高通量药物筛选技术进展及新药开发策略

新药研发过程中．通过筛选而获得具有生物活性的先导化合物．是创新药物研究的关键．目前药物筛选模型已经从传统的整体动物、器官和组织水平发展到细胞和分子水平。创新药物的发现都离不开采用适当的药物作用靶点对大量化合物样品进行筛选．而且筛选规模越大,发现新药的机会就越多。随着计算机技术、生物芯片、蛋白质组学、组合化学等的发展．高通量药物筛选技术应运而生。高通量筛选体系在创新药物筛选中的应用是新药开发研究的一个重要领域。     

药物蛋白质组学与药物发现

21世纪,科学家面临着从基因组到蛋白质组的转变,蛋白质组学是基因组和药物发现的效率。药物蛋白质组学研究不仅有助于发现治疗的可能靶点,也将明显提高药物发现的效率。药物蛋白质组学的研究内容,在临床前包括发现新的治疗靶点和发现针对所有靶点的全部化合物,在临床研究方面应包括药物作用的特异蛋白作为诊断和治疗的标志,或以蛋白质谱的差异来分类者。本文主要综述了蛋白质组学在药物靶点的发现和确认,以有药物发现过程中最有关的技术物研究进展。     

化学基因组学和化学蛋白质组学用于细胞自噬研究

化学基因组学和化学蛋白质组学作为后基因组时代的新技术,是以化学小分子为工具,对细胞的生理过程进行精确干扰,研究有机体和细胞的功能,同时也是新药开发的重要手段。本文综述了化学基因组学和化学蛋白质组学征自噬相关靶点的特异性小分子的发现,及小分子存自噬机理研究中的应用。     

天然活性成分作用靶点识别及确证方法的研究进展

药物靶标的发现和验证是新药研发的关键环节,对新药创制具有源头创新意义。天然产物是新药创制的重要来源,识别其作用靶点不仅为临床预防治疗提供可能新策略,也为进一步阐释中草药及其复方的作用特点及分子机制提供参考依据。随着生命科学和信息学的发展,药物靶点的识别及确证方法不断涌现,生物信息学、网络药理学、蛋白质组学、亲和色谱、药物亲和稳定性、芯片技术、基因敲除技术、RNA干扰等技术的广泛应用,越来越多的天然活性成分的靶点得以识别和验证。因此,本文对近五年来天然活性成分作用靶点识别及确证方法做一简要综述,以供参考。     

基于蛋白质组学技术的新型冠状病毒肺炎研究进展

新型冠状病毒肺炎（coronavirus disease 2019,COVID-19）是一种由严重急性呼吸综合征冠状病毒2 (severe acute respiratory syndrome coronavirus 2,SARS-CoV-2)引发的传染病。此种病毒传染性强、传播速度快,对全球人民的身体健康和生命安全造成严重威胁。蛋白质组学技术以其高通量、高灵敏度的特点,在疾病生物标志物的发现、分子机制研究及治疗靶点研究中扮演着重要角色,并被广泛应用于COVID-19的研究中。本文介绍了SARS-CoV-2的基因组结构及病毒感染过程,总结了目前常用的基于质谱的蛋白质组学研究技术,重点综述了蛋白质组学技术在COVID-19生物标志物的发现、分子机制研究和药物治疗靶标研究中的应用进展,最后展望了蛋白质组学的未来发展方向,以期能够有助于推动蛋白质组学技术在COVID-19精准诊断和治疗中的发展。     

中药网络药理学研究中的生物信息学方法

为了更有效地治疗癌症、心血管疾病、免疫系统疾病等复杂疾病,基于分子网络的多靶点药物发现理念逐渐成为一种新的趋势,而中药整体、辨证、协同的用药观再一次引起了药物发现领域的极大兴趣。中药在治疗复杂慢性疾病方面有确切的疗效和较小的毒副作用。中药网络药理学从分子网络调控的水平上阐明中药的作用机制,为多靶点药物发现提供有益的启示和借鉴,并有可能从临床有效的中药反向开发现代多组分、多靶点新药。针对基于生物分子网络的中药药理学研究路线中的4 个步骤,介绍近年来中药网络药理学研究中相关的生物信息学方法。     

蛋白质组学应用研究进展

蛋白质组学是现代生命科学领域一个新的发展迅速的带头学科,其研究成果为药物筛选、新药开发、临床诊断及新陈代谢途径研究等提供了理论依据和技术基础。该文就蛋白质组学在基础生物学、药物开发、医学、农业等方面的应用进行了综述。     

