Integrating Network Pharmacology, Molecular Docking, Dynamics Simulation and Experimental Validation to Decipher the Antipyretic Mechanisms of Xiaochaihu Granules

Integrating Network Pharmacology, Molecular Docking, Dynamics Simulation and Experimental Validation to Decipher the Antipyretic Mechanisms of Xiaochaihu Granules | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrating Network Pharmacology, Molecular Docking, Dynamics Simulation and Experimental Validation to Decipher the Antipyretic Mechanisms of Xiaochaihu Granules Ming-He Gu, Hong Liu, Cong Bi, Wen-Hui SiTu, Hai-Yong Du, Ai-Hua Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6665313/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Xiaochaihu granules (XCHG), a traditional Chinese herbal formulation, have demonstrated clinical efficacy in fever management, but their precise mode of action remains unclear. Our investigation employed an integrative methodology combining network pharmacology, molecular docking and dynamics simulations), and cellular assays to delineate XCHG's antipyretic mechanisms. Analysis of 18 blood-absorbed components identified from XCHG-treated rat plasma, we identified 17 key targets and 5 key components. GO and KEGG analyses showed that XCHG primarily relates to inflammation, immune regulation, neuroregulation, metabolic control, cell proliferation/apoptosis, and vascular homeostasis, mainly exerting anti-inflammatory effects through these mechanisms. Molecular docking results demonstrated good binding activity between key components and targets, particularly EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein. Subsequent dynamics simulations validated the structural integrity of these ternary complexes. In LPS-stimulated macrophages, XCHG significantly inhibited NO production and downregulation of pro-inflammatory mediators (TNF-α, IL-6, IL-1β, PGE2) and enzymes (iNOS, COX-2). These findings establish that XCHG achieves its antipyretic effects through multi-target engagement of key components with key proteins, subsequently modulating inflammatory cascades and cytokine networks, thereby providing mechanistic support for its clinical application in fever treatment. Biological sciences/Cell biology Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Molecular biology Xiaochaihu granules Fever Network pharmacology Molecular docking Molecular dynamics simulation Experimental validation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Fever represents a protective physiological response against pathogenic invasion, triggered by exogenous or endogenous pyrogens that stimulate immune cells to release inflammatory mediators 1,2 . These mediators act on the thermoregulatory center, elevating the temperature set point and inducing fever 3 . While moderate fever enhances immune defense, excessive febrile responses may lead to systemic complications, including organ dysfunction 4 . Although dexamethasone (Dex) and similar agents are clinically used to manage fever, prolonged administration can cause immunosuppression and other adverse effects 5 . Moreover, single-target anti-inflammatory drugs often fail to comprehensively modulate the complex inflammatory cascade 6 . Xiaochaihu granules (XCHG) is a classical Traditional Chinese Medicine (TCM) formula derived from the ancient medical text "Treatise on Cold Damage Diseases" (Shang Han Lun). This herbal formulation has been clinically used for centuries in Chinese medical practice to treat febrile conditions. It has been widely applied in clinical settings for treating various conditions such as common cold, influenza, hepatitis, and digestive disorders, with particularly remarkable efficacy in addressing symptoms of alternating fever and chills, chest discomfort, and reduced appetite. The commercial preparation utilized in this study was manufactured by Guangzhou Guanghua Pharmaceutical Co., Ltd. The composition of the granules consists of seven medicinal herbs in specific proportions: Bupleurum chinense DC. [Apiaceae] roots (Chaihu), Scutellaria baicalensis Georgi [Lamiaceae] roots (Huangqin), ginger-processed Pinellia ternata [Araceae] tubers (Jiangbanxia), Codonopsis pilosula [Campanulaceae] roots (Dangshen), fresh Zingiber officinale [Zingiberaceae] rhizomes (Shengjiang), Glycyrrhiza uralensis [Fabaceae] roots/rhizomes (Gancao), and Ziziphus jujuba [Rhamnaceae] fruits (Dazao). The crude drug-to-granule ratio is 0.486:1 (w/w), indicating that each gram of granules contains the equivalent of 0.486 g of raw herbal materials. Unlike single-component synthetic drugs, TCM demonstrates a characteristic "multi-component, multi-target, multi-pathway" therapeutic profile, where numerous key components collectively interact with diverse molecular targets through integrated physiological networks. These formulations exert comprehensive therapeutic effects through the regulation of multiple signaling pathways and biological networks in the body, while demonstrating a favorable safety profile with minimal adverse effects 7 . Modern research has demonstrated that the key components in XCHG exert anti-inflammatory effects through multiple mechanisms. For instance, Saikosaponin B2 suppresses PGE2, TNF-α, and IL-1β production during inflammation 8 . Glycyrrhizic acid modulates inflammatory processes through glucocorticoid receptor activation and PI3K/AKT/GSK3β pathway regulation 9 . Furthermore, XCHG has shown efficacy against COVID-19, with research revealing its ability to target coronavirus-associated proteins and regulate key inflammatory pathways including TLR and IL-6/STAT3 signaling 10 . However, XCHG's precise pharmacological mechanisms remain incompletely understood, especially its multi-component, multi-target, and multi-pathway features that require deeper exploration. Recent progress in systems and computational biology has enabled innovative approaches like network pharmacology, molecular docking, and molecular dynamics (MD) simulations to decode TCM components' therapeutic mechanisms 11 . Network pharmacology systematically reveals the pharmacological characteristics of TCM components by constructing "herb-component-target-disease" interaction networks 12,13 . Molecular docking elucidates the binding patterns between key components and target proteins at the molecular level. Molecular dynamics simulations assess the stability and behavior of these molecular complexes over time under physiological conditions 14–16 . This hierarchical, multi-level approach provides unprecedented mechanistic insight into XCHG's anti-pyretic actions. This research systematically investigates XCHG's antipyretic mechanisms through an integrated computational-experimental approach. Network pharmacology analysis identified potential targets of blood-absorbed XCHG components in rats, followed by computational characterization of component-target interactions using molecular docking and dynamics simulations. In vitro validation employing LPS-induced RAW264.7 macrophages demonstrated XCHG's significant modulation of pro-inflammatory mediators, including cytokine secretion (TNF-α, IL-1β, IL-6) and enzyme expression (COX-2, iNOS), along with reduced PGE2 production. The combined strategy elucidates XCHG's fever-alleviating mechanisms at molecular level while establishing a translatable framework for TCM formula research. Materials and Methods Collection of targets for blood-absorbed components of XCHG Based on our previous identification of 18 blood-absorbed XCHG components in rats 17 (Fig.1). The SMILES structures of these components were retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and then submitted to SwissTargetPrediction (http://www.swisstargetprediction.ch/) for target prediction, with the species parameter set to Homo sapiens. After merging and removing duplicates, we obtained the potential targets of XCHG's blood-absorbed components. Collection of fever-related targets Fever-associated targets were systematically retrieved from four major databases: OMIM (https://www.omim.org/), TTD (http://db.idrblab.net/ttd/), DrugBank (https://go.drugbank.com/), and GeneCards (https://www.genecards.org/). After merging non-redundant entries, we cross-referenced these with XCHG's blood-absorbed component targets to identify potential anti-pyretic candidates. Target overlaps were visualized via a Venn diagram tool (https://www.bioinformatics.com.cn/). PPI network construction and key target screening Intersection targets were analyzed using the STRING database (https://www.string-db.org/) under Homo sapiens settings (interaction skey ≥ 0.4; free proteins hidden). The resulting interactions (TSV format) were imported into Cytoscape 3.7.0 for visualization and topological assessment. Network centrality parameters—degree (DC), betweenness (BC), and closeness centrality (CC)—were computed via the CentiScape 2.2 plugin. Targets with DC, BC, and CC values exceeding their respective means were defined as key targets, and a condensed PPI subnetwork was generated. Node properties (size/color intensity) in the visualization reflect interaction degrees, with larger, darker nodes indicating higher connectivity. Final topological validation was performed using Cytoscape’s ‘Network Analyzer’ plugin. Construction of the XCHG's 'herb-component-target-fever' network and screening of key Components To elucidate XCHG's multi-scale therapeutic mechanisms, we constructed an integrated "herb-component-target-fever " network in Cytoscape 3.7.0 by mapping the herb's components to their shared targets with fever-related genes. Topological analysis using the Network Analyzer plugin identified five key components exhibiting the highest degree and betweenness centrality values, indicating their pivotal roles in mediating XCHG's antipyretic effects through multi-target modulation of fever-associated pathways. Functional enrichment analysis Functional enrichment analysis of XCHG’s fever-related targets was performed using the DAVID platform (https://david.ncifcrf.gov/), encompassing Gene Ontology (GO) terms and KEGG pathways 18 . Significant results were visualized as bubble plots and bar charts through the bioinformatics online platform (https://www.bioinformatics.com.cn/), highlighting key biological processes and signaling pathways modulated by XCHG. Molecular docking To investigate the interaction mechanisms between XCHG's key components and key targets, we conducted systematic molecular docking analyses. The key components and six highest-degree targets from the key targets network were selected as ligands and receptors, respectively. Component structures were obtained from PubChem, while target proteins were retrieved from the Protein Data Bank (PDB, https: //www.rcsb.org/) with stringent selection criteria: Homo sapiens origin, X-ray diffraction resolution ≤3 Å, and publication within the last decade. All structures were preprocessed using PyMOL to remove water molecules and original ligands. Molecular docking was performed with AutoDock Vina following grid parameter optimization and charge assignment in AutoDock Tools. Results were visualized in PyMOL after outputting the docking conformations in pdbqt format. Molecular dynamics simulation MD simulations were employed to comprehensively evaluate the structural dynamics and binding characteristics of the receptor-ligand complexes. Using GROMACS 2021, we performed 100-ns simulations on the three most stable complexes identified by docking studies (lowest binding energies). The system was parameterized with the AMBER14SB force field for proteins and GAFF2 for ligands, solvated in an SPC/E water model. After energy minimization (50,000-step steepest descent) and NVT/NPT equilibration at 310K, production runs were conducted. Average Binding free energy calculations using the MM/PBSA method were complemented by extensive trajectory analysis including structural stability assessment through root-mean-square deviation (RMSD), fluctuation (RMSF), and radius of gyration (Rg) measurements. Interaction dynamics were characterized by hydrogen bond formation patterns and solvent-accessible surface area (SASA) variations, while principal component analysis (PCA) and dynamic cross-correlation matrices (DCCM) were employed to elucidate collective motions and residue-residue correlations. Energetic profiling encompassed Gibbs free energy landscape (FEL) construction and residue-specific energy decomposition. The conformational