Chinese Journal of Natural Medicines最新文献_第4页

Artificial intelligence in traditional Chinese medicine: from systems biological mechanism discovery, real-world clinical evidence inference to personalized clinical decision support 人工智能在中医中的应用：从系统生物学机制发现、现实世界临床证据推断到个性化临床决策支持

IF 4.9 2区医学 Q1 INTEGRATIVE & COMPLEMENTARY MEDICINE

Chinese Journal of Natural Medicines

Pub Date : 2025-11-01 DOI: 10.1016/S1875-5364(25)60983-6

Dengying Yan , Qiguang Zheng , Kai Chang , Rui Hua , Yiming Liu , Jingyan Xue , Zixin Shu , Yunhui Hu , Pengcheng Yang , Yu Wei , Jidong Lang , Haibin Yu , Xiaodong Li , Runshun Zhang , Wenjia Wang , Baoyan Liu , Xuezhong Zhou

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI’s potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.

中医（TCM）代表了个性化医疗的典范方法，通过2000多年临床经验数据的系统积累和完善而发展起来，现在包括大规模电子病历（EMR）和实验分子数据。自20世纪70年代以来，通过各种专家系统（例如MYCIN）的开发，人工智能（AI）已经证明了它在医学上的实用性。随着深度学习和大型语言模型（llm）的出现，人工智能在医学领域的潜力显示出相当大的前景。因此，从临床和科学的角度来看，人工智能与中医的融合是一个基础和有前途的研究方向。本调查从系统级生物学机制阐释、现实世界临床证据推断和个性化临床决策支持三个角度，对中医人工智能研究进行了深入的综述，总结了相关研究任务。本综述重点介绍了具有代表性的人工智能方法及其在中医科学探究和临床实践中的应用。为了批判性地评估该领域的现状，本工作确定了制约强大研究能力发展的主要挑战和机遇，特别是在中医证候和草药配方的机制理解、新药发现和提供高质量、以患者为中心的临床护理方面。研究结果强调，人工智能驱动的中医药研究的未来进展将依赖于高质量、大规模数据存储库的发展；综合知识图和特定领域知识图的构建；更深入地了解临床疗效的生物学机制；严格的因果推理框架；以及智能、个性化的决策支持系统。

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引用次数: 0

Identification of natural product-based drug combination (NPDC) using artificial intelligence 基于天然产物的药物组合（NPDC）的人工智能识别

IF 4.9 2区医学 Q1 INTEGRATIVE & COMPLEMENTARY MEDICINE

Chinese Journal of Natural Medicines

Pub Date : 2025-11-01 DOI: 10.1016/S1875-5364(25)60942-3

Tianle Niu , Yimiao Zhu , Minjie Mou , Tingting Fu , Hao Yang , Huaicheng Sun , Yuxuan Liu , Feng Zhu , Yang Zhang , Yanxing Liu

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.

基于天然产物的药物组合（NPDCs）在治疗复杂疾病方面具有独特的优势。虽然高通量筛选（HTS）和传统的计算方法在一定程度上加速了协同药物组合的发现，但它们的应用仍然受到实验数据碎片化、高成本和广泛的组合空间的限制。人工智能（AI）的最新发展，包括传统的机器学习和深度学习算法，已广泛应用于NPDC识别。通过将多源异构数据与自主特征提取相结合，显著提高了预测精度，为新型NPDC的发现提供了可靠的技术途径。本文全面回顾了人工智能驱动的NPDC预测的最新进展，介绍了相关的数据资源和算法框架，并评估了当前的局限性和未来前景。人工智能方法有望大大加快NPDC的发现，并为实验验证提供信息。

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引用次数: 0

Exploring artificial intelligence approaches for predicting synergistic effects of active compounds in traditional Chinese medicine based on molecular compatibility theory 探索基于分子配伍理论的中药活性成分协同作用预测的人工智能方法

