Modular pharmacology: the next paradigm in drug discovery

Introduction: Effective regulation of abnormal targets of disease requires the alteration of either the topological structure or dynamic characteristics of modules in a given target network. In order to disturb the complex direct and indirect target hubs, new approaches in adaptive pharmacology should be developed to regulate target loops using module-based designs.

Areas covered: This review discusses the formation in disease-associated structural networks with multiple drug targets and the optimization of a multi-objective system of modules.

Expert opinion: On the basis of these concepts, modular pharmacology (MP) has emerged as a method that can balance multiple outcomes by regulating the response of property modules on the basis of the benefits and risks of diversified modules and drugs, thereby allowing novel approaches for drug discovery. Further research of pharmacological mechanisms underlying diversity interactions between multiple modules is necessary for a better understanding of the basic therapeutic processes.     

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。     

Discussion on the mechanism of Salvia miltiorrhiza in treating pathological scars based on network pharmacology

In the present study, we aimed to explore the mechanism of Salvia miltiorrhiza in the treatment of pathological scars (PS) by network pharmacology. The active ingredients and drug targets of Salvia miltiorrhiza were screened out through TCMSP database, the disease targets of PS in GeneCards database were obtained, and Venn diagram analysis on drug targets and disease targets was performed, and the intersection was used as the target of Salvia miltiorrhiza for the treatment of PS. Cytoscape software was used to construct a drug-ingredient-target-disease network diagram. A protein-protein interaction network was constructed through String website, its key protein modules and hub genes were screened with Cytoscape software, and GO and KEGG enrichment analyses were performed in DAVID database. Fifty-nine active ingredients, 138 drug targets, and 90 targets of Salvia miltiorrhiza for the treatment of PS were screened out. Core ingredients, such as luteolin and tanshinone IIA, were obtained. The hub genes, such as VEGFA, TP53, JUN, STAT3, AKT1, MAPK1, and PTGS2, and signaling pathways, such as HIF-1, TNF, MAPK, PI3K-Akt, and Jak-STAT, were screened out. Salvia miltiorrhiza might improve PS hypoxia, inflammation, and balance of proliferation and apoptosis of fibroblasts by regulating HIF-1, TNF, MAPK, PI3K-Akt, and Jak-STAT signaling pathways. Moreover, it had the characteristics of multiple centers, multiple targets, and multiple pathways.     

Emerging therapeutic approaches multi-targeting receptor tyrosine kinases and g protein-coupled receptors in cardiovascular disease

Advances in molecular biology and functional genomics have demonstrated that the "one gene-one phenotype-one drug" paradigm, that has dominated pharmaceutical industry and clinical pharmacology thinking, is too simplistic for management of complex polygenic traits. The traditional highly specific drugs with unique target have proven their clinical usefulness. However, they do not always display the required efficacy versus side-effect profile, in major part because polygenic traits are determined by redundant mechanisms. Simultaneously modulating multiple targets may enhance therapeutic efficacy in the treatment of a range of disorders. Multi-targeting can be achieved by the combination of different drugs having specific single target activity. This approach introduces potential problems with pharmacokinetic interactions, toxicity and patient compliance. High efficacy can be achieved, alternatively, by administering selectively non-selective drugs with complex pharmacological profiles directed towards various molecular targets and affording pleiotropic actions. Dual- or multiple-ligands can be discovered accidentally, but can also be rationally designed according to validated medicinal chemical approaches. The merits of multiple-target versus single-target approaches for cardiovascular disease traits are assessed in the present review. The main aim is to make evident the molecular biological basis of the possibility for targeting multiple sites and the subsequently emerging strategies for interventions with superior clinical value by harnessing receptor tyrosine kinases (RTKs) such as VEGFR, PDGFR, bFGFR, as well as G protein-coupled receptors (GPCRs). The premises for lead discovery in this new area and the challenges of medicinal chemistry behind the rational design of multitasked ligands are also discussed.     

