Pharmaceutical composition, preparation method and application

Abstract

The invention discloses a pharmaceutical composition as well as a preparation method and application thereof. The pharmaceutical composition comprises an uncaria-derived anti-depression compound as an active component and pharmaceutically acceptable auxiliary materials. The uncaria sourced antidepressant compound serving as an active ingredient is systematically mined from uncaria based on a hypothesis-free screening strategy in combination with network pharmacology and a molecular docking technology. Experimental data show that the pharmaceutical composition prepared from the screened compound can effectively relieve inflammatory response of microglial cells, can effectively regulate and control VDBP expression, and has the potential of being used for preparing antidepressant drugs.

Description

Technical Field

[0001] The present invention relates to natural medicinal chemistry, and in particular to a pharmaceutical composition, a preparation method and an application thereof. Background Art

[0002] Depressive disorders are a type of disease characterized by low mood or low spirits as core symptoms, accompanied by physical discomfort and cognitive impairment, and significantly affecting an individual's social functioning. Its high prevalence, high recurrence rate, high suicide rate and low treatment rate make it a major challenge in the field of global public health. According to statistics from the World Health Organization, the total burden of depressive disorders has ranked second among human diseases and is expected to jump to first place in 2030. The current mainstream treatment is mainly based on selective serotonin reuptake inhibitors (SSRIs), but there are problems such as delayed onset of action (usually 2-4 weeks) and clinical remission rate of less than 50%. Patients often interrupt treatment due to side effects or insufficient efficacy, resulting in a high relapse rate. This situation has promoted the exploration of new treatment strategies in academia, among which natural medicines have attracted much attention due to their multi-target action characteristics and low risk of adverse reactions.

[0003] Within Traditional Chinese Medicine (TCM), research on natural medicines for depression shows unique potential. Prescriptions for soothing the liver and relieving depression, such as Chaihu Shugan San and Xiaoyao San, recorded in medical texts throughout the ages, are based on the theory that the liver governs emotions and improves depressive symptoms by regulating Qi. Modern research confirms that Chinese herbs such as Coptis chinensis, Acorus gramineus, and Hypericum perforatum can exert antidepressant effects through mechanisms such as regulating monoamine neurotransmitters, inhibiting the release of inflammatory factors, and repairing hippocampal neuronal damage. Of particular note, natural compounds such as hyperforin and paeoniflorin have been shown to have neuroprotective effects comparable to synthetic drugs and can improve side effects such as sexual dysfunction caused by SSRIs, providing important directions for the development of new antidepressant preparations.

[0004] Uncaria rhynchophylla is a typical representative of traditional anticonvulsant and anticonvulsant drugs. Commonly used clinical compound preparations such as Yigansan and Uncaria rhynchophylla have been shown to improve comorbid anxiety and depression. Animal studies have shown that Uncaria rhynchophylla significantly shortens immobility time in a forced swim test, with efficacy comparable to fluoxetine. However, current research has yet to clarify its core active ingredient and specific target, constituting a bottleneck restricting its modern development. Summary of the Invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a pharmaceutical composition comprising an antidepressant compound derived from Uncaria rhynchophylla as an active ingredient. A second purpose is to provide a preparation method and application of the pharmaceutical composition.

[0006] Technical solution: The pharmaceutical composition of the present invention comprises an antidepressant compound derived from Uncaria rhynchophylla as an active ingredient, and pharmaceutically acceptable excipients.

[0007] Preferably, the antidepressant compound derived from Uncaria rhynchophylla is any one of ancustrine, dehydrorhynchophylline, rhynchophylline C, isodehydrorhynchophylline, dehydrorhynchophylline, quercetin, rhynchophylline A, rhynchophylline C, isopterisolepis rhynchophylline, tetrahydroalstonine, vincaine lactone, and rhynchophylline.

[0008] The method for preparing the pharmaceutical composition of the present invention comprises the following steps:

[0009] (1) Screening the active ingredients in Uncaria rhynchophylla to obtain antidepressant compounds derived from Uncaria rhynchophylla;

[0010] (2) Using the screened compound as an active ingredient, adding pharmaceutically acceptable excipients to obtain a pharmaceutical composition.