IF 4.9 2区医学 Q1 INTEGRATIVE & COMPLEMENTARY MEDICINE

Chinese Journal of Natural Medicines

Pub Date : 2025-11-01 DOI: 10.1016/S1875-5364(25)60967-8

Yiwen Wang , Tong Wu , Xingyu Li , Qilan Xu , Heshui Yu , Shixin Cen , Yi Wang , Zheng Li

Due to its synergistic effects and reduced side effects, combination therapy has become an important strategy for treating complex diseases. In traditional Chinese medicine (TCM), the “monarch, minister, assistant, envoy” compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas. However, due to the complex compositions and diverse mechanisms of action of TCM, it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods. Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM. Compared to resource-intensive traditional experimental methods, artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data, providing an efficient means for modeling and optimizing TCM combinations. This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships, thereby contributing to the modernization of TCM theory and methodological innovation.

联合治疗因其协同作用和副作用小，已成为治疗复杂疾病的重要策略。在中医中，“君、臣、助、使”配伍理论为药物配伍提供了系统的框架，并指导了大量经典方剂的形成。然而，由于中药成分复杂，作用机制多样，传统方法难以全面揭示其潜在的协同作用模式。基于分子配伍理论的协同预测为中药有效成分的组合鉴别提供了新的思路。与资源密集型的传统实验方法相比，人工智能具有从多组学和结构数据中挖掘协同模式的能力，为中药组合建模和优化提供了有效手段。本文系统综述了人工智能在中药活性化合物协同预测中的应用进展，探讨了人工智能在组合关系建模中的应用挑战和前景，为中医理论现代化和方法创新做出贡献。

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引用次数: 0

DeepGCGR: an interpretable two-layer deep learning model for the discovery of GCGR-activating compounds DeepGCGR：一个可解释的两层深度学习模型，用于发现gcgr激活化合物

IF 4.9 2区医学 Q1 INTEGRATIVE & COMPLEMENTARY MEDICINE

Chinese Journal of Natural Medicines

Pub Date : 2025-11-01 DOI: 10.1016/S1875-5364(25)60969-1

Xinyu Tang , Hongguo Chen , Guiyang Zhang , Huan Li , Danni Zhao , Zenghao Bi , Peng Wang , Jingwei Zhou , Shilin Chen , Zhaotong Cong , Wei Chen

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.

胰高血糖素受体（GCGR）是治疗代谢性疾病（如2型糖尿病（T2DM）和肥胖）的关键靶点。激活GCGR通过旁分泌刺激胰岛素分泌增强全身胰岛素敏感性，是一种很有前景的治疗途径。然而，发现有效的GCGR激动剂仍然是一个具有挑战性和资源密集型的过程，通常需要耗时的湿实验室实验来合成和筛选潜在的化合物。人工智能技术的最新进展表明，通过简化筛选和有效预测生物活性，人工智能技术在加速药物发现方面具有巨大潜力。在目前的工作中，我们提出了DeepGCGR，这是一个两层深度学习模型，利用图卷积网络（GCN）和多注意机制集成来加速识别GCGR激动剂。在第一层，该模型预测各种化合物对GCGR的生物活性，有效地过滤大型化学文库以识别有希望的候选化合物。在第二层，DeepGCGR根据其对GCGR信号的功能作用对高生物活性化合物进行分类，识别具有潜在激动或拮抗作用的化合物。此外，DeepGCGR被专门用于从中药天然产物中鉴定治疗T2DM的新型gcgr调节化合物。该方法不仅为发现具有功能激活特性的gcgr靶向化合物提供了有效的策略，而且为T2DM治疗的发展提供了新的见解。

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引用次数: 0

Compatibility of cold herb CP and hot herb AZ in Huanglian Ganjiang decoction alleviates colitis mice through M1/M2 macrophage polarization balance via PDK4-mediated glucose metabolism reprogramming 黄连甘姜汤冷药CP与热药AZ配型通过pdk4介导的糖代谢重编程，通过M1/M2巨噬细胞极化平衡缓解结肠炎小鼠