Robust ligand-based modeling of the biological targets of known drugs

Systematic annotation of the primary targets of roughly 1000 known therapeutics reveals that over 700 of these modulate approximately 85 biological targets. We report the results of three analyses. In the first analysis, drug/drug similarities and target/target similarities were computed on the basis of three-dimensional ligand structures. Drug pairs sharing a target had significantly higher similarity than drug pairs sharing no target. Also, target pairs with no overlap in annotated drug specificity shared lower similarity than target pairs with increasing overlap. Two-way agglomerative clusterings of drugs and targets were consistent with known pharmacology and suggestive that side effects and drug-drug interactions might be revealed by modeling many targets. In the second analysis, we constructed and tested ligand-based models of 22 diverse targets in virtual screens using a background of screening molecules. Greater than 100-fold enrichment of cognate versus random molecules was observed in 20/22 cases. In the third analysis, selectivity of the models was tested using a background of drug molecules, with selectivity of greater than 80-fold observed in 17/22 cases. Predicted activities derived from crossing drugs against modeled targets identified a number of known side effects, drug specificities, and drug-drug interactions that have a rational basis in molecular structure.     

基于网络药理学探讨葶苈大枣泻肺汤对儿童肺炎的作用机制研究

目的通过网络药理学方法探讨葶苈大枣泻肺汤治疗儿童肺炎的潜在分子生物学机制。方法从中药系统药理学分析平台(TCMSP)及文献资料中寻找与葶苈大枣泻肺汤中中药相关的所有成分和作用靶点,并构建药物分子-靶点网络;利用疾病靶点数据库(GeneCards和OMIM)选取检索出来的所有与疾病相关的靶点,利用蛋白-蛋白相互作用网络平台STRING V11.0构建药物-疾病蛋白互作网络,利用Biocondoctor生物软件信息包进行GO和KEGG富集分析,预测其作用机制。结果最终筛选得到18个活性化合物,71个有效靶点。GO功能富集分析得到1378个条目(P<0.05),KEGG通路富集分析得到127条信号通路(P<0.05)。结论葶苈大枣泻肺汤治疗儿童肺炎是多成分、多靶点共同作用的结果,为葶苈大枣泻肺汤的临床应用以及儿童肺炎的研究提供了一定理论依据。     

Prediction of triptolide targets in rheumatoid arthritis using network pharmacology and molecular docking

Network pharmacology is a novel approach that uses bioinformatics to predict and identify multiple drug targets and interactions in disease. Here, we used network pharmacology to investigate the mechanism by which triptolide acts in rheumatoid arthritis (RA). We first searched public databases for genes and proteins known to be associated with RA, as well as those predicted to be targets of triptolide, and then used Ingenuity Pathway Analysis (IPA) to identify enriched gene pathways and networks. Networks and pathways that overlapped between RA-associated proteins and triptolide target proteins were then used to predict candidate protein targets of triptolide in RA. The following proteins were found to occur in both RA-associated networks and triptolide target networks: CD274, RELA, MCL1, MAPK8, CXCL8, STAT1, STAT3, c-JUN, JNK, c-Fos, NF-κB, and TNF-α. Docking studies suggested that triptolide can fit in the binding pocket of the six top candidate triptolide target proteins (CD274, RELA, MCL1, MAPK8, CXCL8 and STAT1). The overlapping pathways were activation of Th1 and Th2 cells, macrophages, fibroblasts and endothelial cells in RA, while the overlapping networks were involved in cellular movement, hematological system development and function, immune cell trafficking, cell-to-cell signaling and interaction, inflammatory response, cellular function and maintenance, and cell death and survival. These results show that network pharmacology can be used to generate hypotheses about how triptolide exerts therapeutic effects in RA. Network pharmacology may be a useful method for characterizing multi-target drugs in complex diseases.     

