Xiaotan Sanjie Formula (XTSJF), a traditional Chinese prescription, holds promising potential in addressing gastric cancer (GC). Despite this, the fundamental constituents and underlying mechanisms that define XTSJF’s attributes remain enigmatic. Against this backdrop, this study endeavors to unravel the latent mechanisms driving XTSJF’s impact on GC, leveraging the synergistic prowess of network pharmacology and molecular docking methodologies. To understand the potential mechanism of XTSJF against GC, this study used network pharmacology, molecular docking, and bioinformatics analytic methodologies. There are 135 active components where the active ingredients with a higher degree value are quercetin, β-sitosterol, naringenin, nobiletin, and kaempferol and 167 intersecting targets in which TP53, MAPK3, MAPK1, STAT3, and AKT1 were key targets were identified in XTSJF in the treatment of GC. According to GO and KEGG analyses, XTSJF is mostly involved in the positive control of transcription from the RNA polymerase II promoter, enzyme interaction, and other biological processes in GC. KEGG analysis shows that XTSJF treated GC primarily by regulating signaling pathways including the TNF, PI3K-Akt, and MAPK signaling pathways. According to the results of the PPI network and molecular docking, quercetin, β-sitosterol, naringenin, nobiletin, and kaempferol exhibit stronger affinity with TP53, MAPK3, MAPK1, STAT3, and AKT1. This study indicates the active components of XTSJF as well as its possible molecular mechanism against GC, and it serves as a foundation for future fundamental research. The clinical management of this malignancy has faced substantial challenges due to its marked heterogeneity and robust biological aggressiveness. This intricate nature of gastric cancer (GC) has contributed to the gradual pace of advancements in its clinical treatment. Consequently, an imminent requirement arises for novel antitumor therapeutics characterized by heightened efficacy and reduced toxicity.Gastric cancer, a highly malignant neoplasm, stands prominent among the global top 5 malignancies in terms of both incidence and mortality rates. and effectively boosts the survival rates among patients grappling with GC.,4 The intricate clinical challenges posed by GC necessitate fresh perspectives in treatment. Traditional Chinese Medicine, with its historical underpinnings and demonstrated clinical efficacy, presents itself as a compelling complement to conventional therapeutic regimens. By amalgamating the strengths of both TCM and modern medicine, a promising synergy emerges, offering a renewed outlook for more effective, safer, and holistic approaches toward addressing the global burden of GC.The pressing clinical demand for innovative antitumor agents and efficacious treatment modalities, capable of markedly enhancing patient prognosis, is incontrovertible. In this context, Traditional Chinese Medicine (TCM) emerges as a historical cornerstone in the realm of malignant disease prevention and treatment. Furthermore, TCM assumes a pivotal role as a complementary and alternative strategy within the comprehensive treatment of GC. Notably, TCM brings about significant improvements in the overall quality of life,5,6 Complementary experimental inquiries have shed light on the plausible mechanisms underpinning XTSJF’s anti-gastric cancer effects, encompassing impediment of metastasis, promotion of apoptosis through anti-angiogenic pathways, and restraint of epithelial-mesenchymal transition, among other facets.,8Xiaotan Sanjie Formula (XTSJF), an empirical formula rooted in the “phlegm” theory, has exhibited a steadfast presence in the therapeutic landscape of GC for over 3 decades. The formula’s application in GC prevention and treatment has been extensively documented. Early clinical investigations have unveiled XTSJF’s capability to significantly ameliorate card scores in patients afflicted with advanced gastrointestinal tumors, thereby conferring an extension in survival time.The intricacies intrinsic to GC treatment stem from the formidable spatiotemporal heterogeneity exhibited by the disease, the intricate tumor microenvironment milieu, and the scarcity of prominent mutational targets. In contrast, XTSJF embodies a composite medicinal amalgamation boasting manifold targets and mechanisms, thereby presenting a plausible avenue to surmount the prevailing challenges inherent in GC therapy. Nevertheless, extant studies have faltered in presenting a comprehensive multi-tiered explication of XTSJF’s therapeutic and disease-modulating actions within the context of GC treatment.This review aims to bridge the existing knowledge gap by unraveling the intricate tapestry of XTSJF’s therapeutic potential against gastric malignancies. Through rigorous exploration of its pharmacological attributes and mechanistic engagements, this study seeks to furnish a holistic comprehension of XTSJF’s utility within the domain of GC treatment. By synthesizing empirical wisdom with mechanistic elucidation, a more nuanced understanding of XTSJF’s applicability in addressing the manifold challenges posed by GC is envisaged. The realm of TCM benefits substantially from network pharmacology, with its focal points converging on the aggregation of TCM’s chemical constituents via databases and the application of computational biology to unravel potential component-target associations. The synthesis of a “herbal-component-target-signaling pathway” network culminates from these pursuits, furnishing preliminary insights into the intricate nexus of TCM’s multifarious components, targets, and pathways. This engenders a nuanced comprehension of TCM’s pharmacological underpinnings, facilitating the mastery of its molecular mechanisms. Notably, the confluence of this theory parallels TCM’s cardinal principle of symptom differentiation and treatment, reinforcing its congruence with network pharmacology. As current TCM investigations progressively assimilate network pharmacology methodologies, a corpus of studies converges to proffer foundational and empirical elucidations for diverse TCM conundrums. In this context, this study undertakes a comprehensive network pharmacology exploration to dissect the principal components and molecular biological mechanisms intrinsic to XTSJF in the context of GC. This endeavor strives to furnish pertinent referential resources for subsequent investigations, while concurrently exemplifying the symbiotic synergy between network pharmacology and TCM research.Network pharmacology, heralding a novel paradigm in Chinese medicine research, orchestrates a “drug-target” nexus by amalgamating drug or prescription attributes with potency, thereby effectuating target prediction and mechanism delineation.https://old.tcmsp-e.com/tcmsp.php). Additionally, other active chemical components , not traceable within TCMSP, were procured from the SymMap database. Oral bioavailability (OB) quantifies the extent to which orally ingested medicine enters the bloodstream, while drug similarity (DL) gauges the likeness between a chemical and established drugs.,11 Employing criteria of OB ≥ 30% and DL ≥ 0.18, we ascertained the principal components of XTSJF. Subsequently, we harnessed the resources of PubChem, a repository for organic small molecule bioactivity information, to extract primary chemical components housed within XTSJF’s central molecular structures. Employing pharmacokinetic data retrieval filters set at probability ≥ 0.7, we discerned potential component targets through SwissTargetPrediction (accessible at http://SwissTargetPrediction.ch/). The ensuing identified protein targets were then harmonized into standardized gene names, leveraging the UniProt database.The compounds and their respective target interactions within XTSJF, comprising Pinellia ternate, Rhizoma Arisaematis, Poria, Fructus aurantii immatures, citrus, Bulbus Fritillariae Cirrhosae, Semen Sinapis albae, and Glycyrrhiza uralensis, were gleaned from the TCMSP database  germane to XTSJF’s role in GC’s pathophysiology, we harnessed the STRING database. The overlapping set of targets was fed into this database, utilizing filtering criteria encompassing the “Homo sapiens” organism and a stringent confidence level (0.9). This yielded a repertoire of targeted proteins that formed the basis for constructing a PPI network in Cytoscape 3.9.1. This software not only facilitates network visualization but also computes pivotal network metrics including degree, betweenness centrality, closeness centrality (CC), and average shortest path length. Employing a meticulous approach, we scrutinized these network metrics, favoring target nodes with degree values, betweenness centrality, and CC surpassing the median values within the PPI network. This methodology enabled the identification of 10 core targets for future investigations. In a bid to unravel the biological context of putative GC targets, the DAVID database came into play, allowing the aggregation of valuable Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) data. The outcomes of these analyses were artfully presented through histograms and bubble plots, each category thoughtfully sequenced in order of significance. The spectrum encompassed cellular components, molecular functions, and biological processes, all of which underwent meticulous screening via GO analysis. Additionally, KEGG enrichment analysis unearthed crucial signaling pathways embedded within pivotal biological processes.https://www.rcsb.org/) and save them as PDB format files based on the results of the prior network pharmacology screening. Following that, the protein was configured in PyMOL2.5 to optimize the protein-ligand interactions: Remove additional molecules that were bonded to the target protein structures, as well as any water, organic, and other compounds. The target proteins were then loaded into AutoDockTools1.5.7 to perform hydrogen addition, charge calculations, and receptor designation. The results were then saved in PDBQT format.Search for the target proteins in the RCSB PDB  to gather the overlapping targets of medications and disorders. In total, there are 167 intersecting targets, as shown in Figure .1.0 to gAfter database prediction and removal of duplicate values, the 2 targets, XTSJF and GC, were combined to obtain a total of 167 common targets. A “component-target” network of XTSJF in GC was also constructed Fig. , in whicUploading the intersection target to the STRING database with a confidence level of 0.9 sets, and a PPI network graph of the target was got. We imported the data into Cytoscape 3.9.1 to plot the protein network graph, which comprised 153 nodes and 1652 edges. The corresponding degree values increase with the size of the nodes. Network locations were determined based on the degree values. As shown in Figure P < .05, effective targets were annotated using the DAVID database, and 1126 biological process (BP) items were found. These items were primarily connected to transcription from the RNA polymerase II promoter, gene expression, drug response, apoptotic process, estradiol response, and other topics. There were 68 different cellular components (CC) items, most of which were connected to the macromolecular complex, nucleoplasm, chromatin, and nucleus. There were 139 entries for molecular functions (MF), mostly relating to the interaction of enzymes, proteins with similar structures, RNA polymerase II transcription, etc. We respectively select the top 10 results to draw a bubble chart among BP, CC, and MF. These results are shown