The Consortium for Metabonomic Toxicology (COMET): aims, activities and achievements

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

Metabolomics- and systems-biology-guided discovery of metabolite lead compounds and druggable targets

Metabolomics enables the comprehensive and unbiased analysis of metabolites and lipids in biological systems. In conjunction with high-throughput activity screening, big data and synthetic biology, metabolomics can guide the discovery of lead compounds with pharmacological activity from natural sources and the gut microbiome. In combination with other omics, metabolomics can further unlock the elucidation of compound toxicity, the mode of action and novel druggable targets of disease. Here, we discuss the workflows, limitations and future opportunities to leverage metabolomics and big data in conjunction with systems and synthetic biology for streamlining the discovery and development of molecules of pharmaceutical interest.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

Approaches in metabolomics for regulatory toxicology applications

Innovative methodological approaches are needed to conduct human health and environmental risk assessments on a growing number of marketed chemicals. Metabolomics is progressively proving its value as an efficient strategy to perform toxicological evaluations of new and existing substances, and it will likely become a key tool to accelerate chemical risk assessments. However, additional guidance with widely accepted and harmonized procedures is needed before metabolomics can be routinely incorporated in decision-making for regulatory purposes. The aim of this review is to provide an overview of metabolomic strategies that have been successfully employed in toxicity assessment as well as the most promising workflows in a regulatory context. First, we provide a general view of the different steps of regulatory toxicology-oriented metabolomics. Emphasis is put on three key elements: robustness of experimental design, choice of analytical platform, and use of adapted data treatment tools. Then, examples in which metabolomics supported regulatory toxicology outputs in different scenarios are reviewed, including chemical grouping, elucidation of mechanisms of toxicity, and determination of points of departure. The overall intention is to provide insights into why and how to plan and conduct metabolomic studies for regulatory toxicology purposes.

Low Volume in Vitro Diagnostic Proton NMR Spectroscopy of Human Blood Plasma for Lipoprotein and Metabolite Analysis: Application to SARS-CoV-2 Biomarkers

The utility of low sample volume in vitro diagnostic (IVDr) proton nuclear magnetic resonance (1H NMR) spectroscopic experiments on blood plasma for information recovery from limited availability or high value samples was exemplified using plasma from patients with SARS-CoV-2 infection and normal controls. 1H NMR spectra were obtained using solvent-suppressed 1D, spin-echo (CPMG), and 2-dimensional J-resolved (JRES) spectroscopy using both 3 mm outer diameter SampleJet NMR tubes (100 μL plasma) and 5 mm SampleJet NMR tubes (300 μL plasma) under in vitro diagnostic conditions. We noted near identical diagnostic models in both standard and low volume IVDr lipoprotein analysis (measuring 112 lipoprotein parameters) with a comparison of the two tubes yielding R2 values ranging between 0.82 and 0.99 for the 40 paired lipoprotein parameters samples. Lipoprotein measurements for the 3 mm tubes were achieved without time penalty over the 5 mm tubes as defined by biomarker recovery for SARS-CoV-2. Overall, biomarker pattern recovery for the lipoproteins was extremely similar, but there were some small positive offsets in the linear equations for several variables due to small shimming artifacts, but there was minimal degradation of the biological information. For the standard untargeted 1D, CPMG, and JRES NMR experiments on the same samples, the reduced signal-to-noise was more constraining and required greater scanning times to achieve similar differential diagnostic performance (15 min per sample per experiment for 3 mm 1D and CPMG, compared to 4 min for the 5 mm tubes). We conclude that the 3 mm IVDr method is fit-for-purpose for quantitative lipoprotein measurements, allowing the preparation of smaller volumes for high value or limited volume samples that is common in clinical studies. If there are no analytical time constraints, the lower volume experiments are equally informative for untargeted profiling.

Bioactivity of the Novel Fungicide SYP-14288 Against Plant Pathogens and the Study of its Mode of Action Based on Untargeted Metabolomics

Plant disease is a major threat to crop production, and fungicide application is one of the most effective methods to control plant disease. With emerging issues related to toxic residues and pathogen resistance, new fungicides with novel modes of action are urgently needed. SYP-14288 is a novel fungicide that could efficiently promote respiration and inhibit ATP biosynthesis in target organisms, but its bioactivity against various plant pathogens and exact mode of action are still unknown. In this study, we found that SYP-14288 is highly effective against 31 important plant pathogens belonging to a range of taxonomic groups. In addition, SYP-14288 has demonstrated excellent activity against all life stages of the important fungal plant pathogen Magnaporthe oryzae and is especially effective during the pathogen's high energy consumption stages. SYP-14288 showed good preventative control efficacy against pepper blight and rice blast in the greenhouse and field, respectively. In an untargeted metabolomics assay designed to determine the exact mode of action of SYP-14288, significant changes occurred in 25 metabolites, with the accumulation of seven fatty acid metabolites and a decrease in 18 starch and sugar metabolites (e.g., from the tricarboxylic acid cycle). This suggests that SYP-14288 is an uncoupling agent similar to 2,4-dinitrophenol, which can allow for accumulation of various fatty acids after destroying oxidative phosphorylation coupling, thereby inhibiting the growth of the phytopathogen. These results indicate that the novel uncoupler SYP-14288 is a promising agrochemical in plant disease management.

Twenty years of metabonomics: so what has metabonomics done for toxicology?