[0011] Preferably, the step 1 comprises:

[0012] (11) Obtain and screen the active ingredients of Uncaria rhynchophylla based on the traditional Chinese medicine system pharmacology platform;

[0013] (12) predicting the potential target binding proteins of the active ingredient obtained in step 11 and establishing a gene set of proteins;

[0014] (13) Retrieve the gene names related to depression, merge and remove duplicates, and obtain the gene set related to depression;

[0015] (14) Taking the intersection of the two gene sets obtained in steps 12 and 13, constructing a protein-protein interaction network, topological analysis, and screening gene targets;

[0016] (15) Molecular docking is performed between the protein encoded by the gene target obtained in step 14 and the corresponding active ingredient of Uncaria rhynchophylla screened in step 11 to screen out antidepressant compounds derived from Uncaria rhynchophylla.

[0017] Preferably, the screening criteria for the active ingredients of Uncaria rhynchophylla in step 11 include: oral bioavailability ≥ 30%, and drug-like property ≥ 0.18.

[0018] Preferably, the step 14 includes:

[0019] (141) Take the intersection of the two gene sets obtained in steps 12 and 13;

[0020] (142) The obtained intersection was imported into the STRING database, the species was set to human, the minimum interaction score was ≥0.7, isolated targets were hidden, and other parameters were kept at the default settings to construct a protein–protein interaction network and obtain the analysis results;

[0021] (143) The CytoScape software was used to further experiment with visualization of the PPI network and perform topological analysis;

[0022] (144) Calculate the key node centrality index, score and rank, and obtain the gene target.

[0023] Preferably, the key node centrality index in step 144 includes any one or more of a local topological centrality index, a global path centrality index, a structural robustness-related centrality index, a location and community centrality index, and a composite and biological specific centrality index.

[0024] Preferably, the local topology centrality indicators include degree centrality and clustering coefficient indicators; the global path centrality indicators include betweenness centrality, closeness centrality and bottleneck centrality indicators; the structural robustness-related centrality indicators include edge penetration component and neighborhood / maximum neighborhood component centrality indicators; the position and community centrality indicators include eccentricity centrality and maximum cluster centrality indicators; the composite and biological-specific centrality indicators include biological network centrality indicators.

[0025] Preferably, the step 15 includes:

[0026] (151) obtaining the protein structure encoded by the gene target obtained in step 4 from the protein crystal structure database and performing preprocessing, wherein the preprocessing includes removing water and ligands from the protein structure and adding polar hydrogen;

[0027] (152) The structures of the Uncaria rhynchophylla active components related to the gene targets obtained in step 4 were obtained using the traditional Chinese medicine systems pharmacology platform;

[0028] (153) The protein structure obtained in step 51 was molecularly docked with the structure of the active ingredient of Uncaria rhynchophylla obtained in step 52, and the binding energy was calculated. The compound with a binding energy less than the threshold was the Uncaria rhynchophylla-derived compound.

[0029] Preferably, the molecular docking in step 153 uses a Lamarckian genetic algorithm to optimize the conformational search, and sets a grid frame to cover known active sites; the binding energy threshold is -8.0 kcal / mol.

[0030] The invention relates to the use of the pharmaceutical composition in the preparation of antidepressant drugs.

[0031] Beneficial effects: Compared with the existing technology, the present invention has the following significant advantages: 1. Based on a hypothesis-free screening strategy, combined with network pharmacology and molecular docking technology, compounds with antidepressant activity were systematically mined from Uncaria rhynchophylla for the preparation of pharmaceutical compositions; 2. Experimental data showed that the pharmaceutical compositions prepared from the screened compounds can effectively alleviate the inflammatory response of microglia and effectively regulate VDBP expression, and have the potential to be used in the preparation of antidepressant drugs. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1A Venn diagram showing the intersection of genes encoding proteins that may be targeted by effective monomeric compounds of Uncaria rhynchophylla and genes related to depression;

[0033] Figure 2 This is the relationship diagram between the core target genes of the antidepressant active ingredients of Uncaria rhynchophylla;

