药物作用靶点研究最新进展

通过对我国学者近2年在国内外发表的相关论文进行检索和整理,分类综述针对神经退行性疾病（如阿尔茨海默病、帕金森病等）、心血管疾病（如高血压、心律失常、心衰、冠心病、心肌梗死、动脉粥样硬化等）、脑血管疾病、代谢类疾病（如肥胖症、血脂异常、脂肪肝、糖尿病等）、感染性疾病（如艾滋病、流感、结核病等）、恶性肿瘤、自身免疫性疾病等多种疾病的药物作用靶点研究最新进展。     

Properties of Protein Drug Target Classes

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme.     

Epidemiology of Disappearing Plasmodium vivax Malaria: A Case Study in Rural Amazonia

Background

New frontier settlements across the Amazon Basin pose a major challenge for malaria elimination in Brazil. Here we describe the epidemiology of malaria during the early phases of occupation of farming settlements in Remansinho area, Brazilian Amazonia. We examine the relative contribution of low-density and asymptomatic parasitemias to the overall Plasmodium vivax burden over a period of declining transmission and discuss potential hurdles for malaria elimination in Remansinho and similar settings.

Methods

Eight community-wide cross-sectional surveys, involving 584 subjects, were carried out in Remansinho over 3 years and complemented by active and passive surveillance of febrile illnesses between the surveys. We used quantitative PCR to detect low-density asexual parasitemias and gametocytemias missed by conventional microscopy. Mixed-effects multiple logistic regression models were used to characterize independent risk factors for P. vivax infection and disease.

Principal Findings/Conclusions

P. vivax prevalence decreased from 23.8% (March–April 2010) to 3.0% (April–May 2013), with no P. falciparum infections diagnosed after March–April 2011. Although migrants from malaria-free areas were at increased risk of malaria, their odds of having P. vivax infection and disease decreased by 2–3% with each year of residence in Amazonia. Several findings indicate that low-density and asymptomatic P. vivax parasitemias may complicate residual malaria elimination in Remansinho: (a) the proportion of subpatent infections (i.e. missed by microscopy) increased from 43.8% to 73.1% as P. vivax transmission declined; (b) most (56.6%) P. vivax infections were asymptomatic and 32.8% of them were both subpatent and asymptomatic; (c) asymptomatic parasite carriers accounted for 54.4% of the total P. vivax biomass in the host population; (d) over 90% subpatent and asymptomatic P. vivax had PCR-detectable gametocytemias; and (e) few (17.0%) asymptomatic and subpatent P. vivax infections that were left untreated progressed to clinical disease over 6 weeks of follow-up and became detectable by routine malaria surveillance.     

Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery

The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.     

肿瘤抑制基因DAPK:一个新的药物治疗靶点

死亡相关蛋白激酶(DAPK)是一种新的钙调蛋白(CaM)调节的丝/苏氨酸激酶,是凋亡的正性调节因子。细胞凋亡被认为是控制和治疗肿瘤的最有效方法之一,它与肿瘤的发生、发展和转移有着密切的联系。而DAPK参与多条途径诱导的细胞凋亡,被公认为是一种肿瘤抑制基因。在此我们将重点讨论DAPK促进细胞凋亡的机制,为靶向治疗肿瘤提供方向和理论依据。     

The Glutathione System: A New Drug Target in Neuroimmune Disorders

Glutathione (GSH) has a crucial role in cellular signaling and antioxidant defenses either by reacting directly with reactive oxygen or nitrogen species or by acting as an essential cofactor for GSH S-transferases and glutathione peroxidases. GSH acting in concert with its dependent enzymes, known as the glutathione system, is responsible for the detoxification of reactive oxygen and nitrogen species (ROS/RNS) and electrophiles produced by xenobiotics. Adequate levels of GSH are essential for the optimal functioning of the immune system in general and T cell activation and differentiation in particular. GSH is a ubiquitous regulator of the cell cycle per se. GSH also has crucial functions in the brain as an antioxidant, neuromodulator, neurotransmitter, and enabler of neuron survival. Depletion of GSH leads to exacerbation of damage by oxidative and nitrosative stress; hypernitrosylation; increased levels of proinflammatory mediators and inflammatory potential; dysfunctions of intracellular signaling networks, e.g., p53, nuclear factor-κB, and Janus kinases; decreased cell proliferation and DNA synthesis; inactivation of complex I of the electron transport chain; activation of cytochrome c and the apoptotic machinery; blockade of the methionine cycle; and compromised epigenetic regulation of gene expression. As such, GSH depletion has marked consequences for the homeostatic control of the immune system, oxidative and nitrosative stress (O&NS) pathways, regulation of energy production, and mitochondrial survival as well. GSH depletion and concomitant increase in O&NS and mitochondrial dysfunctions play a role in the pathophysiology of diverse neuroimmune disorders, including depression, myalgic encephalomyelitis/chronic fatigue syndrome and Parkinson’s disease, suggesting that depleted GSH is an integral part of these diseases. Therapeutical interventions that aim to increase GSH concentrations in vivo include N-acetyl cysteine; Nrf-2 activation via hyperbaric oxygen therapy; dimethyl fumarate; phytochemicals, including curcumin, resveratrol, and cinnamon; and folate supplementation.     

