Network Pharmacology and Molecular Docking Combined with In Vivo Validation to Elucidate the Molecular Mechanisms of Adenophorae Radix in Fracture Healing

Abstract

Fracture healing is a multistage regenerative process requiring the coordinated regulation of inflammation, osteogenesis, and bone remodeling, yet pharmacological agents that effectively modulate these processes remain limited. Adenophorae Radix (AR), a traditional medicinal herb used for tissue repair, has not been mechanistically investigated in skeletal regeneration. In this study, a mouse femoral fracture model was employed to evaluate the effects of short-term (7 days) and long-term (5 weeks) oral administration of AR. Bone regeneration was assessed using micro-computed tomography, histological staining, and quantitative real-time polymerase chain reaction. Network pharmacology and molecular docking were applied to predict bioactive AR constituents and their target pathways, followed by in vivo validation. Short-term AR treatment significantly upregulated osteogenic markers, including RUNX2 and osteocalcin, in the bone marrow, indicating early activation of osteoblast differentiation. Long-term administration enhanced bone mineral density, trabecular organization, and callus maturation. Network pharmacology analysis identified cycloartenol acetate, β-sitosterol, and mandenol as major active compounds targeting osteogenesis- and osteoclast-related pathways, converging on HIF1A, PTGS2, and PPARG. Molecular docking demonstrated strong binding affinities between these compounds and their predicted targets, which was supported by increased expression of HIF1A, PTGS2, and PPARG in AR-treated femora. Collectively, these findings suggest that AR promotes fracture healing by regulating osteogenic differentiation and bone remodeling through multi-target transcriptional networks.

1. Introduction

Bone fracture is a pathological disruption of bone integrity resulting from an imbalance between external mechanical stress and the intrinsic mechanical properties of the bone tissue [1]. Bone tissue continuously maintains homeostasis through closely regulated remodeling processes involving osteoblasts, osteoclasts, and different signaling pathways [2]. Fractures disrupt this delicate balance and trigger a complex cascade of inflammatory, angiogenic, and osteogenic responses [3]. Therefore, restoration of bone integrity is highly challenging and frequently complicated by dysregulated inflammation, insufficient osteogenesis, and imbalanced bone remodeling [4]. Fractures, primarily caused by trauma and risk factors, such as aging, osteoporosis, and other metabolic bone diseases, substantially increase the susceptibility to fracture [5,6]. Therefore, the number of patients experiencing fractures has been increasing worldwide with a rapid increase in the elderly population [7,8].

The current clinical management of fractures primarily depends on mechanical stabilization through casting, external fixation, or internal fixation devices, which provide structural support but do not actively promote biological healing [9]. For complicated cases, bone grafting and growth factor therapies, such as bone morphogenetic proteins (BMPs), are employed [10]. However, these approaches are limited by donor-site morbidity, immune rejection risks, high costs, and inconsistent clinical outcomes. Despite these interventions, 5 to 10% of fractures still result in delayed union or nonunion, causing prolonged disability and a substantial healthcare burden [11]. Therefore, novel therapeutic strategies that effectively modulate the fracture microenvironment and enhance bone regeneration are required.

Natural products have emerged as promising alternatives for bone regeneration because of their multitarget bioactive properties, including anti-inflammatory, antioxidant, and osteogenic properties [12]. In the field of bone health, various medicinal herbs, such as Puerariae Radix and Epimedium brevicornum, have been extensively studied for their ability to promote osteoblast differentiation and inhibit bone resorption through the regulation of signaling pathways like Wnt/β-catenin and NF-κB [13,14]. However, there is still a need to explore potent candidates that can simultaneously modulate inflammation and osteogenesis.

Adenophora radix (AR), the root of Adenophora tetraphylla, is a traditional herbal medicine widely used in East Asia as a natural remedy. Previous studies have reported that AR contains different bioactive compounds, including triterpenoid saponins, polysaccharides, and phenolic compounds, which exhibit anti-inflammatory, immunomodulatory, and antioxidant activities [15,16]. Given that AR can effectively suppress pro-inflammatory cytokines (e.g., TNF-α and IL-6) that often hinder early-stage fracture healing, it is a highly plausible candidate for promoting a favorable bone-regenerative microenvironment. However, the potential effects on bone metabolism and fracture healing have not been thoroughly investigated. Considering its established anti-inflammatory properties and critical role in fracture healing, AR may be a novel therapeutic candidate for enhancing bone regeneration.

