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. 2026 Feb 19;11(8):13756–13767. doi: 10.1021/acsomega.5c11326

Exploring the Molecular Mechanism of Shaoyao Gancao Decoction for Trigeminal Neuralgia Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation

Xiaofeng Liu †, Ting Xu †, Wenjuan Lei ‡, Zhongyi Lv §,*, Yifan Zhang ∥,*, Tinghuan Wang †,*
PMCID: PMC12961621  PMID: 41799087

Abstract

Background: Shaoyao Gancao Decoction (SGD) has demonstrated a broad spectrum of analgesic effects and has found application in the management of trigeminal neuralgia (TN). Nonetheless, the underlying molecular mechanisms of this therapeutic intervention remain poorly understood. Objective: This study is designed to elucidate the molecular mechanism of SGD in treating TN by employing an integrated approach that combines network pharmacology, molecular docking, and molecular dynamics simulation. Methods: The active ingredients and associated targets within SGD were screened from the TCMSP database. TN-related targets were extracted from GeneCards, OMIM, CTD, and DisGeNET databases. Subsequently, we constructed a comprehensive TN-SGD-herbs-ingredients-targets network by employing Cytoscape software for visualization. A protein–protein interaction (PPI) network was constructed using the STRING database and further analyzed with Cytoscape, from which we identified pivotal hub genes using three distinct Cytoscape plugins. GO and KEGG enrichment analyses were carried out utilizing R software. Then, molecular docking was executed using AutoDock Vina, and docking results were visualized and augmented with molecular dynamics simulations utilizing BIOVIA Discovery Studio software. Finally, in vitro experiments verified the anti-inflammatory effect of SGD on LPS-treated BV2 cells. Results: A total of 103 active ingredients within SGD, 332 targets associated with TN, and 68 potential therapeutic targets were obtained. We constructed a TN-SGD-herbs-ingredients-targets network and obtained a PPI network of potential therapeutic targets. Then, we extracted seven hub genes from the potential therapeutic targets, including ESR1, JUN, TP53, STAT3, BCL2, AKT1, and ESR2. GO enrichment analyses indicated that SGD affected multiple biological processes and functions, such as responses to xenobiotic stimuli, membrane rafts, DNA binding, and transcription factor binding. KEGG pathway analyses revealed that lipid and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, and chemical carcinogenesis-receptor activation were mainly involved in the therapeutic effects of SGD on TN. Importantly, molecular docking analysis demonstrated substantial binding affinities between the top eight ranked active ingredients and the seven identified hub genes. Furthermore, molecular dynamics simulations validated the binding activity between shinpterocarpin and ESR2. Finally, SGD decreased the levels of TNF-α, IL-1β, and IL-6 and regulated protein expression of ESR1 and ESR2 on LPS-treated BV2 cells, indicating that SGD exerted anti-inflammatory effect on microglia. Conclusion: This study offers valuable insights into the active ingredients of SGD and elucidates their potential molecular mechanisms in the treatment of TN. The findings presented herein lay the groundwork for the development of anti-TN agents rooted in the constituents of SGD.

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1. Introduction

Trigeminal neuralgia (TN) is a common cranial nerve disorder, characterized by abrupt and excruciating pain in the facial distribution area of the trigeminal nerve. Abnormal pain sensitivity is a characteristic feature of TN, significantly diminishing the quality of life for afflicted individuals due to the sudden and intense nature of the pain. Currently, the etiology and pathogenesis of TN are inadequately understood. The prevailing theories implicate neurovascular compression and nerve demyelination as the primary factors contributing to TN. At present, the primary therapeutic approach for TN is centered on the use of the anticonvulsant drug carbamazepine. However, prolonged administration of carbamazepine can lead to drug dependency and the emergence of various complications, including hepatotoxicity and bone marrow suppression. Surgical interventions, such as microvascular decompression, gamma knife radiosurgery, and percutaneous balloon compression, are also employed in the management of TN. Although surgical treatments are particularly effective for patients with TN in short terms, they are invasive procedures fraught with potential surgical complications. These complications encompass facial numbness, facial paralysis, hearing loss, and aseptic meningitis.

