Study on the Mechanism of the Combination of Methotrexate and Leflunomide in the Treatment of Rheumatoid Arthritis Based on Network Pharmacology, Molecular Docking, and in vitro Experimental Verification.

IF 1.6 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS Combinatorial chemistry & high throughput screening Pub Date : 2024-06-20 DOI:10.2174/0113862073285626240604093210
Jinyang Shi, Xinhua Cui, Yang Wang, Yuli Song, Xudong Tang, Junwen Fan, Hongyue Xu, Mingmei Zhu, Wanlu Yu, Lu Yu
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引用次数: 0

Abstract

Background: To date, disease-modifying antirheumatic drugs (DMARDs) are widely used as the primary first-line treatment option for patients with rheumatoid arthritis (RA), and the curative effect of methotrexate (MTX) and leflunomide (LEF; MTX + LEF) is greater than that of single-agent MTX therapy, but the synergistic mechanism of MTX + LEF is unclear.

Methods: First, we explored the mechanism of action of MTX + LEF in RA through network pharmacology and molecular docking. Venn diagram analysis revealed 97 overlapping gene targets of MTX + LEF-RA and STRING, along with Cytoscape plug-in MOCDE and cytoHubba; and GO enrichment analysis revealed that the functions of 97 synergistic targets were related to 123 molecular functions (MF), 63 cell components (CC), and 1,068 biological processes (BP). The Cytoscape plug-in ClueGO demonstrated that these targets were enriched in KEGG pathways of 52 terms, whereas 9 pivotal genes were mainly involved in the signaling pathways of estrogen, Ras, Rap1, PI3K-Akt, relaxin, TNF, AMPK, FoxO, prolactin, IL-17, and adherens junction. Finally, CETSA and DARTS validated the direct binding of MTX or LEF to the selected target proteins EGFR, PPARG, MMP9, and SRC in RAW264.7 cells.

Results: We identified 292 MTX targets and 247 LEF targets from 7 databases. Furthermore, 2,814 potential targets of RA were identified by merging 1,925 targets from 7 databases and 999 differentially expressed genes (DEGs) between normal controls and patients with RA extracted from 5 GEO databases. Nine pivotal genes, ESR1, ALB, CASP3, EGFR, HSP90AA1, SRC, MMP9, PPARG, and IGF1, were identified. Molecular docking verified that both MTX and LEF strongly bind to most of the 9 pivotal proteins except ESR1 and IGF1.

Conclusion: These results contribute to our understanding of the enhancement mechanism of MTX combined with LEF and provide a targeted basis for the clinical treatment of RA.

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基于网络药理学、分子对接和体外实验验证的甲氨蝶呤和来氟米特联合治疗类风湿性关节炎的机制研究。
背景:迄今为止,改变病情抗风湿药(DMARDs)被广泛用作类风湿关节炎(RA)患者的主要一线治疗方案,甲氨蝶呤(MTX)和来氟米特(LEF;MTX + LEF)的疗效高于单药MTX治疗,但MTX + LEF的协同作用机制尚不清楚:首先,我们通过网络药理学和分子对接探索了MTX + LEF在RA中的作用机制。维恩图分析显示,MTX + LEF-RA和STRING的97个基因靶点与Cytoscape插件MOCDE和cytoHubba重叠;GO富集分析显示,97个协同靶点的功能与123个分子功能(MF)、63个细胞组分(CC)和1,068个生物过程(BP)相关。Cytoscape插件ClueGO显示,这些靶标富集在52个术语的KEGG通路中,而9个关键基因主要涉及雌激素、Ras、Rap1、PI3K-Akt、松弛素、TNF、AMPK、FoxO、催乳素、IL-17和粘连结的信号通路。最后,CETSA和DARTS验证了MTX或LEF与RAW264.7细胞中选定的靶蛋白表皮生长因子受体、PPARG、MMP9和SRC的直接结合:结果:我们从 7 个数据库中发现了 292 个 MTX 靶点和 247 个 LEF 靶点。此外,通过合并 7 个数据库中的 1,925 个靶点和从 5 个 GEO 数据库中提取的正常对照组与 RA 患者之间的 999 个差异表达基因(DEGs),我们发现了 2,814 个 RA 潜在靶点。结果发现了九个关键基因:ESR1、ALB、CASP3、表皮生长因子受体、HSP90AA1、SRC、MMP9、PPARG和IGF1。分子对接证实,除 ESR1 和 IGF1 外,MTX 和 LEF 都能与 9 个关键蛋白中的大多数蛋白强结合:这些结果有助于我们理解 MTX 与 LEF 联用的增强机制,并为 RA 的临床治疗提供了靶向依据。
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来源期刊
CiteScore
3.10
自引率
5.60%
发文量
327
审稿时长
7.5 months
期刊介绍: Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.
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