Intervention of traditional Chinese medicine San-Feng-Tong-Qiao-Di-Wan on allergic rhinitis: a network pharmacology and metabolomics study

Introduction

Allergic rhinitis (AR), a common chronic disorder across the world, affects people of all ages (1,2). When exposed to allergens, the nasal mucosa becomes inflamed, causing blockage of the nose, sneezing, and runny nose (3). AR can have a significant impact on quality of life, resulting in emotional disturbances, sleep cycle disorders, and impaired ability to carry out normal daily activities (4,5). Although intranasal antihistamines and steroids are the mainstay of therapy for AR, they are associated with unsatisfactory side effects (5,6). Thus, there is a need for complementary and alternative therapies.

San-Feng-Tong-Qiao-Di-Wan (SFTQDW), a patented traditional Chinese medicine, has been clinically used to treat acute and chronic rhinitis. One of its key ingredients, Scutellaria baicalensis [or “huangqin” (HQ)], has been found to exert significant anti-inflammatory effects and to modulate immunological responses (7,8). The other components of SFTQDW, including Notopterygium [“qianghuo” (QH)], Schizonepeta [“jingjie” (JJ)], and Asarum [“xixin” (XX)], are among the top 10 most commonly used herbal formulae for treating AR (9). We hypothesized that SFTQDW might effectively regulate AR by leveraging synergistic interactions among its components. This study utilized network pharmacology, AR mouse models, clinical data, and serum metabolomics to explore the effects and potential mechanisms of SFTQDW in AR treatment. We present this article in accordance with the ARRIVE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1283/rc).

Methods

Reagents and materials

For use in this study, SFTQDW was purchased from Yangtze River Pharmaceutical Group Co., Ltd. (Taizhou, China); ovalbumin (OVA), aluminum hydroxide, 10% neutral buffered formaldehyde solution, hematoxylin and eosin (H&E), and periodic acid-Schiff (PAS) were purchased from Sigma-Aldrich (St. Louis, MO, USA); Alfaxan was purchased from Jurox Pty Ltd. (Rutherford, Australia); and mouse immunoglobulin E (IgE) enzyme-linked immunosorbent assay (ELISA) kit was purchased from Abcam (Cambridge, UK). Appendix 1 contains reagents for detecting metabolites.

Network pharmacology construction

The procedure for network pharmacology construction was as follows: (I) the candidate targets of AR were screened through a search of the keyword “allergic rhinitis” in the gene map of the Online Mendelian Inheritance in Man (OMIM; https://omim.org/) and GeneCards (https://www.genecards.org/) database. (II) Data on all components of the four Chinese medicinal herbs in SFTQDW (QH, JJ, XX, and HQ) were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. In drug development, two crucial parameters are typically considered in the selection of candidate compounds: oral bioavailability (OB) and drug likeness (DL). In the absorption, distribution, metabolism, and excretion (ADME) processes, OB is one of the most significant pharmacokinetic parameters (10). High OB is frequently used as an essential indicator for determining the DL index of active substances. The substances with OB ≥30% were regarded as high OB. As a qualitative concept applied in drug design to estimate the druggability of a molecule, the DL index is helpful for the rapid screening of active substances. In the DrugBank database, the average DL index is 0.18. In this study, the substances with a DL index ≥0.18 were regarded as having high druggability. These two parameters can determine how the drug will be absorbed and distributed in the human circulatory system, indicating whether a compound is suitable to be used as a drug (11). Accordingly, we screened for active SFTQDW ingredients and their matching targets from the TCMSP database to satisfy both OB ≥30% and DL ≥0.18. (III) The intersection of steps 1 and 2 described above was considered as the predicted target of SFTQDW against AR. These targets were imported into UniProtKB (http://www.uniprot.org/) to standardize the gene and protein names. (IV) A protein-protein interaction (PPI) network was established via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 11.0 database (https://string-db.org/) and Cytoscape 3.7.2 (Cytoscape Consortium, San Diego, CA, USA). Hub genes were obtained using CytoHubba in Cytoscape 3.7.2 (5). The pathway and Gene Ontology (GO) enrichment of potential targets were analyzed via ClueGO in Cytoscape 3.7.2. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was applied with a q value <0.05. (6) The names of four Chinese medicinal herbs in SFTQDW were imported into the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM; http://bionet.ncpsb.org/batman-tcm/) to obtain the enriched KEGG pathways.