The pathogenesis of trigeminal neuralgia is closely related to neuroinflammation. Under physiological conditions, astrocyte activation releases pro-inflammatory cytokines and chemokines, it may be crucial in causing and sustaining neuropathic pain. Also, microglia can release inflammatory cytokines such as IL-1β, TNF-α, IL-6, and chemokines. IL-1β and TNF-α are the key to neuroinflammation, as they can activate the positive feedback loop and cause an inflammatory cascade reaction. Cai et al. reported that the analgesic effect of Gm14461 silencing may be mediated by the inhibition of astrocyte activation and the inflammatory response. Another research has shown that pioglitazone inhibits neuropathic pain in part by reducing astrocyte activation. Therefore, inhibiting the inflammatory response may be one of the effective strategies to alleviate trigeminal neuralgia.

As an adjunctive and complementary therapeutic approach, Traditional Chinese Medicine (TCM) holds significant importance in the management of various ailments. Medicinal plants are important reservoirs of bioactive factors, such as flavonoids with antioxidant, anti-inflammatory, antibacterial, antifungal, and antitumor effects, and Ophiorrhiza rugosa var. prostrata is a traditional medicinal plant used for the management of chest pain, body ache, and earache. In comparison to conventional analgesic drugs, TCM offers distinct advantages of multitude active ingredients and wide therapeutic targets, particularly in the treatment of neuropathic pain. TN has been documented in ancient Chinese literature under various names such as migraine or head wind. The etiology of TN is primarily rooted in the imbalance of wind-cold and wind-heat, which leads to localized pain caused by meridian obstruction arising from liver-qi stagnation. Shaoyao Gancao decoction (SGD), a medicinal formula initially recorded in the Treatise on Febrile Diseases, comprises the potent combination of Paeonia lactiflora (Shaoyao and Baishao) and Glycyrrhiza glabra (Gancao). SGD demonstrates the effect of soothing liver-qi and relieving pain and has been employed in the treatment of diverse pain conditions. Modern pharmacology research and clinical investigation have revealed that SGD has anti-inflammatory, antispasmodic, and analgesic effects and has also been used in the TCM clinical treatment of TN. Although SGD is known to have analgesic activity, the active ingredients of SGD and the specific mechanism of SGD in TN treatment remain unidentified. This may be attributed to the intricate nature of the active ingredients in SGD and multitargets and pathways, which makes it difficult to systematically elucidate the mechanism of SGD for the treatment of TN.

Network pharmacology is an emerging research methodology to explore and analyze the mechanism of drug by constructing a drug-active ingredients-disease-targets network based on systems biology and multidirectional pharmacology, which can predict the internal association and potential mechanism between multiple drug targets and diseases in a network. Molecular docking and molecular dynamics simulations are both simulation approaches that can evaluate interactions between molecules (ligands) and proteins (receptors) and predict the binding affinity. This study delves to investigate the main active ingredients and targets of SGD and explore the potential therapeutic mechanism for TN based on network pharmacology, molecular docking, molecular dynamics simulation, and in vitro experiment (Figure ), and the results may provide valuable ideas for the development of new analgesics.

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Flowchart of this study.

2. Materials and Methods

2.1. Acquisition of Active Ingredients and Targets of SGD

Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was utilized to procure the active ingredients and associated targets of herbs by searching the terms “baishao” and “gancao”. The screening criteria for the ingredients are defined as oral bioavailability (OB) ≥ 30% and druglikeness (DL) ≥ 0.18. The acquired targets were matched with the Uniprot database and converted into gene symbols using the Perl script. The visualization of the top 8 active ingredients was carried out using R software.

