Introduction

Premature ovarian insufficiency (POI), characterized by the cessation of ovarian function before the age of 40, encompasses a spectrum of disorders marked by amenorrhea, elevated gonadotropin levels, and decreased estrogenic activity1. Chemotherapy agents can induce premature ovarian failure, which is manifested by persistent amenorrhea (for more than 12 months) after chemotherapy, a follicle-stimulating hormone level of more than 30 MIU/ml, and a negative pregnancy test2. The rising prevalence of gynecological malignant tumors and the earlier time of onset have led to a notable surge of chemotherapy-induced POI3, resulting in lower fertility rates. Hormone replacement therapy (HRT) is currently the primary treatment method, but it has several disadvantages, including a lengthy treatment cycle and an increased risk of breast cancer and thromboembolism4. Among the emerging therapies, stem cell transplantation represents a novel approach for the treatment of POI. However, this treatment faces with significant challenges because of the discrepancies in reproductive biology between animal models and humans, coupled with the variability in stem cell dosage and surgical techniques5.

In China, the rich heritage of traditional Chinese medicine (TCM) provides a distinctive perspective on multi-target intervention strategies, which have been widely acknowledged by the medical community for their efficacy in treating complex gynaecological diseases with their diverse herbal resources and minimal side effects6. Secoisolariciresinol diglucoside (SDG), a naturally occurring dietary lignan derived from flaxseed, has been demonstrated to exhibit estrogenic properties via metabolic conversion to enterodiol and enterolactone in mammals7. Prior research has substantiated that prolonged administration of SDG tends to accumulate in the ovaries, uterus, and mammary glands of female rats8, influencing the estrous cycle9 and exhibiting a potent scavenging activity against reactive oxygen species (ROS) in ovarian tissue10. Moreover, evidence indicates that SDG can enhance follicle-stimulating hormone receptor (FSHR) expression in granulosa cells, which may contribute to the mitigation of ovarian aging10. However, the specific mechanisms by which it enhances fertility remain to be elucidated.

In this study, we validated the therapeutic effect of SDG on CTX-induced POI in a mouse model. We discovered SDG can activate the PI3K/Akt signaling pathway by network pharmacology and transcriptome analysis, which were further verified by molecular docking, dynamic simulation and molecular biology.

Materials and methods

Preparation of compounds

SDG was solubilized in dimethyl sulfoxide to achieve a 10 mM concentration and subsequently stored at − 80 °C for preservation. This compound was sourced from MedChemExpress (New Jersey, USA).

Animal model and treatment regimen

Construction of POI mouse model

A total of 40 C57BL/6 mice aged 6 weeks, were acquired from the Department of Animal Science at Jiangxi College of Traditional Chinese Medicine. Mice were housed under controlled environmental conditions with 12-hour alternating light/dark cycles, with free access to water and food. (i) We ensured our animal experiment met the standards of the Animal Ethics Committee of Nanchang University and were reviewed and approved by the Animal Ethics Committee of Nanchang University. (ethics approval number: NCULAE-20220624011) (ii) we confirmed that all experiments were performed in accordance with relevant guidelines and regulations. (iii) Besides, our studies involving live animals comply with the ARRIVE guidelines.

Initially, these mice were randomly divided into control group (n = 8) and model group (n = 32). The control group received intraperitoneal injections of saline, while the remaining model group were subjected to a single dose of cyclophosphamide (CTX, 120 mg/kg, Sigma, St. Louis, MO, USA) and busulfan (BU, 30 mg/kg, Sigma, USA) to establish the POI model11.

Animal grouping and medication

The POI model mice were randomly reassigned into four groups after two days injection, and administered varying dosages of SDG (0, 50 mg/kg, 100 mg/kg, 200 mg/kg) via gavage according to body weight for four weeks once a day, which was experimentally determined by the relevant literature12,13 and pre-experiments .

Mice were sacrificed by carbon dioxide anesthesia, then body and ovarian weights were measured. The relative ovarian weight was calculated using the formula: (ovarian weight / body weight) × 1000‰, to account for body weight variances among the mice.