In 1999 the journal Xenobiotica published a perspective article detailing the new concept of metabonomics and its application to toxicology. The approach was to apply analytical chemistry techniques, and in particular ¹H NMR spectroscopy, to profile biofluids and tissues to assess the metabolic effects of xenobiotics. Metabonomics has been shown to be sensitive not only to organ specific toxicity but also provides information on the cells, tissues and mechanisms involved, as well as their interactions with the host's sex, age, diet and environment. This review assesses the impact of metabonomics on drug toxicology over the past twenty years and its future prospects. These applications include:Pharmacometabonomics - the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on physiology.The microbiomes role in toxicology - understanding how xenobiotics can be modified by the microbiome dramatically changing their impact on the host.Development of expert systems for toxicity prediction.Data fusion of different omics to better understand the underlying mechanisms of drug toxicity.Metabonomics and exposome - understanding how multiple environmental toxicants might interact with the host organism to produce their overall phenotype. While there has been huge growth in the use of metabonomics within toxicology these applications are set to increase as the tools become more sensitive and robust, as well as the increased use of both experimental and in silico databases to aid prediction of toxicology.

A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis

Understanding metabotype (multicomponent metabolic characteristics) variation can help to generate new diagnostic and prognostic biomarkers, as well as models, with potential to impact on patient management. We present a suite of conceptual approaches for the generation, analysis, and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of (i) metabolite levels, (ii) metabolic trajectories, (iii) metabolic entropies, and (iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalized medicine, with the aim of improving clinical outcomes for patients, but the framework will have value and utility in areas of metabolic profiling well beyond this exemplar.

The metabolic fate and effects of 2-Bromophenol in male Sprague-Dawley rats

1. The metabolic fate and urinary excretion of 2-bromophenol, a phenolic metabolite of bromobenzene, was investigated in male Sprague-Dawley rats following single intraperitoneal doses at either 0, 100, or 200 mg/kg.2. Urine was collected for seven days and samples analysed using ¹H NMR spectroscopy, inductively coupled plasma (ICP)MS, and UPLC-MS.3. ¹H NMR spectroscopy of the urine samples showed that, at these doses, 2-bromophenol had little effect on endogenous metabolite profiles, supporting histopathology and clinical chemistry data, which showed no changes associated with the administration of 2-bromophenol in this study.4. The use of ICP-MS provided a means for the selective detection and quantification of bromine-containing species and showed that between 15 and 30% of the dose was excreted via the urine over 7 days of the study for both the 100 and 200 mg doses, respectively.5. The bulk of the excretion of Br-containing material had occurred by 8 h post administration. UPLC-MS of urine revealed a number of metabolites of 2-bromophenol, with 2-bromophenol glucuronide and 2-bromophenol sulphate identified as the major species. A number of minor hydroxylated metabolites were also detected as their glucuronide, sulphate, or O-methyl conjugates. There was no evidence for the production of reactive metabolites.

Development of a rapid profiling method for the analysis of polar analytes in urine using HILIC-MS and ion mobility enabled HILIC-MS

INTRODUCTION

As large scale metabolic phenotyping is increasingly employed in preclinical studies and in the investigation of human health and disease the current LC-MS/MS profiling methodologies adopted for large sample sets can result in lengthy analysis times, putting strain on available resources. As a result of these pressures rapid methods of untargeted analysis may have value where large numbers of samples require screening.

OBJECTIVES

To develop, characterise and evaluate a rapid UHP-HILIC-MS-based method for the analysis of polar metabolites in rat urine and then extend the capabilities of this approach by the addition of IMS to the system.

METHODS

A rapid untargeted HILIC LC-MS/MS profiling method for the analysis of small polar molecules has been developed. The 3.3 min separation used a Waters BEH amide (1 mm ID) analytical column on a Waters Synapt G2-Si Q-Tof enabled with ion mobility spectrometry (IMS). The methodology, was applied to the metabolic profiling of a series of rodent urine samples from vehicle-treated control rats and animals administered tienilic acid. The same separation was subsequently linked to IMS and MS to evaluate the benefits that IMS might provide for metabolome characterisation.

RESULTS

The rapid HILIC-MS method was successfully applied to rapid analysis of rat urine and found, based on the data generated from the data acquired for the pooled quality control samples analysed at regular intervals throughout the analysis, to be robust. Peak area and retention times for the compounds detected in these samples showed good reproducibility across the batch. When used to profile the urine samples obtained from vehicle-dosed control and those administered tienilic acid the HILIC-MS method detected 3007 mass/retention time features. Analysis of the same samples using HILIC-IMS-MS enabled the detection of 6711 features. Provisional metabolite identification for a number of compounds was performed using the high collision energy MS/MS information compared against the Metlin MS/MS database and, in addition, both calculated and measured CCS values from an experimentally derived CCS database.

CONCLUSION

A rapid metabolic profiling method for the analysis of polar metabolites has been developed. The method has the advantages of speed and both reducing sample and solvent consumption compared to conventional profiling methods. The addition of IMS added an additional dimension for feature detection and the identification of metabolites.