2.2. Collection of TN-Related Targets

TN-related targets were retrieved from GeneCards, Online Mendelian Inheritance in Man (OMIM), Comparative Toxicogenomics Database (CTD), and DisGeNET databases by searching the term “trigeminal neuralgia”, respectively. Subsequently, the obtained targets were merged and duplicates were removed to obtain comprehensive TN-related targets

2.3. TN-SGD-Herbs-Ingredients-Targets Network Construction

The potential therapeutic targets of SGD on TN were determined by taking the intersection of active-ingredient-associated targets and TN-related targets. Then, we constructed a TN-SGD-herbs-ingredients-targets network using Cytoscape software (Version 3.10.0).

2.4. Protein–Protein Interaction (PPI) Analysis and Hub Genes Screening

To construct the protein–protein interaction (PPI) network, the potential therapeutic targets were input into the STRING database (Version 12.0). A minimum required interaction score of 0.900 was applied, and disconnected nodes were hidden. The resulting interaction data were downloaded in TSV format and imported into Cytoscape software for PPI visualization and subsequent hub gene screening. To improve the robustness of hub gene identification, three complementary Cytoscape pluginsCytoHubba, CytoNCA, and Molecular Complex Detection (MCODE)were jointly applied to evaluate network nodes from different topological and modular perspectives. Hub genes were defined as those consistently identified across these analytical approaches.

2.5. Gene Ontology and KEGG Enrichment Analysis

To examine the mechanism and pathway of SGD in the treatment of TN, we conducted gene ontology (GO) and KEGG pathway enrichment analyses. Additionally, we performed KEGG enrichment analyses to elucidate the functions of the identified hub genes. The “Clusterprofiler” R package was employed for GO and KEGG enrichment analysis in R software (Version 4.3.1).

2.6. Molecular Docking and Molecular Dynamics Simulation

Molecular docking was employed to validate the binding activity between ingredients (ligands) and targets (receptors). The two-dimensional molecular structures of the ingredients, in sdf format, were acquired from the PubChem database and converted to mol2 format using Chem3D software. The protein structures of targets, in pdb format, were downloaded from the PDB database. AutoDock Tools was utilized to remove water molecules, add hydrogen atoms, calculate charges, and configure the grid box for docking. Subsequently, a pdbqt format file containing both ligand and receptor was employed to execute molecular docking using AutoDock Vina. The docking results were visualized using PYMOL (version 4.6.0) and BIOVIA Discovery Studio software (version 2019). A heatmap of binding energy is displayed using “ggplot2” R package.

Molecular dynamics simulation was performed in BIOVIA Discovery Studio using the CHARMM36 force field. The protein–ligand complex was prepared by adding hydrogen atoms and assigning force-field parameters, followed by energy minimization to remove unfavorable contacts. The system was solvated in an explicit periodic water box (TIP3P), and Na+/Cl– ions were added to neutralize the system (0.15 M). Long-range electrostatic interactions were treated using the Particle Mesh Ewald (PME) method, and covalent bonds involving hydrogen atoms were constrained using SHAKE, enabling a 2 fs integration time step. Molecular dynamics simulations were carried out using the Standard Dynamics Cascade protocol, including energy minimization, heating, equilibration, and a production run. The system was heated from 0 to 300 K and equilibrated under NPT conditions. Temperature was maintained at 300 K using a Berendsen thermostat, and pressure was controlled at 1 bar using a Berendsen barostat. The production molecular dynamics was performed for 200 ps, and coordinates were saved every 2 ps for trajectory analyses (temperature, total energy, RMSD, and RMSF).

2.7. Experimental Validation In Vitro

LPS-induced BV2 microglia were used to verify the effects of SGD on neuroinflammation. BV2 cells were purchased from the Chinese Academy of Sciences (Shanghai, China), and cultured in DMEM/high-glucose medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. DMEM/high-glucose medium, penicillin-streptomycin, and LPS were purchased from Servicebio (Wuhan, China). Fetal bovine serum was obtained from HYCEZMBIO (Wuhan, China). SGD granules were provided by Hubei Provincial Integrated Chinese and Western Medicine Hospital and dissolved in pure water in a ratio of 1:1 (Shaoyao/Gancao) to prepare the solution. Then, the solution was filtered with a 0.22 μm filter, and the final concentration of the drug was calculated as 200 g/L (crude drug per g/L). ESR1 (A22181) and ESR2 (A2546) antibodies were obtained from Abclonal (Wuhan, China). The CCK-8 assay was used to determine the cytotoxicity of SGD. ELISA assays were used to test the protein levels of TNF-α, IL-1β, and IL-6 in BV2 cells and supernatants. Western blot was used to detect the protein expression of ESR1 and ESR2 in BV2 cells.