康妇炎胶囊联合头孢噻肟钠治疗子宫内膜炎的临床研究

目的探讨康妇炎胶囊联合头孢噻肟钠治疗子宫内膜炎的临床疗效。方法选取2018年5月-2019年5月在眉山市人民医院治疗的子宫内膜炎患者84例,根据用药的差别分为对照组(42例)和治疗组(42例)。对照组静脉滴注注射用头孢噻肟钠,2.0 g/次加入生理盐水100 mL,2次/d;治疗组在对照组基础上口服康妇炎胶囊,1.2 g/次,3次/d。两组均治疗2周。观察两组患者临床疗效,同时比较治疗前后两组患者临床症状积分,GQOLI-74、FSFI和VAS评分,及血清C反应蛋白(CRP)、白细胞介素-4(IL-4)、白细胞介素-8(IL-8)、肿瘤坏死因子-α(TNF-α)和基质金属蛋白酶-2(MMP-2)水平。结果治疗后,对照组临床有效率为80.95%,显著低于治疗组的97.62%,两组比较差异具有统计学意义(P<0.05)。经治疗,两组患者症状评分均显著下降(P<0.05),且治疗组比对照组下降更明显(P<0.05)。经治疗,两组患者GQOLI-74评分明显升高(P<0.05),而FSFI和VAS评分均明显降低(P<0.05),且治疗组GQOLI-74、FSFI和VAS评分明显好于对照组(P<0.05)。经治疗,两组患者血清CRP、IL-4、IL-8、TNF-α、MMP-9水平均显著降低(P<0.05),且治疗组患者明显低于对照组(P<0.05)。结论康妇炎胶囊联合注射用头孢噻肟钠治疗子宫内膜炎患者可有效改善患者临床症状,降低机体炎症反应,提高患者性生活质量,具有一定的临床推广应用价值。     

藏药三果汤治疗糖尿病作用机制的网络药理学与分子对接技术分析

目的 基于网络药理学及分子对接对三果汤治疗糖尿病靶点及通路进行分析，并通过分子对接进行验证，为基础研究及临床用药提供科学依据。方法 通过中药系统药理学数据库与分子平台（TCMSP）、有机小分子生物活性数据库（PubChem）及文献查阅筛选三果汤中诃子、毛诃子、余甘子的活性成分及作用靶点，使用Genecard数据库、DRUGBANK数据库筛选糖尿病相关靶点，构建“药物-成分-靶点-疾病”网络，取药物疾病靶点交集，使用STRING构建蛋白PPI网络，运用Matescape数据库对交集靶点基因进行GO、KEGG富集分析。将筛选出的有效成分与靶点进行分子对接验证。结果 通过网络药理学收集三果汤及糖尿病靶点信息，采用GO富集分析和KEGG通路分析发现，三果汤和糖尿病共同作用靶点共269个，靶点作用最为突出的为AKT1、HAS2、MAP2、CDH1、PRKCG、PLAT，三果汤作用于糖尿病的通路主要在癌症通路、脂质和动脉粥样硬化、MAPK信号通路。分子对接结果显示，鞣花酸和没食子酸对AKT1、HAS2、MAP2存在亲和力，鞣花酸对HAS2亲和力最好。结论 通过本研究中网络药理学分析及分子对接分析，三果汤中鞣花酸、没食子酸对AKT1、HAS2、MAP2通路靶点具有亲和力，为后续实验提供研究思路。     

基于网络药理学的四君子汤治疗胃炎的作用机制研究

目的通过网络药理学预测四君子汤治疗胃炎的主要活性成分和作用靶点,探讨其多成分-多靶点-多通路的潜在作用机制。方法通过中医药系统药理学数据库和分析平台(TCMSP)数据库收集四君子汤及其中4味中药党参、白术、茯苓、甘草的化学成分,经口服吸收率(OB)、类药性(DL)、半衰期(HL)3个条件筛选得到药物的有效成分,通过SwissTargetPrediction数据库进行有效成分靶点预测;同时从TTD、Drugbank和DisGeNET数据库对胃炎的靶点进行检索及筛选;成分靶点与疾病靶点映射后使用Cytoscape 3.2.1软件构建药物有效成分-靶点蛋白相互作用网络,使用String数据库绘制靶点蛋白-靶点蛋白相互作用网络;对靶点蛋白利用DAVID数据库进行GO分析和KEGG分析,构建核心有效成分-核心靶点-代谢通路网络图。结果从四君子汤中筛选出68个化学成分,涉及治疗胃炎的10个靶点;GO分析结果表明其涉及单孢素代谢过程、杂环代谢过程、药物代谢过程、药物分解过程、类固醇代谢过程等9个生物过程,涉及氧结合、单加氧酶活性、氧化还原酶活性等10个分子功能,包括细胞器膜、内质网膜等6个细胞组成;KEGG分析结果表明其可能通过药物代谢-细胞色素P450、亚油酸代谢、细胞色素P 450对外源性物质代谢的影响、神经活性配体与受体的相互作用、化学致癌、类固醇激素生物合成、视黄醇代谢7个信号通路治疗胃炎。结论四君子汤通过多靶点、多通路治疗胃炎,为今后分子机制的研究奠定一定的基础。     

