mM, respectively (Fig. 4). The remaining compounds showed an inhibition rate of less than 50 % at a concentration of 10 mM, precluding the calculation of their IC50 values.

The inhibitory effects of 20 batches of samples and active ingredients on NA. (a) The dose–effect relationship of active ingredients and positive drugs on the inhibitory effects of NA. (b) The IC50 values of active ingredients and positive drugs in relation to their inhibitory effects on NA. (c) The inhibitory effects of 20 sample batches on NA. (d) Correlation analysis of the inhibitory effects of 20 sample batches on NA and ECI.
Fig. 4
The inhibitory effects of 20 batches of samples and active ingredients on NA. (a) The dose–effect relationship of active ingredients and positive drugs on the inhibitory effects of NA. (b) The IC50 values of active ingredients and positive drugs in relation to their inhibitory effects on NA. (c) The inhibitory effects of 20 sample batches on NA. (d) Correlation analysis of the inhibitory effects of 20 sample batches on NA and ECI.

3.4

3.4 Quantitative analysis of the Q-markers in AG by UHPLC-MS/MS

3.4.1

3.4.1 Optimization of the analysis conditions for UHPLC-MS/MS

The mass spectrometry parameters and chromatographic conditions of the target compounds were optimized in the early stage of this study to achieve the highest resolution chromatogram within a shorter timeframe. The precursor ions (Q1) and product ions (Q3) were selected based on the compound characteristics, while the collision energy (CE) and clustering voltage (DP) parameters were optimized based on the response values of the target ions. The detailed information is shown in Table 1. However, during the methodological investigation, it was found that the content of BC in the samples was extremely low, resulting in insufficient recovery and quantitative limits. Considering the clear dose–response relationship of NA inhibition, the extremely low content of BC would barely affect the overall inhibitory effect of the samples. Therefore, taking into account the measurability requirements of the Q-markers, it was determined to quantitate the other 11 Q-markers excluding BC. In addition, there is isomerism in the target compounds, and the detection of the target compounds mentioned above can be achieved by the retention time in chromatographic column separation. To enhance the separation process within a reasonable running time, various mobile phases (water/acetonitrile, water/methanol, 0.1 % formic acid water/acetonitrile, 0.1 % acetic acid water/acetonitrile) were used for analysis. The final elution conditions were 0.1 % acetic acid concentration in water/acetonitrile, with a flow rate of 0.4 mL/min gradient elution. Simultaneously, the optimal gradient elution time was adjusted to achieve optimal separation, symmetrical peak shapes, and ideal response for each target compound, aiming to reduce overall analysis time. Finally, 50 % methanol, 70 % methanol, methanol, 50 % ethanol, 70 % ethanol and ethanol were used as extraction solvents. The results showed that the extraction rate of Q-markers was the highest and the peak shape was better when 50 % methanol was used as extraction solvent.

3.4.2

3.4.2 Methodological validation

Following optimization of the detection conditions, specificity, the linearity, detection limit, quantification limit, precision, reproducibility, stability, recovery and robustness of the UHPLC-MS/MS method were evaluated. The chromatograms of the mixed reference solution and the sample solution in positive and negative ion modes are shown in Fig. 5. The chromatograms of each negative control sample solution are shown in Supplementary Fig.1. The peak shape of the 11 components tested in the sample solution was satisfactory and consistent with the retention time of the mixed reference substance. Each negative sample have no interference at the peak of the component to be tested, indicating that the method had good specificity. The correlation coefficients for each analyte within their respective concentration ranges were all greater than 0.992, indicating a strong linear relationship. The relative standard deviation (RSD) of the peak area for each component ranged from 1.16 % to 3.96 % after six repeated injections, demonstrating good instrument precision. Reference substance was added to the sample, and the recovery for each component ranged from 96.66 % to 105.04 %, validating the effectiveness of the sample preparation method. Six samples were prepared simultaneously, and the RSD of peak area for each component ranged from 0.42 % to 3.90 %, demonstrating the good repeatability of the preparation method. The results, as shown in Table 3, indicated that the peak area RSD for each component ranged from 2.33 % to 4.61 % at 0, 4, 8, 12, and 24 h post-preparation, indicating good stability of each component. The results of robustness test indicated that the RSD values for 11 components under various chromatographic columns, column temperatures and acetic acid concentrations were in the range of 1.84 % to 4.92 %, 1.62 to 4.62 % and 1.34 % to 4.24 %, respectively. This suggests that the method demonstrated good robustness within a specific range (Cui et al., 2023) (Table 3). The results of the methodological investigation are compliant with the provisions of the Chinese Pharmacopoeia, indicating that the method is sensitive, reliable, and capable of concurrently determining 11 Q-markers (Commission, 2020).

