The Therapeutic Target Database: an internet resource for the primary targets of approved, clinical trial and experimental drugs

Increasing numbers of proteins, nucleic acids and other molecular entities have been explored as therapeutic targets. A challenge in drug discovery is to decide which targets to pursue from an increasing pool of potential targets, given the fact that few innovative targets have made it to the approval list each year. Knowledge of existing drug targets (both approved and within clinical trials) is highly useful for facilitating target discovery, selection, exploration and tool development. The Therapeutic Target Database (TTD) has been developed and updated to provide information on 358 successful targets, 251 clinical trial targets and 1254 research targets in addition to 1511 approved drugs, 1118 clinical trials drugs and 2331 experimental drugs linked to their primary targets (3257 drugs with available structure data). This review briefly describes the TTD database and illustrates how its data can be explored for facilitating target and drug searches, the study of the mechanism of multi-target drugs and the development of in silico target discovery tools.     

The application of RAGE and GECKO in cell-based target discovery screens

While significant advancements have been made in identifying the genes that comprise the human genome, considerable work remains in gaining an understanding of the functions of these gene products. Improved knowledge of protein function is of particular relevance to the drug discovery process, as the elucidation of new targets that are involved in disease processes will most probably lead to improvements in health care. Reverse genetic approaches that attempt to assign protein function on a gene-by-gene basis are labor intensive and have low throughput. Although forward genetic (function-to-gene) approaches often allow for the more efficient identification of disease-relevant drug targets, most existing methodologies are not capable of sampling the entire genome. Here we review current target discovery strategies and discuss two relatively new technologies, RAGE (random activation of gene expression) and GECKO (genome-wide cellular knockout). These tools provide cellular libraries that can be utilized in genome-wide target discovery screens. Examples are given of how these methodologies may facilitate the identification of new drug targets that are involved in human disease and pathology.     

In silico search for drug targets of natural compounds

Natural compounds represent a significant source for the development of novel medicines. Finding the target proteins for a natural compound is the most important step towards understanding its molecular mechanism for therapeutic usage. In fact, the search for target proteins could be considered the first step of the drug discovery and development pipeline. While experimental determination of compound-protein interactions remains very challenging, effective in silico approaches have been developed and have demonstrated appealing advantages, including their low-cost and capability to scale up easily. The goal of this article is to provide an introduction to in silico search for drug targets of natural compounds. I first review currently available natural compounds databases and human gene/protein databases, and the rapidly emerging databases for known drug-target interactions. These resources provide the 'materials' for in silico approaches and define the gold standard of 'positives' for evaluating them. I then introduce three classes of computational methods for target identification of natural compounds, namely molecular docking, quantitative structure-activity relationship (QSAR) modeling, and data mining and integrative analysis. Use of these methods is explained using real examples, and the advantages and disadvantages of each method are compared. As these state-of-the-art methods continue to mature amid significant challenges, this field appears poised for a period of significant growth, with untold benefits to drug discovery and natural product development.     

Bioinformatics and the discovery of novel anti-microbial targets

Genomic research is playing a critical role in the discovery of new anti-microbial drugs. The rapid increase in bacterial and eukaryotic genome sequences allows for new and innovative ways for obtaining antimicrobial protein targets. Here, we describe a two level strategy for target identification and validation using computers (in silico). First, large scale comparative analyses of genome sequences were used to identify highly conserved genes which might be essential for in vitro and/or in vivo survival of bacterial pathogens. Lab-based experiments provided confirmation or validation of the hypothesis of in silico essentiality for over 350 individual genes. Over 200 validated, broad spectrum; yet highly specific gene targets, were identified in community infection pathogens. The second part of the target discovery strategy is an in-depth evolutionary, structural and cellular analysis of key drug targets. As an example, phylogenetic and structural analyses suggest that sequence and binding-pocket conservation in FabH (beta-ketoacyl-ACP synthase III) would allow for the development of small molecule inhibitors not only effective against a broad species spectrum of community bacterial pathogens but also as potential new therapies for tuberculosis and malaria.     