HE staining and follicle enumeration

Ovarian tissues were fixed in 4% paraformaldehyde at room temperature overnight, followed by paraffin embedding. Serial sections of 5 μm thickness were prepared and subjected to hematoxylin and eosin (HE) staining to assess ovarian morphology and follicle counts, adhering to the classification criteria outlined by Chen et al.14.

Transcriptomics analysis

The Gene Expression Omnibus (GEO) database was employed to retrieve gene expression profiles of CTX-induced POI. We applied the limma package in R to analyze the differentially expressed genes (DEGs) in GSE128240, and visualized them by heat map and volcano map The significance thresholds applied were p < 0.05 and |logFC| > 1.

GO and KEGG pathway enrichment

Gene annotations were sourced from the R package org.Hs.eg.db (version 3.1.0), while the latest KEGG Pathway annotations were retrieved from the KEGG REST API (https://www.kegg.jp/kegg/rest/keggapi.html)15,16,17. We mapped the genes to background sets and performed enrichment analysis using the R package clusterProfiler (version 3.14.3), with gene sets ranging from 5 to 5000. P values < 0.05 and FDR < 0.25 determined statistical significance.

Identification of potential SDG targets

The Simplified Molecular-Input Line-Entry System (SMILES) notation and three-dimensional structure of SDG were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Potential targets were identified using resources such as PharmMapper (https://lilabecust.cn/pharmmapper/), SwissTargetPrediction (http://www.swisstargetprediction.ch/), TargetNet (http://targetnet.scbdd.com/), and the Comparative Toxicogenomics Database (CTD, https://ctdbase.org/).

Identification of POI-related targets

We identified genetic targets associated with POI employing the DisGeNET (https://www.disgenet.org/) and GeneCards (https://www.genecards.org/). In order to enhance the dependability of our findings, we only considered POI-related targets exhibiting a gene-disease association score of 0.1 or higher in DisGeNET, and those surpassing a relevance score threshold of 20 in GeneCards, .

Construction of the protein-protein interaction (PPI) network

We integrated PPI data by the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) platform (https://string-db.org/), focusing specifically on Homo sapiens. An interaction confidence score threshold of 0.7 was established, indicating a high level of confidence within the STRING database framework. The resulting network data were visualised using the Cytoscape application.

Cell proliferation assay

KGN cells were seeded in 96-well plates at a density of 4,000 cells per well and exposed to varying concentrations of CTX (250, 500, 1,000, and 1,500 µM) for 24 h to determine the IC50. Subsequently, the cells were co-cultured with varying concentrations of SDG and the optimal concentration of CTX for 24 h. The cell proliferation was then assessed using a CCK-8 assay. (Cell Counting Kit-8, Transgen BioTECH, Beijing, China).

Western blot analysis

Western blotting was conducted as previously described11. Visualization was achieved using a Nikon Eclipse 80i microscope. The primary antibodies included β-actin (20536-1-AP, Proteintech, Wuhan, China), phospho-Akt (T40067, Abmart, Shanghai, China), and Akt (T55561, Abmart, Shanghai, China). Secondary antibodies, both HRP-conjugated and fluorophore-conjugated, were sourced from Elabscience.

Molecular docking simulation

Target protein preparation

The PDB configurations of the PI3K1 and Aktγ molecules were obtained from the Protein Data Bank (https://www.rcsb.org/). In preliminary processing of these structures, we removed the original ligand, solvent molecules and excess protein chains by Pymol v2.5. Gasteiger partial charges and polar hydrogens were then calculated using AutoDock tools to optimise the proteins for docking.

Ligand preparation

The ligand of interest, SDG, was sourced in SDF format from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). We used Open Babel GUI software to convert the structure into 3D PDB format. AutoDock Tools was then employed for the critical steps of hydrogenation and charge calculation.

Molecular docking

The molecular docking procedure was performed using AutoDock Vina to predict the binding affinities between ligands and proteins. Both proteins and SDG structures were converted into the PDBQT format before docking, and the quantification of binding affinity calculated by AutoDock Vina provides insights into potential interactions. Visualization of the docking conformations was handled by Pymol v2.5.