evolution was systematically monitored at 25-ns intervals (0-100 ns) to capture the dynamic behavior throughout the simulation period. Materials XCHG were obtained from Guangzhou Baiyunshan Guanghua Pharmaceutical Co., Ltd (Guangzhou, China). RAW264.7 murine macrophages and DMEM complete medium were sourced from Wuhan Pricella Biotechnology Co., Ltd (Wuhan, China), while LPS was acquired from Sigma-Aldrich (USA). Reagents including the NO assay kit and CCK-8 came from Beyotime Biotechnology (Shanghai, China), with ELISA kits for TNF-α, IL-6, COX-2, IL-1β, PGE2, and iNOS provided by Jiangsu Meimian Industrial Co., Ltd (Jiangsu, China). All primers were commercially synthesized by Genewiz Co., Ltd (Suzhou, China). Cell culture RAW264.7 cells were seeded in DMEM supplemented with 10% FBS, 100 IU/ml penicillin, and 100 IU/ml streptomycin, cultured at 37°C in 5% CO₂ for 1-2 days. When confluence reached about 80%, cells were passaged. Cells in the logarithmic growth phase were used for experiments. Cell viability assay RAW264.7 cells at 80% - 90% confluence were seeded at 1×10⁴ cells per well (100 µL) in a 96-well plate. After 24-hour incubation at 37°C in 5% CO₂ for cell attachment, the supernatant was aspirated and treatment commenced. The control group got 100 µL complete DMEM medium per well; the drug group received 100 µL XCHG solution at 0.5, 1, 1.5, 2, 2.5, and 3 mg/ml. After another 24-hour incubation under the same conditions, the medium was aspirated and 10 µL CCK-8 solutions were added. After 2-hour incubation at 37°C in 5% CO₂, the absorbance at 450 nm was measured with a microplate reader to calculate cell viability. NO release assay RAW264.7 cells were plated in 96-well plates at a density of 1×10⁴ cells per well. The experiment comprised four groups: control, model, positive control (10 μM Dex), and XCHG-treated groups (low, medium, and high doses: 1, 1.5, and 2 mg/ml, respectively). After 24 h of culture, except for the control group, cells in the other groups were induced with LPS (1 μg/ml). Following 24 h of incubation at 37°C with 5% CO₂, supernatants were collected. The supernatant was mixed with Griess reagent and incubated at room temperature for 5 min. Absorbance was measured at 540 nm using a microplate reader. A standard curve was generated using sodium nitrite standards, and NO concentrations in test samples were calculated based on their absorbance values. ELISA assay Cells were seeded in 6-well plates at a density of 3×10⁵ cells/well and divided into the following groups: blank control, model, positive control (10 μM Dex), and XCHG-treated groups (low, medium, and high doses: 1, 1.5, and 2 mg/mL, respectively). After 24 h of culture, all groups except the blank control were stimulated with LPS for 24 h. The cell supernatants were then collected, and the concentrations of TNF-α, IL-6, COX-2, IL-1β, PGE2, and iNOS were determined using ELISA kits according to the manufacturer's instructions. The absorbance was measured at 450 nm, and the cytokine concentrations were calculated based on standard curves. qRT-PCR analysis The culture grouping conditions of RAW264.7 cells was like the ELISA experiment. Total RNA was isolated from the cells using Trizol reagent, followed by reverse transcription with a cDNA synthesis kit. For the quantification of TNF-α and IL-6 genes, qRT-PCR amplification was carried out under specific conditions. Initially, there was a denaturation step at 95°C for 1minute and 30 seconds (1 cycle). This was followed by 40 cycles, each consisting of denaturation at 95°C for 20 seconds, annealing at 64°C for 20 seconds, and extension at 72°C for 30 seconds. Finally, a melting curve analysis was performed, starting at 60°C for 1 minute and increasing by 0.4°C every 15 seconds until 95°C was reached, where it was maintained for 15 seconds (1 cycle). The primers employed in this study are presented in Table 1. Statistical analysis The data are shown as mean ± standard deviation (SD). Statistical significance was assessed using one-way analysis of variance (ANOVA) via GraphPad Prism 10.1.2 software, with a p-value of less than 0.05 regarded as statistically significant. Results Screening of potential targets of XCHG for treating fever Potential therapeutic targets of XCHG were systematically identified through database mining and comparative analysis. The SwissTargetPrediction platform yielded 298 putative targets for the 18 blood-absorbed components. Concurrently, fever-associated genes (n=2,079) were compiled from four disease databases (GeneCards, OMIM, TTD, DrugBank) following deduplication. Intersection analysis through Venn plotting revealed 120 shared targets between component and disease gene sets (Fig. 2a), representing XCHG's potential fever-modulating targets. PPI network construction and key target screening The 120 shared targets were analyzed via STRING to generate a PPI network (120 nodes, 1,432 edges; Fig. 2b). Network visualization in Cytoscape 3.7.0 incorporated node attributes scaled by topological importance, with size and color intensity reflecting connectivity (Degree). Through CentiScape 2.2 analysis, we quantified three centrality parameters (Degree ≥23.87, Closeness≥0.00446, Betweenness ≥109.13) to identify 17 key targets (Fig. 2c) including IL6, TNF, EGFR, STAT3, SRC, ESR1, PTGS2, HSP90AA1, MMP9, PPARG, MAPK3, IL2, CYP3A4, APP, ABCB1, ABCG2, and NTRK2. These targets demonstrated substantial network influence, implicating their functional significance in XCHG's antipyretic activity. The top six key targets by Degree value (Table 2) were subsequently selected for molecular docking with key components. Construction of the XCHG's 'herb-component-target-fever' network and screening of key components The "herb-component-target-fever" interaction network was constructed using Cytoscape 3.7.0 through topological analysis of overlapping targets. The network comprised 140 nodes, including 120 key targets, 18 components, XCHG and the fever (Fig. 2e). Based on Degree values, the key components were identified as key therapeutic components: Oroxylin A, Wogonin, Baicalein, Liquiritigenin and Enoxolone (Table 3). These components are predicted to be the primary key components responsible for XCHG's antipyretic effects. Functional enrichment analysis Functional enrichment analysis revealed XCHG's comprehensive regulatory profile across key biological domains. The analysis identified 528 significantly enriched biological processes (BP), prominently featuring protein phosphorylation and MAPK cascade regulation. Cellular component (CC) analysis (69 terms) demonstrated predominant localization to focal membrane receptor complexes and endoplasmic reticulum structures. Molecular function (MF) assessment (129 terms) highlighted crucial activities including tyrosine kinase function, ATP binding, and enzyme binding (Fig. 3a). These findings collectively indicate XCHG’s ability to regulate phosphorylation signaling (e.g., MAPK cascades) and membrane receptor activity while modulating cellular stress responses. KEGG pathway analysis from the DAVID database identified 160 pathways, with the top 20—including PI3K-Akt, Ras, Rap1, lipid and atherosclerosis, MAPK, C-type lectin receptor signaling, and EGFR tyrosine kinase inhibitor resistance (Fig. 3b)—primarily associated with inflammation, immune regulation, neuroregulation, metabolic control, cell proliferation/apoptosis, and vascular homeostasis. These results demonstrate that XCHG may exert therapeutic effects through modulation of the above pathways, further supporting its multi-target mechanism of action. Molecular docking analysis Molecular docking was performed between XCHG's key components and the six highest-degreetargets (EGFR, IL6, ESR1, STAT3, SRC, TNF), revealing binding energies ranging from -6.3 to -9.3 kcal/mol (Fig. 4), all exceeding the threshold for favorable binding (-5.0 kcal/mol). Notably, three complexes demonstrated particularly strong interactions: EGFR-Enoxolone (-9.3 kcal/mol), ESR1-Liquiritigenin (-8.7 kcal/mol), and SRC-Baicalein (-8.4 kcal/mol), with binding energies significantly below the -7.0 kcal/mol threshold indicating high-affinity binding 19 . Analysis of interaction patterns revealed multiple stabilizing forces including hydrogen bonds (Fig. 5), π-π stacking, and hydrophobic contacts. These three optimal complexes were selected for further MD simulations to investigate their dynamic binding characteristics. Molecular dynamics simulations The structural integrity of protein-ligand complexes was assessed through root-mean-square deviation (RMSD) analysis, with values under 1 nm signifying stable molecular interactions under physiological conditions 20 . RMSD analysis demonstrated that all three complexes (EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein) maintained high structural stability during simulation (Fig. 6a), with RMSD values stabilizing at approximately 0.3 nm, 0.25 nm, and 0.4-0.6 nm, respectively. Compared to the other two complexes, SRC-Baicalein exhibited relatively larger RMSD fluctuations (0.4-0.6 nm), which may be attributed to inherent structural characteristics of different proteins under identical simulation conditions. RMSF was used to assess positional fluctuations of amino acid residues, reflecting regional flexibility 21 . RMSF analysis revealed that all three complexes displayed overall fluctuations below1 nm, indicating limited residue mobility, with flexible regions exhibiting fluctuations between 0.4-0.6 nm (Fig. 6b). Rg was utilized to evaluate overall structural compactness, where larger values indicate structural expansion while smaller values reflect tighter packing 22 . Rg analysis further confirmed the structural compactness of the complexes, with Rg values maintained at approximately 2 nm, 1.75 nm, and 3 nm for the three complexes respectively, showing minimal fluctuations (Fig. 6c). Hydrogen bonds (H-bonds), as critical non-covalent interactions governing complex stability 23 , were quantitatively analyzed across the three complexes. The EGFR-Enoxolone complex formed 2 stable H-bonds, ESR1-Liquiritigenin exhibited 1-2 bonds, while SRC-Baicalein showed the most extensive network with 2-3 persistent H-bonds (Fig. 6d). This H-bond patterning demonstrates stable ligand-receptor recognition, the SRC-Baicalein complex forming the most H-bonds, indicative of superior binding affinity. SASA reflects surface solvent accessibility, where stable SASA profiles indicate well-folded structures 24 . All three complexes exhibited minimalSASA fluctuations (Fig. 6e), further supporting their high structural stability. These results collectively demonstrate that all three complexes maintained excellent binding stability and structuralcompactness throughout the MD simulations. FEL analysis and average binding free energy calculations The FEL was generated using RMSD and Rg data to characterize conformational changes of the complexes during simulations. In the FEL plots, dark-colored regions represent the lowest-energy states while light-colored areas correspond to higher-energy states 25 . The results demonstrated that all three complexes (EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein) formed single, well-defined low-energy clusters (Fig. 7a), indicating high conformational stability and strong binding interactions during the simulations. The MM/PBSA approach quantified binding affinities, with more negative values corresponding to stronger receptor-ligand interactions 26 . The MM/PBSA calculations revealed strongly favorable binding free energies for all three complexes (Fig. 7b): EGFR-Enoxolone (-45.79 kcal/mol), ESR1-Liquiritigenin (-35.58 kcal/mol), and SRC-Baicalein (-32.9 kcal/mol), indicating robust molecular recognition at the binding interfaces. Per-residue energy decomposition analysis (Fig. 7c) identified critical binding site residues with substantial contributions: PHE856 (-2.67 kcal/mol), LEU718 (-2.1 kcal/mol), and VAL726 (-1.99 kcal/mol) in EGFR-Enoxolone; LEU346 (-2.02 kcal/mol), PHE404 (-1.71 kcal/mol), and LEU387 (-1.7 kcal/mol) in ESR1-Liquiritigenin; and GLU310 (-4.0 kcal/mol) in SRC-Baicalein. These energetically favorable interactions significantly stabilized the respective complexes. All three systems demonstrated excellent conformational and binding stability throughout molecular dynamics simulations, providing crucial insights for future studies of protein-ligand interactions. PCA