Therefore, we evaluated the bone healing potential of AR using a closed-fracture mouse model under both short- and long-term treatment conditions. Network pharmacology analysis was conducted to predict the potential molecular targets and signaling pathways associated with the therapeutic effects of AR. Our findings provide the first evidence that AR promotes fracture healing and offer new insights into its therapeutic mechanisms.

2. Results

2.1. Short-Term AR Administration Regulated Expression of Osteoblast- and Osteoclast-Related Genes in the BM of Closed Fracture-Induced Mice

A closed-fracture mouse model was established and treated with AR (200 mg/kg) for 7 days to investigate the effect of AR on fracture healing (Figure 1A). The mRNA expression analyses were performed to evaluate the osteogenic and osteoclastic markers.

In the absence of treatment, fracture induction alone failed to elicit significant alterations in osteogenic- or osteoclast-related gene expression. Conversely, 7-day administration of AR markedly and statistically significantly upregulated runt-related transcription factor 2 (RunX2) and osteocalcin (OCN) expression in the fractures (Figure 1B,D). Furthermore, although osterix (OSX), cellular oncogene fos (c-Fos), and tartrate-resistant acid phosphatase (TRAP) demonstrated a trend toward elevated expression in the AR-treated cohort relative to the untreated fracture controls, these changes were not statistically significant (Figure 1C,E,F). Collectively, these findings suggest that the AR facilitates fracture healing predominantly by enhancing osteogenic gene expression.

2.2. Long-Term AR Administration Regulated Expression of Osteoblast- and Osteoclast-Related Genes in the BM of Closed Fracture-Induced Mice

Micro-CT analysis was performed 4 weeks post-fracture to evaluate long-term structural changes (Figure 2A). At this late time point, periosteal callus was largely resorbed and cortical morphology appeared restored across all fracture groups, indicating progression into the remodeling phase (Figure 2B). Although cortical continuity was observed in both fracture and AR-treated groups, marked deterioration of the trabecular network was evident in the untreated fracture group. In contrast, AR-treated mice exhibited preservation of trabecular architecture in a dose-dependent manner (Figure 2B). Quantitative analysis confirmed that fracture significantly reduced BV/TV and BMD, whereas AR treatment restored these parameters, with the 200 mg/kg group demonstrating the most pronounced recovery (Figure 2C,D). While cortical bridging was comparable among fracture groups at this stage, AR administration primarily influenced trabecular microarchitecture, as reflected by improvements in Tb.N, SMI, trabecular separation, connectivity density, and porosity (Figure 2E–P). Collectively, these results demonstrated that long-term AR administration significantly improved bone mass, trabecular architecture, and biomechanical strength, thereby facilitating fracture healing. Since advanced remodeling occurs at late time points, the analysis focused on distal trabecular bone rather than direct visualization of the cortical fracture line.

2.3. Long-Term AR Administration Regulated Expression of Osteoblast- and Osteoclast-Related Genes in the Closed Fracture-Induced Mice

The mRNA expression of genes related to osteogenesis, osteoclast activity, and cartilage formation was analyzed in bone marrow tissues collected from fractured mice to further elucidate the molecular mechanisms underlying the bone-promoting effects of AR. As shown in Figure 3A–C, AR treatment markedly upregulated the expression of osteogenic markers, including osteocalcin (OCN), alkaline phosphatase (ALP), and Runx2, compared to the fracture-only group, indicating enhanced osteoblast differentiation. Osterix (OSX), another transcription factor essential for osteoblast maturation, was significantly elevated in the AR 200 mg/kg group (Figure 3D). Notably, collagen type II alpha 1 (Col2a1), a marker of chondrogenesis involved in endochondral ossification during fracture repair, also showed an increasing tendency following AR treatment (Figure 3E). In addition, osteoprotegerin (OPG), a regulator of osteoclast differentiation, demonstrated an upward trend. Furthermore, AR administration significantly increased the expression of TRAP, an osteoclast-associated gene (Figure 3G), suggesting that AR may promote coordinated bone formation and remodeling processes essential for fracture repair. Collectively, these data indicate that AR accelerates fracture healing by modulating osteogenic, chondrogenic, and osteoclastic gene expression within the bone marrow niche.

