The compounds were collected from the traditional Chinese medicine systems pharmacology (TCMSP, https://www.tcmspw.com/tcmsp.php) (Ru et al., 2014) database and Traditional Chinese Medicines Integrated Database (TCMID, http://119.3.41.228: 8000/tcmid/) (Xue et al., 2013). Meanwhile, compounds of each herb were supplemented through a literature search in the PubMed database (https://pubmed.ncbi.nlm.nih.gov/). A total of 1,613 compounds were retrieved for 13 herbs, among which there were E. sinica (363), S. armeniacae amarum (113), L. apetalum (68), R. paeoniae rubra (119), A. rugosus (94), M. officinalis (139), A. lancea (49), F. tsaoko (59), Astragalus membranaceus (87), Glycyrrhiza uralensis (280), R. officinale (92), P. cocos (34), and R. Pinelliae Preparata (116). It was worth noting that Gypsum Fibrosum is a mineral drug, and its main composition was CaSO₄•2H₂O. Therefore, its mechanism of action was not included in this study.

The 3D structures of IL-6R (PDB ID: 1P9M) (Ho et al., 2009), TNF-α (PDB ID: 2AZ5) (He et al., 2005), PIK3R1 (PDB ID: 4I6J) (Xing et al., 2013), SRC (PDB ID: 4U5J) (Duan et al., 2014), AKT1 (PDB ID: 4EKL) (Lin et al., 2012), TP53 (PDB ID: 5LAY) (Gollner et al., 2016), and PIK3CA (PDB ID: 7K6M) (Cheng et al., 2021) were downloaded from the RCSB Protein Data Bank (PDB, https://www.rcsb.org/).

3D structures of protein in 2.2.2 were preprocessed by Protein Preparation Wizard. The compounds in 2.2.1 were prepared by the LigPrep module (Sheikh et al., 2018). A total of 1,613 compounds were docked with IL-6R and TNF-α by the Glide standard docking precision method (SP) in Schrődinger 2020-4. The corresponding low-energy conformation was obtained by MMFFs. Next, epik28 (Manchester et al., 2010) used a pH value of 7.0 ± 2.0 to conditionally allocate the ionization state and perform docking calculations.

The binding free energy was used to evaluate the affinity between the ligand and receptor. Lower binding free energy indicates a higher affinity or catalytic activity (Wu et al., 2009). In this study, MM/GBSA in the prime program was used to estimate binding energy (Hou et al., 2011). The formula is: DG bind = E_complex (minimized)—E_ligand (minimized)-E_receptor (minimized).

The initial structures of the protein complex with active compounds (emodin, rhein, and quercetin) were obtained from molecular docking. To further determine the stability of rhein and quercetin with IL-6R and rhein and emodin with the TNF-α complex in a simulated physiological solvent system, we used the Desmond module of Schrödinger to carry out molecular dynamic simulation. The molecular system was solvated with crystallographic water (TIP3P) for a 10-Å buffer region under orthorhombic periodic boundary conditions. The system charges were neutralized by adding Na⁺ and Cl⁻. The molecular system was solvated with crystallographic water (TIP3P) molecules under orthorhombic periodic boundary conditions for a 10 Å buffer region. The system charges were neutralized by adding Na⁺ and Cl⁻. The OPLS_2005 force field was used for energy calculation. After this, an ensemble (NPT) of Nose–Hoover thermostat and barostat was applied to maintain the constant temperature (300°K) and pressure (1 bar) of the systems, respectively. The complex structural dynamic simulation for the best complex was carried out with an NPT ensemble for 50°ns, and the trajectory was set at an interval of 10°ps. The RMSD (root mean square distance) was used to compare the prediction error in disparate models of specific data sets and protein–ligand contacts with the timeline. In addition, the 3D binding interaction pattern of active compounds with protein was visualized by PyMOL.

The parameters of absorption, distribution, metabolism, excretion, and toxicity (ADMET) of emodin, rhein, and quercetin were calculated by using the ProTox-II data platform. In addition, active compounds that conformed to Lipinski’s rule were predicted by the QikProp module of Schrödinger.