Mouse experiments

Six-week-old female BALB/c mice were obtained from Hunan Silaike Jingda Laboratory Animal Co., Ltd. (Changsha, China). Each group consisted of 4–6 mice based on pilot effect size (Cohen’s d ≈ 1.2) and available resources. The mice were randomly assigned using a random-number table by an investigator who was unaware of the treatment codes. All mice were kept in a specific-pathogen-free facility at Guangzhou Medical University with free access to food and water. Two blinded observers independently conducted behavioural scoring, tissue processing, and histological quantification (inter-rater agreement κ >0.80). Two mice died during anaesthesia, resulting in missing data of less than 5%; missing values were addressed using a mixed-effects model. Outliers were identified using Tukey’s method (>1.5× IQR). Animal experiments were carried out under project license (No. 2021489), which was approved by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University, and conducted in compliance with the National Standard of the People’s Republic of China “Laboratory Animal—Guideline for Ethical Review” (GB/T 35892-2018) for animal care and use. All efforts were made to facilitate the welfare of animals and minimize harm.

OVA-induced AR model

The mice were divided into four groups: control, OVA-induced AR without treatment, OVA-induced AR plus SFTQDW 3.00 mg/g body weight, and OVA-induced AR plus SFTQDW 1.45 mg/g body weight. Each group had 4–6 mice. Control mice were given only PBS, with no sensitization or challenge. See Figure 1 for sensitization, challenge, and treatment schedules.

Figure 1 Schematic diagram for the OVA-induced AR mouse model. AR, allergic rhinitis; i.n., intranasal injection; i.p., intraperitoneal injection; o.g., oral gavage; OVA, ovalbumin; SFTQDW, San-Feng-Tong-Qiao-Di-Wan.

Mice were administered an intraperitoneal injection of 200 µL which 100 µg of OVA emulsified in 4 mg of aluminum hydroxide on days 0, 7, and 14. On days 21–27, the mice were intranasally challenged with 800 µg of OVA daily, with the treatment groups orally different oral concentrations of 200 µL of SFTQDW 1 hour before the challenge. Animals were randomly assigned to cages and treatments by an investigator not involved in outcome assessment, and the observer scoring nasal symptoms was blinded to group allocation.

Evaluation of nasal symptoms and the collection of serum samples

After administration of the medication through gavage and a 20-minute rest period, the OVA intranasal challenge was conducted, and the sneezing and nasal rubbing behaviors were observed without bias for 5 minutes. After blood was collected, it was centrifuged at 3,000 rpm for 5 minutes at 4 ℃ for serum collection. This serum was kept at −80 ℃ and then assessed via ELISA.

ELISA and histopathological analyses of nasal tissues

The serum IgE levels were measured with a mouse IgE ELISA kit (Abcam) according to the manufacturer’s instructions. The heads of mice were fixed in 10% neutral buffered formaldehyde solution for a week, decalcified in 0.1 M ethylenediaminetetraacetic acid (EDTA) buffer for 2 weeks, and embedded in paraffin. The blocks were coronally sectioned into 5-µm slices and stained with H&E to measure eosinophils. The remaining slides were stained with PAS to facilitate the analysis of goblet cell hyperplasia.

Participants

A prospective study of patients with AR was conducted at The First Affiliated Hospital of Guangzhou Medical University between December 2020 and June 2021.

