Structural proteomics in drug discovery

High-throughput, automated or semiautomated methodologies implemented by companies and structural genomics initiatives have accelerated the process of acquiring structural information for proteins via x-ray crystallography. This has enabled the application of structure-based drug design technologies to a variety of new structures that have potential pharmacologic relevance. Although there remain major challenges to applying these approaches more broadly to all classes of drug discovery targets, clearly the continued development and implementation of these structure-based drug design methodologies by the scientific community at large will help to address and provide solutions to these hurdles. The result will be a growing number of protein structures of important pharmacologic targets that will help to streamline the process of identification and optimization of lead compounds for drug development. These lead agonist and antagonist pharmacophores should, in turn, help to alleviate one of the current critical bottlenecks in the drug discovery process; that is, defining the functional relevance of potential novel targets to disease modification. The prospect of generating an increasing number of potential drug candidates will serve to highlight perhaps the most significant future bottleneck for drug development, the cost and complexity of the drug approval process.     

Structural proteomics in drug discovery

High-throughput, automated or semiautomated methodologies implemented by companies and structural genomics initiatives have accelerated the process of acquiring structural information for proteins via x-ray crystallography. This has enabled the application of structure-based drug design technologies to a variety of new structures that have potential pharmacologic relevance. Although there remain major challenges to applying these approaches more broadly to all classes of drug discovery targets, clearly the continued development and implementation of these structure-based drug design methodologies by the scientific community at large will help to address and provide solutions to these hurdles. The result will be a growing number of protein structures of important pharmacologic targets that will help to streamline the process of identification and optimization of lead compounds for drug development. These lead agonist and antagonist pharmacophores should, in turn, help to alleviate one of the current critical bottlenecks in the drug discovery process; that is, defining the functional relevance of potential novel targets to disease modification. The prospect of generating an increasing number of potential drug candidates will serve to highlight perhaps the most significant future bottleneck for drug development, the cost and complexity of the drug approval process.     

Gene expression profiling and its practice in drug development

The availability of sequenced genomes of human and many experimental animals necessitated the development of new technologies and powerful computational tools that are capable of exploiting these genomic data and ask intriguing questions about complex nature of biological processes. This gave impetus for developing whole genome approaches that can produce functional information of genes in the form of expression profiles and unscramble the relationships between variation in gene expression and the resulting physiological outcome. These profiles represent genetic fingerprints or catalogue of genes that characterize the cell or tissue being studied and provide a basis from which to begin an investigation of the underlying biology. Among the most powerful and versatile tools are high-density DNA microarrays to analyze the expression patterns of large numbers of genes across different tissues or within the same tissue under a variety of experimental conditions or even between species. The wide spread use of microarray technologies is generating large sets of data that is stimulating the development of better analytical tools so that functions can be predicted for novel genes. In this review, the authors discuss how these profiles are being used at various stages of the drug discovery process and help in the identification of new drug targets, predict the function of novel genes, and understand individual variability in response to drugs.     

Functional assays for screening GPCR targets

G-protein-coupled receptors (GPCRs) are valuable molecular targets for drug discovery. An important aspect of the early drug discovery process is the design and implementation of high-throughput GPCR functional assays that allow the cost-effective screening of large compound libraries to identify novel drug candidates. Several functional assay kits based on fluorescence and/or chemiluminescence detection are commercially available for convenient screen development, each having advantages and disadvantages. In addition, new GPCR biosensors and high-content imaging technologies have recently been developed that hold promise for the development of functional GPCR screens in living cells.     

The use of human tissue in drug discovery

Experiments conducted on human tissue samples are a key component of modern drug discovery programs and complement the use of animal tissue based assays in this process. Such studies can (i) enhance our understanding of disease pathophysiology, (ii) increase (or decrease) confidence that modulating the function of particular molecular targets will have therapeutic benefit (iii) allow comparison of the activities of different agents on particular mechanisms/processes and (iv) provide information on the potential safety risks associated with targets. All of this information is critical in identifying the targets that are most likely to deliver efficacious and safe medicines to address unmet clinical needs. With the introduction of new technologies, human tissue samples are also increasingly being incorporated into drug project screening cascades, including their use in high throughput assays. Improved access to human tissue would undoubtedly further extend the utility of this valuable resource in the drug discovery process.     