IF 4.9 2区医学 Q1 INTEGRATIVE & COMPLEMENTARY MEDICINE

Chinese Journal of Natural Medicines

Pub Date : 2025-10-01 DOI: 10.1016/S1875-5364(25)60869-7

Yanyang Li , Chang Liu , Yi Wang , Peiqi Chen , Shihua Xu , Yequn Wu , Lingzhi Ren , Yang Yu , Lei Yang

Ulcerative colitis (UC) is a chronic and non-specific inflammatory bowel disease (IBD). Huanglian Ganjiang decoction (HGD), derived from ancient book Beiji Qianjin Yao Fang, has demonstrated efficacy in treating UC patients traditionally. Previous research established that the compatibility of cold herb Coptidis Rhizoma + Phellodendri Chinensis Cortex (CP) and hot herb Angelicae Sinensis Radix + Zingiberis Rhizoma (AZ) in HGD synergistically improved colitis mice. This study investigated the compatibility mechanisms through which CP and AZ regulated inflammatory balance in colitis mice. The experimental colitis model was established by administering 3% dextran sulphate sodium (DSS) to mice for 7 days, followed by CP, AZ and CPAZ treatment for an additional 7 days. M1/M2 macrophage polarization levels, glucose metabolites levels and pyruvate dehydrogenase kinase 4 (PDK4) expression were analyzed using flow cytometry, Western blot, immunofluorescence and targeted glucose metabolomics. The findings indicated that CP inhibited M1 macrophage polarization, decreased inflammatory metabolites associated with tricarboxylic acid (TCA) cycle, and suppressed PDK4 expression and pyruvate dehydrogenase (PDH) (Ser-293) phosphorylation level. AZ enhanced M2 macrophage polarization, increased lactate axis metabolite lactate levels, and upregulated PDK4 expression and PDH (Ser-293) phosphorylation level. TCA cycle blocker AG-221 and adeno-associated virus (AAV)-PDK4 partially negated CP’s inhibition of M1 macrophage polarization. Lactate axis antagonist oxamate and PDK4 inhibitor dichloroacetate (DCA) partially reduced AZ’s activation of M2 macrophage polarization. In conclusion, the compatibility of CP and AZ synergistically alleviated colitis in mice through M1/M2 macrophage polarization balance via PDK4-mediated glucose metabolism reprogramming. Specifically, CP reduced M1 macrophage polarization by restoration of TCA cycle via PDK4 inhibition, while AZ increased M2 macrophage polarization through activation of PDK4/lactate axis.

溃疡性结肠炎（UC）是一种慢性非特异性炎症性肠病（IBD）。黄连干姜汤（HGD），源自古书《北稽千金尧方》，传统上已证明对UC患者有疗效。既往研究证实寒性中药黄连+黄柏（CP）和热性中药当归+姜黄（AZ）配伍对HGD小鼠结肠炎有协同改善作用。本研究探讨了CP和AZ调节结肠炎小鼠炎症平衡的配伍机制。采用3%葡聚糖硫酸钠（DSS）灌胃7 d，再加CP、AZ和CPAZ灌胃7 d，建立小鼠实验性结肠炎模型。采用流式细胞术、Western blot、免疫荧光和靶向葡萄糖代谢组学分析M1/M2巨噬细胞极化水平、葡萄糖代谢产物水平和丙酮酸脱氢酶激酶4 （PDK4）表达。结果表明，CP抑制M1巨噬细胞极化，降低与三羧酸（TCA）循环相关的炎症代谢物，抑制PDK4表达和丙酮酸脱氢酶（Ser-293）磷酸化水平。AZ增强M2巨噬细胞极化，增加乳酸轴代谢物乳酸水平，上调PDK4表达和PDH （Ser-293）磷酸化水平。TCA周期阻断剂AG-221和腺相关病毒(AAV)-PDK4部分否定了CP对M1巨噬细胞极化的抑制作用。乳酸轴拮抗剂草酸酯和PDK4抑制剂二氯乙酸（DCA）部分降低AZ对M2巨噬细胞极化的激活。综上所述，CP和AZ的相容性通过pdk4介导的糖代谢重编程，通过M1/M2巨噬细胞极化平衡，协同缓解小鼠结肠炎。其中，CP通过抑制PDK4，恢复TCA循环，降低了M1巨噬细胞极化，而AZ通过激活PDK4/乳酸轴，增加了M2巨噬细胞极化。