and DCCM analysis of complex dynamics To further investigate the conformational changes of proteins in the complexes, we employed PCA and DCCM to evaluate the dynamic behavior of three receptor-ligand systems. PCA identified dominant motion modes, with plot axes representing conformational space dimensions and color gradients (blue to red) indicating simulation progression time 27 . DCCM revealed residue-residue cross-correlations, where color gradients (light blue to pink) represented motion coordination: values approaching 1 indicated synchronized movements, while values near -1 represented anti-correlated motions 28,29 . PCA results (Fig. 8a) demonstrated that the three principal components (PC1, PC2, and PC3) accounted for 57.44%, 43.82%, and 80.93% of the total variance in the EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein complexes, respectively. PC1 primarily captured large-scale conformational changes, while PC2 and PC3 were associated with medium-range motions and localized movements, respectively. Notably, the SRC-Baicalein complex exhibited the highest PC1 contribution (61.55%), indicating its most pronounced conformational flexibility and suggesting greater potential for dynamic responses and structural adaptability. DCCM analysis (Fig. 8b) revealed distinct binding dynamics among the complexes. In the EGFR-Enoxolone complex, correlated motions were predominantly observed within residues 100-150 and 200-250, demonstrating significant cooperative movements in these regions with relatively independent motions elsewhere. The ESR1-Liquiritigenin complex showed strongly coupled dynamics confined to residues 50-100, while other regions displayed more independent motions. Notably, the SRC-Baicalein complex demonstrated the most pronounced correlation patterns, with strong positive couplings (residues 100-150) and anti-correlated motions (residues 300-450) that significantly exceeded those observed in other complexes. This unique dynamic profile, characterized by enhanced residue coordination and pronounced anti-correlations, likely underpins both its superior binding affinity and exceptional conformational adaptability during simulations. Furthermore, structural snapshots extracted at 0, 25, 50, 75, and 100 ns time points from the molecular dynamics trajectories (Fig. 8c) demonstrated EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein maintained stable binding conformations without significant positional drift, confirming their excellent binding stability. Collectively, these findings provide crucial insights into the dynamic behavior and binding characteristics of the protein-ligand complexes, while supporting the therapeutic potential of these ligands as drug candidates. Effect of XCHG on the viability of RAW264.7 cells The CCK-8 assay results (Fig. 9) demonstrated that XCHG at concentrations below 2 mg/ml had minimal impact on RAW264.7 cell viability after 24-hour treatment compared to the control group. However, significant growth inhibition was observed at concentrations exceeding 2 mg/ml. Based on these findings, subsequent experiments were conducted using XCHG concentrations of 1, 1.5, and 2 mg/ml. Effect of XCHG on LPS-induced NO production in RAW264.7 cells As determined by Griess assay, XCHG significantly attenuated LPS-induced NO production in RAW264.7 cells (Fig. 10). The LPS-induced model group showed markedly increased NO secretion compared to the control group ( p < 0.01), confirming successful model establishment. Notably, XCHG treatment at all tested concentrations (low dose: 1 mg/ml, medium dose: 1.5 mg/ml, and high dose: 2 mg/ml) dose-dependently suppressed NO release compared to the model group, demonstrating potent anti-inflammatory effects. Modulatory effects of XCHG on inflammatory mediators in LPS-induced RAW264.7 macrophages Figure 11 demonstrates that the LPS-treated model group exhibited significantly elevated levels of inflammatory biomarkers—TNF-α, IL-6, COX-2, IL-1β, PGE2, and iNOS—compared to untreated controls ( p < 0.01). Both Dex and medium-to-high doses of XCHG significantly suppressed the expression of TNF-α and iNOS ( p < 0.01), while the low-dose XCHG group showed moderate reduction in these parameters (p < 0.05). Moreover, treatment with Dex or any concentration of XCHG resulted in substantial suppression of IL-6, COX-2, IL-1β, and PGE2 relative to the LPS-induced group (p < 0.01). These findings reveal XCHG's concentration-dependent inhibitory effect on endotoxin-induced inflammatory activation in macrophages through comprehensive downregulation of proinflammatory mediators. Notably, the immunomodulatory efficacy of medium and high-dose XCHG treatment approached that of the clinical reference compound Dex. Effect of XCHG on TNF-α and IL-6 mRNA levels in LPS-induced RAW264.7 cells As shown in Fig. 12, compared with the control group, the LPS-induced model group exhibited significantly upregulated mRNA expression levels of TNF-α and IL-6 ( p < 0.01). Relative to the LPS model group, both the Dex group and the medium-and high-dose XCHG groups demonstrated marked downregulation of TNF-α mRNA expression ( p < 0.01). Additionally, the Dex group and high-dose XCHG group significantly suppressed IL-6 mRNA levels ( p < 0.05). Discussion The blood-absorbed components serve as the direct material basis for a drug's pharmacological effects, as only the key components that enter systemic circulation can interact with biological targets to exert therapeutic actions 30,31 . Through systematic analysis of these blood-absorbed components, we can more precisely elucidate the pharmacological substance basis of XCHG and identify its true key components. In our previous pharmacokinetic study on XCHG in rats, we identified 18 key components that can enter systemic circulation. These components primarily fall into two categories: flavonoids and triterpenoid saponins, which significantly influence immune modulation and inflammatory responses. Studies show that baicalin suppresses the NLRP3 inflammasome 32 , while baicalein inhibits COX-2 and iNOS expression 33 . And other components like lobetyolin demonstrate immunomodulatory effects through regulation of glutamine metabolism and inhibition of inflammatory pathways 34 . These scientific findings highlight the presence of multiple inflammation-regulating components in XCHG, suggesting that its therapeutic effects on inflammation and pyrexia likely result from the synergistic actions of diverse components targeting multiple pathways. Based on the network pharmacology analysis results, this study identified five key components in XCHG: Oroxylin A, Wogonin, Baicalein, Liquiritigenin, and Enoxolone (18β-glycyrrhetinic acid), which are likely to constitute the primary material basis for XCHG's antipyretic effects. The analysis simultaneously revealed 120 potential targets associated with XCHG's fever-alleviating effects. Further PPI network analysis highlighted 17 key targets of particular importance. Pathway annotation through GO analysis demonstrated that XCHG's primary targets participate in crucial biological functions including upregulation of MAPK/ERK cascades, stimulation of PI3K-Akt networks, and protein phosphorylation signal transmission, while significantly affecting apoptotic inhibition. These targets predominantly localize in membrane receptor complexes and display molecular characteristics like kinase functionality and ATP-binding properties. Among the 160 enriched biochemical pathways identified by KEGG, the most significant included PI3K-Akt network, Ras-mediated signaling, Rap1 transduction mechanism, lipid metabolism and atherosclerosis, MAPK cascade, C-type lectin receptor interaction, and EGFR inhibitor resistance mechanisms, which relate to inflammatory processes, immune response, neural regulation, metabolic control, cellular proliferation and programmed cell death, and vascular homeostasis. These findings suggest that XCHG's antipyretic effects occur through multi-target and diversified pathway interactions, exemplifying traditional Chinese medicine's holistic therapeutic approach. Unlike single-target pharmaceutical agents, XCHG's key components collectively modulate inflammatory networks, delivering comprehensive efficacy with fewer adverse reactions. Molecular docking combined with MD simulations serves as a crucial computational tool for predicting interaction patterns and structural configurations of biomolecular complexes 35 , providing valuable theoretical guidance for subsequent experimental studies. In the investigation, molecular docking analysis demonstrated that three key components, Enoxolone, Liquiritigenin, and Baicalein exhibited strong binding affinities with key targets EGFR, ESR1, and SRC. These robust molecular interactions suggest these component-target pairs may represent primary key pharmacological combinations underlying XCHG's antipyretic effects. Studies demonstrated that EGFR plays a crucial role in promoting inflammatory responses through Src-dependent transactivation mechanisms, which subsequently regulate signaling pathways including ERK1/2, PI3Kδ/Akt and NF-κB activation. These downstream pathways are critically involved in various inflammatory processes such as inflammatory cell infiltration, tissue fibrosis and airway hyperresponsiveness. As a key component of EGFR signaling networks, SRC physically interacts with activated EGFR and induces phosphorylation at two specific tyrosine residues (Tyr-845 and Tyr-1101), thereby enhancing inflammatory responses and modulating multiple cellular processes including proliferation, differentiation, migration and apoptosis 36,37 . Meanwhile, ESR1 regulates the development and function of innate immune cells by balancing the production of pro- and anti-inflammatory cytokines, while inflammatory cytokines in turn can modulate ESR1 expression and activity 38 . Enoxolone, liquiritigenin, and baicalein demonstrate strong binding affinity with these target proteins, suggesting that XCHG may exert its antipyretic and anti-inflammatory effects by inhibiting the activity of these targets, modulating relevant signaling pathways, reducing the release of pro-inflammatory factors, regulating the hypothalamic thermoregulatory center, and suppressing inflammasome activation. Molecular dynamics simulations further validated the high stability, compactness, and appropriate flexibility of these three protein-ligand complexes. RMSD analysis demonstrated that the EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein complexes maintained stable conformations throughout the 100 ns simulation period. Complementary analyses including RMSF, Rg, hydrogen bonding, and SASA further confirmed the stability of these complexes, with the formation of persistent hydrogen bonds and maintenance of compact structures indicating robust binding interactions. FEL and MM/PBSA analyses revealed that all complexes formed single, well-defined low-energy clusters, demonstrating high conformational stability and strong binding interactions. The calculated average binding free energies provided additional evidence for the stable binding affinities between these ligands and their respective targets. Furthermore, this study utilized an endotoxin-induced inflammation model in murine macrophage line RAW264.7 to verify XCHG's modulatory impact on inflammatory factors. The experimental findings established that XCHG markedly suppressed endotoxin-triggered NO production across all tested concentrations (low, medium, and high), demonstrating robust anti-inflammatory capacity. Immunoassay analysis additionally showed that XCHG reduced levels of critical inflammatory mediators in a concentration-dependent manner, including TNF-α, IL-6, COX-2, IL-1β, PGE2, and iNOS. Medium and high XCHG doses achieved comparable anti-inflammatory efficacy to the reference compound Dex. These observations were further supported at the gene expression level through quantitative PCR, which revealed that medium and high XCHG concentrations substantially decreased transcription of inflammatory cytokines. Together, this evidence suggests that XCHG achieves its inflammation-controlling and fever-reducing properties by attenuating both production and release of inflammatory signaling molecules at protein and transcriptional levels. Cascading reactions between inflammatory factors constitute the key pathological mechanism of fever 39 . When the body is