Introduction

From a scientific perspective, the concepts of ‘yin’ and ‘qi’ in Traditional Chinese Medicine (TCM) do not have direct equivalents in modern biomedical science. However, studies on herbal medicine, including JBOL, often aim to identify bioactive compounds and their mechanisms of action through pharmacological and biochemical approaches. While TCM provides a holistic framework for understanding disease, scientific investigation focuses on identifying active compounds, their molecular targets, and their pharmacokinetics to establish evidence-based conclusions about JBOL’s role in IPF treatment. JBOL is a traditional Chinese medicine agent developed based on more than 30 years of clinical experience to treat idiopathic pulmonary fibrosis (IPF). It consists of twelve traditional Chinese medicines, including Forsythia suspensa (Thunb.) Vahl., Ligusticum chuanxiong Hort., Astragalus membranaceus (Fisch.) Bge., Codonopsis pilosula (Franch.) Nannf., Angelica sinensis (Oliv.) Diels. and et al. According to TCM theory, JBOL possesses the capacity to nourish the yin and enhance the qi, dispelling blood stasis and alleviating phlegm1. In Chinese medicine, the pathophysiology of IPF is closely related to the imbalance of “yin” and “qi”, and JBOL aims to restore lung function and slow down the fibrosis process by regulating these elements. To bridge TCM principles with modern scientific validation, this study employs advanced analytical techniques to uncover the molecular basis of JBOL’s efficacy, ensuring its potential mechanisms align with evidence-based biomedical frameworks.

The chemical composition of JBOL is very complex, including non-volatile components such as flavonoids, glycosides, quinones and sugars, as well as volatile components such as phthalides, terpenes and phenols.The non-volatile components have been extensively analyzed using LC-MS by prior research groups2, whereas the volatile components have received comparatively less attention. The volatile components in JBOL mainly come from Angelica sinensis (Oliv.) Diels. Forsythia suspensa (Thunb.) Vahl. and Ligusticum chuanxiong Hort. The volatile components of Ligusticum chuanxiong Hort. have a variety of pharmacological effects, including analgesic and anti-inflammatory effects3,4;The volatile compounds in Angelica sinensis (Oliv.) Diels. are considered the principal active agents in asthma treatment and are often examined as the main active elements in clinical research5,6; and those of Forsythia suspensa (Thunb.) Vahl. have the efficacy of clearing away heat and detoxifying toxins, and subduing swellings and dispersing knots7,8. However, the volatile components are poorly soluble and volatile, and their composition and content are affected by a variety of factors, which directly affect the efficacy and safety9. Currently, there are few studies on the volatile components of JBOL, which lack accurate content determination and clear elaboration of the relationship between chemical composition and efficacy.

In order to study the volatile components of JBOL in depth, this study used gas chromatography-mass spectrometry (GC-MS) for qualitative and quantitative analyses. GC-MS is the main tool for analyzing volatile components, which can provide high-precision data on chemical composition. In addition, network pharmacology and molecular docking techniques were combined to predict and validate effective active ingredients and targets associated with anti-IPF11,12. This integrated approach not only helps to reveal the potential pharmacological mechanisms of JBOL, but also provides a scientific basis for the quality control and standardization of JBOL. If JBOL has therapeutic potential for idiopathic pulmonary fibrosis (IPF), its efficacy would likely be attributed to its bioactive volatile components, which could exhibit anti-inflammatory, antifibrotic, or antioxidant effects. Scientific validation of JBOL’s effects requires experimental studies, such as in vitro and in vivo models, as well as clinical trials, to determine its impact on molecular pathways involved in fibrosis, oxidative stress, and immune modulation.

The aim of this study was to identify the effective volatile components and their targets in JBOL through GC-MS analysis, network pharmacological prediction, and molecular docking, thus providing a reference for the quality control and standardization of JBOL, and ultimately enhancing the efficacy and safety of JBOL in IPF treatment.