in Figure P < .05, mainly including the MAPK signaling pathway, PI3K-AKT signaling pathway, tumor necrosis factor (TNF) signaling pathway, etc. Select the top 20 pathways to draw a bubble chart, and measure the enrichment degree of KEGG by the number of core targets enriched to this pathway and LG P value, as shown in Figure Under the screening condition of ,16 With binding energies of below −7.0 kcal/mol, all 5 of the primary ingredients demonstrated an exceptionally high binding affinity for MAPK3, showing the drug’s potent affinity for the target. The particular docking outcomes are displayed in Figure The primary ingredients of the XTSJF  were obtained by topological study and calculations. Using AutoDockTools1.5.7, the possible targets and active ingredients of the XTSJF in GC were docked. It was hypothesized that there would be a higher likelihood of pharmacological activity if the conformation of the ligand and receptor binding was more stable. TP53, MAPK3, MAPK1, STAT3, and AKT1 were identified as the top 5 main targets based on degree values  enrichment analysis to illuminate the intricate mechanistic terrain underpinning XTSJF’s therapeutic influence in GC. Substantive GO analysis unveiled target gene enrichment predominantly within key biological processes, encompassing positive regulation of RNA polymerase II promoter transcription, negative regulation of the apoptotic process, and positive regulation of gene expression. Concurrently, KEGG enrichment analysis elucidated multifaceted intersections between XTSJF and critical signaling pathways, with a notable emphasis on the TNF, PI3K-Akt, and MAPK pathways. Among these, the TNF pathway emerges as pivotal, with TNF, a pivotal inflammatory cytokine, orchestrating a dichotomous impact on cytokine release, apoptosis, and programmed cell necrosis. Notably, TNF’s binding to receptors precipitates concurrent activation of both the caspase-8-mediated apoptotic cascade and the NF-kB-mediated inflammatory signaling axis. The intricate nexus between the PI3K/AKT pathway, key transcription factors, and pivotal signaling cascades, such as TGF-β, NFκβ, and Wnt/catenin, holds paramount significance, orchestrating outcomes spanning epithelial-mesenchymal transition, metastasis, and invasion.In light of the network pharmacological insights elucidated above, this study delves into the potential molecular mechanism underpinning XTSJF’s efficacy in GC. Utilizing a judicious approach, we identified 6 active components and 5 representative targets that form the crux of XTSJF’s therapeutic impact. To validate the predictions furnished by network pharmacology, we subjected quercetin, β-sitosterol, naringenin, nobiletin, kaempferol, and luteolin – the 6 key compounds – to molecular docking against TP53, MAPK3, MAPK1, STAT3, and AKT1 proteins, respectively. Impressively, the docking analyses affirmed strong binding interactions between the major active molecules within XTSJF and these hub targets.The next phase of our inquiry centered on corroborating these findings through diverse databases. Remarkably, our exploration unveiled significantly elevated mRNA levels of TP53 and MAPK3 within GC tissues. Furthermore, the TP53 levels exhibited marked variations across pathological stages. Employing the cBioPortal platform, we uncovered genetic alterations within these 5 target entities in a substantial proportion of patients – 57% out of 660 cases. The corroboration of our findings with extant literature reports underscores the robustness of our analytical framework.In essence, this investigation harnesses a comprehensive approach that amalgamates network pharmacology and molecular docking, revealing potential interactions within a multi-component, multi-target, and multi-pathway framework, which are substantiated through molecular docking analyses, enabling a deeper understanding of XTSJF’s mechanistic intricacies within the context of GC. The congruence between our findings and established literature substantiates the potency of our investigative methodology, paving the way for a nuanced comprehension of XTSJF’s clinical utility in GC treatment.Through network pharmacology, we have identified quercetin, β-sitosterol, naringenin, epinephrine, kaempferol, and luteolin as pivotal bioactive constituents of XTSJ’s influence on GC. Nevertheless, we acknowledge that these key components might not comprehensively encapsulate XTSJ’s complete pharmacological effects. Besides, it is imperative to acknowledge the ongoing necessity for refining database accuracy and timeliness, as well as recognizing the evolving nature of web-based information technology. While recognizing the current limitations in database accuracy and the evolving landscape of information technology, we underscore the necessity of corroborative pharmacodynamic and molecular biology investigations to bolster the credibility of our findings.This study offers a preliminary assessment of the pivotal constituents and potential mechanisms underscoring XTSJF’s therapeutic implications in GC, employing an integrative approach encompassing network pharmacology, molecular docking, and bioinformatics analysis. Our investigation points to quercetin, β-sitosterol, naringenin, nobiletin, kaempferol, and luteolin as the principal active components within XTSJF, while protein targets such as TP53, MAPK3, MAPK1, STAT3, and AKT1 emerge as plausible therapeutic targets for XTSJF’s impact on GC. Our findings suggest that XTSJF may harness its therapeutic potential through the modulation of critical pathways, including the PI3K-AKT signaling pathway, MAPK signaling pathway, and TNF signaling pathway. However, it is paramount to acknowledge that existing web-based information technology necessitates further refinement to ensure data accuracy and timeliness. Moreover, the significance of our results rests on the premise of rigorous experimental validation. In summary, this study adopts a multidisciplinary approach, amalgamating network pharmacology, molecular docking, and bioinformatics analysis, to offer a preliminary exploration of XTSJF’s therapeutic role in GC. As we navigate the evolving landscape of data technology, and recognize the essentiality of experimental confirmation, our study paves the way for a more comprehensive understanding of XTSJF’s potential efficacy in the context of GC treatment.Data curation: Xinxin Xu, Shuying Zeng.Formal analysis: Xinxin Xu.Investigation: Xinxin Xu.Supervision: Zhihong Yu.


KEGG signaling pathway enrichment analysis screened 100 pathways (p < 0.05). Molecular docking results showed that ELE had similar affinity with five key targets. In vitro experiments showed that the expressions of STAT3, EGFR and BCL2L1 were significantly decreased, and the expression of CASP9 in the ELE intervention group was significantly increased compared with that in the control group. All in all, ELE may play a key role in the treatment of EC by regulating the expression of STAT3, EGFR, BCL2L1 and CASP9.The study aimed to investigate the mechanism of action of β-elemene (ELE) in the treatment of esophageal cancer (EC). In this study, public databases were used to predict related targets in ELE and EC. The network analysis was performed to identify key targets of ELE in EC treatment. Further, bioinformatics and DAVID databases were used for GO and KEGG enrichment analysis, respectively. Ultimately, molecular docking and in vitro cell experiments were conducted to validate the results of network pharmacology enrichment. As a result, 34 candidate targets for ELE in the treatment of EC were obtained, and five key targets  were identified. GO functional annotation yielded 2200 GO entries ( According to statistics, the number of new EC cases and deaths worldwide in 2020 were 604,000 and 544,000 respectively, of which 50% of the new cases were in China4. Studies have shown that race, gene and lifestyle are inextricably linked to the occurrence and development of EC, among which smoking and drinking have become the most important risk factors for increasing the risk of EC5. Currently, the main treatment for EC is esophagectomy, but it is highly invasive and may greatly impair patients’ quality of life. In addition, surgical treatment is only suitable for patients with early-stage EC6. Unfortunately, most EC patients have already entered the middle and advanced stages of cancer when they seek medical treatment. Compared with 90% for early EC patients after treatment, patients with advanced EC after treatment had an estimated 5-year survival rate of only 6–15%7. Thus, the increasing incidence of EC and the poor prognosis after treatment also highlight the importance of continuous in-depth research on the pathogenesis of EC.Esophageal cancer (EC), one of the most common cancers, has become the leading cause of cancer death8. Beta-elemene (ELE) as a sesquiterpene compound extracted from natural Chinese herb Curcuma wenyujin10, which can exert broad-spectrum anti-tumor activity by inhibiting cell proliferation, arresting cell cycle, regulating the expression of apoptotic genes, inhibiting telomerase activity, and inducing apoptosis and autophagy12. Nowadays, ELE has been widely used in the clinical treatment of various cancers, such as lung cancer13, breast cancer14, and brain tumors15. More recently, a study has also revealed that ELE can inhibit the proliferation and metastasis of EC cells by regulating the phosphorylation of AKT, thereby exerting a therapeutic effect on EC16. However, the mechanism and targets of ELE in the treatment of EC still lack further research.Traditional Chinese Medicine (TCM), especially Chinese herbal medicine, has been widely used to treat cancer in China and many other countries. Numerous studies have shown that TCM can not only relieve the symptoms of cancer patients and improve their quality of life, but also reduce the adverse reactions and complications caused by chemotherapy, radiotherapy or targeted therapy17. Based on this, the study explored the mechanism of action of ELE in the treatment of EC by means of network pharmacology and experimental validation. The related targets of ELE and EC were obtained from massive databases, and key targets were then identified through protein–protein interaction (PPI) network analysis and related literature searches. Finally, the key targets of ELE against EC were verified by in vitro experiments. The network pharmacology analysis process is shown in Fig. Network pharmacology is a new research method that breaks the research model of “one drug, one gene, one disease”, and focuses on comprehensively exploring the mechanism of drug treatment of diseases from multiple components, multiple targets, and multiple pathwaysThe numbers of ELE targets obtained from the TCMSP, CTD, Pubchem and SwissTargetPrediction databases were 25, 11, 17 and 78, respectively. Taking the union of the ELE targets in the above four databases, the final number of ELE targets was 116. The results are presented in Fig. The PPI network was constructed using the STRING database and visualized by Cytoscape software. As a result, the PPI network contained 34 nodes and 336 edges , including BP 2010, CC 90 and MF 100. More precisely, BP analysis indicated that candidate targets were mainly concentrated in negative regulation of mitotic cell cycle, response to drug and gland development. CC analysis showed that the candidate targets were mainly concentrated in PML body, cyclin-dependent protein kinase holoenzyme complex and nuclear envelope. MF analysis showed that the candidate targets