infected by pathogens or induced by exogenous pyrogens, macrophages are first activated to release early inflammatory factors TNF-α and IL-1β, which serve as "alarm signals" and can further induce the production of IL-6 40 –42 . Among these, IL-1β plays a critical initiating role in the febrile process by directly activating the COX-2 promoter through NF-κB nuclear translocation mediated by IκBα phosphorylation, while simultaneously stabilizing COX-2 mRNA via p38 MAPK signaling 43 –46 . IL-6, a pivotal pyrogenic cytokine, has been demonstrated to directly act on the hypothalamic thermoregulatory center 47 . COX-2, as the inducible cyclooxygenase, catalyzes the conversion of arachidonic acid to PGE2 via PGG2/PGH2 intermediates, with its expression level serving as the primary determinant of PGE2 production under pathological conditions 48 . As the definitive pyrogenic mediator, PGE2 directly targets the hypothalamic thermoregulatory center, elevating the thermostatic set point and consequently inducing febrile responses 49 . Inducible iNOS is pivotal in mediating inflammatory responses, as its catalytic product NO upregulates COX-2 and promotes PGE2 production 50 . This synergistic interaction collectively amplifies the cascade of inflammatory responses and febrile progression. This study demonstrated that XCHG markedly decreased levels of NO, TNF-α, IL-6, COX-2, IL-1β, PGE2, and iNOS in LPS-induced RAW264.7 cells, suggesting its anti-inflammatory and fever-reducing actions involve inhibiting these key inflammatory molecules. KEGG analysis indicated that XCHG's main targets were significantly associated with pivotal pathways like PI3K-Akt, Ras, Rap1, and MAPK, which modulate both the release of inflammatory cytokines (e.g., IL-1β, TNF-α, IL-6) and the up regulation of COX-2/iNOS. This suggests that XCHG may potentially interrupt inflammatory signal transduction by intervening in the phosphorylation processes of these pathways, inhibit PGE2 and NO production, and ultimately achieve anti-inflammatory and antipyretic effects. Furthermore, the enrichment of C-type lectin receptor and EGFR-related signaling pathways further elucidates the potential mechanism of action of XCHG at the membrane receptor level. Conclusions This investigation comprehensively revealed the molecular basis of XCHG antipyretic properties by integrating network pharmacology, computational modeling (molecular docking and dynamics), and cellular assays. The findings indicate that XCHG achieves its therapeutic benefits through synergistic modulation of diverse pathways, such as inflammatory responses, immune-neuro-metabolic crosstalk, and vascular function. Key ligand-receptor pairs (e.g., EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein) were identified as potential mediators. In LPS-induced RAW264.7 macrophages, XCHG effectively suppressed the secretion of inflammatory mediators (TNF-α, IL-6, IL-1β), diminished COX-2 and iNOS levels, and PGE2 and NO generation. Unlike single-target agents, XCHG's multi-component synergy enhances therapeutic outcomes, supporting its clinical utility. Further research is needed to validate specific binding mechanisms and detailed signaling cascades. Declarations Author contributions Ming-He Gu was responsible for research design, experimental work, data analysis, and manuscript drafting. Hong Liu, Cong Bi, Wen-Hui SiTu, Hai-Yong Du, and Jun-Hua Zhang collectively contributed to study conceptualization, funding acquisition, and project coordination. Ai-Hua Lin and Yi-Ming Liu performed manuscript reviewing and research supervision. All authors approved the final manuscript. Funding This work was supported by the Guangzhou Science and Technology Program (Grant Number. 202206010112) and the Guangdong Provincial Science and Technology Program (Grant Number. 2023B1212060063). Data Availability The data that support the findings of this study are available in the Supplementary Materials of this paper. Declarations Conflict of interest The authors collectively confirm the absence of any financial or non-financial competing interests related to this research work. Ethical approval Not applicable. References Evans SS, Repasky EA, Fisher DT. Fever and the thermal regulation of immunity: the immune system feels the heat. Rev. Immunol. 15 :335-349. https://doi.org/10.1038/nri3843 (2015). Netea MG, Kullberg BJ, Van der Meer JW. Circulating cytokines as mediators of fever. Infect. Dis. 31. Suppl. 5 : S178-S184. https://doi.org/10.1086/317513 (2000). Dinarello CA. Infection, fever, and exogenous and endogenous pyrogens: some concepts have changed. Endotoxin. 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Tables Table 1 Primer sequences Primer Name Type Sequence (5'-3') Size（bp） IL-6 F CTGCAAGAGACTTCCATCCAG 155 R AGTGGTATAGACAGGTCTGTTGG TNF F CAGGCGGTGCCTATGTCTC 89 R CGATCACCCCGAAGTTCAGTAG Actin F CACCATTGGCAATGAGCGGTTC 130 R AGGTCTTTGCGGATGTCCACGT Table 2 The top six key targets ranked by degree value Target name Degree Closeness centrality Betweenness centrality EGFR 16 1 0.02599597 IL6 16 1 0.02599597 ESR1 16 1 0.02599597 STAT3 15 0.94117647 0.01592653 SRC TNF 15 15 0.94117647 0.94117647 0.01592653 0.0209623 Table 3 The top five components ranked by degree value Ingredient name Degree Closeness centrality Betweenness centrality Oroxylin A 105 0.43621399 0.13701609 Wogonin 105 0.43621399 0.15307644 Baicalein 105 0.43621399 0.17399126 Liquiritigenin 103 0.43383356 0.29743801 Enoxolone 93 0.42343542 0.33684036 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6665313","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471957950,"identity":"8e0e6b1d-65cc-4a80-baa3-110d184dadc7","order_by":0,"name":"Ming-He Gu","email":"","orcid":"","institution":"The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou","correspondingAuthor":false,"prefix":"","firstName":"Ming-He","middleName":"","lastName":"Gu","suffix":""},{"id":471957951,"identity":"d67ce6ad-cdcb-444b-83f2-a307e0ea7dd1","order_by":1,"name":"Hong Liu","email":"","orcid":"","institution":"Guangzhou Baiyunshan 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2","display":"","copyAsset":false,"role":"figure","size":54820038,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-target network analysis. \u003cstrong\u003ea \u003c/strong\u003eVenn diagram of blood-\u003cstrong\u003e \u003c/strong\u003eabsorbed components of XCHG and disease targets. \u003cstrong\u003eb \u003c/strong\u003ePPI network of 120 cross-targets. \u003cstrong\u003ec \u003c/strong\u003eKey target network. \u003cstrong\u003ed \u003c/strong\u003eBlood-absorbed component-target network of XCHG. \u003cstrong\u003ee \u003c/strong\u003eXCHG herb-components-targets-fever network diagram.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6665313/v1/810e74e614615ecd8ab390c1.png"},{"id":84816469,"identity":"d5ca6271-7c68-4469-bb13-3bb23a96e799","added_by":"auto","created_at":"2025-06-17 15:41:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12331095,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analysis.\u003cstrong\u003e a\u003c/strong\u003e GO terms. \u003cstrong\u003eb\u003c/strong\u003e KEGG pathways.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6665313/v1/ea0188252933a617b0703800.png"},{"id":84817365,"identity":"083fe9d1-f600-45fc-a980-b7bb8e8992ec","added_by":"auto","created_at":"2025-06-17 15:49:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1722780,"visible":true,"origin":"","legend":"\u003cp\u003eBinding energy (kcal/mol) heatmap of key components of XCHG and top six key 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protein-ligand complexes.\u003cstrong\u003e a\u003c/strong\u003e RMSD. \u003cstrong\u003eb\u003c/strong\u003e RMSF. \u003cstrong\u003ec\u003c/strong\u003e Rg. \u003cstrong\u003ed\u003c/strong\u003e Number of H-bonds. \u003cstrong\u003ee\u003c/strong\u003e SASA.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6665313/v1/7ce207054aa430266af9c2c1.png"},{"id":84817368,"identity":"b6e72691-24e4-4b9f-b585-6af363253beb","added_by":"auto","created_at":"2025-06-17 15:49:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":11569219,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive energetic analysis of three protein-ligand interactions. \u003cstrong\u003ea \u003c/strong\u003eFEL analysis. \u003cstrong\u003eb\u003c/strong\u003e The average binding free energy, along with its components—van der waals interactions (VDWAALS), electrostatic energy (EEL), polar solvation energy (EGB), non-polar solvation energy (ESURF), molecular mechanics energy (GGAS), and solvation energy (GSOLV). \u003cstrong\u003ec\u003c/strong\u003e Residue energy contributions.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6665313/v1/0b1ad7812b657db8dfbdae93.png"},{"id":84816461,"identity":"1f4e01df-8d76-4c01-81f9-2dc0bf334ddb","added_by":"auto","created_at":"2025-06-17 15:41:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6769724,"visible":true,"origin":"","legend":"\u003cp\u003eConformational and structural analysis of three protein-ligand complexes during molecular dynamics simulation. \u003cstrong\u003ea \u003c/strong\u003ePCA analysis. \u003cstrong\u003eb\u003c/strong\u003e DCCM analysis. \u003cstrong\u003ec\u003c/strong\u003e Structural analysis at 0 s, 25 ns, 50 ns, 75 ns, and 100 ns during the molecular dynamics simulation.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6665313/v1/7804f141c6f921b427099148.png"},{"id":84817371,"identity":"ff329b45-619a-4e90-a051-7b84f725f8a2","added_by":"auto","created_at":"2025-06-17 15:49:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":504913,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of XCHG on the viability of RAW264.7 Cells. Data are presented as mean ± SD (n\u0026nbsp;= 3).\u003csup\u003e ** \u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜0.01 vs. Control.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-6665313/v1/b6909a1fb7954b8c8fa7da56.png"},{"id":84817372,"identity":"c34b561f-7f0e-4a36-8d6e-eddb351cddf0","added_by":"auto","created_at":"2025-06-17 15:49:07","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":423217,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of XCHG on LPS-induced NO production in RAW264.7 cells. Data are presented as mean ± SD (n\u0026nbsp;= 3). \u003csup\u003e##\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u003csup\u003e \u003c/sup\u003e＜0.01 vs. Control, \u003csup\u003e** \u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜0.01 vs. Model.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-6665313/v1/39b8839c22b61275af715136.png"},{"id":84816473,"identity":"200535de-7463-4a6d-b483-cea03cc48145","added_by":"auto","created_at":"2025-06-17 15:41:07","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":20953732,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of XCHG on inflammatory mediators in LPS-induced RAW264.7 cells. \u003cstrong\u003ea\u003c/strong\u003e TNF-α. \u003cstrong\u003eb\u003c/strong\u003e IL-6. \u003cstrong\u003ec\u003c/strong\u003e COX-2. \u003cstrong\u003ed\u003c/strong\u003e IL-1β. \u003cstrong\u003ee\u003c/strong\u003e PGE2. \u003cstrong\u003ef\u003c/strong\u003e iNOS. Data are presented as mean ± SD (n\u0026nbsp;= 3). \u003csup\u003e##\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜0.01 vs. Control, \u003csup\u003e* \u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜0.05 vs. Model, \u003csup\u003e** \u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜0.01 vs. Model.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-6665313/v1/d331187ae0e30a812a61088d.png"},{"id":84816467,"identity":"13169c69-42dc-4602-9f6b-3bfe69182703","added_by":"auto","created_at":"2025-06-17 15:41:07","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1799341,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of XCHG on TNF-α and IL-6 mRNA levels\u003cstrong\u003e \u003c/strong\u003ein LPS-induced RAW264.7 cells.\u003cstrong\u003e a\u003c/strong\u003e Effect of TNF-α mRNA expression. \u003cstrong\u003eb\u003c/strong\u003e Effect of IL-6 mRNA expression. Data are presented as mean ± SD (n\u0026nbsp;= 3). \u003csup\u003e##\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜0.01 vs. Control, \u003csup\u003e*\u003c/sup\u003e\u003csup\u003e\u003cem\u003e \u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜0.05 vs. Model, \u003csup\u003e** \u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜0.01 vs. Model.