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。     

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.     

单克隆抗体药物与血液系统恶性肿瘤的治疗

单克隆抗体凭借其特异性强、副作用较小的优点,越来越广泛地应用于疾病的诊断与治疗。单克隆抗体药物在血液系统恶性肿瘤的治疗中也发挥了重要作用。目前,经美国食品与药品管理局(FDA)批准用于治疗血液系统恶性肿瘤的单克隆抗体药物已有六种,在临床取得良好的治疗效果。单克隆抗体药物主要通过对肿瘤细胞的直接杀伤作用、抗体依赖性细胞介导的细胞毒性反应(ADCC)、补体依赖性细胞毒性反应(CDC)和改变信号通路等机制达到治疗肿瘤的效果。另外,将单克隆抗体与放射性核素、化疗药物和毒素等偶联,用于肿瘤等疾病的靶向治疗研究,成为生物治疗领域的热点。该文对近年来国际上用于血液系统恶性肿瘤治疗的单克隆抗体药物进行了概括和总结,讨论了治疗性单克隆抗体药物存在的问题和应用前景。     

Cancer Cell:新型药物组合或有望治疗恶性白血病

正近日,刊登在国际杂志Cancer Cell上的一篇研究报告中,来自马里兰大学医学院等机构的研究人员开发了一种新型药物组合或有望帮助治疗急性髓性白血病(AML),在诊断为该疾病后,AML能在5年内使得几乎四分之三的患者死亡,目前在美国三个研究中研究研究人员正在招募研究者进行多中心的临床试验。     

脂质体药物传递系统

脂质体已经发展成为一种成熟的传递系统,从脂质体作为载体概念的提出发展到生产制药水平历经了很长的发展阶段,如今脂质体制剂已有效地应用于重要疾病的治疗领域。简述了有关药物传递脂质体的目标和系统,透视了那些正在研究的领域,以及有哪些机会可合理改进脂质体的药物治疗。     

罕见病药物市场与管理

罕见病,又称"孤儿病",是指发病率极低的疾病,绝大部分属于先天性疾病、慢性病,且常常危及生命。近年来,随着公众认知度的提高、国家政策的支持、诊断及治疗技术的进步,罕见病药物市场逐渐发展起来,销售额逐年增加,出现多个"重磅炸弹"级药物。国际大型药企开始抢占罕见病药物市场并且加紧药物研发,目前正在研发的罕见病药物有500多个,主要针对罕见癌症、遗传性疾病、神经类疾病、传染性疾病和自身免疫性疾病等。发达国家和地区在罕见病管理及市场发展方面都已经比较完善,而中国在这方面还比较落后。对中国罕见病药物市场的发展困境进行了分析并提出了几点建议,希望能够促进国内罕见病药物市场的快速发展。     

抗多药耐药靶点及相关药物的研究进展

抗多药耐药是指在疾病的治疗过程中,细胞对多种药物产生广泛的耐受,导致治疗效果不理想的现象。多药耐药多发于感染与肿瘤疾病的治疗中,已成为治愈这2 类疾病的主要障碍。从转运蛋白和离子通道、酶以及核糖体这3 个方面综述抗多药耐药靶点的机制及相关药物的研究进展,旨在为抗多药耐药药物的研发提供参考。     

抗体药物现状及发展趋势

治疗性抗体作为一种具有独特优势的生物靶向治疗药物,已成为目前全球药物研发的热点。截止到2013年2月,已有34个治疗性抗体获得美国FDA批准上市,用于各种疾病的治疗。据统计,目前有多达350余种抗体产品正处于临床试验阶段,其中29个已进入III期临床试验。开发抗体新靶点和新适应证,研究和设计更为安全有效的新型抗体分子及抗体组合疗法,寻找生物标记指导抗体对病人的有效治疗,是当前和今后一段时期内该领域发展的主要方向。本文将综述国际抗体药物研发现状和发展趋势,并对国内抗体药物现状及发展策略予以简要陈述。     

