Abstract
Drug repositioning is gaining attention as a method for developing new drugs due to its low cost, short cycle time, and high success rate. One important approach is to explore new uses for already marketed drugs. In this study, we utilized the strategy of drug repositioning, focusing on the Dan-Lou tablet. We predicted the efficacy of Dan-Lou tablet against non-small cell lung cancer based on gene expression similarity and verified it by in vitro experiments. Next, we performed further analysis and validation using network pharmacology, molecular docking and molecular dynamics. Based on the results, it was concluded that Dan-Lou tablet mainly acted through nine compounds, Quercetin, Luteolin, Scoparone, Isorhamnetin, Eugenol, Genistein, Coumestrol, Hederagenin, Succinic Acid, and mainly targeted CCL2, FEN1, TPI1, RMI2 by six pathways. This discovery not only provides a new idea for the development of Dan-Lou tablet but also provides useful predictive information for clinical treatment. The method we adopted has great development prospects as a way to predict the efficacy of new drugs and their main mechanisms of action, and it has a positive impact on the research and development of new drugs using drug repositioning and the modernization of traditional Chinese medicine.
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Introduction
Drug repositioning, also known as “drug repurposing or drug recirculation”, is a strategy to find new indications through the discovery of approved drugs or potential drugs. As James W Black, winner of the 1988 Nobel Prize in Physiology and Medicine, observed, the richest basis for the discovery of new drugs comes from old drugs1. Effective drug repositioning will have a significant impact on the entire field of drug development. Accelerating the translation of already marketed drugs into new indications, which in turn will benefit mankind.
Drug repositioning is generally divided into computational drug repositioning and experimental drug repositioning. The basis of computational drug repositioning is data, and the main method is a similarity metric based on different data. With the reduction of the cost of High-Throughput Sequencing technology and the continuous accumulation of data related to drug exploration and development, it has become a new research focus of drug repositioning to search for new indications or new uses of drugs through bioinformatics technology, and then reveal the potential treatment mechanism of action. Compared with experimental drug repositioning, computational drug repositioning has the advantages of being faster, less costly, and able to systematically analyze a large amount of data, leading to the discovery of unobserved potential links between drugs.
Transcriptomic data is a powerful tool for analyzing how drugs affect gene expression, revealing the potential biological effects of drugs. Through this data-driven approach, researchers can assess the similarity between traditional Chinese medicines and existing small-molecule drugs. Through similarity analysis, we can discover new efficacies of Chinese medicines. In addition, this research direction is further supported by the provisions in the Requirements for Registration Classification and Application of Traditional Chinese Medicines issued by the National Medical Products Administration in 2020. The policy explicitly categorizes new improved Chinese medicines with increased functionalities as new Chinese medicines, which provides policy support for the discovery of new therapeutic efficacy of Chinese medicines through drug repositioning, and promotes the further development of Chinese medicines and the expansion of clinical applications.
Dan-Lou tablet (DLT) is composed of ten herbs, including Danshen (Radix Salviae Miltiorrhiae), Gualoupi (Fructrs Trichosanthis), Gegen (Radix Puerariae), Chishao (Radix Paeoniae Rubra), Xiebai (Bulbus Allii Macrostemi), Chuanxiong (Rhizoma Chuanxiong), Yujin (Radix Curcumae), Zexie (Rhizoma Alismatis), Huangqi (Radix Astragali), and Gusuibu (Rhizoma Drynariae)2. Among them, Gualoupi and Xiebai play an important role as monarch drugs.
DLT is clinically used for the treatment of coronary heart disease and angina pectoris3. Modern studies have also found it can treat diabetic cardiomyopathy4, atherosclerosis5, hypoxia-induced dyslipidemia6, hyperlipidemia7, and other conditions. In China, DLT has been approved by the China Food and Drug Administration (No. Z20050244), and the National Healthcare Security Administration has included DLT in category B of medical insurance as a reimbursable proprietary Chinese medicine. In order to promote the utilization of DLT, the present study was based on gene expression similarity to discover the new pharmacological efficacy of DLT and preliminary validation was carried out by in vitro experiments. Then the potential mechanisms of action are predicted and validated through network pharmacology and computer simulation, to provide the new experimental basis and methodological reference for future experimental research and clinical application of DLT.