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-6665313/v1/a654886984e1fa62c9aefe71.png"},{"id":84816501,"identity":"b6b460ac-aeab-4465-a3f5-3a045e3ecbb7","added_by":"auto","created_at":"2025-06-17 15:41:10","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":77641073,"visible":true,"origin":"","legend":"","description":"","filename":"XCHGSupplementarymaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-6665313/v1/f8a2544e265d280a66deb419.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Network Pharmacology, Molecular Docking, Dynamics Simulation and Experimental Validation to Decipher the Antipyretic Mechanisms of Xiaochaihu Granules","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFever represents a protective physiological response against pathogenic invasion, triggered by exogenous or endogenous pyrogens that stimulate immune cells to release inflammatory mediators\u003csup\u003e1,2\u003c/sup\u003e. These mediators act on the thermoregulatory center, elevating the temperature set point and inducing fever\u003csup\u003e3\u003c/sup\u003e. While moderate fever enhances immune defense, excessive febrile responses may lead to systemic complications, including organ dysfunction\u003csup\u003e4\u003c/sup\u003e. Although dexamethasone (Dex) and similar agents are clinically used to manage fever, prolonged administration can cause immunosuppression and other adverse effects\u003csup\u003e5\u003c/sup\u003e. Moreover, single-target anti-inflammatory drugs often fail to comprehensively modulate the complex inflammatory cascade\u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eXiaochaihu granules (XCHG) is a classical Traditional Chinese Medicine (TCM) formula derived from the ancient medical text \"Treatise on Cold Damage Diseases\" (Shang Han Lun). This herbal formulation has been clinically used for centuries in Chinese medical practice to treat febrile conditions. It has been widely applied in clinical settings for treating various conditions such as common cold, influenza, hepatitis, and digestive disorders, with particularly remarkable efficacy in addressing symptoms of alternating fever and chills, chest discomfort, and reduced appetite. The commercial preparation utilized in this study was manufactured by Guangzhou Guanghua Pharmaceutical Co., Ltd. The composition of the granules consists of seven medicinal herbs in specific proportions: Bupleurum chinense DC. [Apiaceae] roots (Chaihu), Scutellaria baicalensis Georgi [Lamiaceae] roots (Huangqin), ginger-processed Pinellia ternata [Araceae] tubers (Jiangbanxia), Codonopsis pilosula [Campanulaceae] roots (Dangshen), fresh Zingiber officinale [Zingiberaceae] rhizomes (Shengjiang), Glycyrrhiza uralensis [Fabaceae] roots/rhizomes (Gancao), and Ziziphus jujuba [Rhamnaceae] fruits (Dazao). The crude drug-to-granule ratio is 0.486:1 (w/w), indicating that each gram of granules contains the equivalent of 0.486 g of raw herbal materials. Unlike single-component synthetic drugs, TCM demonstrates a characteristic \"multi-component, multi-target, multi-pathway\" therapeutic profile, where numerous key components collectively interact with diverse molecular targets through integrated physiological networks. These formulations exert comprehensive therapeutic effects through the regulation of multiple signaling pathways and biological networks in the body, while demonstrating a favorable safety profile with minimal adverse effects\u003csup\u003e7\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModern research has demonstrated that the key components in XCHG exert anti-inflammatory effects through multiple mechanisms. For instance, Saikosaponin B2 suppresses PGE2, TNF-α, and IL-1β production during inflammation\u003csup\u003e8\u003c/sup\u003e. Glycyrrhizic acid modulates inflammatory processes through glucocorticoid receptor activation and PI3K/AKT/GSK3β pathway regulation\u003csup\u003e9\u003c/sup\u003e. Furthermore, XCHG has shown efficacy against COVID-19, with research revealing its ability to target coronavirus-associated proteins and regulate key inflammatory pathways including TLR and IL-6/STAT3 signaling\u003csup\u003e10\u003c/sup\u003e. However, XCHG's precise pharmacological mechanisms remain incompletely understood, especially its multi-component, multi-target, and multi-pathway features that require deeper exploration.\u003c/p\u003e\n\u003cp\u003eRecent progress in systems and computational biology has enabled innovative approaches like network pharmacology, molecular docking, and molecular dynamics (MD) simulations to decode TCM components' therapeutic mechanisms\u003csup\u003e11\u003c/sup\u003e. Network pharmacology systematically reveals the pharmacological characteristics of TCM components by constructing \"herb-component-target-disease\" interaction networks\u003csup\u003e12,13\u003c/sup\u003e. Molecular docking elucidates the binding patterns between key components and target proteins at the molecular level. Molecular dynamics simulations assess the stability and behavior of these molecular complexes over time under physiological conditions\u003csup\u003e14–16\u003c/sup\u003e. This hierarchical, multi-level approach provides unprecedented mechanistic insight into XCHG's anti-pyretic actions.\u003c/p\u003e\n\u003cp\u003eThis research systematically investigates XCHG's antipyretic mechanisms through an integrated computational-experimental approach. Network pharmacology analysis identified potential targets of blood-absorbed XCHG components in rats, followed by computational characterization of component-target interactions using molecular docking and dynamics simulations. In vitro validation employing LPS-induced RAW264.7 macrophages demonstrated XCHG's significant modulation of pro-inflammatory mediators, including cytokine secretion (TNF-α, IL-1β, IL-6) and enzyme expression (COX-2, iNOS), along with reduced PGE2 production. The combined strategy elucidates XCHG's fever-alleviating mechanisms at molecular level while establishing a translatable framework for TCM formula research.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eCollection of targets for blood-absorbed components of XCHG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on our previous identification of 18 blood-absorbed XCHG components in rats\u003csup\u003e17\u003c/sup\u003e (Fig.1). The SMILES structures\u0026nbsp;of\u0026nbsp;these\u0026nbsp;components\u0026nbsp;were\u0026nbsp;retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and then submitted to SwissTargetPrediction (http://www.swisstargetprediction.ch/) for target prediction, with the species parameter set to Homo sapiens. After merging and removing duplicates, we obtained the potential targets of XCHG's blood-absorbed components.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollection of fever-related targets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFever-associated targets were systematically retrieved from four major databases: OMIM (https://www.omim.org/), TTD (http://db.idrblab.net/ttd/), DrugBank (https://go.drugbank.com/), and GeneCards (https://www.genecards.org/). After merging non-redundant entries, we cross-referenced these with XCHG's blood-absorbed component targets to identify potential anti-pyretic candidates. Target overlaps were visualized via a Venn diagram tool (https://www.bioinformatics.com.cn/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI network construction and key target screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntersection targets were analyzed using the STRING database (https://www.string-db.org/) under\u0026nbsp;Homo sapiens\u0026nbsp;settings (interaction skey ≥ 0.4; free proteins hidden). The resulting interactions (TSV format) were imported into\u0026nbsp;Cytoscape 3.7.0\u0026nbsp;for visualization and topological assessment. Network centrality parameters—degree (DC), betweenness (BC), and closeness centrality (CC)—were computed via the\u0026nbsp;CentiScape 2.2\u0026nbsp;plugin. Targets with DC, BC, and CC values exceeding their respective means were defined as\u0026nbsp;key targets, and a condensed PPI subnetwork was generated. Node properties (size/color intensity) in the visualization reflect interaction degrees, with larger, darker nodes indicating higher connectivity. Final topological validation was performed using Cytoscape’s\u0026nbsp;‘Network Analyzer’\u0026nbsp;plugin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the XCHG's 'herb-component-target-fever' network and screening of key Components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate XCHG's multi-scale therapeutic mechanisms, we constructed an integrated \"herb-component-target-fever \" network in Cytoscape 3.7.0 by mapping the herb's components to their shared targets with fever-related genes. Topological analysis using the Network Analyzer plugin identified five key components exhibiting the highest degree and betweenness centrality values, indicating their pivotal roles in mediating XCHG's antipyretic effects through multi-target modulation of fever-associated pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis\u0026nbsp;of XCHG’s fever-related targets was performed using the\u0026nbsp;DAVID platform\u0026nbsp;(https://david.ncifcrf.gov/), encompassing\u0026nbsp;Gene Ontology (GO)\u0026nbsp;terms and\u0026nbsp;KEGG pathways\u003csup\u003e18\u003c/sup\u003e. Significant results were visualized as bubble plots and bar charts through the bioinformatics online platform (https://www.bioinformatics.com.cn/), highlighting key biological processes and signaling pathways modulated by XCHG.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the interaction mechanisms between XCHG's key components and key targets, we conducted systematic molecular docking analyses. The key components and six highest-degree targets from the key targets network were selected as ligands and receptors, respectively. Component structures were obtained from PubChem, while target proteins were retrieved from the Protein Data Bank (PDB, https: //www.rcsb.org/) with stringent selection criteria: Homo sapiens origin, X-ray diffraction resolution ≤3 Å, and publication within the last decade. All structures were preprocessed using PyMOL to remove water molecules and original ligands. Molecular docking was performed with AutoDock Vina following grid parameter optimization and charge assignment in AutoDock Tools. Results were visualized in PyMOL after outputting the docking conformations in pdbqt format.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMD simulations were employed to comprehensively evaluate the structural dynamics and binding characteristics of the receptor-ligand complexes. Using GROMACS 2021, we performed 100-ns simulations on the three most stable complexes identified by docking studies (lowest binding energies). The system was parameterized with the AMBER14SB force field for proteins and GAFF2 for ligands, solvated in an SPC/E water model. After energy minimization (50,000-step steepest descent) and NVT/NPT equilibration at 310K, production runs were conducted. Average Binding free energy calculations using the MM/PBSA method were complemented by extensive trajectory analysis including structural stability assessment through root-mean-square deviation (RMSD), fluctuation (RMSF), and radius of gyration (Rg) measurements. Interaction dynamics were characterized by hydrogen bond formation patterns and solvent-accessible surface area (SASA) variations, while principal component analysis (PCA) and dynamic cross-correlation matrices (DCCM) were employed to elucidate collective motions and residue-residue correlations. Energetic profiling encompassed Gibbs free energy landscape (FEL) construction and residue-specific energy decomposition. The conformational evolution was systematically monitored at 25-ns intervals (0-100 ns) to capture the dynamic behavior throughout the simulation period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXCHG were obtained from Guangzhou Baiyunshan Guanghua Pharmaceutical Co., Ltd (Guangzhou, China). RAW264.7 murine macrophages and DMEM complete medium were sourced from Wuhan Pricella Biotechnology Co., Ltd (Wuhan, China), while LPS was acquired from Sigma-Aldrich (USA). Reagents including the NO assay kit and CCK-8 came from Beyotime Biotechnology (Shanghai, China), with ELISA kits for TNF-α, IL-6, COX-2, IL-1β, PGE2, and iNOS provided by Jiangsu Meimian Industrial Co., Ltd (Jiangsu, China). All primers were