A total of six male Sprague-Dawley rats (200 ± 20°g, SPF grade) were purchased from the Laboratory Animal Center of Gansu University of Chinese Medicine (Lanzhou, China) with license number: SCXK (gan) 2020-0001. All animals were housed in the Laboratory Animal Center of Gansu University of Chinese Medicine [animal license number: SCXK (gan) 2020-0009]. The rats were fed standard rodent chow and water ad libitum in a house with a 12 h light/dark cycle (temperature and humidity of 23°C ± 2°C and 60% ± 5%, respectively) for 10 days to adapt to the experimental environment. The experimental protocol, approved by the Committee for Ethics in the Laboratory Animal Center of Gansu University of Chinese Medicine (number: 2021-182), was conducted in strict accordance with the ethical principles in animal research.

The triple quadrupole LC/MS system consisted of an ExionLC (SCIEX, Framingham, MA, United States) and a SCIEX Triple Quad 5,500+ (SCIEX, Framingham, MA, United States) equipped with an ESI source. The chromatographic separation was achieved on a Kinetex C18 column (100 mm × 2.1 mm, 2.6 µm, Kinetex® C18, Phenomenex, United States). The water (containing 0.2% formic acid, solvent A) acetonitrile, and methanol (1:1, v: v, solvent B) system was used as the mobile phase. Analysis was achieved by gradient elution at a flow rate of 0.3 ml/min. The gradient elution program was: 0–2 min, 10% B; 2–5 min, 10%–40% B; 5–15 min, 40%–50% B; 15–30 min, 50%–55% B; 30–50 min, 55%–66% B; 50–63 min, 66%–80% B; and 63–73 min, 80%–100% B. The column and auto-sampler tray temperatures were kept at 30°C and 4°C, respectively. The injection volume was 2 μl. All analytes were quantified using multiple reaction monitoring (MRM) detection in the negative ion mode. The parameters of MRM are shown in Table 1. Other main working parameters were as follows: curtain gas (CUR), ion source gas1 (GS1), and ion source gas2 (GS2) were set at 35, 50, and 50 psi, respectively. IonSpray (IS) voltage, −4,500 V; temperature (TEM), 550°C.

In this study, standard working solutions were made up of mixed standard and single standard. The single standard was made up of quercetin (100 ng/ml). Piceatannol (5,000 ng/ml), emodin (90 ng/ml), rhein (15,000 ng/ml), aloe-emodin (6,000 ng/ml), physcion (150,000 ng/ml), and luteolin (300 ng/ml) were taken 10 µl each to form the mixed standard. Standard working solutions (100 μl) were added to blank plasma (100 μl) to form the calibration standards and stored at 4°C until use.

HSBDF (1.23 g/ml, 20 ml) was filtered, concentrated, dried, and dissolved in 50 ml water (containing 80% analytical-grade methanol) to collect HSBDF extracts (stored at 4°C). Also, the HSBDF extracts were filtered through a 0.22-μm syringe filter when used.

In previous studies, rats fasted overnight with free access to water. The plasma samples (∼0.2 ml) were collected into heparinized microcentrifuge tubes through the caudal vein at 2 h after a single oral administration of HSBDF water decocted. The plasmas were centrifuged at 4,000 rpm for 10 min immediately after collection to obtain plasma. The plasma samples were stored at −80°C until analysis.

The calibration standards and plasma samples were treated with the protein precipitation method using methanol. The procedure was as follows: 100 µl samples were mixed with 800 µl methanol, then vortexed for 5 min, and centrifuged at 13,000 rpm for 10 min. The supernatant was transferred into a clean tube and evaporated with a stream of nitrogen. The residue was dissolved with methanol and was filtered using a 0.22-μm syringe filter to test.

The similarity ensemble approach (SEA) (https://sea.bkslab.org/) (Keiser et al., 2007) was used to predict targets of the key compounds in plasma (emodin, aloe-emodin, rhein, luteolin, and quercetin). The targets were imported into the STRING database (https://string-db.org/) (Szklarczyk et al., 2017), and the organism was set to “Homo sapiens.” The comprehensive score of protein interaction (confidence data >0.9) was used to obtain the PPI information of the target. The major parameter “degree” was used to evaluate its topological features for each node in the interaction network. Based on the degree, bioinformatics (http://www.bioinformatics.com.cn/) was used to visualize the key targets (top 20 targets).

