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Article(id=1239174988071227531, tenantId=1146029695717560320, journalId=1205117082300743687, issueId=1239174986137661844, articleNumber=null, orderNo=null, doi=10.14109/j.cnki.xyylc.2024.04.12, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1657382400000, receivedDateStr=2022-07-10, revisedDate=null, revisedDateStr=null, acceptedDate=1699891200000, acceptedDateStr=2023-11-14, onlineDate=1773371939914, onlineDateStr=2026-03-13, pubDate=1713974400000, pubDateStr=2024-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773371939914, onlineIssueDateStr=2026-03-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773371939914, creator=13701087609, updateTime=1773371939914, updator=13701087609, issue=Issue{id=1239174986137661844, tenantId=1146029695717560320, journalId=1205117082300743687, year='2024', volume='43', issue='4', pageStart='241', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773371939453, creator=13701087609, updateTime=1773372128807, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1239175780396233704, tenantId=1146029695717560320, journalId=1205117082300743687, issueId=1239174986137661844, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1239175780396233705, tenantId=1146029695717560320, journalId=1205117082300743687, issueId=1239174986137661844, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=303, endPage=310, ext={EN=ArticleExt(id=1239174988297719954, articleId=1239174988071227531, tenantId=1146029695717560320, journalId=1205117082300743687, language=EN, title=Antitumor pharmacological mechanism of natural compound tryptanthrin based on network pharmacology, columnId=1207314218647392369, journalTitle=Chinese Journal of New Drugs and Clinical Remedies, columnName=Original Article, runingTitle=null, highlight=null, articleAbstract=
AIM

To clarify antitumor pharmacological mechanism of natural compound tryptanthrin by network pharmacology.

METHODS

The drug-likeness of tryptanthrin molecule was evaluated through ADMETlab 2.0 database,the action targets of tryptanthrin were predicted through PharmMapper and Swiss TargetPrediction database, tumor disease targets were collected by OMIM and DisGeNET database, the potential protein targets of antitumor action of tryptanthrin were then screened out. Using STRING database and Cytoscape 3.7 to sequentially construct protein-protein interaction（PPI）networks and compound target pathway disease networks between protein targets. The GO functional enrichment analysis and KEGG pathway enrichment analysis were performed by DAVID database. AutoDock Vina 1.1.2 as a molecular docking method was used to analyze the interaction between tryptanthrin and the selected core target proteins. The gene expression profiling interactive analysis of the core targets was conducted with the existing clinical tumor database.

RESULTS

A total of 158 antitumor core targets of tryptanthrin were obtained by network pharmacological study, the biological processes of GO functional enrichment analysis had an association with inflammatory response, protein phosphorylation and intracellular signal transduction, etc. The cellular components were relatively close to cytosol, cytoplasm and macromolecular complex,etc. The molecular functions were associated with MAP kinase activity, ATP binding and enzyme binding, etc. The results of KEGG pathway enrichment analysis showed that pathways in cancer, PI3K-AKT signaling pathway, etc were the main pathway that tryptanthrin exerted its antitumor effect, it involved the core targets such as AKT1, PI3K, MAPK1, HSP90AA1,JAK2, NFKB1. The 5 core targets including AKT1, HSP90AA1, PI3K, JAK2, MAPK1 had good binding activities with tryptanthrin, all binding energy were less than -8.6 kcal·mol-1. The results of gene expression profiling interactive analysis of the core targets showed that all five core protein targets played important roles in occurrence and development of human tumor diseases.

CONCLUSION

Tryptanthrin exerted its anti-tumor effect through PI3K-AKT signaling pathway and others.

