Identification of disease-relevant genes for molecularly-targeted drug discovery

The current paradigm for cancer therapy is undergoing a change from non-specific cytotoxic agents to more specific approaches based on unique molecular features of cancer cells. The identification and validation of disease relevant targets are crucial for the development of molecularly targeted anticancer therapies. Advances in our understanding of the molecular basis of cancer together with novel approaches to interfere with signal transduction pathways have opened new horizons for anticancer target discovery. In particular, the image-based large scale analysis of cellular phenotypes that arise from genetic or chemical perturbations paved the way for the identification and validation of disease relevant molecular targets independent of preconceived notions of mechanistic relationships. In addition, novel and sophisticated techniques of genome manipulation allow for the use of mouse models that faithfully recapitulate critical elements of human cancer for target validation in vivo. We believe that these advances will translate into more and better validated drug targets.     

In silico research in drug discovery

Target and lead discovery constitute the main components of today's early pharmaceutical research. The aim of target discovery is the identification and validation of suitable drug targets for therapeutic intervention, whereas lead discovery identifies novel chemical molecules that act on those targets. With the near completion of the human genome sequencing, bioinformatics has established itself as an essential tool in target discovery and the in silico analysis of gene expression and gene function are now an integral part of it, facilitating the selection of the most relevant targets for a disease under study. In lead discovery, advances in chemoinformatics have led to the design of compound libraries in silico that can be screened virtually. Moreover, computational methods are being developed to predict the drug-likeness of compounds. Thus, drug discovery is already on the road towards electronic R&D.     

Receptorome screening for CNS drug discovery

An estimated 50% of currently marketed drugs target G protein-coupled receptors (GPCRs) for a wide variety of indications, including central nervous system (CNS) disorders. Although drug discovery efforts have focused on GPCRs, less than 10% of GPCRs are currently used as drug targets. Thus, GPCRs continue to represent a significant opportunity for future CNS drug development. Identifying the molecular targets of psychoactive compounds may result in the elucidation of novel targets for CNS drug discovery. This commentary will describe discovery-based approaches and provide several recent examples of novel ligand-receptor interactions discovered through systematic screening of the 'receptorome'.     

Drug discovery: selecting the optimal approach

The target-based drug discovery approach has for the past 10-15 years been the dominating drug discovery paradigm. However, within the past few years, the commercial value of novel targets in licensing deals has fallen dramatically, reflecting that the probability of reaching a clinical drug candidate for a novel target is very low. This has naturally led to questions regarding the success of target-based drug discovery and, more importantly, a search for alternatives. This paper evaluates the strengths and limitations of the main drug discovery approaches, and proposes a novel approach that could offer advantages for the identification of disease-modifying treatments.     

Network analysis has diverse roles in drug discovery

Computational biologists use network analysis to uncover relationships between various data types of interest for drug discovery. For example, signalling and metabolic pathways are commonly used to understand disease states and drug mechanisms. However, several other flavours of network analysis techniques are also applicable in a drug discovery context. Recent advances include networks that encompass relationships between diseases, molecular mechanisms and gene targets. Even social networks that mirror interactions within the scientific community are helping to foster collaborations and novel research. We review how these different types of network analysis approaches facilitate drug discovery and their associated challenges.     

Peptide ligands in antibacterial drug discovery: use as inhibitors in target validation and target-based screening

There is an urgent need to develop novel classes of antibiotics to counter the inexorable rise of resistant bacterial pathogens. Modern antibacterial drug discovery is focused on the identification and validation of novel protein targets that may have a suitable therapeutic index. In combination with assays for function, the advent of microbial genomics has been invaluable in identifying novel antibacterial drug targets. The major challenge in this field is the implementation of methods that validate protein targets leading to the discovery of new chemical entities. Ligand-directed drug discovery has the distinct advantage of having a concurrent analysis of both the importance of a target in the disease process and its amenability to functional modulation by small molecules. VITA is a process that enables a target-based paradigm by using peptide ligands for direct in vitro and in vivo validation of antibacterial targets and the implementation of high-throughput assays to identify novel inhibitory molecules. This process can establish sufficient levels of confidence indicating that the target is relevant to the disease process and inhibition of the target will lead to effective disease treatment.     

Fresh approaches to antidepressant drug discovery

Introduction: The success of antidepressant research has long been challenged by a limited mechanistic understanding of depression pathogenesis and antidepressant treatment response. Progress in this field has thereby consistently been hindered by a lack of novel conceptual approaches and sophisticated experimental techniques to dissect the highly intricate neurobiology of depression. Using fresh approaches to investigate the cellular and molecular mechanisms underlying depression will thus be vital for discovery of novel antidepressant targets.