were mainly concentrated in protein phosphatase binding, ubiquitin protein ligase binding, phosphatase binding. The results of the GO functional annotation are shown in Fig. p ˂ 0.05), including pathways in cancer, prostate cancer and small cell lung cancer. The results of the KEGG pathway enrichment analysis are shown in Fig. The results of GO functional , cellular component (CC), and molecular function (MF)) enrichment analysis revealed that 34 candidate targets of ELE for the treatment of EC were enriched in a total of 2200 GO entries , EGFR (− 5.1 kJ·mol−1), CTNNB1 (− 5.5 kJ·mol−1), BCL2L1 (− 5.3 kJ·mol−1) and CASP9 (− 5.4 kJ·mol−1). Molecular docking results are shown in Fig. Molecular docking of ELE with its key candidate targets  for the treatment of EC was applied using the Discovery studio software. As a result, the binding energies for docking with ELE in this study were STAT3 , CTD (http://ctdbase.org/), Pubchem (https://pubchem.ncbi.nlm.nih.gov/) and SwissTargetPrediction (http://swisstargetprediction.ch/) databases. Among them, targets were obtained from the SwissTargetPrediction database through the following two processes: First, the Canonical SMILES structure of ELE was copied from Pubchem. Second, the SMILES structure was input into the SwissTargetPrediction search box to predict the targets of ELE.Potential targets of ELE were obtained from the TCMSP (https://www.ncbi.nlm.nih.gov/), GeneCards (https://www.genecards.org/) and OMIM (https://www.omim.org/) databases.Targets related to EC were obtained from the NCBI (https://string-db.org/), and the species was selected as “Homo sapiens” to construct a PPI network. The “string_interactions file” was downloaded from the PPI network analysis results of the STRING database, and the visualization and topology analysis of the PPI network were then performed using Cytoscape 3.9.1 software.The candidate target list of ELE in the treatment of EC was input to the STRING database (http://www.bioinformatics.com.cn/), and Gene Ontology (GO) functional annotation was performed using the GO enrichment analysis function of the bioinformatics platform. Similarly, the candidate target list of ELE for the treatment of EC was uploaded to the file selection column of the DAVID database (https://david.ncifcrf.gov/) for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.The candidate target list of ELE in the treatment of EC was uploaded to the bioinformatics platform  database, and the 3D chemical structures of ELE were obtained from the PubChem database.Molecular docking of receptor protein with ELE was performed using the Discovery studio software. The 3D structures of all receptor proteins were obtained from the RCSB PDB (−1 penicillin and 100 U·mL−1 streptomycin), and cultured in an incubator containing 5% CO2 at 37 °C. After the cells entered the rapid growth phase, subculture and follow-up experiments were performed.The human EC cell line ECA-109 was purchased from Shanghai Cyberkang Biotechnology Co., Ltd. The ECA-109 cells were added to RPMI-1640  supplemented with 10% fetal bovine serum and 1% antibiotics (100 U·mLCell viability was determined by Cell Counting Kit (CCK-8) assay. Briefly, 2000 ECA-109 cells/well were seeded in a 96-well plate, and after cells were fully attached, cells were then treated with ELE  at different concentrations  for 24, 48 and 72 h. Afterwards, discard the original medium in the 96-well plate, add 10 µL of pre-prepared 10% CCK-8 solution , and incubate in a 37 °C incubator for 0.5–4 h until the color turns orange. Finally, the absorbance value of each well at 450 nm wavelength was measured with a microplate reader.STAT3, EGFR, CTNNB1, BCL2L1 and CASP9 were analyzed. The primer list and sequence are presented in Table TRIzol  was used to extract total RNA from cell samples. Nano Drop 2000  was used to detect RNA concentration and purity. Reverse transcription kit  Co., Ltd) was used for reverse transcription. And ABI 7500 instrument  was employed for fluorescence quantitative detection. Using GAPDH as a reference, the relative mRNA expression levels of The protein expression levels of STAT3, EGFR, BCL2L1 and CASP9 in ECA-109 cells were measured by western blot analysis. Specifically, ELE-treated ECA-109 cells were collected and total cell proteins were extracted in RIPA buffer  for 30 min on ice. Protein concentrations were measured using a BCA protein assay kit  according to the manufacturer’s instructions. The proteins were resolved by SDS loading buffer and denatured at 95 °C for 5 min. Subsequently, a total of 30 μg of proteins were separated by SDS-PAGE on 10% gels, which were then transferred onto PVDF membranes and blocked with 5% skim milk for 2 h at room temperature. The PVDF membranes were incubated with the corresponding primary antibodies ; GAPDH ) overnight at 4 °C. After washing with TBST, the PVDF membranes were incubated with secondary antibody overnight at room temperature. Finally, detection was routinely performed with a chemiluminescent HRP substrate  and an ECL imaging system . The result of gels images was cropped and full-length gels and blots are included in the p-value < 0.05 was considered statistically significant.SPSS 22.0 was used for statistical analysis of experimental data. Data were presented as mean ± standard deviation (SD), and differences between multiple groups were assessed using one-way analysis of variance (ANOVA). A In summary, the present study is the first to systematically explore the possible mechanisms of ELE in the treatment of EC using network pharmacology approach and in vitro validation experiments. The network pharmacological analysis predicted that ELE may exert its therapeutic effects against EC via regulating multiple targets and pathways. In vitro validation experiments confirmed that the possible mechanism of ELE treating EC was to down-regulating the expression of STAT3, EGFR and BCL2L1, and up-regulating the expression of CASP9, thereby improving the progression of EC. This finding may serve as a reference to study the mechanism of action of ELE in the treatment of EC and to supply novel targets for the treatment of EC.Supplementary Figure 1.


Osteoporosis (OP) is a disease caused by multiple factors. Studies have pointed out that isopsoralen (IPRN) is one of the most effective drugs for the treatment of OP. Based on network pharmacological and molecular experimental analysis, the molecular mechanism of IPRN in osteoporosis is clarified.IPRN target genes and OP-related genes were predicted from the databases. Intersections were obtained and visualized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on target genes, which was confirmed by experiments internal and external experiments. Molecular docking was used to verify the binding between IPRN and target proteins. Molecular dynamics (MD) simulates the binding affinity of protein targets and active compounds.87 IPRN target genes and 242 disease-related targets were predicted. The protein–protein interaction (PPI) network identified 18 IPRN target proteins for the treatment of OP. GO analysis indicated that target genes were involved in biological processes. KEGG analysis showed that pathways such as PI3K/AKT/mTOR were associated with OP. Cell experiments (qPCR and WB) found that the expressions of PI3K, AKT, and mTOR in MC3T3-E1 cells at 10 μM, 20 μM, and 50 μM IPRN concentrations, especially at 20 μM IPRN treatment, were higher than those in the control group at 48 h. Animal experiments also showed that compared with the control group, 40 mg/kg/time IPRN could promote the expression of the PI3K gene in chondrocytes of SD rats.This study predicted the target genes of IPRN in the treatment of OP and preliminarily verified that IPRN plays an anti-OP role through the PI3K/AKT/mTOR pathway, which provides a new drug for the treatment of OP. Osteoporosis (OP) is a chronic disease with impaired bone strength caused by low bone density and bone microstructure damage, which mainly occurs in adults, especially postmenopausal women , 2. SincCurrently, On the one hand, the popular biochemical markers of bone turnover (BTM), such as serum bone alkaline phosphatase (bALP) and type I procollagen N propeptide (PINP), provide specific and dynamic indicators of bone turnover mechanism in the balance between bone formation and bone absorption, and can be used as predictive factors of OP occurrence and monitoring indicators of OP patients . On the Epimedium . IP. IPEpimeAt this stage, the development of network pharmacology is particularly rapid and its application is extensive. This is a new discipline based on the theory of systems biology, which analyzes the network of biological systems and selects specific signal nodes to construct a molecular network of multi-target drugs and multi-way regulation . ImproviTherefore, based on network pharmacology, this study explored the role of IPRN in OP through different molecular targets and different pathways and was verified by cell experiments and animal experiments, aiming to provide a new direction for the treatment of OP.https://pubchem.ncbi.nlm.nih.gov/) Compound database, and the retrieved target genes were exported and saved in Excel. Then the STRING (https://string-db.org/) database and DAVID (https://david.ncifcrf.gov/) database were used to unify gene names and IDs ["Isopsoralen" was entered and searched in the PubChem , GeneCards (https://www.GeneCards.org/), and OMIM (Online Mendelian Inheritance in Man) (https://www.omim.org/) databases for retrieval, and the obtained genes were integrated. The duplicated genes were removed, and thus, the target-related genes of OP were finally obtained. According to the method of 2.1, Cytoscape 3.7.0 software was still used to visualize the network diagram of the disease."Osteoporosis" was sequentially entered into PubMed  < 0.05 as the screening criteria.The Metascape platform was used to perform Gene Ontology (GO) annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the targets. And the biological processes, cellular components, molecular functions, and the key signaling pathways of the targets were obtained. The analytical results were then visualized by GraphPad Prism 8.0, with https://www.pdb.org/) Download. Then, AutoDockTools 1.5.6 software is used to read candidate molecules and target protein files, hydrogenate, calculate charges, add atomic types , and saves them in pdbqt format. Then, import AutoDockTools 1.5.6 to build docking grid boxes for each target. The docking is completed through Autodock Vina 1.1.2 software. The lower the vina score is, the more stable the binding between ligand and receptor is, which is used to preliminarily evaluate the binding activity of compounds to targets. Finally, Pymol 1.7.2.1 software was used to output the protein and docking results as complexes, and discovery Studio was used to analyze the force.The predicted target is evaluated by molecular docking. First, the crystal structures of candidate components and targets are obtained from the TCMSP database and RCSB protein database respectively  simulations were carried out by GROMACS 2020.3 software. The MD simulation workflow included four steps: energy minimization, heating, equilibrium, and production dynamics simulation. The simulation box size was optimized with the distance between each atom of the protein and the box greater than 1.0 nm. To make the simulation system electrically neutral, the water molecules were replaced with ClIPRN was purchased from MCE Co., Ltd. . The product number of IPRN was HY-N0763, and its purity is 99.86%. The mouse osteoblast cell line (MC3T3-E1) was obtained from Procell Life Science&Technology Co., Ltd. .MC3T3-E1 cells were cultured in DMEM medium containing 10% fetal bovine serum (FBS) and then were passaged and cryopreserved. The prepared MC3T3-E1 cell suspensions were divided into the control group and the experimental group. In the experimental group, three gradients of IPRN, 10 μM (low concentration), 20 μM (medium concentration), and 50 μM (high concentration), were sequentially added to the complete medium according to the conditions of the previous experiments. At 48 h after administration, cell RNA and protein samples were collected, and GAPDH was used as an internal control to detect the expression of PI3K, AKT, mTOR genes and their phosphorylated proteins by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blot (WB). The primer sequence is shown in Table A total of 9 two-month-old Sprague–Dawley (SD) rats which were SPF grade rats, half male, and half female and weighed 200 ± 20 g, were purchased from Hubei Provincial Laboratory Animal Research Center, license number SCXK (E) 2020–0018. They were fed at the Experimental Animal Center of Xizang Minzu University. Animal experiments complied with ethics and principles.