Article(id=1308455462001734264, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1308455410751525737, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2025-0377, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743609600000, receivedDateStr=2025-04-03, revisedDate=1750435200000, revisedDateStr=2025-06-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889692068, onlineDateStr=2026-09-20, pubDate=1754928000000, pubDateStr=2025-08-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889692068, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889692068, creator=13701087609, updateTime=1789889692068, updator=13701087609, issue=Issue{id=1308455410751525737, tenantId=1146029695717560320, journalId=1189982191388893191, year='2025', volume='60', issue='8', pageStart='2375', pageEnd='2648', issueExtLink='null', onlineDate='null', pubDate='1754928000000', pubDateStr='2025-08-12', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789889679849, creator='13701087609', updateTime=1789893542748, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308471612978786380, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1308455410751525737, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308471612978786381, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1308455410751525737, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2375, endPage=2388, ext={EN=ArticleExt(id=1308455462245003897, articleId=1308455462001734264, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=A novel strategy for development and evaluation of traditional Chinese medicine based on genome-wide pan-receptor drug-target co-screening, columnId=1190335349059588909, journalTitle=Acta Pharmaceutica Sinica, columnName=Professionals Forum, runingTitle=null, highlight=null, articleAbstract=

In the present work, we propose drug-target co-screening strategy, a novel technique for Chinese medicine development and evaluation, which leverages whole-genome pan-receptors and genetically encoded natural compound libraries from medicinal plants and animals. This paper outlines the core components of the strategy, focusing on the identification of novel disease and syndrome-related targets based on clinical efficacy of Chinese medicine, multi-component target screening technologies for herbal medicine, technologies for multi-component identification and target screening and artificial intelligence-powered approaches for discovering active ingredients and elucidating their mechanisms. The strategy demonstrates broad applicability in areas such as the discovery of disease (syndrome) targets, the development of new Chinese medicine drugs, the analysis of multi-target, multi-pathway mechanism of action of Chinese medicine, and the study of drug properties including the five flavors. Centered on whole-genome pan-receptors, drug-target co-screening strategy provides a powerful framework to address key challenges in Chinese medicine research—namely, the ambiguity of therapeutic targets and unclear mechanisms of action. This approach offers strong support for mechanistic studies and paves the way for a new paradigm in modernizing and innovating Chinese medicine drug development.

, authors=San-yin ZHANG1, 2, Ting ZHANG1, 2, Wei CHEN1, 2, Huan LI1, 3, Tai-ping HE1, 4, Han-ting YANG1, Zeng-hao BI1, Zhao-tong CONG1, 2, Shi-lin CHEN1, *, authorsList=San-yin ZHANG, Ting ZHANG, Wei CHEN, Huan LI, Tai-ping HE, Han-ting YANG, Zeng-hao BI, Zhao-tong CONG, Shi-lin CHEN, authorCompany=null, correspAuthors=Shi-lin CHEN, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2025 Acta Pharmaceutica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308455465617224326, articleId=1308455462001734264, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=基于全基因组泛受体的药靶同筛中药开发与评价新策略, columnId=1190335349206389552, journalTitle=药学学报, columnName=专家论坛, runingTitle=null, highlight=null, articleAbstract=

药靶同筛是本草基因组研究团队基于全基因组泛受体和药用动植物基因编码天然多样性成分库提出的中药开发与评价新策略。本文从基于中医临床疗效的病症新靶标发现和中药多组分靶标筛选技术、人工智能赋能药靶同筛的中药活性成分发现及机制解析等方面阐述药靶同筛策略的主要内容, 并阐明其在病(证) 靶标发现、中药新药开发、中药多靶标与途径作用机制解析、中药性味研究等方面的应用。以人类全基因组泛受体为核心的药靶同筛策略, 为中药药效物质基础及作用机制研究提供了有力支持, 有助于解决中药靶标不清、机制不明等关键核心问题, 为中药新药开发和现代化研究提供新的思路与范式。

, authors=张三印1, 2, 张婷1, 2, 陈伟1, 2, 李欢1, 3, 何太平1, 4, 杨含婷1, 毕增昊1, 丛朝彤1, 2, 陈士林1, *, authorsList=张三印, 张婷, 陈伟, 李欢, 何太平, 杨含婷, 毕增昊, 丛朝彤, 陈士林, authorCompany=null, correspAuthors=陈士林, authorNote=

作者贡献:

张三印、张婷、陈伟、李欢负责文章的撰写和修改; 何太平、杨含婷、毕增昊负责文献检索; 丛朝彤负责对文章内容进行客观核对和修改; 陈士林提供选题和撰写思路并指导文章修改。

, correspAuthorsNote=
陈士林, Tel: 86-28-61800086, E-mail:
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张三印 1, 2 , 张婷 1, 2 , 陈伟 1, 2 , 李欢 1, 3 , 何太平 1, 4 , 杨含婷 1 , 毕增昊 1 , 丛朝彤 1, 2 , 陈士林 1, *
药学学报 | 专家论坛 2025,60(8): 2375-2388
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药学学报 |专家论坛 2025 , 60 (8) : 2375 -2388
基于全基因组泛受体的药靶同筛中药开发与评价新策略
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张三印1, 2, 张婷1, 2, 陈伟1, 2, 李欢1, 3, 何太平1, 4, 杨含婷1, 毕增昊1, 丛朝彤1, 2, 陈士林1, *
作者信息
  • 1成都中医药大学本草基因组学研究院, 四川 成都 611137
  • 2成都中医药大学中医药创新研究院, 四川 成都 611137
  • 3南京中医药大学药学院, 江苏 南京 210023
  • 4成都中医药大学基础医学院, 四川 成都 611137
通讯作者:
陈士林, Tel: 86-28-61800086, E-mail:
作者简介:

