Metabolomics in clinical and forensic toxicology,sports anti-doping and veterinary residues

Metabolomics is a multidisciplinary field providing workflows for complementary approaches to conventional analytical determinations. It allows for the study of metabolically related groups of compounds or even the study of novel pathways within the biological system. The procedural stages of metabolomics; experimental design, sample preparation, analytical determinations, data processing and statistical analysis, compound identification and validation strategies are explored in this review. The selected approach will depend on the type of study being conducted. Experimental design influences the whole metabolomics workflow and thus needs to be properly assessed to ensure sufficient sample size, minimal introduced and biological variation and appropriate statistical power. Sample preparation needs to be simple, yet potentially global in order to detect as many compounds as possible. Analytical determinations need to be optimised either for the list of targeted compounds or a universal approach. Data processing and statistical analysis approaches vary widely and need to be better harmonised for review and interpretation. This includes validation strategies that are currently deficient in many presented workflows. Common compound identification approaches have been explored in this review. Metabolomics applications are discussed for clinical and forensic toxicology, human and equine sports anti-doping and veterinary residues.     

Design of small molecule libraries for NMR screening and other applications in drug discovery

There are conceptual differences between high-throughput screening (HTS) and fragment-based screening by NMR. The number of compounds in libraries for NMR screening may be significantly smaller than those used for HTS. Because one relies on a small library its design is significantly important and is the object of this article. A short introduction on fragment-based NMR screening approaches will be provided. Although there are currently very few reports describing the design of libraries of small molecules for NMR screening, aspects of the question of how to compile diverse collections of small molecular fragments useful for drug design were previously addressed for the purposes of combinatorial library design and de novo drug design. As these disciplines are highly interrelated and are applied in an interconnected manner with NMR screening within the drug discovery process, a review of combinatorial library design and especially the building block or fragment selection strategies applied for combinatorial library design and de novo design is well suited to reveal fundamental strategies and potential techniques for the design of NMR screening libraries. This section will be rounded off by a report on hands-on-experience with the design of the Novartis second-site NMR screening library and practical considerations for the design of compound mixtures. Rather than providing an exact protocol general guidelines will be indicated.     

Finding the target after screening the phenotype

Although most screening for new drug leads is being directed at known or emerging molecular targets, there has been a renaissance in screening based on changes in cell or organismal phenotypes. Phenotype-based screening is accompanied by the challenge of identifying the molecular target or targets bound by the drug leads and responsible for their pharmacological activity. A variety of technologies and approaches are being explored for target identification after phenotypic screening. Direct approaches employing affinity chromatography, expression cloning and protein microarrays analyze the compound bound to its target. Indirect approaches are based on comparison of the genome-wide activity profile of the compound with databases of the activity profiles of other compounds with known targets or activity profiles following specific genetic changes. This review will use case studies of target identification efforts and highlight the advantages and disadvantages of the various approaches to target identification after phenotypic screening.     

Advances in metabolite identification

One of the central challenges to metabolomics is metabolite identification. Regardless of whether one uses so-called 'targeted' or 'untargeted' metabolomics, eventually all paths lead to the requirement of identifying (and quantifying) certain key metabolites. Indeed, without metabolite identification, the results of any metabolomic analysis are biologically and chemically uninterpretable. Given the chemical diversity of most metabolomes and the character of most metabolomic data, metabolite identification is intrinsically difficult. Consequently a great deal of effort in metabolomics over the past decade has been focused on making metabolite identification better, faster and cheaper. This review describes some of the newly emerging techniques or technologies in metabolomics that are making metabolite identification easier and more robust. In particular, it focuses on advances in metabolite identification that have occurred over the past 2 to 3 years concerning the technologies, methodologies and software as applied to NMR, MS and separation science. The strengths and limitations of some of these approaches are discussed along with some of the important trends in metabolite identification.     

Metabolomics in food analysis: application to the control of forbidden substances

Metabolomics is a science of interest in food analysis to describe and predict properties of food products and processes. It includes the development of analytical methods with the ultimate goal being the identification of so-called 'quality markers', (i.e. sets of metabolites that correlate with, for example, quality, safety, taste, or fragrance of foodstuffs). In turn, these metabolites are influenced by factors as genetic differences of the raw food ingredients (such as animal breed or crop species differences), growth conditions (such as climate, irrigation strategy, or feeding) or production conditions (such as temperature, acidity, or pressure). In cases where the routine-based measurement of a food property faces some limitations such as the lack of knowledge regarding the target compounds to monitor, monitoring based on a limited set of crucial biomarkers is a good alternative, which is of great interest for food safety purposes regarding growth promoting practices. Such an approach may be more efficient than using a classic approach based on a limited set of known metabolites of anabolic compounds. In this context, screening strategies allowing detection of the physiological response resulting from anabolic compound administration are promising approaches to detect their misuse. The global metabolomics workflow implemented for such studies is presented and illustrated through various examples of biological matrices profiling (tissue, blood, urine) and for different classes of anabolic compounds (steroids, β-agonists and somatotropin).     