commercially synthesized by Genewiz Co., Ltd (Suzhou, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRAW264.7 cells were seeded in DMEM supplemented with 10% FBS, 100 IU/ml penicillin, and 100 IU/ml streptomycin, cultured at 37°C in 5% CO₂ for 1-2 days. When confluence reached about 80%, cells were passaged. Cells in the logarithmic growth phase were used for experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell viability assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRAW264.7 cells at 80% - 90% confluence were seeded at 1×10⁴ cells per well (100 µL) in a 96-well plate. After 24-hour incubation at 37°C in 5% CO₂ for cell attachment, the supernatant was aspirated and treatment commenced. The control group got 100 µL complete DMEM medium per well; the drug group received 100 µL XCHG solution at 0.5, 1, 1.5, 2, 2.5, and 3 mg/ml. After another 24-hour incubation under the same conditions, the medium was aspirated and 10 µL CCK-8 solutions were added. After 2-hour incubation at 37°C in 5% CO₂, the absorbance at 450 nm was measured with a microplate reader to calculate cell viability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNO release assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRAW264.7 cells were plated in 96-well plates at a density of 1×10⁴ cells per well. The experiment comprised four groups: control, model, positive control (10 μM Dex), and XCHG-treated groups (low, medium, and high doses: 1, 1.5, and 2 mg/ml, respectively). After 24 h of culture, except for the control group, cells in the other groups were induced with LPS (1 μg/ml). Following 24 h of incubation at 37°C with 5% CO₂, supernatants were collected. The supernatant was mixed with Griess reagent and incubated at room temperature for 5 min. Absorbance was measured at 540 nm using a microplate reader. A standard curve was generated using sodium nitrite standards, and NO concentrations in test samples were calculated based on their absorbance values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eELISA assay\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were seeded in 6-well plates at a density of 3×10⁵ cells/well and divided into the following groups: blank control, model, positive control (10 μM Dex), and XCHG-treated groups (low, medium, and high doses: 1, 1.5, and 2 mg/mL, respectively). After 24 h of culture, all groups except the blank control were stimulated with LPS for 24 h. The cell supernatants were then collected, and the concentrations of TNF-α, IL-6, COX-2, IL-1β, PGE2, and iNOS were determined using ELISA kits according to the manufacturer's instructions. The absorbance was measured at 450 nm, and the cytokine concentrations were calculated based on standard curves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eqRT-PCR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe culture grouping conditions of RAW264.7 cells was like the ELISA experiment. Total RNA was isolated from the cells using Trizol reagent, followed by reverse transcription with a cDNA synthesis kit. For the quantification of TNF-α and IL-6 genes, qRT-PCR amplification was carried out under specific conditions. Initially, there was a denaturation step at 95°C for 1minute and 30 seconds (1 cycle). This was followed by 40 cycles, each consisting of denaturation at 95°C for 20 seconds, annealing at 64°C for 20 seconds, and extension at 72°C for 30 seconds. Finally, a melting curve analysis was performed, starting at 60°C for 1 minute and increasing by 0.4°C every 15 seconds until 95°C was reached, where it was maintained for 15 seconds (1 cycle). The primers employed in this study are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are shown as mean ± standard deviation (SD). Statistical significance was assessed using one-way analysis of variance (ANOVA) via GraphPad Prism 10.1.2 software, with a p-value of less than 0.05 regarded as statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eScreening of potential targets of XCHG for treating fever\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential therapeutic targets of XCHG were systematically identified through database mining and comparative analysis. The SwissTargetPrediction platform yielded 298 putative targets for the 18 blood-absorbed components. Concurrently, fever-associated genes (n=2,079) were compiled from four disease databases (GeneCards, OMIM, TTD, DrugBank) following deduplication. Intersection analysis through Venn plotting revealed 120 shared targets between component and disease gene sets (Fig. 2a), representing XCHG's potential fever-modulating targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI network construction and key target screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 120 shared targets were analyzed via STRING to generate a PPI network (120 nodes, 1,432 edges; Fig. 2b). Network visualization in Cytoscape 3.7.0 incorporated node attributes scaled by topological importance, with size and color intensity reflecting connectivity (Degree). Through CentiScape 2.2 analysis, we quantified three centrality parameters (Degree ≥23.87, Closeness≥0.00446, Betweenness ≥109.13) to identify 17 key targets (Fig. 2c) including IL6, TNF, EGFR, STAT3, SRC, ESR1, PTGS2, HSP90AA1, MMP9, PPARG, MAPK3, IL2, CYP3A4, APP, ABCB1, ABCG2, and NTRK2. These targets demonstrated substantial network influence, implicating their functional significance in XCHG's antipyretic activity. The top six key targets by Degree value (Table 2) were subsequently selected for molecular docking with key components.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the XCHG's 'herb-component-target-fever' network and screening of key components \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \"herb-component-target-fever\" interaction network was constructed using Cytoscape 3.7.0 through topological analysis of overlapping targets. The network comprised 140 nodes, including 120 key targets, 18 components, XCHG and the fever (Fig. 2e). Based on Degree values, the key components were identified as key therapeutic components: Oroxylin A, Wogonin, Baicalein, Liquiritigenin and Enoxolone (Table 3). These components are predicted to be the primary key components responsible for XCHG's antipyretic effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis revealed XCHG's comprehensive regulatory profile across key biological domains. The analysis identified 528 significantly enriched biological processes (BP), prominently featuring protein phosphorylation and MAPK cascade regulation. Cellular component (CC) analysis (69 terms) demonstrated predominant localization to focal membrane receptor complexes and endoplasmic reticulum structures. Molecular function (MF) assessment (129 terms) highlighted crucial activities including tyrosine kinase function, ATP binding, and enzyme binding (Fig. 3a). These findings collectively indicate XCHG’s ability to regulate phosphorylation signaling (e.g., MAPK cascades) and membrane receptor activity while modulating cellular stress responses. KEGG pathway analysis from the DAVID database identified 160 pathways, with the top 20—including PI3K-Akt, Ras, Rap1, lipid and atherosclerosis, MAPK, C-type lectin receptor signaling, and EGFR tyrosine kinase inhibitor resistance (Fig. 3b)—primarily associated with inflammation, immune regulation, neuroregulation, metabolic control, cell proliferation/apoptosis, and vascular homeostasis. These results demonstrate that XCHG may exert therapeutic effects through modulation of the above pathways, further supporting its multi-target mechanism of action.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular docking was performed between XCHG's key components and the six highest-degreetargets (EGFR, IL6, ESR1, STAT3, SRC, TNF), revealing binding energies ranging from -6.3 to -9.3 kcal/mol (Fig. 4), all exceeding the threshold for favorable binding (-5.0 kcal/mol). Notably, three complexes demonstrated particularly strong interactions: EGFR-Enoxolone (-9.3 kcal/mol), ESR1-Liquiritigenin (-8.7 kcal/mol), and SRC-Baicalein (-8.4 kcal/mol), with binding energies significantly below the -7.0 kcal/mol threshold indicating high-affinity binding\u003csup\u003e19\u003c/sup\u003e. Analysis of interaction patterns revealed multiple stabilizing forces including hydrogen bonds (Fig. 5), π-π stacking, and hydrophobic contacts. These three optimal complexes were selected for further MD simulations to investigate their dynamic binding characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe structural integrity of protein-ligand complexes was assessed through root-mean-square deviation (RMSD) analysis, with values under 1 nm signifying stable molecular interactions under physiological conditions\u003csup\u003e20\u003c/sup\u003e. RMSD analysis demonstrated that all three complexes (EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein) maintained high structural stability during simulation (Fig. 6a), with RMSD values stabilizing at approximately 0.3 nm, 0.25 nm, and 0.4-0.6 nm, respectively. Compared to the other two complexes, SRC-Baicalein exhibited relatively larger RMSD fluctuations (0.4-0.6 nm), which may be attributed to inherent structural characteristics of different proteins under identical simulation conditions. RMSF was used to assess positional fluctuations of amino acid residues, reflecting regional flexibility\u003csup\u003e21\u003c/sup\u003e. RMSF analysis revealed that all three complexes displayed overall fluctuations below1 nm, indicating limited residue mobility, with flexible regions exhibiting fluctuations between 0.4-0.6 nm (Fig. 6b). Rg was utilized to evaluate overall structural compactness, where larger values indicate structural expansion while smaller values reflect tighter packing\u003csup\u003e22\u003c/sup\u003e. Rg analysis further confirmed the structural compactness of the complexes, with Rg values maintained at approximately 2 nm, 1.75 nm, and 3 nm for the three complexes respectively, showing minimal fluctuations (Fig. 6c). Hydrogen bonds (H-bonds), as critical non-covalent interactions governing complex stability\u003csup\u003e23\u003c/sup\u003e, were quantitatively analyzed across the three complexes. The EGFR-Enoxolone complex formed 2 stable H-bonds, ESR1-Liquiritigenin exhibited 1-2 bonds, while SRC-Baicalein showed the most extensive network with 2-3 persistent H-bonds (Fig. 6d). This H-bond patterning demonstrates stable ligand-receptor recognition, the SRC-Baicalein complex forming the most H-bonds, indicative of superior binding affinity. SASA reflects surface solvent accessibility, where stable SASA profiles indicate well-folded structures\u003csup\u003e24\u003c/sup\u003e. All three complexes exhibited minimalSASA fluctuations (Fig. 6e), further supporting their high structural stability. These results collectively demonstrate that all three complexes maintained excellent binding stability and structuralcompactness throughout the MD simulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFEL analysis and average binding free energy calculations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FEL was generated using RMSD and Rg data to characterize conformational changes of the complexes during simulations. In the FEL plots, dark-colored regions represent the lowest-energy states while light-colored areas correspond to higher-energy states\u003csup\u003e25\u003c/sup\u003e.\u0026nbsp;The results demonstrated that all three complexes (EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein) formed single, well-defined low-energy clusters (Fig. 7a), indicating high conformational stability and strong binding interactions during the simulations.\u0026nbsp;The MM/PBSA approach quantified binding affinities, with more negative values corresponding to stronger receptor-ligand interactions\u003csup\u003e26\u003c/sup\u003e.\u0026nbsp;The MM/PBSA calculations revealed strongly favorable binding free energies for all three complexes (Fig. 7b): EGFR-Enoxolone (-45.79 kcal/mol), ESR1-Liquiritigenin (-35.58 kcal/mol), and SRC-Baicalein (-32.9 kcal/mol), indicating robust molecular recognition at the binding interfaces.