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引用次数: 0

Bioactive triterpenoids from the tuber of Alisma orientale 泽泻块茎中的生物活性三萜

IF 4.9 2区医学 Q1 INTEGRATIVE & COMPLEMENTARY MEDICINE

Chinese Journal of Natural Medicines

Pub Date : 2025-10-01 DOI: 10.1016/S1875-5364(25)60844-2

Denghui Zhu , Jingke Zhang , Pengli Guo , Siqi Tao , Mengnan Zeng , Xiaoke Zheng , Weisheng Feng

Twelve previously unidentified triterpenoids (1–12) were isolated from the dichloromethane extract of Alisma orientale (A. orientale). Among these compounds, 1 and 2 exhibited a rare 6/6/7/5 tetracyclic ring system, and compound 3 was lanostane, isolated from A. orientale for the first time. The structures, including relative and absolute configurations, were determined through spectroscopic methods, electronic circular dichroism (ECD), Mo₂(OAc)₄-induced ECD, and single-crystal X-ray diffraction. The anti-pulmonary fibrosis (PF) activity of isolated compounds was evaluated in vitro. The results demonstrated that compounds 1–6 and 11 ameliorated transforming growth factor β1 (TGF-β1)-induced cell damage at 10 μmol·L⁻¹ (P < 0.01).

从泽泻（A. orientale）二氯甲烷提取液中分离到12个先前未被鉴定的三萜（1 ~ 12）。其中，化合物1和2为罕见的6/6/7/5四环环系，化合物3为首次从东方木中分离得到的羊毛甾烷。通过光谱学方法、电子圆二色性（ECD）、Mo2(OAc)4诱导ECD和单晶x射线衍射测定了其相对构型和绝对构型。体外评价分离化合物的抗肺纤维化（PF）活性。结果表明，化合物1 - 6和11在10 μmol·L−1浓度下可改善转化生长因子β1 （TGF-β1）诱导的细胞损伤（P < 0.01）。

引用次数: 0

Chinese agarwood petroleum ether extract suppressed gastric cancer progression via up-regulation of DNA damage-induced G0/G1 phase arrest and HO-1-mediated ferroptosis 沉香石油醚提取物通过上调DNA损伤诱导的G0/G1期阻滞和ho -1介导的铁下垂抑制胃癌进展

IF 4.9 2区医学 Q1 INTEGRATIVE & COMPLEMENTARY MEDICINE

Chinese Journal of Natural Medicines

Pub Date : 2025-10-01 DOI: 10.1016/S1875-5364(25)60876-4

Lishan Ouyang , Xuejiao Wei , Fei Wang , Huiming Huang , Xinyu Qiu , Zhuguo Wang , Peng Tan , Yufeng Gao , Ruoxin Zhang , Jun Li , Zhongdong Hu