Multi-reaction detection chromatograms of 11 Q-markers standard (I) and sample (II) in positive (A) negative (B) ion mode.
Fig. 5
Multi-reaction detection chromatograms of 11 Q-markers standard (I) and sample (II) in positive (A) negative (B) ion mode.
Table 3 The Linear regression data, LOD, and LOQ for the Q-markers in the AG.
Compounds Regression equation Linear range
(μg/mL)
R2 LOQ
(μg/mL)
LOD
(μg/mL)
FTA Y = 524.14020X + 34405.8 1.45–147 0.9933 0.158 0.039
PO Y = 1280.55108X + 3217.47853 0.21–10.425 0.9923 0.161 0.042
VER Y = 644.91506X + 23596.88985 2.3–116 0.9978 0.426 0.092
MG Y = 259.4936X-4425.1039 1.3–13.05 0.9986 0.353 0.176
HMP Y = 129.58793X-3189.41542 0.69–11.25 0.9973 0.600 0.023
CUL Y = 3150.23093X-13705.20537 0.22–10.36 0.9988 0.104 0.052
LG Y = 508.2398X-12552.16773 0.20–10.2 0.9971 0.082 0.020
KOR Y = 85.18389X-977.20287 0.08–10.1 0.9985 0.053 0.020
BI Y = 73.40849X-50442.26 4.16–104 0.9983 0.832 0.034
RT Y = 580.32260X-2037.10498 0.22–11.12 0.9991 0.056 0.028
BER Y = 512.74197X-35.99722 0.23–11.68 0.9994 0.058 0.029

3.4.3

3.4.3 Analysis of the sample

In this study, UHPLC-MS/MS was employed to quantitatively analyze 11 Q-markers in 20 batches of AG. The results indicated that the samples had the highest contents of FTA and VER, ranging from 562.97 to 1829.26 μg/g. FTA is derived from Forsythia suspensa, a principal drug in the prescription. The content of this component showed similar results in a quantitative analysis of Forsythia suspensa (Chen et al., 2017b). VER is simultaneously derived from Rehmannia glutinosa and Pogostemon cablin, which could be one of the reasons for the high content of this component (Li et al., 2022a, Xie et al., 2022). Additionally, the sample also exhibits relatively high content of MG and BI. It is worth noting that MG is the primary active ingredient in Anemarrhena asphodeloides (Zhong et al., 2023), while BI is the active constituent found in Forsythia suspensa (Qi et al., 2021). The tested samples exhibited the lowest contents of BER and KOR (Table 4). The results of this study indicate variations in the levels of different Q-markers among the samples, which may potentially lead to diverse effects on the inhibition of NA activity. At the same time, the content of the same Q-marker in AG was also different among different batches, suggesting the necessity of improving its quality control level. Therefore, this quantitative analysis method is expected to provide a reference for the quality control of AG, ensuring its clinical efficacy and safety. However, further research is required to investigate the contribution of these components to the inhibitory effect of NA. Fig. 5 displayed the multi-reaction detection chromatograms of both the standard and sample.