3. Results

3.1. Active Ingredients and Targets of SGD

SGD, originating from the Treatise on Febrile Diseases, was composed of two herbs, including baishao and gancao (Table S1, Figure A). A total of 103 active ingredients were obtained from the TCMSP database after removing duplicates, including 13 ingredients from baishao and 93 from gancao. In addition, a total of 214 targets were obtained from the TCMSP database after eliminating redundancy. The top 8 ranked active ingredients and molecular structures are shown in Figure B–C.

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Acquisition of active ingredients and targets of SGD, TN-related targets, and potential therapeutic targets of SGD for TN treatment. (A) Crude herb diagram of baishao and gancao. (B) Bar plot showing the top 8 ranked active ingredients in SGD. (C) Molecular structures of the top 8 ranked active ingredients. (D) Collection of TN-related targets. (E) Venn plot of intersection targets between SGD and TN.

3.2. Trigeminal Neuralgia-Related Targets

We obtained 332 targets from the GeneCards database according to a relevance score that is greater than the median. Moreover, 68 targets were obtained from the OMIM database, 172 targets from the CTD database, and 107 targets from the DisGeNet database. Ultimately, a total of 584 TN-related targets were extracted after removing duplicates and visualized in a Venn diagram (Figure D).

3.3. TN-SGD-Herbs-Ingredients-Targets Network

68 candidate therapeutic targets were obtained by merging active ingredient-related targets and TN-related targets (Figure E). Then we imported the prepared file, including all node information, into Cytoscape software for visualization of the network (Figure ). Calculated by Network Analyzer Tools in Cytoscape, the network consisted of 156 nodes and 759 edges.

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Construction of TN-SGD-herbs-ingredients-targets network.

3.4. PPI Construction and Hub Gene Screening

The protein–protein interaction (PPI) network, consisting of 68 nodes, was obtained from the STRING database and reconstructed in Cytoscape to depict the hierarchical relationships among candidate targets. As shown in Figure A, node size was proportional to betweenness centrality, while edge thickness and color reflected the combined interaction score. To identify hub genes within the PPI network, three Cytoscape plugins were applied. First, CytoNCA identified 16 genes based on betweenness centrality, indicating nodes with high global topological importance (Figure B). Next, MCODE detected four densely connected clusters (score > 3) and extracted 23 genes from these modules (Figure C). CytoHubba was then used to rank nodes using the maximum clique centrality (MCC) algorithm, yielding the top 15 candidate genes (Figure D). Given the distinct theoretical foundations of these algorithms, partial differences among the resulting gene sets were expected. Therefore, genes consistently identified across all three methods were selected to enhance the robustness and reduce the method-specific bias. Ultimately, seven overlapping genes were defined as hub genes (Figure E).

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PPI network construction and screening of hub genes. (A) PPI network of 68 potential therapeutic targets extracted from the STRING database is reconstructed using Cytoscape Software. The network included 122 nodes and 488 edges. Color and size represent the degree of nodes and edges, from the lowest (lighter) to the highest (darker). (B) 16 genes are screened using the CytoNCA plugin based on topological property analysis of Betweenness. (C) A total of 23 genes integrated from four clusters using the MCODE plugin (score ≥ 3, node score cutoff = 0.2, K score = 2). (D) The top 15 genes are obtained using the MCC calculation method in the CytoHubba plugin. (E) Venn plot shows that 7 common genes screened from three methods are considered as hub genes.