−∆∆Ct.SD rats were randomly divided into the control group and the experimental group (3 rats in each group) according to the random number table method. The control group was given the same amount of normal saline every day for 7 consecutive days. The experimental group was given IPRN (40 mg/kg/time) continuously for 7 days. At 24 h after the last gavage, the knee joint cartilage tissues of rats in the two groups were collected, and RNA was extracted. Rat β-actin was used as an internal reference, and the relative expression levels of the PI3K gene were calculated according to 2t test, and P < 0.05 was considered to be statistically significant.The control group and the experimental group were repeated three times each group, and the statistical analysis was carried out in turn, and finally, the effect of IPRN on MC3T3-E1 cells and SD rats was obtained. The mean ± standard deviation (SD) of all experimental data was taken. Statistical analyses were performed by GraphPad Prism 8.0 statistical software. The data were compared between the two groups by Student’s As shown in Fig. The number of OP-related genes retrieved from the PubMed, GeneCards, and OMIM databases was 317, 4066, and 34, respectively. The results from the three databases were exported and then merged in Excel to remove duplicates and take the intersected genes, and finally, 242 genes associated with OP were obtained. As shown in Fig. A total of 19 common target genes were obtained from the Venn diagram Fig. . These tAs shown in Fig. We obtained the results of the KEGG enrichment analysis in Figs. http://www.rcsb.org/) download the AKT1 protein structure (PDB ID: 3OS5), PIK3CA protein structure (PDB ID: 4JPS) and mTOR protein structure (PDB ID: 4O75). Then we use AutoDock Vina 1.1.2 software to dock these target proteins  with IPRN. The larger the absolute value of the Docking Score is, the stronger the bond between the compound and the target becomes, and the more stable the molecular conformation becomes. Therefore, we concluded that PIK3CA has the strongest binding force to IPRN, and its molecular structure is the most stable one -IPRN, AKT(3OS5)-IPRN, and mTOR(4O75)- IPRN were subjected to molecular dynamics simulation analyses for 100 ns to evaluate the motion, trajectory, structural features, binding potential, and conformational changes of molecules. The root means square deviation (RMSD) is a good measure of the conformational stability of protein and ligands and is a measure of the extent of deviation in the position of atoms from the starting position. A lower deviation indicates better conformational stability. The changes in RMSD values of the complexes were analyzed in this study. As shown in Fig. P < 0.001). In addition, compared with the control group, IPRN at concentrations of 10, 20, and 50 μM in the 48 h group could significantly increase the expression levels of AKT and mTOR, and it was positively correlated with the dose (P ˂ 0.05). More importantly, WB results showed that the expression of PI3K/p-PI3K, AKT/p-AKT, and mTOR/p-mTOR was significantly increased when 20 μM IPRN was added compared with the control group . This result suggested that IPRN may exert an anti-OP effect by activating the PI3K gene.Knee articular cartilage tissues were collected from rats in the experimental group and the control group, and qRT-PCR was used to detect related genes, and then the Ct values and expression levels of β-actin and PI3K in each group were obtained Fig. . After tOP is a degenerative disease characterized by decreased bone mass, degeneration of bone microarchitecture, and increased risk of bone fragility, which results in fractures and seriously endangers the health of middle-aged and elderly people . At thisAs we have learned, the bone tissue of the human body will be continuously updated with the progression of osteoblast-mediated bone formation and osteoclast-mediated bone resorption. In old age, bone resorption is significantly greater than formation, which causes bone loss. Previous studies have shown that IPRN can promote osteoblast differentiation and mineralization, increase calcium nodule levels and alkaline phosphatase (ALP) activity, and upregulate osteoblast markers including ALP, RUNX2, and COL1A1 . FurtherFrom another perspective, the anti-OP mechanism of prescriptions of TCM involves multiple regulatory pathways, such as PI3K/AKT/m TOR , Wnt/β-cAlthough this study has obtained the above results, it still has its shortcomings. First of all, the research on animal models of OP with TCM preparations is mainly to find new drugs or to further prove efficacy. Animal experiments in this study are relatively simple, just to verify whether the effect of IPRN on OP is obvious. Human OP is divided into primary and secondary, and animal models may not be completely consistent with the disease, so it is still necessary to improve and refine this aspect in subsequent research. Second, the verification of relevant protein expression is lacking in animal experiments. In the future, we are going to add WB, immunohistochemistry (IHC), and other methods to detect the expression of proteins. Despite the above shortcomings, our study still found that IPRN can make an anti-OP difference, which further complements the molecular mechanism of TCM in the treatment of OP.Based on network pharmacology, this study predicted the target genes of IPRN in the treatment of OP and preliminarily verified by in vitro and in vivo experiments that IPRN exerts an anti-OP effect by regulating the PI3K/AKT/mTOR pathway.


Meanwhile, different nitration times revealed that 50 min had the highest nitrogen content (7.97%) with a 1.23 degree of substitution (DS) value. Based on its TG analysis, NC 50 has met the requirements for use as an accelerant for briquettes, with an onset temperature of 91.60 °C and a weight loss of 62.60%. Infrared at peak 1640 cm−1 confirmed the presence of NO2 groups in nitrocellulose successfully formed by nitration. After the addition of nitrocellulose, the calorific value of the briquette increased from 13.54% to 15.47%. Fixed carbon and volatile matter showed the same pattern. The combustion of nitrocellulose-briquette has also been demonstrated by Td10% of degradation, which degraded between 310 and 345 °C.Nitrocellulose (NC) is a conservative material that is used in a variety of applications, such as coating agents, biodegradable plastics, and propellant main charge. Nitrocellulose raw materials are easily obtained from lignocellulose sources, most notably cotton and wood pulp. The egg carton, a recycled paper waste designed for packaging and transporting eggs, is used in this study to make nitrocellulose in pulp form. The effects of different nitration durations  from egg carton pulp bleached with various KOH concentrations  on NC properties were evaluated. The accelerant properties of the NC of nitration time in 50 min were studied in a rice husk charcoal briquette. Rice husk charcoal briquettes are made in various ratios with nitrocellulose as an accelerant . The NC was characterized using Fourier transform infrared (IR) spectroscopy and thermogravimetric (TG) analysis. 1.0 M of bleached egg carton pulp has the highest cellulose content (86.94%) with the presence of crystalline structure of cellulose at peak 1430 cm Nitroce) groups . When di) groups . The nit) groups  because ) groups . Nitroce) groups .Acacia mangium was also used as a raw material for the pulp production of nitrocellulose because it contains more than 80% cellulose. Aside from that, there are non-wood products for nitrocellulose production, such as Schimansky [Purified cotton linters and wood pulp are the most common sources of cellulose used in the production of nitrocellulose. Muvhiiwa et al.  created himansky . The blehimansky . Meanwhihimansky . Pretreahimansky .One of the most common issues in the production of briquettes nowadays is their limited ignition . As a reBecause egg cartons are recyclable, converting them to cellulose is a sustainable option. In this study, egg carton pulp was bleached with 0.6 M, 1.0 M, and 1.5 M KOH as a bleaching agent and then used to make nitrocellulose. Nitrocellulose was produced from egg carton pulp using a nitration process with varying reaction times . FTIR was used to analyze the nitrocellulose product, and TGA was used to determine the temperature of degradation. Various nitration times were used to produce nitrocellulose with varying nitrogen content, degree of substitution, and solubility. Furthermore, this research provides theoretical support for the use of egg carton pulp in the production of nitrocellulose. The goal of this study was to determine the optimal nitrocellulose-to-briquettes ratio in order to produce fuel briquettes with a high calorific value, volatile matter, and fixed carbon.Common egg cartons were retrieved from local eateries and used as the main cellulosic material. It undergoes hydro-pulping to obtain individual strains of fiber before being sieved and screened into a uniform size of 30-mesh size using a sieve shaker. Potassium hydroxide (KOH), distilled water, ethanol, toluene, 72% sulfuric acid, sodium chloride, 10% acetic acid, acetone, sodium hydroxide, acetic acid, pure sulfuric acid, and nitric acid were reagent grade and were used without further purification.The subsequent bleaching to further remove lignin content was conducted with potassium hydroxide (KOH) following  methods The bleached egg carton pulps were soaked in the solution mixture of nitric acid and sulfuric acid. The reaction of egg carton pulp with a 1:1 ratio of undiluted nitric acid to sulfuric acid was utilized. This procedure involves immersing 2 g of pulp in 94 mL of the acid mix  at diffe3. The weight percentage of the binder and CaCO3 in the briquette samples was 4% and 3%, which were constant at different mixing ratios. The feedstock was then poured into the prepared mold after blending. The compaction of the feedstock was performed using a 20-ton hydraulic press machine. A hydraulic press was used to press the coal with pressure of 50 kg/cm2. Each briquette had a holding time of 120 s [The rice husk charcoal and nitrocellulose were weighed around 88–90% and 3–5% on weight percentage and blended at different mixing ratios of 97:3, 96:4, and 95:5, respectively. The blends of the rice husk charcoal and nitrocellulose were mixed with the binder (starch gel) and CaCOof 120 s . The briThe chemical components of treated and untreated recycled egg carton pulp were determined using a standard method. The moisture content of the pulp was determined following TAPPI standard T 264 by drying a sample at 103 ± 5 °C for 24 h. The ethanol-toluene extraction was attained following TAPPI Standard T207 cm-97, using Soxhlet extraction equipment. The hot- and cold-water solubilities were measured according to TAPPI standard T207 os-752 04 cm-88. Klason lignin was determined based on  standardThe CHN elemental analyzer (Vario Macro Cube) was used to evaluate the degree of substitution (DS) and nitrogen content (N%) in nitrocellulose. The degree of substitution (DS) of bleached egg carton pulp nitrocellulose was measured following Equation (1) .