Molecular dynamic simulations of protein-SDG complexes

The molecular dynamics simulations were conducted using the Gromacs software (version 2022.3)18,19 on two SDG-protein complexes, which had been identified as having the lowest binding free energies through molecular docking.The initial step entailed parameterising the SDG molecule with the GAFF force field, which was achieved through the utilisation of the AmberTools22 software. This was followed by the hydrogenation and RESP potential calculations on the small molecule, which were conducted using the Gaussian 16 W software. Subsequently, the calculated potential data were integrated into the topology files of the molecular dynamics simulation system which was maintained at a temperature of 300 K and a pressure of 1 bar throughout the duration of the simulation, employing the Amber99sb force field. In order to emulate the solvent environment, the Tip3p water model was employed to solvate the protein-ligand system, with an appropriate number of Na+/Cl- ions added to neutralise the total charge of the simulation system.

A preparatory phase involving energy minimization via the steepest descent method, comprising 100,000 steps within both NVT (constant volume and temperature) and NPT (constant pressure and temperature) ensembles, each with a coupling constant of 0.1 ps over a duration of 100 ps. The simulation then progressed to a production phase, extending over 5,000,000 steps with a 2 fs timestep, culminating in a comprehensive 100 ns simulation.

Upon completion of the simulation, an analysis of the trajectories was conducted utilising Gromacs’ suite of analytical tools, in order to gain insight into the behaviour of the system under study. To evaluate the structural stability and dynamics of the protein, key parameters were calculated, including the root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA). Furthermore, the binding free energy (ΔGMMGBSA) was calculated to provide additional insight into the interaction forces and binding mechanism between SDG and the target protein.

Statistical analysis

The statistical analysis of data was performed using one-way analysis of variance (ANOVA), facilitated by GraphPad Prism 9 software. The significance thresholds were set at P values < 0.05 (*) for statistical significance. All data are presented as the mean ± standard error, based on a minimum of three independent experiments.

Results

SDG can rescue the ovarian function of CTX-induced POI mice

We evaluated the body weight and ovarian weight after euthanasia by calculating the ovarian index, to elucidate the impact of SDGs on the basic ovarian function status of POI mouse models (Fig. 1A) and relative body weight (Fig. 1B). The ovarian weight ratio in the POI group was significantly reduced compared with the control group (p < 0.05). Conversely, the administration of SDG at dosages of 100 mg/kg and 200 mg/kg markedly mitigated this decrease (p < 0.0001), whereas the 50 mg/kg SDG treatment did not exhibit a significant alteration (p > 0.05). Notably, SDG treatment did not influence the overall body weight of POI mice (p > 0.05). Further morphological analysis revealed a reduction in ovarian volume within the POI group, which was progressively restored with increasing concentrations of SDG (Fig. 1C). To further elucidate the therapeutic potential of SDG in ameliorating POI in mice, we embarked on a histological analysis, employing HE staining to categorize ovarian follicles. Our investigation revealed that, in comparison with the control group, the primordial follicles, primary follicles, secondary follicles and antral follicles in the POI group were significantly diminished, while the situation was improved after administration of SDG, indicating that SDG has a protective effect on ovarian damage caused by chemotherapy drugs (Fig. 1E–G).

Fig. 1
Fig. 1The alternative text for this image may have been generated using AI.
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Morphological and quantitative analysis of ovarian tissue in POI mice following SDG administration. (A) Ovarian index. (B) Relative body weight. (C) Representative ovarian images from each experimental group post-dissection. (D) HE staining of ovaries. The left panel displays the size of the ovaries, with the middle panel focusing on preovulatory follicles—indicated by black arrows marking their diameters. The right panel highlights primordial and primary follicles, with primordial follicles identified by black arrows. (E–G) Quantification of follicular stages, including primordial, primary, secondary, and antral follicles after different treatments for 4 weeks. Data are expressed as mean ± SEM for n = 8 mice per group. Statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 versus control; #p < 0.05, ##p < 0.01, ###p < 0.001 versus POI group.