Areas covered: This article provides an overview of some fundamental problems that depression research is currently facing and critically evaluates how these issues could be addressed by future research. It also discusses novel conceptual and technological advances in the field of neuroscience, particularly in regard to how they may help in providing unprecedented insight into the molecular mechanisms of depression pathogenesis.

Expert opinion: Although progress in antidepressant drug discovery has been limited over recent years, modern innovations in neuroscience, molecular biology, genetics and bioinformatics are just beginning to revolutionize depression research and to reveal novel and promising treatment targets. Integrating findings from a range of relevant experimental models and using the most advanced technology will be vital for the future success of antidepressant drug discovery.     

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.     

Cell-based apoptosis assays in oncology drug discovery

Importance of the field: Screening compounds with a cell-based phenotypic approach complements target-based discovery programs because of the opportunity to investigate targets in the context of the cellular milieu and to discover novel targets. Areas covered in this review: Utilizing a cell-based apoptotic phenotype screen for discovery and optimization of apoptosis inducers and affirming activity as potential anticancer agents in vivo with xenograft models. Subsequently, chemical genetic tools are utilized to identify and validate previously unrecognized cancer targets. Case studies showing the various multidisciplinary approaches utilized for several years are reviewed. What the reader will gain: The interactive nature of the drug and target discovery processes, and insights that come from integration of cellular biology, medicinal chemistry and animal research. Take home message: Phenotype proapoptotic screen followed by chemical genetics is useful for anticancer drug research, for the discovery of potential drugs and identification of druggable targets.     

High-throughput gene expression analysis for drug discovery

The ability to rapidly survey and compare gene expression levels between reference and test samples is moving the drug discovery process towards a more genomic orientation. The success of the Human Genome Project and related private genomics initiatives, combined with new technologies to probe, image and access expression data, are responsible for this transformation. This article reviews the history, status and future direction of high-throughput gene expression analysis. It describes classical approaches, explains the development of methods such as differential display for discovering novel genes, and discusses how microarray technology is exploiting collections of known sequences to pinpoint drug targets.     

Chemogenomics knowledge-based strategies in drug discovery

In the postgenomic age of drug discovery, targets can no longer be viewed as singular objects having no relationship to one another. All targets are now visible and the systematic exploration of selected target families appears to be a promising way to speed up and further industrialize target-based drug discovery. Chemogenomics refers to such systematic exploration of target families and aims to identify all possible ligands of all target families. Because biology works by applying prior knowledge to an unknown entity, chemogenomics approaches are expected to be especially effective within the previously well-explored target families, for which, in addition to the protein sequence and structure information, considerable knowledge of pharmacologically active structural classes and structure-activity relationships exists. For the new target families, chemical knowledge will have to be generated and beyond biological target validation, the emphasis is on chemistry to provide the molecules with which their novel biology and pharmacology can be studied. Using examples from the previously most successfully explored target families, the GPCR family in particular, we summarize herein our current chemogenomics knowledge-based strategies for drug discovery, which are founded on the high integration of chem and bioinformatics, thereby providing a molecular informatics frame for the exploration of the new target families.     

New approaches for pediatric rhabdomyosarcoma drug discovery: targeting combinatorial signaling

Introduction: Rhabdomyosarcomas (RMS) are rare heterogeneous pediatric tumors that are treated by surgery, chemotherapy and irradiation. New therapeutic approaches are needed, especially in the advanced stages to target the pro-oncogenic signals. Exploring the molecular interactions of the regulatory signals and their roles in the developmental aspects of different subtypes of RMS is essential to identify potential targets and develop new therapeutic drugs. Areas covered: Insights into different drug discovery approaches are discussed with specific emphasis on gene expression profiling, fusion protein, role of small interfering RNA (siRNA)- and microRNA (miRNA)-based discovery approaches, targeting cancer stem cells, and in vitro and in vivo model systems. Targeting some overexpressed signals along with the possibilities of combination therapy of validated drug targets is discussed. Additionally, methods to overcome the limitations of discovery-based research are briefly discussed. Expert opinion: Due to drug resistance, ineffective therapy in advanced stages and relapse, there is a demand to explore new drug targets and discovery approaches. Implementing miRNA-based profiling would reveal the extent of miR-based regulation, various biomarkers and potential targets in RMS. A suitable combination of innovative techniques and the use of model systems might assist the identification and validation of novel targets and drug discovery methods. Combining specific drugs along with type-specific target inhibition of overexpressed mRNAs through siRNA approaches would enable the development of personalized therapy.     