GO and KEGG enrichment was carried out using DAVID (https://david.ncifcrf.gov/) databases. A total of 151 targets of key compounds in plasma were imported using the DAVID database. The select identifier was set to official gene, list type was set to gene list, species was set to “Homo sapiens,” and the threshold was set at p < 0.05. Bioinformatics and GOChord of R language were used to construct the GO and pathway-target diagram, respectively.

A549 and MLE-12 cells were purchased from Zhong Qiao Xin Zhou Biotechnology Co., Ltd. (Shanghai, China) and maintained at 37°C under 5% CO2 in DME/F-12 and 1,640 medium supplemented with 10% fetal bovine serum, respectively.

HSBDF condensed extract of 1.23 g/ml was lyophilized using a freeze dryer (EYELA, Japan). Finally, 15.38 g of lyophilized powder was obtained (yield, 11.23%). The dry powder was stored in an 80°C refrigerator until use.

Cell viability of A549 and MLE-12 cells were measured with the CCK-8 assay after compound intervention. The cells were seeded in 96-well plates at a density of 5 × 10³ cells/well in 200 μl of culture media and grown for 4 h. Second, they were treated with different concentrations of LPS, quercetin, luteolin, emodin, rhein, aloe-emodin, and HSBDF. Subsequently, 10 μl of CCK-8 solution was added to each well, and the plates were incubated for 1 h. The optical density of each well was determined at 450 nm using a microplate reader (Bio-Rad, Hercules, CA, United States).

EdU proliferation kit was employed for the determination of DNA synthetic capacity in proliferating cells. A549 cells were cultured in 96-well plates and grown for 4 h. After being treated for 24 h with compounds, the cells were strictly conducted following the instructions of the kit. Live cell imaging system (OLYMPUS, Japan, 20×) and ImageJ software were employed for calculating the number of EdU-positive cells (green cells). The percentage of EdU-positive cells to total Hoechst 33342-positive cells (blue cells) was represented as the EdU-positive rate.

The A549 cells culture medium was gathered and centrifuged (3,000 r/min×20 min) at 4°C. The supernatant liquid was taken out for detection of MDA, T-SOD, TNF-α, and IL-6. Enzyme-Linked Immunosorbent Assay (ELISA) (TNF-α and IL-6), MDA, and T-SOD steps were followed as per the instructions of the reagent kit.

FITC-conjugated annexin V was used to detect apoptosis. A549 cells were cultured in six-well plates (1 × 10⁵ cells/well) for 4 h before treatment. After adding LPS, quercetin, emodin, rhein, and HSBDF, the cells were incubated for 24 h. Next, the cells were gathered and stained using the annexin V−FITC/PI apoptosis detection kit following the manufacturer’s instructions. The resulting fluorescence signal was detected by flow cytometry (BD, FACSCelesta).

Analyses were performed using GraphPad Prism 6 software. The results are expressed as mean ± standard deviation (SD) of at least three independent experiments. The statistical differences between the two groups were compared by Student’s t-test. p < 0.05 was considered statistically significant.

To explore the anti-inflammatory effect of HSBDF, 1,613 compounds were docked with IL-6R and TNF-α by Schrődinger. The compound with a docking score of ≤ −5 Kcal/mol was known as the potentially effective compound (Hsin et al., 2013). It can be seen that the total number of compounds with potential inhibitory activities was IL-6R (96) and TNF-α (111) as shown in Table 2, indicating that the formula had potential effects on inhibiting inflammation by acting on IL-6R and TNF-α.

In terms of a single herb, it was found that Glycyrrhiza uralensis, Ephedra sinica, and Rheum officinale were the top three docking herbs with two targets, respectively. Previous studies also demonstrated that Glycyrrhiza polysaccharide (50 mg/kg) had a certain therapeutic effect on lung inflammation and oxidative damage in COPD mice (Wu et al., 2020); Ephedra sinica water plays an important role in distillates in arthritic rats (Yeom et al., 2006); Rheum officinale ethanol extract can effectively improve the pulmonary inflammation in mice infected with mycoplasma pneumonia (Gao et al., 2021). These results suggested that Glycyrrhiza uralensis, Ephedra sinica, and Rheum officinale were key herbs to inhibit inflammation, which was of great significance to ameliorate ALI.