Transcriptomic shows the PI3K/Akt pathway plays a crucial role in the pathogenic process of POI

In order to reveal the potential mechanisms of chemotherapy - induced POI, we utilized the GEO database and selected the GSE128240 data set for mRNA sequencing data analysis. The limma package in R was employed to identify 636 differentially expressed genes (DEGs), comprising 265 that were upregulated and 372 that were downregulated. The volcano plot (Fig. 2A) and heat map (Fig. 2B) highlighted these DEGs between POI and control groups. GO enrichment analysis indicates that the effects of chemotherapy may be mediated by molecular functions including protein phosphorylation, transcriptional regulation, alterations in apoptotic processes, and protein binding (Fig. 2C). In addition, the PI3K/Akt signaling pathway is related to the pathogenesis of POI (Fig. 2D).

Fig. 2
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The transcriptomic landscape of POI mice. (A) A volcano plot illustrates the landscape of differentially expressed genes contrasting control and POI groups. (B) A heat map provides the differentially expressed genes in the control group versus the POI group. (C) GO enrichment analysis reveals the top five enriched biological themes. (D) The KEGG pathway analysis.

Network pharmacology indicates that SDG treats CTX-induced POI through the PI3K/Akt pathway

We used the network pharmacology approach to delineate the mechanism of SDG. Initially, we utilized the PubChem database to acquire the SMILES notation and the three-dimensional structure of SDG (Fig. 3A). Subsequently, we sorted out 341 putative SDG targets after deduplication from a series of databases including PharmMapper, SwissTargetPrediction, TargetNet and Comparative Toxicology Database (Supplementary Table 1). Furthermore, we extracted 316 POI-associated targets from DisGeNET and GeneCards (Supplementary Table 2). Leveraging these datasets, we employed the Venn Diagram tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) to identify 39 potential therapeutic targets (Fig. 3B and Supplementary Table 3) which is the foundation for constructing a PPI network (Fig. 3C). Analysis of the PPI network’s topological features unveiled eight hub targets—ERBB2, MMP9, ESR1, ALB, AKT1, CASP3, EGFR, IGF1—based on their DC, BC, and CC metrics. These hub targets are postulated to play pivotal roles in mediating the therapeutic effects of SDG on POI. Subsequent functional enrichment analyses illuminated that these targets predominantly converge on cellular processes such as response to chemical stimulus, cell proliferation regulation, and phosphorylation regulation (Fig. 3D,E). In addition, KEGG enrichment analysis emphasized the important roles of the PI3K/Akt, MAPK and FoxO signaling pathways (Fig. 3F), providing a new direction for finding potential treatment pathways.

Fig. 3
Fig. 3The alternative text for this image may have been generated using AI.
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Network pharmacology unveils SDG’s therapeutic landscape in POI. (A) The 3D molecular structure of SDG. (B) A Venn diagram highlights the intersection of SDG and POI targets. (C) A PPI network of potential therapeutic targets. The node sizes and colors are illustrated from large to small and yellow to red in descending order of degree values. (D) GO enrichment analysis enumerates the top ten significantly enriched biological themes. BP, biological process; CC, cell component; MF, molecular function. (E) The top 10 enriched biological processes. (F) KEGG enrichment analysis.

SDG alleviates CTX-induced damage by activating PI3K/Akt signaling pathway in KGN cells

In the initial study, we determined that 500 μm CTX is the 50% cytotoxic concentration for KGN cells via CCK-8 assay (Fig. 4A). Subsequently, we determined the therapeutic concentration of SDG using the same method and found that 400 µM is the therapeutic threshold of SDG, and the therapeutic effect is dose - dependent (Fig. 4B).

Fig. 4
Fig. 4The alternative text for this image may have been generated using AI.
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SDG counters CTX-induced toxicity in granulosa cells via PI3K/Akt pathway modulation. (A) The cell viability of KGN treated with different concentrations of CTX. (B) The cell viability of KGN treated with different concentrations of SDG and 500 μm CTX. (C–F) The expression levels of PI3K/Akt pathway proteins in KGN cells after treatment with 500 μm CTX or co-treatment with 500 μm CTX and 400 μm SDG. (G) The microscopic morphology of KGN cells after treatment with 500 μm CTX or co-treatment with 500 μm CTX and 400 μm SDG. Statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 versus control. #p < 0.05, ##p < 0.01, ###p < 0.001 versus CTX group.