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目的

采用网络药理学方法观察天然化合物色胺酮的抗肿瘤药理机制。

方法

通过ADMETlab 2.0数据库对色胺酮分子的类药性进行评估，运用PharmMapper、Swiss TargetPrediction数据库预测色胺酮的作用靶点，借助OMIM数据库、DisGeNET数据库收集肿瘤疾病靶点，筛选出色胺酮抗肿瘤作用潜在蛋白靶点。利用STRING数据库、Cytoscape 3.7软件依次构建蛋白靶点之间的蛋白质-蛋白质相互作用（PPI）网络、“化合物-靶点-通路-疾病”网络；基于DAVID数据库进行GO功能富集分析、KEGG通路富集分析。采用AutoDock Vina 1.1.2软件进行分子对接，分析色胺酮与筛选出的核心靶点蛋白之间的相互作用。借助现有临床肿瘤数据库，对核心靶点开展基因表达谱交互分析。

结果

通过网络药理学研究共获得色胺酮抗肿瘤核心靶点158个，GO功能富集分析的生物过程与炎症反应、蛋白质磷酸化、细胞内信号转导等关联，细胞组成与胞浆、细胞质、高分子复合物等较为密切，分子功能与MAPK酶活性、ATP绑定、酶结合等相关。KEGG通路富集分析表明癌症的途径、PI3K-AKT信号通路等是色胺酮发挥抗肿瘤作用的主要途径，涉及核心靶点AKT1、PI3K、MAPK1、HSP90AA1、JAK2、NFKB1等。色胺酮与5种核心靶点AKT1、HSP90AA1、PI3K、JAK2、MAPK1之间有较好的结合活性，结合能均不超过-8.6 kcal·mol-1。色胺酮抗肿瘤核心靶点的基因表达谱交互分析结果显示上述5种核心蛋白靶点均在人类肿瘤疾病发生发展中扮演重要作用。

结论

色胺酮通过PI3K-AKT等信号通路发挥抗肿瘤作用。

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陈岩勤，
周威
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杨应勇，男，高级工程师，硕士，主要从事中药新药的研究，E-mail: 。

陈岩勤，女，副教授，硕士生导师，博士，主要从事感染与免疫的研究，E-mail: 。

周威，男，教授，博士生导师，博士，主要从事抗肿瘤药物开发与分析，E-mail:

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杨应勇，男，高级工程师，硕士，主要从事中药新药的研究，E-mail: 。

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杨应勇，男，高级工程师，硕士，主要从事中药新药的研究，E-mail: 。

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陈岩勤，女，副教授，硕士生导师，博士，主要从事感染与免疫的研究，E-mail: 。

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陈岩勤，女，副教授，硕士生导师，博士，主要从事感染与免疫的研究，E-mail: 。

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周威，男，教授，博士生导师，博士，主要从事抗肿瘤药物开发与分析，E-mail:

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周威，男，教授，博士生导师，博士，主要从事抗肿瘤药物开发与分析，E-mail:

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红色五角星标记靶点为色胺酮抗肿瘤靶点

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A：AKT1，B：HSP90AA1，C：PI3Kα，D：JAK2，E：MAPK1

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A：AKT1，B：HSP90AA1，C：PI3Kα，D：JAK2，E：MAPK1

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通路名称介中心度接近中心度节点度
hsa05200：癌症的途径0.01460.402236
hsa04151: PI3K-AKT信号通路0.00720.385126
hsa04625: C型凝集素受体信号通路0.00380.378622
hsa04660: T细胞受体信号通路0.00310.377021
hsa01522：内分泌抵抗0.00270.375520
hsa04917：催乳素信号通路0.00220.373919
hsa04068: FoxO信号通路0.00260.373919
hsa05212：胰腺癌0.00150.372418
hsa04668: TNF信号通路0.01300.372418
hsa04210：细胞凋亡0.00290.372418
hsa05220：慢性粒细胞白血病0.00160.369316
hsa05235：癌症中PD-L1的表达和PD-1检查点通路0.00170.369316
hsa05215：前列腺癌0.00170.369316
hsa05231：癌症中的胆碱代谢0.00120.367815
hsa04370: VEGF信号通路0.00150.366314
hsa05210：结直肠癌0.00070.366314
hsa05221：急性髓系白血病0.01140.364913
hsa05218：黑色素瘤0.00080.364913
hsa05223：非小细胞肺癌0.00080.364913
hsa05214：胶质瘤0.00060.364913
), ArticleFig(id=1239213765401563676, tenantId=1146029695717560320, journalId=1205117082300743687, articleId=1239174988071227531, language=CN, label=表1, caption=