蛋白质组学-引领后基因组时代

蛋白质组学是建立在高通量筛选技术的基础上发展的方法学,用于研究细胞功能网络模块中蛋白相互作用及在疾病或病变中蛋白和蛋白相互作用所发生的系统动态的差异变化;其研究技术奠基于双向凝胶电泳。及至世纪之交,随着质谱及蛋白质芯片的引进,蛋白质组学已广泛应用在生命科学上。其在医学上的应用,主要旨在发现疾病的特异性蛋白质分子或其蛋白质纹印,以揭示疾病的发生机制,也作为早期诊断、分子分型、疗效及预后判断的依据,并找出可能成为新药物设计的分子靶点,为疾病提供新的治疗方案。随着人类基因序列的完成,蛋白质组学热浪掀起了后基因组年代的序幕,人类将更深入地了解疾病和生命的本源。现就蛋白质组学10年来的发展历程、研究技术、在人类疾病中的应用及未来展望等作出精简的评述。     

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.     

蛋白质组学技术在恶性肿瘤研究中应用的新进展

人类基因组计划的完成使人类进入后基因组时代,蛋白质组学有了飞速的发展。作为基因的产物,蛋白质在人类的生命活动中扮演了重要的角色,21种不同的氨基酸可以根据遗传基因组合成数量庞大的蛋白质,而且又由于蛋白质的翻译后修饰使得蛋白质在表型上千变万化。不同的蛋白质在人体内具有相应的功能,而就现今发病人数不断增加,发病年龄越发年轻的肿瘤而言,蛋白质在其发生发展、侵袭以及转移等生物学特为中也起到了至关重要的作用。随着蛋白质组学的理论和技术的不断发展,该理论和技术已经被广泛应用于研究恶性肿瘤的发生和发展机制、侵袭和转移机制、各种生物标志物和药物作用靶点及赖药机制等方面的研究,发现了不少与之相关的差异性表达蛋白质。本文主要对蛋白质组学技术在恶性肿瘤研究中应用的最新进展进行了综述。     

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.     

Navigating the kinome

Although it is increasingly being recognized that drug-target interaction networks can be powerful tools for the interrogation of systems biology and the rational design of multitargeted drugs, there is no generalized, statistically validated approach to harmonizing sequence-dependent and pharmacology-dependent networks. Here we demonstrate the creation of a comprehensive kinome interaction network based not only on sequence comparisons but also on multiple pharmacology parameters derived from activity profiling data. The framework described for statistical interpretation of these network connections also enables rigorous investigation of chemotype-specific interaction networks, which is critical for multitargeted drug design.     

Network pharmacology: the next paradigm in drug discovery

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.     

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.     

The relationship between rational drug design and drug side effects

Previous analysis of systems pharmacology has revealed a tendency of rational drug design in the pharmaceutical industry. The targets of new drugs tend to be close with the corresponding disease genes in the biological networks. However, it remains unclear whether the rational drug design introduces disadvantages, i.e. side effects. Therefore, it is important to dissect the relationship between rational drug design and drug side effects. Based on a recently released drug side effect database, SIDER, here we analyzed the relationship between drug side effects and the rational drug design. We revealed that the incidence drug side effect is significantly associated with the network distance of drug targets and diseases genes. Drugs with the distances of three or four have the smallest incidence of side effects, whereas drugs with the distances of more than four or smaller than three show significantly greater incidence of side effects. Furthermore, protein drugs and small molecule drugs show significant differences. Drugs hitting membrane targets and drugs hitting cytoplasm targets also show differences. Failure drugs because of severe side effects show smaller network distances than approved drugs. These results suggest that researchers should be prudent on rationalizing the drug design. Too small distances between drug targets and diseases genes may not always be advantageous for rational design for drug discovery.     

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.     

抗癌药物作用预测计算方法的研究现状与展望

对精准医疗即个体化医疗理念的探讨与实践是当下医学研究的热门课题,如果精准医疗的设想实现可为患者提供更加精确有效的治疗方案,而对癌症的研究是医学界尚未攻破且意义重大的研究课题,也是和精准医疗结合最密切的课题之一。应用生物信息学的计算方法可以通过分析患者的概况来为癌症患者的药物选择提供有效方案,从而提高癌症患者的生存率。通过参考多篇使用计算方法研究抗癌药物作用的研究成果,从数据源和网络分析、机器学习和深度学习等计算方法两个方面总结了当前的研究成果,并对该课题存在的问题与未来发展趋势做出了分析与展望。