Inclusion criteria (12):

• Participants must have intermittent or persistent AR in the acute phase and be aged between 18 and 50 years;
• Must have at least 2 nasal symptoms (sneezing, rhinorrhea, nasal itching, obstruction) and ocular symptoms (itching, redness, tearing);
• Must have a baseline Visual Analog Scale (VAS) score of ≥4;
• Must provide signed informed consent.

Exclusion criteria:

• Participants with asthma, chronic obstructive pulmonary disease (COPD), autoimmune diseases, or other systemic inflammatory disorders;
• Pregnant or lactating individuals;
• Individuals who have taken antihistamines, intranasal/topical steroids, or immunotherapy within 4 weeks prior to enrollment;
• Individuals who have taken traditional Chinese medicine or immunomodulators within 3 months prior to enrollment.

Sample size:

• Assuming a minimal clinically important difference of 1.5 VAS points with a standard deviation (SD) of 2.0, α=0.05, and power =80%, 28 paired observations are needed;
• Allowing for a 15% dropout rate, 34 patients were enrolled;
• Patients administered oral SFTQDW for 7 days and assessed using standardized procedures throughout the study. Before and after treatment, patients completed questionnaires that included the VAS score, the Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ), and Epworth Sleepiness Scale (ESS) scores. Blood samples were collected on days 0 and 7 for metabolomics analysis.

The study was approved by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (No. GYFYY-2020-100), and the requirement for individual consent was waived due to the retrospective nature of the analysis. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol (version 5.0, dated July 27, 2020) was finalized before data collection began, but was not registered in a public registry; it is available upon request.

Collection of serum samples from patients with AR

Approximately 4 mL of peripheral venous blood was collected from the patients and immediately centrifuged at 3,000 rpm for 5 min at 4 ℃. The serum was collected and frozen at −80 ℃ until it was needed for analysis.

Pretreatment of serum samples, derivation, and chromatographic and mass spectrometry conditions

A total of 50 µL of serum was first mixed with 200 µL of precooled methanol and processed with a previously described method (13). The residue was stored at −20 ℃ before derivatization. The derivatization process and mass spectrometry conditions were similar to those used in our previous study (13).

Metabolomics data processing and analysis

A metabolomics analysis of 86 metabolites was conducted to characterize the dynamic changes of patients with AR during treatment with SFTQDW via a Nexera X2 UHPLC system (Shimadzu, Kyoto, Japan) and a TripleTOF 5600+ accurate-mass Q-TOF/MS system (AB SCIEX, Framingham, MA, USA). The metabolomics data were processed with MarkerView version 1.3.1, MultiQuant version 3.0.3, and Analyst TF version 1.7.1 software (AB SCIEX). Heatmaps and Venn diagrams were generated via the XianTao website (http://www.xiantao.love/) and the E Venn website (http://www.ehbio.com/test/venn/#/), respectively. In addition, we included all significant metabolites and genes from network pharmacology to construct the metabolite-gene-disease interaction network using MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/).

Statistical analysis

All data were analyzed using SPSS 25.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism 9.0. The sample size of 4–6 mice per group was determined based on a pilot effect size of Cohen’s d ≈ 1.2 and resource availability. Outliers were identified using Tukey’s method (>1.5× IQR) and excluded only if traceable to technical error; two mortalities (<5% missing data) were included in a linear mixed-effects model with random intercept. Behavioral scores and histological quantification were performed by two investigators blinded to treatment allocation. Normally distributed numerical variables are presented as mean ± SD and compared using the independent samples t-test or one-way analysis of variance. Non-normally distributed data were analyzed with the Mann-Whitney U test. For metabolomics, significant metabolites were defined as those meeting VIP >1, fold-change >1.5 or <0.67, and FDR-adjusted P<0.05. Correlations between metabolites and clinical indicators were assessed using Spearman’s rank test.