色胺酮抗肿瘤作用网络的信号通路

, figureFileSmall=null, figureFileBig=null, tableContent=
通路名称介中心度接近中心度节点度
hsa05200：癌症的途径0.01460.402236
hsa04151: PI3K-AKT信号通路0.00720.385126
hsa04625: C型凝集素受体信号通路0.00380.378622
hsa04660: T细胞受体信号通路0.00310.377021
hsa01522：内分泌抵抗0.00270.375520
hsa04917：催乳素信号通路0.00220.373919
hsa04068: FoxO信号通路0.00260.373919
hsa05212：胰腺癌0.00150.372418
hsa04668: TNF信号通路0.01300.372418
hsa04210：细胞凋亡0.00290.372418
hsa05220：慢性粒细胞白血病0.00160.369316
hsa05235：癌症中PD-L1的表达和PD-1检查点通路0.00170.369316
hsa05215：前列腺癌0.00170.369316
hsa05231：癌症中的胆碱代谢0.00120.367815
hsa04370: VEGF信号通路0.00150.366314
hsa05210：结直肠癌0.00070.366314
hsa05221：急性髓系白血病0.01140.364913
hsa05218：黑色素瘤0.00080.364913
hsa05223：非小细胞肺癌0.00080.364913
hsa05214：胶质瘤0.00060.364913
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基于网络药理学探讨天然化合物色胺酮的抗肿瘤药理机制
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杨应勇 1 , 杨勇 1 , 梁妍 2 , 宋辉 3 , 张晓燕 3 , 陈岩勤 2 , 周威 2
中国新药与临床杂志 | 论著 2024,43(4): 303-310
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中国新药与临床杂志 | 论著 2024, 43(4): 303-310
基于网络药理学探讨天然化合物色胺酮的抗肿瘤药理机制
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杨应勇1 , 杨勇1, 梁妍2, 宋辉3, 张晓燕3, 陈岩勤2 , 周威2
作者信息
  • 1.贵州神奇药业有限公司 贵州神奇药物研究院，贵州 贵阳　550004
  • 2.贵州医科大学药学院，贵州 贵安新区　561113
  • 3.贵州医科大学基础医学院，贵州 贵安新区　561113

    • 杨应勇，男，高级工程师，硕士，主要从事中药新药的研究，E-mail: 。

    陈岩勤，女，副教授，硕士生导师，博士，主要从事感染与免疫的研究，E-mail: 。

    周威，男，教授，博士生导师，博士，主要从事抗肿瘤药物开发与分析，E-mail:

通讯作者:

陈岩勤，
周威
Antitumor pharmacological mechanism of natural compound tryptanthrin based on network pharmacology
Ying-yong YANG1 , Yong YANG1, Yan LIANG2, Hui SONG3, Xiao-yan ZHANG3, Yan-qin CHEN2 , Wei ZHOU2
Affiliations
  • 1.Guizhou Shenqi Institute of Pharmaceutical Research, Guizhou Shenqi Pharmaceutical Co., Ltd., Guiyang GUIZHOU 550004, China
  • 2.School of Pharmacy, Guizhou Medical University, Gui’an New District GUIZHOU 561113, China
  • 3.School of Basic Medicine, Guizhou Medical University, Gui’an New District GUIZHOU 561113, China
出版时间: 2024-04-25 doi: 10.14109/j.cnki.xyylc.2024.04.12
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摘要
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目的

采用网络药理学方法观察天然化合物色胺酮的抗肿瘤药理机制。

方法

通过ADMETlab 2.0数据库对色胺酮分子的类药性进行评估，运用PharmMapper、Swiss TargetPrediction数据库预测色胺酮的作用靶点，借助OMIM数据库、DisGeNET数据库收集肿瘤疾病靶点，筛选出色胺酮抗肿瘤作用潜在蛋白靶点。利用STRING数据库、Cytoscape 3.7软件依次构建蛋白靶点之间的蛋白质-蛋白质相互作用（PPI）网络、“化合物-靶点-通路-疾病”网络；基于DAVID数据库进行GO功能富集分析、KEGG通路富集分析。采用AutoDock Vina 1.1.2软件进行分子对接，分析色胺酮与筛选出的核心靶点蛋白之间的相互作用。借助现有临床肿瘤数据库，对核心靶点开展基因表达谱交互分析。