Results

Identification of volatile components in JBOL

In accordance with the experimental conditions described in “2.4.1,” the total ion chromatogram (TIC) of the volatile chemical components in JBOL was acquired via GC-MS analysis, as illustrated in Fig. 1 (Fig. 1). According to pertinent literature and the NIST.11 standard mass spectrometry library3,4,5,6,7,8, 48 components were identified via the solvent extraction method, whereas 23 components were found through the water vapor distillation method. Following the merging and deduplication process, there were a total of 58 components detailed in Table 1.

Fig. 1
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The TICdiagram of volatile components in JBOL. (A: solvent extraction method, B: water vapor distillation method)

Table 1 Identification results of volatile components in JBOL.
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Network pharmacology

Drug targets and disease targets

This analysis identified 2,519 targets associated with 58 active compounds, resulting in 714 targets after deduplication. Utilizing “idiopathic pulmonary fibrosis” as the search term, the inquiry in the GeneCards, OMIM, and TTD databases produced 4563, 369, and 30 targets respectively, culminating in a total of 4962 targets. Following filtration and deduplication, 1267 target genes associated with IPF were identified, including SRC Proto-Oncogene, Non-Receptor Tyrosine Kinase (SRC), Signal transducer and activator of transcription 3 (STAT3), ‌Interleukin−1β (IL-1β), RAC-alpha serine/threonine-protein kinase (AKT1), Epidermal growth factor receptor (EGFR) etc.

PPI network construction

The intersection of disease targets and component targets yielded a total of 167 overlapping targets. Through the PPI network analysis, we had identified 24 core targets, such as STAT3, EGFR, SRC, among others, which had played a crucial role in the pathogenesis of IPF. The detailed network structure and the list of core targets had been presented in Fig. 2 and the Table 2, respectively. This finding had provided important targets for subsequent drug development and mechanistic studies.

Fig. 2
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The PPI network of intersection targets.

Table 2 Key targets.
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Ingredient-target network

The active ingredients and intersecting targets were imported into Cytoscape 3.8.0 software to construct an ‘active ingredient-target gene-pathway’ network for visualisation and analysis (Fig. 3). The analysed network consisted of 220 nodes and 838 edges. According to the degree value, the higher ranked active ingredients were Senkyunolide I, Senkyunolide H, 4-terpineolide, α-Terpineol, Ligustilide, and so on.

In the ingredient-target network, we identified key active ingredients, such as Senkyunolide I and α-Terpineol, which exhibited significant interactions with targets related to IPF. These ingredients may serve as potential drug candidates and warrant further investigation.

Fig. 3
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Ingredient-target network (Rectangle: target site; triangle: active ingredient).

GO enrichment analysis and KEGG pathway analysis

The intersecting targets were uploaded to the DAVID website and KEGG database, GO analysis and KEGG analysis was performed individually. The top 10 entries were selected and visualized based on the P-value (Fig. 4).Then, a total of 161 pathways were obtained from KEGG pathway analysis, and the top 25 signalling pathway were involved in cancer pathway, AGE-RAGE signalling pathway, lipid and atherosclerosis, EGFR tyrosine kinase inhibitor resistance and PI3K-Akt signalling pathway. Based on the linkage between the target genes and KEGG pathways, a bubble map was constructed using a bioinformatics platform (http://www.bioinformatics.com.cn/) (Fig.4).

Fig. 4
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GO analysis and KEGG analysis. (A: GO function enrichment analysis; B: KEGG pathway analysis) (Fig. 5).

Fig. 5
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Ingredient-target-pathway network (Rhomboid: active component; rectangle: target; triangle: signaling pathway).