\u0026nbsp;Per-residue energy decomposition analysis (Fig. 7c) identified critical binding site residues with substantial contributions: PHE856 (-2.67 kcal/mol), LEU718 (-2.1 kcal/mol), and VAL726 (-1.99 kcal/mol) in EGFR-Enoxolone; LEU346 (-2.02 kcal/mol), PHE404 (-1.71 kcal/mol), and LEU387 (-1.7 kcal/mol) in ESR1-Liquiritigenin; and GLU310 (-4.0 kcal/mol) in SRC-Baicalein. These energetically favorable interactions significantly stabilized the respective complexes. All three systems demonstrated excellent conformational and binding stability throughout molecular dynamics simulations, providing crucial insights for future studies of protein-ligand interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA and DCCM analysis of complex dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the conformational changes of proteins in the complexes, we employed PCA and DCCM to evaluate the dynamic behavior of three receptor-ligand systems. PCA identified dominant motion modes, with plot axes representing conformational space dimensions and color gradients (blue to red) indicating simulation progression time\u003csup\u003e27\u003c/sup\u003e. DCCM revealed residue-residue cross-correlations, where color gradients (light blue to pink) represented motion coordination: values approaching 1 indicated synchronized movements, while values near -1 represented anti-correlated motions\u003csup\u003e28,29\u003c/sup\u003e.\u0026nbsp;PCA results (Fig. 8a) demonstrated that the three principal components (PC1, PC2, and PC3) accounted for 57.44%, 43.82%, and 80.93% of the total variance in the EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein complexes, respectively. PC1 primarily captured large-scale conformational changes, while PC2 and PC3 were associated with medium-range motions and localized movements, respectively. Notably, the SRC-Baicalein complex exhibited the highest PC1 contribution (61.55%), indicating its most pronounced conformational flexibility and suggesting greater potential for dynamic responses and structural adaptability. DCCM analysis (Fig. 8b) revealed distinct binding dynamics among the complexes. In the EGFR-Enoxolone complex, correlated motions were predominantly observed within residues 100-150 and 200-250, demonstrating significant cooperative movements in these regions with relatively independent motions elsewhere. The ESR1-Liquiritigenin complex showed strongly coupled dynamics confined to residues 50-100, while other regions displayed more independent motions. Notably, the SRC-Baicalein complex demonstrated the most pronounced correlation patterns, with strong positive couplings (residues 100-150) and anti-correlated motions (residues 300-450) that significantly exceeded those observed in other complexes. This unique dynamic profile, characterized by enhanced residue coordination and pronounced anti-correlations, likely underpins both its superior binding affinity and exceptional conformational adaptability during simulations. Furthermore, structural snapshots extracted at 0, 25, 50, 75, and 100 ns time points from the molecular dynamics trajectories (Fig. 8c) demonstrated EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein maintained stable binding conformations without significant positional drift, confirming their excellent binding stability. Collectively, these findings provide crucial insights into the dynamic behavior and binding characteristics of the protein-ligand complexes, while supporting the therapeutic potential of these ligands as drug candidates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of XCHG on the viability of RAW264.7 cells\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CCK-8 assay results (Fig. 9) demonstrated that XCHG at concentrations below 2 mg/ml had minimal impact on RAW264.7 cell viability after 24-hour treatment compared to the control group. However, significant growth inhibition was observed at concentrations exceeding 2 mg/ml. Based on these findings, subsequent experiments were conducted using XCHG concentrations of 1, 1.5, and 2 mg/ml.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of XCHG on LPS-induced NO production in RAW264.7 cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs determined by Griess assay, XCHG significantly attenuated LPS-induced NO production in RAW264.7 cells (Fig. 10). The LPS-induced model group showed markedly increased NO secretion compared to the control group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), confirming successful model establishment. Notably, XCHG treatment at all tested concentrations (low dose: 1 mg/ml, medium dose: 1.5 mg/ml, and high dose: 2 mg/ml) dose-dependently suppressed NO release compared to the model group, demonstrating potent anti-inflammatory effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModulatory effects of XCHG on inflammatory mediators in LPS-induced RAW264.7 macrophages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 11 demonstrates that the LPS-treated model group exhibited significantly elevated levels of inflammatory biomarkers—TNF-α, IL-6, COX-2, IL-1β, PGE2, and iNOS—compared to untreated controls (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). Both Dex and medium-to-high doses of XCHG significantly suppressed the expression of TNF-α and iNOS (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), while the low-dose XCHG group showed moderate reduction in these parameters (p \u0026lt; 0.05). Moreover, treatment with Dex or any concentration of XCHG resulted in substantial suppression of IL-6, COX-2, IL-1β, and PGE2 relative to the LPS-induced group (p \u0026lt; 0.01). These findings reveal XCHG's concentration-dependent inhibitory effect on endotoxin-induced inflammatory activation in macrophages through comprehensive downregulation of proinflammatory mediators. Notably, the immunomodulatory efficacy of medium and high-dose XCHG treatment approached that of the clinical reference compound Dex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of XCHG on TNF-α and IL-6 mRNA levels in LPS-induced RAW264.7 cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. 12, compared with the control group, the LPS-induced model group exhibited significantly upregulated mRNA expression levels of TNF-α and IL-6 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). Relative to the LPS model group, both the Dex group and the medium-and high-dose XCHG groups demonstrated marked downregulation of TNF-α mRNA expression (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). Additionally, the Dex group and high-dose XCHG group significantly suppressed IL-6 mRNA levels (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe blood-absorbed components serve as the direct material basis for a drug's pharmacological effects, as only the key components that enter systemic circulation can interact with biological targets to exert therapeutic actions\u003csup\u003e30,31\u003c/sup\u003e. Through systematic analysis of these blood-absorbed components, we can more precisely elucidate the pharmacological substance basis of XCHG and identify its true key components. In our previous pharmacokinetic study on XCHG in rats, we identified 18 key components that can enter systemic circulation. These components primarily fall into two categories: flavonoids and triterpenoid saponins, which significantly influence immune modulation and inflammatory responses. Studies show that baicalin suppresses the NLRP3 inflammasome\u003csup\u003e32\u003c/sup\u003e, while baicalein inhibits COX-2 and iNOS expression\u003csup\u003e33\u003c/sup\u003e. And other components like lobetyolin demonstrate immunomodulatory effects through regulation of glutamine metabolism and inhibition of inflammatory pathways\u003csup\u003e34\u003c/sup\u003e. These scientific findings highlight the presence of multiple inflammation-regulating components in XCHG, suggesting that its therapeutic effects on inflammation and pyrexia likely result from the synergistic actions of diverse components targeting multiple pathways.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the network pharmacology analysis results, this study identified five key components in XCHG: Oroxylin A, Wogonin, Baicalein, Liquiritigenin, and Enoxolone (18β-glycyrrhetinic acid), which are likely to constitute the primary material basis for XCHG's antipyretic effects. The analysis simultaneously revealed 120 potential targets associated with XCHG's fever-alleviating effects. Further PPI network analysis highlighted 17 key targets of particular importance. Pathway annotation through GO analysis demonstrated that XCHG's primary targets participate in crucial biological functions including upregulation of MAPK/ERK cascades, stimulation of PI3K-Akt networks, and protein phosphorylation signal transmission, while significantly affecting apoptotic inhibition. These targets predominantly localize in membrane receptor complexes and display molecular characteristics like kinase functionality and ATP-binding properties. Among the 160 enriched biochemical pathways identified by KEGG, the most significant included PI3K-Akt network, Ras-mediated signaling, Rap1 transduction mechanism, lipid metabolism and atherosclerosis, MAPK cascade, C-type lectin receptor interaction, and EGFR inhibitor resistance mechanisms, which relate to inflammatory processes, immune response, neural regulation, metabolic control, cellular proliferation and programmed cell death, and vascular homeostasis. These findings suggest that XCHG's antipyretic effects occur through multi-target and diversified pathway interactions, exemplifying traditional Chinese medicine's holistic therapeutic approach. Unlike single-target pharmaceutical agents, XCHG's key components collectively modulate inflammatory networks, delivering comprehensive efficacy with fewer adverse reactions.\u003c/p\u003e\n\u003cp\u003eMolecular docking combined with MD simulations serves as a crucial computational tool for predicting interaction patterns and structural configurations of biomolecular complexes\u003csup\u003e35\u003c/sup\u003e, providing valuable theoretical guidance for subsequent experimental studies. In the investigation, molecular docking analysis demonstrated that three key components, Enoxolone, Liquiritigenin, and Baicalein exhibited strong binding affinities with key targets EGFR, ESR1, and SRC. These robust molecular interactions suggest these component-target pairs may represent primary key pharmacological combinations underlying XCHG's antipyretic effects.\u0026nbsp;Studies demonstrated that EGFR plays a crucial role in promoting inflammatory responses through Src-dependent transactivation mechanisms, which subsequently regulate signaling pathways including ERK1/2, PI3Kδ/Akt and NF-κB activation. These downstream pathways are critically involved in various inflammatory processes such as inflammatory cell infiltration, tissue fibrosis and airway hyperresponsiveness. As a key component of EGFR signaling networks, SRC physically interacts with activated EGFR and induces phosphorylation at two specific tyrosine residues (Tyr-845 and Tyr-1101), thereby enhancing inflammatory responses and modulating multiple cellular processes including proliferation, differentiation, migration and apoptosis\u003csup\u003e36,37\u003c/sup\u003e. Meanwhile, ESR1 regulates the development and function of innate immune cells by balancing the production of pro- and anti-inflammatory cytokines, while inflammatory cytokines in turn can modulate ESR1 expression and activity\u003csup\u003e38\u003c/sup\u003e.\u0026nbsp;Enoxolone, liquiritigenin, and baicalein demonstrate strong binding affinity with these target proteins, suggesting that XCHG may exert its antipyretic and anti-inflammatory effects by inhibiting the activity of these targets, modulating relevant signaling pathways, reducing the release of pro-inflammatory factors, regulating the hypothalamic thermoregulatory center, and suppressing inflammasome activation.\u0026nbsp;Molecular dynamics simulations further validated the high stability, compactness, and appropriate flexibility of these three protein-ligand complexes. RMSD analysis demonstrated that the EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein complexes maintained stable conformations throughout the 100 ns simulation period. Complementary analyses including RMSF, Rg, hydrogen bonding, and SASA further confirmed the stability of these complexes, with the formation of persistent hydrogen bonds and maintenance of compact structures indicating robust binding interactions. FEL and MM/PBSA analyses revealed that all complexes formed single, well-defined low-energy clusters, demonstrating high conformational stability and strong binding interactions. The calculated average binding free energies provided additional evidence for the stable binding affinities between these ligands and their respective targets.