Materials and methods
Analyzing the differentially expressed genes of DLT
The gene expression profiling data of DLT were obtained from the high-throughput gene expression database (Gene Expression Omnibus, GEO, (accessible using https://www.ncbi.nlm.nih.gov/geo/), and the gene expression profiling data of DLT in GEO was GSE179789 (Affymetrix Human Genome U133 Plus 2.0 Array, GPL570). The overall design of the dataset is that microarray gene expression analysis was used to identify the differentially expressed genes (DEGs) in peripheral blood mononuclear cells of healthy people and patients before and after DLT treatment. The GSE179789 data contains 24 peripheral blood mononuclear cells samples, which are 8 healthy people, 8 patients with coronary artery disease, and 8 patients with coronary artery disease after DLT treatment. 16 patient samples were selected based on refined diagnostic criteria. The healthy participants also met strict inclusion criteria. The critical exclusion criteria were unstable angina, current pregnancy, mental illness, or cognitive dysfunction8. To reduce confounding factors, we therefore chose 8 of these patients with coronary artery disease versus 8 patients with coronary artery disease after treatment with DLT, for a total of 16 samples for differential gene analysis. The processed data from the GEO database were downloaded manually, and the BiocManager package in R 4.3.3 language software was used for the extraction of probe-gene correspondence, and the limma package was used for the standard data difference analysis. The visualization of differentially expressed gene data was performed by using the ggplot2 and ggrepel package for volcano plot and the pheatmap package for heat map. Subsequently, we performed KEGG enrichment analysis on the obtained differential genes using the DAVID online website (accessible at https://david.ncifcrf.gov/) to verify the accuracy of the data.
Identification of new drug efficacy based on gene expression similarity
Small molecule compound gene expression data were obtained from Connectivity Map (CMap, accessible at https://clue.io/#l1000). The CMap is a resource that uses cellular responses to perturbation to find relationships between diseases, genes, and therapeutics. It contains over one million gene expression signatures from treatment of a variety of cell types with perturbagens that span a range of small-molecule compounds, gene overexpression and gene knockdown reagents. Changes in gene expression (collectively termed a differential expression signature) that arise from a disease or from perturbagen treatment can be compared for similarity to all perturbational signatures in the database. Perturbations that elicit highly similar expression signatures are termed “connected”; their similar transcriptional effects suggest they confer related physiological effects on the cell. Cmap database’s scoring guidelines involve a connectivity score that provides three measures of confidence including Kolmogorov-Smirnov enrichment statistic, a false discovery rate, and STAR Methods9. We mapped the differential genes of interest as query signatures to the Cmap database. A score of -100-100 is used to reflect the relevance as defined by the scoring guidelines described above. Positive values indicate a positive correlation, the larger the absolute value, the stronger the correlation. Negative values indicate a negative correlation, with larger absolute values indicating less correlation.
We mapped the differential genes of DLT to the Cmap database to obtain a list involving small molecules with their corresponding scores. A positive similarity score indicated that the compound has the same pharmacological effect as DLT, and the larger the value of the absolute value, the more similar it is. A negative similarity score indicated that the compound has the opposite pharmacological effect to DLT, and the larger the value of the absolute value, the less similar the pharmacological effect is.
Verification of efficacy
Preparation of lyophilized powder of DLT
DLT was obtained from Jilin Cornell Pharmaceutical Co. Ltd. (20230708). Crushed 20 g of DLT, added 200 mL of 75% ethanol, ultrasonic extraction for 30 min, repeated three times, combined the extracts, concentrated, and lyophilized.
Cell culture
Human lung cancer cell line A549 was bought from the Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences. A549 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) with 10% fetal bovine serum (FBS) at 37 °C in 5% CO2. All reagents were purchased from Servicebio.
CCK-8 cell viability assay
A549 cells have been placed using 96-well plates with 10000 cells/well. Medium of 100 µL containing FBS (10%) was added to each well. 24 h later, DLT with different concentrations has been applied for the treatment. 24 h later, the medium was replaced with medium containing 10% CCK-8 reagent and incubated for 1 h. The absorbance at 450 nm has been detected. The blank group was set up with only medium added and the control group was cells cultured in normal medium. Three replicate experiments were performed for each group. The cell viability (%) rate was calculated as (ODtreatment - ODblank) / (ODcontrol - ODblank) × 100%.