基于网络药理学与分子对接探讨二妙散治疗宫颈人乳头瘤病毒感染的作用机制

目的 基于网络药理学和分子对接探讨二妙散治疗宫颈人乳头瘤病毒（HPV）感染作用机制。方法 通过中药系统药理学数据库与分析平台检索二妙散的有效成分和作用靶点;GeneCards、OMIM数据库检索宫颈HPV感染相关疾病靶点。将药物有效成分作用靶点与疾病靶点取交集;利用STRING数据库构建交集靶点的蛋白相互作用（PPI）网络,利用Cytoscape3.8.2软件对PPI进行拓扑分析;将潜在作用靶点导入DAVID数据库进行基因本体功能（GO）和京都基因与基因组百科全书（KEGG）通路富集分析;利用AutoDock Tools 1.5.7软件和Pymol软件对核心作用靶点与有效成分进行分子对接及可视化。结果 获得二妙散有效成分28个,作用靶点为221个,筛选出核心靶点5个,包括蛋白激酶B1(Akt1)、肿瘤蛋白p53(TP53)、雌激素受体α(ESR1)、转录因子AP-1(JUN)、半胱氨酸天冬氨酸蛋白酶3(CASP3)。GO富集分析显示,潜在作用靶点与细胞增殖的正向调节、细胞凋亡过程的负调控、转录的正调控、RNA聚合酶II启动子转录的正调控等生物过程有关;KEGG富集分析结果包括磷脂酰肌醇...     

基于网络药理学探讨舒肝解郁胶囊治疗抑郁症的作用机制

目的：基于网络药理学预测舒肝解郁胶囊治疗抑郁症的主要活性成分及其作用靶点,探讨其多成分-多靶点-多通路的作用机制。方法：采用超高效液相色谱-四级杆/静电场轨道阱高分辨质谱（UHPLC-Q-Orbitrap MS）技术对舒肝解郁胶囊中的化合物准确定性,借助中药系统药理学分析平台（TCMSP）和Swiss Tartget Predict数据库对舒肝解郁胶囊中化学成分的作用靶点进行预测,借助OMIM数据库和GeneCard数据库检索抑郁症相关基因,应用UniProt数据库分析药物靶点和疾病基因。采用Cytoscape软件构建蛋白质相互作用（PPI）关系网络,在DAVID数据库对关键靶点（GO）富集分析和（KEGG）通路分析。结果：通过对舒肝解郁胶囊中36个主要化学成分定性分析,对13个有对照品成分进行网络药理学探索,发现舒肝解郁胶囊治疗抑郁症的552个核心靶点及MAPK信号通路、TNF信号通路、PI3K-Akt信号通路等主要通路。结论：舒肝解郁胶囊发挥治疗抑郁症的作用体现了中药多成分、多靶点、多途径的协同作用特点,为进一步探索舒肝解郁胶囊治疗抑郁症的机制奠定了理论基础。     

基于网络药理学和分子对接分析薤白治疗心肌缺血再灌注损伤的作用机制

目的：基于网络药理学方法探讨薤白治疗心肌缺血再灌注损伤的作用机制。方法：在中药系统药理学分析平台（TCMSP）上检索和筛选薤白的活性成分、作用靶点;利用OMIM数据库和CTD数据库搜集疾病靶点;找出薤白作用靶点和疾病靶点共有交集靶点,分子对接软件SYBYL验证薤白活性成分与交集靶点结合活性。然后利用String数据库和Cytoscape软件绘制靶点蛋白互作网络,利用DAVID数据库对靶点进行信号通路富集分析,利用Cytoscape软件构建薤白活性成分-靶点-信号通路网络。结果：筛选获得薤白活性成分10个,治疗疾病作用靶点19个。蛋白互作分析结果显示,17个靶点蛋白存在相互作用关系,IL6、TNF、NOS3、CCL2、VEGFA、SOD1、CAT与10个及以上蛋白存在互作关系。信号通路富集结果显示,上述靶点主要与流体剪切应力与动脉粥样硬化、过氧化物酶体、HIF-1信号通路、松弛素信号通路、细胞因子-细胞因子-受体相互作用、IL-17信号通路以及肿瘤坏死因子信号通路等23条信号通路有关。结论：薤白通过多成分、多靶点、多途径治疗心肌缺血再灌注损伤,其作用机制主要通过调控流体剪切应力、氧化应激及炎性反应来发挥作用。     