结果

通过网络药理学研究共获得色胺酮抗肿瘤核心靶点158个，GO功能富集分析的生物过程与炎症反应、蛋白质磷酸化、细胞内信号转导等关联，细胞组成与胞浆、细胞质、高分子复合物等较为密切，分子功能与MAPK酶活性、ATP绑定、酶结合等相关。KEGG通路富集分析表明癌症的途径、PI3K-AKT信号通路等是色胺酮发挥抗肿瘤作用的主要途径，涉及核心靶点AKT1、PI3K、MAPK1、HSP90AA1、JAK2、NFKB1等。色胺酮与5种核心靶点AKT1、HSP90AA1、PI3K、JAK2、MAPK1之间有较好的结合活性，结合能均不超过-8.6 kcal·mol-1。色胺酮抗肿瘤核心靶点的基因表达谱交互分析结果显示上述5种核心蛋白靶点均在人类肿瘤疾病发生发展中扮演重要作用。

结论

色胺酮通过PI3K-AKT等信号通路发挥抗肿瘤作用。

色胺酮  /  抗肿瘤药，植物  /  网络药理学  /  药理作用分子作用机制（中药）
AIM

To clarify antitumor pharmacological mechanism of natural compound tryptanthrin by network pharmacology.

METHODS

The drug-likeness of tryptanthrin molecule was evaluated through ADMETlab 2.0 database,the action targets of tryptanthrin were predicted through PharmMapper and Swiss TargetPrediction database, tumor disease targets were collected by OMIM and DisGeNET database, the potential protein targets of antitumor action of tryptanthrin were then screened out. Using STRING database and Cytoscape 3.7 to sequentially construct protein-protein interaction（PPI）networks and compound target pathway disease networks between protein targets. The GO functional enrichment analysis and KEGG pathway enrichment analysis were performed by DAVID database. AutoDock Vina 1.1.2 as a molecular docking method was used to analyze the interaction between tryptanthrin and the selected core target proteins. The gene expression profiling interactive analysis of the core targets was conducted with the existing clinical tumor database.

RESULTS

A total of 158 antitumor core targets of tryptanthrin were obtained by network pharmacological study, the biological processes of GO functional enrichment analysis had an association with inflammatory response, protein phosphorylation and intracellular signal transduction, etc. The cellular components were relatively close to cytosol, cytoplasm and macromolecular complex,etc. The molecular functions were associated with MAP kinase activity, ATP binding and enzyme binding, etc. The results of KEGG pathway enrichment analysis showed that pathways in cancer, PI3K-AKT signaling pathway, etc were the main pathway that tryptanthrin exerted its antitumor effect, it involved the core targets such as AKT1, PI3K, MAPK1, HSP90AA1,JAK2, NFKB1. The 5 core targets including AKT1, HSP90AA1, PI3K, JAK2, MAPK1 had good binding activities with tryptanthrin, all binding energy were less than -8.6 kcal·mol-1. The results of gene expression profiling interactive analysis of the core targets showed that all five core protein targets played important roles in occurrence and development of human tumor diseases.

CONCLUSION

Tryptanthrin exerted its anti-tumor effect through PI3K-AKT signaling pathway and others.

