Effect of food on systemic exposure to niflumic acid following postprandial administration of talniflumate

PURPOSE: Talniflumate was designed as a prodrug of niflumic acid, a potent analgesic and anti-inflammatory drug, which is widely prescribed for treating rheumatoid diseases. The prandial effect on talniflumate absorption remains unclear; therefore, this study investigated the effect of food on the systemic exposure to niflumic acid in healthy volunteers. METHODS: Volunteers received a single 740-mg dose of talniflumate 30 min after consuming a high-fat breakfast, a low-fat breakfast, or no food (fasting condition). Plasma concentrations of both talniflumate and niflumic acid were measured using validated high-performance liquid chromatography coupled to tandem mass spectrometry. RESULTS: The maximum concentration of niflumic acid was 224 +/- 193 ng/ml at approximately 2.7 h in the fasted condition compared with 886 +/- 417 ng/ml (p < 0.05) at 1.8 h and 1,159 +/- 508 ng/ml (p < 0.01) at 2.2 h with the low- and high-fat meals, respectively. The mean area under the curve from zero to infinity (AUC(inf)) values after the low- and high-fat meals were four- and fivefold, respectively, the value while fasting (p < 0.05). CONCLUSIONS: It is strongly recommended that talniflumate be taken after a meal to increase systemic exposure to its active metabolite. Our results suggest a reduction in the daily dosage of talniflumate when taken with food

Direct Infusion MS-Based Lipid Profiling Reveals the Pharmacological Effects of Compound K‑Reinforced Ginsenosides in High-Fat Diet Induced Obese Mice

The serum lipid metabolites of lean and obese mice fed normal or high-fat diets were analyzed via direct infusion nanoelectrospray–ion trap mass spectrometry followed by multivariate analysis. In addition, lipidomic biomarkers responsible for the pharmacological effects of compound K-reinforced ginsenosides (CK), thus the CK fraction, were evaluated in mice fed high-fat diets. The obese and lean groups were clearly discriminated upon principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) score plot, and the major metabolites contributing to such discrimination were triglycerides (TGs), cholesteryl esters (CEs), phosphatidylcholines (PCs), and lysophosphatidylcholines (LPCs). TGs with high total carbon number (>50) and low total carbon number (<50) were negatively and positively associated with high-fat diet induced obesity in mice, respectively. When the CK fraction was fed to obese mice that consumed a high-fat diet, the levels of certain lipids including LPCs and CEs became similar to those of mice fed a normal diet. Such metabolic markers can be used to better understand obesity and related diseases induced by a hyperlipidic diet. Furthermore, changes in the levels of such metabolites can be employed to assess the risk of obesity and the therapeutic effects of obesity management

Quantitative Determination of Octylonium in Human Plasma by LC–MS

The purpose of this investigation was to develop a method for measuring the concentration of octylonium in human plasma. Hydrochloric acid was added to the plasma samples before pretreatment to improve the stability of the octylonium. After liquid–liquid extraction with ethylacetate and isopropanol (10:1), the analytes were separated by chromatography on a reversed-phase C18 column and detected by LC–MS–MS with electrospray ionization–ionization. The coefficient of variation for the precision of the assay was less than 10.1%, and the accuracy ranged from 98.0 to 106.5%. The limit of quantification or sensitivity was 0.2 ng mL−1. This method was validated by measuring octylonium in the plasma of healthy human subjects after administration of a single 120-mg oral dose of octylonium bromide. Thus, a highly sensitive and accurate analytical method was developed to determine the concentration of octylonium in human plasma. </p

Additional file 2: of The efficacy and stability of an information and communication technology-based centralized monitoring system of adherence to immunosuppressive medication in kidney transplant recipients: study protocol for a randomized controlled trial

SPIRIT 2013 Checklist: recommended items to address in a clinical trial protocol and related documents*. (DOC 123 kb

Comparison of the distribution of metabolite intensity levels for control and drug-dosed (low, middle, high) groups.

<p>Box plots indicate the distribution of magnitudes of peak intensity levels of key metabolic phenotype in each group. The box is drawn from the 25^th to 75^th percentiles in the distribution of intensities. The median, or 50^th percentile, is drawn as a black horizontal line inside the box. The whiskers (lines extending from the box) describe the spread of the data within the 10^th and 90^th percentiles.</p

Chemical structure of sparfloxacin and its metabolic pathway.

<p>Chemical structure of sparfloxacin and its metabolic pathway.</p

PLS model validity.

