In silico prediction of phytoconstituents from Ehretia laevis targeting TNF-α in arthritis

Q3 Medicine Digital Chinese Medicine Pub Date : 2021-10-01 DOI:10.1016/j.dcmed.2021.09.003
Subhash R. Yende , Sapan K. Shah , Sumit K. Arora , Keshav S. Moharir , Govind K. Lohiya
{"title":"In silico prediction of phytoconstituents from Ehretia laevis targeting TNF-α in arthritis","authors":"Subhash R. Yende ,&nbsp;Sapan K. Shah ,&nbsp;Sumit K. Arora ,&nbsp;Keshav S. Moharir ,&nbsp;Govind K. Lohiya","doi":"10.1016/j.dcmed.2021.09.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>Rheumatoid arthritis (RA) is an autoimmune disease involving the synovial lining of the major joints. Current therapies have noteworthy side effects. Our study involved <em>in silico</em> evaluation of <em>Ehretia laevis</em> (<em>E. laevis</em>) phytoconstituents targeting tumor necrosis factor-<em>α</em> (TNF-<em>α</em>).</p></div><div><h3>Methods</h3><p>Molecular docking studies performed to investigate the binding pattern of the plant <em>E. laevis</em> phytoconstituents along with the crystal structure of TNF-<em>α</em> (PDB ID: 2AZ5) using AutoDock Vina followed by a study of interacting amino acid residues and their influence on the inhibitory potentials of the active constituents. Further the pharmacokinetic profile and toxicity screening carried out using SwissADME and pkCSM.</p></div><div><h3>Results</h3><p>The docked results suggest that lupeol (− 9.4 kcal/mol) and <em>α</em>-amyrin (− 9.4 kcal/mol) has best affinity towards TNF-<em>α</em> compared to standard drug thalidomide (− 7.4 kcal/mol). The active chemical constituents represents better interaction with the conserved catalytic residues, leading to the inhibition/blockade of the TNF-<em>α</em>-associated signaling pathway in RA. Furthermore, pharmacokinetics and toxicity parameters of these phytochemicals were within acceptable limits according to ADMET studies.</p></div><div><h3>Conclusion</h3><p>The binding potential of phytoconstituents targeting TNF-<em>α</em> showed promising results. Nonetheless, it encourages the traditional use of <em>E. laevis</em> and provides vital information on drug development and clinical treatment.</p></div>","PeriodicalId":33578,"journal":{"name":"Digital Chinese Medicine","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589377721000318/pdfft?md5=4b53abc9a35b066bd74795d91b60a0cc&pid=1-s2.0-S2589377721000318-main.pdf","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Chinese Medicine","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589377721000318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 8

Abstract

Objective

Rheumatoid arthritis (RA) is an autoimmune disease involving the synovial lining of the major joints. Current therapies have noteworthy side effects. Our study involved in silico evaluation of Ehretia laevis (E. laevis) phytoconstituents targeting tumor necrosis factor-α (TNF-α).

Methods

Molecular docking studies performed to investigate the binding pattern of the plant E. laevis phytoconstituents along with the crystal structure of TNF-α (PDB ID: 2AZ5) using AutoDock Vina followed by a study of interacting amino acid residues and their influence on the inhibitory potentials of the active constituents. Further the pharmacokinetic profile and toxicity screening carried out using SwissADME and pkCSM.

Results

The docked results suggest that lupeol (− 9.4 kcal/mol) and α-amyrin (− 9.4 kcal/mol) has best affinity towards TNF-α compared to standard drug thalidomide (− 7.4 kcal/mol). The active chemical constituents represents better interaction with the conserved catalytic residues, leading to the inhibition/blockade of the TNF-α-associated signaling pathway in RA. Furthermore, pharmacokinetics and toxicity parameters of these phytochemicals were within acceptable limits according to ADMET studies.

Conclusion

The binding potential of phytoconstituents targeting TNF-α showed promising results. Nonetheless, it encourages the traditional use of E. laevis and provides vital information on drug development and clinical treatment.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
青黄体植物成分靶向关节炎TNF-α的计算机预测
目的类风湿关节炎(RA)是一种累及主要关节滑膜的自身免疫性疾病。目前的治疗方法有明显的副作用。我们的研究涉及针对肿瘤坏死因子-α (TNF-α)的laevis (E. laevis)植物成分的硅片评价。方法采用AutoDock Vina软件进行分子对接研究,研究紫叶藤植物成分与肿瘤坏死因子α (PDB ID: 2AZ5)晶体结构的结合模式,并研究相互作用氨基酸残基及其对活性成分抑制电位的影响。进一步使用SwissADME和pkCSM进行药代动力学分析和毒性筛选。结果与标准药物沙利度胺(- 7.4 kcal/mol)相比,lupeol (- 9.4 kcal/mol)和α-amyrin (- 9.4 kcal/mol)对TNF-α的亲和力最好。活性化学成分与保守的催化残基表现出更好的相互作用,导致RA中TNF-α-相关信号通路的抑制/阻断。此外,根据ADMET的研究,这些植物化学物质的药代动力学和毒性参数在可接受的范围内。结论植物成分与TNF-α的结合潜力较好。尽管如此,它还是鼓励传统地使用紫肠杆菌,并提供有关药物开发和临床治疗的重要信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Digital Chinese Medicine
Digital Chinese Medicine Medicine-Complementary and Alternative Medicine
CiteScore
1.80
自引率
0.00%
发文量
126
审稿时长
63 days
期刊最新文献
Distribution of traditional Chinese medicine pattern types and prognostic risk factors in patients undergoing percutaneous coronary intervention (PCI): a systematic review and meta-analysis Research status and prospect of tongue image diagnosis analysis based on machine learning Thoughts on the system construction of digital Chinese medicine Differences in pulse manifestations at Cunkou based on simplified modeling of tactile sensing A novel deep learning based cloud service system for automated acupuncture needle counting: a strategy to improve acupuncture safety
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1