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代谢组学分析:通过网络药理学和对接研究揭示炎症性疾病的治疗潜力。

Metabolomic Profiling of : Unveiling Therapeutic Potential for Inflammatory Diseases through Network Pharmacology and Docking Studies.

作者信息

Mallepura Adinarayanaswamy Yashaswini, Padmanabhan Deepthi, Natarajan Purushothaman, Palanisamy Senthilkumar

机构信息

Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur 603203, Tamil Nadu, India.

Department of Biology, West Virginia State University, Institute, WV 25112-1000, USA.

出版信息

Pharmaceuticals (Basel). 2024 Mar 26;17(4):423. doi: 10.3390/ph17040423.

Abstract

Medicinal plants have been utilized since ancient times for their therapeutic properties, offering potential solutions for various ailments, including epidemics. Among these, , a member of the family, has been traditionally employed to address numerous conditions such as diarrhea, cancer, and fever. In this study, employing HR-LCMS/MS(Q-TOF) analysis, we identified 113 compounds from the methanolic extract of . Utilizing Lipinski's rule of five, we evaluated the drug-likeness of these compounds using SwissADME and ProTox II. SwissTarget Prediction facilitated the identification of potential inflammatory targets, and these targets were discerned through the Genecard, TTD, and CTD databases. A network pharmacology analysis unveiled hub proteins including CCR2, ICAM1, KIT, MPO, NOS2, and STAT3. Molecular docking studies identified various constituents of , exhibiting high binding affinity scores. Further investigations involving in vivo testing and genomic analyses of metabolite-encoding genes will be pivotal in developing efficacious natural-source drugs. Additionally, the potential of molecular dynamics simulations warrants exploration, offering insights into the dynamic behavior of protein-compound interactions and guiding the design of novel therapeutics.

摘要

药用植物自古以来就因其治疗特性而被利用,为包括流行病在内的各种疾病提供了潜在的解决方案。其中,[植物名称],[植物所属科名]科的一员,传统上被用于治疗多种病症,如腹泻、癌症和发烧。在本研究中,我们采用高分辨液相色谱-质谱联用/质谱(四极杆飞行时间质谱)分析,从[植物名称]的甲醇提取物中鉴定出113种化合物。利用Lipinski的五规则,我们使用SwissADME和ProTox II评估了这些化合物的类药性。SwissTarget Prediction有助于识别潜在的炎症靶点,这些靶点通过Genecard、TTD和CTD数据库得以识别。网络药理学分析揭示了包括CCR2、ICAM1、KIT、MPO、NOS2和STAT3在内的枢纽蛋白。分子对接研究确定了[植物名称]的各种成分,其表现出高结合亲和力分数。涉及体内测试和代谢物编码基因的基因组分析的进一步研究对于开发有效的天然来源药物至关重要。此外,分子动力学模拟的潜力值得探索,它能深入了解蛋白质-化合物相互作用的动态行为并指导新型治疗药物的设计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a45c/11054655/46414b221850/pharmaceuticals-17-00423-g001.jpg

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