Potential of Mesalazine Therapeutic Drug Monitoring by Measuring Fecal Excretion in Patients With Ulcerative Colitis.

BACKGROUND: Therapeutic drug monitoring of mesalazine (5-ASA) in patients with ulcerative colitis is unavailable. Mucosal 5-ASA concentrations are assumed to be higher during remission, but biopsy is not practical. Therefore, we investigated the feasibility of measuring mesalazine levels in feces. To explore the potential role of fecal mesalazine measurements in therapeutic drug monitoring, we compared the dry fecal concentration and daily fecal excretion of 5-ASA and its metabolite N-acetyl-5-ASA in patients with ulcerative colitis with active and quiescent disease. METHODS: Adults with ulcerative colitis on oral mesalazine and scheduled for colonoscopy were eligible for inclusion in this cross-sectional study. Stool and urine samples were collected for 48 and 24 hours, respectively, and rectal biopsies were performed. (N-acetyl-)5-ASA was measured using mass spectrometry. Biochemically active disease was defined as a fecal calprotectin level above 100 mcg/g and endoscopically active disease as any activity following the endoscopic Mayo score (≥1). RESULTS: Approximately 28 patients were included in the study. Daily fecal excretion of (N-acetyl-)5-ASA did not differ between patients with (n = 13) and without (n = 15) endoscopically active disease [median 572 mg/d versus 597 mg/d ( P = 0.86) for 5-ASA and 572 mg/d versus 554 mg/d ( P = 0.86) for N-acetyl-5-ASA]. The same applied to the fecal concentration [median 9.7 mcg/mg dry weight versus 10.3 ( P = 0.53) and 12.0 versus 9.9 ( P = 0.89)]. The results were comparable when the biochemical disease activity definition was used. The mucosal concentrations and urinary excretion of (N-acetyl-)5-ASA did not differentiate between quiescent and active activity. CONCLUSIONS: Fecal (N-acetyl-)5-ASA measurements do not correlate with disease activity, which renders it an unsuitable tool for therapeutic drug monitoring of mesalazine.

Comparative Analysis of the Effects of Fish Oil and Fenofibrate on Plasma Metabolomic Profiles in Overweight and Obese Individuals.

SCOPE: The drug fenofibrate and dietary fish oils can effectively lower circulating triglyceride (TG) concentrations. However, a detailed comparative analysis of the effects on the plasma metabolome is missing. METHODS AND RESULTS: Twenty overweight and obese subjects participate in a double-blind, cross-over intervention trial and receive in a random order 3.7 g day-1 n-3 fatty acids, 200 mg fenofibrate, or placebo treatment for 6 weeks. Four hundred twenty plasma metabolites are measured via gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). Among the treatments, 237 metabolites are significantly different, of which 22 metabolites change in the same direction by fish oil and fenofibrate, including a decrease in several saturated TG-species. Fenofibrate additionally changes 33 metabolites, including a decrease in total cholesterol, and total lysophosphatidylcholine (LPC), whereas 54 metabolites are changed by fish oil, including an increase in unsaturated TG-, LPC-, phosphatidylcholine-, and cholesterol ester-species. All q < 0.05. CONCLUSION: Fenofibrate and fish oil reduce several saturated TG-species markedly. These reductions have been associated with a decreased risk for developing cardiovascular disease (CVD). Interestingly, fish oil consumption increases several unsaturated lipid species, which have also been associated with a reduced CVD risk. Altogether, this points towards the power of fish oil to change the plasma lipid metabolome in a potentially beneficial way.

Characterization of Clinical Absorption, Distribution, Metabolism, and Excretion and Pharmacokinetics of Velsecorat Using an Intravenous Microtracer Combined with an Inhaled Dose in Healthy Subjects.