(1)DS=11 is the funnel mass with the dry residues, and m2 is the mass of the empty funnel.To investigate the influence of nitrating time on solubility, the dried NC (0.2 g) were dissolved in 10 mL of acetone for 1 h to achieve dissolution. The undissolved nitrocellulose was filtered off and left to dry . The sol−1 with Perkin Elmer Spectrum 400 FT-IR/NIR with Imaging System.The structure of egg carton pulp and nitrocellulose pulps were identified using Fourier transform infrared (FT-IR) spectroscopy in the range of 4000–400 cm2 atmosphere to investigate the change in properties of egg carton pulps and nitrocellulose as a function of temperature. The pulps (6–10 mg) were analyzed under an N2 atmosphere (20 mL min−1), with a heating rate of 10 °C min−1 with a temperature from 30 to 600 °C.Thermogravimetric analysis (TGA) was performed using Mettler Toledo TGA SDTA 851e with a continuous flow of Nc is the (wt%) fixed carbon obtained for each briquette sample, Vm is the (wt%) volatile matter, Mc is the moisture obtained, and Ac is the (wt%) ash content obtained for each briquette sample. The volatile matter is an unstable substance that frequently transitions quickly to another form or vaporizes, expressed as a percentage.The calorific value of a briquette was determined by measuring the heat generated by the full combustion of a specified amount of it, expressed in calories per gram. A bomb calorimeter  was used to measure the calorific value. This test was carried out to determine the standard quality of the briquettes’ fuel power. Meanwhile, the percentage of fixed carbon was measured by Equation (3).p ≤ 0.05 in the comparison test. These analyses were performed using Statistical Analysis System (SAS).Properties measurement data (ANOVA) was used to compare chosen parameters, and significant differences were identified with a p ≤ 0.05. The increased cellulose content could be attributed to the partial removal of hemicellulose and lignin during bleaching. Some lignin in fibers cannot be easily degraded due to the presence of strong carbon-carbon linkages that are resistant to chemical attack [Based on statistical analysis, there are significant differences in cellulose concentrations between 1.0 M and 1.5 M at l attack . It is ap ≤ 0.05) from other concentrations. However, at 1.0 M, bleached egg carton pulp has the lowest cold-water solubility (5.96%), with no significant differences at 1.5 M. The existence of inorganic matter, tannins, gums, and sugar can be determined using cold and hot water solubilities [The bleached egg carton pulp with the highest ethanol-toluene solubility (5.71%) and hot-water-soluble extractives was found at 1.0 M. (8.15%). The statistical analysis reveals that 1.0 M of ethanol-toluene solubility and hot water solubility differ significantly , maximum degradation temperature (Tmax), and char residue. According to the onset temperature, 1.0 M bleached egg carton pulp has the highest temperature at 294.01 °C. The initial degradation increased from 0.6 M to 1.0 M before decreasing slightly when 1.5 M was reached. A similar pattern was observed for maximum degradation temperature (Tmax) values. The highest egg carton pulp degradation temperature indicates an increase in pulp crystallinity during the bleaching process [The weight loss, decomposition temperature, and thermal stability of the bleached egg carton pulp were all evaluated using thermogravimetric analysis . Table 2 process . Increas process , the higThe degradation in the range of 198.9–200.1 °C was attributed to lignocellulosic component degradation, primarily hemicellulose, and some that remained unbleached . Hemicelp ≤ 0.05.The nitrogen content of nitrocellulose follows the same pattern . The nit−1. This band corresponds to residual hydroxyl groups of nitrocellulose. The peak appeared at 2880 cm−1 due to C–H stretching vibration. It shows that the band assignment is due to alkane CH stretching absorptions from the carbons of the glucose structures [−1 is the appearance of carbonyl groups (C=O) of nitrocellulose. It shows the intensity of C=O increases with the increasing nitration time. This band indicates an intermolecular interaction involving the nitrocellulose hydroxyl group  [−1, and it is attributed to the stretching of CN [−1) as a result of hydrogen bonding and did not overlap with the nitrate asymmetric stretching vibration (vaNO2) at 1640 cm−1 [2 link, producing NO2 gas at 1640 cm−1 [The FTIR spectra of egg carton pulp and nitrocellulose are shown in ructures . The abs···H–O–) . It showng of CN . The car640 cm−1 . Nitroce640 cm−1 . The sumRice husk charcoal briquettes with varying ratios of rice husk charcoal to nitrocellulose content were created. In this study, three different ratios of rice husk to nitrocellulose  were used to evaluate nitrocellulose properties, and the samples were labeled B2, B3, and B4, respectively. In contrast, briquettes with no nitrocellulose content were used as controls and designated as B1. −1. Nitrocellulose is produced by nitrating cellulose, so the sources of cellulose are important. Increasing the concentration of KOH to 1.0 M resulted in high thermal degradation at Td10%, which could be attributed to the high percentage of cellulose. When the reaction time for the nitration process of egg carton pulp samples for nitrocellulose production was changed to 60 min, the nitrogen content and degree of substitution increased before decreasing. This is due to the reverse reaction, in which nitrocellulose takes precedence over the nitration reaction of cellulose due to hydrolysis. At the optimal reaction time of 50 min, nitrocellulose with the highest nitrogen content of 7.97% and 1.23 DS was obtained. Nitrocellulose dissolved in acetone with a high nitrogen content and variable solubility could be used in a variety of applications, including printing inks, nail lacquer, leather finishes, as well as gun cotton and flash papers. This was further demonstrated by thermal degradation, in which the NC 50 degraded faster and lost more weight than the NC 40. It demonstrates that nitrocellulose has lower thermal stability due to its combustible properties. Nitro groups were detected in nitrocellulose FTIR spectra that were successfully attached to egg carton pulp. It is expected that egg carton pulp will be a better candidate for NC resource and utilization. Based on briquette proximate analysis, the nitrocellulose content influences the calorific value, volatile matter, and fixed carbon content. The nitrocellulose content increased the energy content of the briquette, which is of high quality.Because it is recyclable paper and easy to obtain, egg carton pulp was used as a raw material for accelerant in the production of briquette, and the additional accelerant in briquette was added to improve briquette ignition. This is due to the fact that briquettes take longer to heat up. Furthermore, the presence of cellulose content is an important source of nitrocellulose production. As the concentration of KOH increased, the amount of cellulose in the bleached egg carton pulp increased. The 1.0 M bleaching pulp had a high cellulose content of 86.94% and a low lignin content (1.01%). The presence of cellulose is indicated by the presence of a band at 1420 cm


Targeting kinase activity is considered to be an attractive therapeutic strategy to overcome acute myeloid leukemia (AML) since aberrant activation of the kinase pathway plays a pivotal role in leukemogenesis through abnormal cell proliferation and differentiation block. Although clinical trials for kinase modulators as single agents remain scarce, combination therapies are an area of therapeutic interest. In this review, the author summarizes attractive kinase pathways for therapeutic targets and the combination strategies for these pathways. Specifically, the review focuses on combination therapies targeting the FLT3 pathways, as well as PI3K/AKT/mTOR, CDK and CHK1 pathways. From a literature review, combination therapies with the kinase inhibitors appear more promising than monotherapies with individual agents. Therefore, the development of efficient combination therapies with kinase inhibitors may result in effective therapeutic strategies for AML. Acute myeloid leukemia (AML) is a clonal disease that is derived from abnormally and, occasionally, poorly differentiated cells of the hematopoietic system . ImproveAccumulated evidences suggests that abnormal activation of signal transduction pathways plays a pivotal role in leukemogenesis, through the blocking of differentiation and abnormal cell proliferation . Kinase To follow this issue in the present review, the author summarizes attractive kinase pathways for therapeutic targets and the combination strategies for these pathways. During a search of the literature on PubMed with the keywords “kinase”, “inhibitor”, “combination”, “AML”, “therapy”, and “clinical”, more than 500 papers were found to have been published to date. Based on the search, the FLT3, PI3K/AKT/mTOR, MAPK, AXL, CDK, and CHK1 pathways appear to be common pathways targeted by kinase inhibitor combination therapies. In the present review, the author summarizes these attractive kinase pathways for therapeutic targets and the combination strategies for these pathways.Mutations of FLT3 comprise one of the most frequently identified types of genetic alterations in AML . As the p = 0.002) and reduced relapse rates (p = 0.02); it was also remarkably advantageous for FLT3-ITD high-allelic ratio patients (2-year OS of 82%) [Several resistance mechanisms for FLT3 inhibitor therapy have been shown, such as additional mutations in the kinase domain in internal tandem duplication (ITD) patients , or anot of 82%) .FLT3 inhibitors are tyrosine kinase inhibitors and are classified into first- and second-generation inhibitors based on their kinase specificity and potency . First-gAs there are many excellent reviews on FLT3 inhibitors and their combinations ,27,28,29This combination was shown to be well tolerated in older patients with untreated FLT3-ITD AML , or undeThe phosphatidylinositol 3-kinase/AKT/mammalian target of rapamycin (PI3K/AKT/mTOR) signaling pathway is one of the key aberrant intracellular axes in AML . ChemothBertacchini et al.  investigAnother kinase pathway related to AKT/mTOR signaling involves PIM kinases and p38a (MAPK14) . SignaliMeja et al.  found thRecently, metformin, a classic hypoglycemic drug for diabetes, was reported to synergistically sensitize AML cells to Ara-C through mTORC1/P70S6K pathway inhibition . However, concerns about combinations with PI3K/AKT inhibitors have been raised. Liang et al.  revealedClinical trials on mTOR inhibitors have been conducted. In