To determine anti-inflammatory key compounds, we selected the compounds, which conformed to Lipinski’s rules (MW < 500, H-bond donor<5, H-bond acceptor<10, log P (O/W) < 5, and rotor ≤10) and docking score ≤−5 Kcal/mol, to analyze further. Based on the Pharmacopoeia of the People’s Republic of China (2020) and the literature of every herb’s main components, seven key compounds were selected from the top 50 compounds with high docking scores (Table 3). These compounds’ binding free energy was further calculated in Schrődinger.

The results indicated that piceatannol, emodin, aloe-emodin, rhein, physcion, luteolin, and quercetin had potential effects on inhibiting inflammation. Therefore, these compounds were considered key compounds to explore further. Also, we found that these compounds were mainly distributed in Glycyrrhiza uralensis, E. sinica, and R. officinale, which further provides support that Glycyrrhiza uralensis, E. sinica, and R. officinale were key herbs to treat ALI.

It was well known that network pharmacology contributes to exploring the potential mechanism of TCM in prevention and treatment of disease (Li et al., 2021). Therefore, in order to analyze the mechanism of key compounds in plasma, top 20 in 151 (confidence data >0.9) targets of PPI are listed in Figure 2A. The result showed that the top 20 targets of degree were TP53, SRC, PIK3R1, PIK3CA, AKT1, EGFR, CDK1, ESR1, PTPN11, CASP3, PTK2, FYN, MAPK8, LCK, CCNB1, CCNB2, AR, GSK3B, APP, and PTPN1.

In order to further validate the prediction of network pharmacology, five nodes, namely, TP53, SRC, PIK3R1, PIK3CA, and AKT1 with the highest degree were taken as examples to be analyzed. Their crystal structure was subjected and docked with corresponding compounds. Finally, constituent with docking score ≤ −5 (Kcal/mol) was selected to analyze. The total proportion of TP53 (5/5), SRC (5/5), PIK3R1 (5/5), PIK3CA (5/5), and AKT1 (4/5) was 96%, and the results are shown in Figure 2B. The results of molecular docking showed that the prediction accuracy was high, which verified the result of PPI.

It was reported that SRC could inhibit TLR4-induced inflammatory cytokines and promote anti-inflammatory cytokine IL-10, which plays an important role in the treatment of acute and chronic inflammation (Li et al., 2019). The pharmacological study suggested that G. baccata extract can upregulate AKT1 gene expressions to protect the cells against oxidative damage and inflammation (Martínez et al., 2021). Moreover, the studies also suggested that TP53, PIK3R1, and PIK3CA were vital for inflammation (Eyileten et al., 2018; Busbee et al., 2021; Reske et al., 2021). Accordingly, the results indicate that emodin, aloe-emodin, rhein, luteolin, and quercetin may play an important role in inhibiting anti-inflammatory and anti-oxidative damage.

To explore the regulatory mechanisms of key compounds in plasma, a total of 267 terms were enriched (p < 0.01), and the top five of GO enrichment were visualized, as shown in Figure 2C. Among which, biological processes (BP) include protein phosphorylation, peptidyl-serine phosphorylation, and protein autophosphorylation, etc; cell composition (CC) includes cytosol, plasma membrane, extracellular exosome, cytoplasm, and nucleoplasm; and molecular function (MF) includes protein kinase activity, protein serine/threonine kinase activity, ATP binding, and so on.

Moreover, we had used 151 targets to enrich 107 pathways (p < 0.05), and the top 20 targets and top 20 pathways are shown in Figure 2D. It can be seen that the HIF-1 signaling pathway, the ErbB signaling pathway, the TNF signaling pathway, and so on may be the key regulated pathways of emodin, aloe-emodin, rhein, luteolin, and quercetin. Also, studies have shown that the HIF-1α signaling pathway was related to inflammation, and arginine can regulate HIF-1α to suppress the inflammatory response (Chen et al., 2020). The Er7bB signaling pathway was associated with oxidative stress, and it was reported that the ErbB4 system of the heart was activated at early stages of chronic heart failure to enhance the cardiomyocyte resistance to oxidative stress (Shchendrigina et al., 2020). The TNF signaling pathways and inflammatory pathways can suppress inflammation (Liang et al., 2021).

Network pharmacology results suggested that emodin, aloe-emodin, rhein, luteolin, and quercetin likely treated ALI by regulating the biological function and pathways related to anti-inflammatory and anti-oxidative damage.

