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Insulin is as a major postprandial hormone with profound effects on carbohydrate, fat, and protein metabolism. In the absence of exogenous insulin, patients with type 1 diabetes exhibit a variety of metabolic abnormalities including hyperglycemia, glycosurea, accelerated ketogenesis, and muscle wasting due to increased proteolysis. We analyzed plasma from type 1 diabetic (T1D) humans during insulin treatment (I+) and acute insulin deprivation (I-) and non-diabetic participants (ND) by (1)H nuclear magnetic resonance spectroscopy and liquid chromatography-tandem mass spectrometry. The aim was to determine if this combination of analytical methods could provide information on metabolic pathways known to be altered by insulin deficiency. Multivariate statistics differentiated proton spectra from I- and I+ based on several derived plasma metabolites that were elevated during insulin deprivation (lactate, acetate, allantoin, ketones). Mass spectrometry revealed significant perturbations in levels of plasma amino acids and amino acid metabolites during insulin deprivation. Further analysis of metabolite levels measured by the two analytical techniques indicates several known metabolic pathways that are perturbed in T1D (I-) (protein synthesis and breakdown, gluconeogenesis, ketogenesis, amino acid oxidation, mitochondrial bioenergetics, and oxidative stress). This work demonstrates the promise of combining multiple analytical methods with advanced statistical methods in quantitative metabolomics research, which we have applied to the clinical situation of acute insulin deprivation in T1D to reflect the numerous metabolic pathways known to be affected by insulin deficiency.

The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.

Insulin is as a major postprandial hormone with profound effects on carbohydrate, fat, and protein metabolism. In the absence of exogenous insulin, patients with type 1 diabetes exhibit a variety of metabolic abnormalities including hyperglycemia, glycosurea, accelerated ketogenesis, and muscle wasting due to increased proteolysis. We analyzed plasma from type 1 diabetic (T1D) humans during insulin treatment (I+) and acute insulin deprivation (I-) and non-diabetic participants (ND) by 1H nuclear magnetic resonance spectroscopy and liquid chromatography-tandem mass spectrometry. The aim was to determine if this combination of analytical methods could provide information on metabolic pathways known to be altered by insulin deficiency. Multivariate statistics differentiated proton spectra from I- and I+ based on several derived plasma metabolites that were elevated during insulin deprivation (lactate, acetate, allantoin, ketones). Mass spectrometry revealed significant perturbations in levels of plasma amino acids and amino acid metabolites during insulin deprivation. Further analysis of metabolite levels measured by the two analytical techniques indicates several known metabolic pathways that are perturbed in T1D (I-) (protein synthesis and breakdown, gluconeogenesis, ketogenesis, amino acid oxidation, mitochondrial bioenergetics, and oxidative stress). This work demonstrates the promise of combining multiple analytical methods with advanced statistical methods in quantitative metabolomics research, which we have applied to the clinical situation of acute insulin deprivation in T1D to reflect the numerous metabolic pathways known to be affected by insulin deficiency.

For many years, basic and clinical researchers have taken advantage of the analytical sensitivity and specificity afforded by mass spectrometry in the measurement of proteins. Clinical laboratories are now beginning to deploy these work flows as well. For assays that use proteolysis to generate peptides for protein quantification and characterization, synthetic stable isotope-labeled internal standard peptides are of central importance. No general recommendations are currently available surrounding the use of peptides in protein mass spectrometric assays. The Clinical Proteomic Tumor Analysis Consortium of the National Cancer Institute has collaborated with clinical laboratorians, peptide manufacturers, metrologists, representatives of the pharmaceutical industry, and other professionals to develop a consensus set of recommendations for peptide procurement, characterization, storage, and handling, as well as approaches to the interpretation of the data generated by mass spectrometric protein assays. Additionally, the importance of carefully characterized reference materials-in particular, peptide standards for the improved concordance of amino acid analysis methods across the industry-is highlighted. The alignment of practices around the use of peptides and the transparency of sample preparation protocols should allow for the harmonization of peptide and protein quantification in research and clinical care.

This study aims to investigate the metabolic difference between male and female healthy adults using a combination of GC–MS and NMR metabolomics techniques. While metabolomics has shown wide applications in characterizing the status and progression of many diseases, physiological factors such as gender often contribute high levels of variability that can hinder the detection of biomarkers of interest, such as in disease detection. We carried out a detailed exploration of gender related metabolic profiling of human urine using a Headspace-SPME/GC–MS approach and detected over two hundred peaks. Fifty-nine metabolites were identified using the NIST library. 1 H NMR spectroscopy was also utilized, and resulted in the identification of eighteen metabolites. We find that both GC–MS and NMR are able to capture human gender metabolic differences, and their combination allows a significantly better understanding of this difference. Subtle differences between genders are found to be related to the metabolism of fats, amino acids, and TCA cycle intermediates.