3.5. GO and KEGG Analysis

GO and KEGG enrichment analyses were carried out to explore the biological functions and pathways of SGD on TN treatment. A total of 2124 items were enriched in GO analyses with p < 0.05, including 1915 entries in biological processes (BP), 64 in cellular components (CC), and 146 in molecular functions (MF). As shown in Figure A, a bubble plot was drawn by using R software. Biological processes showed that the therapeutic targets were mainly enriched in response to xenobiotic stimulus, cellular response to cadmium ion, and response to nutrient levels. Cellular component revealed that targets were mainly enriched in membrane raft, membrane microdomain, and RNA polymerase II transcription regulator complex. Molecular functions showed that targets were mainly related to DNA-binding transcription factor binding, phosphatase binding, and RNA polymerase II-specific DNA-binding transcription factor binding. In addition, a total of 162 pathways were enriched in KEGG enrichment analysis with p < 0.05 (Figure B). Therapeutic targets were significantly related to lipid and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, chemical carcinogenesis-receptor activation, and endocrine resistance. Furthermore, we performed KEGG enrichment analysis to investigate hub genes involved in the pathway (Figure C).

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GO and KEGG enrichment analysis. (A) GO analysis for the 68 potential therapeutic targets of SGD for TN. The bar plot shows the top 5 significantly enriched items in BP, CC, and MF. (B) KEGG enrichment analysis for 68 potential therapeutic targets. The bar plots show the top 20 significantly enriched KEGG items. (C) KEGG enrichment analysis for the screened hub genes. The C-net plot shows the correlation between seven hub genes and the top 20 enriched KEGG pathways.

3.6. Verification by Molecular Docking and Molecular Dynamics Simulation

Molecular docking was employed to confirm the interaction between the top 8 active ingredients and 7 hub genes (Table S2). As shown in Figure A, all binding energies between active ligands and receptors were less than −5.0 kcal/mol, indicating that the active ingredients had good binding activity to the targets. Notably, the binding energies between ESR1/ESR2 and ligands were lower than −8.0 kcal/mol (Figure B). Subsequently, we visually presented the binding modes between each target and the corresponding ingredient with the lowest binding energy (Figure C–H).

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Molecular docking. (A) Heatmap of molecular docking results. (B) Top six active ingredients and targets ranked by the lowest binding energy (binding energy ≤ −9 kcal/mol). (C) The binding mode of ESR2 with Shinpterocarpin. (D) The binding mode of ESR1 docked with licochalcone a. (E) The binding mode of ESR2 docked with quercetin. (F) The binding mode of ESR1 docked with naringenin. (G) The binding mode of ESR1 docked with formonometin. (H) The binding mode of ESR2 docked with naringenin.

Molecular dynamics simulations were performed to provide atomistic insights into the dynamic stability of the shinpterocarpin–ESR2 complex. Given the favorable docking result (low binding energy), molecular dynamics simulation was conducted to further evaluate the stability of the binding mode. As shown in Figure A–B, the temperature and total energy profiles remained stable over the simulation time, indicating that the system reached equilibrium. RMSD analysis demonstrated that the ESR2 backbone maintained a relatively stable conformation after shinpterocarpin binding (Figure D). In addition, RMSF analysis revealed residue-level flexibility patterns of ESR2 in the complex (Figure C), and the backbone and side-chain RMSF profiles are presented in Figure E.

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Molecular dynamics simulation of shinpterocarpin and ESR2. (A) Temperature (K) vs time (ps) of shinpterocarpin and ESR2. (B) Total Energy (kcal/mol) vs Time of shinpterocarpin and ESR2. (C) RMSF diagram of shinpterocarpin and ESR2. (D) RMSD diagram of shinpterocarpin and ESR2. (E) RMSF diagram of the main chain and the side chain.