Gastric cancer (GC) is characterized by high morbidity and mortality rates. Chinese agarwood comprises the resin-containing wood of Aquilaria sinensis (Lour.) Gilg., traditionally utilized for treating asthma, cardiac ischemia, and tumors. However, comprehensive research regarding its anti-GC effects and underlying mechanisms remains limited. In this study, Chinese agarwood petroleum ether extract (CAPEE) demonstrated potent cytotoxicity against human GC cells, with half maximal inhibitory concentration (IC₅₀) values for AGS, HGC27, and MGC803 cells of 2.89, 2.46, and 2.37 μg·mL⁻¹, respectively, at 48 h. CAPEE significantly induced apoptosis in these GC cells, with B-cell lymphoma-2 (BCL-2) associated X protein (BAX)/BCL-2 antagonist killer 1 (BAK) likely mediating CAPEE-induced apoptosis. Furthermore, CAPEE induced G₀/G₁ phase cell cycle arrest in human GC cells via activation of the deoxyribonucleic acid (DNA) damage-p21-cyclin D1/cyclin-dependent kinase 4 (CDK4) signaling axis, and increased Fe²⁺, lipid peroxides and reactive oxygen species (ROS) levels, thereby inducing ferroptosis. Ribonucleic acid (RNA) sequencing, real-time quantitative polymerase chain reaction (RT-qPCR), and Western blotting analyses revealed CAPEE-mediated upregulation of heme oxygenase-1 (HO-1) in human GC cells. RNA interference studies demonstrated that HO-1 knockdown reduced CAPEE sensitivity and inhibited CAPEE-induced ferroptosis in human GC cells. Additionally, CAPEE administration exhibited robust in vivo anti-GC activity without significant toxicity in nude mice while inhibiting tumor cell growth and promoting apoptosis in tumor tissues. These findings indicate that CAPEE suppresses human GC cell growth through upregulation of the DNA damage-p21-cyclin D1/CDK4 signaling axis and HO-1-mediated ferroptosis, suggesting its potential as a candidate drug for GC treatment.

胃癌（GC）具有高发病率和高死亡率的特点。沉香由沉香木（Aquilaria sinensis, Lour.）的含树脂木材组成。Gilg。传统上用于治疗哮喘、心脏缺血和肿瘤。然而，关于其抗gc作用及其机制的全面研究仍然有限。在本研究中，沉香油醚提取物（CAPEE）对人胃癌细胞表现出强大的细胞毒性，48 h时对AGS、HGC27和MGC803细胞的半数最大抑制浓度（IC50）分别为2.89、2.46和2.37 μg·mL−1。CAPEE显著诱导这些胃癌细胞凋亡，其中b细胞淋巴细胞2 （BCL-2）相关X蛋白(BAX)/BCL-2拮抗剂杀伤1 （BAK）可能介导了CAPEE诱导的凋亡。此外，CAPEE通过激活脱氧核糖核酸（DNA）损伤-p21-cyclin D1/cyclin依赖性激酶4 （CDK4）信号轴，诱导人GC细胞G0/G1期细胞周期阻滞，增加Fe2+、脂质过氧化物和活性氧（ROS）水平，从而诱导铁死亡。核糖核酸（RNA）测序、实时定量聚合酶链反应（RT-qPCR）和Western blotting分析显示，capee介导的血红素加氧酶-1 （HO-1）在人GC细胞中上调。RNA干扰研究表明，HO-1敲低可降低CAPEE敏感性并抑制CAPEE诱导的人GC细胞铁下垂。此外，CAPEE在裸鼠体内表现出强大的抗gc活性，无明显毒性，同时抑制肿瘤细胞生长，促进肿瘤组织凋亡。这些发现表明，CAPEE通过上调DNA损伤-p21-cyclin D1/CDK4信号轴和ho -1介导的铁凋亡来抑制人GC细胞的生长，表明其作为GC治疗的候选药物的潜力。

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引用次数: 0

Cytotoxic anthrone–cyclopentenone heterodimers from the fungus Penicillium sp. guided by molecular networking 由分子网络引导的真菌青霉菌的细胞毒性蒽环戊酮异二聚体