Biomarker technology roundup: from discovery to clinical applications, a broad set of tools is required to translate from the lab to the clinic

Biomarkers are being utilized throughout the drug discovery and development process to understand fundamental biological processes and relationships. Specific biomarkers for disease states, prognosis, and response to therapy have been applied to screening tissues and serum, and serve as new tools in the development of therapeutics, to segment the population for specific treatments. The use of specific biomarkers to screen subjects to determine clinical trial eligibility, and for early toxicology studies, holds the potential to decrease drug failure rates in the later phases of the clinical trial process. Traditional research tools have been employed to study the genes, proteins, and metabolites of interest. In addition, new technologies and permutations of existing technologies have been developed particularly for investigation in the preclinical and clinical phases of drug development. More importantly, the transition of a compound from preclinical to clinical is aided by technologies that span both process segments. Identification of biomarkers that can be studied throughout the development process requires technologies that are both feasible and cost-effective for large patient populations.     

Potassium ion channels and human disease: phenotypes to drug targets?

A significant difficulty faced by the pharmaceutical industry is the initial identification and selection of macromolecular targets upon which de novo drug discovery programs can be initiated. A drug target should have several characteristics: known biological function; robust assay systems for in vitro characterization and high-throughput screening; and be specifically modified by and accessible to small molecular weight compounds in vivo. Ion channels have many of these attributes and can be viewed as suitable targets for small molecule drugs. Potassium (K⁺) ion channels form a large and diverse gene family responsible for critical functions in numerous cell types, tissues and organs. Recent discoveries, facilitated by genomics technologies combined with advanced biophysical characterization methods, have identified novel K⁺ channels that are involved in important physiologic processes, or mutated in human inherited disease. These findings, coupled with a rapidly growing body of information regarding modulatory channel subunits and high resolution channel structures, are providing the critical information necessary for validation of K⁺ channels as drug targets.     

Novel putative drugs and key initiating genes for neurodegenerative disease determined using network-based genetic integrative analysis

Understanding the genetic causes of neurodegenerative disease (ND) can be useful for their prevention and treatment. Among the genetic variations responsible for ND, heritable germline variants have been discovered in genome-wide association studies (GWAS), and nonheritable somatic mutations have been discovered in sequencing projects. Distinguishing the important initiating genes in ND and comparing the importance of heritable and nonheritable genetic variants for treating ND are important challenges. In this study, we analysed GWAS results, somatic mutations and drug targets of ND from large databanks by performing directed network-based analysis considering a randomised network hypothesis testing procedure. A disease-associated biological network was created in the context of the functional interactome, and the nonrandom topological characteristics of directed-edge classes were interpreted. Hierarchical network analysis indicated that drug targets tend to lie upstream of somatic mutations and germline variants. Furthermore, using directed path length information and biological explanations, we provide information on the most important genes in these created node classes and their associated drugs. Finally, we identified nine germline variants overlapping with drug targets for ND, seven somatic mutations close to drug targets from the hierarchical network analysis and six crucial genes in controlling other genes from the network analysis. Based on these findings, some drugs have been proposed for treating ND via drug repurposing. Our results provide new insights into the therapeutic actionability of GWAS results and somatic mutations for ND. The interesting properties of each node class and the existing relationships between them can broaden our knowledge of ND.     

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.     

Pharmacogenetics in drug development

Over the last two decades, identification of polymorphisms that influence human diseases has begun to have an impact on the provision of medical care. The promise of genetics lies in its ability to provide insights into an individual's susceptibility to disease, the likely nature of the disease and the most appropriate therapy. For much of its history, pharmacogenetics (PGx-the use of genetic information to impact drug choice) has been limited to comparatively simple phenotypes such as plasma drug levels. Progress in genetics technologies has broadened the scope of PGx efficacy and safety studies that can be implemented, impacting on a broad spectrum of drug discovery and development activities. Recent PGx data show the ability of this approach to generate information that can be applied to dose selection, efficacy determination and safety issues. This in turn will lead to significant opportunities to affect both the approach to clinical development and the probability of success--the latter being an important aspect for pharmaceutical companies and for the patients who will benefit from these new medicines.     

Chemical proteomics and its application to drug discovery

The completion of the human genome sequencing project has provided a flood of new information that is likely to change the way scientists approach the study of complex biological systems. A major challenge lies in translating this information into new and better ways to treat human disease. The multidisciplinary science of chemical proteomics can be used to distill this flood of new information. This approach makes use of synthetic small molecules that can be used to covalently modify a set of related enzymes and subsequently allow their purification and/or identification as valid drug targets. Furthermore, such methods enable rapid biochemical analysis and small-molecule screening of targets thereby accelerating the often difficult process of target validation and drug discovery.     