A549 is a human alveolar epithelial cell line. It is often used to research lung diseases, such as ARDS (Lin et al., 2020), ALI (Geiser et al., 2001; Zhao et al., 2021). Therefore, we used A549 to further study ALI. To determine the optimal concentration and time of LPS, cell counting kit-8 (CCK-8) was used to detect the viability of A549 cells in this study. As shown in Figure 3, LPS markedly decreased the cell viability of A549 cells in a dose- and time-dependent manner. The cell viability was reduced by approximately 50% when A549 was subjected to 6.25 μg/ml of LPS compared with blank (p < 0.01) in 24 h, indicating that 6.25 μg/ml of LPS is deleterious to the epithelial cell. LPS of 12.5, 25, 50, or 100 μg/ml decreased cell viability more prominently than 6.25 μg/ml LPS did, which demonstrated that LPS induced cell viability loss in a dose-dependent manner and that the lowest effective injury dose of 24 h LPS treatment was 6.25 μg/ml (Liu et al., 2021). Consequently, further experiments were performed with 6.25 μg/ml of LPS at 24 h.

In order to determine the effect of HSBDF and key compounds in plasma on the viability of A549 cells, CCK-8 was used to detect the viability of cells treated with different concentrations of drug for 24 h (Figure 4). The results showed that HSBDF and key compounds in plasma can increase the cytotoxicity of A549 cells in a dose-dependent manner. Moreover, we found that A549 cell proliferation was markedly promoted with low concentration of quercetin (1.5625 μM), emodin (3.125 μM), rhein (1.5625 μM), and HSBDF (0.03125 mg/ml), whereas aloe-emodin and luteolin did not. The cell viability was further tested for normal epithelial cells (MLE12) after treating with low concentration of quercetin, emodin, and rhein. (Supplementary Figure S1). We found that the viability of lung epithelial cells, such as A549 and MLE-12, can be increased by these key compounds.

To further evaluate the effects of HSBDF, quercetin, emodin, and rhein on A549 cells grown in an inflammatory environment, the EdU proliferation kit and cck-8 were used to detect proliferating cells and the viability of cells, respectively. As shown in Figure 5A, we found that inflammation stimulated by LPS inhibited the proliferation of A549 cells, whereas inhibition of proliferation was decreased by quercetin (1.5625 μM), emodin (3.125 μM), rhein (1.5625 μM), and HSBDF (0.03125 mg/ml). The stimulative proliferation of quercetin (1.5625 μM), emodin (3.125 μM), rhein (1.5625 μM), and HSBDF (0.03125 mg/ml) on A549 cells cultured in an inflammatory environment was also confirmed by EdU proliferation staining (Figures 5B1,B2). These results demonstrate that quercetin, emodin, rhein, and HSBDF were effective in inhibiting inflammation. Therefore, HSBDF (0.03125 mg/ml), quercetin (1.5625 μM), emodin (3.125 μM), and rhein (1.5625 μM) were active compounds for further experiments.

To determine the effect of HSBDF and active compounds on the expression of inflammatory cytokine, ELISA was used to measure the expression levels of IL-6 and TNF-α in the cell supernatant liquid. The elevated expressions of TNF-α and IL-6 induced by LPS in A549 cells were inhibited by quercetin (1.5625 μM), emodin (3.125 μM), rhein (1.5625 μM), and HSBDF (0.03125 mg/ml) (Figure 6A).

Simultaneously, the anti-oxidative stress of HSBDF and active compounds was further researched in an inflammatory environment. We found that the oxidative stress injury was weakened after treatment of HSBDF and active compounds. The SOD was enhanced significantly by active compounds, while the MDA was decreased (Figure 6B).

These results further verify that network pharmacology results indicate that emodin, rhein, and quercetin have the function of anti-inflammation and act against oxidative damage.

LPS has been known to cause alveolar epithelial cell apoptosis that characterizes ALI (Hou et al., 2021). Therefore, the anti-apoptosis of HSBDF and active compounds was further investigated in an inflammatory environment. We investigated the effects of drugs on the apoptosis of A549 cells in an inflammatory environment by flow cytometry. We found that LPS increased A549 cell apoptosis. The apoptosis was significantly decreased after the intervention of quercetin, emodin, rhein, and HSBDF in an inflammatory environment (Figure 7).