After determining the effective dose, we conducted western blot analysis. Interestingly, no marked variance was observed in the phosphorylation levels of Akt (p-Akt) across the three study groups (Fig. 4D). However, the Akt expression in the CTX treatment group was relatively decreased compared with the control group and this trend was significantly reversed after combined treatment with SDG (Fig. 4E). Furthermore, CTX administration resulted in a significant upregulation of the p-Akt/Akt ratio compared to the control group, while SDG attenuated this effect (Fig. 4F).

Microscopic examination of KGN cell morphology and density provided additional insights into the protective effects of SDG. The CTX-treated cells exhibited marked alterations, characterized by reduced cell density and irregular shapes. Conversely, the co-administration of SDG appeared to mitigate these cytotoxic effects, as evidenced by improvements in both cell morphology and density (Fig. 4G).

Collectively, these findings substantiate the role of SDG in ameliorating CTX-induced cellular damage through the modulation of the PI3K/Akt signaling pathway, thereby illuminating a potential therapeutic strategy for the mitigation of chemotherapeutic toxicity.

Molecular docking reveals SDG’s interaction with PI3K/Akt signaling components

To further elucidate the mechanism of SDG on the PI3K/Akt signaling pathway, molecular docking experiments were conducted with PI3Kγ and Akt1 of the pathway. The predicted optimal binding conformations underscore the pivotal roles of hydrogen bonds, π-π stacking interactions, and hydrophobic contacts in mediating the binding modes of SDG to these proteins, as well as in positioning and anchoring SDG within the binding pockets (Fig. 5). Docking data shows that SDG exhibits high affinity for both PI3Kγ and Akt1 (Table 1), indicating that this ligand has excellent affinity and selectivity for these targets. Notably, the stable binding conformations observed across multiple modes point to the potential of SDG as a selective modulator of the PI3K/Akt pathway. Compared to Akt1, the relatively lower RMSD values for PI3Kγ highlight a more consistent binding topology, indicating a more precise interaction pattern with PI3Kγ. It suggests that SDG can achieve therapeutic effects on POI by modulating the PI3K/Akt signaling pathway.

Fig. 5
Fig. 5The alternative text for this image may have been generated using AI.
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The optimal docking conformations of SDG with PI3Kγ and Akt1. (A) SDG-PI3Kγ; (B) SDG-Akt1.

Table 1 Binding energies of the nine most favorable conformations of SDG towards PI3Kγ and Akt1.
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Molecular dynamics simulations confirm SDG-protein complexes’ stability and interaction

In an extensive 100 ns molecular dynamics simulation, the stability of the small ligand SDG in complex with proteins PI3Kγ and Akt1 was scrutinized through RMSD analysis (Fig. 6A,B). For the PI3Kγ complex, the protein moiety maintained a stable RMSD around 0.2 nm, signifying negligible structural perturbations throughout the simulation period. In contrast, the RMSD values for SDG exhibited pronounced variability, oscillating between 0.4 and 1.0 nm, which intimates a comparatively less stable ligand-protein interaction. The overall complex RMSD followed the ligand’s pattern suggesting that the ligand’s binding dynamics significantly influence the observed RMSD variability (Fig. 6A). On the other hand, the Akt1 complex showcased exceptional stability, with the protein’s RMSD consistently below 0.1 nm and the ligand’s RMSD exhibiting minor fluctuations between 0.2 and 0.6 nm. This stability was mirrored in the complex’s RMSD, highlighting a potentially more robust interaction (Fig. 6B).

