基于组合药物网络的疾病关联性分析

组合药物在复杂疾病的治疗中形成了多靶点,多环节上的密切联系,对疾病的治疗效果也可达到单种药物治疗意想不到的效果。组合药物中各单药功能各异但联用后治疗效果更佳,说明所对应疾病之间可能存在某种关系。通过研究疾病间关联关系,可能会发现治疗某种疾病的新靶标,从而在新药的研发中取得新的进展。本文以DCDB(组合药物数据库)中的药物组合为数据源构建组合药物网络,并通过网络聚类算法得到了33个独立且内部联系紧密的药物模块。其中7组药物模块所包含的组合药物用于治疗两种或两种以上疾病,说明这些疾病之间存在一定的关联关系。对这些关系进行论证,结果表明,组合药物网络是发现疾病关联关系的一种有效手段。     

面向药物发现和精准医疗的基因表达谱分析

作为功能基因组学中重要的组成部分,基因表达谱在生物学、医学和药物研发等多个领域发挥着重要作用.特别是随着精准医疗概念的提出,整合多组学数据用于个性化医疗是未来的发展趋势.本文从基因表达谱的基本概念出发,重点介绍面向药物发现的基因表达谱分析方法,即基于关联图谱的方法、基于基因调控网络的方法和基于多组学数据整合的方法.系统整理了各种方法的研究进展,特别是在抗癌药物研发领域的最新进展,为利用基因表达谱数据进行药物研发提供方法借鉴.     

基因表达时间延迟调控关系识别软件ITdGR

基因表达调控网络的深入研究有利于分子药物靶标的发现以及推新药的研发,是未来生物医学研究的重要内容。针对基因表达调控的时间延迟问题,我们初步设计开发了一套基于基因表达谱数据识别基因表达时间延迟调控关系的软件ITdGR(Identification of Time-delayed Gene Regulations)。并已经成功地将该软件应用于酿酒酵母细胞周期的基因表达谱数据中,识别出的调控关系与已有的知识相符。该软件为基因调控网络重构以及基因表达动态研究提供了一个方便和快捷的工具。     

RNA干扰与抗病毒感染

RNA干扰(RNAi)是由小干扰RNA(siRNA)引发的生物细胞内同源基因的转录后基因沉默(PTGS)现象,是一种古老的生物抵抗外在感染的防御机制。RNAi因其在维持基因组稳定、调控基因表达和保护基因组免受外源核酸侵入等方面发挥的重要作用,已被广泛用于探索基因功能、基因治疗和新药的研发。外源导入siRNA引发的RNAi可以特异性抑制病毒的复制与感染,为抗病毒感染治疗开辟了一条新的途径。     

人肠道菌群与肺癌关系的研究进展

肠道菌群是由不同种类微生物组成的一个大群体,对宿主的代谢、内分泌系统和免疫系统有着较大的影响。近年来大量的研究发现肠道微生物群落结构变化与消化系统疾病、精神疾病、呼吸系统疾病等多种疾病的发生密切相关,其中关于肠道菌群与肺癌的研究发展迅速。在这篇综述中,我们系统地回顾了肠道常见菌群及肠道菌群与肺癌的关系,并探讨了调节肠道菌群在疾病预防和治疗方面的作用,以期能更加全面地了解肠道菌群在肺癌发生发展中的作用,从而为肺癌的预防、诊断及治疗提供新的方向。     

国际抗糖尿病药物研发新进展

根据美国药物研究与生产商协会( PhRMA) 发布的相关报告和新药数据库中的数据,对2013 年至今进入Ⅲ期临床试验或递交新药申请(NDA)/ 生物制剂许可申请（BLA）的用于治疗糖尿病及其相关疾病的65 种候选新药的临床研发情况进行综述。将这些候选新药分为非胰岛素类、胰岛素类和复方制剂类抗糖尿病药,并重点对递交NDA/BLA 或已获得批准的抗糖尿病新药开发进行了分析和讨论。     

《细胞》：特殊基因或为引发非小细胞肺癌“祸首”

近日来自新加坡基因组研究院（Genome Institute of Singapore）的科学家们在新研究中找到了可能引发非小细胞肺癌的“罪魁祸首”。这一研究发现为肺癌药物的研发开创了新的途径。相关研究论文于2012年1月5日在线发表在《细胞》（Cell）杂志上。     