Gene set enrichment analysis of non-small cell lung cancer
Non-small cell lung cancer (NSCLC) gene expression profiles were obtained from GSE18842 of the GEO database, whose platform is also GPL570. The data contains RNA expression matrices for 46 tumors and 45 control lung tissues. We used the R packages limma, org.Hs.eg.db, cluster Profiler, and enrichplot to first read and collate the expression parameter information of all transcriptome genes and the customized gene set file. We then performed Gene Set Enrichment Analysis (GSEA) enrichment analyses. GSEA is a computational biology method used to analyze gene expression data with the aim of revealing patterns of gene expression associated with a particular biological process, pathway, or function. The core idea of GSEA is to determine the degree of enrichment of the members of a gene set by ordering them in the context of the overall gene expression data. Specifically, GSEA ranks all genes according to their expression levels under different conditions. It then starts at one end of the gene set and calculates how far the genes in that gene set deviate from the sorted list. If the genes in a gene set are centrally aligned in one of the positions in the sorted list, it indicates that the gene set is enriched in a specific biological process or pathway. This process is quantified by constructing an Enrichment Score. GSEA of the gene expression matrix of NSCLC could help to find the pathways associated with the disease and the targets enriched in the pathways leading to a better understanding of the disease mechanisms at the molecular level. Finally, we visualized the results by Bubble plots by the enrichplot package. A P-value less than 0.05 was considered as significant.
Determination of the key targets of anti-NSCLC exertion by DLT
We compared the DEGs of DLT with the results of GSEA above. As a result, we obtained the key pathways by which DLT exerts its anti-NSCLC effects, and subsequently identified the key targets for its drug efficacy. The results were visualized using the Gseavis software package and the key genes were labeled in the graph.
Identification of the chemical constituents at role in DLT
HERB 2.0 database (accessible at http://47.92.70.12/) is a natural medicine database platform that integrates systematic reviews & meta-analyses, clinical trials, reference mining data and high-throughput experimental data. This database provides functions such as browsing, searching, viewing, analyzing, and downloading for TCM herbs, TCM herbal active ingredients, TCM formulae, target genes, diseases, systematic review & meta-analyses, clinical trials, reference mining data and high-throughput experiments. Ingredients of herbs were collected from multiple TCM databases, including SymMap, TCMID, TCMSP, and TCM-ID. Target Related Ingredients were collected from multiple databases and the PubMed database. The ingredients of DLT and key target-related ingredients were obtained from the HERB 2.0 database. The two types of ingredient sets obtained were analyzed and counted by one-to-one correspondence between herbal ingredients and target ingredients. Finally, the relationship between ingredients and targets was obtained after de-duplication. To check whether the ingredients screened met the drug properties of the compounds, SwissADME (accessible at https://www.swissadme.ch/) was used to determine the drug properties of the compounds. There ingredients that passed the screening were listed as active ingredients. In the end, a list of relationships between the DLT-Herb-Active Ingredient-Target-Pathway was obtained. The relationship was imported into Cytoscaper 3.10.0 software and analyzed using the AnalyzeNetwork tool. Then, the relationship was visualized using Cytoscaper 3.10.0 software.
External validation of core targets
The most critical targets obtained from the above analysis were analyzed for gene expression and pathological stages using GEPIA 2.010 (accessible at http://gepia2.cancer-pku.cn/#index), |log2FC|Cutoff:1, p-value Cutoff:0.01. GEPIA is mainly based on TCGA, GTEx, and other databases for analysis.
Molecular docking analysis
Based on the above results, we molecularly docked the core active components with the core target proteins to verify their binding ability using various software tools.
For the ligand compounds, the first step was to carry out the download of their 3D structures in PubChem (accessible at https://pubchem.ncbi.nlm.nih.gov/) as SDF format files, which were converted to Mol2 format using Open Babel GUI software. Next, the compounds were hydrogenated, set up as ligands, checked and selected the torsion bond, and finally exported to PDBQT format for storage, using AutoDock Tools 1.5.7 software, for subsequent docking.