Ingredient-target-pathway network

The top 25 pathways enriched by active ingredients, core targets, and signaling pathway were imported into Cytoscape (3.8.0) software to create a network for visualization and analysis, depicted in Fig. 6. The primary active components, based on their degree values, included Senkyunolide I, Senkyunolide H, 4-Pinene, baicalein, α-piniferol, α-Terpineol, Hambelin, α-Terpineol, and Sedanolide. The highest-ranked targets included EGFR, PIK3CD, MAPK14, PIK3CB, and PIK3CA, among others (Fig. 5). These findings underscore the potential of the identified components and targets in modulating disease pathways, providing valuable insights for future drug development efforts.

Fig. 6
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Schematic representation of docking (A: Molecular docking modeling of STAT3 with Senkyunolide H; B: Molecular docking modeling of SRC with Senkyunolide I; C: Molecular docking modeling of EGFR with 4-terpineol; D: Molecular docking modeling of EGFR with α-Terpineol; E: Molecular docking modeling of STAT3 with Sedanolide; F: Molecular docking modeling of STAT3 with Ligustilide).

Molecular docking

Through the integrated evaluation of the “Ingredients-disease-target” Degree value and the KEGG Degree value of ingredient content, we identified six ligands: Senkyunolide H, Senkyunolide I, 4-terpineol, α-Terpineol, Sedanolide, and Ligustilide. These were subsequently docked with five receptor proteins: STAT3, EGFR, SRC, AKT1, and IL-1β (Table 3). The docking results of the active ingredient-target proteins indicate that the affinity values were all less than or equal to -5.13 kcal/mol.

In the molecular docking analysis, we identified six key compounds and evaluated the docking activity of each compound with different targets. To more intuitively demonstrate the interactions between these compounds and their corresponding targets, we selected the docking combination with the highest binding energy for each compound and its corresponding target as the representative and performed molecular docking visualization (Fig. 6).

Table 3 Results of molecular Docking.
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Quantitative analysis of 6 chemical components

Determination of evaluation indicators

After identifying six key components through network pharmacology screening and molecular docking validation, we had further confirmed the identification of the six active compounds by comparing them with reference substances. (Fig. 7) By comparing the retention times and mass spectra, Sedanolide, Ligustilide, α-Terpineol, 4-Terpineol, Senkyunolide H, and Senkyunolide I were ultimately recognized as quantitative markers.

Fig. 7
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The GC-MS chromatogram overlap maps of volatile components in JBOL (A: solvent extraction method, B: water vapor distillation method; ).

Methodology validation

Take injection measurements on standard solutions with varying concentrations in Section “Determination of volatile components content in multiple batches of JBOL” in accordance with the experimental setup in Section “Methodological examination”. The linear regression equation was presented in Table 4, indicating a good linear relationship among the six components within the linear range. The RSD values for the stability of the six compounds ranged from 2.6 to 4.7%, signifying that the test solution remained stable for 12 h and was more suitable for injection and analysis within this timeframe. The RSD values for precision ranged from 0.61 to 3.5%, indicating satisfactory instrumental precision. Additionally, the RSD values for reproducibility ranged from 0.84 to 3.0%, demonstrating that the quantitative assay method exhibited good reproducibility (Table 5). Furthermore, the average recoveries of the approach ranged from 88.58 to 101.73% (Table 6), demonstrating the method was accuracy in determining the target compounds.

Table 4 Linear regression equations and ranges for 6 components.
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Table 5 RSD values for precision, repeatability and stability tests.
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Table 6 Results of sample recovery test (n = 6).
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Table 7 Content of volatile components in different batches of JBOL.
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Content determination

As a result of the above study, we confirmed that the extraction method of the samples and the analytical methods for the content determination of six components had passed the test. So the contents of these six volatile components could be determined according to the identified preparation and assay methods, and finally the contents of Sedanolide, Ligustilide, α-Terpineol, 4-Terpineol, Senkyunolide H, and Senkyunolide I in eight batches of JBOLwere measured, and the result was shown in Table 7; Fig. 8. It showed the difference of the contents of six components among eight batches was small, which meant that JBOL has a high degree of consistency among different batches.