Predictive models of hepatotoxicity using gene expression data from primary rat hepatocytes

With the aim of evaluating the usefulness of an in vitro system for assessing the potential hepatotoxicity of compounds, the paper describes several methods of obtaining mathematical models for the prediction of compound-induced toxicity in vivo. These models are based on data derived from treating rat primary hepatocytes with various compounds, and thereafter using microarrays to obtain gene expression 'profiles' for each compound. Predictive models were constructed so as to reduce the number of 'probesets' (genes) required, and subjected to rigorous cross-validation. Since there are a number of possible approaches to derive predictive models, several distinct modelling strategies were applied to the same data set, and the outcomes were compared and contrasted. While all the strategies tested showed significant predictive capability, it was interesting to note that the different approaches generated models based on widely disparate probesets. This implies that while these models may be useful in ascribing relative potential toxicity to compounds, they are unlikely to provide significant information on underlying toxicity mechanisms. Improved predictivity will be obtained through the generation of more comprehensive gene expression databases, covering more 'toxicity space', and by the development of models that maximize the observation, and combination, of individual differences between compounds.     

鉴定植物天然产物生物活性组分的新方法（英文）

Countless studies have been devoted to the scientific evaluation of the safety and/or efficacy of botanical natural products.Investigators involved in such studies face a unique set of challenges.Natural products differ from their pharmaceutical counterparts in that they are typically complex mixtures,for which the identities and quantities of components present are not known.To further complicate matters,the composition of these mixtures will vary depending on source material and method of preparation.Investigators conducting clinical trials with complex botanical natural products must choose from a myriad of potential preparations,which may vary greatly in composition.In making such decisions,it is extremely useful to know which components of the mixture are most likely to be responsible for its purported biological activity(the"active constituents").The gold standard approach for identifying active constituents of botanical natural products is bioassay-guided fractionation,in which the mixture is subjected to successive rounds of purification and bioassays until an active compound is identified.Bioassay guided fractionation has historically played a critical role in drug discovery,but is,nonetheless,fraught with challenges.The process is biased towards the most abundant and easily isolatable mixture components,which may not be the most biologically active.Furthermore,if multiple compounds contribute either additively,antagonistically,or synergistically to the observed biological activity of the mixture,activity may be lost upon isolation.As a complementary strategy to bioassay-guided fractionation,our research group has developed untargeted metabolomics strategies to aid in the identification of bioactive mixture components.These strategies involve profiling botanical mixtures using ultraperformance chromatography coupled to high resolving power mass spectrometry.The resulting chemical data is then integrated with biological assay data using biochemometric data analysis strategies.Several case studies will be presented illustrating how this approach can be applied,including for the identification of compounds from the botanical green(Camellia sinensis)that inhibit drug metabolizing enzymes.Such studies are being conducted as part of the Center for Excellence in Natural Product Drug Interaction Studies(Na PDI),which is supported by a cooperative agreement with the National Center for Complementary and Integrative Health,a component of the National Institutes of Health.     

Practical metabolomics in drug discovery

Importance of the field: Metabolomics is increasingly becoming an important field in the pharmaceutical industry to support the discovery and development of therapeutic agents. It allows the comprehensive and simultaneous profiling of hundreds of discrete biologically important molecules, including amino acids, sugars, lipids and exogenous substances from biological fluids and tissues. Metabolomics is the 'omics' field that most represents the interplay of internal biological regulation and external environmental influences on disease, thereby being of particular importance to disease mitigation and management. Areas covered in this review: Technological advances in the experimental work flow, analytical detection strategies and bioinformatics tools have enabled metabolomics studies to become increasingly comprehensive, robust and informative for the understanding of disease, drug action and the development of biomarkers. This review will focus on the practical aspects of metabolomics studies as they have been applied to the study of mammalian biological systems, specifically targeted to the steps of experimental design with regard to sample preparation, sample analysis and data analysis of both polar and non-polar metabolites. What the reader will gain: The reader will gain an overview of the field of metabolomics as it applies to drug development and the practical issues involved with experimental design. We will discuss the various methods of sample preparation and analysis as they apply to different classes of metabolites and highlight recent advances in the field that illustrate these methods. Take home message: The field of metabolomics is a rapidly expanding discipline that is being applied to various aspects of drug development. The large diversity of metabolites found in nature dictates that different methods be developed for the investigation of different classes of metabolites. As the field of metabolomics continues to mature, it is likely that it will play an increasingly important role in the characterization of disease and the future development of biomarkers to assess drug efficacy and safety.     