作者贡献:

张三印、张婷、陈伟、李欢负责文章的撰写和修改; 何太平、杨含婷、毕增昊负责文献检索; 丛朝彤负责对文章内容进行客观核对和修改; 陈士林提供选题和撰写思路并指导文章修改。

A novel strategy for development and evaluation of traditional Chinese medicine based on genome-wide pan-receptor drug-target co-screening
San-yin ZHANG1, 2, Ting ZHANG1, 2, Wei CHEN1, 2, Huan LI1, 3, Tai-ping HE1, 4, Han-ting YANG1, Zeng-hao BI1, Zhao-tong CONG1, 2, Shi-lin CHEN1, *
Affiliations
  • 1Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
  • 2Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
  • 3School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
  • 4School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
出版时间: 2025-08-12 doi: 10.16438/j.0513-4870.2025-0377
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药靶同筛是本草基因组研究团队基于全基因组泛受体和药用动植物基因编码天然多样性成分库提出的中药开发与评价新策略。本文从基于中医临床疗效的病症新靶标发现和中药多组分靶标筛选技术、人工智能赋能药靶同筛的中药活性成分发现及机制解析等方面阐述药靶同筛策略的主要内容, 并阐明其在病(证) 靶标发现、中药新药开发、中药多靶标与途径作用机制解析、中药性味研究等方面的应用。以人类全基因组泛受体为核心的药靶同筛策略, 为中药药效物质基础及作用机制研究提供了有力支持, 有助于解决中药靶标不清、机制不明等关键核心问题, 为中药新药开发和现代化研究提供新的思路与范式。

中药  /  药靶同筛  /  泛受体  /  人工智能  /  靶标发现

In the present work, we propose drug-target co-screening strategy, a novel technique for Chinese medicine development and evaluation, which leverages whole-genome pan-receptors and genetically encoded natural compound libraries from medicinal plants and animals. This paper outlines the core components of the strategy, focusing on the identification of novel disease and syndrome-related targets based on clinical efficacy of Chinese medicine, multi-component target screening technologies for herbal medicine, technologies for multi-component identification and target screening and artificial intelligence-powered approaches for discovering active ingredients and elucidating their mechanisms. The strategy demonstrates broad applicability in areas such as the discovery of disease (syndrome) targets, the development of new Chinese medicine drugs, the analysis of multi-target, multi-pathway mechanism of action of Chinese medicine, and the study of drug properties including the five flavors. Centered on whole-genome pan-receptors, drug-target co-screening strategy provides a powerful framework to address key challenges in Chinese medicine research—namely, the ambiguity of therapeutic targets and unclear mechanisms of action. This approach offers strong support for mechanistic studies and paves the way for a new paradigm in modernizing and innovating Chinese medicine drug development.