This open-label, single-period study describes the human absorption, distribution, metabolism, excretion, and pharmacokinetics of velsecorat (AZD7594). Healthy subjects received inhaled velsecorat (non-radiolabeled; 720 µg) followed by intravenous infusion of carbon 14 (14C)-velsecorat (30 µg). Plasma, urine, and feces were collected up to 168 hours post-dose. Objectives included identification and quantification of velsecorat and its metabolites (i.e., drug-related material) in plasma and excreta, and determining the elimination pathways of velsecorat by measuring the rate and route of excretion, plasma half-life (t1/2), clearance, volume of distribution and mean recovery of radioactivity. On average, 76.0% of administered 14C dose was recovered by the end of the sampling period (urine = 24.4%; feces = 51.6%), with no unchanged compound recovered in excreta, suggesting that biliary excretion is the main elimination route. Compared with intravenous 14C-velsecorat, inhaled velsecorat had a longer t1/2 (27 versus 2 hours), confirming that plasma elimination is absorption-rate-limited from the lungs. Following intravenous administration, t1/2 of 14C-drug-related material was longer than for unchanged velsecorat, and 20% of the 14C plasma content was related to unchanged velsecorat. The geometric mean plasma clearance of velsecorat was high (70.7 l/h) and the geometric mean volume of distribution at steady state was 113 l. Velsecorat was substantially metabolized via O-dealkylation of the indazole ether followed by sulfate conjugation, forming the M1 metabolite, the major metabolite in plasma. There were 15 minor metabolites. Velsecorat was well tolerated, and these results support the progression of velsecorat to phase 3 studies. SIGNIFICANCE STATEMENT: This study describes the human pharmacokinetics and metabolism of velsecorat, a selective glucocorticoid receptor modulator, evaluated via co-administration of a radiolabeled intravenous microtracer dose and a non-radiolabeled inhaled dose. This study provides a comprehensive assessment of the disposition of velsecorat in humans. It also highlights a number of complexities associated with determining human absorption, distribution, metabolism, and excretion for velsecorat, related to the inhaled route, the high metabolic clearance, sequential metabolite formation and the low intravenous dose.

Decontamination of Toxic Industrial Chemicals and Fentanyl by Application of the RSDL® Kit.

PURPOSE: This study investigated the decontamination effectiveness of selected toxic industrial chemicals using RSDL® (Reactive Skin Decontamination Lotion Kit; Emergent BioSolutions Inc.; https://www.rsdl.com/). MATERIALS AND METHODS: Quantitative analytical methods were developed for dermal toxic compounds of varying physicochemical properties: sulfuric acid, hydrofluoric acid, ammonia, methylamine, hydrazine, phenylhydrazine, 1,2-dibromoethane, capsaicin, and fentanyl. These methods were subsequently used to evaluate the decontamination effectiveness on painted metal substrates at an initial chemical contamination level of 10g/m2 (0.1g/m2 for fentanyl). RESULTS: The decontamination effectiveness ranged from 97.79% to 99.99%. DISCUSSION AND CONCLUSION: This study indicates that the RSDL kit may be amenable for use as an effective decontaminant for material substrates beyond the classical chemical warfare agents and the analytical methods may be used for future decontamination assessment studies using contaminated skin or other materials.

Dietary medium chain fatty acid supplementation leads to reduced VLDL lipolysis and uptake rates in comparison to linoleic acid supplementation.

Dietary medium chain fatty acids (MCFA) and linoleic acid follow different metabolic routes, and linoleic acid activates PPAR receptors. Both these mechanisms may modify lipoprotein and fatty acid metabolism after dietary intervention. Our objective was to investigate how dietary MCFA and linoleic acid supplementation and body fat distribution affect the fasting lipoprotein subclass profile, lipoprotein kinetics, and postprandial fatty acid kinetics. In a randomized double blind cross-over trial, 12 male subjects (age 51±7 years; BMI 28.5±0.8 kg/m2), were divided into 2 groups according to waist-hip ratio. They were supplemented with 60 grams/day MCFA (mainly C8:0, C10:0) or linoleic acid for three weeks, with a wash-out period of six weeks in between. Lipoprotein subclasses were measured using HPLC. Lipoprotein and fatty acid metabolism were studied using a combination of several stable isotope tracers. Lipoprotein and tracer data were analyzed using computational modeling. Lipoprotein subclass concentrations in the VLDL and LDL range were significantly higher after MCFA than after linoleic acid intervention. In addition, LDL subclass concentrations were higher in lower body obese individuals. Differences in VLDL metabolism were found to occur in lipoprotein lipolysis and uptake, not production; MCFAs were elongated intensively, in contrast to linoleic acid. Dietary MCFA supplementation led to a less favorable lipoprotein profile than linoleic acid supplementation. These differences were not due to elevated VLDL production, but rather to lower lipolysis and uptake rates.

Building multivariate systems biology models.