For receptor proteins, PDB format files of core target proteins were downloaded via the RCSB protein database (accessible at https://www.rcsb.org/). The PDB file was imported into PyMol software to remove water molecules and ligand impurities and add full hydrogen, and the result was imported into AutoDock Tools 1.5.6, which was set up as a receptor and exported in PDBQT format for subsequent docking. Before docking, the Grid function of AutoDockTools 1.5.6 software was recognized to identify the structural domains on the protein receptor that can bind to the drug (active pocket). Based on the parameters obtained in the previous step, semiflexible docking of the ligand and target was performed using AutoDock software, and the results were finally visualized using PyMOL software.
Molecular dynamics simulation
By numerically solving Newton’s equations of motion, Molecular dynamics simulation (MD) provides insights into the dynamic behavior of molecules at atomic levels. It helps predict molecular interactions, conformational changes, and thermodynamic properties. Ligand-protein complexes with the lowest molecular docking binding energy were subjected to kinetic simulations using GROMACS 2022.311. For small molecule preprocessing, AmberTools22 was used to add GAFF force field to small molecules, while Gaussian 16 W was used to hydrogenate small molecules and calculate RESP potential. Potential data was added to the topology file of the molecular dynamics system. The simulation conditions were carried out at static temperature of 300 K and atmospheric pressure (1 Bar). The simulation box was filled with TIP3P, and the protein was positioned at the center of the box, maintaining a distance of 1.2 nm from the box boundaries, and the total charge of the simulation system was neutralized by adding an appropriate number of Na+ ions. For the simulations, the steepest descent algorithm was first used to minimize the whole system, including ions, solvents, and proteins, and then carried out the isothermal isovolumic ensemble (NVT) equilibrium and isothermal isobaric ensemble (NPT) equilibrium for 100,000 steps, respectively, with the coupling constant of 0.1 ps and the duration of 100 ps. Finally, 100 ns molecular dynamics simulations were performed. The 100 ns MD simulation of the protein-ligand complexes provided a broad scope for conformational flexibility investigation. At the end of the simulation, the software’s proprietary tool was used to analyze the trajectory, and the root-mean-square variance (RMSD), root-mean-square fluctuation (RMSF), and protein rotation radius of each amino acid trajectory were calculated, combined with the free energy (MMGBSA), free energy topography, Hydrogen bonds analysis, and other Data. Visualization of the results was carried out using R 4.3.3 language software.
Results
Identification of differentially expressed genes in DLT
Genes with P < 0.05 were screened as significant differential genes. The above differentially expressed genes were categorized into up and down-regulated genes using Fold change (FC) as the criterion, and the differentially expressed genes with log2FC > 0.3 were up-regulated genes (up), and the differentially expressed genes with log2FC<-0.3 were down-regulated (down). Finally, 240 up-regulated genes and 241 down-regulated genes were obtained, and their volcano maps are shown in Fig. 1(a), and the 100 genes with the largest and smallest differences were taken to draw the heatmap, as in Fig. 1(b). Figure 1(b) before and after show the group of coronary artery disease patients who did not take DLT and the group who took DLT after 8 weeks, respectively.
Subsequently, we performed KEGG analysis of the differential genes of DLT. The results showed iron death as the better enriched pathway, and a review showed that a growing number of studies support the notion that ferroptosis — an iron-dependent form of non-apoptotic cell death that involves the accumulation of lipid hydroperoxides — has a pathophysiological role in the development of cardiovascular diseases12. This indicated that the differential genes that have been identified in DLT were somewhat closely biologically linked to coronary-like diseases. Moreover, the data of DLT were obtained from patients with coronary artery disease. This also proves the reliability of the data. We selected pathways with P < 0.05 to be shown in Supplementary Table S1.
Visualization of differential genes in DLT. (a) Volcano map of differentially expressed genes in DLT. (b) Heatmap of differentially expressed genes in DLT.
Prediction of the new efficacy of DLT
Mapping the differential genes of DLT to the CMap database, we obtained 2429 small molecule compounds related to their expression. Based on the literature, the group summarized the pharmacological effects of the top five small molecule compounds with clear pharmacological effects, as shown in Table 1. According to the results, we found that the pharmacological effects of DLT’s analogous compounds were mainly related to anti-inflammatory Neuro-modulating and anti-tumors, and its anti-tumor effects were the most prominent. Therefore, we further focused the disease on NSCLC to carry out the follow-up study.
