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Metabolite Identification (metabolite + identification)
Selected AbstractsImportant roles of the hyphenated HPLC-DAD-MS-SPE-NMR technique in metabonomicsMAGNETIC RESONANCE IN CHEMISTRY, Issue S1 2009Huiru Tang Abstract Metabolite identification is a key step for metabonomics study. A fully automated hyphenation of HPLC-diode-array detector (DAD) mass spectrometry (MS) solid phase extraction (SPE),NMR spectroscopy (HPLC-DAD-MS-SPE-NMR) is one of the most efficient methods to determine the structure of a given unknown metabolite in a complex mixture (metabonome) and hence represents one of the most important analytical techniques for the further development of metabonomics. In this review, some recent applications of this technique in identifying novel and trace metabolites in plant extracts and drug metabolism have been discussed. Modification of this hyphenated technique, enabling multiple trappings of strong polar metabolites for biofluids, needs further development. Copyright © 2009 John Wiley & Sons, Ltd. [source] Metabolite identification of a new antitumor agent icotinib in rats using liquid chromatography/tandem mass spectrometryRAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 14 2008Zhongmin Guan Icotinib, 4-[(3-ethynylphenyl)amino]-6,7-benzo-12-crown-4-quinazoline, is a new antitumor agent. The metabolic pathway of icotinib in rats was studied using liquid chromatography/tandem mass spectrometry (LC/MSn) analysis. Full scan and selected ion monitoring modes were used to profile the possible metabolites of icotinib in rat urine, feces and bile samples. Four phase I metabolites (M1,M4) and two phase II metabolites (M5, M6) were detected and characterized. Multiple-stage mass spectrometry and nuclear magnetic resonance (NMR) spectrometry were employed to elucidate structures of metabolites. Icotinib was metabolized to open the crown ether ring to form the main phase I metabolites. During metabolism, a reactive metabolite was formed. Using semicarbazide as a trapping agent, an intermediate arising from opening of the crown ether ring was detected as an aldehyde product by LC/MS/MS. These data indicated that ring opening of the crown ether was triggered by hydroxylation at the 8,-position of the ring to form a hemiacetal intermediate, which was further oxidized or reduced. Finally, the metabolic pathway of icotinib in rats was proposed. Copyright © 2008 John Wiley & Sons, Ltd. [source] Metabolite identification of small interfering RNA duplex by high-resolution accurate mass spectrometryRAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 12 2008Yan Zou On-line liquid chromatography/electrospray ionization high-resolution mass spectrometry (LC/ESI-HRMS) using an LTQ-Orbitrap mass spectrometer was employed to investigate the metabolite profiles of a model siRNA duplex designated HBV263. The HBV263 duplex was incubated in rat and human serum and liver microsomes in vitro. The siRNA drug and its metabolites were then extracted using a liquid-liquid extraction followed by solid-phase extraction (LLE-SPE), and analyzed by LC/ESI-MS. High-resolution accurate mass data enabled differentiation between two possible metabolite sequences with a monoisotopic molecular mass difference of less than 1,Da. ProMass deconvolution software was used to provide semi-automated data processing. In vitro serum and liver microsome incubation samples afforded different metabolite patterns: the antisense strand of the duplex was degraded preferentially in rat and human serum, while the sense strand of the duplex was less stable in rat and human liver microsomes. Copyright © 2008 John Wiley & Sons, Ltd. [source] The autocorrelation matrix probing biochemical relationships after metabolic fingerprinting with CEELECTROPHORESIS, Issue 7 2009Santiago Angulo Abstract Fingerprinting together with statistical analysis is often employed to compare samples in metabonomic studies of a disease. Correlation algorithms can aid by extracting information based on the variation patterns of key metabolites. This information can be linked to metabolite identification or to specific up/down-regulated biochemical pathways. Matlab-based software employing the Pearson's correlation algorithm was applied to urine electropherograms from 20 mice infected with the schistosoma parasite. The fingerprints were the sum of electropherograms analysed with normal and reverse polarity, in two different modes MEKC and CZE and with two different capillaries (uncoated and polyacrylamide coated) to provide a broad picture of the samples. Hippurate, a metabolite that was depleted