[0034] Figure 3 This is a bubble diagram of the KEGG pathway enrichment of the core target genes of the antidepressant active ingredients of Uncaria rhynchophylla;

[0035] Figure 4 This is the GO enrichment analysis bar graph of the core target genes of the antidepressant active ingredients of Uncaria rhynchophylla;

[0036] Figure 5 This is the heat map of the molecular docking binding energy of the core target gene encoding protein and the effective monomer components of Uncaria rhynchophylla;

[0037] Figure 6 Figure 2 is the molecular docking result diagram of the core target gene encoding protein and the effective monomer components of Uncaria rhynchophylla, among which A is JAK2-anecarboxylic acid, B is JAK2-dehydrorhynchophylline, C is JAK2-quercetin, D is JAK2-dehydrorhynchophylline, E is JAK2-rhynchophylline C, F is PTPN11-rhynchophylline A, G is JAK2-rhynchophylline A, H is JAK2-dehydrorhynchophylline, I is JAK2-rhynchophylline A, and J is TYK2-rhynchophylline A, K are JAK2-hyperoptera-rhynchophylline, L is JAK2-tetrahydroalstonine, M is PTPN11-hypergonin lactone, N is PTPN11-hypergonin C, O is JAK2-hypergonin C, P is JAK2-hypergonin lactone, Q is MAPK1-hypergonin lactone, R is JAK2-tetrahydroalstonine, S is JAK3-tetrahydroalstonine, T is MAPK1-hypergonin, and U is MAPK3-hypergonin;

[0038] Figure 7 The protective effects of high and low concentrations of rhynchophylline, dehydrorhynchophylline, isorhynchophylline, isodehydrorhynchophylline, rhynchophylline, and dehydrorhynchophylline on the LPS-induced microglial inflammation model, n=6, t-test, *p<0.05, **p<0.01, ***p<0.001;

[0039] Figure 8 The cytoprotective effects of isodehydrorhynchophylline, dehydrorhynchophylline, and rhynchophylline on LPS-induced BV2 cell inflammation model, n = 6, t-test, *p < 0.05, **p < 0.01;

[0040] Figure 9Effects of therapeutic or preventive use of isodehydrorhynchophylline and rhynchophylline on VDBP expression in LPS-induced microglial inflammation model, n = 2, t-test, *p < 0.05;

[0041] Figure 10 This is a flow chart for the screening and biological validation of antidepressant compounds derived from Uncaria rhynchophylla. DETAILED DESCRIPTION

[0042] The technical solution of the present invention is further described below.

[0043] Example 1: Screening of antidepressant compounds derived from Uncaria rhynchophylla

[0044] 1. Obtain and screen the active ingredients of Uncaria rhynchophylla based on the traditional Chinese medicine system pharmacology platform

[0045] By searching the keyword "Uncaria rhynchophylla" in the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) (http: / / www.tcmsp-e.com), 65 monomer components of Uncaria rhynchophylla were obtained. Thirty-three effective monomer components with potential bioactivity were screened out based on the criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18, as shown in Table 1.

[0046] Table 1 Effective monomer components with potential biological activity in Uncaria rhynchophylla

[0047]

[0048]

[0049] 2. Predict potential target binding proteins of active ingredients and establish protein gene sets

[0050] The 33 selected compounds were searched in the Swiss Target Prediction (www.swisstargetprediction.ch) and TCMSP (http: / / www.tcmsp-e.com) databases to obtain information on their potential target proteins. After conversion to the Universal Protein Resources database (Uniprot) (http: / / www.uniprot.org), a total of 773 gene sets encoding proteins that could be targeted by the effective monomeric compounds of Uncaria rhynchophylla were obtained, as shown in Table 2.

[0051] Table 2 Gene sets encoding proteins that may be targeted by effective monomeric compounds of Uncaria rhynchophylla

[0052]

[0053]

[0054] 3. Retrieve the gene names related to depression, merge and remove duplicates, and obtain the gene set related to depression

[0055] Disease targets related to depression were retrieved from three databases: GeneCards (www.genecards.org), OMIM (Online Mendelian Inheritance in Man) (www.omim.org), and DisGeNET (https: / / disgenet.com). The keywords included "Depressive Disorder", "Major Depressive Disorder", "Depression", and "Depressive-like behavior". After the retrieval, the genes obtained from the three databases were merged and deduplicated, resulting in a total of 13,218 gene names related to depression.