3.7. SGD Inhibited LPS-Mediated BV2 Neuroinflammation by Regulating Protein Expression of ESR1 and ESR2

We performed in vitro experiments to verify the anti-inflammatory effect of SGD on LPS-treated BV2 cells. Our results showed that SGD had no obvious cytotoxicity to BV2 cells within a concentration of 10 g/L (Figure A) for 24 h, and the half-maximal inhibitory concentration (IC50) of SGD was 65.56 g/L (Figure B). Then, we examined the levels of inflammatory cytokines in LPS-induced BV2 cells and SGD-treated BV2 cells. LPS (1 μg/mL) was added to BV2 cells for 3 h to simulate neuroinflammation, and a concentration series of SGD (SGD-L, 5 g/L; SGD-M, 10 g/L; SGD-H, 20 g/L) was used to treat LPS-induced BV2 cells for 24 h. We found that the levels of TNF-α, IL-1β, and IL-6 were significantly increased in BV2 cells after treatment with LPS, and SGD decreased the levels of TNF-α, IL-1β, and IL-6 both in BV2 cells (Figure C) and cell culture supernatants (Figure D). According to the predicted results of network pharmacology and molecular docking, we further examined the protein expression of ESR1 and ESR2. The results indicated that SGD might have an anti-inflammatory effect on LPS-induced BV2 cells by inhibiting the expression of ESR1 and ESR2 (Figure E–F).

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SGD inhibited LPS-mediated BV2 neuroinflammation by regulating protein expression of ESR1 and ESR2. (A) SGD had no obvious cytotoxicity to BV2 cells within a concentration of 10 g/L for 24 h. *p < 0.05, **p < 0.01, compared to 0 g/L groups. (B) The half-maximal inhibitory concentration (IC50) of SGD was 65.56 g/L. (C) The levels of TNF-α, IL-1β, and IL-6 were significantly increased in BV2 cells. **p < 0.01, compared to control groups. #p < 0.05, ##p < 0.01, compared to LPS groups. (D) The levels of TNF-α, IL-1β, and IL-6 were significantly increased in BV2 cell culture supernatants. **p < 0.01, compared to control groups. #p < 0.05, ##p < 0.01, compared to LPS groups. (E, F) SGD inhibited the protein expression of ESR1 and ESR2 in LPS-induced BV2 cells. **p < 0.01, compared to control groups. ##p < 0.01, compared to LPS groups.

3.8. Statistical Analysis

All the experimental data were presented as the means ± standard deviation. Statistical analyses were performed using GraphPad Prism 8.0 software. Student’s t-tests were used to calculate p-values, and a p-value < 0.05 indicated significance.

4. Discussion

Trigeminal neuralgia is a severe paroxysmal neuralgia occurring in the facial trigeminal nerve distribution. Focal demyelination of primary trigeminal afferent fibers in the vicinity of the trigeminal roots entering the pons is the underlying pathophysiological mechanism of TN. The etiology of TN remains unclear, and inflammation is suspected to play a crucial role in in the development and progression of TN. In TCM clinic, the diagnosis and treatment system is based on the principle of “disease-syndrome-prescription”, which possesses distinct advantages in disease prevention and treatment. The concept of “syndrome” serves as the connection between disease and the selection of herbs, while the “prescription” is the product of disease diagnosis and treatment based on the manifestation of the corresponding “syndrome”. The TCM diagnosis and treatment system formed by “disease-syndrome-prescription” coincides with the research paradigm of integrated network pharmacology for exploring the interaction and mechanism of drug-target-disease. The TCM discriminative diagnosis and treatment system coincides with the network pharmacology, which is based on exploring the drug-target-disease interaction mechanism. Therefore, network pharmacology is an important approach to explore the mechanism in the dialectical treatment of diseases with TCM.

TN, categorized as “facial paralysis” in TCM, is mainly caused by “wind” and “fire-heat” toxins. Therefore, the main strategy of TCM in TN treatment is focused on dispelling “wind” toxins and removing “fire” toxins. SGD, first mentioned in “Treatise on Febrile Diseases”, is a representative formula for the treatment of pain syndromes. It has been reported that the main components of baishao, including total glycosides of paeony, kaempferol, and β-sitosterol, can regulate immunity, improve mitochondrial function, and alleviate inflammatory response to exert analgesic effects. Furthermore, the main constituents of gancao, including quercetin, liquiritigenin, and licorice polysaccharides, are capable of scavenging reactive oxygen radicals, activating interleukins, inhibiting neuroinflammatory reactions, and improving immune function. Taking into account the multicomponent and multitarget characteristics of the TCM formula, this study utilizes network pharmacology to reveal the potential pharmacological mechanisms of SGD for TN treatment. Further, we validate the binding affinity and stability between active ingredients and targets by molecular docking and molecular dynamics simulation.