Histological examination was performed in the mid-diaphyseal fracture region at 4 weeks post-injury to evaluate tissue organization during the remodeling phase (Figure 4). At this late time point, the fracture site in all groups predominantly exhibited restored cortical continuity with evidence of ongoing remodeling. Clear periosteal callus tissue was not observed, consistent with progression into the remodeling stage of murine fracture healing.

Compared with the fracture-only group, AR-treated groups showed more organized cortical architecture and improved structural alignment. High-magnification views (40×) revealed increased numbers of osteoblast-like cuboidal cells lining bone surfaces in AR-treated animals (Figure 4A), suggesting active bone formation during the remodeling phase. TRAP staining was conducted in the same mid-diaphyseal region to assess osteoclast activity (Figure 4B). AR-treated groups exhibited increased TRAP-positive multinucleated cells along bone surfaces compared with the fracture-only group, indicating enhanced remodeling activity.

Collectively, these findings suggest that AR modulates cellular activity at the fracture site and promotes coordinated bone remodeling during the late healing phase.

2.4. Network Pharmacology and Molecular Docking Predicted Potential Mechanisms of AR in Bone Fracture

Recently, in silico molecular docking and network topological analysis have been extensively utilized as robust methodologies to identify bioactive compounds and key regulatory modules in complex disease models [17,18,19]. Following this integrative approach, network pharmacology analysis was performed to identify the potential molecular mechanisms underlying the bone-regenerative effects of AR. A compound–target network was constructed, revealing that several bioactive constituents of AR, including cycloartenol acetate, β-sitosterol, and mandenol, were associated with osteogenic and osteoclastic regulation (Figure 5A).

Among the 131 predicted AR targets, 79 overlapped with the osteogenesis-related genes (Figure 5B). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses demonstrated the significant involvement of overlapping targets in pathways associated with osteoblast differentiation, lipid metabolism, and the PI3K–Akt and PPAR signaling pathways (Figure 5C,D).

The protein–protein interaction (PPI) network (Figure 5E) identified PPARG, PTGS2, HIF1A, ESR1, GSK3B, and HMGCR as key hub genes potentially mediating AR-induced osteogenesis (Figure 5F–H).

Similarly, an analysis of osteoclast-related genes revealed 59 targets shared between AR and osteoclastogenesis (Figure 5I). Enrichment analysis indicated that these targets were largely involved in T-cell receptor signaling, estrogen signaling, and MAPK pathways (Figure 5J,K). PPI network mapping (Figure 5L–O) further identified PPARG, PTGS2, HIF1A, KDR, and NR3C1 as central regulators of AR-mediated osteoclast modulation.

Molecular docking was performed to validate the interactions between the key AR compounds and core targets identified from the network pharmacology analysis. As summarized in Figure 6A, docking score heatmaps revealed strong binding affinities (−9.0 to −10.4 kcal/mol) between cycloartenol acetate and the active sites of PTGS2 and PPARG, suggesting these as primary binding targets. In addition, β-sitosterol and phthalic acid exhibited favorable interactions with HIF1A and PPARG, indicating potential regulatory effects on osteogenesis- and metabolism-related signaling. Docking visualization confirmed stable interactions through hydrogen bonding and hydrophobic contacts between the compounds and key amino acid residues in each target protein (Figure 6B–P). These findings further support the idea that the osteogenic effects of AR are mediated by multi-target binding, primarily involving PTGS2, PPARG, and HIF1A.

The mRNA expression of the key target genes (HIF1A, PTGS2, and PPARG) was examined in the bone marrow of fractured femora to experimentally validate the network pharmacology and docking predictions.

As demonstrated in Figure 7, AR treatment significantly upregulated the expression of all three genes compared to the fracture group. In particular, PTGS2 and PPARG expression was markedly increased in the AR 200 mg/kg group, which is consistent with the predicted strong binding affinities of AR-derived compounds to these targets. These findings confirm that AR enhances osteogenic signaling, partly through the activation of HIF1A-, PTGS2-, and PPARG-related pathways, thereby supporting the computational predictions.

3. Discussion

Fracture healing is orchestrated through a spatiotemporal sequence of inflammation, osteogenesis, and remodeling, governed by reciprocal signaling between immune and skeletal cells [20]. This dynamic process depends on early osteogenic transcriptional activation within the bone marrow niche, followed by angiogenic coupling and osteoclast-mediated remodeling [21]. The present study identified AR as a natural modulator that accelerates this coordinated repair cascade by reprogramming the osteogenic and metabolic transcriptional pathways.