From drug target to leads--sketching a physicochemical pathway for lead molecule design in silico

The discovery of new pharmaceuticals via computer modeling is one of the key challenges in modern medicine. The advent of global networks of genomic, proteomic and metabolomic endeavors is ushering in an increasing number of novel and clinically important targets for screening. Computational methods are anticipated to play a pivotal role in exploiting the structural and functional information to understand specific molecular recognition events of the target macromolecule with candidate hits leading ultimately to the design of improved leads for the target. In this review, we sketch a system independent, comprehensive physicochemical pathway for lead molecule design focusing on the emerging in silico trends and techniques. We survey strategies for the generation of candidate molecules, docking them with the target and ranking them based on binding affinities. We present a molecular level treatment for distinguishing affinity from specificity of a ligand for a given target. We also discuss the significant aspects of drug absorption, distribution, metabolism, excretion and toxicity (ADMET) and highlight improved protocols required for higher quality and throughput of in silico methods employed at early stages of discovery. We present a realization of the various stages in the pathway proposed with select examples from the literature and from our own research to demonstrate the way in which an iterative process of computer design and validation can aid in developing potent leads. The review thus summarizes recent advances and presents a viewpoint on improvements envisioned in the years to come for automated computer aided lead molecule discovery.     

Chemogenomic approaches to rational drug design

Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.     

The promise of genomics to identify novel therapeutic targets

The cataloguing of the human genome has provided an unprecedented prospectus for target identification and drug discovery. A current analysis indicates that slightly more than 3000 unique protein encoding loci are potentially amenable to pharmacological intervention (the 'druggable genome', which can be queried at http://function.gnf.org/druggable). However, the assessment of genome sequence data has not resulted in the anticipated acceleration of novel therapeutic developments. The basis for this shortfall lies in the significant attrition rates endemic to preclinical/clinical development, as well as the often underestimated complexity of gene function in higher order biological systems. To address the latter issue, a number of strategies have emerged to facilitate genomics-driven target identification and validation, including cellular profiling of gene function, in silico modelling of gene networks, and systematic analyses of protein complexes. The expectation is that the integration of these and other systems-based technologies may enable the conversion of potential genomic targets into functionally validated molecules, and result in practicable gene-based drug discovery pipelines.     

High-throughput virtual screening for drug discovery in parallel

With the influx of targets generated by genomics and proteomics initiatives, a new drug discovery paradigm is emerging. Many companies are setting up target family platforms that tackle multiple targets and therapeutic areas simultaneously. Virtual screening (VS) techniques are a fundamental component of such platforms for in silico filtering of compound collections and prioritization of chemistry and screening efforts. At the heart of these, structure-based docking and scoring methods are especially effective in identifying bioactive molecules if the structure of a target is available. As structural genomics maps the structural space of the proteome, these techniques are expected to become commonplace. In light of this, an overview of the latest developments in VS methodology is given here. In particular, emphasis is placed on those techniques adaptable to high-throughput VS in parallel drug discovery platforms. The first examples of docking across multiple targets have already appeared in the literature and will be reviewed here.     

Chemical genomics strategy for the discovery of new anticancer agents

Chemical genomics represents a cooperation of biology and chemistry to identify and intervene the biological targets. Small molecules with diverse structural characteristics should be used to validate the target through interfering with the biological processes. Because of the limitation of existing chemical libraries, the diversity can be exploited using both the molecular design techniques; structure-based design and ligand-based design. These methods can guide the selection of small molecules with optimal binding properties to desired biological targets. Studies of potential molecular targets for novel anticancer drug discovery including in silico screening, QSAR, and de novo design demonstrated the importance of chemical genomics strategy to find the chemical probes and drug lead compounds.     

A Discovery Funnel for Nucleic Acid Binding Drug Candidates

Computational approaches are becoming increasingly popular for the discovery of drug candidates against a target of interest. Proteins have historically been the primary targets of many virtual screening efforts. While in silico screens targeting proteins has proven successful, other classes of targets, in particular DNA, remain largely unexplored using virtual screening methods. With the realization of the functional importance of many non-cannonical DNA structures such as G-quadruplexes, increased efforts are underway to discover new small molecules that can bind selectively to DNA structures. Here, we describe efforts to build an integrated in silico and in vitro platform for discovering compounds that may bind to a chosen DNA target. Millions of compounds are initially screened in silico for selective binding to a particular structure and ranked to identify several hundred best hits. An important element of our strategy is the inclusion of an array of possible competing structures in the in silico screen. The best hundred or so hits are validated experimentally for binding to the actual target structure by a high-throughput 96-well thermal denaturation assay to yield the top ten candidates. Finally, these most promising candidates are thoroughly characterized for binding to their DNA target by rigorous biophysical methods, including isothermal titration calorimetry, differential scanning calorimetry, spectroscopy and competition dialysis.This platform was validated using quadruplex DNA as a target and a newly discovered quadruplex binding compound with possible anti-cancer activity was discovered. Some considerations when embarking on virtual screening and in silico experiments are also discussed.     

