Genomics strategies for antifungal drug discovery--from gene discovery to compound screening

The use of genomics tools to discover new genes, to decipher pathways or to assign a function to a gene is just beginning to have an impact. Genomics approaches have been applied to both antibacterial and antifungal target discovery in order to identify a new generation of antibiotics. This review discusses genomics approaches for antifungal drug discovery, focusing on the areas of gene discovery, target validation, and compound screening. A variety of methods to identify fungal genes of interest are discussed, as well as methods for obtaining full-length sequences of these genes. One approach is well-suited to organisms having few introns (Candida albicans), and another for organisms with many introns (Aspergillus fumigatus). To validate broad spectrum fungal targets, the yeast Saccharomyces cerevisiae was used as a model system to rapidly identify genes essential for growth and viability of the organism. Validated targets were then exploited for high-throughput compound screening.     

Genomics and proteomics: the new millennium of drug discovery and development

One of the most pressing issues facing the pharmaceutical and biotechnology industry is the tremendous dropout rate of lead drug candidates. Over the last two decades, several new genomic technologies have been developed in hopes of addressing the issues of target identification and lead candidate optimization. Gene expression microarray is one of these technologies and this review describes the four main formats, which are currently available: (a) cDNA; (b) oligonucleotide; (c) electrokinetic; and (d) fiberoptic. Many of these formats have been developed with the goal of screening large numbers of genes. Recently, a high-throughput array format has been developed where a large number of samples can be assayed using arrays in parallel. In addition, focusing on gene expression may be only one avenue in preventing lead candidate failure. Proteomics or the study of protein expression may also play a role. Two-dimensional polyacrylamide gel electrophoresis (2-DE) coupled with mass spectroscopy has been the most widely accepted format to study protein expression. However, protein microarrays are now being developed and modified to a high-throughput screening format. Examples of several gene and protein expression studies as they apply to drug discovery and development are reviewed. These studies often result in large data sets. Examples of how several statistical methods (principal components analysis [PCA], clustering methods, Shannon entropy, etc.) have been applied to these data sets are also described. These newer genomic and proteomic technologies and their analysis and visualization methods have the potential to make the drug discovery and development process less costly and more efficient by aiding to select better target and lead candidates.     

Mining protein lists from proteomics studies: applications for drug discovery

Importance of the field: In recent years, proteomics has become a common technique applied to a wide spectrum of scientific problems, including the identification of diagnostic biomarkers, monitoring the effects of drug treatments or identification of chemical properties of a protein or a drug. Although being significantly different in scientific essence, the ultimate result of the majority of proteomics studies is a protein list. Thousands of independent proteomics studies have reported protein lists in various functional contexts. Areas covered in this review: We review here the spectrum of scientific problems where proteomics technology was applied recently to deliver protein lists. The available bioinformatics methods commonly used to understand the properties of the protein lists are compared. What the reader will gain: The types and common functional properties of the reported protein lists are discussed. The range of scientific problems where this knowledge could be potentially helpful with a focus on drug discovery issues is explored. Take home message: Reported protein lists represent a valuable resource which can be used for a variety of goals, ranging from biomarkers discovery to identification of novel therapeutic implications of known drugs.     

In silico tools used for compound selection during target-based drug discovery and development

Introduction: The target-based drug discovery process, including target selection, screening, hit-to-lead (H2L) and lead optimization stage gates, is the most common approach used in drug development. The full integration of in vitro and/or in vivo data with in silico tools across the entire process would be beneficial to R&D productivity by developing effective selection criteria and drug-design optimization strategies.

Areas covered: This review focuses on understanding the impact and extent in the past 5 years of in silico tools on the various stage gates of the target-based drug discovery approach.