Results

Network pharmacology supported the use of SFTQDW in AR treatment

The study collected 63 different chemical compounds from four herbs in SFTQDW and predicted that they could affect 179 different potential targets. HQ, XX, JJ, and QH were found to have 116, 102, 178, and 47 putative targets, respectively. Among these, 16 targets were identified as potential targets for SFTQDW in treating AR (as depicted in Figure 2A). The detailed information on SFTQDW putative targets can be found in table available at https://cdn.amegroups.cn/static/public/jtd-2025-1283-1.xlsx. All the intersected targets were normalized to their official symbols via the UniProt database.

Figure 2 Network pharmacology analysis of SFTQDW’s treatment effect for AR. (A) The Venn plot of SFTQDW in treating AR (HQ, XX, JJ, and QH are the components of SFTQDW). (B) The PPI network of SFTQDW in treating AR. The node color and size reflect its degree. The nodes with red borders represent the hub genes. (C) The GO enrichment analysis of potential targets as determined by ClueGO. (D) The KEGG pathways enrichment analysis as determined by ClueGO. All pathways have q value <0.05. **, P<0.01. AR, allergic rhinitis; GO, Gene Ontology; HQ, huangqin (Scutellaria baicalensis); JJ, jingjie (Schizonepeta); KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction; QH, qianghuo (Notopterygium); SFTQDW, San-Feng-Tong-Qiao-Di-Wan; XX, xixin (Asarum).

To identify the hub genes of SFTQDW against AR among the 16 targets, a PPI network was constructed via Cytoscape. Figure 2B provides a comprehensive view of the relationships within 15 targets (the other genes were not connected). Next, CytoHubba calculated hub genes, and the top 5 genes, based on the scores of 10 computational methods, were considered hub genes, including caspase-3 (CASP3), prostaglandin-endoperoxide synthase 2 (PTGS2), cholinergic receptor muscarinic 2 (CHRM2), acetylcholinesterase (ACHE), and Jun proto-oncogene (JUN). GO and KEGG pathway enrichment analyses related to the 16 targets were performed through Figure 2C,2D. The top terms in GO analysis were negative regulation of muscle contraction (GO:0045932), acetylcholine receptor signaling pathway (GO:0095500), response to nicotine (GO:0035094), and membrane depolarization (GO:0051899). KEGG enrichment analysis indicated that the significantly affected pathways were primarily related to the regulation of lipolysis in adipocytes. The expected significant KEGG pathways of HQ (Figure S1A), XX (Figure S1B), JJ (Figure S1C), and QH (Figure S1D) in the BATMAN-TCM analysis in network pharmacology illustrated the potential pathways of SFTQDW’s action against AR. The top 5 hub genes (CASP3, PTGS2, CHRM2, ACHE, and JUN) are located at the intersection of two BATMAN-TCM-predicted modules: “regulation of lipolysis in adipocytes” enriched by HQ/XX/JJ and “acetylcholine receptor signaling” enriched by QH/XX. These modules hint at the dual-lipid/neuro axis seen in the metabolomics and symptom data.

SFTQDW relieved inflammatory reactions of OVA-induced AR

The effectiveness of SFTQDW was verified through a study conducted on an AR mouse model. The AR group exhibited increased sneezing behavior, nose rubbing, and serum IgE levels in comparison to the control group (Figure 3A-3C). Additionally, the number of eosinophils (Figure 3D-3H) and goblet cells (Figure 3I-3M) in nasal tissues were significantly higher in the AR group, indicating successful induction of the AR model. After administration of SFTQDW treatment, the symptoms of AR were considerably alleviated, and the inflammation of nasal tissues was significantly improved. It is worth noting that the positive effects of SFTQDW were more pronounced in the 3.00 mg/g group than in the 1.45 mg/g group. The high-dose group showed greater reductions in sneezing and eosinophilia, as seen in Figure 3A,3H. This reflects the anticipated downstream effects of PTGS2-centric regulation of lipolysis and CHRM2-mediated membrane depolarization highlighted by network pharmacology in Figure 2B-2D, suggesting that the same hub genes operate across species.