traditional Chinese medicine  /  drug-target co-screening  /  pan-receptor  /  artificial intelligence  /  target discovery
张三印, 张婷, 陈伟, 李欢, 何太平, 杨含婷, 毕增昊, 丛朝彤, 陈士林. 基于全基因组泛受体的药靶同筛中药开发与评价新策略. 药学学报, 2025 , 60 (8) : 2375 -2388 . DOI: 10.16438/j.0513-4870.2025-0377
San-yin ZHANG, Ting ZHANG, Wei CHEN, Huan LI, Tai-ping HE, Han-ting YANG, Zeng-hao BI, Zhao-tong CONG, Shi-lin CHEN. A novel strategy for development and evaluation of traditional Chinese medicine based on genome-wide pan-receptor drug-target co-screening[J]. Acta Pharmaceutica Sinica, 2025 , 60 (8) : 2375 -2388 . DOI: 10.16438/j.0513-4870.2025-0377
中医药在多种慢性病和疑难杂症治疗方面具有独特优势, 但中医药研究仍面临成分复杂、靶标和机制不清等挑战。以受体为靶标的药物, 占美国FDA批准上市药物靶标的60%以上[1, 2]; G蛋白偶联受体(G protein-coupled receptors, GPCRs)、离子通道受体、酪氨酸激酶受体等, 作为主要的人类蛋白靶标, 参与多种疾病的发生和发展[3]。其中, GPCRs是目前开发最为成功的药靶之一, 约40%的上市药物以其为靶标[4]。离子通道受体与多种疾病(如心力衰竭、高血压、肥胖症等) 密切相关[5], 约18%的上市药物以其为靶标。核受体是最大的真核转录调节因子超家族, 影响蛋白质合成, 参与生长发育、细胞分化和代谢等过程[6], 已成为新药开发策略中的主要靶标之一[7]。截至2024年, FDA批准了80个蛋白激酶受体药物上市[8]。因此, 靶标泛受体药物开发具有重要价值。
随着高通量筛选、生物信息学、基因组学、代谢组学、蛋白质组学等前沿技术蓬勃发展, 众多国内外研究团队投身中医药研究, 提出了诸多创新性研究范式。Li等[9]融合中医理论与现代药理学、药物化学、生物技术等现代科学理论, 提出“配伍-拼合”药物研发理论。此外, 人工智能(artificial intelligence, AI) 已成为药物发现的关键利器, 有力推动了新药研发向个性化、智能化迈进, 大幅缩短了研发周期和成本[10]。鉴于现有药物半数以上以受体为作用靶标, 本团队结合中医药特色与现代科技, 以泛受体为核心, 针对某一类或多种药物靶标, 如GPCRs、核受体等多种受体靶标, 提出基于全基因组泛受体的药靶同筛中药筛选与评价新策略, 突破了传统单向筛选模式的局限性, 以期挖掘中药来源的新药、提升药物研发效率与成功率, 为中医药发展及疾病靶标与药物发现提供新思维与新方法, 推动传统中药研究与现代科技深度融合, 助力揭示中药作用机制与药效物质基础, 尤其是在药物靶标发现与验证领域, 为中药现代化带来新契机与新路径。
随着化学、生物、生物信息学等技术的发展, 对中药(复方) 化学成分的研究已较为深入, 国内许多团队对其多靶标、多途径作用机制进行了探索, 提出了“系统-系统”[11]、“化学分析-体内代谢-生物机制”[12]、“中药化学物质组解析-中药多成分药代动力学-药代-药效关联性分析”[13]、“部分”对“整体”活性贡献[14]、中药复方在靶器官的“时-量-效”关系[15]、“中药成分-直接靶标-间接靶标-生物效应”[16]等不同的研究策略与方法。这些研究策略和技术方法为阐述中药复方疗效的整体调控机制、构建中药新药发现新范式、推动形成复杂疾病的药物研发新理论、促进中医药原创思维转化为产业竞争力起到了积极作用。如发现片仔癀可通过肠道菌群及其代谢物(如牛磺酸、次牛磺酸和胆汁酸等) 抑制结肠癌, 并发现其主要成分人参皂苷F2和人参皂苷Re对结直肠癌细胞和原代类器官有抑制作用[17]。化湿败毒颗粒通过7种成分、5个靶标、2条途径的“多成分-多靶标-多通路”的整合调节发挥预防和治疗新冠肺炎的药效作用[18], 充分展示了中药复方药物的独特优势。
随着中医药研究新范式的不断涌现, 中药复方作用机制研究也取得诸多新突破, 但中医药的发展和研究仍存在问题, 如: ①长期缺乏自主知识产权药物靶标的“卡脖子”问题; ②基于中医药临床有效的天然先导化合物发现不足; ③ “多成分”下的药效物质不明确、作用机制不清楚。如何结合中医药临床疗效和自身特点, 运用前沿科学技术, 建立适用于中医药的新靶标和创新药物发现模型及评价体系, 推动中药作用机制科学解析是目前亟需解决的问题。
本团队在本草基因组学研究的基础上, 提出适合于中药新药发现和评价的—药靶同筛策略, 基于中医药临床疗效和自身特点, 围绕中医临床优势病种队列数据和药理机制研究, 运用多组学、生物信息学以及AI等技术手段, 挖掘基于共性生物学机制的中医病证新靶标; 通过在分子、细胞、组织、动物及临床患者等多层次进行靶标验证, 并结合药用动植物基因编码天然多样性成分库, 开展基于人类全基因组泛受体的中药新药发现与作用机制评价研究, 立足于中医药临床疗效, 通过识别新靶标、筛选创新药物、解析中药作用机制, 为中药新药发现与评价提供理论依据和技术支撑。
