网络药理学与药物发现研究进展

将生物学网络与药物作用网络整合,分析药物在网络中与节点或网络模块的关系,由寻找单一靶点转向综合网络分析,就形成了网络药理学.通过系统生物学的研究方法进行网络药理学分析,能够在分子水平上更好的理解细胞以及器官的行为,加速药物靶点的确认以及发现新的生物标志物.这使得我们有可能系统地预测和解释药物的作用,优化药物设计,发现影响药物作用有效性和安全性的因素,从而设计多靶点药物或药物组合.本文综述了网络药理学的新近研究进展,介绍在生物学网络的各个层面上网络药理学的研究和应用,展望网络药理未来的发展方向,对药物发现具有重要意义.     

基于网络药理学和分子对接研究黄芪治疗糖尿病心肌病的作用机制

目的:运用网络药理学和分子对接方法研究黄芪治疗糖尿病心肌病(DCM)的作用机制.方法:利用中药系统药理学技术平台数据库(TCMSP)收集黄芪的成分及其相关靶点;通过GeneCards、NCBI、OMIM数据库获取DCM相关疾病靶点.取黄芪成分靶点与DCM疾病靶点的交集基因,作为黄芪对DCM作用的潜在关键靶点基因,将交集...     

蛋白质组学技术在网络药理学中的应用

蛋白质组学发展至今已日趋成熟,在生物医药相关领域研究中的应用显著增加,与之相关的样品制备技术、蛋白定量方法及先进的质谱仪器也得到了快速发展。网络药理学是近年来提出的新药发现新策略,是药理学的新兴分支学科,它从整体的角度探索药物与疾病的关联性,发现药物靶标,指导新药研发。将蛋白质组学技术应用于网络药理学研究,能使研究人员系统地预测和解释药物的作用,加速药物靶点的确认,从而设计多靶点药物或药物组合。综述了蛋白质组学技术的新近研究进展,并简单概述了其在网络药理学中的应用。     

基于网络药理学探讨丹参-丹皮配伍抗脑缺血损伤的作用机制

本文旨在通过网络药理学和分子对接方法探讨丹参-丹皮活性成分治疗脑卒中的潜在分子机制。首先基于中药系统药理学分析平台筛选丹参、丹皮的活性成分及其作用靶点,利用CTD、TTD和GeneCards数据库收集脑卒中相关靶点。然后将药物和疾病靶点取交集,借助STRING数据库获取靶点间相互作用关系,利用R语言的ClusterProfiler包对其进行生物功能和通路富集分析。最后,通过Cytoscape软件构建蛋白质-蛋白质相互作用和成分-靶点-通路网络图,并利用AutoDock Vina软件对网络中的关键靶点及对应成分进行分子对接验证。结果显示丹参-丹皮成分作用于脑卒中的靶点67个,GO分析显示其主要参与脂多糖应答,细菌来源的分子反应,氧化应激等生物学过程。KEGG通路富集共得到149条通路(P<0.05),主要涉及AGE-RAGE信号通路、IL-17信号通路、TNF信号通路等。分子对接结果显示,筛选的主要活性成分与其对应靶蛋白均具有较好的结合活性。综上,本研究通过网络药理学预测了丹参-丹皮治疗脑卒中可能的药效物质基础及其作用机制,为进一步挖掘其药效成分和临床扩大使用范围提供科学依据。     