Table 4 The content of 11 Q-markers (μg/g) and the construction of ECI in 20 batches of AG.
No.
Sample
FTA PO VER MG HMP CUL LG KOR BI RT BER ECI
S1 711.37 39.49 1331.10 170.92 43.63 9.10 49.48 6.04 180.42 77.11 62.71 333.10
S2 907.35 39.73 1485.37 184.24 28.70 9.46 64.57 8.42 228.24 112.37 41.90 395.62
S3 933.02 49.41 1638.09 227.96 45.50 9.27 49.53 6.73 179.62 104.65 69.97 419.12
S4 1043.44 40.54 1829.26 217.76 45.01 8.67 67.34 7.17 209.92 110.31 68.13 460.84
S5 921.59 70.70 1729.69 181.03 36.24 8.38 53.07 7.41 176.42 106.61 54.05 422.98
S6 746.25 45.30 1609.24 69.17 31.86 7.95 52.39 3.11 141.81 45.67 44.60 354.13
S7 813.42 49.65 1520.51 189.24 26.15 8.12 49.76 7.37 163.96 105.44 39.94 374.66
S8 769.90 36.75 1592.66 224.58 36.89 9.49 62.87 5.55 202.89 84.77 58.60 379.10
S9 725.42 27.25 1667.22 173.32 40.44 6.46 52.50 5.04 169.27 74.64 65.02 368.09
S10 690.06 45.77 1006.11 158.96 31.06 6.52 39.98 6.56 148.36 80.49 47.10 290.36
S11 640.57 25.25 1475.22 164.06 26.13 6.46 49.05 4.48 161.14 66.76 39.38 326.19
S12 562.97 31.74 1453.17 62.29 28.66 7.04 44.58 2.75 124.75 36.48 39.34 294.73
S13 646.78 43.19 1570.55 167.27 35.42 7.61 46.70 5.24 154.79 74.55 56.17 340.91
S14 665.83 51.62 1545.43 185.23 36.89 7.56 41.63 4.98 144.01 90.39 62.66 344.90
S15 788.20 37.03 1608.57 137.82 26.14 7.83 51.47 5.64 176.53 70.73 43.67 371.49
S16 677.19 21.36 890.43 58.93 24.34 5.54 36.80 2.44 109.07 29.36 34.44 256.81
S17 577.60 23.14 1454.14 90.54 23.37 6.46 48.83 5.24 162.81 69.57 36.27 303.68
S18 785.58 31.40 1035.16 211.82 29.74 7.99 53.84 5.20 185.26 78.23 47.16 320.10
S19 578.29 27.72 1453.45 204.78 24.63 7.87 47.58 4.04 160.15 70.06 39.75 314.95
S20 742.51 29.02 948.45 161.36 19.65 6.99 38.73 4.41 137.19 57.94 30.98 290.10
Correlation
(R)
0.69 0.39 0.67 0.47 0.47 0.44 0.67 0.47 0.56 0.55 0.52 0.81

3.5

3.5 Construction of ECI

The efficacy weight was calculated by normalizing the IC50 values of the 11 Q-markers in AG against the NA inhibition rate. FTA had the largest effect weight, indicating the highest activity level. The effect of BER had the smallest weight, indicating the weakest activity. The ECI was calculated by combining the Q-markers content of each sample batch, based on Formula (4).

(4)
Z m = 0.209 × X F T A + 0.117 × X P O + 0.102 × X V E R + 0.093 × X M G + 0.084 × X H M P + 0.082 × X C U L + 0.081 × X L G + 0.078 × X K O R + 0.070 × X B I + 0.046 × X R T + 0.036 × X B E R

The results were shown in Table 4, where the maximum ECI value is 460.84 (sample S4), and the minimum ECI value is 256.81 (sample S16). The correlation analysis showed that the R value of ECI and biological titer correlation analysis was 0.81. The NA inhibitory activity of each batch of samples is significantly correlated with the calculated ECI results, showing a positive relationship. Specifically, higher ECI values correspond to stronger NA inhibitory activity in AG. Furthermore, the correlation analysis revealed R values ranging from 0.69 to 0.39 between the content of each component and NA inhibitory activity. These values consistently remained lower than the R value for NA inhibitory activity and the ECI. Thus, compared to using the content of each individual component as the sole indicator, the ECI offers a more scientific and reasonable approach to characterizing the pharmacological activity of AG.