NMR Metabolomics Protocols for Drug Discovery

Drug discovery is an extremely difficult and challenging endeavor with a very high failure rate. The task of identifying a drug that is safe, selective, and effective is a daunting proposition because disease biology is complex and highly variable across patients. Metabolomics enables the discovery of disease biomarkers, which provides insights into the molecular and metabolic basis of disease and may be used to assess treatment prognosis and outcome. In this regard, metabolomics has evolved to become an important component of the drug discovery process to resolve efficacy and toxicity issues and as a tool for precision medicine. A detailed description of an experimental protocol is presented that outlines the application of NMR metabolomics to the drug discovery pipeline. This includes (1) target identification by understanding the metabolic dysregulation in diseases, (2) predicting the mechanism of action of newly discovered or existing drug therapies, (3) and using metabolomics to screen a chemical lead to assess biological activity. Unlike other OMICS approaches, the metabolome is "fragile" and may be negatively impacted by improper sample collection, storage, and extraction procedures. Similarly, biologically irrelevant conclusions may result from incorrect data collection, preprocessing or processing procedures, or the erroneous use of univariate and multivariate statistical methods. These critical concerns are also addressed in the protocol.

A Metabolomics Approach to Pharmacotherapy Personalization

The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of "omics" technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided.

Liver metabolomics study reveals protective function of Phyllanthus urinaria against CCl4-induced liver injury

Phyllanthus Urinaria L. (PUL) is a traditional Chinese medicine used to treat hepatic and renal disorders. However, the mechanism of its hepatoprotective action is not fully understood. In the present study, blood biochemical indexes and liver histopathological changes were used to estimate the extent of hepatic injury. GC/MS and LC/MS-based untargeted metabolomics were used in combination to characterize the potential biomarkers associated with the protective activity of PUL against CCl₄-induced liver injury in rats. PUL treatment could reverse the increase in ALT, AST and ALP induced by CCl₄ and attenuate the pathological changes in rat liver. Significant changes in liver metabolic profiling were observed in PUL-treated group compared with liver injury model group. Seventeen biomarkers related to the hepatoprotective effects of PUL against CCl₄-induced liver injury were screened out using nonparametric test and Pearson's correlation analysis (OPLS-DA). The results suggested that the potential hepatoprotective effects of PUL in attenuating CCl₄-induced hepatotoxicity could be partially attributed to regulating L-carnitine, taurocholic acid, and amino acids metabolism, which may become promising targets for treatment of liver toxicity. In conclusion, this study provides new insights into the mechanism of the hepatoprotection of Phyllanthus Urinaria.

Metabolic Fingerprints from the Human Oral Microbiome Reveal a Vast Knowledge Gap of Secreted Small Peptidic Molecules

Metabolomics is the ultimate tool for studies of microbial functions under any specific set of environmental conditions (D. S. Wishart, Nat Rev Drug Discov 45:473–484, 2016, https://doi.org/10.1038/nrd.2016.32). This is a great advance over studying genes alone, which only inform about metabolic potential. Approximately 25,000 compounds have been chemically characterized thus far; however, the richness of metabolites such as SMs has been estimated to be as high as 1 × 10³⁰ in the biosphere (K. Garber, Nat Biotechnol 33:228–231, 2015, https://doi.org/10.1038/nbt.3161). Our classical, one-at-a-time activity-guided approach to compound identification continues to find the same known compounds and is also incredibly tedious, which represents a major bottleneck for global SM identification. These challenges have prompted new developments of databases and analysis tools that provide putative classifications of SMs by mass spectral alignments to already characterized tandem mass spectrometry spectra and databases containing structural information (e.g., PubChem and AntiMarin). In this study, we assessed secreted peptidic SMs (PSMs) from 27 oral bacterial isolates and a complex oral in vitro biofilm community of >100 species by using the Global Natural Products Social molecular Networking and the DEREPLICATOR infrastructures, which are methodologies that allow automated and putative annotation of PSMs. These approaches enabled the identification of an untapped resource of PSMs from oral bacteria showing species-unique patterns of secretion with putative matches to known bioactive compounds.

Recent research indicates that the human microbiota play key roles in maintaining health by providing essential nutrients, providing immune education, and preventing pathogen expansion. Processes underlying the transition from a healthy human microbiome to a disease-associated microbiome are poorly understood, partially because of the potential influences from a wide diversity of bacterium-derived compounds that are illy defined. Here, we present the analysis of peptidic small molecules (SMs) secreted from bacteria and viewed from a temporal perspective. Through comparative analysis of mass spectral profiles from a collection of cultured oral isolates and an established in vitro multispecies oral community, we found that the production of SMs both delineates a temporal expression pattern and allows discrimination between bacterial isolates at the species level. Importantly, the majority of the identified molecules were of unknown identity, and only ~2.2% could be annotated and classified. The catalogue of bacterially produced SMs we obtained in this study reveals an undiscovered molecular world for which compound isolation and ecosystem testing will facilitate a better understanding of their roles in human health and disease.

IMPORTANCE Metabolomics is the ultimate tool for studies of microbial functions under any specific set of environmental conditions (D. S. Wishart, Nat Rev Drug Discov 45:473–484, 2016, https://doi.org/10.1038/nrd.2016.32). This is a great advance over studying genes alone, which only inform about metabolic potential. Approximately 25,000 compounds have been chemically characterized thus far; however, the richness of metabolites such as SMs has been estimated to be as high as 1 × 10³⁰ in the biosphere (K. Garber, Nat Biotechnol 33:228–231, 2015, https://doi.org/10.1038/nbt.3161). Our classical, one-at-a-time activity-guided approach to compound identification continues to find the same known compounds and is also incredibly tedious, which represents a major bottleneck for global SM identification. These challenges have prompted new developments of databases and analysis tools that provide putative classifications of SMs by mass spectral alignments to already characterized tandem mass spectrometry spectra and databases containing structural information (e.g., PubChem and AntiMarin). In this study, we assessed secreted peptidic SMs (PSMs) from 27 oral bacterial isolates and a complex oral in vitro biofilm community of >100 species by using the Global Natural Products Social molecular Networking and the DEREPLICATOR infrastructures, which are methodologies that allow automated and putative annotation of PSMs. These approaches enabled the identification of an untapped resource of PSMs from oral bacteria showing species-unique patterns of secretion with putative matches to known bioactive compounds.