Systems biology methods using large-scale "omics" data sets face unique challenges: integrating and analyzing near limitless data space, while recognizing and removing systematic variation or noise. Herein we propose a complementary multivariate analysis workflow to both integrate "omics" data from disparate sources and analyze the results for specific and unique sample correlations. This workflow combines principal component analysis (PCA), orthogonal projections to latent structures discriminate analysis (OPLS-DA), orthogonal 2 projections to latent structures (O2PLS), and shared and unique structures (SUS) plots. The workflow is demonstrated using data from a study in which ApoE3Leiden mice were fed an atherogenic diet consisting of increasing cholesterol levels followed by therapeutic intervention (fenofibrate, rosuvastatin, and LXR activator T-0901317). The levels of structural lipids (lipidomics) and free fatty acids in liver were quantified via liquid chromatography-mass spectrometry (LC-MS). The complementary workflow identified diglycerides as key hepatic metabolites affected by dietary cholesterol and drug intervention. Modeling of the three therapeutics for mice fed a high-cholesterol diet further highlighted diglycerides as metabolites of interest in atherogenesis, suggesting a role in eliciting chronic liver inflammation. In particular, O2PLS-based SUS2 plots showed that treatment with T-0901317 or rosuvastatin returned the diglyceride profile in high-cholesterol-fed mice to that of control animals.

Lipidomics reveals multiple pathway effects of a multi-components preparation on lipid biochemistry in ApoE*3Leiden.CETP mice.

BACKGROUND: Causes and consequences of the complex changes in lipids occurring in the metabolic syndrome are only partly understood. Several interconnected processes are deteriorating, which implies that multi-target approaches might be more successful than strategies based on a limited number of surrogate markers. Preparations from Chinese Medicine (CM) systems have been handed down with documented clinical features similar as metabolic syndrome, which might help developing new intervention for metabolic syndrome. The progress in systems biology and specific animal models created possibilities to assess the effects of such preparations. Here we report the plasma and liver lipidomics results of the intervention effects of a preparation SUB885C in apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE*3Leiden.CETP) mice. SUB885C was developed according to the principles of CM for treatment of metabolic syndrome. The cannabinoid receptor type 1 blocker rimonabant was included as a general control for the evaluation of weight and metabolic responses. METHODOLOGY/PRINCIPAL FINDINGS: ApoE*3Leiden.CETP mice with mild hypercholesterolemia were divided into SUB885C-, rimonabant- and non-treated control groups. SUB885C caused no weight loss, but significantly reduced plasma cholesterol (-49%, p<0.001), CETP levels (-31%, p<0.001), CETP activity (-74%, p<0.001) and increased HDL-C (39%, p<0.05). It influenced lipidomics classes of cholesterol esters and triglycerides the most. Rimonabant induced a weight loss (-9%, p<0.05), but only a moderate improvement of lipid profiles. In vitro, SUB885C extract caused adipolysis stimulation and adipogenesis inhibition in 3T3-L1 cells. CONCLUSIONS: SUB885C, a multi-components preparation, is able to produce anti-atherogenic changes in lipids of the ApoE*3Leiden.CETP mice, which are comparable to those obtained with compounds belonging to known drugs (e.g. rimonabant, atorvastatin, niacin). This study successfully illustrated the power of lipidomics in unraveling intervention effects and to help finding new targets or ingredients for lifestyle-related metabolic abnormality.

Linking biological activity with herbal constituents by systems biology-based approaches: effects of Panax ginseng in type 2 diabetic Goto-Kakizaki rats.

Although a number of animal experiments and clinical trials have investigated the effects of ginseng roots on diabetes, the relationship between their therapeutic effects on diabetes and the quality and the growth age of this herb have not yet been reported. This study systematically investigated the effects of 3- to 6-year-old ginseng roots on glycemic and plasma lipid control in a rat model of type 2 diabetes. Six groups of male Goto-Kakizaki (GK) rats received either metformin, 3- to 6-year-old ginseng roots, or no treatment. The treatments were administered twice daily for 9 weeks. A combined approach was used that involved applying liquid chromatography-mass spectrometry-based lipidomics, measuring biochemical parameters and profiling the components of ginseng roots of different ages. Compared to the untreated controls, treatment with 4- and 6-year-old ginseng roots significantly improved glucose disposal, and 5-year-old ginseng treatment significantly increased high density lipoprotein cholesterol. Treatment with 6-year-old ginseng significantly decreased total plasma triacylglyceride (TG) and very-low-density lipoprotein cholesterol and improved plasma glycated hemoglobin (HbA1c). In addition, treatment with 4- to 6-year-old ginseng influenced plasma lipidomics in diabetic GK rats by reducing TG lipid species. Metformin significantly reduced fasting blood glucose by 41% and reduced HbA1c by 11%, but showed no effects on the plasma lipid parameters. The present study demonstrates that ginseng roots show growth age-dependent therapeutic effects on hyperlipidemia and hyperglycemia in diabetic GK rats. These age-dependent effects may be linked with the variation in both the ratios and concentrations of specific bioactive ginsenosides in ginseng roots of different growth ages. This study introduced novel systems biology-based approaches for linking biological activities with potential active components in herbal mixtures.

Semi-automated non-target processing in GC × GC-MS metabolomics analysis: applicability for biomedical studies.

Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC-MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC-MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC-MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC-MS and GC-MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC-MS and GC × GC-MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC-MS processing compared to targeted GC-MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC-MS were somewhat higher than with GC-MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC-MS was demonstrated; many additional candidate biomarkers were found with GC × GC-MS compared to GC-MS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-010-0219-6) contains supplementary material, which is available to authorized users.

Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping.

Analytical errors caused by suboptimal performance of the chosen platform for a number of metabolites and instrumental drift are a major issue in large-scale metabolomics studies. Especially for MS-based methods, which are gaining common ground within metabolomics, it is difficult to control the analytical data quality without the availability of suitable labeled internal standards and calibration standards even within one laboratory. In this paper, we suggest a workflow for significant reduction of the analytical error using pooled calibration samples and multiple internal standard strategy. Between and within batch calibration techniques are applied and the analytical error is reduced significantly (increase of 25% of peaks with RSD lower than 20%) and does not hamper or interfere with statistical analysis of the final data.

Metabolic profiling of the response to an oral glucose tolerance test detects subtle metabolic changes.

BACKGROUND: The prevalence of overweight is increasing globally and has become a serious health problem. Low-grade chronic inflammation in overweight subjects is thought to play an important role in disease development. Novel tools to understand these processes are needed. Metabolic profiling is one such tool that can provide novel insights into the impact of treatments on metabolism. METHODOLOGY: To study the metabolic changes induced by a mild anti-inflammatory drug intervention, plasma metabolic profiling was applied in overweight human volunteers with elevated levels of the inflammatory plasma marker C-reactive protein. Liquid and gas chromatography mass spectrometric methods were used to detect high and low abundant plasma metabolites both in fasted conditions and during an oral glucose tolerance test. This is based on the concept that the resilience of the system can be assessed after perturbing a homeostatic situation. CONCLUSIONS: Metabolic changes were subtle and were only detected using metabolic profiling in combination with an oral glucose tolerance test. The repeated measurements during the oral glucose tolerance test increased statistical power, but the metabolic perturbation also revealed metabolites that respond differentially to the oral glucose tolerance test. Specifically, multiple metabolic intermediates of the glutathione synthesis pathway showed time-dependent suppression in response to the glucose challenge test. The fact that this is an insulin sensitive pathway suggests that inflammatory modulation may alter insulin signaling in overweight men.

Correlation network analysis for data integration and biomarker selection.

High-throughput biomolecular profiling techniques such as transcriptomics, proteomics and metabolomics are increasingly being used in in vivo studies to recognize and characterize effects of xenobiotics on organs and systems. Of particular interest are biomarkers of treatment-related effects which are detectable in easily accessible biological fluids such as blood. A fundamental challenge in such biomarker studies is selecting among the plethora of biomolecular changes induced by a compound and revealed by molecular profiling, to identify biomarkers which are exclusively or predominantly due to specific processes. In this work we present a cross-compartment correlation network approach, involving no a priori supervision or design, to integrate proteomic, metabolomic and transcriptomic data for selecting circulating biomarkers. The case study we present is the identification of biomarkers of drug-induced hepatic toxicity effects in a rodent model. Biomolecular profiling of both blood plasma and liver tissue from Wistar Hannover rats administered a toxic compound yielded many hundreds of statistically significant molecular changes. We exploited drug-induced correlations between blood plasma analytes and liver tissue molecules across study animals in order to nominate selected plasma molecules as biomarkers of drug-induced hepatic alterations of lipid metabolism and urea cycle processes.

Atherosclerosis and liver inflammation induced by increased dietary cholesterol intake: a combined transcriptomics and metabolomics analysis.

BACKGROUND: Increased dietary cholesterol intake is associated with atherosclerosis. Atherosclerosis development requires a lipid and an inflammatory component. It is unclear where and how the inflammatory component develops. To assess the role of the liver in the evolution of inflammation, we treated ApoE*3Leiden mice with cholesterol-free (Con), low (LC; 0.25%) and high (HC; 1%) cholesterol diets, scored early atherosclerosis and profiled the (patho)physiological state of the liver using novel whole-genome and metabolome technologies. RESULTS: Whereas the Con diet did not induce early atherosclerosis, the LC diet did so but only mildly, and the HC diet induced it very strongly. With increasing dietary cholesterol intake, the liver switches from a resilient, adaptive state to an inflammatory, pro-atherosclerotic state. The liver absorbs moderate cholesterol stress (LC) mainly by adjusting metabolic and transport processes. This hepatic resilience is predominantly controlled by SREBP-1/-2, SP-1, RXR and PPARalpha. A further increase of dietary cholesterol stress (HC) additionally induces pro-inflammatory gene expression, including pro-atherosclerotic candidate genes. These HC-evoked changes occur via specific pro-inflammatory pathways involving specific transcriptional master regulators, some of which are established, others newly identified. Notably, several of these regulators control both lipid metabolism and inflammation, and thereby link the two processes. CONCLUSION: With increasing dietary cholesterol intake the liver switches from a mainly resilient (LC) to a predominantly inflammatory (HC) state, which is associated with early lesion formation. Newly developed, functional systems biology tools allowed the identification of novel regulatory pathways and transcriptional regulators controlling both lipid metabolism and inflammatory responses, thereby providing a rationale for an interrelationship between the two processes.

