Innovative approaches to novel antibacterial drug discovery

Increasing antibiotic resistance in microorganisms and new emerging pathogens have become a major problem in our society. Rising to satisfy this urgent medical need is a recent confluence of powerful new drug discovery technologies: combinatorial chemistry; sequence and functional genomic analysis; and novel methods of high-throughput screening. The combination of these technologies will bring to bear untapped power in the search for new antimicrobials.     

Computational chemistry approaches to drug discovery in signal transduction

The advent of therapeutic strategies aimed at targeting specific macromolecular components of deregulated signaling pathways associated with particular disease states has given rise to the idea that it should be possible to design ligands as drug candidates to these targets from first principles. This concept has been beckoning for a long time but structure-based ligand design only became feasible once it was possible to determine the 3-D structures of molecular targets at atomic resolution. However, structure-based design turned out to be difficult, chiefly because under physiological conditions both receptors and ligands are not static but they behave dynamically. While it is possible to design ligands with high steric and electronic complementarity to a receptor site, it is always uncertain how biologically relevant the assumed conformations of both ligand and receptor actually are. The fact that it remains beyond our current abilities to predict with sufficient accuracy the affinity between hypothetical ligand and receptor poses is in part connected with this problem and continues to confound the reliable prediction of drug-like ligands for therapeutic targets. Nevertheless, significant progress has been made and so-called virtual screening methods that use computational methods to dock candidate ligands into receptor sites and to score the resulting complexes are now used routinely as one of the components in drug discovery screening campaigns. Here an overview is given of the underlying principles, implementations, and applications of structure-guided computational design technologies. Although the emphasis is on receptor-based strategies, mention will also be made of some of the more established ligand-based approaches, such as similarity analyses and quantitative structure-activity relationship methods.     

New approaches to antidepressant drug discovery: beyond monoamines

All available antidepressant medications are based on serendipitous discoveries of the clinical efficacy of two classes of antidepressants more than 50 years ago. These tricyclic and monoamine oxidase inhibitor antidepressants were subsequently found to promote serotonin or noradrenaline function in the brain. Newer agents are more specific but have the same core mechanisms of action in promoting these monoamine neurotransmitters. This is unfortunate, because only approximately 50% of individuals with depression show full remission in response to these mechanisms. This review summarizes the obstacles that have hindered the development of non-monoamine-based antidepressants, and provides a progress report on some of the most promising current strategies.     

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.     

GPCRs: an update on structural approaches to drug discovery

G-protein-coupled receptors (GPCRs) are a major opportunity for drug discovery in the post-genomic era. There are thought to be more than 500 therapeutically relevant GPCRs out of a total of over 700 identified to date, although only one, rhodopsin, has been the subject of a full 3D X-ray crystallography study. Two structurally related proteins, bacteriorhodopsin and sensory rhodopsin, which are not GPCRs but are part of the seven-helix membrane receptor family, have also been the subject of X-ray crystallographic studies and have been used in GPCR modeling studies. The significant differences between these rhodopsin structures, the relatively low sequence homology between individual GPCRs, and some difficulties in rationalizing point-mutation data suggests that homology-based molecular modeling alone will not provide the accurate structural information on individual receptors required for ligand design and in silico screening. In the absence of such structural information, several approaches can be used to assist in the discovery of ligands.     

Modern approaches to drug discovery and design: setting the scene

The raison d'ítre for the drug discovery and development process is to provide safe and effective treatments for diseases. Bringing a new drug to market, however, is a time-consuming and expensive process and it remains an imperative for drug companies that they identify ways in which they can accelerate the identification of potential targets and their screening and development in order to maintain a competitive edge. Successful drug discovery efforts include biochemical, biophysical, genetic and immunological approaches, targeting such processes as signal transduction, cell cycle control, apoptosis, gene regulation and metastasis. As the number of these biological targets increases, reliance on bioinformatics and chemoinformatics to improve decision making, by identifying characteristics of successful drugs and sharing knowledge gained within the scientific community, has become a burgeoning area in the post-genomic era of drug discovery.     

Novel and revisited approaches in antituberculosis drug discovery

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Image source (SEM of Mtb): NIAID.     

Fragment-based approaches in drug discovery and chemical biology

Fragment-based approaches to finding novel small molecules that bind to proteins are now firmly established in drug discovery and chemical biology. Initially developed primarily in a few centers in the biotech and pharma industry, this methodology has now been adopted widely in both the pharmaceutical industry and academia. After the initial success with kinase targets, the versatility of this approach has now expanded to a broad range of different protein classes. Herein we describe recent fragment-based approaches to a wide range of target types, including Hsp90, β-secretase, and allosteric sites in human immunodeficiency virus protease and fanesyl pyrophosphate synthase. The role of fragment-based approaches in an academic research environment is also examined with an emphasis on neglected diseases such as tuberculosis. The development of a fragment library, the fragment screening process, and the subsequent fragment hit elaboration will be discussed using examples from the literature.     

