Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.     

Chemical genetics strategies for identification of molecular targets

Chemical genetics is an emerging field that can be used to study the interactions of chemical compounds, including natural products, with proteins. Usually, the identification of molecular targets is the starting point for studying a drug’s mechanism of action and this has been a crucial step in understanding many biological processes. While a great variety of target identification methods have been developed over the last several years, there are still many bioactive compounds whose target proteins have not yet been revealed because no routine protocols can be adopted. This review contains information concerning the most relevant principles of chemical genetics with special emphasis on the different genomic and proteomic approaches used in forward chemical genetics to identify the molecular targets of the bioactive compounds, the advantages and disadvantages of each and a detailed list of successful examples of molecular targets identified with these approaches.     

Metabolic network modeling and simulation for drug targeting and discovery

Systems biology has greatly contributed toward the analysis and understanding of biological systems under various genotypic and environmental conditions on a much larger scale than ever before. One of the applications of systems biology can be seen in unraveling and understanding complicated human diseases where the primary causes for a disease are often not clear. The in silico genome-scale metabolic network models can be employed for the analysis of diseases and for the discovery of novel drug targets suitable for treating the disease. Also, new antimicrobial targets can be discovered by analyzing, at the systems level, the genome-scale metabolic network of pathogenic microorganisms. Such applications are possible as these genome-scale metabolic network models contain extensive stoichiometric relationships among the metabolites constituting the organism's metabolism and information on the associated biophysical constraints. In this review, we highlight applications of genome-scale metabolic network modeling and simulations in predicting drug targets and designing potential strategies in combating pathogenic infection. Also, the use of metabolic network models in the systematic analysis of several human diseases is examined. Other computational and experimental approaches are discussed to complement the use of metabolic network models in the analysis of biological systems and to facilitate the drug discovery pipeline.     

Microbial genomics: rhetoric or reality?

The availability of complete genome sequences of many bacterial species is facilitating numerous computational approaches for understanding bacterial genomes. One of the major incentives behind the genome sequencing of many pathogenic bacteria is the desire to better understand their diversity and to develop new approaches for controlling human diseases caused by these microorganisms. This task has become even more urgent with the rapid evolution of antibiotic resistance among many bacterial pathogens. Novel drug targets are required in order to design new antimicrobials against antibiotic-resistant pathogens. The complete genome sequences of an ever increasing number of pathogenic microbes constitute an invaluable resource and provide lead information on potential drug targets. This review focuses on in silico analyses of microbial genomes, their host-specific adaptations, with specific reference to genome architecture, design, evolution, and trends in computational identification of microbial drug targets. These trends underscore the utility of genomic data for systematic in silico drug target identification in the post-genomic era.     

基于语义的疾病表型相似性

已知一种药物可用于治疗某疾病,则该药物可能对与该疾病具有相似表型的其他疾病有疗效。因此,大规模地计算疾病表型相似性可辅助发现的疾病新的治疗方法。我们从OMIM下载了3742种疾病的表型信息,从Mesh词库下载13721个关联解剖学和疾病症状的注释词。我们将以上的Mesh词逐一在3742种疾病的表型信息文本中搜索,得到每种疾病涉及的Mesh词汇列表,进而基于语义分析的方法系统地计算了疾病表型的两两相似性矩阵。我们发现疾病关联生物通路最多的有肿瘤生物通路,胰岛素信号通路,肥大心肌病通路和细胞粘附通路等。随疾病对表型相似度的增加,其更涉及相同KEGG生物通路的概率亦增加,证明了本文方法的可靠性。疾病表型相似性可作为疾病在基因水平相似性的补充,有望为药物发现研究提供一条新途径。     

Using mouse forward genetics to define novel target space

Rapid advances in genomics technologies have identified a wealth of new therapeutic targets, but typically these targets are weakly validated with only circumstantial evidence to link them to human disease. The next challenge is testing gene-to-disease connections in a relevant animal model, a time-consuming and uncertain process using conventional reverse-genetic approaches such as knockout and transgenic mice. By contrast, forward genetics proceeds by measuring a physiological process that is relevant to disease, then identifying the gene products that impinge on this process. This ‘phenotype-first’ approach solves the bottleneck of target validation by using clinically relevant assays in a mammalian whole-animal system as a discovery platform. As an unbiased approach to gene discovery and validation, forward genetics will identify novel drug targets and increase the success rate of drug development.     