基于网络药理学和分子对接探索桑白皮治疗糖尿病周围神经病变的潜在机制

本研究运用网络药理学和分子对接方法对中药桑白皮治疗糖尿病周围神经病变(DPN)的活性成分、潜在作用靶点和信号通路进行研究,探索桑白皮治疗DPN的可能作用机制。首先从中药系统药理学数据库(TCMSP)筛选出桑白皮的活性成分及靶点基因。通过GeneCards数据库及OMIM数据库筛选出DPN的疾病靶点基因,并用Cytoscape软件构建“药物-有效成分-靶基因-疾病”中药调控网络图。将有效成分靶标与疾病靶标上传到STRING数据库,构建蛋白互作网络图(PPI),并使用R语言对得到的PPI进行核心基因的筛选。运用R语言对关键靶点进行GO富集分析和KEGG通路富集分析。其次从活性成分及靶点基因中根据degree值筛选出前3个关键成分,并将该网络中的基因靶点以degree值高低进行排序,选择前3个核心靶点,然后从RCSB数据库下载相关蛋白的结构,使用Pymol软件去除溶剂分子与配体,使用AutoDock软件进行分子对接。最后通过酶联免疫吸附实验和荧光光谱实验验证网络药理学富集分析的结果。最终预测到31个桑白皮活性成分,312个活性成分相关靶点,120个桑白皮-糖尿病周围神经病变共同有效靶点。活性成分中度值最高的为槲皮素,其次为山柰酚。PPI网络核心基因为转录因子AP-1(JUN)、丝裂原活化蛋白激酶1(MAPK1)、转录因子p65(RELA)、丝氨酸-苏氨酸蛋白激酶1(AKT1)、白介素6(IL-6)等;GO富集分析显示会影响基因的转录、细胞因子表达和蛋白激酶活性等;KEGG通路富集分析显示AGE-RAGE信号通路、流体剪切力和动脉粥样硬化为显著性最高的通路,其次为卡波西肉瘤相关疱疹病毒感染、MAPK信号通路、人巨细胞病毒感染、TNF信号通路。分子对接结果显示关键成分中槲皮素与对应靶点具有较好的结合活性。酶联免疫吸附实验提示桑白皮能够降低IL-6和TNF-α的表达,荧光光谱实验证实桑白皮能够减少AGEs。可见中药桑白皮治疗糖尿病周围神经病变具有多成分、多靶点、多功能、多通路的作用特点,其潜在的作用机制可能与AGE-RAGE信号通路、肿瘤坏死因子信号通路等有关。     

基于网络药理学和分子对接技术探究清震汤治疗偏头痛的作用机制

基于网络药理学及分子对接技术探究清震汤治疗偏头痛的作用机制。利用中药系统药理学平台,结合文献报道,获取清震汤中3味中药的活性成分和作用靶点,借助UniProt数据库对靶点蛋白名称进行规范。通过DrugBank、GeneCards等数据库获取偏头痛相关靶点。运用在线Venny作图平台,得到清震汤治疗偏头痛的潜在作用靶点。通过STRING平台构建潜在靶点PPI网络,将所得蛋白互作信息导入Cytoscape 3.7.1进行图像优化及提取核心基因,运用DAVID数据库对潜在作用靶点进行富集分析,采用Cytoscape 3.7.1构建“中药-化合物-靶点-通路”调控网络并进行拓扑分析,使用Autodock软件进行分子对接验证。网络药理学分析结果显示,清震汤中治疗偏头痛可能与槲皮素、山奈酚、豆甾醇等40个化学成分有关,IL6、CXCL8、TNF、PTGS2等为关键靶点。富集分析得到GO条目436条,KEGG通路92条,主要涉及TNF信号通路,神经信号传递通路等。分子对接结果显示,上述活性成分与相关靶点具有较好的结合活性。该研究初步表明,清震汤中多种活性成分通过作用于IL6、CXCL8、TNF、PTGS2等关键靶点,调节多条信号通路,发挥治疗偏头痛的作用。     