网络药理学:认识药物及发现药物的新理念

网络药理学是指将药物作用网络与生物网络整合在一起,分析药物在此网络中与特定节点或模块的相互作用关系,从而理解药物和机体相互作用的科学。网络药理学突破传统的"一个药物一个靶标,一种疾病"理念,代表了现代生物医药研究的哲学理念与研究模式的转变。以系统生物学和网络生物学基本理论为基础的网络药理学具有整体性、系统性的特点,注重网络平衡(或鲁棒性)和网络扰动,强调理解某个单一生物分子(如基因、mRNA或蛋白等)在生物体系中的生物学地位和动力学过程要比理解其具体生物功能更为重要,揭示药物作用的生物学和动力学谱要比揭示其作用的单个靶标或几个"碎片化"靶标更重要,对认识药物和发现药物的理念产生了深远影响。     

Chemogenomics knowledge-based strategies in drug discovery

In the postgenomic age of drug discovery, targets can no longer be viewed as singular objects having no relationship to one another. All targets are now visible and the systematic exploration of selected target families appears to be a promising way to speed up and further industrialize target-based drug discovery. Chemogenomics refers to such systematic exploration of target families and aims to identify all possible ligands of all target families. Because biology works by applying prior knowledge to an unknown entity, chemogenomics approaches are expected to be especially effective within the previously well-explored target families, for which, in addition to the protein sequence and structure information, considerable knowledge of pharmacologically active structural classes and structure-activity relationships exists. For the new target families, chemical knowledge will have to be generated and beyond biological target validation, the emphasis is on chemistry to provide the molecules with which their novel biology and pharmacology can be studied. Using examples from the previously most successfully explored target families, the GPCR family in particular, we summarize herein our current chemogenomics knowledge-based strategies for drug discovery, which are founded on the high integration of chem and bioinformatics, thereby providing a molecular informatics frame for the exploration of the new target families.     

Identification of drug candidates and repurposing opportunities through compound–target interaction networks

Introduction: System-wide identification of both on- and off-targets of chemical probes provides improved understanding of their therapeutic potential and possible adverse effects, thereby accelerating and de-risking drug discovery process. Given the high costs of experimental profiling of the complete target space of drug-like compounds, computational models offer systematic means for guiding these mapping efforts. These models suggest the most potent interactions for further experimental or pre-clinical evaluation both in cell line models and in patient-derived material.

Areas covered: The authors focus here on network-based machine learning models and their use in the prediction of novel compound–target interactions both in target-based and phenotype-based drug discovery applications. While currently being used mainly in complementing the experimentally mapped compound–target networks for drug repurposing applications, such as extending the target space of already approved drugs, these network pharmacology approaches may also suggest completely unexpected and novel investigational probes for drug development.

Expert opinion: Although the studies reviewed here have already demonstrated that network-centric modeling approaches have the potential to identify candidate compounds and selective targets in disease networks, many challenges still remain. In particular, these challenges include how to incorporate the cellular context and genetic background into the disease networks to enable more stratified and selective target predictions, as well as how to make the prediction models more realistic for the practical drug discovery and therapeutic applications.     

Functional human tissue assays

Functional human tissue assays can be used to measure a vast range of physiological effects at the level of the organ, cell or even gene. In relation to drug discovery, such assays have been used in three main areas: discovery biology, in vitro efficacy pharmacology, and safety pharmacology. The most common area to which assays have been applied has been discovery biology, to investigate the mechanisms underlying a novel drug target or to validate that a target identified in a particular tissue is capable of eliciting a physiological response. Furthermore, as the available assays develop, they are considered an important adjunct to routine safety and efficacy pharmacology tests. Such approaches are often superior to extrapolation from animal data.     

Chemogenomic approaches to rational drug design

Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.     

中药复方网络药理学研究的文献分析

目的:综述网络药理学方法在中药复方研究中的应用进展,为中药复方现代化研究提供参考与借鉴.方法:以"网络药理学""中药复方"为关键词,在中国知网、万方数据以及维普网等数据库中组合查询2006年5月-2020年5月发表的文献,筛选文献,对其所用数据库、分析平台、软件进行统计汇总;在进行计量分析的基础上,对网络药理学在中药复...