4

4 Conclusion

AG is a Chinese patent medicine that has been available in the market for several years and has shown significant efficacy in treating influenza. However, its active ingredients and mechanism of action are still unclear. The existing quality control components are only evaluated based on a chemical index, and do not correlate with its efficacy. This limitation affects the level of quality evaluation. The Q-marker is the primary active ingredient that is closely associated with efficacy. A scientific selection and evaluation of the Q-marker can ensure the quality control and clinical usefulness of drugs (Xiang et al., 2018). In this study, high-throughput technology was used to characterize the components of AG in order to elucidate their material basis. Furthermore, serum pharmacochemistry studies were conducted to identify the components of AG that are present in serum, thus providing a foundation for screening effective components. The potential Q-markers of AG in the treatment of influenza were confirmed through a series of steps, including molecular docking virtual screening and in vitro evaluation of NA inhibition using a kit. To investigate the testability of potential Q-markers, UHPLC-MS/MS was used for confirmation of the final Q-markers and determination of its content. Subsequently, the ECI of AG was calculated based on the dose–effect relationship of the Q-markers, and its rationality was verified. In conclusion, this study is based on investigating the characteristics of Q-markers related to the testability, quality transfer, traceability, effectiveness, and compatibility contribution of TCM compounds. The study identified FTA, PO, VER, MG, HMP, CUL, LG, KOR, BI, RT, and BER as the Q-markers for AG in the treatment of influenza. Modern pharmacological studies have demonstrated the NA inhibitory activity of several components including FTA (Xie et al., 2023), PO (Wu et al., 2011), VER (Chen et al., 2015), MG (Yoo et al., 2021), BI (Zhao and Chen, 2014), and RT (Ling et al., 2020), which aligns well with the findings of our research.

This study introduces a novel approach to assess the quality of Chinese patent medicine, in which Q-markers and ECI complement each other. This method allows the calculation of ECI through Q-marker screening, as well as the validation of Q-marker confirmation through ECI analysis. The discovery and evaluation strategy of Q-markers not only promotes the improvement of the quality control level of AG, but also provides a valuable reference for the systematic exploration of Q-markers in the quality evaluation and process control of traditional Chinese medicine prescriptions. However, the in vivo content and effects of each Q-marker investigated in this study remain unclear. To address this, future research will focus on conducting network pharmacology analysis, serum pharmacokinetics studies, and in vivo pharmacodynamic experiments (Liao et al., 2018, Liu et al., 2021). Concurrently, the hemagglutinin (HA) protein, a key player in virus entry, is also a target for the development of anti-influenza virus therapeutic drugs. Currently, studies have shown that drugs inhibit influenza virus by targeting both HA and NA. Consequently, drugs are found to inhibit influenza virus infection through the combined action of HA and NA. Therefore, future investigation can focus on examining AG's inhibitory effect on HA and NA targets, and subsequently exploring the drug's mechanism in the viral infection life cycle (Lao et al., 2023).

Funding

This research was supported by Sichuan Science and Technology Program (Grant No.: 2022YFS0431), National Interdisciplinary Innovation Team of Traditional Chinese Medicine (Grant No.: ZYYCXTD-D-202209).

CRediT authorship contribution statement

Shimin Tian: Data curation, Writing – original draft. Xiaorong Wei: Methodology, Data curation. Jiao Song: Methodology, Data curation. Xinfu Cai: Investigation. Qiang Shang: Investigation. Dong Li: Formal analysis. Chuan Zheng: Conceptualization, Supervision, Writing – review & editing, Project administration. Li Han: Conceptualization, Supervision, Writing – review & editing, Project administration. Dingkun Zhang: Conceptualization, Supervision, Writing – review & editing, Project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Appendix A

Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.arabjc.2023.105580.

Appendix A

Supplementary material

The following are the Supplementary data to this article:

Supplementary data 1

Supplementary data 1

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