In this study, 121 active ingredients of SGD were initially screened from TCMSP, of which quercetin, kaempferol, licochalcone a, 7-methoxy-2-methyl isoflavone, naringenin, shinpterocarpin, β-sitosterol, and formononetin were ranked as the top 8 ingredients in terms of the number of targets. It has been recognized that the inflammatory response induced by interleukins on the trigeminal ganglion and nerve endings plays an important role in the pathogenesis of TN. Specifically, IL-1β and IL-6 can increase neuronal sensitivity to trigger the release of related neurotransmitters to down-regulate the pain threshold, which contributed to generating and transmitting pain sensations. It has been reported that injections of TNF-α and IL-1β into the infraorbital foramen may induce TN-like symptoms and pathological changes in rats, further supporting the involvement of inflammatory factors in the development and progression of TN. Licochalcone a was reported to attenuate neuropathic pain in rats by inhibiting microglia activation and inflammatory responses through the inhibition of p38 phosphorylation and the release of inflammatory factors such as TNF-α, IL-1, and IL-6. Tyrosine protein kinase (C-terminal Src kinase, CSK) has been proven to be involved in a multitude of metabolic pathways and processes, including cell growth and differentiation, cell migration, and immune responses. It has been demonstrated that Shinpterocarpin was able to selectively bind to CSK to exert biological effects. In addition, naringenin regulated voltage-gated ion channels to reverse abnormal nociceptive information on pain, especially calcium ion and sodium ion channels, both of which were key channels for the transmission and modulation of pain signals. The analgesic effects of naringenin had been illustrated in postsurgical pain models and neuropathic pain models. This study confirmed that SGD had no obvious toxicity to BV2 cells at concentrations below 10 g/L. We further verified that SGD reduced the LPS-induced BV2 inflammatory response by decreasing the levels of TNF-α, IL-1β, and IL-6. These results suggested that the analgesic effect of SGD may be exerted by inhibiting the inflammatory response of microglia. Our findings in this study identified core targets of SGD for analgesic and anti-inflammatory effects on TN, including ESR1, ESR2, TP53, JUN, AKT1, STAT3, and BCL2. Estrogen receptors are divided into two subtypes, namely ESR1 and ESR2, both of which are widely distributed in the nervous system and perform essential biological functions upon binding with estrogen. Phytoestrogens, a class of bioactive compounds with steroid structures, can activate ESR to exert important biological functions in the body. In this study, it was found that there were a variety of phytoestrogens in the active ingredients of SGD, such as β-sitosterol, isoflavones, etc. Formononetin, also reported as a phytoestrogen member of the flavonoid family, played significant neuroprotective effects in the nervous system. , Additionally, formononetin could upregulate the expression of ESR2 to reduce the release of inflammatory factors in microglia, thereby attenuating neuroinflammatory responses. , Consequently, we speculated that the active ingredients of SGD inhibited the inflammatory response by activating ESR, maintaining the stability of nerve cells, preventing the incoming nociceptive nerve signals, and achieving a certain degree of analgesia. Our in vitro experimental results also further confirmed that SGD inhibited the expression of ESR1 and ESR2 in LPS-induced BV2 cells.