This study aims to investigate the metabolic difference between male and female healthy adults using a combination of GC-MS and NMR metabolomics techniques. While metabolomics has shown wide applications in characterizing the status and progression of many diseases, physiological factors such as gender often contribute high levels of variability that can hinder the detection of biomarkers of interest, such as in disease detection. We carried out a detailed exploration of gender related metabolic profiling of human urine using a Headspace-SPME/GC-MS approach and detected over two hundred peaks. Fifty-nine metabolites were identified using the NIST library. super(1)H NMR spectroscopy was also utilized, and resulted in the identification of eighteen metabolites. We find that both GC-MS and NMR are able to capture human gender metabolic differences, and their combination allows a significantly better understanding of this difference. Subtle differences between genders are found to be related to the metabolism of fats, amino acids, and TCA cycle intermediates.

This study aims to investigate the metabolic difference between male and female healthy adults using a combination of GC-MS and NMR metabolomics techniques. While metabolomics has shown wide applications in characterizing the status and progression of many diseases, physiological factors such as gender often contribute high levels of variability that can hinder the detection of biomarkers of interest, such as in disease detection. We carried out a detailed exploration of gender related metabolic profiling of human urine using a Headspace-SPME/GC-MS approach and detected over two hundred peaks. Fifty-nine metabolites were identified using the NIST library. ^sup 1^H NMR spectroscopy was also utilized, and resulted in the identification of eighteen metabolites. We find that both GC-MS and NMR are able to capture human gender metabolic differences, and their combination allows a significantly better understanding of this difference. Subtle differences between genders are found to be related to the metabolism of fats, amino acids, and TCA cycle intermediates.[PUBLICATION ABSTRACT]

Background Respiratory syncytial virus (RSV) infection presents a significant health challenge in young children, elderly and immunocompromised patients. To date, there are no effective treatments available. EDP-938 was designed to meet this unmet medical need and is currently in Phase 2 clinical trials. Herein we report its preclinical pharmacokinetic (PK) and pharmacodynamic (PD) properties. Methods The pharmacokinetics of EDP-938 following single intravenous and oral doses were determined in mice, rats, dogs, and monkeys. In vitro cellular permeability and metabolic stability were assayed using Caco-2 cells and human liver microsomes, respectively. In vivo pharmacodynamic efficacy of EDP-938 was conducted in the African green monkey model, in which animals experimentally challenged with RSV were orally dosed twice daily with 100 mg/kg EDP-938 for 6 days starting 24 hours prior to infection. Results EDP-938 was well absorbed in the preclinical species with oral bioavailability values ranging from 27.1% in dogs, 35.4% in mice, 35.7% in rats, and 39.5% in monkeys, after a single oral dose when formulated in 0.5% methylcellulose. EDP-938 showed a moderate in vitro permeability of 3.6 x 10–6 cm/sec in Caco-2 cells. Based on the outcome of these absorption studies, EDP-938 was projected to have good oral absorption in humans. EDP-938 had low intrinsic clearance of 5 mL/minute/mg in human liver microsomes. Moreover, EDP-938 demonstrated potent antiviral efficacy in an African green monkey model of RSV infection. In untreated monkeys the RSV RNA viral load in the bronchoalveolar lavage fluid peaked at 106 copies/mL on day 5 post-infection, by comparison in animals treated with EDP-938 the viral load was below the limit of detection by day 3 post-infection. The PK/PD modeling suggested that plasma trough concentrations ≥10 × EC90 led to >4-log viral load reduction in EDP-938 treated monkeys. Conclusion The favorable preclinical PK and PD properties of EDP-938 support its further clinical development as a novel treatment for RSV infection. Disclosures All authors: No reported disclosures.

Insulin is as a major postprandial hormone with profound effects on carbohydrate, fat, and protein metabolism. In the absence of exogenous insulin, patients with type 1 diabetes exhibit a variety of metabolic abnormalities including hyperglycemia, glycosurea, accelerated ketogenesis, and muscle wasting due to increased proteolysis. We analyzed plasma from type 1 diabetic (T1D) humans during insulin treatment (I+) and acute insulin deprivation (I-) and non-diabetic participants (ND) by .sup.1 H nuclear magnetic resonance spectroscopy and liquid chromatography-tandem mass spectrometry. The aim was to determine if this combination of analytical methods could provide information on metabolic pathways known to be altered by insulin deficiency. Multivariate statistics differentiated proton spectra from I- and I+ based on several derived plasma metabolites that were elevated during insulin deprivation (lactate, acetate, allantoin, ketones). Mass spectrometry revealed significant perturbations in levels of plasma amino acids and amino acid metabolites during insulin deprivation. Further analysis of metabolite levels measured by the two analytical techniques indicates several known metabolic pathways that are perturbed in T1D (I-) (protein synthesis and breakdown, gluconeogenesis, ketogenesis, amino acid oxidation, mitochondrial bioenergetics, and oxidative stress). This work demonstrates the promise of combining multiple analytical methods with advanced statistical methods in quantitative metabolomics research, which we have applied to the clinical situation of acute insulin deprivation in T1D to reflect the numerous metabolic pathways known to be affected by insulin deficiency.