Endophytes: exploiting biodiversity for the improvement of natural product-based drug discovery

Abstract

Endophytes, microorganisms that colonize internal tissues of all plant species, create a huge biodiversity with yet unknown novel natural products, presumed to push forward the frontiers of drug discovery. Next to the clinically acknowledged antineoplastic agent, paclitaxel, endophyte research has yielded potential drug lead compounds with antibacterial, antiviral, antioxidant, insulin mimetic, anti-neurodegenerative and immunosuppressant properties. Furthermore, while being implicated in livestock neurotoxicosis, some endophyte-produced alkaloids have been shown to display insecticidal activity. The endophyte-host relationship is postulated to be a ‘balanced antagonism’. Moreover, the plausibility of horizontal gene transfer (HGT) hypothesis is taken into account. Knowledge of the genetic background of endophytic natural product biosynthesis is discussed on the basis of loline alkaloids, ergopeptines, lolitrems and maytansinoids. The current dynamic progress in genomics will contribute to a better understanding of endophytic microbes and to further exploiting them as a source of pharmaceutically relevant compounds.     

Genetics-directed drug discovery for combating Mycobacterium tuberculosis infection

Mycobacterium tuberculosis (Mtb), the pathogen of tuberculosis (TB), is one of the most infectious bacteria in the world. The traditional strategy to combat TB involves targeting the pathogen directly; however, the rapid evolution of drug resistance lessens the efficiency of this anti-TB method. Therefore, in recent years, some researchers have turned to an alternative anti-TB strategy, which hinders Mtb infection through targeting host genes. In this work, using a theoretical genetic analysis, we identified 170 Mtb infection-associated genes from human genetic variations related to Mtb infection. Then, the agents targeting these genes were identified to have high potential as anti-TB drugs. In particular, the agents that can target multiple Mtb infection-associated genes are more druggable than the single-target counterparts. These potential anti-TB agents were further screened by gene expression data derived from connectivity map. As a result, some agents were revealed to have high interest for experimental evaluation. This study not only has important implications for anti-TB drug discovery, but also provides inspirations for streamlining the pipeline of modern drug discovery.     

Systematic discovery of drug interaction mechanisms

Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome‐wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions.     

斑马鱼在新药发现中的应用

在过去20年里,斑马鱼已成为一种重要的模式脊椎动物,在发育、遗传、免疫、肿瘤和毒理等诸多研究领域中被广泛应用。近年来,斑马鱼作为活体模型越来越多地应用于某些生物学过程的药物筛选。通过斑马鱼初步筛选,在药物研发初期可确定化合物的生物学活性、毒性以及副作用等。最近的研究还发现,斑马鱼不仅用于新药筛选,还可用于药物结构的优化。本文重点介绍斑马鱼在新药发现中的应用。     

量子点在新药开发中的应用

由于具有优异的光学特性,量子点在生物医学领域内的研究和应用取得了一些有意义的进展,同时也引起了新药开发人员的兴趣.本文概述了量子点在新药开发中所具有的优势,分析了量子点在药物传输、药物筛选和药靶确证方面的潜在应用,进一步讨论了当前量子点应用于新药开发存在的问题和不足.     

Stereoselective and isozyme-selective drug interactions

A surprisingly large number of marketed drugs are racemic mixtures. The pharmacokinetic literature on racemic drugs contains a vast amount of information on drug–drug interactions derived from the measurement of total drug concentrations in plasma and urine. The appreciation of the role of stereochemistry in drug interactions with racemic warfarin resulted in a long-overdue scientific rigor being applied to the study of drug interactions. It also compelled us to recognize that much of the literature was uninterpretable. A better understanding of oxidative metabolism, particularly the complexity of the cytochrome P-450 family of enzymes, has also strengthened the scientific basis of drug interactions. We now recognize that investigators and clinicians must consider both stereoselectivity and isozyme selectivity in the study of drug interactions to understand the nature of the interaction so as to more effectively use new and potent drugs. © 1993 Wiley-Liss, Inc.     