基于网络药理学-分子对接研究桑不同入药部位防治糖尿病的作用机制

采用网络药理学-分子对接研究桑不同入药部位防治糖尿病的作用机制,从TCMSP、TCMID等多个中药数据库获得桑不同部位桑叶、桑椹、桑枝中的成分信息,结合OMIM、TTD等疾病数据库获得糖尿病靶点信息,利用Cytoscape 3.7.2软件分别构建不同部位的“活性成分-疾病靶点”的复杂网络及拓扑分析,应用WebGestalt工具进行通路富集分析。使用Autodock vina软件对桑各入药部位主要活性成分与作用靶点进行分子对接。结果得到桑的不同入药部位主要有11个差异活性成分,调控MAPK8、AKT1、VEGFA、IL6、PPARG等32个核心靶点。靶点主要涉及急性炎症反应、有机氮化合物的反应、细胞增殖调控、对胰岛素刺激反应等生物过程。通过介素-17信号通路、肿瘤坏死因子信号通路、PI3K-AKT信号通路和MAPK信号通路等来发挥治疗糖尿病的作用。本研究通过网络药理学分析桑不同入药部位防治糖尿病的活性成分群及作用机制,为桑的不同入药部位防治糖尿病的作用关系给出了参考依据。     

甘草抗动脉粥样硬化机制的网络药理学研究

利用网络药理学方法探讨甘草在抗动脉粥样硬化中的分子机制。本研究利用中医药系统药理学数据库和分析平台(traditional Chinese medicine systems pharmacology database and analysis platform,TCMSP)分析甘草中的有效活性成分,并获得有效成分的作用靶点。通过Cytoscape软件构建可视化靶点互相作用网络,对网络中的关键靶点进行基因本体(GO)富集分析和KEGG通路富集分析。结果显示甘草中40种有效活性成分的预测靶点共97个,47个靶点与动脉粥样硬化(AS)相关,其中18个是血管保护药物和脂质修饰药物的作用靶点,表明甘草可作为调控AS发展的药物。基于97个预测靶点的GO富集分析,发现甘草可参与多种生物学过程,尤其是应对外源性刺激,以及参与细胞凋亡等过程。通过构建甘草靶点与AS疾病靶点相互作用网络(PPI),确定了AKT1、MAPK3、MAPK1、JUN和CASP3等关键靶点,并对关键靶点进行KEGG富集分析,结果表明甘草主要影响调控细胞增殖、生存以及凋亡的细胞信号转导相关通路,并激活先天免疫相关信号通路,调节炎性细胞因子释放,从而发挥抗动脉粥样硬化作用。甘草具有多成分、多靶点、多途径的作用特点,主要通过PI3K-AKT信号途径、MAPK信号途径、NOD样受体信号通路调控细胞增殖和凋亡,同时发挥免疫调控作用,从而影响动脉粥样硬化的发展,由此可见,甘草可作为动脉粥样硬化疾病治疗的候选中草药。     

基因芯片技术及其在补血中药研究中的应用

补血中药在我国中医药宝库中占有十分重要的地位,中药调控造血的分子机制与细胞因子网络直接相关。多维超高通量药物筛选体系的建立和从分子水平系统说明中药作用的药理学机制,使传统的中药理论与国际通用的医学理论模式接轨,是中药现代化、国际化的迫切要求。由于中药重整体、多靶点、多环节的作用方式,使得传统的研究技术难以完整地阐明其作用机制;而现今发展起来的高密度基因芯片技术,可以同时研究上千种基因的作用模式,进行平行基因分析,因此可以用来检测疾病状态下和中药作用后成千上万个基因的表达模式,并对其进行定性和定量分析,从而使从整体和分子水平上阐明中药作用机制成为可能。基因芯片这种高通量、快速、平行的基因信息处理和分析技术,是实现这一目标的绝佳实验手段。在药物领域对于药物靶标的发现、多靶位同步超高通量药物筛选、药物作用的分子机理、中医药基础理论现代化、药物活性及毒性评价等都有其他方法无可比拟的优越性。因此,建立以基因芯片技术为核心的多靶点、多层次、多水平的中药多维超高通量筛选体系具有极其重要的意义。     

