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      Compound Characterization and Metabolic Profile Elucidation after In Vitro Gastrointestinal and Hepatic Biotransformation of an Herniaria hirsuta Extract Using Unbiased Dynamic Metabolomic Data Analysis

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          Abstract

          Herniaria hirsuta L. (Caryophyllaceae) is used for treatment of urinary stones and as a diuretic. Little is known about the active compounds and the mechanism of action. The phytochemical composition of H. hirsuta was comprehensively characterized using UHPLC-UV-HRMS (Ultrahigh-Performance Liquid Chromatography-Ultraviolet-High Resolution Mass Spectrometry) data. An in vitro gastrointestinal model was used to simulate biotransformation, which allowed the monitoring of the relative abundances of individual compounds over time. To analyze the longitudinal multiclass LC–MS data, XCMS, a platform that enables online metabolomics data processing and interpretation, and EDGE, a statistical method for time series data, were used to extract significant differential profiles from the raw data. An interactive Shiny app in R was used to rate the quality of the resulting features. These ratings were used to train a random forest model. The most abundant aglycone after gastrointestinal biotransformation was subjected to hepatic biotransformation using human S9 fractions. A diversity of compounds was detected, mainly saponins and flavonoids. Besides the known saponins, 15 new saponins were tentatively identified as glycosides of medicagenic acid, acetylated medicagenic acid and zanhic acid. It is suggested that metabolites of phytochemicals present in H. hirsuta, most likely saponins, are responsible for the pharmaceutical effects. It was observed that the relative abundance of saponin aglycones increased, indicating loss of sugar moieties during colonic biotransformation, with medicagenic acid as the most abundant aglycone. Hepatic biotransformation of this aglycone resulted in different metabolites formed by phase I and II reactions.

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          Prodrugs: design and clinical applications.

          Prodrugs are bioreversible derivatives of drug molecules that undergo an enzymatic and/or chemical transformation in vivo to release the active parent drug, which can then exert the desired pharmacological effect. In both drug discovery and development, prodrugs have become an established tool for improving physicochemical, biopharmaceutical or pharmacokinetic properties of pharmacologically active agents. About 5-7% of drugs approved worldwide can be classified as prodrugs, and the implementation of a prodrug approach in the early stages of drug discovery is a growing trend. To illustrate the applicability of the prodrug strategy, this article describes the most common functional groups that are amenable to prodrug design, and highlights examples of prodrugs that are either launched or are undergoing human trials.
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            Significance analysis of time course microarray experiments.

            Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. By using our method, 7,409 genes are called significant at a 1% false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source edge software package.
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              Mass spectrometry in the structural analysis of flavonoids.

              Flavonoids are very common and widespread secondary plant metabolites. They have a wide range of biological and physiological activities and serve as chemotaxonomic marker compounds. Therefore, they have been extensively investigated both in the past and during recent years. The interest in them is still increasing. In the search for new compounds, and also in quality control, there is a need to have reliable methodology for the analysis of flavonoids. Mass spectrometry can make an invaluable contribution because of its high sensitivity, possibilities of coupling with liquid chromatography and the availability of powerful tandem mass spectrometric techniques. A review of currently available mass spectrometric methodology used in the structure elucidation of flavonoids is presented. Sample preparation, liquid chromatographic/mass spectrometric analysis and tandem mass spectrometric procedures for the characterization of flavonoid aglycones, O-glycosides, C-glycosides and acylated glycosides are considered. Copyright 2004 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Metabolites
                Metabolites
                metabolites
                Metabolites
                MDPI
                2218-1989
                16 March 2020
                March 2020
                : 10
                : 3
                : 111
                Affiliations
                [1 ]Natural Products & Food Research and Analysis (NatuRA), Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; anastasia.vanderauwera@ 123456uantwerpen.be (A.V.d.A.); sebastiaan.bijttebier@ 123456gmail.com (S.B.); nina.hermans@ 123456uantwerpen.be (N.H.); kenn.foubert@ 123456uantwerpen.be (K.F.)
                [2 ]Adrem Data Lab, Department of Mathematics—Computer Sciences, University of Antwerp, Middelheimlaan 1, 2020 Antwerp, Belgium; charlie.beirnaert@ 123456uantwerpen.be (C.B.); kris.laukens@ 123456uantwerpen.be (K.L.)
                Author notes
                [* ]Correspondence: laura.peeters@ 123456uantwerpen.be ; Tel.: +32-3265-9096
                Author information
                https://orcid.org/0000-0003-3007-2569
                https://orcid.org/0000-0002-8217-2564
                https://orcid.org/0000-0002-8288-4254
                https://orcid.org/0000-0003-3946-7313
                Article
                metabolites-10-00111
                10.3390/metabo10030111
                7142424
                32188118
                223945d2-97e9-4b4d-9616-4cc8330be9a0
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 05 February 2020
                : 12 March 2020
                Categories
                Article

                herniaria hirsuta,caryophyllaceae,saponins,urolithiasis,machine learning,dynamic metabolomics

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