tryptanthrin  /  antineoplastic agents, phytogenic  /  network pharmacology  /  molecular mechanisms of pharmacological action（TCD）
杨应勇, 杨勇, 梁妍, 宋辉, 张晓燕, 陈岩勤, 周威. 基于网络药理学探讨天然化合物色胺酮的抗肿瘤药理机制. 中国新药与临床杂志, 2024 , 43 (4) : 303 -310 . DOI: 10.14109/j.cnki.xyylc.2024.04.12
Ying-yong YANG, Yong YANG, Yan LIANG, Hui SONG, Xiao-yan ZHANG, Yan-qin CHEN, Wei ZHOU. Antitumor pharmacological mechanism of natural compound tryptanthrin based on network pharmacology[J]. Chinese Journal of New Drugs and Clinical Remedies, 2024 , 43 (4) : 303 -310 . DOI: 10.14109/j.cnki.xyylc.2024.04.12
正文
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肿瘤是人类第二大杀手，严重影响人类生命健康。大量基础及临床研究表明天然药物通过多种途径发挥抗肿瘤作用，能延长肿瘤患者生存期、提高患者生活质量。来自青黛等天然药物的化合物色胺酮（tryptanthrin）被研究证实在体外多种肿瘤细胞、体内荷瘤实验动物中展现出良好的广谱抗肿瘤药理作用，且安全性高，作为抗肿瘤先导化合物具有较好的新药开发前景[1-3]。色胺酮能够通过杀伤肿瘤细胞、诱导肿瘤细胞凋亡、抑制DNA合成、影响多药耐药基因表达等发挥抗肿瘤效应，本课题组前期研究报道了色胺酮体内外抗乳腺癌作用[4-6]。本研究采用网络药理学的方法，旨在全面了解天然化合物色胺酮的抗肿瘤作用及其药理机制，为该类先导化合物抗肿瘤新药研发提供必要的药学研究数据。
材料与方法
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化学成分
采用ADMETlab 2.0数据库计算色胺酮化学分子的分子量（MW）、氢键受体数（nHA）、氢键供体数（nHD）、脂水分配系数（logP）、可旋转键数（nRot）、拓扑极性分子表面积（TPSA）等理化信息，并根据Lipinski、Veber类药性规则验证色胺酮化学分子。
药物靶点与疾病靶点
利用在线对接模拟工具Pharm-Mapper数据库（Norm Fit > 0.2）、Swiss TargetPrediction数据库（probability > 0）对色胺酮的作用靶点进行预测。借助OMIM数据库（Approved）、DisGeNET数据库（Scoregda > 0），以“Cancer”检索，收集与人类癌症密切相关的基因蛋白。对两类靶点取交集，获得色胺酮抗肿瘤的潜在作用交集靶点。
蛋白质-蛋白质相互作用（PPI）网络的构建与分析
在STRING数据库中获得潜在作用交集靶点的PPI，取置信度≥0.7。以靶点为节点nodes、PPI为边edges，构建PPI网络。网络节点重要度的评价指标以介中心度（betweenness centrality）、接近中心度（closeness centrality）、节点度（degree）表示。采用Cytoscape 3.7软件对PPI网络进行分析。
GO功能富集分析、KEGG通路富集分析与化合物-靶点-通路-疾病网络分析
分析色胺酮抗肿瘤作用靶点的生物学功能，利用DAVID数据库对交集靶点进行GO功能富集分析、KEGG通路富集分析，设定阈值FDR<0.05，n≥10。根据生物学信息筛选与肿瘤有关通路，通过Cytoscape 3.7软件构建色胺酮抗肿瘤的化合物-靶点-通路-疾病网络图。以介中心度、接近中心度、节点度为条件，进行网络拓扑学分析,进行网络可视化呈现。以P < 0.05表示信号通路为显著富集，获得色胺酮抗肿瘤网络核心靶点。
分子对接与基因表达谱交互分析
将色胺酮分子与筛选出的抗肿瘤核心靶点进行分子对接，从RCSB蛋白数据库中获得靶蛋白的三维结构文件，经AutoDockTools 1.5.6软件对蛋白晶体结构进行初始化处理，包括去除溶剂分子、水分子和配体，加氢、合并非极性氢，能量初始化等，准备pdbqt文件，对接位点定义与相应的原始配体活性位点对应。对色胺酮分子的三维结构进行MM2力场优化。