Fig. 8
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Volatile content of different batches of JBOL.

Discussion

The chemical composition of traditional Chinese medicine is intricate, with its efficacy primarily reliant on its diverse chemical constituents. Volatile components are particularly significant as noted in the “Shennong Ben Cao Jing Hundred Records”. It recorded that the gas of the incense was positive, and the positive gas was to remove the evils of obscenity. The volatile constituents in traditional Chinese medicine predominantly serve to alleviate epidermal conditions, clear heat and detoxify, eliminate dampness, warm the body, regulate qi, activate blood circulation, and dispel blood stasis. However, JBOL as a clinical preparation of traditional Chinese medicine with obvious curative effect on IPF and related lung diseases13, has little research on its volatile components.

In this study, we analyzed the chemical constituents in JBOL using GC-MS technique and identified the volatile constituents through comparison of reference substance and database recognition, which resulted a total of 58 compounds in identification. From the identification results, we found that the volatile components extracted by water vapor distillation method had less phthalides and ketones and more alkenes and terpenes. In contrast, phthalides and ketones obtained through solvent extraction method were relatively more retained. It may be due to the low boiling point of alkenes and terpenoids, which can be extracted from the oral liquid by water vapor. And the properties of phthalides, ketones and other components is relatively stable, which is difficult to be extracted by water vapor distillation, and can be extracted by the solvent extraction method, because the polarity of volatile components is very small14,15.

The GO and KEGG analyses demonstrated a significant enrichment in pathways related to cancer, inflammation, and cellular signaling. In particular, the identification of key signaling pathways, such as the AGE-RAGE and PI3K-Akt pathways, underscored the potential of the identified targets in modulating disease progression. These findings provided valuable insights into the molecular mechanisms underlying the disease and highlighted potential therapeutic targets for further investigation.

The docking results of the active ingredient-target proteins indicate that the affinity values were all less than or equal to -5.13 kcal/mol, demonstrating that the six screened components exhibit strong binding affinity to the five targets. The resulting compounds are relatively stable, suggesting their potential significance in the therapeutic process of IPF, which holds reference value for clinical research.

Through the network pharmacological analysis, the main active ingredients of JBOL were finally obtained, such as Sedanolide, Ligustunolide, α-Terpineol, 4-Terpeneol, Senkyunolide H and Senkyunolide I, which were mainly derived from Ligusticum chuanxiong Hort., Forsythia suspensa (Thunb.) Vahl., Angelica sinensis (Oliv.) Diels, and so on. Among them, phthalides account for the majority of the compounds, and Ligustilide, Senkyunolide H, Senkyunolide I, and Sedanolide belong to this category16. Ligustilide and Senkyunolide have strong anticholinergic effect and obvious asthmatic effect, and significant efficacy in antioxidant and anti-inflammatory properties17,18. Sedanolide is also one of the phthalides, which also has anti-inflammatory and antibacterial effects. Among monoterpenes, α-Terpineol and 4-Terpineol have the highest content. The relevant studies have shown that, α-terpineol showed anti-tumor activity against many tumor cell lines (lung, breast, leukemia, and colorectal ) by reducing NF-kB expression19, and also has anti-inflammatory20 and cardiovascular protective21 effects. 4-terpineol suppresses proliferation and promotes apoptosis in cytoplasmic cells by inhibiting the PI3K/Akt signaling pathway, demonstrating significant anticancer effects22,23.