In vitro validation of DLT against non-small cell lung cancer
The lyophilized powder of DLT was dissolved using DEME containing 10% FBS. 1, 3, 5, 8, 10 and 15 mg/mL of medium containing the drug was configured as therapeutic drug. The survival of the cells after 24 h of drug intervention was calculated. Histograms of cell survival and concentration of drug administered were plotted and analyzed for significance using Graphpad Pism9.5 and the results are shown in Fig. 2. According to the results, it can be concluded that the cell survival rate decreased gradually with the increase of the concentration of DLT, and the IC50 was 6.14 mg/mL. So, we concluded that DLT has the activity against NSCLC.
Histogram of A549 cells viability with regard to the concentration of DLT.
Gene set enrichment analysis of non-small cell lung cancer
We took the differential expression matrix of the GSE18842 dataset analyzed by the limma package as the object of study and performed GSEA. A total of 20,824 expressed genes were analyzed using GSEA. According to the enrichment analysis results, 103 pathways were significantly enriched (P < 0.05), and the top 30 pathways from the results of the analysis were visualized, as shown in Fig. 3. The vertical coordinates in the graph represent the pathways enriched, and the horizontal coordinates represent the proportion of all genes enriched in each pathway. The size of the bubbles in the graph represents the number of genes enriched in this pathway, and the color of the bubbles represents the significance of the enrichment. Cell cycle, DNA replication, Biosynthesis of amino acid, Osteoclast differentiation, Cytokine-cytokine receptor interaction were the first five pathways. After literature review, all of the above pathways are strongly associated with NSCLC29,30,31,32,33,34. For instance, berberine could significantly inhibit the growth of NSCLC cells through down-regulating DNA replication and repair related proteins RRM1, RRM2, LIG1 and POLE2 in a dose-dependent manner in NSCLC cells29. Yang, Y30. et al. found that HIF-1α via METTL3 regulation of the m6A modification of CDK2AP2 mRNA drives smoking-induced progression of NSCLC through promoting cell proliferation. Similarly, Wang, Z32. et al. choose TGF-β, Activin and TRAIL as the key cytokines and cytokine receptors. In their research, the Camellia. leave. saponinstreatment might promote T cell differentiation and tumor immune response by inhibiting the expression of three TGF phenotypes (TGF-β1, TGF-β2, and TGF-β3), thereby inhibiting tumor angiogenesis and invasion. In addition to that, Vorinostat initiates G1/G2 cell-cycle arrest and disrupts vascular endothelial growth factor (VEGF) signaling in a cell-dependent and dose-dependent manner, resulting in anti-NSCLC effects34.
Bubble map of the top 30 pathways from NSCLC gene set analysis results.
Identification of key targets for the efficacy of DLT in the treatment of NSCLC
Removing the disease pathway, we compared 481 differential genes of DLT and genes enriched in the top 30 pathways by GSEA. By taking intersections, we obtained a target-pathway relationship, which involves 13 targets, and 14 pathways. Thirteen targets were also labeled in Fig. 1(a). Figure 4 shows the KEGG pathway enrichment results, which mainly contained 14 pathways related to DLT. The genes labeled in the figure are key genes.
Plots of GSEA analysis.
Identification of the chemical constituents at role in DLT
From the HERB 2.0 database, 1435 compounds that contain 10 traditional Chinese medicines of DLT were obtained after the de-duplication process. Meanwhile, 286 small molecule chemical compounds corresponding to 13 key targets of DLT associated with the treatment of NSCLC were identified from this database after the de-duplication process. The two groups of compounds were further analyzed to obtain a relationship between 38 compounds and 4 targets.
Further, we used the SwissADME database to screen 38 compounds. We downloaded the compounds’ Canonical SMILES from PubChem, entered the compounds’ SMILES in SwissADME “Enter a list of SMILES here”, set the conditions of GI absorption as “High”, Durglikeness as at least two “Yes”, MLOGP < 5. After screening, 24 compounds were obtained, which were regarded as the active ingredients that exerted medicinal effects, as shown in Supplementary Table S2. Finally, we obtained a list of relationships involving 7 herbs, 24 compounds, 4 targets, and 6 pathways, as shown in Supplementary Table S3.