The rapid induction of RUNX2 and OCN expression following AR administration indicated that AR functions in the early phase of callus formation to prime osteoblast differentiation [22,23]. RUNX2 is a master regulator that initiates the transition of mesenchymal progenitors into pre-osteoblasts, whereas OCN marks the terminal maturation of the osteoblasts responsible for matrix mineralization [24]. The concurrent increase in these markers within 1 week suggests that AR bypasses the typical temporal delay between osteoblast lineage commitment and matrix deposition. This may involve upstream enhancement of canonical Wnt/β-catenin or BMP signaling, as reported for plant sterols such as β-sitosterol that stabilize β-catenin and promote osteogenic gene transcription [25,26,27]. Thus, AR may function as an osteoinductive cue that accelerates the initiation of bone formation, rather than merely supporting the remodeling phase.

Fracture healing is a multi-stage biological process involving inflammation, angiogenesis, chondrogenesis, osteogenesis, and remodeling. In the present network pharmacology workflow, we specifically prioritized osteogenesis and osteoclast differentiation because these represent the final effector arms of skeletal repair—bone formation and resorption coupling—which directly correspond to our in vivo experimental readouts. Thus, the computational filtering was intentionally centered on the osteoblast–osteoclast axis rather than the broader fracture-repair terminology.

Network pharmacology and docking analyses revealed that HIF1A, PTGS2, and PPARG were the principal nodes mediating AR activity. These targets converge at the interface between inflammation and energy metabolism, which are essential for effective bone repair. HIF1A orchestrates hypoxia-adaptive responses and stimulates angiogenesis, which is a prerequisite for nutrient and oxygen delivery during callus formation [28]. PTGS2 (COX-2) modulates both osteoblast and osteoclast activity through prostaglandin E₂ synthesis, thereby fine-tuning bone turnover under inflammatory conditions [29]. PPARG governs lipid metabolism and osteoblast–adipocyte lineage balance; its activation within controlled limits can enhance osteoblast differentiation by regulating mitochondrial bioenergetics and oxidative stress [30,31]. Although these genes are not exclusive to osteogenesis or osteoclast differentiation, they function as upstream integrators that coordinate hypoxia adaptation, inflammatory signaling, and metabolic regulation—processes that converge on osteoblast–osteoclast coupling during fracture repair. Therefore, their identification reflects regulation of the effector axis rather than a disconnect in the analytical strategy.

Histological and TRAP analyses revealed that AR not only promoted osteoblast activity, but also increased the number of osteoclasts in the callus, indicating accelerated remodeling. The induction of TRAP expression and presence of multinucleated TRAP-positive cells imply that synchronized bone resorption is required for structural refinement [32]. In addition to their established roles in osteoclast differentiation, c-Fos and TRAP are also linked to inflammatory signaling pathways that influence the bone microenvironment during fracture repair. This coupling effect is consistent with PTGS2 activation, which enhances osteoclast differentiation through PGE₂–EP₄ signaling, but in a context-dependent manner that supports balanced turnover rather than excessive resorption [33]. Thus, AR appears to facilitate the transition from immature woven bone to mature lamellar bone by coordinating osteoblast–osteoclast crosstalk.

The validation of HIF1A, PTGS2, and PPARG upregulation in the bone marrow suggests that the AR influences the marrow niche as a whole rather than acting on a single cellular population. The bone marrow microenvironment is increasingly recognized as a regulatory center that links metabolism, hypoxia, and immune signaling in skeletal repair [34,35]. The AR likely shifts the niche toward an anabolic state characterized by increased mitochondrial activity, controlled inflammation, and pro-angiogenic gene expression, by modulating these transcriptional hubs. This aligns with a systems-level model in which multi-component natural extracts exert “network pharmacological” effects not through one dominant target, but through distributed regulation of cellular states. Clinically, delayed union and nonunion remain critical challenges owing to insufficient biological stimulation despite mechanical stabilization. The present findings suggest that AR may serve as an adjunctive biological modulator that enhances intrinsic repair capacity by activating osteogenic transcriptional programs and metabolic adaptation.