[0056] 4. Screening of potential key target genes for Uncaria rhynchophylla anti-depression, construction of protein-protein interaction network, topological analysis, and screening of gene targets

[0057] The genes encoding the proteins that may be targeted by the effective monomer compounds of Uncaria rhynchophylla are intersected with the depression-related genes and a Venn diagram is drawn, as shown in Figure 1 As shown in Table 3, 639 potential key target genes for Uncaria rhynchophylla antidepressant effects were obtained.

[0058] Table 3 Potential key target genes for Uncaria rhynchophylla's anti-depressant effects

[0059]

[0060]

[0061] The potential gene targets of the antidepressant active monomer components of Uncaria rhynchophylla were imported into the STRING database (v11.5) to construct a protein-protein interaction (PPI) network. The following settings were made: species was Homo sapiens (human), the minimum interaction score (Confidence Score) was ≥ 0.7, isolated targets were hidden, and other parameters remained at default settings. The PPI analysis results were obtained and exported in TSV format.

[0062] The PPI network was further visualized using CytoScape (v3.9.0), and the "CytoHubba" plug-in was used for topological analysis. The local topological centrality indices including degree centrality and clustering coefficient were calculated respectively; the global path centrality indices included betweenness centrality, closeness centrality, and bottleneck centrality; the structural robustness-related centrality indices included edge permeation component (EPC), neighborhood component centrality (MNC), and maximum neighborhood component centrality (DMNC); the position and community centrality indices included eccentricity centrality (EcCentricity) and maximum cluster centrality (MCC); the composite and biological specific centrality indices included biological network centrality (BNC). Finally, 10 core antidepressant target genes of the effective components of Uncaria rhynchophylla were screened out using the MCC scores. The MCC scores are shown in Table 4. The relationships between the core antidepressant target genes of the effective components of Uncaria rhynchophylla are shown in Table 4. Figure 2 shown.

[0063] Table 4 Names and Maximum Clique Centrality (MCC) scores of key nodes in the PPI network

[0064] Ranking Core target gene name MCC score 1 STAT3 6852578 2 PTPN11 6426458 3 JAK1 5029086 4 STAT1 4935786 5 JAK3 4870468 6 JAK2 4597976 7 MAPK1 4314224 8 MAPK3 4297688 9 TYK2 4157880 10 IL6 3686438

[0065] Among them, STAT3 (Signal Transducer and Activator of Transcription 3) is signal transduction and transcription activator protein 3; JAK1 (Janus Kinase 1) is Janus kinase 1; STAT1 (Signal Transducerand Activator of Transcription 1) is signal transduction and transcription activator protein 1; JAK3 (Janus Kinase3) is Janus kinase 3; JAK2 (Janus Kinase 2) is Janus kinase 2; MAPK1 (Mitogen-Activated Protein Kinase1) is mitogen-activated protein kinase 1; MAPK3 (Mitogen-Activated Protein Kinase3) is mitogen-activated protein kinase 3; TYK2 (Tyrosine Kinase 2) is tyrosine kinase 2; IL6 (Interleukin-6) is interleukin-6.

[0066] 5. Functional enrichment analysis

[0067] In order to further analyze the biological functions and signaling pathways of the core targets, the present invention imported the 10 core target genes of drug diseases screened out into the DAVID database (v6.8) for gene ontology (GO) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis;

[0068] Among them, GO enrichment analysis includes biological process (BP), cellular component (CC) and molecular function (MF) analysis. At the same time, in the KEGG pathway enrichment analysis, significant signal pathways with p < 0.05 were selected, and finally the above enrichment results were visualized using Origin software with enrichment bubble charts and enrichment bar charts, as shown in Tables 5-6 and Figure 3-4 shown.