We proceeded to visualize the network relationship graphs between DLT, single herbs, active ingredients, targets, and pathways using Cytoscape 3.10.0 software. As in Fig. 5, the shades of colors in the graph were arranged according to the value of degree, and the darker the color indicated that in the network of relationships, the stronger the association. The analysis results showed that Quercetin, Luteolin, Scoparone, Isorhamnetin, Eugenol, Genistein, Coumestrol, Hederagenin, and Succinic Acid were the nine compounds with the strongest associations in the network of relationship, and thus we regarded them as key active ingredients. In addition, CCL2, FEN1, TPI1, and RMI2 are the most critical targets.
Therefore, based on these database analyses, we predicted that the anti-NSCLC effects of DLT may mainly exert their efficacy through the key active ingredients components of seven herbs, and it affects targets such as CCL2, FEN1, TPI1, and RMI2 through seven pathways.
DLT-Herb-Active Ingredient-Target-Pathway network. The green diamonds are DLT, the light pink circles represent compounds, the orange squares represent targets, the light blue squares on the left represent herbs, and the light purple squares on the right represent pathways.
External validation of core targets
We used GEPIA 2.0 online web analytics to detect the differential expression of key core target genes between NSCLC tissues and normal lung tissues NSCLC includes many types such as adenocarcinoma (LUAD), squamous cell carcinoma (LUSC) and large cell carcinoma. Expression analysis and pathological stage analysis of four genes, CCL2, FEN1, TPI1 and RMI2, in LUAD and LUSC using GEPIA2.0 database, and the results were shown in Figs. 6 and 7. In addition, we analyzed the differential expression of the four genes in the GSE18842 dataset and used the ggplot2 package to draw gene expression box-and-line plots as in Fig. 8. By analyzing Figs. 6 and 8, we found that the four genes were differentially expressed in the same way in the clinical dataset as in the GEPIA 2.0 database. This proves the reliability of the GSE18842 dataset. Second, we found that LUSC was significantly different in four key genes (P < 0.05), but LUAD was significantly different only in FEN1 and RMI2 (P < 0.05). Moreover, by analyzing Fig. 7, the expression level of FEN1 and TPI1 changed significantly with the pathological stage in LUAD.
mRNA expression levels of core genes in cancer genome map (TCGA) and genotypic tissue expression (GTEx) databases. Red represents lung cancer tissue, and black represents normal lung tissue. *, P < 0.05. (a) CCL2. (b) FEN1. (c) TPI1. (d) RMI2.
Stage diagram of core gene mRNA expression levels and pathological stages in the GEPIA database. (a) LUAD-CCL2. (b) LUSC- CCL2. (c) LUAD-FEN1. (d) LUSC-FEN1. (e) LUAD-TPI1. (f) LUSC- TPI1. (g) LUAD-RMI2. (h) LUSC-RMI2.
Expression of core targets in the GSE18842 dataset.
Molecular docking analysis
Based on the above results, the compounds of the nine core components that are associated with four core targets were selected as docking compounds for simple validation of the above results. Protein PDB structures of CCL2 (P13500, 1DOK), FEN1 (P39748, 5ZOD), TPI1 (P60174, 4POC) and RMI2 (Q9H9A7, 3MXN) were downloaded through the RCSB database. Nine compounds in 3D SDF format files were downloaded by the PubChem database.
The results of the docking are shown in Table 2. Kcal/mol is the binding energy obtained by the software. Its 3D model is illustrated in Fig. 9. It is generally accepted that a docking score of less than 0 kcal/mol indicates that the component has the potential for spontaneous binding to the target, and less than − 7 kcal/mol is considered to have strong docking affection35. From the results, it could be concluded that the active compounds all bind well to the active pocket of the protein and all had the potential for spontaneous binding, which proved the accuracy of the above projections results. Hederagenin had the strongest affinity with CCL2. Hederagenin performed interactions with amino acid residues in the CCL2 active site, such as GLU-50, THR-32, and SER-33 through five hydrogen bonds with bond lengths of 1.9, 1.9, 2.8, 2.8, and 1.6, respectively.