基于网络药理学与分子对接探究黄芩有效成分对酒精性肝病的作用机制及效果验证

通过网络药理学和分子对接技术探究中药黄芩治疗酒精性肝病的作用机制，并通过体外细胞实验验证黄芩有效成分对酒精性肝病的治疗效果。在TCMSP、Swiss ADME和Swiss Target Prediction数据库中检索获得黄芩有效成分及其作用靶点；在GeneCards、OMIM、DisGeNET、TTD和PharmGKB数据库中检索获得酒精性肝病相关的疾病靶点；利用String数据库构建靶点相互作用网络；通过Metascape数据库对关键靶点进行京都基因与基因组百科全书(KEGG)通路富集分析、基因本体(GO)富集分析。采用Cytoscape 3.8.0软件构建黄芩治疗酒精性肝病的“有效成分-靶点-通路”互作网络，并筛选出黄芩有效成分和关键靶点进行分子对接。基于网络药理学和分子对接结果，采用体外细胞实验初步验证预测结果。将黄芩有效成分进行ADME筛选后共获得27个，且这27个有效成分可以通过257个基因靶点对酒精性肝病起到治疗作用，其中关键核心靶点有SRC、AKT1、PIK3R1、STAT3、PIK3CA等。KEGG信号通路富集分析结果显示，黄芩治疗酒精性肝病的主要信号通路包括癌症的途...     

A survey of web resources and tools for the study of TCM network pharmacology

Background: Traditional Chinese medicine (TCM) treats diseases in a holistic manner, while TCM formulae are multi-component, multi-target agents at the molecular level. Thus there are many parallels between the key ideas of TCM pharmacology and network pharmacology. These years, TCM network pharmacology has developed as an interdisciplinary of TCM science and network pharmacology, which studies the mechanism of TCM at the molecular level and in the context of biological networks. It provides a new research paradigm that can use modern biomedical science to interpret the mechanism of TCM, which is promising to accelerate the modernization and internationalization of TCM. Results: In this paper we introduce state-of-the-art free data sources, web servers and softwares that can be used in the TCM network pharmacology, including databases of TCM, drug targets and diseases, web servers for the prediction of drug targets, and tools for network and functional analysis. Conclusions: This review could help experimental pharmacologists make better use of the existing data and methods in their study of TCM.     

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds

Background

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target.

Methodology

We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery.

Conclusions

This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.     

Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate.     

Network pharmacology and drug discovery

The pharmaceutical industry has historically relied on particular families of 'druggable' proteins against which to develop compounds with desired actions. But proteins rarely function in isolation in and outside the cell; rather, proteins operate as part of highly interconnected cellular networks. Network pharmacology is an emerging area of pharmacology which utilizes network analysis of drug action. By considering drug actions in the context of the cellular networks, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. Network pharmacology can provide new approaches for drug discovery for complex diseases. This review introduced the recent progress of network pharmacology and its importance to understand the mechanism of drug actions and drug discovery.     

Evaluating the Pharmacological Mechanism of Chinese Medicine Si-Wu-Tang through Multi-Level Data Integration

Si-Wu-Tang (SWT) is a Traditional Chinese Medicine (TCM) formula widely used for the treatments of gynecological diseases. To explore the pharmacological mechanism of SWT, we incorporated microarray data of SWT with our herbal target database TCMID to analyze the potential activity mechanism of SWT''s herbal ingredients and targets. We detected 2,405 differentially expressed genes in the microarray data, 20 of 102 proteins targeted by SWT were encoded by these DEGs and can be targeted by 2 FDA-approved drugs and 39 experimental drugs. The results of pathway enrichment analysis of the 20 predicted targets were consistent with that of 2,405 differentially expressed genes, elaborating the potential pharmacological mechanisms of SWT. Further study from a perspective of protein-protein interaction (PPI) network showed that the predicted targets of SWT function cooperatively to perform their multi-target effects. We also constructed a network to combine herbs, ingredients, targets and drugs together which bridges the gap between SWT and conventional medicine, and used it to infer the potential mechanisms of herbal ingredients. Moreover, based on the hypothesis that the same or similar effects between different TCM formulae may result from targeting the same proteins, we analyzed 27 other TCM formulae which can also treat the gynecological diseases, the subsequent result provides additional insight to understand the potential mechanisms of SWT in treating amenorrhea. Our bioinformatics approach to detect the pharmacology of SWT may shed light on drug discovery for gynecological diseases and could be utilized to investigate other TCM formulae as well.     