基于表达及网络拓扑结构挖掘动脉粥样硬化风险疾病基因

基于功能基因组信息、网络拓扑结构信息整合分析方法,利用基因表达谱数据和蛋白质互作数据挖掘动脉粥样硬化(AS)风险疾病基因,为从基因组层面研究动脉粥样硬化提供了新的视角．经过差异表达分析,支持向量机(SVM)的机器学习方法双重筛选,可以鉴别出可信度水平较高的风险疾病基因,对于研究动脉粥样硬化疾病基因在网络中的拓扑性质,建立基因与疾病发生发展过程的联系,提供了新的思路．得到了巨噬细胞样本中59个风险疾病基因,泡沫细胞中61个风险疾病基因．这些风险基因与已知疾病基因共享大部分动脉粥样硬化病变相关生物学过程及信号通路．并应用到对其他复杂疾病致病机理的研究中．     

基因组解析与新药开发

由于ＤＮＡ微阵列技术 ,基因表达的解析已成为可能 ,个体基因差异也正在被发现 ,并产生了一个新的领域———药物基因组学 ,药物开发的模式发生了根本性的改变。基因组解析将为许多新药开发提供目标 ,新的药物筛选系统正在形成 ,基于新的作用功能的先导化合物正在被发现 ,利用ＤＮＡ微阵列技术而实施药理学与安全性评价 ,从基因序列开始对药物标靶的立体构造进行预测 ,从而选择最优秀的化合物。对于临床试验 ,诊断患者的基因多态性 ,筛选最合适的试验人群 ,提高新药的通过率 ,根据个体的基因差异使给药个体化 ,减少副作用 ,加速新药的开发。…     

真菌感染与致病真菌研究面临的挑战

真菌感染的临床地位日益突出,已成为医院感染的重要组成。随着国内外对真菌感染和致病真菌研究的不断深入,新的挑战也应运而生。近年来,真菌感染的致病菌谱与以往相比不尽相同,许多新的致病菌种被逐一报道,而原有的真菌引起的疾病也出现了新的临床表现。抗真菌药物研发加速和广泛应用的同时,各种简捷灵敏的药敏试验方法被建立起来并加以标准化,但致病真菌的耐药现象则成为困扰临床的难题。从分子耐药机制入手寻找打破真菌耐药壁垒的突破口,是真菌学研究和新药研制的目标;注重早期诊断,完善治疗方案,是治疗真菌感染的关键。     

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large‐scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross‐validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue‐specific expression information on the drug targets. We further show that disease‐specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease‐specific signatures.     

Gene expression signature-based approach identifies a pro-resolving mechanism of action for histone deacetylase inhibitors

Despite several therapies being currently available to treat inflammatory diseases, new drugs to treat chronic conditions with less side effects and lower production costs are still needed. An innovative approach to drug discovery, the Connectivity Map (CMap), shows how integrating genome-wide gene expression data of drugs and diseases can accelerate this process. Comparison of genome-wide gene expression data generated with annexin A1 (AnxA1) with the CMap revealed significant alignment with gene profiles elicited by histone deacetylase inhibitors (HDACIs), what made us to hypothesize that AnxA1 might mediate the anti-inflammatory actions of HDACIs. Addition of HDACIs (valproic acid, sodium butyrate and thricostatin A) to mouse macrophages caused externalization of AnxA1 with concomitant inhibition of cytokine gene expression and release, events that occurred independently as this inhibition was retained in AnxA1 null macrophages. In contrast, novel AnxA1-mediated functions for HDACIs could be unveiled, including promotion of neutrophil apoptosis and macrophage phagocytosis, both steps crucial for effective resolution of inflammation. In a model of acute resolving inflammation, administration of valproic acid and sodium butyrate to mice at the peak of disease accelerated resolution processes in wild type, but much more modestly in AnxA1 null mice. Deeper analyses revealed a role for endogenous AnxA1 in the induction of neutrophil death in vivo by HDACIs. In summary, interrogation of the CMap revealed an unexpected association between HDACIs and AnxA1 that translated in mechanistic findings with particular impact on the processes that regulate the resolution of inflammation. We propose non-genomic modulation of AnxA1 in immune cells as a novel mechanism of action for HDACIs, which may underlie their reported efficacy in models of chronic inflammatory pathologies.     

Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease

For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.     

药物重定位的计算分析方法

全新结构药物的研发存在周期长、耗资大、风险高的问题.通过各种技术预测已有药物的新适应症,即药物重定位,可以缩短药物研发时间、降低研发成本和风险.由于疾病种类和已知药物的数量繁多,完全通过实验筛选已知药物的新用途仍然具有很高的成本.随着组学和药物信息学数据的积累,药物重定位进入到了理性设计和实验筛选相结合的阶段,药物重定位的计算预测已经成为计算生物学和系统生物学的重要研究方向.本文将目前药物重定位计算分析的策略归纳为药物-靶标关系分析、药物-药物关系分析和药物-疾病关系分析,对已报道的技术方法及其成功应用实例进行了综述.     