采用分子对接软件AutoDock Vina 1.1.2进行分子对接打分，评价色胺酮分子与核心靶点的结合强度与活性。结合能< 0时,配体与受体结合的构象稳定，能量越低其结合的可能性越大；结合能≤-5.0 kcal·mol-1时，化合物与靶点具有良好的亲和力；结合能≤-7.0 kcal·mol-1时，化合物与靶点具有很好的亲和力。对分子对接结果最佳构象进行可视化处理，通过PyMOL 2.5软件处理最低结合能模型。
围绕色胺酮抗肿瘤的核心靶点，进一步运用GEPIA数据库进行肿瘤核心靶点的基因表达谱交互分析。按照总生存期（overall survival）方法，绘制箱线图（boxplot）和生存曲线（survival curve），进一步验证本次确定的色胺酮抗肿瘤核心靶点。
结果
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色胺酮类药性与抗肿瘤潜在靶点
根据ADMETlab 2.0计算得到色胺酮化学分子的MW 248.06、nHA 4、nHD 0、logP 2.183、nRot 0、TPSA 51.96，符合Lipinski、Veber等类药性规则，通过20%生物利用度，推断色胺酮具有较好成药性，但是生物利用度有待改善，值得后续开展其新药开发工作。
色胺酮抗肿瘤PPI网络
将158个交集基因导入STRING，共获得158个节点、1 215个边，平均节点度15.4，PPI富集P < 1.0-16。利用Cytoscape进行PPI网络分析，根据节点度筛选的前10位PPI核心靶点分别是HSP90AA1、PIK3CA、PIK3R1、HDAC1、ESR1、AKT1、RELA、JAK2、JAK3、NFKB1，见图1。
GO功能和KEGG通路的富集分析
对色胺酮抗肿瘤靶点进行富集分析，色胺酮抗肿瘤交集靶点能够富集到生物过程496种、细胞组成66种、分子功能110种。生物过程与炎症反应、蛋白质磷酸化、细胞内信号转导、肽基丝氨酸磷酸化、基因表达的正调控等相关性较大；细胞组成与胞浆、细胞质、大分子复合物、PI3K复合物等关系较为密切；分子功能与MAPK活性、ATP绑定、酶结合、蛋白激酶活性、蛋白质结合、PI3K酶活性等相关。分别选择排名前20个靶点进行条形柱状图展示，见图2。KEGG通路富集分析，共富集到信号通路158条，与C型凝集素受体信号通路、T细胞受体信号通路、内分泌抵抗、癌症的途径、慢性粒细胞白血病、TNF信号通路、VEGF信号通路、PI3K-AKT信号通路密切相关。靶点有效富集在癌症的途径、PI3K-AKT信号通路中，选择前20条通路进行气泡图展示，见图3。
化合物-靶点-通路-疾病网络
将上述158个交集靶点与20条KEGG通路导入Cytoscape 3.7软件，得到化合物-靶点-通路-疾病网络图，见图4。该网络图包含182个节点，676条边，其中绿色六边形代表色胺酮分子，圆形代表药物作用靶点，菱形代表通路，红色方形代表肿瘤疾病。节点大小或颜色深浅代表度值的高低，靶点的重要性与度值呈正相关。经过网络拓扑学分析，以介中心度、接近中心度、节点度为阈值，排名前10的网络核心靶点分别是AKT1、MAPK1、MAPK3、PIK3R1、PIK3CB、PIK3CA、PIK3CD、RAF1、CCND1、RELA。化合物-靶点-通路-疾病网络分析显示：癌症的途径、PI3K-AKT信号通路等是色胺酮发挥抗肿瘤的主要途径，见表1。色胺酮通过PI3K-AKT信号通路发挥抗肿瘤作用，作用靶点包括AKT1、MAPK1（ERK）、PI3K、RAF1、CCND1、RELA、JAK2等，见图5。
核心靶点验证
依据上述富集分析结果和本课题组前期实验数据，选择AKT1（PDB ID：4ejn）、PI3Kα（PDB ID：4jps）、MAPK1（PDB ID：6g9n）、HSP90AA1（PDB ID：3r91）、JAK2（PDB ID：3rvg）5个代表性核心靶点与色胺酮分子进行分子对接。靶点蛋白三维结构逐一通过PDB ID检索下载。AutoDock Vina 1.1.2分子对接结果显示色胺酮与上述蛋白晶体结合强度：AKT1（-10.4 kcal·mol-1）> HSP90AA1（-10 kcal·mol-1）>JAK2（-9.1 kcal·mol-1）> PI3K（-9 kcal·mol-1）> MAPK1（-8.6 kcal·mol-1）。
对分子对接结果的最佳构象（第一构象）进行可视化处理，分析色胺酮小分子-核心蛋白之间相互作用，发现色胺酮分子上的5位N原子、6位C=O羰基容易与靶点蛋白氨基酸残基形成氢键作用，色胺酮分子其他位置元素、基团跟靶点蛋白以疏水相互作用为主。AKT1-色胺酮结合能为-10.4 kcal·mol-1，色胺酮与氨基酸残基Ser205A形成氢键作用，分别与氨基酸残基Asp292A、Ile290A、Leu264A、Lys268A、Thr211A、Thr291A、Trp80A、Tyr272A和Val270A形成疏水相互作用，见图6。