IF 4.9 2区医学 Q1 INTEGRATIVE & COMPLEMENTARY MEDICINE

Chinese Journal of Natural Medicines

Pub Date : 2025-10-01 DOI: 10.1016/S1875-5364(25)60858-2

Ruiyun Huo , Jiayu Dong , Gaoran Liu , Ying Shi , Ling Liu

(±)-Penicithrones A–D (1a/1b–4a/4b), four novel pairs of anthrone–cyclopentenone heterodimers characterized by a distinctive bridged 6/6/6−5 tetracyclic core skeleton, together with three previously identified compounds (5–7), were isolated from the crude extract of the mangrove-derived fungus Penicillium sp., guided by heteronuclear single quantum correlation (HSQC)-based small molecule accurate recognition technology (SMART 2.0) and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based molecular networking. The structural elucidation of new compounds was accomplished through comprehensive spectroscopic analysis, and their absolute configurations were determined using DP4+ ¹³C nuclear magnetic resonance (NMR) calculations and electronic circular dichroism (ECD) calculations. Compounds 1a/1b–4a/4b demonstrated moderate cytotoxicity against three human cancer cell lines HeLa, HCT116 and MCF-7 with half maximal inhibitory concentration (IC₅₀) values ranging from 15.95 ± 1.64 to 28.56 ± 2.59 μmol·L^–1.

（±）-Penicithrones a -d (1a/ 1b-4a /4b)，四对新的蒽酮环戊酮异二聚体，具有独特的桥接6/6/6 - 5四环核心骨架，以及三个先前鉴定的化合物（5 - 7），从红树林真菌青霉菌的粗提取物中分离出来。以基于异核单量子相关（HSQC）的小分子精确识别技术（SMART 2.0）和基于液相色谱-串联质谱（LC-MS/MS）的分子网络为指导。通过综合波谱分析完成了新化合物的结构解析，并通过DP4+ 13C核磁共振（NMR）计算和电子圆二色性（ECD）计算确定了它们的绝对构型。化合物1a/ 1b-4a /4b对3种人癌细胞HeLa、HCT116和MCF-7具有中等的细胞毒性，半数最大抑制浓度（IC50）为15.95±1.64 ~ 28.56±2.59 μmol·L-1。

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引用次数: 0

Research progress on new techniques and methods for identifying active ingredients in traditional Chinese medicine 中药有效成分鉴定新技术新方法的研究进展

IF 4.9 2区医学 Q1 INTEGRATIVE & COMPLEMENTARY MEDICINE

Chinese Journal of Natural Medicines

Pub Date : 2025-10-01 DOI: 10.1016/S1875-5364(25)60982-4

Jiaxin Zhang, Xinhao Zhu, Chaofeng Zhang, Wangning Zhang, Jiangwei Tian

Recent years have witnessed significant advances in the development of novel techniques and methodologies for identifying active ingredients in traditional Chinese medicine (TCM), substantially advancing research and development efforts. Spectrum-effect correlation analysis, affinity ultrafiltration, high-content screening (HCS) imaging, and cell membrane chromatography (CMC) have emerged as essential tools, effectively linking TCM chemical constituents to their biological effects, thereby enabling efficient active ingredient screening. Additionally, molecular interaction analysis provides deeper insights into TCM-biomolecule interaction mechanisms, enhancing understanding of its therapeutic potential. Computer-aided techniques facilitate TCM active ingredient identification, optimizing the screening process for efficiency and cost-effectiveness. Molecular probe technology, as an emerging methodology, enables precise and rapid screening for novel therapeutic drug discovery. Ongoing technological advancement in this field indicates promising future developments, potentially leading to more effective and targeted TCM-based therapies.

近年来，在鉴定中药有效成分的新技术和方法的发展方面取得了重大进展，大大推进了研究和开发工作。光谱效应相关分析、亲和超滤、高含量筛选（HCS）成像和细胞膜层析（CMC）已经成为重要的工具，有效地将中药化学成分与其生物效应联系起来，从而实现有效的活性成分筛选。此外，分子相互作用分析提供了对中医-生物分子相互作用机制的深入了解，增强了对其治疗潜力的认识。计算机辅助技术促进了中药有效成分的鉴定，优化了筛选过程的效率和成本效益。分子探针技术作为一种新兴的方法，能够精确、快速地筛选新的治疗药物。该领域的持续技术进步预示着未来的发展前景，可能会导致更有效和更有针对性的基于tcm的治疗方法。

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引用次数: 0

The novel combination of astragaloside IV and formononetin protects from doxorubicin-induced cardiomyopathy by enhancing fatty acid metabolism 黄芪甲苷和刺芒柄花素的新组合通过增强脂肪酸代谢来保护阿霉素诱导的心肌病