Despite these findings, several limitations of the present study should be acknowledged. First, although our network pharmacology and docking analyses identified key bioactive components like β-sitosterol and cycloartenol acetate, we did not perform a direct quantitative characterization of the AR extract (e.g., HPLC/MS). Future research focusing on the chemical standardization of the extract is necessary to ensure pharmacological consistency. Second, this study lacked a clinical positive control group, such as Teriparatide or BMP-2; however, our primary goal was to establish a ‘proof-of-concept’ for AR’s intrinsic potential in a field where specific pharmacological options for fracture acceleration remain limited. Lastly, while our docking validation focused on high-confidence core targets, further broad-spectrum screening will be required to fully elucidate the synergistic interactions of AR’s minor constituents. Addressing these points in follow-up studies will facilitate the clinical translation of AR as a therapeutic candidate for bone regeneration.

4. Materials and Methods

4.1. Preparation of AR

Dried Adenophorae radix was purchased from the Jung Do Herbal Drug Co. (Seoul, Republic of Korea). The materials were extracted with 70% ethanol by boiling for 2 h, and the extract was concentrated by rotary vacuum evaporator. After evaporating, the extract was freeze-dried to yield a powdered extract. The obtained powder was stored at 4 °C.

4.2. Animals

Six-week-old C57BL/6J male mice were purchased from Daehan Biolink Co., Ltd. (Eumseong, Republic of Korea). Animals were housed under controlled environmental conditions. All animal experiments were performed in accordance with the “Guide for the Care and Use of Laboratory Animals, 8th edition” (National Institutes of Health, 2011) and approved by the “Animal Care and Use Guidelines” of Kyung Hee University, Seoul, Republic of Korea (Approval number: KHSASP-25-196).

A closed mid-diaphyseal fracture was induced in the right femur using a standardized weight-drop apparatus (50 g steel ball, 100 cm height). This model was chosen to evaluate the effects of AR on the natural secondary bone healing process, characterized by extensive callus formation. Notably, no internal fixation was employed, providing a mechanical environment that facilitates endochondral ossification. Fracture induction was confirmed by manual palpation and by observation of impaired limb function immediately following injury.

To ensure ethical standards and animal welfare, comprehensive health monitoring was performed daily throughout the experimental period. We specifically monitored for clinical signs of distress, including significant body weight loss (monitored every 2 days), changes in food and water intake, and alterations in coat condition. Post-operative pain was managed according to the institutional guidelines, and animals were housed in a controlled environment to limit excessive mechanical stress while allowing for normal weight-bearing. No animals exhibited a weight loss of more than 20% or severe morbidity necessitating early euthanasia, and the consistency of the fracture was confirmed by the uniform callus development observed in the control group.

4.3. Drug Administration

The short-term and long-term experiments were designed to address different phases of fracture healing. In the short-term study, AR was administered immediately after fracture induction to evaluate its effects on early molecular responses. In contrast, the long-term study included 1 week of pre-treatment followed by 4 weeks of post-fracture administration to assess sustained effects of AR on bone remodeling and trabecular microarchitecture.

4.3.1. Short-Term Administration

The mice for short-term administration were randomly divided into 3 groups: (1) NOR group (normal mice, vehicle treated (p.o.), n = 6); (2) Fracture group (fractured mice, vehicle treated (p.o.), n = 6); (3) AR 200 group (fractured mice, AR 200 mg/kg (p.o.), n = 6).

4.3.2. Long-Term Administration

The mice for long-term administration were randomly divided into 4 groups: (1) NOR group (normal mice, vehicle treated (p.o.), n = 6); (2) Fracture group (fractured mice, vehicle treated (p.o.), n = 7); (3) AR 40 group (fractured mice, AR 40 mg/kg (p.o.), n = 7); (4) AR 200 group (fractured mice, AR 200 mg/kg (p.o.), n = 7).

4.4. Tissue Preparation

Bone marrow in the femur was collected in phosphate-buffered saline (PBS) and filtered through a cell strainer. The femurs were stored at −80 °C until use. For bone RNA extraction, bone samples were rinsed with PBS and rapidly frozen in liquid nitrogen. The frozen bones were ground into a fine powder using a mortar and pestle under liquid nitrogen. For the long-term study (Week 6), Femoral tissues for histological analysis were fixed in 4% paraformaldehyde and stained with H&E or TRAP.