AKT belongs to the serine/threonine protein kinase and is mainly involved in regulating biological processes, such as cell growth, proliferation, and vascular regeneration. In the animal model of neuropathic pain, central sensitization of the spinal cord played a key role in neuro-inflammation and chronic neuropathic pain, and the PI3K/AKT signaling pathway was proved to be a key pathway involved in the process. Studies have confirmed that many active ingredients can regulate the expression of AKT, including quercetin, kaempferol, and naringenin. The activated protein 1­(AP-1) was formed by c-JUN and c-FOS when neurons were stimulated by inflammatory factors. Then, the intracellular downstream molecules, such as TP53 and BCL-2, were activated to exert antiapoptosis and participate in nerve damage repair response. We reasoned that SGD may also play a therapeutic efficacy on neuropathic pain by regulating the expression of AKT, JUN, FOS, and other targets apart from inflammatory pain. In addition, STAT3 is also an important target involved in the activation of glial cells and the formation of neuropathic pain. Studies have shown that peripheral nerve injury can activate the phosphorylation of STAT3 to participate in the occurrence of neuropathic pain. Inhibiting the JAK/STAT3 signaling pathway can reduce the proliferation of astrocytes and recover from tactile allodynia.

We performed GO and KEGG enrichment analyses to explore the mechanism of SGD for the treatment of TN. GO annotation analysis results showed that therapeutic targets were mainly involved in the cellular response to cadmium ions, membrane microdomains, and DNA-binding transcription factor binding. Therefore, it can be speculated that the mechanism of SGD in treating TN was mainly related to the control of ion channels, functions of the cell membrane, and regulation of transcription factors. In KEGG pathway enrichment analysis, potential therapeutic genes are mainly enriched in inflammation- and immunity-related signaling pathways, such as lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, and TNF signaling pathway. These results revealed that SGD exerted therapeutic efficacy on TN through the synergistic effect of multicompounds, multitargets, and multipathways. In molecular docking, eight active ingredients and seven targets were used to verify the interaction, and the results suggested that there was great binding energy between ingredients and targets. In particular, the affinity between shinpterocarpin and ESR2 was the lowest, indicating the strongest binding activity. Subsequently, molecular dynamics simulations also confirmed this result. Shinpterocarpin might be a promising drug for the treatment of TN.

However, our study has several limitations. First, network pharmacology depends on available databases and in silico prediction, so the identified targets and pathways may vary with database coverage, algorithm performance, and parameter settings. The multicomponent nature of SGD and metabolism-related effects are also difficult to fully capture computationally. Second, PPI analysis, docking, and molecular dynamics were used to prioritize candidate interactions, but docking relies on simplified scoring and static structures. Molecular dynamics was performed only for the top-ranked shinpterocarpin–ESR2 complex and was not extended to other high-affinity pairs. Third, experimental validation focused on ESR1 and ESR2 in a cellular model, and broader pathway-level assessment and in vivo confirmation were beyond the scope of this study. Overall, our findings highlight ESR1 and ESR2 as plausible targets and provide a basis for further investigation of SGD in TN.

5. Conclusion

In this study, we employed network pharmacology to elucidate the potential mechanism of SGD in the treatment of TN. We further performed molecular docking and molecular dynamics simulations to preliminarily verify the interaction between active ingredients and hub targets. Our findings suggested that SGD exerted a therapeutic effect on TN by intervening with ESR1, ESR2, AKT, STAT3, and other targets, which provided a theoretical basis for the clinical application of SGD and new ideas for the development of novel analgesics.

Supplementary Material

ao5c11326_si_001.pdf (254.5KB, pdf)

Data used to support the results of this study were included within the article and Supporting Information.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.5c11326.

  • Table S1: Formulations of Shaoyao Gancao Decoction (SGD); Table S2: Ligands and receptors for molecular docking; Supplementary-WB (PDF)

#.

X.F.L., T.X., and W.J.L. contributed equally to this study. X.F.L. and T.H.W. contributed to the design, analysis, and interpretation of data; drafting of the manuscript. T.X. and W.J.L. contributed to the critical revision of the manuscript. Z.Y.L. and Y.F.Z. contributed to the methodology. X.F.L. wrote the manuscript. All authors read and approved the final manuscript.

The authors declare no competing financial interest.

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Data Availability Statement

Data used to support the results of this study were included within the article and Supporting Information.

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