Author Video: An author video summary of this article is available.

UPLC-MS for metabolomics: a giant step forward in support of pharmaceutical research

Metabolomics is a relatively new and rapidly growing area of post-genomic biological research. As use of metabolomics technology grows throughout the spectrum of drug discovery and development, and its applications broaden, its impact is expanding dramatically. This review seeks to provide the reader with a brief history of the development of metabolomics, its significance and strategies for conducting metabolomics studies. The most widely used analytical tools for metabolomics: NMR, LC-MS and GC-MS, are discussed along with considerations for their use. Herein, we will show how metabolomics can assist in pharmaceutical research studies, such as pharmacology and toxicology, and discuss some examples of the importance of metabolomics analysis in research and development.

NMR Chemical Shift Ranges of Urine Metabolites in Various Organic Solvents

Signal stability is essential for reliable multivariate data analysis. Urine samples show strong variance in signal positions due to inter patient differences. Here we study the exchange of the solvent of a defined urine matrix and how it affects signal and integral stability of the urinary metabolites by NMR spectroscopy. The exchange solvents were methanol, acetonitrile, dimethyl sulfoxide, chloroform, acetone, dichloromethane, and dimethyl formamide. Some of these solvents showed promising results with a single batch of urine. To evaluate further differences between urine samples, various acid, base, and salt solutions were added in a defined way mimicking to some extent inter human differences. Corresponding chemical shift changes were monitored.

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine

"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

Quality assurance of metabolomics

Metabolomics promises a holistic phenotypic characterization of biological responses to toxicants. This technology is based on advanced chemical analytical tools with reasonable throughput, including mass-spectroscopy and NMR. Quality assurance, however – from experimental design, sample preparation, metabolite identification, to bioinformatics data-mining – is urgently needed to assure both quality of metabolomics data and reproducibility of biological models. In contrast to microarray-based transcriptomics, where consensus on quality assurance and reporting standards has been fostered over the last two decades, quality assurance of metabolomics is only now emerging. Regulatory use in safety sciences, and even proper scientific use of these technologies, demand quality assurance. In an effort to promote this discussion, an expert workshop discussed the quality assurance needs of metabolomics.

The goals for this workshop were 1) to consider the challenges associated with metabolomics as an emerging science, with an emphasis on its application in toxicology and 2) to identify the key issues to be addressed in order to establish and implement quality assurance procedures in metabolomics-based toxicology. Consensus has still to be achieved regarding best practices to make sure sound, useful, and relevant information is derived from these new tools.

Development of a Rapid Microbore Metabolic Profiling Ultraperformance Liquid Chromatography-Mass Spectrometry Approach for High-Throughput Phenotyping Studies

A rapid gradient microbore ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) method has been developed to provide a high-throughput analytical platform for the metabolic phenotyping of urine from large sample cohorts. The rapid microbore metabolic profiling (RAMMP) approach was based on scaling a conventional reversed-phase UPLC-MS method for urinary profiling from 2.1 mm × 100 mm columns to 1 mm × 50 mm columns, increasing the linear velocity of the solvent, and decreasing the gradient time to provide an analysis time of 2.5 min/sample. Comparison showed that conventional UPLC-MS and rapid gradient approaches provided peak capacities of 150 and 50, respectively, with the conventional method detecting approximately 19 000 features compared to the ∼6 000 found using the rapid gradient method. Similar levels of repeatability were seen for both methods. Despite the reduced peak capacity and the reduction in ions detected, the RAMMP method was able to achieve similar levels of group discrimination as conventional UPLC-MS when applied to rat urine samples obtained from investigative studies on the effects of acute 2-bromophenol and chronic acetaminophen administration. When compared to a direct infusion MS method of similar analysis time the RAMMP method provided superior selectivity. The RAMMP approach provides a robust and sensitive method that is well suited to high-throughput metabonomic analysis of complex mixtures such as urine combined with a 5-fold reduction in analysis time compared with the conventional UPLC-MS method.

Emerging applications of metabolomics in drug discovery and precision medicine

Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.

Comparative metabonomic analysis of hepatotoxicity induced by acetaminophen and its less toxic meta-isomer

The leading cause of drug-induced liver injury in the developed world is overdose with N-acetyl-p-aminophenol (APAP). A comparative metabonomic approach was applied to the study of both xenobiotic and endogenous metabolic profiles reflective of in vivo exposure to APAP (300 mg/kg) and its structural isomer N-acetyl-m-aminophenol (AMAP; 300 mg/kg) in C57BL/6J mice, which was anchored with histopathology. Liver and urine samples were collected at 1 h, 3 h and 6 h post-treatment and analyzed by ¹H nuclear magnetic resonance (NMR) spectroscopy and gas chromatography–mass spectrometry (liver only). Histopathology revealed the presence of centrilobular necrosis from 3 h post-APAP treatment, while an AMAP-mediated necrotic endpoint was not observed within the timescale of this study, yet two of five treated mice showed minimal centrilobular eosinophilia. The ¹H-NMR xenobiotic metabolic profile of APAP-treated animals comprised of mercapturate (urine and liver) and glutathionyl (liver) conjugates detected at 1 h post-treatment. This finding corroborated the hepatic endogenous metabolic profile which showed depletion of glutathione from 1 h onwards. In contrast, AMAP glutathionyl conjugates were not detected, nor was AMAP-induced depletion of hepatic glutathione observed. APAP administration induced significant endogenous hepatic metabolic perturbations, primarily linked to oxidative and energetic stress, and perturbation of amino acid metabolism. Early depletion of glutathione was followed by depletion of additional sulfur-containing metabolites, while altered levels of mitochondrial and glycolytic metabolites indicated a disruption of energy homeostasis. In contrast, AMAP administration caused minimal, transient, distinct metabolic perturbations and by 6 h the metabolic profiles of AMAP-treated mice were indistinguishable from those of controls.