Evolving cryo-EM structural approaches for GPCR drug discovery

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Genomic approaches to fungal pathogenicity

Within a few years, the genome sequences of a large number of medically and agriculturally important fungi will be known. With this resource come the promises of genomic approaches to study pathogenicity and host-fungus interactions. Genomics is particularly attractive for these questions, as conventional genetic and biochemical approaches are limited in many pathogenic fungi. Recent work has applied signature-tagged mutagenesis and DNA microarray analysis to virulence studies in several fungal species, and novel approaches, such as protein arrays and genomic deletion libraries, are being developed in Saccharomyces cerevisiae and have significant potential in other fungi. High-throughput gene-discovery approaches should greatly increase our understanding of fungal pathogenesis.     

Bioinformatics approaches for new drug discovery: a review

Prolonged antibiotic therapy for the bacterial infections has resulted in high levels of antibiotic resistance. Initially, bacteria are susceptible to the antibiotics, but can gradually develop resistance. Treating such drug-resistant bacteria remains difficult or even impossible. Hence, there is a need to develop effective drugs against bacterial pathogens. The drug discovery process is time-consuming, expensive and laborious. The traditionally available drug discovery process initiates with the identification of target as well as the most promising drug molecule, followed by the optimization of this, in-vitro, in-vivo and in pre-clinical studies to decide whether the compound has the potential to be developed as a drug molecule. Drug discovery, drug development and commercialization are complicated processes. To overcome some of these problems, there are many computational tools available for new drug discovery, which could be cost effective and less time-consuming. In-silico approaches can reduce the number of potential compounds from hundreds of thousands to the tens of thousands which could be studied for drug discovery and this results in savings of time, money and human resources. Our review is on the various computational methods employed in new drug discovery processes.     

High-throughput approaches to catalyst discovery

High-throughput synthesis and screening approaches to catalyst discovery and optimization are systematically changing the way in which catalyst research is conducted. Increased rates of innovation, cost effectiveness, improved intellectual property, reduced time to market and an improved probability of success are some of the attractive features that demand consideration. Advances made over the past few years reveal that any initial skepticism is waning, and high-throughput approaches to catalyst discovery are now being implemented broadly in industrial and academic laboratories.     

New approaches to antibiotic discovery

New antibiotics are urgently required by human medicine as pathogens emerge with developed resistance to almost all antibiotic classes. Pioneering approaches, methodologies and technologies have facilitated a new era in antimicrobial discovery. Innovative culturing techniques such as iChip and co-culturing methods which use ‘helper’ strains to produce bioactive molecules have had notable success. Exploiting antibiotic resistance to identify antibacterial producers performed in tandem with diagnostic PCR based identification approaches has identified novel candidates. Employing powerful metagenomic mining and metabolomic tools has identified the antibiotic’ome, highlighting new antibiotics from underexplored environments and silent gene clusters enabling researchers to mine for scaffolds with both a novel mechanism of action and also few clinically established resistance determinants. Modern biotechnological approaches are delivering but will require support from government initiatives together with changes in regulation to pave the way for valuable, efficacious, highly targeted, pathogen specific antimicrobial therapies.     

microRNA计算发现方法的研究进展

microRNA (miRNA)是近几年发现的一类长度为～21 nt的内源非编码小RNA, 在植物和动物中发挥着重要而广泛的调控功能。它的发现主要有cDNA克隆测序和计算发现两条途径。由于cDNA克隆测序方法受miRNA表达的时间和组织特异性以及表达水平的影响, 而计算发现可以弥补其不足, 因此miRNA的计算发现方法研究受到了广泛的重视。文章对近几年计算发现miRNA的研究进展进行了综述, 根据计算发现方法的本质, 将计算发现方法归纳为5类, 分别是同源片段搜索方法、基于比较基因组学的预测方法、基于序列和结构特征打分的预测方法、结合作用靶标的预测方法和基于机器学习的预测方法, 并对各类方法的原理、核心思想、优点和局限性进行了分析, 最后探讨了进一步的发展方向。     

Combinatorial approaches to materials discovery

Using a mixture of scientific intuition, iteration and serendipity, combinatorial materials science is an approach to the discovery and study of new materials that combines high-speed chemical synthesis, high-throughput screening and high-capacity information processing to create, analyse and interpret large numbers of new and diverse material compositions. Technology has now been developed that makes this powerful integration possible. The classes of materials under investigation include catalysts, luminescent, optical, magnetic and dielectric materials, and structural polymers.