Figure 3 The effects of SFTQDW on AR mice. (A,B) Allergic symptom scores of (A) sneezing and (B) nasal scratching in SFTQDW-treated AR mice on day 28. (C) IgE levels in the serum of SFTQDW-treated AR mice. (D-G) Histological changes of the nasal tissues after SFTQDW treatment. Representative H&E-stained images are indicated (200× magnification; scale bar =50 µm). (H) Bar graphs represent the quantification of eosinophil cell counts. (I-L) Goblet cell hyperplasia under PAS staining. Goblet cells were stained purple (200× magnification; scale bar =50 µm). (M) Bar graph representing the quantification of goblet cell counts. The values are expressed as the mean ± SD. *, P<0.05. SFTQDW-3.00, SFTQDW 3.00 mg/g treatment group; SFTQDW-1.45, SFTQDW 1.45 mg/g treatment group. AR, allergic rhinitis; H&E, hematoxylin and eosin; IgE, immunoglobulin E; PAS, periodic acid-Schiff; SD, standard deviation; SFTQDW, San-Feng-Tong-Qiao-Di-Wan.

SFTQDW treatment ameliorated acute symptoms in patients with AR

Given the efficacy of SFTQDW in the AR mouse model, we were interested in assessing its effects on patients with AR. After exclusion criteria were applied, 32 patients were selected for the study. The basic information on patient enrollment is presented in Table 1. Posttreatment, the VAS, RQLQ, and ESS scores (Figure 4A-4C) were significantly lower than on day 0, indicating that SFTQDW treatment ameliorated acute symptoms, enhanced patients’ quality of life, and relieved daytime sleepiness. The reduction in nasal-congestion scores and the down-regulation of CHRM2/ACHE parallel the GO term “membrane depolarization” (GO:0051899), indicating that the cholinergic hub identified in silico is suppressed in patients, linking symptom relief to the neuro-lipid model.

Figure 4 Objective scores, metabolomics profiling results, and a metabolite-gene-disease network of patients with AR. (A-C) Objective scores of patients. (A) VAS, (B) RQLQ, and (C) ESS scores. The data are expressed as the mean ± SD. n=32. *, P<0.05. (D-H) The metabolomics profiling results. (D) PCA of all samples. (E) Heat map of the cluster analysis for all metabolites. (F) Violin plots of the significantly upregulated metabolites. (G) Violin plots of the significantly downregulated metabolites. (H) The important metabolites and targets in AR identified by the integrated analysis of metabolomics and network pharmacology. The metabolite-gene-disease network provides a global view of the functional relationships between metabolites, connected genes in network pharmacology, and target diseases, including AR. The significantly upregulated metabolites are depicted in red, the downregulated ones are depicted in green, and the genes are depicted in pink. The related diseases are shown in blue, with AR highlighted by a red dashed square. A: day 7; B: day 0. AR, allergic rhinitis; ESS, Epworth Sleepiness Scale; HETE, hydroxyeicosatetraenoic acid; HPODE, hydroperoxyoctadecadienoic acid; PC, principal component; PCA, principal component analysis; PGE2, prostaglandin E2; RQLQ, Rhinoconjunctivitis Quality of Life Questionnaire; SD, standard deviation; VAS, Visual Analog Scale.