4.5. RNA Extraction

Total RNA was extracted using the TRIzol (Invitrogen, Carlsbad, CA, USA) reagent according to the manufacturer’s protocol. The bone marrow and powdered bone tissue were transferred to a microcentrifuge tube, and TRIzol reagent was added for homogenization using a tissue homogenizer. The homogenized samples were incubated on ice for 15 min and centrifuged at 12,000× g for 10 min at 4 °C. The supernatant was transferred to a new tube and mixed with an equal volume of isopropanol to precipitate RNA. After centrifugation, the supernatant was discarded and the RNA pellet was air-dried. The pellet was then washed with 75% ethanol and centrifuged at 7500× g for 5 min at 4 °C. The final RNA pellet was air-dried again and dissolved in 25 μL of RNase-free water, followed by incubation at 55 °C for 10 min to complete RNA solubilization.

4.6. Micro-CT Analysis

The structural changes in the fractured femurs were evaluated using a micro-CT scanner (Skyscan 1172, Bruker, Belgium). Quantitative analysis was conducted in the distal femoral metaphyseal region proximal to the growth plate, excluding cortical bone. This region was selected because it is a standard anatomical site for assessing trabecular parameters associated with osteoblast and osteoclast activity. Quantitative parameters were analyzed using CTAn software, version 1.18.

4.7. Polymerase Chain Reaction (PCR) and Quantitative Real-Time Reverse Transcription-Polymerase Chain Reaction (qRT-PCR)

Gene expression in the bone marrow was evaluated by PCR. RNA samples were quantified with Nanodrop 2000 Spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). The RNA samples were synthesized to cDNA by using TOPscript™ RT DryMIX (Enzynomics, Daejeon, Republic of Korea). qRT-PCR was performed with TOP real™ qPCR 2X PreMIX (SYBR Green; Enzynomics (Daejeon, Republic of Korea)) and a CFX Connect real-time PCR system (Bio-Rad Laboratories, Hercules, CA, USA). The gene primers were generated by COSMO Genetech (Seoul, Republic of Korea), and each primer sequence in the 5′ to 3′ direction is as follows: RunX2 (Forward; 5′-ACT CTT CTG GAG CCG TTT ATG-3′, Reverse; 5′-GTG AAT CTG GCC ATG TTT GTG-3′), Osteocalcin (Forward; 5′-AAG CAG GAG GGC AAT AAG GT-3′, Reverse; 5′-TTT GTA GGC GGT CTT CAA GC-3′), Osterix (Forward; 5′-CGC TTT GTG CCT TTG AAA T-3′, Reverse; 5′-CCG TCA ACG ACG TTA TGC-3′), c-fos (Forward; 5′-CGG GTT TCA ACG CCG ACT A-3′, Reverse; 5′-TTG GCA CTA GAG ACG GAC AGA-3′), Osteoprotegerin (Forward; 5’-TGA GAG AAC GAG AAA GAC CTG C-3’, Reverse; 5’-CGG ATT GAA CCT GAT TCC CTA T-3’), Col2a1 (Forward; 5’-ACT GGT AAA GTG GGG CAA GAC-3′, Reverse; 5′-CCA CAC CAA ATT CCT GTT CA-3′), Alkaline phosphatase (Forward; 5′-AAC CCA GAC ACA AGC ATT CC-3′, Reverse; 5′-GAG AGC GAA GGG TCA GTC AG-3′), TRAP (Forward; 5′-ACA CAG TGA TGC TGT GTG GCA ACT C-3′, Reverse; 5′-CCA GAG GCT TCC ACA TAT ATG ATG G-3′), HIF1a (Forward; 5′-GAA ATG GCC CAG TGA GAA AA-3′, Reverse; 5′-CTT CCA CGT TGC TGA CTT GA-3′), PPARG (Forward; 5′-TGT TCG CCA AGG TGC TCC AG-3′, Reverse; 5′-TGA AGG CTC ATG TCT GTC TCT GTC-3′), and COX-2 (Forward; 5′-TGG GGT GAT GAG CAA CTA TT-3′, Reverse; 5′-AAG GAG CTC TGG GTC AAA CT-3′).

4.8. Histological and Histomorphometric Analysis

The harvested femur samples were fixed in 10% neutral buffered formalin for 48 h. For mineral removal, the specimens were decalcified in a 10% EDTA solution (pH 7.4) at room temperature for 3–4 weeks, with the solution replaced every 3 days. Once decalcification was confirmed, the samples were dehydrated through a graded series of ethanol, cleared in xylene, and embedded in paraffin.