Expert opinion: There are a large number of in silico tools available for establishing selection criteria and drug-design optimization strategies in the target-based approach. However, the inconsistent use of in vitro and/or in vivo data integrated with predictive in silico multiparameter models throughout the process is contributing to R&D productivity issues. In particular, the lack of reliable in silico tools at the H2L stage gate is contributing to the suboptimal selection of viable lead compounds. It is suggested that further development of in silico multiparameter models and organizing biologists, medicinal and computational chemists into one team with a single accountable objective to expand the utilization of in silico tools in all phases of drug discovery would improve R&D productivity.     

Current tools for norovirus drug discovery

ABSTRACT

Introduction: Rapid transmission of norovirus often occurs due to its low infectious dosage, high genetic diversity and its short incubation time. The viruses cause acute gastroenteritis and may lead to death. Presently, no effective vaccine or selective drugs accepted by the United States Food and Drug Administration (FDA) are available for the treatment of norovirus. Advances in the development of norovirus replicon cell lines, GII.4-Sydney HuNoV strain human B cells, and murine and gnotobiotic pig norovirus models have facilitated the discovery of effective small molecule inhibitors in vitro and in vivo.

Areas covered: This review gives a brief discussion of the biology and replication of norovirus before highlighting the discovery of anti-norovirus molecules. The article coverage includes: an overview of the current state of norovirus drug discovery, the targeting of the norovirus life cycle, the inhibition of structural and nonstructural proteins of norovirus such as proteases and polymerase, and the blockage of virus entry into host cells. Finally, anti-norovirus drugs in the clinical development stage are described.

Expert opinion: The current approach for the counteraction of norovirus focuses on the inhibition of viral RNA polymerase, norovirus 3C-like protease and the structural proteins VP1 as well as the blockade of norovirus entry. Broad-spectrum anti-norovirus molecules, based on the inhibition of 3C-like protease, have been developed. Other host factors and ways to overcome the development of resistance through mutation are also being examined. A dual approach in targeting viral and host factors may lead to an effective counteraction of norovirus infection. Current successes in developing norovirus replicon harboring cells and norovirus infected human cells, as well as murine norovirus models and other animal models such as piglets have facilitated the discovery of effective drugs and helped our understanding of its mechanism of action.     

Applying proteomics to drug discovery

Proteomics is a technology that has come to prominence over the last few years largely as a result of the advances that have been made in the equipment and software associated with the performance and analysis of two dimensional (2D) gel electrophoresis. With this technique it is now possible to resolve and identify proteins on 2D gels with a high degree of reproducibility and sensitivity. This facilitates the detection and quantification of thousands of proteins from complex biological samples in a single analysis and, more significantly, the comparison of these data accurately and reproducibly between samples. Thus, qualitative and quantitative assessments of changes are possible between the healthy and diseased state, in the presence and absence of drug, or between responders and non-responders. The added ability of carrying out such analysis at a high throughput opens up the possibilities for using proteomics to great effect throughout the drug discovery process. This review outlines the proteomic process and indicates areas where its potential has begun to be realised.     

Introduction to chemical proteomics for drug discovery and development

A fundamental goal of chemical proteomics is to identify target proteins for bioactive small molecules and then apply them to drug discovery and development as valid and drugable targets. Here, we introduce integrated technologies for the rapid identification of target proteins, methodologies for validating them as drugable targets, and applications of chemical proteomics in drug discovery and development.     

Maximizing computational tools for successful drug discovery

Drug discovery is an iterative cycle of identifying promising hits followed by lead optimization via bioisosteric replacements. In the search for compounds affording good bioactivity, equal importance should also be placed on achieving those with favorable pharmacokinetic properties. Thus, the balance and realization of both key properties is an intricate problem that requires great caution. In this editorial, the authors explore the available computational tools in the context of the extant of big data that has borne out via advents of the Omics revolution. As such, the selection of appropriate computational tools for analyzing the vast number of chemical libraries, target proteins and interactomes is the first step toward maximizing the chance for success. However, in order to realize this, it is also necessary to have a solid foundation on the big concepts of drug discovery as well as knowing which tools are available in order to give drug discovery scientists the best opportunity.     