Metabolomics profiling in patients with AR

The application of SFTQDW treatment induced metabolic changes in patients with AR, as evidenced by the segregation of serum samples between days 0 and 7 (as depicted in Figure 4D). The heatmap in Figure 4E displays a clear grouping of data points into two distinct clusters. A total of 27 differentially expressed metabolites were identified between days 0 and 7. As shown in Figure 4F, nine significant metabolites were upregulated, namely 13(S)-hydroperoxyoctadecadienoic acid (HPODE), 11(S)-hydroxyeicosatetraenoic acid (HETE), 8(S)-HETE, isodeoxycholic acid, behenic acid, lignoceric acid, isovaleric acid, valeric acid, and valine. In contrast, Figure 4G presents the 18 significant metabolites that were downregulated, including 9(S)-HPODE, 15(S)-HETE, 12(S)-HETE, 5(S)-HETE, 12(S)-hydroxyeicosapentaenoic acid (HEPE), prostaglandin E2 (PGE2), 9,11-octadecadienoic acid, palmitic acid, 15-methyl palmitic acid, alanine, arginine, isoleucine, methionine, serine, tyrosine, asparagine, succinate, and palmitelaidic acid. Enrichment analysis revealed that the significantly upregulated metabolites (as illustrated in Figure S2A) were significantly enriched in linoleic acid metabolism, valine, leucine, and isoleucine biosynthesis, as well as pantothenate and coenzyme A biosynthesis. On the other hand, the downregulated metabolites (as depicted in Figure S2B) were notably associated with aminoacyl transfer RNA biosynthesis, alanine, aspartate, glutamate metabolism, arachidonic acid metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis. The PTGS2-derived lipid mediators 5(S)-HETE and PGE2 were down-regulated among the 27 differential metabolites, while 13(S)-HPODE was up-regulated. This aligns with the predicted PTGS2-anchored network and correlates with the VAS drop from 4 to 2.

Specific metabolites and targets closely related to AR identified through integrated analysis of metabolomics and network pharmacology

To identify the specific pathways related to SFTQDW’s effect in AR, we established a network of interactions among metabolites, genes, and diseases (Figure 4H) in an effort to identify the metabolites and targets that are most closely linked to AR. Our findings suggest that SFTQDW has the potential to target PTGS2 and regulate 5(S)-HETE, which is directly associated with AR.

Discussion

The epithelium of the nasal mucosa is an essential element in the respiration process, as it is constantly exposed to dust and allergens in the atmosphere. Contact with these substances triggers an inflammatory response from eosinophils, neutrophils, and lymphocytes, releasing reactive oxygen species (ROS), which induces oxidative stress (14), causing inflammation symptoms such as congestion and a runny nose (15). This occurs through the activation of inflammasomes and transcription factors, leading to the increased production of inflammatory cytokines, impairing the cilia of the nasal mucosa, and altering the expression of adhesion molecules (16-18). This, in turn, causes changes in permeability and an overproduction of mucus. Additionally, ROS can break peptide bonds (19), change the charge of proteins, and oxidize certain amino acids (20,21), which causes the destruction of proteins by proteases and reduces oxidatively modified enzyme activities (22). Furthermore, inflammation can lead to oxidative stress reactions, aggravating allergic inflammation and creating a cyclical relationship (16,23-25). Consequently, persistent symptoms can reduce the quality of life of individuals with AR, and drugs that can effectively control AR are necessary. Modulating the inflammatory changes associated with the generation of ROS has shown promise as a therapeutic approach (26).