Further insights were gleaned from the root mean square fluctuation (RMSF) analyses, which revealed distinctive flexibility profiles for PI3Kγ and Akt1 upon binding with SDG (Fig. 6C,D). PI3Kγ exhibited RMSF peaks at residues 200, 400, 600, and 800, suggesting regions of high flexibility, with fluctuations ranging from 0.2 to nearly 1.0 nm (Fig. 6C). Meanwhile, the tetrameric Akt1 showed a more restrained fluctuation pattern across all four chains, with RMSF values spanning from 0.1 to 0.8 nm, indicating a generally more stable structure (Fig. 6D). The ΔGMMGBSA calculations further quantified the interactions, yielding − 68.78 kcal/mol with PI3Kγ and − 58.73 kcal/mol with Akt1. These data demonstrated that under simulated conditions, SDG can directly bind to both proteins and form stable complexes.

Fig. 6
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Calculated RMSD, RMSF, and H-bond number of two SDG-protein complexes during simulation. (A, C, E) RMSD, RMSF and H-bond number of SDG-PI3Kγ complex. (B, D, F) RMSD, RMSF and H-bond number of SDG-Akt1 complex.

The dynamics of hydrogen bond formation between SDG and the respective proteins further elucidated the nature of their interactions (Fig. 6E,F). During the 100-nanosecond molecular dynamics simulation, the number of hydrogen bonds formed between SDG and PI3Kγ can reach approximately 10 at certain time points (Fig. 6E). In the complex with Akt1, the hydrogen bond count exhibits minor fluctuations, primarily ranging between 4 and 8 (Fig. 6F).

The investigation extended to analyzing the Rg and SASA of the protein complexes post-SDG binding (Fig. 7A–D). Variations in Rg across the simulation timeline indicated that SDG binding imparts a degree of dynamism and structural flexibility to the proteins (Fig. 7A,B). Moreover, alterations in SASA suggested that SDG binding modulates the solubility and surface characteristics of PI3Kγ and Akt1 (Fig. 7C,D), highlighting the intricate molecular interplay and the consequential structural adaptations induced by SDG binding.

Fig. 7
Fig. 7The alternative text for this image may have been generated using AI.
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Calculated Rg and SASA of two SDG-protein complexes during simulation. (A, C) Rg and SASA of SDG-PI3Kγ complex. (B, D) Rg and SASA of SDG-Akt1 complex.

Discussion

With the rise in gynaecological malignancies, there has been a rise in chemotherapy and radiotherapy-induced POI3. The etiology of POI is multifaceted, encompassing early follicular attrition, hindered follicular development or maturation, oocyte pool depletion, and ovarian resistance syndrome20. HRT is a conventional means of POI management, but it is related to the increased risk of adverse cardiovascular events21,22 and breast cancer23, which increases the necessity of finding new therapies. In this background, Chinese herbal medicine is a promising therapeutic option due to its multi-target, few side effects and high acceptability. Traditional Chinese medicine posits the kidneys as the essence’s reservoir, integral to reproductive capabilities24. SDG, a flaxseed derivative, is recognised for its beneficial effects on liver and kidney function, and has been demonstrated to possess anti-inflammatory25,antioxidant10, and anti-aging properties26. However, the precise role and mechanism by which it reduces POI remain uncertain.

Our study employed a mouse model of CTX-induced POI11 followed by administration of different concentrations of SDG for 4 weeks. The ovarian index declined post-CTX exposure, but was significantly improved in the SDG-treated groups. The CTX-induced model resulted in a marked reduction in follicle count across all stages, as seen in the histopathological analyses. This observation aligns with findings from prior research27, underscoring the hypothesis that the etiology of POI is rooted in the excessive activation and subsequent depletion of follicles28. Notably, administration of SDG led to a pronounced recovery in the follicle count at all stages, with particular emphasis on the restoration of primordial and antral follicles. Concurrently, there was a relative decrease in the number of atretic follicles. These findings collectively indicate the efficacy of SDG in mitigating the effects of CTX-induced POI in mice, thereby suggesting a potential therapeutic avenue for addressing this condition.