Nuclear magnetic resonance based metabolomics and liver diseases: Recent advances and future clinical applications

Metabolomics is defined as the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. It is an “omics” technique that is situated downstream of genomics, transcriptomics and proteomics. Metabolomics is recognized as a promising technique in the field of systems biology for the evaluation of global metabolic changes. During the last decade, metabolomics approaches have become widely used in the study of liver diseases for the detection of early biomarkers and altered metabolic pathways. It is a powerful technique to improve our pathophysiological knowledge of various liver diseases. It can be a useful tool to help clinicians in the diagnostic process especially to distinguish malignant and non-malignant liver disease as well as to determine the etiology or severity of the liver disease. It can also assess therapeutic response or predict drug induced liver injury. Nevertheless, the usefulness of metabolomics is often not understood by clinicians, especially the concept of metabolomics profiling or fingerprinting. In the present work, after a concise description of the different techniques and processes used in metabolomics, we will review the main research on this subject by focusing specifically on in vitro proton nuclear magnetic resonance spectroscopy based metabolomics approaches in human studies. We will first consider the clinical point of view enlighten physicians on this new approach and emphasis its future use in clinical “routine”.

Metabolomics and its application to the evaluation of the efficacy and toxicity of traditional Chinese herb medicines

Traditional Chinese herb medicines (TCHMs) have been used in the treatment of a variety of diseases for thousands of years in Asian countries. The active components of TCHMs usually exert combined synergistic therapeutic effects on multiple targets, but with less potential therapeutic effect based on routine indices than Western drugs. These complex effects make the assessment of the efficacy of TCHMs and the clarification of their underlying mechanisms very challenging, and therefore hinder their wider application and acceptance. Metabolomics is a crucial part of systems biology. It allows the quantitative measurement of large numbers of the low-molecular endogenous metabolites involved in metabolic pathways, and thus reflects the fundamental metabolism status of the body. Recently, dozens of metabolomic studies have been devoted to prove the efficacy/safety, explore the underlying mechanisms, and identify the potential biomarkers to access the action targets of TCHMs, with fruitful results. This article presents an overview of these studies, focusing on the progress made in exploring the pharmacology and toxicology of various herbal medicines.

Metabolomics and Personalized Medicine

Current clinical practice strongly relies on the prognosis, diagnosis, and treatment of diseases using methods determined and averaged for the specific diseased cohort/population. Although this approach complies positively with most patients, misdiagnosis, treatment failure, relapse, and adverse drug effects are common occurrences in many individuals, which subsequently hamper the control and eradication of a number of diseases. These incidences can be explained by individual variation in the genome, transcriptome, proteome, and metabolome of a patient. Various "omics" approaches have investigated the influence of these factors on a molecular level, with the intention of developing personalized approaches to disease diagnosis and treatment. Metabolomics, the newest addition to the "omics" domain and the closest to the observed phenotype, reflects changes occurring at all molecular levels, as well as influences resulting from other internal and external factors. By comparing the metabolite profiles of two or more disease phenotypes, metabolomics can be applied to identify biomarkers related to the perturbation being investigated. These biomarkers can, in turn, be used to develop personalized prognostic, diagnostic, and treatment approaches, and can also be applied to the monitoring of disease progression, treatment efficacy, predisposition to drug-related side effects, and potential relapse. In this review, we discuss the contributions that metabolomics has made, and can potentially still make, towards the field of personalized medicine.

Metabonomics and diagnostics

Metabonomic techniques have considerable potential in the field of clinical diagnostics, typifying the application of a translational research paradigm. Care must be taken at all stages to apply appropriate methodology with accurate patient selection and profiling, and rigorous data acquisition and handling, to ensure clinical validity.An ever-increasing number of publications in a wide range of diseases and diverse patient groups suggest a variety of potential clinical uses; prospective studies in large validation cohorts are required to bring metabonomics into routine clinical practice. In this chapter, the utility of metabonomics as a diagnostic tool will be discussed.

A correction method for systematic error in (1)H-NMR time-course data validated through stochastic cell culture simulation

Background

The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct systematic error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed.

Results

Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were then scaled to simulated compound concentrations. Finally, the scaled trends were perturbed using a combination of random and systematic errors. To detect systematic errors, a nonparametric fit was applied to each trend and percent deviations calculated at every timepoint. Systematic errors could be identified at time-points where the median percent deviation exceeded a threshold value, determined by the choice of smoothing model and the number of observed trends. Regardless of model, increasing the number of observations over a time-course resulted in more accurate error estimates, although the improvement was not particularly large between 10 and 20 samples per trend. The presented algorithm was able to identify systematic errors as small as 2.5 % under a wide range of conditions.