Integration of two-dimensional LC-MS with multivariate statistics for comparative analysis of proteomic samples.

LC-MS-based proteomics requires methods with high peak capacity and a high degree of automation, integrated with data-handling tools able to cope with the massive data produced and able to quantitatively compare them. This paper describes an off-line two-dimensional (2D) LC-MS method and its integration with software tools for data preprocessing and multivariate statistical analysis. The 2D LC-MS method was optimized in order to minimize peptide loss prior to sample injection and during the collection step after the first LC dimension, thus minimizing errors from off-column sample handling. The second dimension was run in fully automated mode, injecting onto a nanoscale LC-MS system a series of more than 100 samples, representing fractions collected in the first dimension (8 fractions/sample). As a model study, the method was applied to finding biomarkers for the antiinflammatory properties of zilpaterol, which are coupled to the beta2-adrenergic receptor. Secreted proteomes from U937 macrophages exposed to lipopolysaccharide in the presence or absence of propanolol or zilpaterol were analysed. Multivariate statistical analysis of 2D LC-MS data, based on principal component analysis, and subsequent targeted LC-MS/MS identification of peptides of interest demonstrated the applicability of the approach.

Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation.

A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.

Assessing the performance of statistical validation tools for megavariate metabolomics data.

Statistical model validation tools such as cross-validation, jack-knifing model parameters and permutation tests are meant to obtain an objective assessment of the performance and stability of a statistical model. However, little is known about the performance of these tools for megavariate data sets, having, for instance, a number of variables larger than 10 times the number of subjects. The performance is assessed for megavariate metabolomics data, but the conclusions also carry over to proteomics, transcriptomics and many other research areas. Partial least squares discriminant analyses models were built for several LC-MS lipidomic training data sets of various numbers of lean and obese subjects. The training data sets were compared on their modelling performance and their predictability using a 10-fold cross-validation, a permutation test, and test data sets. A wide range of cross-validation error rates was found (from 7.5% to 16.3% for the largest trainings set and from 0% to 60% for the smallest training set) and the error rate increased when the number of subjects decreased. The test error rates varied from 5% to 50%. The smaller the number of subjects compared to the number of variables, the less the outcome of validation tools such as cross-validation, jack-knifing model parameters and permutation tests can be trusted. The result depends crucially on the specific sample of subjects that is used for modelling. The validation tools cannot be used as warning mechanism for problems due to sample size or to representativity of the sampling.

Characterization of anti-inflammatory compounds using transcriptomics, proteomics, and metabolomics in combination with multivariate data analysis.

The discovery of new anti-inflammatory drugs is often based on an interaction with a specific target, although other pathways often play a primary or secondary role. Anti-inflammatory drugs can be categorized into classes, based on their mechanism of action. In this article we investigate the possibility to characterize novel anti-inflammatory compounds by three holistic methods. For this purpose, we make use of macrophage-like U937 cells which are stimulated with LPS in the absence or presence of an anti-inflammatory compound. Using micro-arrays, 2-D gel electrophoresis and a LC-MS method for lipids the effects on the transcriptome, proteome and metabolome of the exposed cells is investigated. The expression patterns are subsequently analyzed using in-house developed pattern recognition tools. Using the methods described above, we have examined the effects of six anti-inflammatory compounds. Our results demonstrate that different classes of anti-inflammatory compounds show distinct and characteristic mRNA, protein, and lipid expression patterns, which can be used to categorise known molecules and to discover and classify new leads. The potential of our approach is illustrated by the analysis of several beta (2)-adrenergic agonists (beta2-agonists). In addition to their primary pharmacological target, beta2-agonists posses certain anti-inflammatory properties. We were able to show that zilpaterol, a poorly characterized beta2-agonist, gives rise to an almost identical expression pattern as the beta2-agonists clenbuterol and salbutamol. Furthermore we have identified specific mRNA, protein and lipid markers for the anti-inflammatory compounds investigated in this study.