From the PPI network in network pharmacology, we can know that the volatile constituents of JBOL mostly exert their anti-IPF effects via AKT1, STAT3, EGFR, IL-1β, and SRC. Subsequently, the GO function and KEGG pathway enrichment analysis of the intersection targets revealed that these targets were enriched in pathways associated with inflammatory response, tumor response, cellular senescence, autophagy, and immune functions24,25,26. The primary pathways identified included the cancer pathway, AGE-RAGE signaling pathway, EGFR signaling pathway, PI3K-Akt signaling pathway, and HIF-1 signaling pathway.The AGE-RAGE signaling pathway has been identified as being linked to the inflammatory process. AGEs can activate RAGE receptors, subsequently trigger various intracellular signaling pathway, resulting in the expression of pro-inflammatory cytokines including IL-6 and IL-1β, thereby inducing an inflammatory response and oxidative damage in IPF27,28. AKT1 is a member of the protein kinase family and serves as a crucial component in the PI3K-Akt signaling pathway29.The Akt pathway conveys signals to the downstream mTOR pathway upon activation, producing apoptosis, oxidative stress, mesenchymal transition, and other biological processes, hence increasing fibrogenesis30. Activation of the PI3K/Akt signaling pathway facilitates the polarization of macrophages from M1 to M2 phenotypes, resulting in a substantial elevation in the secretion of pro-inflammatory cytokines, including TNF-α and IL-1β, thereby exacerbating lung injury and inflammatory responses in patients with IPF31. The study found that the inhibition of AKT1 could partially obstruct PI3K-Akt pathway signaling, therefore demonstrating anti-IPF effects32,33. Whereas, EGFR and SRC stimulate the release of pro-inflammatory cytokines via the activation of the NF-κB signaling pathway, hence aggravating lung injury. STAT3 is a crucial transcription factor in the organism that modulates various biological processes, including cell proliferation, apoptosis, epithelial-mesenchymal transition (EMT), and glucose metabolism34. During EMT, epithelial cells lose their adhesive polarity and undergo various metabolic alterations, leading to the development of a mesenchymal phenotype.EMT is intricately linked to organ fibrosis, with numerous investigations validating that type 2 EMT facilitates fibrosis in organs like the liver, lungs, and kidneys35,36. STAT3 is a pivotal protein in the HIF-1 inflammatory signaling cascade, facilitating the development of CD4 + T cells into the Th17 subpopulation and influencing Th17/Treg immunological homeostasis. Hence, inhibiting STAT3 activity can mitigate alveolar epithelial cell fibrosis37,38. The specific action relationship was shown in Fig. 9.

Fig. 9
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Schematic diagram of possible mechanisms of action.

Furthermore, the findings of this study provide a scientific basis for optimizing the formulation of JBOL and developing combination therapy strategies. For instance, by increasing the content of Ligustunolide and Senkyunolide I, or by developing derivatives based on these compounds, it may be possible to further enhance the anti-IPF efficacy of JBOL. Additionally, the combination of JBOL with existing anti-fibrotic drugs could potentially produce synergistic effects, thereby more effectively slowing disease progression and improving patient outcomes.

The methodology validation presents good precision and reproducibility, with RSD values ranging from 0.61 to 4.7%. However, the Discussion could further emphasize how these validation results support the robustness of the quantitative assay method for clinical or therapeutic applications, particularly regarding the consistency of compound stability and recovery rates.

Finally, the high degree of consistency observed across the eight batches of JBOL samples provided a robust foundation for their reliability and quality control in prospective therapeutic applications. Nevertheless, to ensure their long-term efficacy and safety in clinical settings, ongoing research and monitoring are imperative. Through stringent quality control measures and clinical surveillance, we can effectively mitigate the impact of inter-batch variability, thereby ensuring that patients receive stable and effective treatment.

In this study, GC-MS in conjunction with the NIST.11 database was employed to identify the volatile constituents in JBOL, and network pharmacology and molecular docking methodologies was applied to preliminarily investigate the primary active components and potential mechanisms of these volatile constituents in JBOL for the treatment of IPF. Our study showed that the chemical components of Sedanolide, Ligustilide, α-Terpineol, 4-Terpineol, Senkyunolide H, and Senkyunolide I in JBOL might act on the signaling pathway such as AGE-RAGE, EGFR, PI3K-Akt, and HIF-1 by modulating AKT1, STAT3, EGFR, IL-1β, and SCR, thus exerting an anti-IPF effect. Furthermore, the aforementioned six active components were quantified, and the variations in their composition across different batches were studied. This study initially examined the multi-target and multi-pathway characteristics and mechanisms of JBOL in the treatment of IPF, offering a reference for subsequent animal and cellular experiments to validate the mechanism of JBOL in combating IPF, as well as establishing a theoretical foundation for further research and development of JBOL.