The challenges of modeling mammalian biocomplexity

Understanding the relationships between human genetic factors, the risks of developing major diseases and the molecular basis of drug efficacy and toxicity is a fundamental problem in modern biology. Predicting biological outcomes on the basis of genomic data is a major challenge because of the interactions of specific genetic profiles with numerous environmental factors that may conditionally influence disease risks in a nonlinear fashion. 'Global' systems biology attempts to integrate multivariate biological information to better understand the interaction of genes with the environment. The measurement and modeling of such diverse information sets is difficult at the analytical and bioinformatic modeling levels. Highly complex animals such as humans can be considered 'superorganisms' with an internal ecosystem of diverse symbiotic microbiota and parasites that have interactive metabolic processes. We now need novel approaches to measure and model metabolic compartments in interacting cell types and genomes that are connected by cometabolic processes in symbiotic mammalian systems.     

Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.     

长链非编码RNA作为潜在药物靶点的研究进展

随着生命科学的不断发展,2012年DNA元件百科全书（ENCODE）项目进一步丰富了人类基因组功能元件的相关信息。该项目 发现人类基因组超过80%的序列会被转录,其中大部分转录本是非编码RNA（ncRNA）。目前,在这些非编码RNA中,小RNA的研究 相对深入,而长链非编码RNA（lncRNA）的研究相对较少。越来越多研究表明,很多lncRNA参与到人类重大疾病的发生、发展过程之 中,并且一些动物实验证实lncRNA可作为药物靶点。因此,从lncRNA角度筛选新的药物靶点也越来越受到研究者的关注。重点总结了 lncRNA的生物学功能及作为潜在药物靶点的研究进展。     

Biological and Functional Properties of Wedelolactone in Human Chronic Diseases

Medicinal herbs are well known and studied over the past millennia in most of the developing countries as a rational means of treatment against various diseases and disorders. Wedelolactone (WDL), a major bioactive compound in Eclipta prostrata L (Eclipta alba L), has been reported with potential benefits in human health against chronic diseases. However, a comprehensive study on WDL pharmacological benefits in various ailments, to the best of our knowledge, is not yet reported. Thereof, the present review provides the recent therapeutic applications in reference to biological and functional activities against major human chronic diseases, including cardiovascular, cancer, diabetes mellitus, liver disease, Alzheimer’s disease, and androgenetic alopecia. In this study, we collected all the relevant experimental information on WDL from Scientific databases such as PubMed, Web of Science, Science Direct, and Google Scholar. Conclusively, WDL is recognized as a key anti-oxidant with both specific regulator and inhibitor of major drug targetable proteins in human chronic diseases and disorders. Hence, WDL as a novel therapeutic bioactive molecule is advised to explore further for relevant pharmacological activities.     

Genetics and genomics of behavioral and psychiatric disorders

Psychiatric conditions are to some degree under genetic influences. Despite the application of advanced genetic and molecular biological technologies, the genetic bases of the human behavioral traits and psychiatric diseases remains largely unresolved. Conventional genetic linkage approaches have not yielded definitive results, possibly because of the absence of objective diagnostic tests, the complex nature of human behavior or the incomplete penetrance of psychiatric traits. However, recent studies have revealed some genes of interest using multifaceted approaches to overcome these challenges. The approaches include using families in which specific behaviors segregate as a mendelian trait, utilization of endophenotypes as biological intermediate traits, identification of psychiatric disease phenotypes in genomic disorders, and the establishment of mouse models.     