The PI3K/Akt pathway regulates many cellular processes, including proliferation, survival, apoptosis, cell cycle regulation, DNA repair and metabolism29. The PI3K/Akt pathway plays a key role in oocyte maturation and follicular development. This is achieved through the activation of PI3K, which facilitates the phosphorylation of Akt30. Concurrently, the quantification of PI3K/Akt pathway constituents within intrafollicular granulosa cells (GCs) serves as a prognostic marker for oogenetic capability31. The inhibition of the PI3K/Akt pathway has been demonstrated to induce granulosa cell apoptosis32, whereas its activation has been shown to prevent apoptosis and autophagy, thereby alleviating ovarian dysfunction33. Numerous traditional Chinese medicinal compounds have been documented to modulate this pathway, influencing germ cell maturation and female reproductive health34,35.

As an emerging discipline, network pharmacology combines the principles of systems biology with network analysis to provide specific signal nodes for the design of multi - target drugs. This field is developing rapidly, providing new models for drug mechanism elucidation and drug development36. Leveraging network pharmacological methodologies, our investigation identified 39 putative target genes of SDG for the treatment of POI. Subsequent PPI and enrichment analyses suggest that the regulation of SDG on PI3K/Akt signaling plays an important role in the treatment of POI. Complementary transcriptomic analyses in POI murine models pinpointed aberrations in PI3K/Akt pathway regulation as potential pathogenic mechanisms. These findings align with extant literature, reinforcing the seminal role of the PI3K/Akt pathway in both the pathogenesis and treatment of POI.

To elucidate the predictive accuracy of network pharmacology for target identification, we embarked on a series of in vitro investigations. These experiments require the pretreatment of KGN cells with CTX first, followed by therapeutic intervention with SDG. Our findings revealed that CTX exposure detrimentally impacted cellular proliferation and morphology, whereas SDG conferred protection against CTX-induced damage. Moreover, we observed a diminution in Akt molecule expression post-CTX treatment, a trend that was reversed by SDG administration. Intriguingly, the levels of p-Akt remained largely unaltered, hinting at the possibility that both chemotherapeutic agents and SDG might exert direct regulatory effects on the PI3K/Akt signaling cascade through modulation of Akt expression. Nonetheless, the precise mechanisms through which SDG elevates Akt molecule expression, thereby activating the PI3K/Akt pathway, remain elusive. the exact mechanism by which SDGs increase Akt molecule expression and activate the PI3K/Akt pathway remains elusive. To further our understanding, we employed molecular docking and dynamics analyses to investigate the interaction between SDG and the PI3K/Akt molecules. Our computational studies indicated robust binding affinity of SDG towards both molecules which led us to hypothesize that SDG might influence the structural conformation of PI3K and Akt, thereby impeding their degradation and culminating in the activation of the PI3K/Akt pathway.

Nevertheless, our study still has limitations. The initial network pharmacological analysis was hampered by database constraints, precluding the acquisition of tissue-specific disease and drug target genes. This limitation potentially led to discrepancies between predicted and actual targets in POI ovarian tissues. To mitigate this issue, we supplemented our network pharmacological approach with experimental validation and further substantiated our findings through molecular docking and dynamics simulations. Another limitation stems from our use of KGN cells instead of primary GCs for validation, which might introduce disparities in molecular expression and could further influence the veracity of our conclusions. Additionally, we did not conduct any rescue studies to verify whether SDG alleviates chemotherapy drug damage through the PI3K/Akt pathway. Collectively, these factors underscore the necessity for further foundational research to deepen our comprehension of SDG’s therapeutic mechanisms in POI treatment and to advance the clinical application of TCM.

Conclusion

In this investigation, we employed a synergistic approach that integrated network pharmacology with empirical validation to elucidate the therapeutic efficacy of SDG in mitigating ovarian damage induced by chemotherapeutic agents. Our findings substantiate that SDG ameliorates follicular depletion and ovarian damage via modulation of the PI3K/Akt signaling pathway, thereby offering a promising avenue for the management of POI. This study not only corroborates the clinical applicability of SDG in addressing POI but also pioneer novel perspectives for exploring TCM research methodologies. Drawing on the evidence presented, we advocate for the potential of SDG as a superior complementary and alternative medicinal option in the treatment landscape of POI. Future investigations will delve deeper into the mechanistic insights and therapeutic implications of SDG in this context.