国家中医药管理局通过持续发布中医优势病种目录, 结合基因组学、代谢组学、AI等现代技术手段及海量临床数据积累, 为中医优势病种的靶标发现提供了理论基础和研究思路。此外, 基于规范化临床队列, 通过多组学技术对比健康人群与患者群体的生物标志物差异, 锁定潜在药物靶标; 借助生物信息学分析和全基因组泛受体研究, 系统解析靶标的作用网络与安全性, 并在分子、细胞、动物及临床层面进行多维度验证; 最终形成“病症机制解析-靶标智能筛选-中药精准评价”的创新策略, 既保留中医整体辨证特色, 又实现作用靶标的精准定位, 为中药新药发现与评价提供新的理论支撑。
青蒿素治疗疟疾临床疗效显著, 研究发现青蒿素可能通过抑制恶性疟原虫ATP酶6 (plasmodium falciparumcalcium ATPase 6, PfATP6) 起到杀灭疟原虫的效果, 并通过靶标确认, PfATP6作为疟疾的新药靶标[19]。Yin等[20]基于传统中医针灸防治哮喘的临床疗效出发, 发现并证明了肌动蛋白结合蛋白2 (transgelin-2, TG2) 是治疗哮喘的新靶标。基于该靶标, 利用虚拟筛选技术, 从6 000个化合物中筛选出了具有“类针刺”舒张气管平滑肌作用的先导化合物TSG12, 为进一步的新药发现奠定基础。Han等[21]研究发现番荔枝内酯类似物AA005抗肥胖效应依赖于其与线粒体内膜的线粒体三功能蛋白α亚基相互作用, 结果表明, 线粒体三功能蛋白可能是一个很有价值的治疗靶标。通过临床研究发现中药的作用靶标, 结合现代组学技术、细胞和动物实验、网络药理学分析以及分子验证, 建立中药与疾病靶标之间的关系的研究方法不仅可以揭示中药的分子机制, 还能够为靶向药物研发和临床精准医学提供新思路(图1)。
近年来, 随着亲和质谱法(affinity mass spectrometry, A-MS)、表面等离子共振(surface plasmon resonance, SPR) 技术、生物膜干涉(bio-layer interferometry, BLI) 技术、高通量筛选和虚拟筛选(图2) 等先进技术的广泛应用, 研究人员能够高效地从复杂中药体系中鉴定出与特定靶标结合的活性成分, 并深入探讨其作用机制。A-MS是结合了亲和层析和质谱技术的高灵敏度分析方法, 用于研究分子间的相互作用。Zhang等[22]提出了通过A-MS法筛选多种中药粗提物, 成功鉴定出可结合血清素2C受体的活性配体。细胞膜色谱法(cell membrane chromatography, CMC) 是利用细胞膜的生物特性进行药物筛选和评估的亲和色谱法。Ding等[23]通过CMC方法, 结合质谱鉴定从四逆汤中筛选出多种线粒体靶向成分, 包括准葛尔乌头碱、8-姜酚和异甘草素; Ding等[24]从中药五味子提取物中鉴定出了五种作用于跨膜蛋白分化20的潜在活性化合物。SPR是一种无标签、实时监测分子相互作用的先进技术, 已被用于中药靶向药物的筛选。Chen等[25]通过SPR研究连花清瘟胶囊治疗COVID-19的作用机制, 发现大黄酸、连翘酯苷A、连翘酯苷I、新绿原酸及其异构体能作用于血管紧张素转化酶2, 并对其产生抑制作用。BLI是一种能够实时监测分子间相互作用的光学传感技术。Guo等[26]采用基于BLI与超高效液相色谱-质谱联用技术, 从天然药物中快速识别淀粉样蛋白-β (Aβ) 结合抑制剂。结果显示, 黄芩中的黄芩苷及开心散中的聚多孔菌酸C、脱氢土木香酸和土木香酸对Aβ表现出显著结合亲和力。亲和毛细管电泳技术是一种通过受体或配体进行亲和作用前后的电泳谱图来筛选药物中的活性物质的方法。Li等[27]结合亲和毛细管电泳技术与液质联用技术, 从泽兰提取物中筛选出槲皮素和芦丁可作为表皮生长因子受体的抑制剂。
此外, 基于活性检测的高通量筛选技术也在不断进步, 也为中药多组分靶标和药效物质的发现提供了支持。药用动植物基因编码生物多样性成分库涵盖了亿量级的天然产物, 通过高通量智能筛选技术获得靶标相关的候选化合物。随着AI技术的快速发展, 通过AlphaFold3等新型算法能够获得精确度较高的靶标蛋白结构, 为基于结构的高通量虚拟筛选提供了基础。这些技术的综合应用不仅有助于阐明中药复方的整体药效和多重作用机制, 还为中药新药研发、质量控制和临床应用提供了科学依据。
在癌症、糖尿病、病毒和细菌感染等复杂疾病中, 多靶标药物已进入临床应用, 如用于治疗肥胖症和糖尿病的GLP-1R/GCGR/GIPR多重激动剂[28], 以及拮抗血清素2A受体(serotonin 2A receptor, 5-HT2AR) 同时激活血清素1A受体(serotonin 1A receptor, 5-HT1AR) 的双向调节剂[29]。许多中药有效成分也已被证明具有多靶标作用潜力, 高通量筛选、组学分析和信号通路检测等技术的发展为中药多成分、多靶标的研究提供了有利的条件, 能够更简单、有效地实现中药成分与药物靶标“多对多”的筛选。
以GPCRs为例, 近年来已有多种被开发用于复杂体系的多靶标筛选技术(图3)。PRESTO-Salsa技术能大规模评估几乎所有的非嗅觉GPCRs (近400个靶标), 与过去的PRESTO-Tango技术相比, 具有更高的通量和更低的样本需求, 已用于绘制人类和微生物代谢物在GPCRs中的生物活性[30], 可结合多组学研究揭示中药复方的整体作用机制。ONE-GO生物传感器技术旨在实现快速、简单的药物筛选和生物分析, 可同时评估多种G蛋白和受体的生物学效应, 也可以在各种细胞类型(包括原代细胞系) 中的评价配体活性。该技术结合了生物识别元件和GPCRs下游信号检测系统, 能够以高效、准确的方式检测分子间的相互作用, 已成为GPCRs研究和药物发现的有力工具[31]。DCy-FIR技术平台利用酵母菌株库设计了300个潜在的GPCR-Gα蛋白的偶联组合, 成功复现了多种已知的配体-受体相互作用, 达到了100%的准确率, 同时也成功发现多种全新相互作用, 展示了该技术在识别GPCRs配体方面的优势[32]。