the early 2010s, the results of the phase Ib GOELAMS study of the mTOR inhibitor RAD001  and the One of the most aberrantly activated oncogenic pathways in AML is the RAS-RAF-MEK-ERK (MAPK) pathway . HoweverMAPK-interacting kinases 1 and 2 (MNK1/2) are downstream effectors of this pathway that control the activation of eukaryotic translation factor 4E (eIF4E)  Figure . eIF4E wAntiapoptotic Bcl-2 family members are critical for the survival of AML cells. The combination of venetoclax and tomivosertib showed synergistic anti-leukemic responses in AML cells . In a siAXL, named after the Greek word “anexelekto”, meaning uncontrolled, is a member of the TAM family of receptor tyrosine kinases. AXL potentially drives cell proliferation through effector molecules in the PI3K/AKT/mTOR, RAS/RAF/MEK/ERK, JAK/STAT, and NF-kB signaling pathways . InhibitC Cyclin-dependent kinases (CDKs) 1, 2, 4, and 6 are mainly involved in regulation of the cell cycle, while CDK7, 8, and 9 play roles in regulating transcription to further influence survival and cell proliferation by driving the target gene expressions . Among tCDK9 is a transcriptional regulator of myeloid cell leukemia-1 (MCL-1) that can influence apoptosis induction ,74. CDK9Recently, histone methyltransferase EZH2 loss and the subsequent reduction in trimethylation of histone H3K27 were reported to result in the de-repression of HOX genes as a novel pathway for acquired resistance to tyrosine kinase inhibitors and cytotoxic drugs in AML . SpecifiThe DNA damage checkpoint is regulated by two signaling pathways, ATM-CHK2-p53 and ATR-CHK1-cdc25A, of which the ATM-CHK2-p53 pathway is impaired in many cancers  Figure . ConsequA major cause of treatment failure is resistance to chemotherapeutic agents, and one strategy to overcome such chemoresistance is to target the antiapoptotic Bcl-2 protein. The Bcl-2-selective inhibitor ABT-199 showed encouraging preclinical results ,89. Mcl-


Accurate identification of molecular and toxicological functions of potential drug candidates is crucial for drug discovery and development. This may aid in the evaluation of the risks of genotoxicity and carcinogenesis. In addition, in silico characterization of existing and new drugs might offer clues for future investigations and aid in the development of anticancer treatments. Using next-generation knowledge discovery (NGKD) methodology, we endeavored to establish a risk assessment of anticancer drugs for their molecular mechanism(s) and genotoxicity.This study was performed at the Faculty of Applied Medical Sciences, King Abdulaziz University (KAU), Jeddah, Saudi Arabia, in November 2022. Using innovative in silico model systems, we assessed the molecular mechanism of action and toxicity of around 20 distinct substances such as Deguelin, Etoposide, Camptothecin, Cytarabine (Ara-C), Cisplatin, Hydroxyurea, Trichostain A, Antimycin, Colchicine, 2-deoxyglucose, Tunicamycin, Thapsigargin, Vinblastin, Docetaxel, Oxaliplatin, Methotrexate, 5-flurouracil, Bleomycin, Taxol (Paclitaxel), and Apicidin. Using the Ingenuity Pathway Analysis (IPA) knowledge base, the number of targets for each compound was determined in silico. Subsequently, they were examined using Fisher’s exact test and Benjamini Hochberg Multiple Testing Correction (P<0.05) and submitted to core analysis with IPA to decode the biological and toxicological activities differently controlled by these drugs. In addition, a multiple comparison module in IPA was used to compare the core analyses of each molecule. In addition, we obtained the top 100 protein targets of Etoposide, Camptothecin, and Ara-C using SwissTargetPrediction, as well as the key pathways and gene ontologies affected by these drugs and disease associations using the WebGestalt tool.We identified distinct toxicological signatures and canonical signaling pathways in tumor cell lines regulated by these 20 anticancer drugs. These signaling pathways included cell death and apoptosis in addition to molecular processes, p53 signaling, and aryl hydrocarbon receptor signaling. The TP53 signaling pathway is utilized by these agents to effectively trigger cell death and apoptosis, and p53 functions as a master regulator in a variety of cellular stress responses, including genotoxic stress.Our research has laid the groundwork for the discovery of additional biomarkers that assess both the safety and effectiveness of treatment. Our mechanism based “NGKD” tools have more relevance for the identification of safer therapies and has the potential to lead to the rational screening of drug candidates targeting specific molecular networks and canonical pathways implicated in cancer and genotoxicity. In addition, the combination of protein, microRNA and metabolome profiles may be essential for the development of translatable biomarkers for the safety and efficacy of pharmacotherapeutic agents.Our research has laid the groundwork for the discovery of additional biomarkers that assess both the safety and the effectiveness of a treatment. In drug discovery and development, an accurate understanding of the molecular and toxicological activities of anti-cancer drugs is absolutely necessary.4To decode the mechanistic interpretation that was based on toxicological functions, molecular signaling pathways, and intracellular signaling molecules that were differentially impacted by 20 different anti-cancer drugs including Etoposide, Camptothecin, and Cytarabine (Ara-C), the next generation knowledge discovery (NGKD) approach was utilized in the current study.Besides, an array of open source in silico tools such as SwissTargetPrediction, and WebGestalt are available for further in-depth knowledge discovery.This study was performed at the Faculty of Applied Medical Sciences, King Abdulaziz University (KAU), Jeddah, Saudi Arabia, in November 2022. This research did not require Institutional Review Board (IRB) clearance since neither animal models nor human volunteers were employed. We used the Ingenuity Pathway Analysis (IPA) software for knowledge discovery, data analysis, and interpretation.6We used cutting-edge in silico model systems to assess the effectiveness and toxicity of over 20 distinct anti-cancer agents such as Deguelin, Etoposide, Camptothecin, Ara-C, Cisplatin, Hydroxyurea, Trichostain A, Antimycin, Colchicine, 2-deoxyglucose, Tunicamycin, Thapsigarin,Vinblastin, Docetaxel, Oxaliplatin, Methotrexate, 5-flurouracil, Bleomycin, Taxol (Paclitaxel), and Apicidin. To estimate the number of genes regulated by each drug in tumor cell lines, an in-silico analysis was performed using the Ingenuity Pathway Analysis (IPA) knowledge base . They were then submitted to core analysis utilizing IPA to interpret both biological and toxicological functions differentially regulated by these drugs, using Fisher’s exact test and Benjamini Hochberg Multiple Testing Correction (P < 0.05).9To determine the molecular function and genotoxicity of Etoposide, Camptothecin, and Ara-C, target prediction was carried out using the SwissTargetPrediction online application, using updated bioactivity data and retrained and redefined similarity criteria.9The WebGestalt tool (wGSEA) used the Over Representation Analysis (ORA) of the molecular targets of Etoposide, Camptothecin, and Ara-C generated from Swiss Target Prediction.12In this study, we investigated the efficacy and toxicity of 20 different anticancer compounds using state-of-the-art in silico methodologies. We used the IPA knowledgebase to obtain molecular targets in mammalian cells and tissues to study the mechanisms of action of 20 different compounds that are important in experimental therapeutics and molecular toxicology. This allowed us to study the mechanisms of action and genotoxicity of the compounds. Here, we compared each of the core analysis results using the multiple comparison module in IPA.This allowed the generation of hierarchical clusters for the most important canonical pathways, diseases, and biological and toxicological functions. Etoposide, Camptothecin, and Ara-C each have their own distinct set of toxicological effects, which we have categorized as hepatotoxicity, cardiotoxicity, and nephrotoxicity, respectively . On the The tumor suppressor p53 acts as a key regulator of a wide variety of cellular stress responses, including genotoxic stress . Using tUsing the GLAD4U disease database, the WebGestalt analysis of camptothecin protein targets found that gene sets implicated in Drug Interaction with Drug, Vascular Diseases, Inhibitory Concentration 50, Drug Resistance, Pathologic Processes, Cardiovascular Diseases, Depression, Pain, Breast Diseases, and Mouth Neoplasms were significantly (P<0.05) impacted . WebGestThe accurate molecular and toxicological identification of potential drugs and natural products is necessary for the research and development of new pharmaceuticals.In addition, these chemicals utilize the TP53 signaling pathway to effectively induce cell death and apoptosis in tumor cell lines. In many different cellular stress responses, including the response to genotoxic stress, the tumor suppressor p53 acts as a major regulator, and mutations in the p53 gene result in cancer.17It binds to topoisomerase II and inhibits its activity, resulting in breaks in either single or double strands of DNA, as well as obstruction of DNA replication and transcription, leading to apoptotic cell death. It mostly affects the cell cycle’s G2 and S stages.These translocations may lead to some forms of leukemia.24Additionally, it inhibits viral replication and suppresses the immune system. Within the cell, Ara-C is converted to the triphosphate form, where it competes with cytidine for DNA inclusion. Due to steric hindrance by the arabinose sugar, DNA replication is halted, particularly during the S phase of the cell cycle. Similarly, this drug inhibits DNA polymerase, leading to a reduction in DNA replication and repair. Our NGKD approach showed thatAra-C can interact with proteins implicated in a variety of neoplasms, drug interactions, and drug toxicity.Ara-C may cause transitory increases in blood enzyme and bilirubin levels and that this may play a role in the development of clinically evident acute liver damage with jaundice.We conducted this study using NGKD methods based on in silico tools. Hence, it is important to validate these findings using appropriate in vitro and in vivo studies using compounds with anti-cancer potential. In addition, a combination of high-throughput proteomics, transcriptomics, and metabolomics experiments may be necessary to decipher translatable biomarkers for the safety and effectiveness of new pharmacotherapeutic drugs.Using a variety of “NGKD” platforms, we were able to identify the processes underlying the genotoxicity of many different anti-cancer medicines. The advantage of our study is that the “NGKD -based approach” makes the mechanism-based risk estimation for the compounds with anti-cancer potential easy and precise since there are no broad-mechanism-based assays and the methods that are currently available are laborious and time-consuming. It is important to note that our mechanism based “NGKD” approach may be considered as a preliminary step in the development of new treatments and may be adopted for rational screening of novel drugs with anti-cancer potential in the future.PNP, MR, MIN, KG: Conceptualization, methodology, data analysis, writing the original draft, co-responsibility and integrity of the study.MR, MIN: Manuscript correction and editing.KG: Manuscript final editing for submission.