潜在性药物靶点PARP16的研究进展

PARP16属于聚腺苷二磷酸核糖聚合酶(PARP)家族成员,是一种单ADP-核糖基转移酶,与其他家族成员不同,它是位于内质网的锚定跨膜蛋白.内质网未折叠蛋白反应期间,会激活内质网的两个压力传感器PERK和IRE1α,PARP16在这个过程中发挥着重要功能.通过对它们单ADP-核糖基化,激活其生物活性而对癌症、心血管疾病...     

核酸适配体在靶向药物传递中的研究进展

抗癌药物的毒副作用限制了其临床应用,纳米药物载体可实现药物在病灶部位的聚集而不影响正常组织,从而降低药物毒副作用.在药物载体表面修饰靶向配体,以提高药物载体主动靶向进入到细胞的能力,可有效地将药物释放到靶细胞,大大提高药效.核酸适配体(aptamer)作为一种新型的靶向分子,近几年已被运用到靶向药物传递的研究中.本文介绍了几种适配体靶向载药体系,如适配体-药物、适配体-脂质体、适配体-聚合物胶束、适配体-聚合物纳米颗粒、适配体-金属颗粒以及适配体-支化聚合物等载药体系,并对当前研究的热点以及存在的问题和不足进行了评述.     

The Concept of Additivity of Drug Combinations in the Case of Quantal Response

The concept of additivity of drug combinations is widely accepted in pharmacology and toxicology. Up to now, no general statistical methods to test that property are available. The present paper gives a mathematical formulation of additivity, a method to fit dose response surfaces under additivity assumption and a statistical test.     

Target Essentiality and Centrality Characterize Drug Side Effects

To investigate factors contributing to drug side effects, we systematically examine relationships between 4,199 side effects associated with 996 drugs and their 647 human protein targets. We find that it is the number of essential targets, not the number of total targets, that determines the side effects of corresponding drugs. Furthermore, within the context of a three-dimensional interaction network with atomic-resolution interaction interfaces, we find that drugs causing more side effects are also characterized by high degree and betweenness of their targets and highly shared interaction interfaces on these targets. Our findings suggest that both essentiality and centrality of a drug target are key factors contributing to side effects and should be taken into consideration in rational drug design.     

基于生物信息学方法发现潜在药物靶标

药物靶点通常是在代谢或信号通路中与特定疾病或病理状态有关的关键分子.通过绑定到特定活动区域抑制这个关键分子进行药物设计.确定特定疾病有关的靶标分子是现代新药开发的基础.在药物靶标发现的过程中,生物信息学方法发挥了不可替代的重要的作用,尤其适用于大规模多组学数据的分析.目前,已涌现了许多与疾病相关的数据库资源,基于生物网络特征、多基因芯片、蛋白质组、代谢组数据等建立了多种生物信息学方法发现潜在的药物靶标,并预测靶标可药性和药物副作用.     

小分子药靶——RNA药靶研究进展

对RNA药靶的特点、研究策略和针对RNA药靶的小分子药物筛选方法,进行了综述.RNA的三级结构作为分子相互作用的识别位点和结合位点对RNA的生物功能的实现具有重要决定作用,RNA分子同蛋白质一样将成为新型小分子药物的作用靶点.     

小分子药物靶标寻找方法研究进展

许多微生物的次生代谢物属于小分子活性化合物,在医疗及农业领域发挥着重要的作用。在基因组学、蛋白质组学与生物信息学等技术的推动下,一些新的小分子药物靶标寻找方法应运而生了,这些新的方法主要是基于细胞中基因或蛋白质的表达量、蛋白质的亲和性、稳定性等各种特性进行靶标寻找的。小分子药物靶标寻找方法的发展加快了阐明小分子药物作用机理的历程,也为发现新的靶标资源以便于进一步筛选活性更高的药物提供了技术保障。     

代谢组学在肿瘤药物靶点筛选中的应用进展

肿瘤是一种多因素参与造成机体各系统功能平衡紊乱的代谢性疾病,代谢重编程是恶性肿瘤的重要特征之一.研究"代谢指纹图谱"的代谢组学,通过揭示肿瘤或药物引起的宿主内源性代谢物的变化,为肿瘤药物靶点的筛选提供了可能.但目前对代谢组在肿瘤药物靶点筛选中的整体性综述并不多见,因此,本文在介绍了代谢组学筛选肿瘤药物靶点的流程的基础上...     

Development Of The Novel Drug Releasing System Triggered By Hybridization With Target Sequence

We have already established the strategy of synchronous activation by hybridization, in which the highly reactive cross-linking agent, 2-amino-6-vinylpurine nucleoside analog, can be generated from its stable precursors, the phenylsulfide derivatives, by a hybridization-promoted activation process with selectivity to cytosine. In this study, this in situ activation system was applied to the method for the drug releasing system triggered by hybridization with the target sequence.