[0069] Table 5 KEGG analysis enriched pathways and related information of the 10 core target genes

[0070] Enrichment Factor KEGG pathway -lgP Gene 0.083333 Th17 cell differentiation 21.17016 9 0.049383 JAK-STAT signaling pathway 16.59178 8 0.041237 Kaposi's sarcoma-associated herpesvirus infection 15.95356 8 0.078652 PD-1 Expression / PD-L1 Checkpoint Pathway in Cancer 15.7441 7 0.012121 Herpes simplex virus infection pathway 8.428399 6 0.046154 Inflammatory bowel disease 5.947519 3 0.043478 Adipokine signaling pathway 5.868823 3

[0071] Table 6 GO enrichment analysis results and related information of 10 key genes

[0072]

[0073]

[0074] 5. Molecular docking and screening of compounds derived from Uncaria rhynchophylla.

[0075] AutoDock Vina (v1.2.3) was used to perform molecular docking analysis on the effective monomer components with potential biological activity in the above-mentioned Uncaria rhynchophylla and the antidepressant core target genes of the effective components of Uncaria rhynchophylla.

[0076] The 3D structures of key target proteins encoded by the core antidepressant target genes of 10 active ingredients in Uncaria rhynchophylla were downloaded from the Protein Data Bank (PDB) and preprocessed using PyMOL, including water removal, ligand addition, and polar hydrogen addition. The 3D structures of potentially bioactive monomeric components in Uncaria rhynchophylla were downloaded from PubChem or TCMSP. The core target genes include STAT3, PTPN11, JAK1, STAT1, JAK3, JAK2, MAPK1, MAPK3, TYK2, and IL6. Potentially bioactive monomeric components in Uncaria rhynchophylla include isobupleurolactone, dehydrorhynchophylline B, dehydrorhynchophylline, ancustrine, dehydrorhynchophylline, rhynchophylline B, rhynchophylline C, isodehydrorhynchophylline, quercetin, rhynchophylline, rhynchophylline A, isopterisopterine, rhynchophylline C, tetrahydroalstonine, vinculin lactone, and rhynchophylline.

[0077] The structural data were imported into AutoDock Vina software, and the Lamarkian Genetic Algorithm was used to optimize the conformational search. A grid box was set to cover the known active sites to ensure the maximum possible binding area for calculation of binding energies (kcal·mol⁻¹). The binding energy threshold was set at -8.0 kcal / mol. Compounds with binding energies below the threshold are listed in Table 8, representing the antidepressant compounds derived from Uncaria rhynchophylla.

[0078] Table 8 Docking results of core target gene-encoded proteins and Uncaria rhynchophylla effective monomer components

[0079]

[0080]

[0081] The best binding model in the docking results is selected and a 3D visualization is drawn using PyMOL, as shown in the following example: Figure 5-6 shown.

[0082] Example 2: Biological Validation of Uncaria rhynchophylla-derived Antidepressant Compounds

[0083] The required standards of six Uncaria rhynchophylline monomer components, including Rrynchophylline, Iso-Rrynchophylline, Corynantheine, Iso-Corynantheine, Hirsutine, and Hirsuteine, were purchased from Beijing Zhongke Huabiao Biotechnology Research Institute. They were dissolved in PBS solution and sonicated to obtain a storage solution with a concentration of 1000 μM. Then, according to experimental needs, an appropriate amount of the storage solution was diluted with PBS to the drug concentration required for the drug intervention experiment. Among them, the Rhynchophylline standard contained Rhynchophylline A and Rhynchophylline C, and Iso-Corynantheine was used as a control compound for screening the effective monomer components.

[0084] 1. Biocompatibility assessment of antidepressant compounds derived from Uncaria rhynchophylla

[0085] (1) 0.5×10 5 BV2 cells were pre-cultured in a 37°C, 5% CO2 incubator for 24 hours.

[0086] (2) After the cells were stably attached, the old complete medium was discarded and the cells in the high-concentration group were replaced with complete medium containing 0.1 μM final concentrations of rhynchophylline, isorhynchophylline, dehydrorhynchophylline, isodehydrorhynchophylline, tricholoma, and dehydrorhynchophylline, respectively. LPS-complete medium solution with a final concentration of 100 ng / mL was used as a control.