Functional cell-based uHTS in chemical genomic drug discovery

The availability of genomic information significantly increases the number of potential targets available for drug discovery, although the function of many targets and their relationship to disease is unknown. In a chemical genomic research approach, ultra-high throughput screening (uHTS) of genomic targets takes place early in the drug discovery process, before target validation. Target-selective modulators then provide drug leads and pharmacological research tools to validate target function. Effective implementation of a chemical genomic strategy requires assays that can perform uHTS for large numbers of genomic targets. Cell-based functional assays are capable of the uHTS throughput required for chemical genomic research, and their functional nature provides distinct advantages over ligand-binding assays in the identification of target-selective modulators.     

Trends in ion channel drug discovery: advances in screening technologies

Ion channels mediate and regulate crucial electrical functions throughout the body. They are therapeutic drug targets for a variety of disorders and, in some cases, the direct cause of unwanted side-effects. Advances in medical genetics have increased our knowledge of ion channel structure–function relationships and identified disease-causing mutations in ion channel genes. The recognized importance of these proteins in health and disease has led to an active search for ion channel targets in the multi-billion-dollar worldwide drug discovery market. Trends in ion channel screening technologies have focused on increasing throughput and enhancing information content of assays through electrophysiological approaches. The ability to study ion channels by voltage clamp and their time-, voltage- and state-dependent drug interactions with enhanced throughput will ultimately play a key role in the development of novel, safe ion channel-targeted drugs.     

Intrabodies as drug discovery tools and therapeutics

Within the biomedical and pharmaceutical communities there is an ongoing need to find new technologies that can be used to elucidate disease mechanisms and provide novel therapeutics. Antibodies are arguably the most powerful tools in biomedical research, and antibodies specific for extracellular or cell-surface targets are currently the fastest growing class of new therapeutic molecules. However, the majority of potential therapeutic targets are intracellular, and antibodies cannot readily be leveraged against such molecules, in the context of a viable cell or organism, because of the inability of most antibodies to form stable structures in an intracellular environment. Advances in recent years, in particular the development of intracellular screening protocols and the definition of antibody structures that retain their antigen-binding function in an intracellular context, have allowed the robust isolation of a subset of antibodies that can function in an intracellular environment. These antibodies, generally referred to as intrabodies, have immense potential in the process of drug development and may ultimately become therapeutic entities in their own right.     

Progress in functional genomics approaches to antifungal drug target discovery

Antifungal drug discovery is starting to benefit from the enormous advances in the genomics field, which have occurred in the past decade. As traditional drug screening on existing targets is not delivering the long-awaited potent antifungals, efforts to use novel genetics and genomics-based strategies to aid in the discovery of novel drug targets are gaining increased importance. The current paradigm in antifungal drug target discovery focuses on basically two main classes of targets to evaluate: genes essential for viability and virulence or pathogenicity factors. Here we report on recent advances in genetics and genomics-based technologies that will allow us not only to identify and validate novel fungal drug targets, but hopefully in the longer run also to discover potent novel therapeutic agents. Fungal pathogens have typically presented significant obstacles when subjected to genetics, but the creativity of scientists in the anti-infectives field and the cross-talk with scientists in other areas is now yielding exciting new tools and technologies to tackle the problem of finding potent, specific and non-toxic antifungal therapeutics.     

Current situation and future usage of anticancer drug databases

Cancer is a deadly disease with increasing incidence and mortality rates and affects the life quality of millions of people per year. The past 15 years have witnessed the rapid development of targeted therapy for cancer treatment, with numerous anticancer drugs, drug targets and related gene mutations been identified. The demand for better anticancer drugs and the advances in database technologies have propelled the development of databases related to anticancer drugs. These databases provide systematic collections of integrative information either directly on anticancer drugs or on a specific type of anticancer drugs with their own emphases on different aspects, such as drug–target interactions, the relationship between mutations in drug targets and drug resistance/sensitivity, drug–drug interactions, natural products with anticancer activity, anticancer peptides, synthetic lethality pairs and histone deacetylase inhibitors. We focus on a holistic view of the current situation and future usage of databases related to anticancer drugs and further discuss their strengths and weaknesses, in the hope of facilitating the discovery of new anticancer drugs with better clinical outcomes.     

Will genetics really revolutionize the drug discovery process?

Genetics has played only a modest role in drug discovery, but new technologies will radically change this. Whole genome sequencing will identify new drug discovery targets, and emerging methods for the determination of gene function will increase the ability to select robust targets. Detection of single nucleotide polymorphisms and common polymorphisms will enhance the investigation of polygenic diseases and the use of genetics in drug development. Oligonucleotide arraying technologies will allow analysis of gene expression patterns in novel ways.     

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.     

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.