Materials and methods

Sample source

The sample was JBOL sourced from Shandong Hongjitang Pharmaceutical Group Co., Ltd. The batch numbers of JBOL involved were 2,401,001, 2,309,002, 2,307,001, 2,304,001, 2,302,001, 2,212,001, 2,205,001, and 2,201,001.

Chemicals and reagents

Sedanolide and Senkyunolide H were purchased from Suzhou Yani Biotechnology Co., Ltd, batch numbers are YJ068 and CFS202301, respectively. Ligustilide was purchased from Chengdu Gleip Bio-technology Co., Ltd, batch number is 10361). α-Terpineol and 4-Terpineol were purchased from Shandong Boloda Bio-Tech Co., Limited with batch numbers SYC-240611 and TXC-240305 respectively. Senkyunolide I was purchased from China Academy of Food and Drug Administration, batch numbers are 112071 − 20302. The methanol solution was of mass spectrometry grade, and other reagents such as ether and ethyl acetate were of chromatography grade.

Preparation of standards

Accurately weigh the following reference standards and place them into volumetric flasks of the specified capacities, dissolve in methanol, and dilute to the mark: For the 10 ml volumetric flask, add Sedanolide (98% purity, 10.07 mg), Ligustilide (98.38% purity, 17.40 mg), Senkyunolide H (98% purity, 10.01 mg), and Senkyunolide I (99.5% purity, 10.00 mg). For the 20 ml volumetric flask, add α-Terpineol (95.76% purity, 27.01 mg) and 4-Terpineol (95.51% purity, 19.68 mg). Standards of Sedanolide, Ligustilide, Senkyunolide H, Senkyunolide I, α-Terpineol, and 4-Terpineol were precisely weighed, dissolved in methanol, and shaken to prepare reference solutions with concentrations of 0.9869 mg/ml, 1.8269 mg/ml, 0.9810 mg/ml, 0.9950 mg/ml, 1.2932 mg/ml, and 0.9398 mg/ml, respectively. 8 ml of α-Terpineol reference solution and 12 ml of 4-Terpineol reference solution were combined in a 20 ml volumetric flask to prepare reference solutions of α-Terpineol at 0.5173 mg/ml and 4-Terpineol at 0.5639 mg/ml.

Qualitative analysis

Measurement condition

A triple quadrupole gas chromatography-mass spectrometry (GC-MS/MS, Agilent, USA) system equipped with an HP-5MS capillary column (0.250 mm×30 m×0.25 μm, − 60 ℃~325 ℃~350 ℃) was employed. The temperature program commenced at an initial temperature of 55 ℃, subsequently increasing to 135 ℃ at a rate of 20 ℃/min, then to 160 ℃ at a rate of 5 ℃/min, followed by an ascent to 225 ℃ at a rate of 6 ℃/min, maintaining 225 ℃ at a rate of 20 ℃/min, and finally reaching 270 ℃ at a rate of 20 ℃/min, maintained for 5 min. The pre-sample inlet temperature was 250 ℃, and the temperature of the transmission line was 300 °C. In addition, the column flow rate was kept at 1.44 mL/min, and the sample injection amount was 0.2 µL. The mode was non-shunt, the scanning method of MS1 scan was adopted, at scanning range of 50–500 m/z in scanning time of 300 ms.

Sample Preparation

Because the volatile components in JBOL are so complicated, it’s hard to make sure that all of the compounds are included in the test solution using just one method. That’s why the volatile components in JBOL were taken out using two different methods: the solvent extraction method39 and the water vapour distillation method40.