Gancao fuzi decoction (GFD) in the treatment of cold-dampness obstruction-type knee osteoarthritis (KOA) remain unclear.The key active components and potential molecular mechanism of To explore the mechanism of GFD in the treatment of cold-dampness obstruction syndrome-type KOA by network pharmacology.The potential active components and targets of the four herbs in GFD  were screened using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The targets of KOA were obtained with the Comparative Toxicogenomics Database (CTD), the GeneCards database, and the DisGeNET database, and the common targets of the drugs and disease were ultimately obtained. Cytoscape (v.3.7.1) was used to draw the active component-target network, and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (v.11.0) database was used to construct the protein interaction network. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the intersecting targets.A total of 102 potential active components and 208 targets of GFD in the treatment of cold-dampness obstruction syndrome-type KOA were screened. GFD treatment was found to be closely related to many inflammatory signalling pathways in the treatment of KOA.The effect of GFD on cold-dampness obstruction syndrome-type KOA is mediated by multicomponent, multitarget, and multichannel mechanisms, which provides the basis for further experimental study of its pharmacodynamic material basis and mechanism. The Gancao fuzi decoction (GFD) originates from a treatise on Shanghan Lun written by Zhang Zhongjing and is composed of four herbs: Fuzi, Guizhi, Baizhu, and Gancao. GFD has the effects of dispelling, and removing dampness, warming the meridian, dispersing cold, warming yang, and tonifying the middle. This prescription is famous for the treatment of cold-dampness obstruction-type KOA. Cold-dampness obstruction syndrome is a type of KOA in basic traditional Chinese medicine (TCM). Dampness, an external evil, plays a key role in the pathological development of bone and joint diseases. In the clinic, all patients with wind cold-dampness Bi can flexibly use GFD [ use GFD . HoweverThe “one drug, one gene, one disease” model, often followed in new drug research and development, maybe one of the main reasons for the failure of many new drugs in clinical trials. A variety of clinical diseases, such as KOA, are multigene and multifactor diseases , 11, and22.1The active ingredients of GFD were extracted from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) . The com2.2In the Comparative Toxicogenomics Database (CTD) , GeneCar2.3The disease-related targets overlapped with the active components of GFD. In this study, we obtained the intersecting genes related to GFD and KOA targets and generated a Venny map using the online Venny map platform . The tar2.4The compound-target network was visualized using Cytoscape (v.3.7.1) . Informa2.5Homo sapiens”, and high confidence = 0.9 was set as the lowest interaction score.Protein-protein interactions (PPIs) are the basis of cell function and play an important role in regulating the physiological and pathological state of the body. In PPI networks, nodes are often used to represent proteins, connection lines are used to represent PPIs, and the node size, colour, connection length, and thickness represent the topological parameters of the network . To furt2.6https://david.ncifcrf.gov/home.jsp) [p < 0.05. GO enrichment analysis and KEGG pathway annotation analysis of the potential targets of GFD in the treatment of cold-dampness obstruction-type KOA were conducted, and the important signalling pathway of GFD in the treatment of KOA was identified.The potential targets of GFD in the treatment of KOA (the common targets obtained as described in Section 1.3) were input into the list of target gene names through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (ome.jsp) , the spe33.11.A total of 620 types of chemical constituents of GFD, including 65, 220, 55, and 280 types of constituents of Fuzi, Guizhi, Baizhu and Gancao, respectively, were retrieved from the TCMSP. A total of 127 types of active ingredients of GFD, including 21, 7, 7, and 92 types of ingredients of Fuzi, Guizhi, Baizhu, and Gancao, respectively, were screened based on the criteria OB ≥ 30% and DL ≥ 0.18. Because 25 compounds had no corresponding targets, a subsequent analysis was not conducted. The analysis revealed 208 targets of the active ingredients (excluding repeated targets). Specific information on the selected active ingredients is shown in Table 3.2Using “osteoarthritis, knee” as the keywords, 4602 related disease targets were retrieved from the CTD, 2040 were retrieved from GeneCards and 368 were retrieved from DisGeNET. After merging the three datasets and removing duplicate items, a total of 5843 genes related to KOA were obtained.3.31). These 167 targets may be key in linking the therapeutic effect of GFD on KOA.The active component target and disease target datasets were intersected, and a total of 167 targets of active components related to KOA were obtained for subsequent analysis . The network topology analysis showed that the network concentration, density, and heterogeneity were 0.402, 0.031, and 1.652, respectively. The average node degree was 9.58, and 96 nodes had above-average degrees. The average mediation centrality of the nodes was 0.006, and 42 nodes showed above-average mediation centrality. According to the topological properties of the degree value and intermediate centrality of the network nodes, the core nodes were selected and analysed. These nodes that connect the most compounds or targets play a key role in the whole network and may be key compounds or targets. Table 2 lists the 25 key nodes with above-average degree and centrality values and their topological parameters in the compound-target network.One hundred two active monomers and 208 targets of GFD were selected to construct the active component-target interaction network using Cytoscape (v.3.7.1). The yellow nodes represent active ingredients of GFD, and the green nodes are targets of the active ingredients. The network contained 2970 edges representing the interactions between targets and chemical components, which reflect the multi-component and multitarget characteristics of GFD . The results from the PPI analysis revealed 148 nodes and 1430 edges. The nodes represent proteins, each edge represents a PPI relationship, and the average degree was 9.66. Fig. (4) shows a bar chart of the top 15 target targets in terms of degree value. These targets are considered the key targets of GFD in the treatment of cold-dampness obstruction-type KOA.The 167 common targets obtained in Section 2.3 were inputted into STRING (v.11.0) for analysis, and the protein interaction results are shown in Fig. (3.65). The main BP terms were related to positive regulation of transcription from the RNA polymerase II promoter, response to a drug, negative regulation of the apoptotic process, and inflammatory response, and the extracellular space, nucleoplasm, nuclear chromatin, and external side of plasma membrane were the main enriched CC terms. The main MF terms were related to identical protein binding, protein binding, and protein heterodimerization activity.The 167 common targets of GFD and KOA were analysed by GO enrichment analysis, and 881 GO entries, including 685 biological processes (BP), 72 cellular component (CC) and 124 molecular functions (MF) terms, were obtained. According to the p-value and number of enriched genes, the top 10 BP, CC, and MF terms with high enrichment were selected to visualize and generate a bubble diagram . The main biological pathways identified in the analysis included the TNF signalling pathway, PI3K-Akt signalling pathway, VEGF signalling pathway, B-cell receptor signalling pathway, FoxO signalling pathway, and NF-kappa B signalling pathway.KEGG analysis of the intersecting targets of GFD and KOA showed that the key targets were enriched in 129 biological signalling pathways. According to the Benjamini‒Hochberg correction method and a 4KOA is a chronic degenerative osteoarthropathy with an increasing incidence rate. The incidence rate of KOA is approximately 4%-13% of the world’s population, and KOA has thus become a major disease affecting human health . The quaIn this study, quercetin, kaempferol, beta-sitosterol, 7-methoxy-2-methyl isoflavone, and formononetin were found to be the core compounds of GFD. These core components regulate the TNF signalling pathway, MAPK signalling pathway, p53 signalling pathway, PI3K Akt signalling pathway, HIF-1 signalling pathway, T-cell receptor signalling pathway, and VEGF signalling pathway by affecting PTGS2, ESR1, HSP90, NOS2, JUN, MAPK1, MAPK3, TNF, TP53, IL6, EGFR, and other proteins to play a role in the treatment of cold-dampness obstruction-type KOA.The pathogenesis of KOA is complex and involves many cytokines, proteins, and signalling pathways . ArticulIn osteoarticular diseases, p53 can block the cell cycle, promote apoptosis and accelerate cartilage degradation by inhibiting DNA replication , 33. StuAlthough this study presents several findings, there remain some defects. On the one hand, the lack of TCM syndrome-related gene target databases affects the accuracy of network pharmacology in predicting the mechanism of action, target, and pathway of Chinese herbal medicines. On the other hand, the current drug data focus on the interactions between drugs for the prevention and treatment of diseases, but research methods based on network pharmacology cannot reveal the synergistic effects of Chinese herbal medicine, which is a field that needs to be further explored in the future.Based on network pharmacology, this study linked the core components of GFD with KOA-related targets of cold-dampness obstruction syndrome and intuitively showed the biological pathway and targets of GFD in the treatment of KOA. This study revealed that GFD may play a role in the treatment of cold-dampness obstruction syndrome-type KOA by inhibiting the release of related inflammatory factors and suppressing chondrocyte apoptosis through related pathways and targets. By applying network pharmacology, we screened the commonly used compounds for the treatment of this disease and preliminarily identified the correlation between the active components of GFD and cold-dampness obstruction syndrome-type KOA, which can provide certain directions and ideas for future experimental research on GFD.