Navigating tuberculosis drug discovery with target-based screening

Introduction: Mycobacterium tuberculosis kills more people than any other bacterial pathogen. New drugs are required to shorten the treatment time and provide a viable therapy for drug-resistant and latent forms of tuberculosis. The tuberculosis field has advanced considerably since the publication of the M. tuberculosis genome sequence. Today, researchers can build a high definition map of the pathogen's traits and behavior and select individual targets for chemical disruption. Areas covered: This review examines the discovery of current clinical and candidate tuberculosis drugs. It outlines recent developments in the selection of molecular targets for the discovery of new anti-mycobacterial agents. It appraises techniques that incorporate target knowledge into the screening protocol. These techniques include in silico, in vitro enzyme-based, differential antisense sensitivity and gene expression screening systems. The review also looks ahead to further techniques that may be applied in tuberculosis drug discovery. Expert opinion: The adoption of an 'either/or' approach to targeted or random tuberculosis drug screening is not expected. The historical success of random screening in providing the tuberculosis drugs currently in clinical use is likely to ensure that non-targeted protocols retain an important role in drug screening. However, a number of M. tuberculosis inhibitors in lead optimization and preclinical development have been discovered using targeted methods. Realization of the first clinically-approved tuberculosis drugs derived from targeted screening and continued refinements in targeted screening technologies are likely to increase the adoption of targeted approaches in the future.     

Chemical ligands, genomics and drug discovery

The sequencing of the human genome and numerous pathogen genomes has resulted in an explosion of potential drug targets. These targets represent both an unprecedented opportunity and a technological challenge for the pharmaceutical industry. A new strategy is required to initiate small-molecule drug discovery with sets of incompletely characterized, disease-associated proteins. One such strategy is the early application of combinatorial chemistry and other technologies to the discovery of bioactive small-molecule ligands that act on candidate drug targets. Therapeutically active ligands serve to concurrently validate a target and provide lead structures for downstream drug development, thereby accelerating the drug discovery process.     

A rational approach to maximize success rate in target discovery

To overcome the problem of high attrition rates in the drug discovery process, an efficient strategy how to identify, select, characterize and validate the most suitable drug targets before embarking on the resource-intense steps of lead discovery and lead optimization is mandatory. We have implemented such an efficient strategy consisting of (i) Target Selection based on gene expression analyses of drugable target genes in clinical samples and relevant in vitro model systems, to identify candidate targets with a specific tissue distribution and presence in human disease; (ii) Target Assessment exploiting the three-dimensional structure of proteins for detailed binding site analysis, to estimate the drugability of the protein for small-molecule inhibitor binding as well as selectivity profiles; and (iii) Target Validation providing evidence for a functional role in in vitro model systems, thus corroborating the biological hypothesis underlying the therapeutic concept. This rational approach has led to the discovery of drug targets for Lead Discovery, maximizing the likelihood for achieving target-selective inhibition by small-molecule inhibitors with minimal in vivo side effects and a therapeutic effect based on a sound biological hypothesis.     

The purinome, a complex mix of drug and toxicity targets

Much attention has focused on the development of protein kinases as drug targets to treat a variety of human diseases including diabetes, cancer, hypertension and arthritis. To date, Gleevec is one example of a drug targeting protein that has successfully treated human cancer. Several other protein kinase inhibitors are in clinical development. However, protein kinases are in fact part of a larger collection of some 2000 distinct proteins expressed by the genome that like the protein kinases also bind purines (the purinome), either to be utilized as substrates or as co-factors in the form of NAD, NADP and co-enzyme A. The solution structures of many representative gene family members within the purinome show these proteins bind purines in a similar orientations to that observed in all protein kinases. Several non-protein kinase purine utilizing proteins are established drug targets such as HMG CoA reductase, dihydrofolate reductase, phosphodiesterase and HSP90. Searches of OMIM identifies many purine utilizing enzymes that are associated with inborn errors in metabolism. Inhibition of any one of which by a drug could lead to an undesirable side effect. The purinome is therefore somewhat of a drug discovery mixed blessing. It is a rich source of therapeutic targets, but also contains a large collection of diverse proteins whose inhibition could result in an adverse outcome. Drug discovery within the purinome should therefore encompass strategies that enable broad assessment of selectivity across the entire purinome at the earliest stages of the discovery process. In this article we review the purinome within the context of drug discovery and discuss approaches for avoiding off target binding during the discovery/lead optimization process with particular emphasis on use of proteome mining technology.     

Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.     

Screening the receptorome: an efficient approach for drug discovery and target validation

The receptorome, comprising at least 5% of the human genome, encodes receptors that mediate the physiological, pathological and therapeutic responses to a vast number of exogenous and endogenous ligands. Not surprisingly, the majority of approved medications target members of the receptorome. Several in silico and physical screening approaches have been devised to mine the receptorome efficiently for the discovery and validation of molecular targets for therapeutic drug discovery. Receptorome screening has also been used to discover, and thereby avoid, the molecular targets responsible for serious and unforeseen drug side effects.     

High-throughput electrophysiology: an emerging paradigm for ion-channel screening and physiology

Ion channels represent highly attractive targets for drug discovery and are implicated in a diverse range of disorders, in particular in the central nervous and cardiovascular systems. Moreover, assessment of cardiac ion-channel activity of new chemical entities is now an integral component of drug discovery programmes to assess potential for cardiovascular side effects. Despite their attractiveness as drug discovery targets ion channels remain an under-exploited target class, which is in large part due to the labour-intensive and low-throughput nature of patch-clamp electrophysiology. This Review provides an update on the current state-of-the-art for the various automated electrophysiology platforms that are now available and critically evaluates their impact in terms of ion-channel screening, lead optimization and the assessment of cardiac ion-channel safety liability.     

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediting the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.     

The devil is still in the details--driving early drug discovery forward with biophysical experimental methods

This review comments on some recent trends and insights in the field of lead identification and optimization with a bias toward the increased use of biophysical methods, particularly in combination with three-dimensional structural information. While high-throughput screening, combinatorial chemistry and, most recently, in silico virtual screening techniques have made well-resourced but only partially successful attempts to meet the challenge of identifying new drug candidates by playing 'the large numbers game', another group of technologies are now approaching the same challenge from what might be considered the opposite extreme. The common strategy of these technologies is to focus on a smaller set of low-molecular-weight compounds whose interactions with a target are characterized with the aid of sensitive assays, most often high-quality biophysical techniques such as biosensors, calorimetry, nuclear magnetic resonance spectroscopy and X-ray crystallography. The advantages of such an approach include more optimal and chemically attractive starting points, immediate access to reliable measurements of binding properties, the mapping of ligand interactions on the atomic level and, most importantly, a greater control of experimental errors at the initial stages of drug discovery where compounds are either discovered or lost. When correctly supported, this more careful approach appears to deliver quality leads, even for the so-called 'difficult' targets. As these techniques are complementary to traditional methods, companies should be less hesitant to invest in them. The biophysical methods that are used to drive this approach have made something of a return to drug discovery after having been discarded for being too slow, too expensive or too old-fashioned by the over-optimistic supporters of high-throughput and statistical/computational in silico methods.     

Novel approaches to antimalarial drug discovery

Major advances in our understanding of malaria parasite biology have been made. Coupled with the completion of the malaria genome, this has presented exciting opportunities for target-based antimalarial drug discovery. However, the unraveling of more validated biological targets will not necessarily translate into the identification of new chemical entities that are effective against drug resistant parasites in the long term. As history has already shown, development of antiplasmodial agents aimed at a single parasite target or specialized process has failed to stem the tide of drug resistance. This review highlights recent starting points and/or approaches to antimalarial drug discovery with particular emphasis on innovative efforts, which are not necessarily based on the identification of new drug targets and attendant inhibitor design. Approaches covered include utilization of validated chemical scaffolds, bioprecursor and carrier prodrugs, double drug development and/or multi-therapeutic strategies, use of metallocenic scaffolds, the medicinal chemistry of antimalarial natural products and in silico drug design.