Based on the docking results, we demonstrate the reliability of the predictive method.
3D interactions of active compounds with protein. (a) quercetin-CCL2. (b) Isorhamnetin-CCL2. (c) eugenol-CCL2. (d) Hederagenin-CCL2. (e) scoparone-CCL2. (f) Luteolin-CCL2. (g) Genistein-CCL2. (h) Coumestrol-FEN1. (i) Genistein-FEN1. (j) Luteolin-FEN1. (k) Coumestrol-RMI2. (l) Succinic Acid-TPI1.
Molecular dynamics simulations
Here, we performed dynamical simulations of Hederagenin in complex with the 1DOK protein with the lowest molecular docking binding energy described above to further study the interaction between the active compound and the target protein. After running MD simulations for 100 ns, we analyzed the dynamic behaviors of the complex.
The RMSD was used to determine whether the system reached dynamic equilibrium or not, and from the RMSD (Fig. 10a), the ligand-protein complex had been stable in the kinetic simulation system, almost equilibrated near 0.30 nm, which also proved the reliability of the protein-ligand complex docking results and the stability of the complex structure. From the RMSF (Fig. 10b), it could be obtained that residues 17–20, 22–25, 45–50, and 55–60 of the protein are more flexible. Hydrogen bonding as well as hydrophobic interactions play an important role in the preservation of protein conformation. From Fig. 10(d) we saw that hydrogen bonding, as a strong non-covalent force, was stabilized in the complex, which is dominated by hydrogen bonding between some key residues in the proteins and some important groups in the small molecules, with up to five hydrogen bonds in the system. From Fig. 10(c), it can be seen that the ligand acts as the active site of the protein mainly by binding to 15 amino acids, mainly residue 49 of LYS amino acid of A chain, residue 7 of ALA amino acid of B chain, residue 9 of VAL amino acid of B chain and residue 31 of ILE amino acid of B chain.
Based on the above results, we further demonstrate the reliability of the prediction method.
Molecular dynamics simulation analysis of the Hederagenin-CCL2 complex. (a) RMSD curve of ligand (Hederagenin, red line), protein (CCL2, blue line), and complex (black line). (b) RMSF curve of CCL2(black, chain A; rad, chain B). (c) The energy contribution of hot residue of the complex. (d) Hydrogen bonds of the complex.
Discussion
Lung cancer is one of the most common malignant tumors in the world and has the highest mortality rate among all cancers. Based on histopathologic features, lung cancer can be divided into small cell lung cancer (SCLC) and NSCLC, of which NSCLC is the most common type of lung cancer, accounting for about 80–85% of all lung cancers. Nearly 70% of NSCLC patients are diagnosed at a late stage, and the 5-year survival rate is only 15%. Thus, there is an immediate need to develop highly effective and low-toxicity drugs for the treatment of NSCLC. In recent years, the treatment of NSCLC by traditional Chinese medicines has attracted a great deal of attention from many researchers, and many herbs have been found to have significant inhibitory effects on lung cancer. TCM treatment of cancer has unique advantage. We have found that DLT can be a potential drug for the treatment of lung cancer through drug repositioning, and it is of great significance.
In this study, we predicted an anti-NSCLC effect of DLT based on gene expression similarity and performed preliminary validation by in vitro experiments. We continued to predict the key targets and their active components that exert their medicinal effects. It was concluded that DLT mainly acted through nine compounds, Quercetin, Luteolin, Scoparone, Isorhamnetin, Eugenol, Genistein, Coumestrol, Hederagenin, Succinic Acid, which are from Yujin, Chishao, Huangqi, Danshen, Gegen, Chuanxiong, and Xiebai, and mainly targeted CCL2, FEN1, TPI1, RMI2 by six pathways. This also reflected the multi-component, multi-target, multi-pathway mechanism of action of traditional Chinese medicine.