The Nuclear Overhauser Effect in the lead identification process

In the past several years nuclear magnetic resonance (NMR) spectroscopy has emerged as a valuable tool in the drug discovery field. In such context, several NMR-based techniques have been developed aimed at the identification and subsequent optimization of novel binders for a given protein target. Among the different NMR approaches, those relying on the transferred Nuclear Overhauser Effect (tr-NOE) appear to be particularly useful as in some instances, in addition to binding, tr-NOE may provide also structural information on the binding mode of a ligand. In the current work we will reiterate the basic principles and applications that are related to measurements of tr-NOEs. The tr-NOE can be applied as a screening tool to recognize ligands for a given target protein in a mixture of compounds or to identify pair of molecules that bind to a protein simultaneously on adjacent sites (interligand NOEs). Moreover, in the case of peptide-ligands, tr-NOEs furnish intra-molecular distance constraints that can be used to determine their bioactive conformation. Starting from the conformation thus obtained, a pharmacophoric model can be derived and later used to search within a 3D database of small molecules to find new potentially active non-peptide compounds that fit the pharmacophore. We will report examples of each of the above mentioned strategies.     

Predictive models of hepatotoxicity using gene expression data from primary rat hepatocytes

With the aim of evaluating the usefulness of an in vitro system for assessing the potential hepatotoxicity of compounds, the paper describes several methods of obtaining mathematical models for the prediction of compound-induced toxicity in vivo. These models are based on data derived from treating rat primary hepatocytes with various compounds, and thereafter using microarrays to obtain gene expression ‘profiles’ for each compound. Predictive models were constructed so as to reduce the number of ‘probesets’ (genes) required, and subjected to rigorous cross-validation. Since there are a number of possible approaches to derive predictive models, several distinct modelling strategies were applied to the same data set, and the outcomes were compared and contrasted. While all the strategies tested showed significant predictive capability, it was interesting to note that the different approaches generated models based on widely disparate probesets. This implies that while these models may be useful in ascribing relative potential toxicity to compounds, they are unlikely to provide significant information on underlying toxicity mechanisms. Improved predictivity will be obtained through the generation of more comprehensive gene expression databases, covering more ‘toxicity space’, and by the development of models that maximize the observation, and combination, of individual differences between compounds.     

QSAR approaches to predicting toxicity

Due to the demands of time and the high cost of testing compounds for toxicity in test animals, it would be an advantage to be able to estimate the toxic response of chemical agents using theoretical approaches. Predicting whether a compound will be toxic or nontoxic is a classification problem and the methods of studying quantitative structure activity relationships (QSAR) can be used for this purpose [Hansch, C. (1969) Accounts Chem. Res., 2, 232]. It should be recognized, however, that formulating the QSAR problem as one of active vs. inactive makes it different from classical QSAR problems. This requires that methods be applied that can predict the category of a compound to be used, i.e., so-called methods of pattern recognition (Varmuza, K. (1983) J. Chem. Info. Comp. Sci. 23, 6) being required. There are several methods of pattern recognition that can be used with some being more suitable than others. The nature of this unique QSAR problem, the appropriate methods to apply, and some of the pitfalls of applying QSAR techniques to predicting toxicity are discussed.     

Docking and scoring in virtual screening for drug discovery: methods and applications

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.     

Platforms for the identification of GPCR targets, and of orthosteric and allosteric modulators

Areas covered in this review: The review provides a summary of old and new approaches for GPCR target identification and for the screening of molecules acting on GPCR targets. The new findings in the field are presented as well as an opinion about how these developments may help GPCR drug discovery. Importance in the field: GPCRs have been the most useful family of proteins in terms of targets for drug discovery. The expectations for GPCR target identification and discovery of new drugs acting on 'old' or 'new' GPCR targets are very high. Given the fact that the pace at which new 'GPCR drugs' appear in the market is decreasing and since the new developments in the field are not being translated into drug discovery there is a need to review the field from a critical perspective. Take home message: To overcome the limitation of the old approaches used in GPCR target identification and drugs discovery new approaches are required. In particular successful approaches in GPCR drug discovery should take into account that the real GPCR targets for a given disease are not GPCR monomers but GPCR heteromers. What the reader will gain: The reader will gain an overview of the strategies currently used and their pros and cons. The reader will also understand that new strategies may help in accelerating the access of GPCR into the market, and also notice that successful strategies should take advantage of the new findings in the field of GPCRs.     