针对AKT1、PI3K、MAPK1、HSP90AA1、JAK2核心靶点，开展基因表达谱交互分析。现有临床肿瘤数据库数据统计结果显示AKT1、PI3K、MAPK1、HSP90AA1表达水平与乳腺癌发生发展、患者生存期正相关；JAK2则表现出负相关。AKT1的风险比（HR）为1.3（P = 0.13）、PI3K的HR为1.4（P = 0.037）、MAPK1的HR为1.3（P = 0.13）、HSP90AA1的HR为1.3（P = 0.079）、JAK2的HR为0.77（P = 0.11），见图7。
讨论
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现有研究报道显示色胺酮具有良好的抗肿瘤、抗炎活性等治疗作用，具有较好的安全性和新药开发前景[4]。网络药理学以系统生物学为基础，将药理学和生物信息学结合起来，进行全面的药物分析研究。网络药理学认为药物在体内的过程是一个复杂的作用网络，即多成分-多靶点-多途径作用过程[7，8]。目前，该技术能够指导药物先导化合物发现、药效与药理机制研究、临床药物治疗等方面，比较适合作为解决当前复杂药物研究开发过程中瓶颈问题的有效手段之一。
围绕AKT1、PI3K、MAPK1、HSP90AA1、JAK2核心靶点的临床前实验结果与临床肿瘤数据进行分析，反映了上述5种蛋白靶点在人类肿瘤疾病发生发展中扮演重要角色，证明它们是色胺酮发挥抗肿瘤作用的作用靶点。本研究通过网络药理学等手段宏观阐明了色胺酮抗肿瘤的可能药理作用机制，涉及癌症的途径、PI3K-AKT信号通路、C型凝集素受体信号通路、T细胞受体信号通路、内分泌抵抗等。PI3K-AKT信号通路在细胞增殖、凋亡、生长、分化、转录、迁移多方面发挥关键作用，PI3K作为一种胞内磷脂肌醇激酶，通过产生特异性磷酸化反应而发挥作用；AKT1作为蛋白激酶家族之一，是该通路下游核心调控因子，影响肿瘤细胞增殖、代谢。但是由于人类肿瘤疾病的类型众多，各类肿瘤发生发展、恶性癌变程度、临床治疗手段也都不尽相同，因此在研究这类疾病和色胺酮抗肿瘤药物的过程中，既要了解肿瘤的共同性质，也要分析具体肿瘤疾病的自身特点。总之，本研究为后续色胺酮药物分子合理开发、系列色胺酮衍生物活性分子设计、抗肿瘤作用靶点、药效与药理机制、药物开发等指明了研究方向。
基金
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  • 贵州省科技支撑计划项目(黔科合支撑〔2021〕一般415)
  • 贵州省自然科学基金(黔科合基础〔2020〕1Y404)
  • 贵州省自然科学基金(黔科合基础〔2020〕1Y380)
  • 贵州省大学生创新创业训练计划项目(201510660024)
  • 贵州医科大学教学改革研究项目(JG2021042)
文献
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2024年第43卷第4期
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文章信息
doi: 10.14109/j.cnki.xyylc.2024.04.12
  • 接收时间：2022-07-10
  • 首发时间：2026-03-13
  • 出版时间：2024-04-25
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出版历史
  • 收稿日期：2022-07-10
  • 录用日期：2023-11-14
基金
贵州省科技支撑计划项目(黔科合支撑〔2021〕一般415)
贵州省自然科学基金(黔科合基础〔2020〕1Y404)
贵州省自然科学基金(黔科合基础〔2020〕1Y380)
贵州省大学生创新创业训练计划项目(201510660024)
贵州医科大学教学改革研究项目(JG2021042)
作者信息
    1.贵州神奇药业有限公司 贵州神奇药物研究院，贵州 贵阳　550004
    2.贵州医科大学药学院，贵州 贵安新区　561113
    3.贵州医科大学基础医学院，贵州 贵安新区　561113

通讯作者:

陈岩勤，
周威
参考文献
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https://castjournals.cast.org.cn/joweb/zgxyylczz/CN/10.14109/j.cnki.xyylc.2024.04.12
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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