We further investigated the above results in the literature. For the nine compounds, except for Succinic Acid, the remaining eight compounds had been reported in the literature to be related to NSCLC36,37,38,39,40,41,42,43,44. Endometrial cancer can be treated with succinic acid45. That’s why we think even more that succinic acid can be used as a potential anti-NSCLC active compound. We detailed quercetin, which has the strongest association. Li H43et al. showed that quercetin as the main active ingredient in Yang-Yin-Qing-Fei-Tang has effective inhibitory activity against NSCLC, Li R44et al. showed that the main active ingredient in kiwi root against NSCLC contains quercetin. For the 4 targets, there were literature reports of association with NSCLC46,47,48,49. We elaborated on CCL2, which has the strongest association. CCL2 is a member of the CC chemokine family, originally described as a “tumor-derived chemokine”, and has been shown to have a direct effect on tumor growth in an autocrine and paracrine manner in a variety of cancers, including cancers of the breast, lung, cervix, ovary, sarcoma, and prostate49.
In addition, DLT include ten Chinese herbs, which are somewhat closely related to NSCLC. For example, Danshen of extracted by methanol with ultrasonic had been shown to have anti-NSCLC effects50, Yujin has the efficacy of promoting the flow of qi and relieves depress and clearing away heart heat and cool blood, and attributes to the liver, heart, and lung channels, and Huangqi has the efficacy of invigorating spleen and supplementing the middle and elevating the yang and attributes to spleen and lung channel such as Huangqi Fuzheng Decoction has proved to have good efficacy in NSCLC. Based on TCM theory, the pivotal pathogenesis of NSCLC is the weakness of qi with phlegm and blood stasis, which plays a fundamental role throughout the oncogenic and progressive process of NSCLC51. DLT have the effect of broadening the chest and promoting Yang, boosting blood circulation and eliminating blood stasis.
In conclusion, the above analysis shows that our prediction results are accurate, which also indicates that our prediction methodology is reliable. The accuracy and comprehensiveness of the data depends on the updating and improvement of the databases, which is a limitation that must be recognized. Therefore, further experimental and clinical trials are needed to investigate the pharmacological activity and mechanism of action of DLT against NSCLC. It is believed that with further research on gene expression profiles, human biological responses, and biological pathways, the data will be more complete and the results obtained will be more accurate. However, due to the low cost and certain degree of confidence of this method, we believe that it is a good way to predict the new efficacy of drugs, which can be used to reduce the cost of new drug research and development, shorten the cycle of new drug research and development, and improve the success rate of new drug research and development.
Conclusion
Through a series of analytical predictions and validations of gene expression similarity, simple in vitro experiments, network pharmacology, molecular docking, molecular dynamics, and literature research, we concluded that DLT has anti-NSCLC activity and it is predicted that DLT mainly acted through nine compounds, Quercetin, Luteolin, Scoparone, Isorhamnetin, Eugenol, Genistein, Coumestrol, Hederagenin, Succinic Acid, which are from Yujin, Chishao, Huangqi, Danshen, Gegen, Chuanxiong, and Xiebai, and mainly targeted CCL2, FEN1, TPI1, RMI2 by six pathways. This provides valuable advice for the subsequent development and utilization of DLT. Overall, the method we adopted has great development prospects as a way to predict the efficacy of new drugs and their main mechanisms of action.
Data availability
All data generated or analyzed during this study are included in this published article and its supplementary information files.
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Funding
This work was financially supported by National Natural Science Foundation of China Regional Cooperation Project (U21A20407), Natural Science Foundation of Gansu Province (23JRRA1303), Scientific Research Project of Key Laboratory of Quality Control of Chinese Medicinal Materials and Drinking Tablets of State Drug Administration of China (2023GSMPA-KL13), Gan Medical Products Administration Scientific Research Project (2022GSMPA00124), Science and Technology Innovation Project of China Academy of Chinese Medical Sciences (CI2021A05403), National Natural Science Foundation of China (No. 81603401) and Special Fund for Basic Research Expenses of Central Public Welfare Research Institutes (ZZ13-YQ-078).
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Zhang, J., Lin, Z., Zhang, Y. et al. Bioinformatics-based drug repositioning and prediction of the main active ingredients and potential mechanisms of action for the efficacy of Dan-Lou tablet. Sci Rep 14, 23297 (2024). https://doi.org/10.1038/s41598-024-74243-5
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DOI: https://doi.org/10.1038/s41598-024-74243-5