The solvent extraction method was to take 30 mL of JBOL and adding 3 g of anhydrous sodium sulfate (Na2SO4). The sample was thoroughly mixed with the anhydrous Na2SO4, followed by ultrasonic treatment for 10 min to accelerate the dehydration process and ensure comprehensive contact between the anhydrous Na2SO4 and the moisture present in the sample. After dehydration with anhydrous Na2SO4, 20 mL of JBOL was precisely measured and placed in a separating funnel. An equal amount of ether was added and shaken three times for extraction, and the ether solution was combined respectively and evaporated to dryness. Then, the residue was dissolved in 2 mL of ether. Finally, the test solution was obtained through filtering with a 0.22 μm filter membrane before determination.

The procedure for water vapor distillation was to precisely measure 100 ml of JBOL, heat it to collect volatile components for 2 h, and add 2 ml of ethyl acetate in extractor to dissolve the volatile oil. Then, the ethyl acetate solution layer was placed in a 10 ml volumetric flask, scaled with ethyl acetate, and shake thoroughly.

In both sample processing methods, Parafilm had been used to seal the containers (such as volumetric flasks) during the dehydration process and solvent transfer to prevent solvent volatilization. This step had ensured the integrity of the samples and the accuracy of subsequent analyses.

GC-MS characterization of volatile components

The JBOL with batch number 2,309,002 was processed according to the method described in “4.4.2”. Subsequently, under the experimental conditions outlined in “4.4.1”, the total ion chromatogram (TIC) of its volatile chemical components was analyzed using “Qualitative Analysis 10.0”. By consulting relevant literature and utilizing the standard mass spectral library NIST.11 for identification3,4,5,6,7,8, a minimum of 70% similarity between the mass spectra of the peaks in the samples and the mass spectra in the NIST.11 standard spectral library was used to filter the search results.the results from both the solvent extraction method and the steam distillation method were determined separately. Following this, the results from the two methods were combined and duplicates were removed to obtain the final composition of volatile components in the Jinbei Oral Liquid.

Network pharmacology

Acquisition of drug targets and disease targets

The volatile constituents of JBOL were ascertained by GC-MS characterization results, and the pertinent parameters of the individual components were retrieved from the PubChem databases (https://pubchem.ncbi.nlm.nih.gov/) (accessed on 10th August 2024) and TCMSP databases (https://www.tcmsp-e.com/) (accessed on 10th August 2024). The respective targets of each component were forecasted using the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) (accessed on 12th August 2024), whereas components absent from this database were additionally evaluated through the Pharm Mapper database (https://lilab-ecust.cn/pharmmapper/) (accessed on 12th August 2024). Disease targets were identified using searches in Gene Cards databases (https://www.genecards.org/) (accessed on 14th August 2024), OMIM databases (https://omim.org/) (accessed on 14th August 2024), TTD databases (https://db.idrblab.net/ttd/) (accessed on 14th August 2024), and other databases with the term “idiopathic pulmonary fibrosis”.

Construction of protein-protein interactions (PPIs) network

The targets of the active ingredient-IPF intersection were imported into the STRING database (https://cn.string-db.org/) (accessed on 20th August 2024) to acquire information regarding the protein interaction network. The screening requirements for the “organisms” designated as “Homo sapiens” stipulated a minimum interaction score of “medium confidence (0.4).” Thereafter, the produced data was extracted and loaded into Cytoscape (3.8.0) to create a PPI network. The degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC) of the node in the PPI network were computed, The node size and color represented the degree value, with larger nodes and darker colors corresponding to higher degree values. Core targets were identified by meeting or exceeding the median of Betweenness Centrality, Closeness Centrality, and Degree. The primary targets were identified according to the degree value inside the PPI network.