Longitudinal sections were cut at a thickness of 5 μm using a microtome. Sections were stained with H&E for structural evaluation. For osteoclast visualization, TRAP staining was performed using a commercial kit according to the manufacturer’s instructions, followed by counterstaining with hematoxylin to visualize nuclei.

4.9. Network Pharmacology and Molecular Docking Analysis

Main compounds of AR were collected using TCMSP (https://www.tcmsp-e.com/tcmsp.php (accessed on 13 August 2025)). Active compounds that met the criteria (Molecular weight ≤ 500, Oral bioavailability ≥ 30, Drug likeness ≥ 0.18) were selected. These thresholds were established based on the Lipinski’s ‘Rule of Five’ and the standard parameters recommended by the TCMSP database. An MW ≤ 500 was selected to prioritize compounds with favorable membrane permeability. The OB and DL filters were applied to identify constituents that are likely to possess high gastrointestinal absorption and structural properties similar to existing therapeutic drugs, thereby ensuring the pharmacological relevance of the identified compounds. Potential target genes of the active compounds were identified using the Swiss Target Prediction database (https://www.swisstargetprediction.ch/ (accessed on 13 August 2025)). Genecards database (https://www.genecards.org/ (accessed on 13 August 2025)) was used to find osteo-related genes. Osteogenesis and osteoclast differentiation were selected as primary biological terms because osteoblast–osteoclast coupling represents the core effector mechanism of fracture repair. Since our in vivo endpoints primarily evaluated trabecular microarchitecture and osteoclastic activity, we focused the network pharmacology analysis on these two functional axes to align computational predictions with experimental outcomes. The intersection of target genes from AR active compounds and osteo-related genes was determined using Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/ (accessed on 13 August 2025)). Protein–protein interaction (PPI) network was constructed using STRING database (v.12.0; https://string-db.org/ (accessed on 14 August 2025)) and imported into Cytoscape v.3.7.1 software. The CytoHubba and CytoNCA plug-ins were utilized to assess topological features such as degree, betweenness, and closeness centralities. Hub and core targets were identified based on these network parameters. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the ShinyGO platform (ver. 0.80; https://bioinformatics.sdstate.edu/go/ (accessed on 15 August 2025)). Biological processes and signaling pathways with p-values < 0.05 were considered significantly enriched. Molecular docking analysis was conducted to assess the osteogenic potential of the active compounds identified from AR. The three-dimensional structures of these compounds were obtained from the PubChem database, whereas the crystal structures of target proteins were retrieved from the RCSB protein Data Bank (PDB). Prior to docking, all water molecules and co-crystallized ligands were removed using PyMOL software, version 2.5. Docking simulations were then carried out with AutoDock Vina (version 1.1.2) to calculate the binding affinities between the compounds and target proteins, including peroxisome proliferator-activated receptor gamma (PPARG), hypoxia-inducible factor 1-alpha (HIF1a), and prostaglandin-endoperoxide synthase 2 (PTGS2; also known as COX-2). The active binding sites were defined during the docking procedure, and the resulting ligand–protein interactions were visualized and analyzed using LigPlot+ (PPARG PDBID; 2PRG, HIF1a PDBID; 4H6J, COX2 PDBID; 6COX).

4.10. Statistical Analysis

All statistical analyses were performed by GraphPad Prism ver. 8.0.1 (GraphPad Software Inc., San Diego, CA, USA). Data are expressed as mean ± standard error of the mean (SEM). Before applying parametric tests, the normality of the data distribution and the homogeneity of variances were confirmed using the Shapiro–Wilk test and Levene’s test, respectively. One-way analysis of variances (ANOVA) was conducted with Dunnett’s post hoc test as the data satisfied these statistical prerequisites. A p-value less than 0.05 was considered statistically significant.

5. Conclusions

In summary, AR accelerates fracture healing not only by stimulating osteoblast differentiation but also by reprogramming the bone marrow microenvironment through the HIF1A–PTGS2–PPARG-centered transcriptional networks. This integrative modulation of hypoxia, inflammation, and metabolism underscores the potential of the AR as a system-level therapeutic candidate for enhancing skeletal regeneration.