Conclusion

Both the simulation framework and error correction method represent examples of time-course analysis that can be applied to further developments in ¹H-NMR methodology and the more general application of quantitative metabolomics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0197-4) contains supplementary material, which is available to authorized users.

Nuclear magnetic resonance: a key metabolomics platform in the drug discovery process

Metabolomics is an innovative tool that is now emerging in the drug discovery process. Indeed, its ability to follow the dynamic perturbations in the metabolome resulting from pathologies but also from drug treatment and or/toxicity is of value for the development of new therapeutic approaches. Nuclear magnetic resonance (NMR) spectroscopy, which is an important analytical technique for several steps of the lead discovery, validation and optimization processes, has been described, together with mass spectrometry (MS) as one of the major platform that could be used for metabolomics studies. This review highlights why NMR could be considered a key tool for the application of metabolomics in drug discovery.

Metabonomics in the preclinical and environmental toxicity field

Preclinical studies assess both efficacy and safety of new drugs through a series of assays used to identify potential target organs and determine safety thresholds. However, despite these efforts, too many drugs prove toxic to humans during clinical phases or later on the market. This paper reviews how metabonomics, one of the key players in systems biology, should be able to assist toxicologists in better predicting the adverse effects of xenobiotics.

Investigation of Host-Gut Microbiota Modulation of Therapeutic Outcome

A broader understanding of factors underlying interindividual variation in pharmacotherapy is important for our pursuit of "personalized medicine." Based on knowledge gleaned from the investigation of human genetics, drug-metabolizing enzymes, and transporters, clinicians and pharmacists are able to tailor pharmacotherapies according to the genotype of patients. However, human host factors only form part of the equation that accounts for heterogeneity in therapeutic outcome. Notably, the gut microbiota possesses wide-ranging metabolic activities that expand the metabolic functions of the human host beyond that encoded by the human genome. In this review, we first illustrate the mechanisms in which gut microbes modulate pharmacokinetics and therapeutic outcome. Second, we discuss the application of metabonomics in deciphering the complex host-gut microbiota interaction in pharmacotherapy. Third, we highlight an integrative approach with particular mention of the investigation of gut microbiota using culture-based and culture-independent techniques to complement the investigation of the host-gut microbiota axes in pharmaceutical research.

Transgenic mice and metabolomics for study of hepatic xenobiotic metabolism and toxicity

INTRODUCTION

The study of xenobiotic metabolism and toxicity has been greatly aided by the use of genetically modified mouse models and metabolomics.

AREAS COVERED

Gene knockout mice can be used to determine the enzymes responsible for the metabolism of xenobiotics in vivo and to examine the mechanisms of xenobiotic-induced toxicity. Humanized mouse models are especially important because there exist marked species differences in the xenobiotic-metabolizing enzymes and the nuclear receptors that regulate these enzymes. Humanized mice expressing CYPs and nuclear receptors including the pregnane X receptor, the major regulator of xenobiotic metabolism and transport were produced. With genetically modified mouse models, metabolomics can determine the metabolic map of many xenobiotics with a level of sensitivity that allows the discovery of even minor metabolites. This technology can be used for determining the mechanism of xenobiotic toxicity and to find early biomarkers for toxicity.

EXPERT OPINION

Metabolomics and genetically modified mouse models can be used for the study of xenobiotic metabolism and toxicity by: i) comparison of the metabolomics profiles between wild-type and genetically modified mice, and searching for genotype-dependent endogenous metabolites; ii) searching for and elucidating metabolites derived from xenobiotics; and iii) discovery of specific alterations of endogenous compounds induced by xenobiotics-induced toxicity.

Metabolomics in the pharmaceutical industry

Metabolomics has roots in the pharmaceutical industry that go back nearly three decades. Initially focused on applications in toxicology and disease pathology, more recent academic and commercial efforts have helped advance metabolomics as a tool to reveal the molecular basis of biological processes and pharmacological responses to drugs. This article will discuss areas where metabolomic technologies and applications are poised to have the greatest impact in the discovery and development of pharmaceuticals.

A gas chromatography-mass spectrometry based study on serum metabolomics in rats chronically poisoned with hydrogen sulfide

Hydrogen sulfide poisoning is a common occupational hazard, whose mortality and incidence rates are first and second, respectively, among occupational poisoning incidents in China. The main target organs of its toxicity are in the central nervous system and respiratory system. However, there are currently no specific direct tests that can be used to diagnose poisoned patients. In this study, we developed a serum metabonomic method using orthogonal partial least squares-discriminate analysis (OPLS-DA), based on gas chromatography-mass spectrometry (GC/MS) to evaluate the effect of chronic poisoning by hydrogen sulfide in rats. The OPLS-DA data demonstrated that the model group (n = 60) differed significantly from the control group (n = 30), suggesting that the metabolic profiles of the two groups are markedly different. Alterations in the levels of some metabolites such as citrate, galactose, lactate, mannose, inositol, urea, phosphate, alanine and valine were detected by OPLS-DA analysis. We observed changes in metabolic pathways including lipid metabolism, energy metabolism and amino metabolism in the model group. Our results indicate that GC/MS-based metabonomic methods may provide novel detection means for chronic hydrogen sulfide poisoning.