[0087] In the low-concentration group, cells were replaced with complete medium containing 0.01 μM final concentrations of rhynchophylline, isorhynchophylline, dehydrorhynchophylline, isodehydrorhynchophylline, tricholoma, and dehydrorhynchophylline, respectively. LPS-complete medium solution with a final concentration of 100 ng / mL was used as a control. Culture was continued for 24 h.

[0088] (3) Add 10 μL of CCK8 solution to each well and incubate in a 37°C, 5% CO2 incubator for 2 hours. Measure the absorbance at 450 nm using a microplate reader and calculate the relative cell viability according to the following formula:

[0089] Relative cell viability = (A s -A b ) / (A c -A b )×100%

[0090] Among them, A s 、A b and A c are the absorbance at 450 nm of the sample, blank control and negative control, respectively.

[0091] The results are as follows Figure 7 As shown in the results, this study evaluated the protective effects of six Rhynchophylla alkaloid components at different concentrations on the LPS-induced microglial (BV2) inflammation model. Specifically, this study used two doses, high concentration (0.1μM) and low concentration (0.01μM), to test the effects of the six alkaloids on the microglial inflammatory response. High concentrations of Rhynchophylline, Isorhynchophylline, Isodehydrorhynchophylline and Dehydrorhynchophylline failed to exert the expected protective effect in the microglial inflammation model, but instead showed certain cytotoxicity, which may be related to their excessive stimulation of microglia or the resulting cell dysfunction. In contrast, at low concentrations, Isodehydrorhynchophylline, Rhynchophylline and Dehydrorhynchophylline showed significant protective effects on the microglial inflammation model. These components effectively alleviated the inflammatory response of microglia, further verifying their role as potential neuroprotective agents.

[0092] 2. Uncaria rhynchophylla-derived antidepressant compounds alleviate LPS-induced microglial inflammatory injury model

[0093] (1) 0.5×10 5 BV2 cells were incubated in a 37°C, 5% CO2 incubator for 24 hours to allow them to adhere and grow stably. Before administration, cell morphology was observed under a microscope and abnormal cell wells were removed to ensure the reliability of the experimental data.

[0094] (2) In the preventive drug administration experiment, after the cells were stably attached to the wall, the old complete culture medium was discarded and the cells in the experimental group were replaced with complete culture medium containing 1, 2, 5, 10, 20, and 50 nM final concentrations of dehydrorhynchophylline, 2, 5, 10, and 20 nM final concentrations of isodehydrorhynchophylline, or 1, 2, 5, 10, 20, and 50 nM final concentrations of rhynchophylline. The blank treatment group was replaced with fresh complete culture medium and cultured for another 24 hours to simulate the preventive effect of the drug.

[0095] After the preventive administration, the culture medium was removed and the cells were gently washed with PBS. Then, LPS-complete culture medium solution with a final concentration of 100 ng / mL was added to the experimental groups except the blank control group and incubated for 4 h to induce inflammatory damage.

[0096] After LPS treatment, 10 μL of CCK8 solution was added to each well and incubated in a 37°C, 5% CO2 incubator for 2 hours. The absorbance at 450 nm was measured using a microplate reader, and the relative cell viability was calculated according to the following formula to evaluate the preventive effect of the active ingredients of Uncaria rhynchophylla:

[0097] Relative cell viability = (A s -A b ) / (A c-A b )×100%

[0098] Among them, A s 、A b and A c are the absorbance at 450 nm of the sample, blank control and negative control, respectively.

[0099] (3) In therapeutic drug administration experiments.

[0100] The results are as follows Figure 8 As shown in the figure, based on the above experimental results, this study further selected isorhynchophylline, rhynchophylline, and dehydrorhynchophylline for in-depth investigation. To refine their pharmacological activities, this study designed multiple concentration gradients to evaluate their protective effects in a LPS-induced microglial inflammation model. As shown in the figure, dehydroisorhynchophylline and rhynchophylline exhibited significant neuroprotective effects at specific concentrations, significantly alleviating the inflammatory response of microglia.