在GPCRs研究中, 细胞表达系统具有重要意义。目前常用的细胞表达系统在研究中各有优劣, 例如昆虫细胞系统在结构研究中可高效表达重组蛋白, 但存在蛋白修饰不全等问题; 哺乳动物细胞系能进行多种翻译后修饰, 利于GPCRs功能研究, 但在某些方面如基因表达稳定性上存在不足。为加速GPCRs靶向药物筛选, 本团队前期提出并构建了全基因组泛GPCRs细胞文库的三种策略[33]。基因过表达策略可利用第二代慢病毒转染系统构建文库, 确保蛋白长期稳定表达; PRESTO-Tango技术[30]能通过转录因子激活系统, 实现高通量功能表达和检测; CRISPR/Cas9技术则可构建全基因组GPCRs基因编辑细胞文库, 用于表型筛选和基因验证。在药物筛选方面, 通过整合虚拟筛选与全基因组泛GPCRs细胞文库依赖的高通量筛选平台, 可提高药物发现效率; 在机制研究方面, 有助于深入探究GPCRs在疾病病因、发病机制及翻译后修饰中的作用; 在药物安全性评估方面, 能为药物研发提供更可靠的数据, 减少对动物实验的依赖。
为了更全面、高效地探索传统药物与GPCRs的关系, 本团队前期提出并构建了全基因组泛GPCRs药物发现平台。该平台主要包括三个关键部分: GPCRs稳转细胞资源库、高通量筛选系统和药理药效评估技术体系。GPCRs稳转细胞资源库包含800余种GPCR基因过表达细胞株, 确保蛋白长期稳定表达。高通量筛选系统涵盖了“一对一”“多对多”等多种筛选策略, 能够快速、高效地筛选出潜在的GPCRs配体。药理评估则从基因型到表型全面评估药物的作用效果, 通过结合多组学技术, 如转录组学、蛋白质组学和代谢组学等, 深入研究药物对细胞和机体的影响, 揭示其潜在的作用机制和脱靶效应, 为药物的优化和开发提供重要依据[34]。该平台通过快速筛选和评估大量传统药物成分, 可提高药物研发效率, 加速药物研发进程, 降低研发成本。同时, 有助于阐明传统药物的治疗机制, 从分子层面解释传统药物在临床应用中的有效性, 为其合理应用和进一步开发提供科学依据。
药物发现是药物研发过程中的关键环节, 涉及靶标识别、先导化合物发现和优化、药效和毒性评价等。传统药物发现方法面临周期长、成本高的问题, 一种新药的发现至少需要10年的研发周期和10亿美元的投入[35]。为了应对这些挑战, 研究者将AI引入药物研发领域, 逐渐成为了药物发现领域的关键工具。截至2023年底, 全球已有700多家企业专注于AI药物研发, 多款AI药物已进入临床阶段。AI赋能的新药研发, 正引领制药领域向个性化药物和智能化研发方向迈进(图4)。
随着高通量测序技术的发展, 多组学数据不断产生, 为基于AI的靶标筛选提供了数据基础。利用AI技术整合和分析多源数据, 通过比较疾病和正常状态下的差异特征可识别潜在靶标[36]。研究者通过对TCGA-GTEx数据库的RNA-Seq数据进行联合分析, 发现胞质分裂调控蛋白1 (protein regulating cytokinesis 1, PRC1)在多种癌组织中异常表达, 是潜在的致癌驱动因素。同时还发现PRC1的异常表达还与免疫微环境中促癌性TH2细胞的增加有关, 这进一步增强了其作为癌症治疗靶标的潜力[37]。然而, 单一组学存在信息不全面、难以揭示多层次调控机制、难以处理疾病异质性等问题。多组学整合则能够以系统性视角捕捉不同分子层次之间的相互作用。作为多组学数据整合分析的关键策略, AI能够全面解析生物系统中的复杂关系, 潜在的靶标可以通过其在疾病和健康状态下的中心性、相互作用模式、调控关系和功能路径等特征进行识别, 使得从多层次的生物数据中识别靶标成为可能, 提高了靶标发现的准确性和效率[38]。
基于GPCRs及其配体的多组学数据, Arora等[39]构建了包含GPCRs、GPCRs配体及配体合成酶的相互作用网络。研究发现, 多种GPCRs与其配体及配体合成酶在不同癌症亚型中受到差异性调节, 并且与患者的生存模式密切相关。进一步分析还发现, 受体-配体(或酶) 对可以更有效地对患者进行分层, 说明GPCRs网络在调节癌症表型中具有协同作用。通过大规模癌症细胞药物筛选, 作者发现毒蕈碱型受体、腺苷受体、5-羟色胺受体和趋化因子受体是诸多具有抑制细胞生长和增殖药物的靶标, 并通过实验验证了两种腺苷受体抑制剂对肝癌细胞系(HepG2) 生长的抑制作用, 证实了这些药物具有潜在的抗癌效果[39]。不仅为理解GPCRs在癌症中的作用提供了新的视角, 也为抗癌药物靶标发现提供了新线索。随着图像处理技术的快速发展, 基于图像的表型分析方法在药物靶标领域日益受到关注, 通过提取生物图像中的信息识别潜在的药物靶标。研究团队[40]采用行人重识别(person re-identification) 方法开发了用于识别线粒体的动态表型特征的MitoReID方法, 通过提取单个细胞线粒体在时间序列变化中的图像特征, MitoReID方法能够预测未知化合物的潜在靶标。
源于中药的化合物是新药发现的宝库, 因其独特的结构和广泛的生物活性, 已成为寻找创新药物的重要资源。目前, 结合AI技术、高效率的活性成分识别与筛选方法, 成为了现代药物发现的重要研究方向。基于AI的活性成分识别与筛选包括数据整合与预处理、特征提取与表示、模型构建等步骤。笔者研究团队[10, 35]在前期工作中详细阐述了各个步骤涉及的方法与技术。以下将以GPCRs配体发现领域的GPCRs-配体互作、配体设计、配体活性预测等问题为例, 简述AI在活性成分识别与筛选方面的应用情况。
GPCRs与其配体间的相互作用具有高度的选择性和特异性, 不同配体对特定GPCRs表现出了独特的亲和力。基于GPCRs-配体互作数据集, Seo等[41]提出了预测GPCRs-配体互作的模型, 为GPCRs-配体相互作用的预测提供了新方法, 对孤儿GPCRs配体筛选具有重要意义。此外, 识别GPCRs中配体的结合区域对于药物设计也至关重要。Di Rienzo等[42]提出了识别GPCRs中的配体结合位点计算模型, 成功预测了秀丽线虫嗅觉神经元GPCRs中的配体结合位点, 为理解嗅觉受体的功能机制提供了新线索。