<p>(A) Plot of predicted QTc vs. actual (measured) QTc from the PLS model using the cross-validation method. Predicted values from the PLS model in which all predicted QTc values show a linear relationship with actual measured QTc values (R² = 0.9884). Colour from blue to red indicates increasing QTc values. RMSEE specifies the root mean square error of the estimation (the fit) for observations in the workset. The values were predicted by exclusion of 1/7^th of the data from the model and predicting the excluded data that are not part of model building. (B) Internal validation of the PLS model by 20 permutation tests to confirm predictability and data overfitting shows that all R² (goodness of fit) and Q² (predictability of model) values from the permuted models (left) are smaller than those of the original model (far right), demonstrating the validity of the PLS model. (C) Internal validation of the PLS model with 100 permutation tests to use stricter validation criteria. (D and E) Plots for normalised intensities of LysoPC (18∶1) (D) and L-aspartic acid (E), which exhibit a negative and positive correlation, respectively, with QTc.</p

PCA and PLS modelling of plasma LC–MS metabolic data for predicting the drug-induced QT prolongation of sparfloxacin.

<p>(A) PCA score plot (t<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060556#pone.0060556-Beringer1" target="_blank">[1]</a> vs. t<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060556#pone.0060556-DeSarro1" target="_blank">[2]</a>) obtained from guinea pig plasma samples. Obviously separated clustering of dose groups and the control group was shown by PCA; in addition, dose-dependent metabolomic modification was detected. (B) Loading plot for the above PLS model in which each point represents a metabolic feature detected from plasma LC–MS data and is plotted as its respective coefficient from PLS component 1 vs. its coefficient from PLS component 2. The arrow indicates a positive relationship with the QTc. Metabolite variables with larger coefficient values (positive or negative) have a stronger correlation with the QTc (marked by red boxes; VIP>1.5) and were used to build the PLS model for predicting cardiovascular toxicity. The inset green bar plot shows the correlation coefficients for the key identified metabolites.</p

Pattern Recognition Analysis for Hepatotoxicity Induced by Acetaminophen Using Plasma and Urinary ¹H NMR-Based Metabolomics in Humans

Drug-induced liver injury (DILI) is currently an increasingly relevant health issue. However, available biomarkers do not reliably detect or quantify DILI risk. Therefore, the purpose of this study was to comparatively evaluate plasma and urinary biomarkers obtained from humans treated with acetaminophen (APAP) using a metabolomics approach and a proton nuclear magnetic resonance (NMR) platform. APAP (3 g/day, two 500 mg tablets every 8 h) was administered to 20 healthy Korean males (age, 20–29 years) for 7 days. Urine was collected daily before and during dosing and 6 days after the final dose. NMR spectra of these urine samples were analyzed using principal component analysis (PCA) and partial least-squares-discrimination analysis. Although the activities of aspartate aminotransferase and lactate dehydrogenase were significantly increased 7 days post-APAP treatment, serum biochemical parameters of aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, total bilirubin, γ-glutamyl transpeptidase, and lactate dehydrogenase were within normal range of hepatic function. However, urine and plasma ¹H NMR spectroscopy revealed different clustering between predosing and after APAP treatment for global metabolomic profiling through PCA. Urinary endogenous metabolites of trimethylamine-N-oxide, citrate, 3-chlorotyrosine, phenylalanine, glycine, hippurate, and glutarate as well as plasma endogenous metabolites such as lactate, glucose, 3-hydroxyisovalerate, isoleucine, acetylglycine, acetone, acetate, glutamine, ethanol, and isobutyrate responded significantly to APAP dosing in humans. Urinary and plasma endogenous metabolites were more sensitive than serum biochemical parameters. These results might be applied to predict or screen potential hepatotoxicity caused by other drugs using urinary and plasma ¹H NMR analyses

Names and associated metabolic pathways for the identified metabolites in increasing order of their VIP values.

*<p>Metabolites were identified by interpreting their fragmentation patterns (MS/MS spectra) and conducting a database search. LysoPC, lysophosphatidylcholine; PC, phosphatidylcholine; CDP, cytidine-5′-diphosphate; APGPR, Ala-Pro-Gly-Pro-Arg. ^**Human Metabolome Database. ^aVariable importance in the projection. All abbreviations used for pathways and reactions are from KEGG identifiers (<a href="http://www.genome.jp/kegg/kegg3.html" target="_blank">http://www.genome.jp/kegg/kegg3.html</a>).</p