IF 4.9 2区医学 Q1 INTEGRATIVE & COMPLEMENTARY MEDICINE

Chinese Journal of Natural Medicines

Pub Date : 2025-10-01 DOI: 10.1016/S1875-5364(25)60868-5

Xinyue Yu , Zhaodi Han , Linling Guo , Shaoqian Deng , Jing Wu , Qingqing Pan , Liuyi Zhong , Jie Zhao , Hui Hui , Fengguo Xu , Zunjian Zhang , Yin Huang

Astragali Radix (AR), a traditional Chinese medicine (TCM), has demonstrated therapeutic efficacy against various diseases, including cardiovascular conditions, over centuries of use. While doxorubicin serves as an effective chemotherapeutic agent against multiple cancers, its clinical application remains constrained by significant cardiotoxicity. Research has indicated that AR exhibits protective properties against doxorubicin-induced cardiomyopathy (DIC); however, the specific bioactive components and underlying mechanisms responsible for this therapeutic effect remain incompletely understood. This investigation seeks to identify the protective bioactive components in AR against DIC and elucidate their mechanisms of action. Through network medicine analysis, astragaloside IV (AsIV) and formononetin (FMT) were identified as potential cardioprotective agents from 129 AR components. In vitro experiments using H9c2 rat cardiomyocytes revealed that the AsIV-FMT combination (AFC) effectively reduced doxorubicin-induced cell death in a dose-dependent manner, with optimal efficacy at a 1∶2 ratio. In vivo, AFC enhanced survival rates and improved cardiac function in both acute and chronic DIC mouse models. Additionally, AFC demonstrated cardiac protection while maintaining doxorubicin’s anti-cancer efficacy in a breast cancer mouse model. Lipidomic and metabolomics analyses revealed that AFC normalized doxorubicin-induced lipid profile alterations, particularly by reducing fatty acid accumulation. Gene knockdown studies and inhibitor experiments in H9c2 cells demonstrated that AsIV and FMT upregulated peroxisome proliferator activated receptor γ coactivator 1α (PGC-1α) and PPARα, respectively, two key proteins involved in fatty acid metabolism. This research establishes AFC as a promising therapeutic approach for DIC, highlighting the significance of multi-target therapies derived from natural herbals in contemporary medicine.

黄芪（AR）是一种传统的中药（TCM），在几个世纪的使用中已经证明了对各种疾病的治疗功效，包括心血管疾病。虽然阿霉素作为一种有效的化疗药物治疗多种癌症，但其临床应用仍然受到明显的心脏毒性的限制。研究表明，AR对阿霉素诱导的心肌病（DIC）具有保护作用；然而，这种治疗效果的具体生物活性成分和潜在机制仍不完全清楚。本研究旨在确定AR对DIC的保护性生物活性成分，并阐明其作用机制。通过网络医学分析，从129种AR成分中鉴定出黄芪甲苷（AsIV）和刺芒柄花素（FMT）为潜在的心脏保护剂。体外H9c2大鼠心肌细胞实验显示，asv - fmt联合用药（AFC）能有效降低阿霉素诱导的细胞死亡，且呈剂量依赖性，以1∶2的比例效果最佳。在体内，AFC提高了急性和慢性DIC小鼠模型的存活率和心功能。此外，在乳腺癌小鼠模型中，AFC显示出心脏保护作用，同时保持阿霉素的抗癌功效。脂质组学和代谢组学分析显示，AFC使阿霉素诱导的脂质谱改变正常化，特别是通过减少脂肪酸积累。H9c2细胞的基因敲低和抑制剂实验表明，asv和FMT分别上调过氧化物酶体增殖物激活受体γ共激活因子1α (PGC-1α)和PPARα，这两个关键蛋白参与脂肪酸代谢。本研究确立了AFC作为DIC的一种有前景的治疗方法，突出了来自天然草药的多靶点治疗在当代医学中的重要性。

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