\u003c/p\u003e\n\u003cp\u003eFurthermore, this study utilized an endotoxin-induced inflammation model in murine macrophage line RAW264.7 to verify XCHG's modulatory impact on inflammatory factors. The experimental findings established that XCHG markedly suppressed endotoxin-triggered NO production across all tested concentrations (low, medium, and high), demonstrating robust anti-inflammatory capacity. Immunoassay analysis additionally showed that XCHG reduced levels of critical inflammatory mediators in a concentration-dependent manner, including TNF-α, IL-6, COX-2, IL-1β, PGE2, and iNOS. Medium and high XCHG doses achieved comparable anti-inflammatory efficacy to the reference compound Dex. These observations were further supported at the gene expression level through quantitative PCR, which revealed that medium and high XCHG concentrations substantially decreased transcription of inflammatory cytokines. Together, this evidence suggests that XCHG achieves its inflammation-controlling and fever-reducing properties by attenuating both production and release of inflammatory signaling molecules at protein and transcriptional levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCascading reactions between inflammatory factors constitute the key pathological mechanism of fever\u003csup\u003e39\u003c/sup\u003e. When the body is infected by pathogens or induced by exogenous pyrogens, macrophages are first activated to release early inflammatory factors TNF-α and IL-1β, which serve as \"alarm signals\" and can further induce the production of IL-6\u003csup\u003e40\u003c/sup\u003e\u003csup\u003e–42\u003c/sup\u003e. Among these, IL-1β plays a critical initiating role in the febrile process by directly activating the COX-2 promoter through NF-κB nuclear translocation mediated by IκBα phosphorylation, while simultaneously stabilizing COX-2 mRNA via p38 MAPK signaling\u003csup\u003e43\u003c/sup\u003e\u003csup\u003e–46\u003c/sup\u003e.\u0026nbsp;IL-6, a pivotal pyrogenic cytokine, has been demonstrated to directly act on the hypothalamic thermoregulatory center\u003csup\u003e47\u003c/sup\u003e. COX-2, as the inducible cyclooxygenase, catalyzes the conversion of arachidonic acid to PGE2 via PGG2/PGH2 intermediates, with its expression level serving as the primary determinant of PGE2 production under pathological conditions\u003csup\u003e48\u003c/sup\u003e. As the definitive pyrogenic mediator, PGE2 directly targets the hypothalamic thermoregulatory center, elevating the thermostatic set point and consequently inducing febrile responses\u003csup\u003e49\u003c/sup\u003e. Inducible iNOS is pivotal in mediating inflammatory responses, as its catalytic product NO upregulates COX-2 and promotes PGE2 production\u003csup\u003e50\u003c/sup\u003e. This synergistic interaction collectively amplifies the cascade of inflammatory responses and febrile progression. This study demonstrated that XCHG markedly decreased levels of NO, TNF-α, IL-6, COX-2, IL-1β, PGE2, and iNOS in LPS-induced RAW264.7 cells, suggesting its anti-inflammatory and fever-reducing actions involve inhibiting these key inflammatory molecules. KEGG analysis indicated that XCHG's main targets were significantly associated with pivotal pathways like PI3K-Akt, Ras, Rap1, and MAPK, which modulate both the release of inflammatory cytokines (e.g., IL-1β, TNF-α, IL-6) and the up regulation of COX-2/iNOS. This suggests that XCHG may potentially interrupt inflammatory signal transduction by intervening in the phosphorylation processes of these pathways, inhibit PGE2 and NO production, and ultimately achieve anti-inflammatory and antipyretic effects. Furthermore, the enrichment of C-type lectin receptor and EGFR-related signaling pathways further elucidates the potential mechanism of action of XCHG at the membrane receptor level.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis investigation comprehensively revealed the molecular basis of XCHG antipyretic properties by integrating network pharmacology, computational modeling (molecular docking and dynamics), and cellular assays. The findings indicate that XCHG achieves its therapeutic benefits through synergistic modulation of diverse pathways, such as inflammatory responses, immune-neuro-metabolic crosstalk, and vascular function. Key ligand-receptor pairs (e.g., EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein) were identified as potential mediators. In LPS-induced RAW264.7 macrophages, XCHG effectively suppressed the secretion of inflammatory mediators (TNF-α, IL-6, IL-1β), diminished COX-2 and iNOS levels, and PGE2 and NO generation. Unlike single-target agents, XCHG's multi-component synergy enhances therapeutic outcomes, supporting its clinical utility. Further research is needed to validate specific binding mechanisms and detailed signaling cascades.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eMing-He Gu was responsible for research design, experimental work, data analysis, and manuscript drafting. Hong Liu, Cong Bi, Wen-Hui SiTu, Hai-Yong Du, and Jun-Hua Zhang collectively contributed to study conceptualization, funding acquisition, and project coordination. Ai-Hua Lin and Yi-Ming Liu performed manuscript reviewing and research supervision. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the Guangzhou Science and Technology Program (Grant Number. 202206010112) and the Guangdong Provincial Science and Technology Program (Grant Number. 2023B1212060063).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are available in the Supplementary Materials of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors collectively confirm the absence of any financial or non-financial competing interests related to this research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEvans SS, Repasky EA, Fisher DT. 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Inducible nitric oxide synthase binds, S-nitrosylates, and activates cyclooxygenase-2. \u003cstrong\u003e310\u003c/strong\u003e:1966-1970. https://doi.org/10.1126/science.1119407 (2005).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Primer sequences\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003ePrimer Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eSequence (5\u0026apos;-3\u0026apos;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eSize（bp）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;IL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eCTGCAAGAGACTTCCATCCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 119px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eAGTGGTATAGACAGGTCTGTTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;TNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eCAGGCGGTGCCTATGTCTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 119px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eCGATCACCCCGAAGTTCAGTAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;Actin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eCACCATTGGCAATGAGCGGTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 119px;\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220px;\"\u003e\n \u003cp\u003eAGGTCTTTGCGGATGTCCACGT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eThe top six key targets ranked by degree value\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eTarget name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eCloseness centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eBetweenness centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;EGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.02599597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;IL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.02599597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;ESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.02599597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;STAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.94117647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.01592653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eSRC\u003c/p\u003e\n \u003cp\u003eTNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.94117647\u003c/p\u003e\n \u003cp\u003e0.94117647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.01592653\u003c/p\u003e\n \u003cp\u003e0.0209623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eThe top five components ranked by degree value\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIngredient name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCloseness centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBetweenness centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOroxylin A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43621399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13701609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWogonin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43621399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.15307644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBaicalein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43621399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17399126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLiquiritigenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43383356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.29743801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnoxolone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42343542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33684036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Xiaochaihu granules, Fever, Network pharmacology, Molecular docking, Molecular dynamics simulation, Experimental validation ","lastPublishedDoi":"10.21203/rs.3.rs-6665313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6665313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Xiaochaihu granules (XCHG), a traditional Chinese herbal formulation, have demonstrated clinical efficacy in fever management, but their precise mode of action remains unclear. Our investigation employed an integrative methodology combining network pharmacology, molecular docking and dynamics simulations), and cellular assays to delineate XCHG's antipyretic mechanisms. Analysis of 18 blood-absorbed components identified from XCHG-treated rat plasma, we identified 17 key targets and 5 key components. GO and KEGG analyses showed that XCHG primarily relates to inflammation, immune regulation, neuroregulation, metabolic control, cell proliferation/apoptosis, and vascular homeostasis, mainly exerting anti-inflammatory effects through these mechanisms. Molecular docking results demonstrated good binding activity between key components and targets, particularly EGFR-Enoxolone, ESR1-Liquiritigenin, and SRC-Baicalein. Subsequent dynamics simulations validated the structural integrity of these ternary complexes. In LPS-stimulated macrophages, XCHG significantly inhibited NO production and downregulation of pro-inflammatory mediators (TNF-α, IL-6, IL-1β, PGE2) and enzymes (iNOS, COX-2). These findings establish that XCHG achieves its antipyretic effects through multi-target engagement of key components with key proteins, subsequently modulating inflammatory cascades and cytokine networks, thereby providing mechanistic support for its clinical application in fever treatment.","manuscriptTitle":"Integrating Network Pharmacology, Molecular Docking, Dynamics Simulation and Experimental Validation to Decipher the Antipyretic Mechanisms of Xiaochaihu Granules","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 15:41:02","doi":"10.21203/rs.3.rs-6665313/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5fdc75b2-c540-44bf-a3fa-5f4057ab2459","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50118265,"name":"Biological sciences/Cell biology"},{"id":50118266,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":50118267,"name":"Biological sciences/Drug discovery"},{"id":50118268,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-10-16T19:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 15:41:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6665313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6665313","identity":"rs-6665313","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}