脑靶向寡核苷酸药物策略用于衰老相关脑部疾病治疗

寡核苷酸药物近10年发展迅速,已有多款应用于临床治疗。因其设计便捷、序列灵活、特异性高,有望解决许多靶点难成药的困境,并且其临床转化周期和成本较低,目前已成为新兴生物技术药物研发的前沿领域。脑部疾病包括多种目前无法治愈的疾病,如神经退行性疾病、胶质瘤、运动神经元疾病等,其中很多与年龄相关,被认为是衰老相关脑部疾病。因其病因复杂,许多靶点难成以药,同时由于脑部特殊屏障系统“血脑屏障”的存在,导致大部分药物无法实现脑部病灶的有效积累,众多小分子药物遭遇临床转化失败。寡核苷酸类药物的特异性和序列灵活性提供了新的成药可能性,但同样面临脑部递送的挑战。尽管目前已有多款寡核苷酸类药物应用于医疗市场,但脑靶向寡核苷酸药物仍然极为罕见,随着纳米递送和脑靶向基团研究的逐渐成熟,未来5~10年寡核苷酸药物用于脑部疾病治疗将成为可能。本文针对本领域重点话题如寡核苷酸药物临床批准的应用案例、脑靶向寡核苷酸药物的递送瓶颈和当前策略,以及衰老相关脑部疾病的寡核苷酸药物潜在靶点进行了梳理,同时对临床转化中的难点和面临的挑战展开了综述和讨论。     

纳米药物递送系统在基于化疗的联合治疗中的研究进展

肿瘤是一种病理过程复杂的疾病。大多数肿瘤患者接受化疗和放疗，但这些治疗通常只对部分有效，并产生各种严重的副作用。因此，有必要开发新的治疗策略。联合治疗是目前肿瘤治疗的热点，联合用药引起的多种协同作用是提高抗肿瘤活性的关键。纳米药物递送系统的出现对临床治疗产生了深远的影响。药物的体内递送常不能达到令人满意的治疗效果，而纳米药物递送系统可以实现肿瘤靶向给药，在提高抗肿瘤效果的同时降低药物的毒副作用。本文介绍了多种基于化疗的联合治疗方法，重点阐述了纳米药物递送系统在基于化疗的联合治疗中的运用，并对该领域面临的挑战和未来发展方向进行了展望。     

A Network-Based Classification Model for Deriving Novel Drug-Disease Associations and Assessing Their Molecular Actions

The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug) was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer’s disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer’s disease.     

Convergence and divergence of genetic and modular networks between diabetes and breast cancer

Diabetes mellitus (DM) and breast cancer (BC) can simultaneously occur in the same patient populations, but the molecular relationship between them remains unknown. In this study, we constructed genetic networks and used modularized analysis approaches to investigate the multi‐dimensional characteristics of two diseases and one disease subtype. A text search engine (Agilent Literature Search 2.71) and MCODE software were applied to validate potential subnetworks and to divide the modules, respectively. A total of 793 DM‐related genes, 386 type 2 diabetes (T2DM) genes and 873 BC‐related genes were identified from the Online Mendelian Inheritance in Man database. For DM and BC, a total of 99 overlapping genes, 9 modules, 29 biological processes and 7 pathways were identified. Meanwhile, for T2DM and BC, 56 overlapping genes, 5 modules, 20 biological processes and 12 pathways were identified. Based on the Gene Ontology functional enrichment analysis of the top 10 non‐overlapping modules of the two diseases, 10 biological functions and 5 pathways overlapped between them. The glycosphingolipid and lysosome pathways verified molecular mechanisms of cell death related to both DM and BC. We also identified new biological functions of dopamine receptors and four signalling pathways (Parkinson's disease, Alzheimer's disease, Huntington's disease and long‐term depression) related to both diseases; these warrant further investigation. Our results illustrate the landscape of the novel molecular substructures between DM and BC, which may support a new model for complex disease classification and rational therapies for multiple diseases.     

Disease-driven detection of differential inherited SNP modules from SNP network

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.     

Functional Module Connectivity Map (FMCM): A Framework for Searching Repurposed Drug Compounds for Systems Treatment of Cancer and an Application to Colorectal Adenocarcinoma

Drug repurposing has become an increasingly attractive approach to drug development owing to the ever-growing cost of new drug discovery and frequent withdrawal of successful drugs caused by side effect issues. Here, we devised Functional Module Connectivity Map (FMCM) for the discovery of repurposed drug compounds for systems treatment of complex diseases, and applied it to colorectal adenocarcinoma. FMCM used multiple functional gene modules to query the Connectivity Map (CMap). The functional modules were built around hub genes identified, through a gene selection by trend-of-disease-progression (GSToP) procedure, from condition-specific gene-gene interaction networks constructed from sets of cohort gene expression microarrays. The candidate drug compounds were restricted to drugs exhibiting predicted minimal intracellular harmful side effects. We tested FMCM against the common practice of selecting drugs using a genomic signature represented by a single set of individual genes to query CMap (IGCM), and found FMCM to have higher robustness, accuracy, specificity, and reproducibility in identifying known anti-cancer agents. Among the 46 drug candidates selected by FMCM for colorectal adenocarcinoma treatment, 65% had literature support for association with anti-cancer activities, and 60% of the drugs predicted to have harmful effects on cancer had been reported to be associated with carcinogens/immune suppressors. Compounds were formed from the selected drug candidates where in each compound the component drugs collectively were beneficial to all the functional modules while no single component drug was harmful to any of the modules. In cell viability tests, we identified four candidate drugs: GW-8510, etacrynic acid, ginkgolide A, and 6-azathymine, as having high inhibitory activities against cancer cells. Through microarray experiments we confirmed the novel functional links predicted for three candidate drugs: phenoxybenzamine (broad effects), GW-8510 (cell cycle), and imipenem (immune system). We believe FMCM can be usefully applied to repurposed drug discovery for systems treatment of other types of cancer and other complex diseases.     

Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms

Background

Complex diseases are associated with altered interactions between thousands of genes. We developed a novel method to identify and prioritize disease genes, which was generally applicable to complex diseases.

Results

We identified modules of highly interconnected genes in disease-specific networks derived from integrating gene-expression and protein interaction data. We examined if those modules were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies. First, we analyzed publicly available gene expression microarray and genome-wide association study (GWAS) data from 13, highly diverse, complex diseases. In each disease, highly interconnected genes formed modules, which were significantly enriched for genes harboring disease-associated SNPs. To test if such modules could be used to find novel genes for functional studies, we repeated the analyses using our own gene expression microarray and GWAS data from seasonal allergic rhinitis. We identified a novel gene, FGF2, whose relevance was supported by functional studies using combined small interfering RNA-mediated knock-down and gene expression microarrays. The modules in the 13 complex diseases analyzed here tended to overlap and were enriched for pathways related to oncological, metabolic and inflammatory diseases. This suggested that this union of the modules would be associated with a general increase in susceptibility for complex diseases. Indeed, we found that this union was enriched with GWAS genes for 145 other complex diseases.

Conclusions

Modules of highly interconnected complex disease genes were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies.     

Disease gene interaction pathways: a potential framework for how disease genes associate by disease-risk modules

Background

Disease genes that interact cooperatively play crucial roles in the process of complex diseases, yet how to analyze and represent their associations is still an open problem. Traditional methods have failed to represent direct biological evidences that disease genes associate with each other in the pathogenesis of complex diseases. Molecular networks, assumed as ‘a form of biological systems’, consist of a set of interacting biological modules (functional modules or pathways) and this notion could provide a promising insight into deciphering this topic.

Methodology/Principal Findings

In this paper, we hypothesized that disease genes might associate by virtue of the associations between biological modules in molecular networks. Then we introduced a novel disease gene interaction pathway representation and analysis paradigm, and managed to identify the disease gene interaction pathway for 61 known disease genes of coronary artery disease (CAD), which contained 46 disease-risk modules and 182 interaction relationships. As demonstrated, disease genes associate through prescribed communication protocols of common biological functions and pathways.

Conclusions/Significance

Our analysis was proved to be coincident with our primary hypothesis that disease genes of complex diseases interact with their neighbors in a cooperative manner, associate with each other through shared biological functions and pathways of disease-risk modules, and finally cause dysfunctions of a series of biological processes in molecular networks. We hope our paradigm could be a promising method to identify disease gene interaction pathways for other types of complex diseases, affording additional clues in the pathogenesis of complex diseases.     

NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.     

Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state “signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.     

Network medicine: a network-based approach to human disease

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.     

Identifying causal genes and dysregulated pathways in complex diseases

In complex diseases, various combinations of genomic perturbations often lead to the same phenotype. On a molecular level, combinations of genomic perturbations are assumed to dys-regulate the same cellular pathways. Such a pathway-centric perspective is fundamental to understanding the mechanisms of complex diseases and the identification of potential drug targets. In order to provide an integrated perspective on complex disease mechanisms, we developed a novel computational method to simultaneously identify causal genes and dys-regulated pathways. First, we identified a representative set of genes that are differentially expressed in cancer compared to non-tumor control cases. Assuming that disease-associated gene expression changes are caused by genomic alterations, we determined potential paths from such genomic causes to target genes through a network of molecular interactions. Applying our method to sets of genomic alterations and gene expression profiles of 158 Glioblastoma multiforme (GBM) patients we uncovered candidate causal genes and causal paths that are potentially responsible for the altered expression of disease genes. We discovered a set of putative causal genes that potentially play a role in the disease. Combining an expression Quantitative Trait Loci (eQTL) analysis with pathway information, our approach allowed us not only to identify potential causal genes but also to find intermediate nodes and pathways mediating the information flow between causal and target genes. Our results indicate that different genomic perturbations indeed dys-regulate the same functional pathways, supporting a pathway-centric perspective of cancer. While copy number alterations and gene expression data of glioblastoma patients provided opportunities to test our approach, our method can be applied to any disease system where genetic variations play a fundamental causal role.