Multiple independent analyses reveal only transcription factors as an enriched functional class associated with microRNAs

Background

The process of drug discovery and development is time-consuming and costly, and the probability of success is low. Therefore, there is rising interest in repositioning existing drugs for new medical indications. When successful, this process reduces the risk of failure and costs associated with de novo drug development. However, in many cases, new indications of existing drugs have been found serendipitously. Thus there is a clear need for establishment of rational methods for drug repositioning.

Results

In this study, we have established a database we call “PharmDB” which integrates data associated with disease indications, drug development, and associated proteins, and known interactions extracted from various established databases. To explore linkages of known drugs to diseases of interest from within PharmDB, we designed the Shared Neighborhood Scoring (SNS) algorithm. And to facilitate exploration of tripartite (Drug-Protein-Disease) network, we developed a graphical data visualization software program called phExplorer, which allows us to browse PharmDB data in an interactive and dynamic manner. We validated this knowledge-based tool kit, by identifying a potential application of a hypertension drug, benzthiazide (TBZT), to induce lung cancer cell death.

Conclusions

By combining PharmDB, an integrated tripartite database, with Shared Neighborhood Scoring (SNS) algorithm, we developed a knowledge platform to rationally identify new indications for known FDA approved drugs, which can be customized to specific projects using manual curation. The data in PharmDB is open access and can be easily explored with phExplorer and accessed via BioMart web service (http://www.i-pharm.org/, http://biomart.i-pharm.org/).     

Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.     

Intuitive repositioning of an anti-depressant drug in combination with tivozanib: precision medicine for breast cancer therapy

Despite the existing therapies and lack of receptors such as HER-2, estrogen receptor and progesterone receptor, triple-negative breast cancer is one of the most aggressive subtypes of breast cancer. TNBCs are known for their highly aggressive metastatic behavior and typically migrate to brain and bone for secondary site propagation. Many diseases share similar molecular pathology exposing new avenues in molecular signaling for engendering innovative therapies. Generation of newer therapies and novel drugs are time consuming associated with very high resources. In order to provide personalized or precision medicine, drug repositioning will contribute in a cost-effective manner. In our study, we have repurposed and used a neoteric combination of two drug molecules namely, fluvoxamine and tivozanib, to target triple-negative breast cancer growth and progression. Our combination regime significantly targets two diverse but significant pathways in TNBCs. Subsequent analysis on migratory, invasive, and angiogenic properties showed the significance of our repurposed drug combination. Molecular array data resulted in identifying the specific and key players participating in cancer progression when the drug combination was used. The innovative combination of fluvoxamine and tivozanib reiterates the use of drug repositioning for precision medicine and subsequent companion diagnostic development.

     

Drug Repositioning for Diabetes Based on 'Omics' Data Mining

Drug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical ‘omics’ data. We analyzed data from genome wide association studies (GWAS), proteomics and metabolomics studies and revealed a total of 992 proteins as potential anti-diabetic targets in human. Information on the drugs that target these 992 proteins was retrieved from the Therapeutic Target Database (TTD) and 108 of these proteins are drug targets with drug projects information. Research and preclinical drug targets were excluded and 35 of the 108 proteins were selected as druggable proteins. Among them, five proteins were known targets for treating diabetes. Based on the pathogenesis knowledge gathered from the OMIM and PubMed databases, 12 protein targets of 58 drugs were found to have a new indication for treating diabetes. CMap (connectivity map) was used to compare the gene expression patterns of cells treated by these 58 drugs and that of cells treated by known anti-diabetic drugs or diabetes risk causing compounds. As a result, 9 drugs were found to have the potential to treat diabetes. Among the 9 drugs, 4 drugs (diflunisal, nabumetone, niflumic acid and valdecoxib) targeting COX2 (prostaglandin G/H synthase 2) were repurposed for treating type 1 diabetes, and 2 drugs (phenoxybenzamine and idazoxan) targeting ADRA2A (Alpha-2A adrenergic receptor) had a new indication for treating type 2 diabetes. These findings indicated that ‘omics’ data mining based drug repositioning is a potentially powerful tool to discover novel anti-diabetic indications from marketed drugs and clinical candidates. Furthermore, the results of our study could be related to other disorders, such as Alzheimer’s disease.     

Systematic drug repositioning based on clinical side-effects

Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naïve Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this “clinical phenotypic assay”.