[0101] 3. Uncaria rhynchophylla-derived antidepressant compounds specifically target VDBP

[0102] Identifying Plasma Biomarkers with high specificity for major depressive disorder: A multi-level proteomics study. published by Shi et al. in 2020 and Vitamin D-binding protein in plasma microglia-derived extracellular vesicles as a potential biomarker for major depressive disorder. published by Zhang et al. in 2024 both suggest that brain-derived VDBP may play an important role in the occurrence and development of depression. Among them, the expression of VDBP in microglia-derived extracellular vesicles in the plasma of depressed patients was significantly reduced and negatively correlated with the Hamilton Depression Rating Scale (HAMD-24) score. This phenomenon was further verified in depressive-like mice and naturally depressed rhesus monkey models.

[0103] In addition, the latest study published by Kong et al. in 2025, "Microglia-Derived Vitamin DBinding Protein Mediates Synaptic Damage and Induces Depression by Binding to the Neuronal Receptor Megalin," further revealed the key role of VDBP in the interaction between microglia and neurons and clarified its potential regulatory pathway in the pathogenesis of depression. These findings further support the potential value of brain-derived VDBP as a molecular diagnostic biomarker for depressive disorders.

[0104] Therefore, in the present invention, the antidepressant properties of the compounds were further evaluated by their effects on VDBP.

[0105] (1) 0.5×10 5 BV2 cells were incubated in a 37°C, 5% CO2 incubator for 24 hours to allow them to adhere and grow stably. Before administration, cell morphology was observed under a microscope and abnormal cell wells were removed to ensure the reliability of the experimental data.

[0106] (2) In the preventive administration experiment, after the cells were stably attached to the wall, the old culture medium was discarded, and 100 μL of fresh complete culture medium was added to the blank control group and LPS group. The remaining cells were added with 100 μL of fresh complete culture medium containing 1 nM final concentration of rhynchophylline, 5 nM final concentration of rhynchophylline, 5 nM final concentration of isodehydrorhynchophylline, or 10 nM final concentration of isodehydrorhynchophylline, and the culture was continued for 24 hours.

[0107] After the prophylactic administration, the culture medium was removed and the cells were gently washed twice with PBS. Subsequently, 100 μL of LPS solution with a final concentration of 100 ng / mL was added to all experimental groups except the blank control group and incubated for 4 hours to induce inflammatory damage. After LPS treatment, the cells were set aside for further use.

[0108] (3) In the therapeutic drug administration experiment, after the cells adhered to the wall, 100 μL of 100 ng / mL LPS solution was first added to the groups except the blank control group and incubated for 4 hours to induce inflammatory damage. Then, the old culture medium was discarded, and 100 μL of fresh complete culture medium was added to the blank control group and LPS group. The remaining cells were added with 100 μL of fresh complete culture medium containing 1 nM final concentration of rhynchophylline, 5 nM final concentration of rhynchophylline, 5 nM final concentration of isodehydrorhynchophylline, or 10 nM final concentration of isodehydrorhynchophylline, and cultured for another 24 hours. After the end, the cells were set aside for use.

[0109] (4) The cells obtained in steps 2 and 3 were washed with PBS, and then 150 μL / well of RIPA lysis buffer was added, and 1% protease inhibitors and phosphatase inhibitors were added to extract proteins. After electrophoresis, the protein samples were transferred to PVDF membranes; after blocking with blocking solution, rabbit VDBP primary antibody and mouse β-actin primary antibody were used to incubate at 4°C for 16 hours; after the primary antibody incubation was completed and washed, horseradish peroxidase-conjugated secondary antibody of the corresponding resistance was used to incubate at room temperature for 1 hour; after washing, enhanced chemiluminescence solution was added to develop the images, and grayscale analysis was performed on the obtained images.

[0110] The results are as follows Figure 9 As shown in Figure 2, after the therapeutic use of a specific concentration of rhynchophylline, this study observed a significant decrease in VDBP expression in the LPS-induced microglial inflammation model. This result suggests that rhynchophylline may exert its neuroprotective effects by specifically targeting VDBP and has the potential to treat depression.