Construction of ingredient-target network

The volatile components and anticipated targets of JBOL were input into Cytoscape (3.8.0) software to create the ingredient-target network diagram, which can clarify the intricate interactions among components and targets, and to further examine the functional mechanisms of JBOL active components.

Pathway enrichment analysis and gene ontology term performance

The overlapping targets were imported into the DAVID database (https://david.ncifcrf.gov/) (accessed on 10th September 2024) for Gene Ontology (GO) analysis and KEGG database for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The GO analysis had three modules: biological process (BP), molecular function (MF), and cellular component (CC). The leading 10 entries from each module were selected for bar chart. The top 25 pathways were designated for enrichment analysis and depicted in bubble charts to investigate the primary pathways by which JBOL volatile components exert their anti-IPF effects.

Construction of ingredient-target-pathway network

The Cytoscape (3.8.0) software application was used to establish a ingredient-target-pathway network, facilitating the visualization and elucidation of the intricate interactions, and pathways to further explore the functional mechanisms of the active ingredients of JBOL employed against IPF.

Molecular docking

Based on the Degree value ranking in network pharmacology analysis and the composition of drug components, five principal targets and six essential active ingredients were identified for molecular docking validation, The selection was based on the top-ranking degree values and binding energy thresholds from the network pharmacology analysis. The 3D structures of ligands and receptors were sourced from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and PDB database (http://www.rcsb.org/) (accessed on 19th September 2024), respectively. The active ingredients and primary target proteins were hydrogenated and dehydrated utilizing PyMOL software (https://www.pymol.org/), while molecular docking of the key active ingredients and primary target proteins was validated using AutoDockTools (1.5.6) software (https://autodock.scripps.edu/), with a binding energy of ≤-5 kJ/mol deemed indicative of successful docking. Ultimately, PyMOL software was employed to show the results exhibiting enhanced activity.

Quantitative analysis

Selection of evaluation indicators

After identifying the key components of JBOL through network pharmacology screening and molecular docking validation, we had further confirmed the selected key active compounds by comparing them with reference substances. Specifically, we had conducted GC-MS analysis using standard samples under the experimental conditions described in “4.4.2” and had compared the corresponding peaks in the sample with those of the standards. Sedanolide, Ligustilide, α-Terpineol, 4-Terpineol, Senkyunolide H, and Senkyunolide I were ultimately recognized as quantitative markers.

Chromatographic and mass spectrometry conditions

All sample analyses were conducted under the same chromatographic and mass spectrometric conditions as described in Section “Measurement condition”, but the injection mode was changed from non-shunt to pulsed and non-shunt to improve the reproducibility and sensitivity of the analysis.

Methodological examination

The linearity, precision, repeatability, stability and recovery rates of 6 components were determined based on the above chromatographic and extraction conditions. A linear regression equation connecting the peak areas with the relevant concentrations of the standard solutions was constructed using the peak areas derived from the GC-MS analysis of standard solutions that had been injected at different concentrations. The linear equation was obtained by using the mass concentration of each control (X, µg/mL) as the abscissa and the peak area of each component (Y) as the vertical coordinate. The sample was prepared according to the method of “4.4.2” by taking 2,309,002 batches of JBOL as test material, injected continuously for 6 times to check the precision of machine, and tested at 0 h, 2 h, 4 h, 8 h, 12 h respectively for stability assessment. In addition, 6 identical samples were prepared and injected for repeatability analysis. The combination of the above results, the known content of the sample (batch number 2309002) was divided into 6 copies, added the reference solutions in a ratio of 1:1 respectively to measure the recovery rate of each component.

Determination of volatile components content in multiple batches of JBOL

The 2,401,001, 2,309,002, 2,307,001, 2,304,001, 2,302,001, 2,212,001, 2,205,001, and 2,201,001 batches of JBOL were prepared according to the method of “4.4.2” as the test solution, and then injected continuously according to the chromatographic conditions under “4.7.2” to determine the content of each volatile component and compare the differences between different batches.