从头设计配体是当前药物发现领域的前沿方向, 利用AI技术能高效探索广阔的化学空间, 生成具有特定生物活性的全新分子。研究者利用生成张量强化学习模型, 学习ZINC数据库的460万个化合物和ChEMBL数据库中的已知κ-阿片类受体抑制剂, 同时通过定义奖励函数评估分子的合成可行性、溶解度、血脑屏障穿透性等关键药物特性, 生成具有潜在κ-阿片类受体拮抗活性的全新分子[43]。随后, 化学合成了评分排名靠前的分子, 并在体外实验中验证了它们与阿片类受体的亲和力和功能拮抗活性。实验结果表明, 所有合成的化合物都能与受体结合, 其中两个具有最高结合亲和力的化合物对κ-阿片类受体具有明显的拮抗作用。
配体的生物活性对于药物发现至关重要。IC50、EC50、Ki和Kd是描述配体生物活性的指标, 反映了配体与GPCRs作用的效价及亲和力信息。通过整理36种GPCRs的配体生物活性数据, 并利用图的特征对配体进行编码, Velloso等[44]提出了预测GPCRs配体生物活性的模型pdCSM-GPCR, 同时还发现了生物活性高的配体中富含的子结构。该工作不仅展示了如何利用AI技术开发预测配体生物活性的方法, 也为设计更有效的GPCR药物提供重要的结构线索。
尽管已有大量组学数据和中药相关数据, 但各类数据仍分散在不同平台和数据库中, 使得研究者难以全面理解中药活性成分的作用机制及其与疾病之间的关系。此外, 系统化整合方法的缺乏, 使得对中药的研究往往局限于单一视角, 未能充分利用多组学数据的优势。因此, 开发多组学与中药知识融合的智能平台, 是加速中药活性成分发现与机制解析的重要途径。
以英矽智能开发的PandaOmics平台为例, PandaOmics通过整合公开文献、临床试验的文本数据以及基因组学、蛋白质组学等多组学数据, 基于专有的人工智能算法, 实现了对多维数据的深度融合和分析, 在药物发现领域发挥了重要作用, 特别是在特发性肺纤维化、肾纤维化、胶质母细胞瘤等疾病的靶标筛选和药物发现方面, 展现了卓越的效果。利用PandaOmics平台, 研究人员发现了特发性肺纤维化的靶标TNIK (Traf2/NCK互作激酶) 及其抑制剂, 并已开启Ⅱ期临床试验[45]。
为了在中药活性成分的筛选中实现类似的突破性进展, 可以借鉴PandaOmics平台的成功经验, 开发融合多组学数据和中药相关数据的智能化平台。首先, 收集与疾病相关的基因组、转录组、蛋白质组和代谢组等多组学数据, 以及中药、中药成分等相关数据, 并对不同来源的数据进行清洗和格式化, 以消除数据冗余和不一致性。其次, 采用AI技术对多组学数据和中药数据进行高效整合, 构建“中药-活性成分-细胞-靶标-疾病”多层次关联网络; 通过网络分析方法, 识别潜在的药物靶标, 解析其在复杂生物过程中的作用。第三, 针对潜在靶标, 运用AI技术筛选并优化候选成分的结构与功能, 以提高其成药性。因此, 开发融合多组学数据与中药知识的智能平台, 不仅将促进中药活性成分的筛选和作用机制的解析, 还将有助于发现新的药物靶标和优化候选成分, 从而加速中药的现代化和新药研发。
药靶同筛结合了药物和靶标的筛选过程, 旨在同时识别潜在的药物分子和它们潜在作用的生物靶标。这种方法特别适用于中医药研究, 因为中药通常包含多种活性成分, 这些成分可能对多个靶标有影响, 从而产生治疗效果。基于全基因组泛受体的药靶同筛策略, 可以更全面地评估中药的潜在治疗作用(图5)。
在新药研发中, 确定疾病靶标是至关重要环节。Ji等[46]提出一种高通量的混检策略以鉴定药物的靶标, 通过该方法分析5种细胞系药物-靶标相互作用结果, 揭示在不同细胞系之间的靶标和结合亲和力差异, 并且进一步验证了B-Raf原癌基因和酪蛋白激酶2分别是巴非替尼和阿西贝利的潜在脱靶靶标。Wei等[47]开发了一个人软骨类器官高通量药物筛选系统, 发现了α2肾上腺素受体作为软骨再生的新靶标。研究表明, α2肾上腺素受体抑制剂酚妥拉明能够促进软骨分化并抑制软骨肥大, 为软骨损伤治疗提供了新的策略。研究者以中药药效成分为工具探针, 通过靶标“钩钓”技术系统揭示了蟾酥、五味子、野马追等中药代表性成分的直接靶标蛋白及参与疾病相关进程的分子生物学机制, 为“清热解毒、补肾宁心、消肿利湿”等中药功效提供了微观证据, 同时也发现了免疫炎症[48]、肿瘤[49]、神经退行[50]等重大疾病治疗新靶标。Wang等[51]构建了基于病证结合动物模型的药效评价体系, 从疾病、证候、症状三个维度进行评价, 重点突破了证候的规范化、症状的客观化、临床与基础指标等效转化等中医特色的关键问题。前期本团队建立了基于全基因组泛GPCRs细胞株库和基因编码天然多样性成分库, 通过药靶同筛策略进行大规模的筛选和评价, 为病症靶标的发现奠定了基础。
本草基因组团队基于人类全基因组泛受体, 提出的药靶同筛策略整合了基于临床队列数据, 多组学分析、基因编辑、高通量筛选、AI等方法的中医优势病种的病证新靶标挖掘技术, 为新的先导化合物的发现提供技术支撑。三氧化二砷是从中药砒霜中提取的化合物, 用于治疗急性早幼粒细胞白血病, 它的发现为白血病的治疗提供了新的方法[52]。红曲中的洛伐他汀是降胆固醇药物的先导化合物, 它的发现促进了心血管疾病治疗药物的发展[53]。靛玉红是从中药青黛中发现的活性成分, 具有抗白血病的作用, 通过结构优化, 得到了新药甲异靛[54]。紫杉醇[55]、长春碱[56]等活性成分, 作为先导化合物, 经过成药性优化后, 顺利被应用于临床。由此可见, 中药是发现先导化合物的宝库, 且越来越多的筛选技术用于发现先导化合物。亲和超滤质谱技术被用于从中药中筛选活性成分, 如酪氨酸酶、葡萄糖苷酶、黄嘌呤氧化酶等的抑制剂[57]。基于线粒体的离心超滤/液相色谱/质谱法被用于从中药中筛选潜在的靶向线粒体的化合物, 发现了23种活性化合物, 其中12种是新型化合物[58]。奔曜科技与浙江大学长三角智慧绿洲创新中心未来健康实验室联合开发的平台, 每天可筛选2 500多个中药成分, 为中药的加速开发提供了数智新动力。