[0111] The process flow diagram of the above embodiment is as follows Figure 10 shown.

Claims

1. A pharmaceutical composition, characterized in that The pharmaceutical composition comprises an antidepressant compound derived from Uncaria rhynchophylla as an active ingredient and pharmaceutically acceptable excipients.

2. The pharmaceutical composition according to claim 1, characterized in that The antidepressant compound derived from Uncaria rhynchophylla is any one of ancustrine, dehydrorhynchophylline, rhynchophylline C, isodehydrorhynchophylline, dehydrorhynchophylline, quercetin, rhynchophylline A, rhynchophylline C, isopterisolepis rhynchophylline, tetrahydroalstonine, vincaine lactone, and rhynchophylline.

3. A method for preparing the pharmaceutical composition according to claim 1 or 2, characterized in that the steps include: (1) Screening the active ingredients in Uncaria rhynchophylla to obtain antidepressant compounds derived from Uncaria rhynchophylla; (2) Using the screened compound as an active ingredient, adding pharmaceutically acceptable excipients to obtain a pharmaceutical composition.

4. The method for preparing the pharmaceutical composition according to claim 3, wherein The step 1 comprises: (11) Obtain and screen the active ingredients of Uncaria rhynchophylla based on the traditional Chinese medicine system pharmacology platform; (12) predicting the potential target binding proteins of the active ingredient obtained in step 11 and establishing a gene set of proteins; (13) Retrieve the gene names related to depression, merge and remove duplicates, and obtain the gene set related to depression; (14) Taking the intersection of the two gene sets obtained in steps 12 and 13, constructing a protein-protein interaction network, topologically analyzing, and screening gene targets; (15) Molecular docking is performed between the protein encoded by the gene target obtained in step 14 and the corresponding active ingredient of Uncaria rhynchophylla screened in step 11 to screen out antidepressant compounds derived from Uncaria rhynchophylla.

5. The method for preparing the pharmaceutical composition according to claim 4, wherein The screening criteria for the active ingredients of Uncaria rhynchophylla in step 11 include: oral bioavailability ≥ 30% and drug-like property ≥ 0.

18.

6. The method for preparing the pharmaceutical composition according to claim 4, wherein: The step 14 comprises: (141) Take the intersection of the two gene sets obtained in steps 12 and 13; (142) The obtained intersection was imported into the STRING database, the species was set to human, the minimum interaction score was ≥0.7, isolated targets were hidden, and other parameters were kept at the default settings to construct a protein–protein interaction network and obtain the analysis results; (143) The CytoScape software was used to further experiment with visualization of the PPI network and perform topological analysis; (144) Calculate the key node centrality index, score and rank, and obtain the gene target.

7. The method for preparing the pharmaceutical composition according to claim 6, wherein The key node centrality index in step 144 includes any one or more of a local topological centrality index, a global path centrality index, a structural robustness-related centrality index, a location and community centrality index, and a composite and biological specific centrality index.

8. The method for preparing the pharmaceutical composition according to claim 4, wherein The step 15 comprises: (151) obtaining the protein structure encoded by the gene target obtained in step 4 from the protein crystal structure database and performing preprocessing, wherein the preprocessing includes removing water and ligands from the protein structure and adding polar hydrogen; (152) The structures of the Uncaria rhynchophylla active components related to the gene targets obtained in step 4 were obtained using the traditional Chinese medicine systems pharmacology platform; (153) The protein structure obtained in step 51 was molecularly docked with the structure of the active ingredient of Uncaria rhynchophylla obtained in step 52, and the binding energy was calculated. The compound with a binding energy less than the threshold was the Uncaria rhynchophylla-derived compound.

9. The method for preparing the pharmaceutical composition according to claim 8, characterized in that: In step 153, the molecular docking uses a Lamarckian genetic algorithm to optimize the conformational search, and a grid frame is set to cover the known active sites; the binding energy threshold is -8.0 kcal / mol.

10. Use of the pharmaceutical composition according to any one of claims 1 to 2 in the preparation of antidepressant drugs.

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