AI-powered therapeutic target discovery

Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.     

Combining label-free cell phenotypic profiling with computational approaches for novel drug discovery

Introduction: Drug discovery is a long and costly process. Innovations and paradigm shifts are essential for continuous improvement in the productivity of pharmaceutical R&D.

Areas covered: The author reviews the progress of label-free cell phenotypic and computational approaches in early drug discovery since 2004 and proposes a novel paradigm, which combines both approaches.

Expert opinion: Label-free cell phenotypic profiling techniques offer an unprecedented and integrated approach to comprehend drug–target interactions in their native environments. However, these approaches have disadvantages associated with the lack of molecular details. Computational approaches, including ligand-, structure- and phenotype-based virtual screens, have become versatile tools in the early drug discovery process. However, these approaches mostly predict the binding of drug molecules to targets of interest and are limited to targets that are either well annotated for ligands or that are structurally resolved. Thus, combining label-free cell phenotypic profiling with computational approaches can provide a potential paradigm to accelerate novel drug discovery by taking advantages of the best of both approaches.     

Proteomics: a major new technology for the drug discovery process

Proteomics is a new enabling technology that is being integrated into the drug discovery process. This will facilitate the systematic analysis of proteins across any biological system or disease, forwarding new targets and information on mode of action, toxicology and surrogate markers. Proteomics is highly complementary to genomic approaches in the drug discovery process and, for the first time, offers scientists the ability to integrate information from the genome, expressed mRNAs, their respective proteins and subcellular localization. It is expected that this will lead to important new insights into disease mechanisms and improved drug discovery strategies to produce novel therapeutics.     

Using model-system genetics for drug-based target discovery

The combination of medicinal chemistry and model-organism genetics is emerging as a powerful tool for the discovery and validation of drug targets. Model systems can be used to identify the cognate target for compounds that demonstrate in vivo efficacy but have unknown mechanisms of action. Alternatively, drugs with known cognate targets can be used to probe biochemical pathways in model organisms, revealing new targets and mechanisms within these pathways. In both cases, the availability of human genomic sequence data is opening up new opportunities for accelerating target discovery.     

Structural bioinformatic approaches to the discovery of new antimycobacterial drugs

Integrated bioinformatic approaches to drug discovery exploit computational techniques to examine the flow of information from genome to structure to function. Informatics is being be used to accelerate and rationalize the process of antimycobacterial drug discovery and design, with the immediate goals to identify viable drug targets and produce a set of critically evaluated protein target models and corresponding set of probable lead compounds. Bioinformatic approaches are being successfully applied in the selection and prioritization of putative mycobacterial drug target genes; computational modelling and x-ray structure validation of protein targets with drug lead compounds; simulated docking and virtual screening of potential lead compounds; and lead validation and optimization using structure-activity and structure-function relationships. By identifying active sites, characterizing patterns of conserved residues and, where relevant, predicting catalytic residues, bioinformatics provides information to aid the design of selective and efficacious pharmacophores. In this review, we describe selected recent progress in antimycobacterial drug design, illustrating the strengths and limitations of current structural bioinformatic approaches as tools in the fight against tuberculosis.     

Fragment-based lead discovery on G-protein-coupled receptors

Introduction: G-protein-coupled receptors (GPCRs) form one of the largest groups of potential targets for novel medications. Low druggability of many GPCR targets and inefficient sampling of chemical space in high-throughput screening expertise however often hinder discovery of drug discovery leads for GPCRs. Fragment-based drug discovery is an alternative approach to the conventional strategy and has proven its efficiency on several enzyme targets. Based on developments in biophysical screening techniques, receptor stabilization and in vitro assays, virtual and experimental fragment screening and fragment-based lead discovery recently became applicable for GPCR targets.

Areas covered: This article provides a review of the biophysical as well as biological detection techniques suitable to study GPCRs together with their applications to screen fragment libraries and identify fragment-size ligands of cell surface receptors. The article presents several recent examples including both virtual and experimental protocols for fragment hit discovery and early hit to lead progress.

Expert opinion: With the recent progress in biophysical detection techniques, the advantages of fragment-based drug discovery could be exploited for GPCR targets. Structural information on GPCRs will be more abundantly available for early stages of drug discovery projects, providing information on the binding process and efficiently supporting the progression of fragment hit to lead. In silico approaches in combination with biological assays can be used to address structurally challenging GPCRs and confirm biological relevance of interaction early in the drug discovery project.