Methods for computer-aided chemical biology. Part 1: Design of a benchmark system for the evaluation of compound selectivity

Computational drug design and discovery methods have traditionally put much emphasis on the identification of novel active compounds and the optimization of their potency. For chemical genetics and genomics applications, an important task is the identification of small molecules that are selective against target families, subfamilies, or individual targets and can be used as molecular probes for specific functions. In order to develop or tune computational methods for such applications, there is a need for molecular benchmark systems that focus on compound selectivity, rather than biological activity (in qualitative terms) or potency. We have constructed a selectivity-oriented test system that consists of 26 compound selectivity sets against 13 individual targets belonging to three distinct families and contains a total of 558 selective compounds. The targets were chosen because of pharmaceutical relevance and the availability of suitable ligands, privileged structural motifs and/or target structure information. Compound selectivity sets were characterized by structural diversity, chemical scaffold and selectivity range analysis. The test system is made freely available and should be useful for the development of computational approaches in chemical biology.     

ID NMR Methods in ligand-receptor interactions

The drug discovery process often involves the screening of compound libraries to identify drug candidates capable of binding to target macromolecules. New approaches in biological and chemical research are driving a change in the pharmaceutical industry. Recent advances in NMR spectroscopy such as affinity NMR techniques, which detect binding of a small molecule with a "receptor", have been shown to be valuable tools to perform rapid screening of compounds for biological activity. These NMR observable events include using relaxation, chemical shift perturbations, translational diffusion, and magnetization transfer. These one dimensional NMR methods increase both the throughput of screening and yield crucial data on the mode of binding. The practical utility of these techniques will be described.     

Immunomodulants in HIV infection

The current antiretroviral therapy has improved the clinical outcome of HIV-infected patients. However, drug toxicity, the emergence of drug-resistant HIV variants and the incomplete reconstitution of immune response underline the need for additional therapeutic approaches. Adjuvant therapies with immunomodulants, such as cytokines, immunosuppressants, or compounds selectively targeting HIV-specific immunity, are being intensively investigated as potential supplements to antiretroviral therapy. Although much data have been generated, there has been little evidence of the clinical efficacy of these strategies to date, and the need for new effective and reproducible immune surrogate markers able to identify the actual improvement derived from an immunotherapeutic strategy is becoming a priority. The demonstration of a possible role in inducing a better control of HIV infection and the identification of settings in which these therapies should be more effective will be essential if immunomodulants are to be included in the therapeutic armamentarium against HIV.     

Pharmacophore model generation for microtubule-stabilizing anti-mitotic agents (MSAAs) against ovarian cancer

Microtubules have always remained a mainstay in the discussion of anti-cancer drugs. Research is being carried out since over a decade on the microtubule-binding agents. The already present drugs have acquired clinical disadvantages or limitations, most importantly, the acquired resistance. Microtubule-stabilizing anti-mitotic agents (MSAAs) has proved important therapeutic agents for the treatment of cancer. The development of new and potent MSAAs is of both clinical and research interests. Ligand-based pharmacophore modeling is playing a key role for the identification of ligand features for the particular targets. We present a model for designing the pharmacophore onto the set of 12 compounds of 10 different classes and two FDA approved standard drugs. The ligand-based pharmacophore model with one hydrophobic or aromatic group, two Hydrogen-bond acceptors and one Hydrogen-bond donor has been identified to facilitate the discovery of highly potent MSAAs. The result indicates that the in silico methods are useful in predicting the biological activity of the compound or compound library by screening it against a predicted pharmacophore. Ligand Scout 3.02 has been used to predict the pharmacophore features for MSAAs and the distances between pharmacophore features have been calculated through the software VMD. This discovery will help in the identification of more potent MSAAs.     

Application of two-dimensional nuclear magnetic resonance spectroscopy to quality control of ginseng commercial products

Ginseng has been used as a powder or a crude extract of the plant roots. The quality control of commercial ginseng preparations is difficult due to the diverse compounds present. Most previous quality control methods using TLC or HPLC-UV (or -MS) cannot be expected to cover a wide range of compounds in the commercial ginseng preparations. In this study, the metabolic fingerprinting of ginseng preparations was performed by (1)H-NMR spectroscopy. Although (1)H-NMR spectroscopy could provide information about the total profile of the compounds present, low resolution and overlapping signals make it difficult for further identification of each compound. For overcoming the problem two-dimensional J-resolved NMR spectra and multivariate data analysis techniques was applied for the analysis. Principal component analysis (PCA) of projected J-resolved NMR spectra shows a clear discrimination among those samples by principal component 1 and principal component 3. The loading plot of PC values obtained from all NMR signals indicates that alanine, arginine, choline, fumaric acid, inositol, sucrose as well as ginsenosides are important metabolites to differentiate the preparations from each other. This method allows an efficient discrimination of a ginseng preparation in less than 15 minutes without any pre-purification steps.