Sample collection and preparation of biofluids and extracts for NMR spectroscopy

Metabonomics is a cross-disciplinary science that overlaps with analytical chemistry, biology, and statistical analysis. The techniques commonly used are proton nuclear magnetic resonance ((1)H NMR) spectroscopy and mass spectrometry (MS). Applying (1)H NMR on cell extracts provides a rapid and comprehensive screening of the most abundant metabolites allowing the quantitation of typically 20-70 compounds (depending on the type of sample) including amino and organic acids, sugars, amines, nucleosides, phenolic compounds, osmolytes, and lipids produced at sublevel millimolar concentrations. The sample preparation is usually kept minimal making the method particularly suited to high-throughput analysis (up to 100 samples/24 h with the use of a 60-holder autosampler). This chapter describes procedures for profiling liquids and solids of biological origin from plants, food, microbes, and mammalian systems.

Constrained randomization and multivariate effect projections improve information extraction and biomarker pattern discovery in metabolomics studies involving dependent samples

Analytical drift is a major source of bias in mass spectrometry based metabolomics confounding interpretation and biomarker detection. So far, standard protocols for sample and data analysis have not been able to fully resolve this. We present a combined approach for minimizing the influence of analytical drift on multivariate comparisons of matched or dependent samples in mass spectrometry based metabolomics studies. The approach is building on a randomization procedure for sample run order, constrained to independent randomizations between and within dependent sample pairs (e.g. pre/post intervention). This is followed by a novel multivariate statistical analysis strategy allowing paired or dependent analyses of individual effects named OPLS-effect projections (OPLS-EP). We show, using simulated data that OPLS-EP gives improved interpretation over existing methods and that constrained randomization of sample run order in combination with an appropriate dependent statistical test increase the accuracy and sensitivity and decrease the false omission rate in biomarker detection. We verify these findings and prove the strength of the suggested approach in a clinical data set consisting of LC/MS data of blood plasma samples from patients before and after radical prostatectomy. Here OPLS-EP compared to traditional (independent) OPLS-discriminant analysis (OPLS-DA) on constrained randomized data gives a less complex model (3 versus 5 components) as well a higher predictive ability (Q2 = 0.80 versus Q2 = 0.55). We explain this by showing that paired statistical analysis detects 37 unique significant metabolites that were masked for the independent test due to bias, including analytical drift and inter-individual variation.

Metabolic phenotyping applied to pre-clinical and clinical studies of acetaminophen metabolism and hepatotoxicity

Acetaminophen (APAP, paracetamol, N-acetyl-p-aminophenol) is a widely used analgesic that is safe at therapeutic doses but is a major cause of acute liver failure (ALF) following overdose. APAP-induced hepatotoxicity is related to the formation of an electrophilic reactive metabolite, N-acetyl-p-benzoquinone imine (NAPQI), which is detoxified through conjugation with reduced glutathione (GSH). One method that has been applied to study APAP metabolism and hepatotoxicity is that of metabolic phenotyping, which involves the study of the small molecule complement of complex biological samples. This approach involves the use of high-resolution analytical platforms such as NMR spectroscopy and mass spectrometry to generate information-rich metabolic profiles that reflect both genetic and environmental influences and capture both endogenous and xenobiotic metabolites. Data modeling and mining and the subsequent identification of panels of candidate biomarkers are typically approached with multivariate statistical tools. We review the application of multi-platform metabolic profiling for the study of APAP metabolism in both in vivo models and humans. We also review the application of metabolic profiling for the study of endogenous metabolic pathway perturbations in response to APAP hepatotoxicity, with a particular focus on metabolites involved in the biosynthesis of GSH and those that reflect mitochondrial function such as long-chain acylcarnitines. Taken together, this body of work sheds much light on the mechanism of APAP-induced hepatotoxicity and provides candidate biomarkers that may prove of translational relevance for improved stratification of APAP-induced ALF.

Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping

Proton nuclear magnetic resonance (NMR)-based metabolic phenotyping of urine and blood plasma/serum samples provides important prognostic and diagnostic information and permits monitoring of disease progression in an objective manner. Much effort has been made in recent years to develop NMR instrumentation and technology to allow the acquisition of data in an effective, reproducible, and high-throughput approach that allows the study of general population samples from epidemiological collections for biomarkers of disease risk. The challenge remains to develop highly reproducible methods and standardized protocols that minimize technical or experimental bias, allowing realistic interlaboratory comparisons of subtle biomarker information. Here we present a detailed set of updated protocols that carefully consider major experimental conditions, including sample preparation, spectrometer parameters, NMR pulse sequences, throughput, reproducibility, quality control, and resolution. These results provide an experimental platform that facilitates NMR spectroscopy usage across different large cohorts of biofluid samples, enabling integration of global metabolic profiling that is a prerequisite for personalized healthcare.

Metabolic profiling of serum samples by 1H nuclear magnetic resonance spectroscopy as a potential diagnostic approach for septic shock

OBJECTIVES

To determine whether a nuclear magnetic resonance-based metabolomics approach can be useful for the early diagnosis and prognosis of septic shock in ICUs.

DESIGN

Laboratory-based study.

SETTING

University research laboratory.