中药复方是中医治疗疾病的主要形式, 药物配伍不是药物的简单相加, 而是遵循配伍原则选择合适药物组合, 最后达到增效减毒目的, 揭示其作用机制是中药现代化研究的重要组成部分[59]。Zhang等[60]通过复方黄黛片治疗急性早幼粒细胞白血病的研究利用现代科学的语言来描述、解释传统中医药“君臣佐使”这一重要组方原理。中西医结合“分子配伍”理论坚持中医药理论, 充分发挥中药多成分、多靶标、多环节的综合疗效优势, 采用现代制剂方式制成现代中药, 克服了传统中药复方物质基础不明和西药不良反应较大的缺点, 兼取了中药复方综合作用的优势和西药单体针对性强的优点[61]。
2023年, 诺华公司心衰药物“诺欣妥(2015年7月)” 2023年的销售额超60亿美元, 同比增长31%。与联合用药不同, 诺华公司将两个已知分子(已上市高血压药物缬沙坦和脑啡肽酶抑制剂[62]) 结合成一个具有专利保护的新分子实体, 通过共晶带来的理化特性改变, 进一步放大了两个活性组分间的协同效果, 可在增强心脏保护神经内分泌系统的同时抑制肾素-血管紧张素-醛固酮系统等[63], 以治疗心脏衰竭[62]。Liao等[64]首次提出中药分子新药研发“合策略”, 发明并证实丹参素异丙酯、丹参素冰片酯等多个分子组方的创新候选药; Chen等[65]在其研究中提出了中西医结合“分子配伍”理论, 并成功研发了原创抗癌新药榄香烯脂质体, 这是全球第一个仅含碳氢元素的抗癌新药; Harrison等[66]以传统天然产物为基础进行创新药研发, 其自主研发的创新分子由小檗碱及熊去氧胆酸形成。相较于盐酸小檗碱、熊去氧胆酸或两者的简单联用, 创新分子HTD1801有非常独特的理化特性, 表现出水溶性和亲脂性的双重改善[67]。因此, 以传统天然产物为基础, 结合全基因组泛受体药靶同筛策略, 有助于促进分子配伍创新药物的研发。
中医“异病同治”的理论在于辨证与辨病相结合, 在证同的基础上进一步扩大原有的治疗范围, 从而发现新的适应症。在历代医家的研究中, 特别是张仲景的《伤寒论》中发现证同均用一方, 即异病证同而同方, 从而大大地扩展了原有方剂的治疗范围[68]。妇科经典方定经汤具有疏肝补肾之功效, 是治疗妇科月经失调肝郁肾虚证的代表方剂[69], 同时肝郁肾虚也是男科病症的主要证型之一, 故已有研究发现定经汤应用于阳痿、早泄等男科病症的治疗中, 均取得较好疗效。除了通过同方同证来发现新的适应症外, 也可以通过共同的病机来探索新的适应症。阳痿和气瘿虽为不同疾病, 但核心病机均为肝气郁滞, 因此, 临床中利用柴胡类方从肝论治阳痿和气瘿[70]。综上所述, 基于“异病同治”理论, 可以通过同方同证、发病机制相同和共同的分子生物学机制来发现新的适应症。
在非酒精性脂肪肝炎和糖尿病中, 核受体FXR (farnesoid X receptor, FXR) 介导了胆汁酸和糖脂代谢异常, 然而激活肝脏FXR可改善非酒精性脂肪肝炎[71]和糖尿病[72]。二甲双胍可通过多种机制降低血糖, 包括减少肝脏的糖异生、提高外周组织对葡萄糖的利用、抑制肠道吸收葡萄糖等[73]。在非糖尿病疾病的治疗中, 二甲双胍可降低心血管疾病的风险, 并且对已经发生心血管疾病的糖尿病患者有保护作用[74], 这表明基于特定靶标的“异病同治”策略在中药研究中是可行的。紫杉醇是一种从红豆杉树皮中提取的天然次生代谢产物, 通过促进微管蛋白聚合, 抑制微管蛋白的解聚, 从而阻止癌细胞的有丝分裂和触发细胞凋亡, 尤其在卵巢癌、子宫癌和乳腺癌等癌症的治疗中显示出特效[75]。这些研究表明通过识别不同疾病背后的共同生物学途径或靶标, 可以探索已有药物在新的治疗领域中的应用, 发现其新的适应症。
中医药的核心理念是整体观念, 强调人体各个系统和器官之间在生理功能上是紧密相连的。在病理状态下, 某一部位的病变会不可避免地波及其他区域或者整个身体的平衡, 反之亦然, 整体的健康状况也会对局部病变造成影响[76]。因此, 在中药的研究中, 必须考虑到各种成分之间的相互作用, 包括它们的协同效应和相互增强的功效。目前药物开发是基于“一种疾病, 一个靶标, 一个分子”的理念。然而, 大多数疾病的发病机制是复杂多样的, 甚至同一种疾病的症状也各不相同。因此, 针对单一靶标的药物往往难以满足治疗需求[29]。Zhang等[77]开发了一种中药微球靶标捕捞策略, 筛选出28种关键靶标, 以GNAS为关键靶标, 从参芪降糖颗粒中筛选到26个化合物, 经过后续验证其中7个具有很强的结合活性。5-HT2AR[78]和5-HT1AR[79]是非典型抗精神病药物的关键作用靶标。Chen等[29]基于选择性拮抗5-HT2AR (如匹莫范色林) 的药物和5-HT1AR激动剂(如坦度螺酮), 设计出同时具有5-HT1AR激动剂和5-HT2AR拮抗剂活性, 并且规避多巴胺D2受体拮抗活性的多靶标分子, 有望在更广泛的精神疾病领域, 如精神分裂症、痴呆症中得以应用。本团队提出的药靶同筛从疾病–靶标–药物出发, 更易揭示中药“多靶标、多途径”的作用机制; 研究泛受体可进一步解析中药的多靶标协同作用机制, 阐述中医药治疗疾病的科学内涵, 促进新药(如分子配伍创新药物等) 开发。