Pathway as a Pharmacological Target for Herbal Medicines: An Investigation from Reduning Injection

As a rich natural resource for drug discovery, Traditional Chinese Medicine (TCM) plays an important role in complementary and alternative medical systems. TCM shows a daunting complexity of compounds featuring multi-components and multi-targets to cure diseases, which thus always makes it extremely difficult to systematically explain the molecular mechanisms adequately using routine methods. In the present work, to reveal the systematic mechanism of herbal formulae, we developed a pathway-based strategy by combining the pathways integrating, target selection, reverse drug targeting and network analysis together, and then exemplified it by Reduning injection (RDN), a clinically widely used herbal medicine injection, in combating inflammation. The anti-inflammatory effects exerted by the major ingredients of RDN at signaling pathways level were systematically investigated. More importantly, our predicted results were also experimentally validated. Our strategy provides a deep understanding of the pharmacological functions of herbal formulae from molecular to systematic level, which may lead to more successful applications of systems pharmacology for drug discovery and development.     

Herbogenomics: from traditional Chinese medicine to novel therapeutics

Traditional Chinese medicine (TCM) has a long history of development and application and has demonstrated on evidence basis its efficacy in the treatment of many diseases affecting multiple organ systems. In particular, TCM is effective in the prevention and treatment of chronic diseases and metabolic syndromes. However, the value of TCM has not been fully recognized worldwide due to the lack of definitive information of active ingredients in almost any TCM preparation. Novel functional genomics and proteomics approaches provide alternate perspectives on the mechanism of action of TCM. The target molecules on which TCM either activates or inactivates can be identified by functional genomics and proteomics, thus the affected critical signaling pathway cascades leading to effective recovery of chronic diseases can be studied. Several TCM preparations have been available for the treatment of liver fibrosis and cirrhosis, even advanced liver cirrhosis that has been shown to be irreversible and has no US-FDA approved therapy. In the TCM-treated livers with fibrosis and cirrhosis, some critical molecules that are significantly involved in the recovery can be identified through functional genomics and proteomics studies. These molecules become novel targets for drug discovery and development and candidates for the development of gene therapy. Gene therapy developed based on this strategy for the treatment of advanced liver fibrosis and cirrhosis in animal models has obtained promising results. This process thus establishes a herbogenomics approach to understand mechanisms of action of TCM and to identify effective molecular targets for the discovery and development of novel therapeutics.     

Sulfonamide derivatives as multi-target agents for complex diseases

Sulfonamide derivatives are frequently seen structural motifs in medicinal chemistry. Almost a century after Gerhard Domagk’s pioneering work leading to the first sulfonamide antibiotic Prontosil, sulfa-drugs are still widely utilized in various pharmaceutical applications due to their antibacterial, antiviral, antimalarial, antifungal, anticancer, antidepressant, or other properties. In the past few years, the interest in sulfonamides has increased as their broad range of bioactivity and versatile structure make them excellent candidates for repurposing old drugs or developing new multi-target agents in the emerging field of polypharmacology.This digest aims to provide an overview of recent advances in sulfonamide-based bioactive compounds, their importance in drug discovery and development emphasizing multi-target approaches for complex diseases, and their novel contribution to contemporary medicinal chemistry.     

Computational methods and applications for quantitative systems pharmacology

Background: Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes. Results: Various computational methods such as ADME/T evaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient for QSP have been developed. We reviewed the major progresses and broad applications in medical guidance, drug discovery and exploration of pharmacodynamic material basis and mechanism of traditional Chinese medicine. Conclusion: QSP has significant achievements in recent years and is a promising approach for quantitative evaluation of drug efficacy and systematic exploration of mechanisms of action of drugs.