Network pharmacology analysis identified 63 chemical components in SFTQDW mapped to 179 potential targets, with 16 linked to AR treatment. A PPI network pinpointed CASP3, PTGS2, CHRM2, ACHE, and JUN as core targets. GO and KEGG enrichment showed enrichment in “acetylcholine receptor signaling pathway”, “membrane depolarization”, and “regulation of lipolysis in adipocytes”, indicating a multi-faceted approach to anti-AR effects. Animal and human experiments further validated the efficacy of oral SFTQDW administration in managing AR symptoms. CHRM2 and ACHE were enriched in the “acetylcholine receptor signaling” and “membrane depolarization” entries, indicating hyperresponsiveness of nasal mucosal parasympathetic nerves. AR symptoms in the mouse model improved, and VAS scores for sneezing and nasal congestion decreased in clinical patients, supporting the regulation of the acetylcholine pathway. To further explore the pharmacodynamic action of SFTQDW, a serum metabolomics study was conducted. The study revealed that nine metabolites were upregulated and 18 were downregulated after drug administration. Network analysis identified PTGS2 as a core target. The drug significantly downregulated its downstream lipid mediators 5(S)-HETE and PGE2, in line with the “lipolysis regulation” pathway prediction. The driving metabolites were identified as 13(S)-HPODE, 5(S)-HETE, and PGE2. 13(S)-HPODE is an antioxidant/anti-inflammatory lipid that inhibits COX-2 activity and reduces ROS production (27-29). On the other hand, 5(S)-HETE and PGE2 can recruit eosinophils and amplify Th2 inflammation as products of PTGS2 (30-32). SFTQDW was found to increase 13(S)-HPODE levels while decreasing 5(S)-HETE and PGE2 levels, indicating its ability to promote linoleic acid metabolism towards inflammation resolution through bidirectional regulation of the same pathway. The pro-resolving branch starts with linoleic acid, leading to 13-HPODE and then 13-HODE. In contrast, the pro-inflammatory branch begins with linoleic acid, resulting in 15-HPETE and 5(S)-HETE. SFTQDW increased 13(S)-HPODE and decreased 5(S)-HETE, changing the balance between the two pathways and reducing downstream HETE and leukotriene production. This shift in pathway flow from pro-inflammatory to inflammation resolution in linoleic acid metabolism is in line with the “regulation of lipolysis in adipocytes” pathway. PTGS2 was identified as a key target of SFTQDW through network pharmacology.

Metabolomics confirmed a significant decrease in its product, 5(S)-HETE, validating the prediction. Animal studies showed downregulation of PTGS2 protein expression in the nasal mucosa, consistent with the decrease in 5(S)-HETE. Inhibition of the “PTGS2 to 5(S)-HETE” axis is a key mechanism of SFTQDW’s anti-AR effect. The downregulation of short-chain fatty acids (isovalerate and valerate) and the upregulation of valine further inhibit Th2 cytokine release (33), explaining the rapid alleviation of clinical symptoms.

The neuro-lipid metabolism network, as predicted by SFTQDW through “16 intersection targets related to PPI core genes to GO/KEGG functional entries”, was closed-loop verified at the metabolomics level. The key molecular event connecting prediction and phenotype was identified as the downregulation of the PTGS2/5(S)-HETE axis.

SFTQDW may alleviate AR according to the integration of network pharmacology and metabolomics; however, the mechanistic evidence is currently descriptive and has only been validated at the metabolite level in an OVA-induced mouse model. Nine human metabolites, such as 13-HPODE, 8/11(S)-HETE, behenic/lignoceric acid, and valine, showed an increase after treatment, suggesting a potential connection to anti-inflammatory effects. However, the lack of a placebo group in the patient sample makes it difficult to rule out regression-to-the-mean, natural fluctuations, or placebo responses as other possible reasons. No randomized, blinded, or placebo-controlled trial in human participants was conducted. Since SFTQDW is already approved for clinical use, our objective was to suggest a mechanistic hypothesis rather than reassess its efficacy. To improve the enrichment analysis and include a human control group, future studies should incorporate robust statistical methods, molecular dynamics simulations, and prospective, controlled clinical validation.

Conclusions

By integrating network pharmacology, an AR mouse model, and patient data, this study was able to determine the efficacy and underlying mechanisms of SFTQDW. It was found that SFTQDW could control inflammation-associated metabolites, thereby achieving the desired effect in AR treatment.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the ARRIVE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1283/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1283/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1283/prf

Funding: This work was supported by the Guangzhou Municipal Science and Technology Bureau (No. 202201020450).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1283/coif). All authors report that this work was supported by the Guangzhou Municipal Science and Technology Bureau (No. 202201020450). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All animal experiments were approved by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (No. 2021489), and conducted in compliance with the National Standard of the People’s Republic of China “Laboratory Animal—Guideline for Ethical Review” (GB/T 35892-2018) for animal care and use. The study was approved by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (No. GYFYY-2020-100) and the requirement for individual consent was waived due to the retrospective nature of the analysis. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Gray)