SUBJECTS

Serum samples from septic shock patients and ICU controls (ICU patients with systemic inflammatory response syndrome but not suspected of having an infection) were collected within 24 hours of admittance to the ICU.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

H nuclear magnetic resonance spectra of septic shock and ICU control samples were analyzed and quantified using a targeted profiling approach. By applying multivariate statistical analysis (e.g., orthogonal partial least squares discriminant analysis), we were able to distinguish the patient groups and detect specific metabolic patterns. Some of the metabolites were found to have a significant impact on the separation between septic shock and control samples. These metabolites could be interpreted in terms of a biological human response to septic shock and they might serve as a biomarker pattern for septic shock in ICUs. Additionally, nuclear magnetic resonance-based metabolomics was evaluated in order to detect metabolic variation between septic shock survivors and nonsurvivors and to predict patient outcome. The area under the receiver operating characteristic curve indicated an excellent predictive ability for the constructed orthogonal partial least squares discriminant analysis models (septic shock vs ICU controls: area under the receiver operating characteristic curve = 0.98; nonsurvivors vs survivors: area under the receiver operating characteristic curve = 1).

CONCLUSIONS

Our results indicate that nuclear magnetic resonance-based metabolic profiling could be used for diagnosis and mortality prediction of septic shock in the ICU.

Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN

Data processing for 1D NMR spectra is a key bottleneck for metabolomic and other complex-mixture studies, particularly where quantitative data on individual metabolites are required. We present a protocol for automated metabolite deconvolution and quantification from complex NMR spectra by using the Bayesian automated metabolite analyzer for NMR (BATMAN) R package. BATMAN models resonances on the basis of a user-controllable set of templates, each of which specifies the chemical shifts, J-couplings and relative peak intensities for a single metabolite. Peaks are allowed to shift position slightly between spectra, and peak widths are allowed to vary by user-specified amounts. NMR signals not captured by the templates are modeled non-parametrically by using wavelets. The protocol covers setting up user template libraries, optimizing algorithmic input parameters, improving prior information on peak positions, quality control and evaluation of outputs. The outputs include relative concentration estimates for named metabolites together with associated Bayesian uncertainty estimates, as well as the fit of the remainder of the spectrum using wavelets. Graphical diagnostics allow the user to examine the quality of the fit for multiple spectra simultaneously. This approach offers a workflow to analyze large numbers of spectra and is expected to be useful in a wide range of metabolomics studies.

Metabolomic fingerprinting: challenges and opportunities

Systems biology has primarily focused on studying genomics, transcriptomics, and proteomics and their dynamic interactions. These, however, represent only the potential for a biological outcome since the ultimate phenotype at the level of the eventually produced metabolites is not taken into consideration. The emerging field of metabolomics provides complementary guidance toward an integrated approach to this problem: It allows global profiling of the metabolites of a cell, tissue, or host and presents information on the actual end points of a response. A wide range of data collection methods are currently used and allow the extraction of global or tissue-specific metabolic profiles. The great amount and complexity of data that are collected require multivariate analysis techniques, but the increasing amount of work in this field has made easy-to-use analysis programs readily available. Metabolomics has already shown great potential in drug toxicity studies, disease modeling, and diagnostics and may be integrated with genomic and proteomic data in the future to provide in-depth understanding of systems, pathways, and their functionally dynamic interactions. In this review we discuss the current state of the art of metabolomics, its applications, and future potential.

Omics technologies and their applications to evaluate metal toxicity in mice M. spretus as a bioindicator

Metals are important components of living organisms since many biological functions critically depend on their interaction with some metal in the cell. However, human activities have increased toxic metal levels in the terrestrial and aquatic ecosystems affecting living organisms. The impact of metals on cellular metabolism and global homeostasis has been traditionally assessed in free-living organisms by using conventional biomarkers; however, to obtain a global vision of metal toxicity mechanisms and the responses that metals elicit in the organisms, new analytical methodologies are needed. We review the use of omics approaches to assess the response of living organisms under metal stress illustrating the possibilities of different methodologies on the basis of our previous results. Most of this research has been based on free-living mice Mus spretus, a conventional bioindicator used to monitor metal pollution in Doñana National Park (DNP) (SW Spain), which is an important European biological reserve for migrating birds affected by agricultural, mining and industrial activities. The benefits of using omic techniques such as heterologous microarrays, proteomics methodologies (2-DE, iTRAQ®), metallomics, ionomics or metabolomics has been remarked; however, the complexity of these areas requires the integration of omics to achieve a comprehensive assessment of their environmental status. This article is part of a Special Issue entitled: Environmental and structural proteomics.

BIOLOGICAL SIGNIFICANCE

This work presents new contributions in the study of environmental metal pollution in terrestrial ecosystems using Mus spretus mice as bioindicator in Doñana National Park (SW Spain) and surroundings. In addition, it has been demonstrated that the integration of omics multi-analytical approaches provides a very suitable approach for the study of the biological response and metal interactions in exposed and free-living mice (Mus musculus and Mus spretus, respectively) under metal pollution.

A metabonomic analysis of serum from rats treated with ricinine using ultra performance liquid chromatography coupled with mass spectrometry

A metabonomic approach based on ultra performance liquid chromatography coupled with mass spectrometry (UPLC/MS) was used to study the hepatotoxicity of ricinine in rats. Potential biomarkers of ricinine toxicity and toxicological mechanism were analyzed by serum metabonomic method. The significant differences in the metabolic profiling of the control and treated rats were clear by using the principal components analysis (PCA) of the chromatographic data. Significant changes of metabolite biomarkers like phenylalanine, tryptophan, cholic acid, LPC and PC were detected in the serum. These biochemical changes were related to the metabolic disorders in amino acids and phospholipids. This research indicates that UPLC/MS-based metabonomic analysis of serum samples can be used to predict the hepatotoxicity and further understand the toxicological mechanism induced by ricinine. This work shows that metabonomics method is a valuable tool in drug mechanism study.