ABC transporters: human disease and pharmacotherapeutic potential

Adenosine triphosphate (ATP)-binding cassette (ABC) transporters are a 48-member superfamily of membrane proteins that actively transport a variety of biological substrates across lipid membranes. Their functional diversity defines an expansive involvement in myriad aspects of human biology. At least 21 ABC transporters underlie rare monogenic disorders, with even more implicated in the predisposition to and symptomology of common and complex diseases. Such broad (patho)physiological relevance places this class of proteins at the intersection of disease causation and therapeutic potential, underlining them as promising targets for drug discovery, as exemplified by the transformative CFTR (ABCC7) modulator therapies for cystic fibrosis. This review will explore the growing relevance of ABC transporters to human disease and their potential as small-molecule drug targets.     

Yeast as a tool to uncover the cellular targets of drugs

Knowledge of the spectrum of cellular proteins targeted by experimental therapeutic agents would greatly facilitate drug development. However, identifying the targets of drugs is a daunting challenge. The yeast Saccharomyces cerevisiae is a valuable model organism for human diseases and pathways because it is genetically tractable and shares many functional homolog with humans. In yeast, it is possible to increase or decrease the expression level of essentially every gene and measure changes in drug sensitivity to uncover potential targets. It is also possible to infer mechanism of action from comparing the changes in mRNA expression elicited by drug treatment with those induced by gene deletions or by other drugs. Proteins that bind drugs directly can be identified using yeast protein chips. This review of the use of yeast for discovering targets of drugs discusses the advantages and drawbacks of each approach and how combining methods may reveal targets more efficiently.     

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.     

Searching for synthetic lethality in cancer

The incentive to develop personalised therapy for cancer treatment is driven by the premise that it will increase therapeutic efficacy and reduce toxicity. Understanding the underlying cellular and molecular basis of the disease has been extremely important in the design of these novel therapies; however, identifying new drug targets for personalised therapies remains problematic. This review describes how the biological concept of synthetic lethality has been successfully implemented to identify new therapeutic approaches and targets in models from yeast through to human cells. We also discuss how recent technical advances combined with an increased understanding of the complexity of cellular networks may facilitate therapeutic advances in the future.     

Emerging Mechanisms and Targeted Therapy of Ferroptosis in Neurological Diseases and Neuro-oncology

Ferroptosis is a novel type of cell death characterized by iron-dependent lipid peroxidation that involves a variety of biological processes, such as iron metabolism, lipid metabolism, and oxidative stress. A growing body of research suggests that ferroptosis is associated with cancer and neurodegenerative diseases, such as glioblastoma, Alzheimer''s disease, Parkinson''s disease, and stroke. Building on these findings, we can selectively induce ferroptosis for the treatment of certain cancers, or we can treat neurodegenerative diseases by inhibiting ferroptosis. This review summarizes the relevant advances in ferroptosis, the regulatory mechanisms of ferroptosis, the participation of ferroptosis in brain tumors and neurodegenerative diseases, and the corresponding drug therapies to provide new potential targets for its treatment.     

Genomic and epigenomic instability, fragile sites, schizophrenia and autism

Increasing evidence links genomic and epigenomic instability, including multiple fragile sites regions to neuropsychiatric diseases including schizophrenia and autism. Cancer is the only other disease associated with multiple fragile site regions, and genome and epigenomic instability is a characteristic of cancer. Research on cancer is far more advanced than research on neuropsychiatric disease; hence, insight into neuropsychiatric disease may be derived from cancer research results. Towards this end, this article will review the evidence linking schizophrenia and other neuropsychiatric diseases (especially autism) to genomic and epigenomic instability, and fragile sites. The results of studies on genetic, epigenetic and environmental components of schizophrenia and autism point to the importance of the folate-methionine-transulfuration metabolic hub that is diseases also perturbed in cancer. The idea that the folate-methionine-transulfuration hub is important in neuropsychiatric is exciting because this hub present novel targets for drug development, suggests some drugs used in cancer may be useful in neuropsychiatric disease, and raises the possibility that nutrition interventions may influence the severity, presentation, or dynamics of disease.