化学蛋白质组学与药物靶点的发现

小分子药物靶点的发现对于生物和医学的研究者而言,是一项既重要又艰巨的任务,医学和药学界研究工作者急切需要发现和确认新的靶点。为了克服药物靶点确认的瓶颈,已经发展了许多新技术用以研究小分子化合物与蛋白质分子间的相互作用,其中包括化学蛋白质组学方法。化学蛋白质组是全蛋白质组学研究的一个亚类,化学蛋白质组学是利用能够与靶蛋白质发生特异性相互作用的化学小分子来干扰和探测蛋白质组,在分子水平上系统揭示特定蛋白质的功能以及蛋白质与化学小分子的相互作用,从而准确找到药物作用靶点的组学研究方法。化学蛋白质组学技术和方法不断成熟,在药物作用靶点的发现、确认和药物多靶点研究等方面都将起到重要的作用,并将大大提高药物发现的效率。     

Scientific workflows as productivity tools for drug discovery

Large pharmaceutical companies annually invest tens to hundreds of millions of US dollars in research informatics to support their early drug discovery processes. Traditionally, most of these investments are designed to increase the efficiency of drug discovery. The introduction of do-it-yourself scientific workflow platforms has enabled research informatics organizations to shift their efforts toward scientific innovation, ultimately resulting in a possible increase in return on their investments. Unlike the handling of most scientific data and application integration approaches, researchers apply scientific workflows to in silico experimentation and exploration, leading to scientific discoveries that lie beyond automation and integration. This review highlights some key requirements for scientific workflow environments in the pharmaceutical industry that are necessary for increasing research productivity. Examples of the application of scientific workflows in research and a summary of recent platform advances are also provided.     

Systems biology visualization tools for drug target discovery

Importance of the field: Post-genome drug development has been driven by the need to study biological perturbations at the molecular system level. Systems biology visualization tools can help researchers extract hidden patterns from complex and large Omics data sets, model disease molecular mechanisms, and identify drug targets and drugs with good pharmacological and toxicological profiles. Areas covered in this review: This review covers basic concepts in developing and applying information visualization tools to systems biology. We describe a framework and basic data representation schemes for visual data analysis in systems biology. We review major application areas of these visualization tools within drug discovery by focusing on early-stage drug discovery tasks such as disease biology modeling, target identifications and lead identification. We also show case studies and summarize our experience using visualization tools as lessons to our readers. What the reader will gain: The reader will understand what visualization tools are available for diverse types of systems biology studies in drug discovery and understand how these tools can help advance drug development. Take home message: In spite of the complexity inherent in systems biology, proper use of information visualization tools may reveal emerging properties hidden in the data and enhance chances of success for drug discovery.     

Strategies for bringing drug delivery tools into discovery

The past decade has yielded a significant body of literature discussing approaches for development and discovery collaboration in the pharmaceutical industry. As a result, collaborations between discovery groups and development scientists have increased considerably. The productivity of pharma companies to deliver new drugs to the market, however, has not increased and development costs continue to rise. Inability to predict clinical and toxicological response underlies the high attrition rate of leads at every step of drug development. A partial solution to this high attrition rate could be provided by better preclinical pharmacokinetics measurements that inform PD response based on key pathways that drive disease progression and therapeutic response. A critical link between these key pharmacology, pharmacokinetics and toxicology studies is the formulation. The challenges in pre-clinical formulation development include limited availability of compounds, rapid turn-around requirements and the frequent un-optimized physical properties of the lead compounds. Despite these challenges, this paper illustrates some successes resulting from close collaboration between formulation scientists and discovery teams. This close collaboration has resulted in development of formulations that meet biopharmaceutical needs from early stage preclinical in vivo model development through toxicity testing and development risk assessment of pre-clinical drug candidates.