The integration of investigative toxicology in the drug discovery process

Summary— Integrating toxicology early in the drug discovery process adds value by providing the earliest possible identification of a compound's potential for toxicological and pathological effects relevant to intended clinical use. With this approach true ‘lead’ candidates, with a high probability of clinical success, are identified and advanced while reducing effort and resources expended on compounds without the requisite therapeutic index. Resources are focussed on the speed of getting a discovery ‘lead’ into early clinical development, defining the mechanisms of observed preclinical toxicity and their relevance to human use, and developing early safety data with in vitro test systems ahead of in vivo systems where possible, thus reducing animal use.     

Systematic identification of proteins that elicit drug side effects

Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large‐scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug–target relations to identify overrepresented protein–side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations.     

Microarray technology GEM microarrays and drug discovery

Incyte Genomics' GEM™ Gene Expression Microarray is a proven genomics tool used by a large number of pharmaceutical companies to speed up the drug discovery and development process. The development and integration of this technology, together with Incyte's sequence databases and clone resources, have resulted in GEM microarrays that span approximately 60,000 human genes as well as approximately 60,000 plant, rat, mouse, yeast, and bacterial genes. The technology underlying the use of these arrays and their application to the drug discovery process is highlighted. Journal of Industrial Microbiology & Biotechnology (2002) 28, 180–185 DOI: 10.1038/sj/jim/7000136 Received 16 November 2000/ Accepted in revised form 01 March 2001     

A solid-phase glycosyltransferase assay for high-throughput screening in drug discovery research

Glycosyltransferases mediate changes in glycosylation patterns which, in turn, may affect the function of glycoproteins and/or glycolipids and, further downstream, processes of development, differentiation, transformation and cell-cell recognition. Such enzymes, therefore, represent valid targets for drug discovery. We have developed a solid-phase glycosyltransferase assay for use in a robotic high-throughput format. Carbohydrate acceptors coupled covalently to polyacrylamide are coated onto 96-well plastic plates. The glycosyltransferase reaction is performed with recombinant enzymes and radiolabeled sugar-nucleotide donor at 37°C, followed by washing, addition of scintillation counting fluid, and measurement of radioactivity using a 96-well -counter. Glycopolymer construction and coating of the plastic plates, enzyme and substrate concentrations, and linearity with time were optimized using recombinant Core 2 1-6-N-acetylglucosaminyltransferase (Core 2 GlcNAc-T). This enzyme catalyzes a rate-limiting reaction for expression of polylactosamine and the selectin ligand sialyl-Lewis^x in -glycans. A glycopolymer acceptor for 1-6-N-acetylglucosaminyltransferase V was also designed and shown to be effective in the solid-phase assay. In a high-throughput screen of a microbial extract library, the coefficient of variance for positive controls was 9.4%, and high concordance for hit validation was observed between the Core 2 GlcNAc-T solid-phase assay and a standard solution-phase assay. The solid-phase assay format, which can be adapted for a variety of glycosyltransferase enzymes, allowed a 5–6 fold increase in throughput compared to the corresponding solution-phase assay.     

Novel computational biology methods and their applications to drug discovery

Computational biology methods are now firmly entrenched in the drug discovery process. These methods focus on modeling and simulations of biological systems to complement and direct conventional experimental approaches. Two important branches of computational biology include protein homology modeling and the computational biophysics method of molecular dynamics. Protein modeling methods attempt to accurately predict three-dimensional (3D) structures of uncrystallized proteins for subsequent structure-based drug design applications. Molecular dynamics methods aim to elucidate the molecular motions of the static representations of crystallized protein structures. In this review we highlight recent novel methodologies in the field of homology modeling and molecular dynamics. Selected drug discovery applications using these methods conclude the review.     

Chemical Mixtures: An Unsolvable Riddle?