中药药性理论是研究中药的性质、性能及其运用规律的理论, 包括四气、五味、归经、升降沉浮、有毒无毒等方面。其中, 五味包括酸、苦、甘、辛、咸, 不同的味道具有不同的治疗作用。《本草备要》记载: “凡药酸者能涩能收、苦者能泻能燥能坚、甘者能补能和能缓、辛者能散能润能横行、咸者能下能软坚、淡者能利窍能渗泄”。GPCRs是主要的嗅觉和味觉感受器[80-82], 主要参与识别苦味[83, 84]、甜味[85]、鲜味[86, 87]以及脂肪味[88]。2000年, Adler等[89]第一次在哺乳动物中发现与苦味相关的基因。截至目前, 人类已鉴定出25个苦味受体(type 2 bitter taste receptor, TAS2Rs) 基因[90], 这些受体在舌头、大脑[91]、肺[92]、胃[93]、肠道[94]等多种器官组织中表达, 与呼吸系统、心血管系统、内分泌系统等多种系统疾病相关。中医学认为苦味药其性沉降, 有降气、燥湿、泄热、坚阴等功效。《黄帝内经》记载“肺苦气上逆, 急食苦以泄之”, 苦味药沉降的特性常用于治疗肺失宣降之证。TAS2Rs在人体气道平滑肌(airway smooth muscle, ASM) 上表达, 苦味剂激活相应受体导致钙钾离子通道开放, 引起ASM舒张[95], 其气管舒张作用甚至优于β肾上腺素能受体激动剂[96, 97], 为慢性阻塞性肺病、支气管哮喘等肺部疾病的临床中西医结合治疗提供直接证据。其次, 苦味受体也在肠道中表达[98], 黄连是苦味药的代表, 小檗碱是其发挥作用的主要有效成分, Yue等[99]研究发现小檗碱可以通过TAS2R介导的GLP-1 (glucagon-like peptide-1) 的分泌途径调节人体内葡萄糖稳态。黄芪多糖(astragalus polysaccharides, APS) 是黄芪的重要成分, 具有调控血糖、保护心血管等作用[100]。Luo等[101]发现, APS可以促进大鼠肠道中甜味受体2/3 (taste receptor 2 and 3, T1R2/T1R3) 的表达水平, 增加GLP-1的合成与释放, 促进葡萄糖的转运功能从而达到治疗糖尿病的作用[102]。由此可见, 中药“五味”可通过调节GPCRs中的味觉受体发挥药理作用。因此, 研究泛受体可为解析中药药性的相关机制提供新思路, 有利于提高对中药发挥作用的现代医学认识, 为临床应用提供依据。
GPCRs、核受体、酪氨酸激酶受体、离子通道等蛋白家族占FDA批准上市药物靶标的60%以上, 参与多种疾病的发生和发展[3], 是重要的药物靶标。本团队提出的基于全基因组泛受体和药用动植物基因编码天然多样性成分库的药靶同筛策略, 通过高通量药物筛选平台, 结合单细胞组学等前沿技术, 确认候选活性配体的类型及其作用机制; 同时对筛选出的天然产物进行结构优化与改造, 并结合体外细胞模型与体内动物实验开展系统的生物活性评价, 从疾病-药物-靶标出发, 更为系统全面地阐释了中医药“多成分、多靶标”的作用机制, 是更适合中医药复杂体系的筛选策略, 有利于破解中医药疗效不明、机制不清等核心难题, 推动中药新药研发与现代化进程。
药靶同筛策略可应用于靶标和药物发现、分子配伍和中药“多靶标、多途径”作用机制解析等研究, 笔者团队已构建了800余种GPCRs稳转细胞库和药用动植物基因编码天然多样性成分库化合物库, 及AI赋能的高通量药物筛选和验证平台, 同时也构建了完整的泛受体下游信号验证平台, 可为中药药效物质基础及作用机制研究提供有力支持, 为中药新药开发和现代化研究提供新的思路与范式。

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2025年第60卷第8期
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doi: 10.16438/j.0513-4870.2025-0377
  • 接收时间:2025-04-03
  • 首发时间:2026-09-20
  • 出版时间:2025-08-12
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  • 收稿日期:2025-04-03
  • 修回日期:2025-06-21
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    1成都中医药大学本草基因组学研究院, 四川 成都 611137
    2成都中医药大学中医药创新研究院, 四川 成都 611137
    3南京中医药大学药学院, 江苏 南京 210023
    4成都中医药大学基础医学院, 四川 成都 611137

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2种不同金属材料的力学参数
科
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属数
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genus
种数
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species
占总种数比例
Percentage of
total species (%)
属
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种数
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species
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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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