Danggui Sini is a traditional Chinese medicine prescription for treating peripheral nerve injury (PNI). We studied the mechanisms of this decoction through network pharmacology analysis and molecular docking. Using R language and Perl software, the active components and predicted targets of Danggui Sini, as well as the related gene targets of PNI, were mined through TCMSP, GeneCards, OMIM, TTD, and DrugBank. The network diagram of active components and intersection targets was constructed using Cytoscape software and the STRING database. The CytoNCA plug-in was used to screen out the core compounds and key targets. The genes were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment. AutoDock was used to analyze the molecular docking of key targets and core compounds of diseases. The drug component disease target regulatory network showed that the key components included quercetin, kaempferol, naringenin, and licochalcone A, which play key roles in the whole network and may be the primary compounds associated with the action of Danggui Sini against PNI. PPI network topology analysis showed high degree values for RELA, JUN, MAPK1, RB1, and FOS. Enrichment analysis showed that the core targets of Danggui Sini participated in pathways associated with neurogenesis-multiple diseases. Molecular docking showed that the active ingredients in Danggui Sini had a good binding ability with key targets. We conclude that many active components of Danggui Sini play therapeutic roles in PNI treatment by regulating RELA, JUN, MAPK1, RB1, and FOS, and multiple other targets in inflammation, immunity, and lipid metabolism. Therefore, neural function is slow to recover, incomplete, and accompanied by pain. During the delay in postoperative regeneration, nerve mismatch leads to further delays in functional repair for several days to several months.–5 Only 40% to 50% of patients who received autologous nerve transplantation recovered sufficient functionality.,7 This delay in functional recovery indicates the need for drug intervention. Therefore, new drug treatment strategies are needed to promote the functional recovery of patients with PNI.Peripheral nerve injury (PNI) is a serious disease that leads to partial functional impairment and permanent disability of the segmental motor and sensory and autonomic nerves. Most peripheral nerve injuries require surgical repair so that axons can regenerate and attach to the distal end of the nerve. Functional recovery following surgery is limited by the size of the nerve space, neuroma, and scar tissue formation. The Danggui Sini decoction is a prescription from the Treatise on Febrile Diseases composed of Angelica sinensis , Ramulus Cinnamomi , asarum , tongcao (TC), peony , jujube , and Glycyrrhiza uralensis . A. sinensis and cinnamon twig are the primary drugs in the prescription. Asarum and peony are official medicines. Tongcao, jujube, and G. uralensis are adjuvants. The whole prescription warms meridians, disperses cold, nourishes the blood, and dredges the pulse. Modern research has demonstrated the pharmacological effects of A. sinensis, such as hematopoiesis, antioxidation, anticancer, neuroprotection, and immune regulation, and indicated its use for the treatment of nervous system diseases.,10 Ramulus Cinnamomi (volatile oil) has many pharmacological effects, such as anti-inflammatory, anti-tumor, antibacterial, anti-diabetes, anti-obesity, and neuroprotection and can also be used for the treatment of nervous system diseases.,12 Peony has antioxidant, anti-inflammatory, anti-tumor, antibacterial, anti-virus, cardiovascular protection, and neuroprotective properties. Asarum has analgesic, anti-inflammatory, neuroprotective, cardiovascular protection, antitussive, immunosuppressive, antitumor, and microbicide effects. The pharmacological effects of G. uralensis are anti-inflammatory, antioxidant, anti-allergic, and antibacterial. Tongcao has anti-inflammatory and other biological properties. Jujube elicits anti-cancer, anti-oxidation, anti-inflammatory, anti-hyperlipidemia, anti-hyperglycemia, immune regulation, neuroprotection, sedation, and anti-virus effects. Cheng et al found that the Danggui Sini decoction can significantly decrease the expression levels of NF-κB and mRNA, which play protective roles in nerve cells. However, the mechanism of the Danggui Sini decoction in the treatment of PNI remains unclear owing toa lack of systematic analysis.Chinese herbal medicine is rich in resources and has been used for thousands of years, providing a promising means to treat PNI. Traditional Chinese Medicine (TCM) prescriptions comprise a variety of compounds with different structures and functions that act on multiple targets for multi-level treatment. Systematically analyzing the multi-component and multi-target mechanisms of TCM at the network level, its holistic view, and systems theory are consistent with the holistic concept of TCM and the principles of multi-target and multi-component TCM prescriptions. It is an effective technical method to study the compatibility and pharmacological effects of TCM.,21 Molecular docking technology can improve pharmacological experiments by allowing the theoretical simulation of interactions between receptors and ligands and predicting their binding mode and affinity. Both have been widely used in TCM to explore treatment mechanisms associated with diseases that affect the nervous, circulatory, and respiratory systems; diabetes; cancer; and osteoarthropathy.–27The scope of network pharmacology is expanding, and current research has aimed to clarify the effective components and therapeutic activities of TCM.This study aimed to predict the targets of the Danggui Sini decoction through constructing protein–protein interaction (PPI) networks, screening topologies, and performing molecular docking analysis. Our results revealed the mechanism of Danggui Sini decoction in the treatment of PNI and provide a basis for future pharmacological experiments and clinical application. The overall flow chart of this study is shown in Figure https://tcmsp-e.com/), A. sinensis, Ramulus Cinnamomi, asarum, tongcao, peony, jujube, and licorice were used as keywords to search for the active ingredients. Oral bioavailability (OB) ≥ 30% and drug like (DL) ≥ 0.18 were used to screen for the target active ingredient group. Then, we matched the active ingredients with potential targets one by one according to the TCMSP database using UniProt (https://uniprot.org/). The target was standardized to “Homo sapiens” as the target of the active ingredient. Using Perl (https://www.perl.org/),the gene targets of the effective components of the Danggui Sini decoction were obtained.In the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (https://db.idrblab.net/ttd/), DrugBank (https://www.drugbank.com), GeneCards (https://www.genecards.org), Online Mendelian Inheritance in Man (OMIM) (https://www.omim.org), and PharmGKB (https://www.Pharmgkb.org) database, we used the keyword “peripheral nerve injury” to retrieve disease targets. Using R (Venn diagram package), we merged disease-related genes, removed duplicate targets, and integrated PNI-related targets.In the Therapeutic Target Database (TTD) (https://string-db.org/); the species was set to “homo sapiens,” the lowest interaction threshold was set to the highest confidence (0.9), and the PPI network information was obtained. Then, topology analysis of the PPI network was carried out using the CytoNCA plug-in in Cytoscape 3.8.2, including the betweenness centrality (BC), degree centrality (DC), closeness centrality (CC), and centrality of the eigenvector (EC). The local average connectivity (LAC) and network centrality (NC) scoring conditions were filtered twice. For screening, the value corresponding to the median node was calculated. Nodes higher than this value were defined as representing significant effective compounds and key targets.Using R and the Venn diagram package, the gene targets of the effective components were analyzed to determine the target of the Danggui Sini decoction for the treatment of PNI. Cytoscape 3.8.2 was used to construct the network diagram of active ingredients and intersection targets. We then input the intersection target into STRING  analysis of biological processes, cell composition, and molecular functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The significance threshold was https://www.ncbi.nlm.nih.gov/geo/), and chemistry software was used to convert the 2D structure into a 3D structure. From the RCSB Protein Data Bank (PDB) database (http://www.rcsb.org/), we downloaded the 3D structure PDB file of the core target protein, used PyMOL to remove water molecules and small molecule ligand molecules, used autodock to dock molecules based on human protein, and used Vina and PyMOL to plot the results with the lowest binding energy between each target and pharmacodynamic components.The mol format of the 2D structure of small molecule ligands was downloaded from the PubChem database . The grid on the right represents the target genes for the treatment of PNI (expressed in blue). The larger the square area in the grid, the more components connected with the gene. The top 9 active components in the target are quercetin, isoflavone, glabridin, kaempferol, naringenin, formononetin, licochalcone A, beta sitosterol, and stigmasterol, which can interact with 143, 108, 67, 49, 33, 29, 28, 28, and 26 target proteins, respectively. Regarding targets, 11.31% of the targets interact with 20 or more compounds, and20% interact with ≥ 30 compounds. The top 7 targets are PTGS2, ESR1, HSP90AB1, AR, NOS2, PPARG, and SCN5A, which can interact with 103, 82, 77, 68, 67, 64, and 57 compounds, respectively.Cytoscape 3.8.2 was used to establish the interaction network between active ingredients and targets. The nodes represent TCM, active ingredients, and targets, and the edges represent the interaction between active ingredients and target diseases. The network contained 340 nodes and 1729 edges  were selected for molecular docking with the 4 key pharmacodynamic components . The lower the energy when the conformation of the ligand and receptor is stable, the greater the possibility of action. The molecular docking junctions showed that the binding energy between RELA and quercetin, naringenin, licochalcone A, and kaempferolis −7.7, −7.4, −6.7, and −8.5 kcal/mol, respectively; that between FOS and quercetin and licochalcone A is −8.2 and −6.1 kcal/mol, respectively; that between RB1 and quercetin is −7.0 kcal/mol; and that between MAPK1 and licochalcone A, quercetin, and naringeninis −7.3, −8.4, and −8.4 kcal/mol, respectively  that regulate biological processes such as neurodegeneration, apoptosis, immune response, and lipid metabolism through molecular docking targets and enriched pathways, protect nerve cells after PNI, and prevent reinjury. This study theoretically explained the adjuvant effect of Danggui Sini on PNI. Building on the results of the present study, we aim in future work to provide biological experimental evidence for the therapeutic effect of Danggui Sini on PNI.www.editage.cn) for English language editing.We would like to thank Editage (Conceptualization: Yan Wang.Data curation: Ning Zhang, Qian Zhang, Ruisu Zhang, Yan Wang.Formal analysis: Ning Zhang.Methodology: Dandan Zhang, Ruisu Zhang.Software: Dandan Zhang, Qian Zhang.Writing – original draft: Ning Zhang, Dandan Zhang, Yan Wang.Writing – review & editing: Ning Zhang, Dandan Zhang.