It is difficult to overstate the complexity of assessing risks from chemical mixtures. For every valid reason to assess risks from mixtures, there appears an equally valid question as to whether it is possible to do so in a scientifically rigorous and relevant manner. Because so few data exist for mixtures, current mixture assessment methods must rely on untested assumptions and simplifications. That the accuracy of risk estimates improve with the number of chemicals assessed together as mixtures is a valid assumption only if assessment methods for mixtures are better than those based on individual chemicals. On the other hand, arbitrarily truncating a mixture assessment to make it manageable may lead to irrelevant risk estimates. Ideally, mixture assessments should be as broad as necessary to improve accuracy and reduce uncertainty over assessments that only use toxicity data for single chemicals. Further broadening the scope may be ill advised because of the tendency to increase rather than decrease uncertainty. Risk assessment methods that seek to be comprehensive at the expense of increased uncertainty can hardly be viewed as improvements. It would be prudent to verify that uncertainty can be reduced before burdening the risk assessment process with more complexity.     

Evaluating Chemical Interaction Studies for Mixture Risk Assessment

We describe a set of criteria to evaluate the quality of data and interpretations in chemical interaction studies. These criteria reflect the consensus of the literature on interaction analysis developed over decades of research in pharmacology, toxicology, and biometry; address common pitfalls in published interaction studies; and can be easily applied to common methods of interaction analysis. The criteria apply broadly to interaction data for drugs, pesticides, industrial chemicals, food additives, and natural products and are intended to assist risk assessors who must evaluate interaction studies for use in component-based mixture risk assessments. The criteria may also assist researchers interested in conducting interaction studies to inform mixture risk assessment. The criteria are also intended to serve larger scientific goals, including increasing the repeatability of results obtained in chemical interaction studies, enhancing the reliability of conclusions drawn from interaction data, providing greater consistency of interpretations among various analysts, and decreasing uncertainty in using interaction data in risk assessments. We describe the basis for each criterion and demonstrate their utility by using them to evaluate interaction studies from the recent toxicological and pharmacological literature, which serve as examples of different types of data sets that the risk assessor may encounter.     

"Click"化学在药物发现中的应用

“click”化学是最近几年发展起来的一种新技术,它在药物发现的许多方面均有着广泛的应用:快速合成小分子化合物库并从中筛选先导化合物、进行靶标导向的活性小分子合成以及在生物耦联技术等方面的应用。     

中国新药研究开发现状

随着国民经济的持续发展和生活水平的不断提高,健康状况与生命质量已经成为我国新时期社会发展的重大课题。人口老龄化和农村医药市场的拓展为生物医药产业提供了前所未有的成长空间。经过多年的不懈努力,我国自主的创新药物研究体系已经初步形成,以提升国际竞争力为导向,医药产业正在实现由仿制为主向创新为主的历史性转变。     

Genetic variation in a tropical tree species influences the associated epiphytic plant and invertebrate communities in a complex forest ecosystem

Genetic differences among tree species, their hybrids and within tree species are known to influence associated ecological communities and ecosystem processes in areas of limited species diversity. The extent to which this same phenomenon occurs based on genetic variation within a single tree species, in a diverse complex ecosystem such as a tropical forest, is unknown. The level of biodiversity and complexity of the ecosystem may reduce the impact of a single tree species on associated communities. We assessed the influence of within-species genetic variation in the tree Brosimum alicastrum (Moraceae) on associated epiphytic and invertebrate communities in a neotropical rainforest. We found a significant positive association between genetic distance of trees and community difference of the epiphytic plants growing on the tree, the invertebrates living among the leaf litter around the base of the tree, and the invertebrates found on the tree trunk. This means that the more genetically similar trees are host to more similar epiphyte and invertebrate communities. Our work has implications for whole ecosystem conservation management, since maintaining sufficient genetic diversity at the primary producer level will enhance species diversity of other plants and animals.