in the infected group and is present in both polarities, was chosen as a test variable; it correlated with itself to a p value of <0.000. Phenylacetylglycine, a metabolite shown as over expressed in the disease, was positively correlated to three metabolites in its same pathway with a correlation coefficient of 0.7 and p<0.000 to phenylalanine, 0.7 and p<0.000 to 2-hydroxyphenylacetic and 0.55 and p<0.003 to phenylacetate. The study shows that the autocorrelation matrix is able to provide extra information from data files acquired by CE analyses. It underlined an up-regulated metabolic path by association in the schistosoma infection model. [source] Pharmaceutical metabolites in the environment: Analytical challenges and ecological risks,ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 12 2009Mary D. Celiz Abstract The occurrence of human and veterinary pharmaceuticals in the environment has been a subject of concern for the past decade because many of these emerging contaminants have been shown to persist in soil and water. Although recent studies indicate that pharmaceutical contaminants can pose long-term ecological risks, many of the investigations regarding risk assessment have only considered the ecotoxicity of the parent drug, with very little attention given to the potential contributions that metabolites may have. The scarcity of available environmental data on the human metabolites excreted into the environment or the microbial metabolites formed during environmental biodegradation of pharmaceutical residues can be attributed to the difficulty in analyzing trace amounts of previously unknown compounds in complex sample matrices. However, with the advent of highly sensitive and powerful analytical instrumentations that have become available commercially, it is likely that an increased number of pharmaceutical metabolites will be identified and included in environmental risk assessment. The present study will present a critical review of available literature on pharmaceutical metabolites, primarily focusing on their analysis and toxicological significance. It is also intended to provide an overview on the recent advances in analytical tools and strategies to facilitate metabolite identification in environmental samples. This review aims to provide insight on what future directions might be taken to help scientists in this challenging task of enhancing the available data on the fate, behavior, and ecotoxicity of pharmaceutical metabolites in the environment. [source] Mass defect filter technique and its applications to drug metabolite identification by high-resolution mass spectrometryJOURNAL OF MASS SPECTROMETRY (INCORP BIOLOGICAL MASS SPECTROMETRY), Issue 7 2009Haiying Zhang Abstract Identification of drug metabolites by liquid chromatography/mass spectrometry (LC/MS) involves metabolite detection in biological matrixes and structural characterization based on product ion spectra. Traditionally, metabolite detection is accomplished primarily on the basis of predicted molecular masses or fragmentation patterns of metabolites using triple-quadrupole and ion trap mass spectrometers. Recently, a novel mass defect filter (MDF) technique has been developed, which enables high-resolution mass spectrometers to be utilized for detecting both predicted and unexpected drug metabolites based on narrow, well-defined mass defect ranges for these metabolites. This is a new approach that is completely different from, but complementary to, traditional molecular mass- or MS/MS fragmentation-based LC/MS approaches. This article reviews the mass defect patterns of various classes of drug metabolites and the basic principles of the MDF approach. Examples are given on the applications of the MDF technique to the detection of stable and chemically reactive metabolites in vitro and in vivo. Advantages, limitations, and future applications are also discussed on MDF and its combinations with other data mining techniques for the detection and identification of drug metabolites. Copyright © 2009 John Wiley & Sons, Ltd. [source] An algorithm for thorough background subtraction from high-resolution LC/MS data: application to the detection of troglitazone metabolites in rat plasma, bile, and urineJOURNAL OF MASS SPECTROMETRY (INCORP BIOLOGICAL MASS SPECTROMETRY), Issue 9 2008Haiying Zhang Abstract Interferences from biological matrices remain a major challenge to the in vivo detection of drug metabolites. For the last few decades, predicted metabolite masses and fragmentation patterns have been employed to aid in the detection of drug metabolites in liquid chromatography/mass spectrometry (LC/MS) data. Here we report the application of an accurate mass-based background-subtraction approach for comprehensive detection of metabolites formed in vivo using troglitazone as an example. A novel algorithm was applied to check all ions in the spectra of control scans within a specified time window around an analyte scan for potential background subtraction from that analyte spectrum. In this way, chromatographic fluctuations between control and analyte samples were dealt with, and background and matrix-related signals could be effectively subtracted from the data of the analyte sample. Using this algorithm with a ± 1.0 min control scan time window, a ± 10 ppm mass error tolerance, and respective predose samples as controls, troglitazone metabolites were reliably identified in rat plasma and bile samples. Identified metabolites included those reported in the literature as well as some that had not previously been reported, including a novel sulfate conjugate in bile. In combination with mass defect filtering, this algorithm also allowed for identification of troglitazone metabolites in rat urine samples. With a generic data acquisition method and a simple algorithm that requires no presumptions of metabolite masses or fragmentation patterns, this high-resolution LC/MS-based background-subtraction approach provides an efficient alternative for comprehensive metabolite identification in complex biological matrices. Copyright © 2008 John Wiley & Sons, Ltd. [source] Metabolites of an orally active antimicrobial prodrug, 2,5-bis(4-amidinophenyl)furan-bis- O -methylamidoxime, identified by liquid chromatography/tandem mass spectrometryJOURNAL OF MASS SPECTROMETRY (INCORP BIOLOGICAL MASS SPECTROMETRY), Issue 4 2004Lian Zhou Abstract DB75 (2,5-bis(4-amidinophenyl)furan) is a promising antimicrobial agent against African trypanosomiasis and Pneumocystis carinii pneumonia. However, it suffers from poor oral activity in rodent models for both infections. In contrast, a novel prodrug of DB75, 2,5-bis(4-amidinophenyl)furan-bis- O -methylamidoxime (DB289), has excellent oral activity. DB289 is currently undergoing clinical investigation as a candidate drug to treat primary stage African trypanosomiasis and Pneumocystis carinii pneumonia. In this study, metabolites of DB289 formed after incubation with freshly isolated rat hepatocytes were characterized using liquid chromatography/ion trap mass spectrometry. Administration of DB289 and octadeuterated DB289 in a 1 : 1 mixture greatly facilitated metabolite identification by providing isotope patterns with twin ions separated by 8 m/z units in the ratio 1 : 1, in the extracted ion chromatograms of molecular ions and in the product ion mass spectra of metabolites. Ten metabolites were identified. Series of O -demethylations and N -dehydroxylations led to the metabolic activation of DB289 to DB75 with the production of four intermediate phase I metabolites. Phase II glucuronidation and sulfation led to the formation of four glucuronide and one sulfate metabolites. Copyright © 2004 John Wiley & Sons, Ltd. [source] Characterization of in vitro and in vivo metabolic pathways of the investigational anticancer agent, 2-methoxyestradiolJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 7 2007Nehal J. Lakhani Abstract The aim of this study was to characterize the metabolic pathways of 2-methoxyestradiol (2ME2), an investigational anticancer drug. In vitro metabolism studies were performed by incubation of 2ME2 with human liver microsomes under various conditions and metabolite identification was performed using liquid chromatography-tandem mass spectrometry. In microsomal mixtures, four major oxidative metabolites and two glucuronic acid conjugates were observed originating from 2ME2. Human liver S9 protein fraction was used to screen for in vitro sulfation but no prominent conjugates were observed. The total hepatic clearance as estimated using the well-stirred model was approximately 712 mL/min. In vivo metabolism, assessed using 24-h collections of urine from cancer patients treated with 2ME2 revealed that <0.01% of the total administered dose of 2ME2 is excreted unchanged in urine and about 1% excreted as glucuronides. Collectively, this suggests that glucuronidation and subsequent urinary excretion are elimination pathways for 2ME2. © 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 96: 1821,1831, 2007 [source] Analytical strategies for identifying drug metabolitesMASS SPECTROMETRY REVIEWS, Issue 3 2007Chandra Prakash Abstract With the dramatic increase in the number of new chemical entities (NCEs) arising from combinatorial chemistry and modern high-throughput bioassays, novel bioanalytical techniques are required for the rapid determination of the metabolic stability and metabolites of these NCEs. Knowledge of the metabolic site(s) of the NCEs in early drug discovery is essential for selecting compounds with favorable pharmacokinetic credentials and aiding medicinal chemists in modifying metabolic "soft spots". In development, elucidation of biotransformation pathways of a drug candidate by identifying its circulatory and excretory metabolites is vitally important to understand its physiological effects. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have played an invaluable role in the structural characterization and quantification of drug metabolites. Indeed, liquid chromatography (LC) coupled with atmospheric pressure ionization (API) MS has now become the most powerful tool for the rapid detection, structure elucidation, and quantification of drug-derived material within various biological fluids. Often, however, MS alone is insufficient to identify the exact position of oxidation, to differentiate isomers, or to provide the precise structure of unusual and/or unstable metabolites. In addition, an excess of endogenous material in biological samples often suppress the ionization of drug-related material complicating metabolite identification by MS. In these cases, multiple analytical and wet chemistry techniques, such as LC-NMR, enzymatic hydrolysis, chemical derivatization, and hydrogen/deuterium-exchange (H/D-exchange) combined with MS are used to characterize the novel and isomeric metabolites of drug candidates. This review describes sample preparation and introduction strategies to minimize ion suppression by biological matrices for metabolite identification studies, the application of various LC-tandem MS (LC-MS/MS) techniques for the rapid quantification and identification of drug metabolites, and future trends in this field. © 2007 Wiley Periodicals, Inc., Mass Spec Rev [source] ,All-in-One' analysis for metabolite identification using liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry with collision energy switchingRAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 18 2005Mark Wrona The removal of bottlenecks in discovery stage metabolite identification studies is an ongoing challenge for the pharmaceutical industry. We describe the use of an ,All-in-One' approach to metabolite characterization that leverages the fast scanning and high mass accuracy of hybrid quadrupole time-of-flight mass spectrometry (QqToFMS) instruments. Full-scan MS and MS/MS data is acquired using collision energy switching without the preselection, either manually or in a data-dependent manner, of precursor ions. The acquisition of ,clean' MS/MS data is assisted by the use of ultrahigh-performance chromatography. Data acquired using this method can then be mined post-acquisition in a number of ways. These include using narrow window extracted ion chromatograms (nwXICs) for expected biotransformations, XICs for the product ions of the parent compound and/or expected modification of these product ions, and neutral loss chromatograms. This approach has the potential to be truly comprehensive for the determination of in vitro biotransformations in a drug discovery environment. Copyright © 2005 John Wiley & Sons, Ltd. [source] Increasing throughput and information content for in vitro drug metabolism experiments using ultra-performance liquid chromatography coupled to a quadrupole time-of-flight mass spectrometerRAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 6 2005Jose Castro-Perez The field of drug metabolism has been revolutionized by liquid chromatography/mass spectrometry (LC/MS) applications with new technologies such as triple quadrupoles, ion traps and time-of-flight (ToF) instrumentation. Over the years, these developments have often relied on the improvements to the mass spectrometer hardware and software, which has allowed users to benefit from lower levels of detection and ease-of-use. One area in which the development pace has been slower is in high-performance liquid chromatography (HPLC). In the case of metabolite identification, where there are many challenges due to the complex nature of the biological matrices and the diversity of the metabolites produced, there is a need to obtain the most accurate data possible. Reactive or toxic metabolites need to be detected and identified as early as possible in the drug discovery process, in order to reduce the very costly attrition of compounds in late-phase development. High-resolution, exact mass measurement plays a very important role in metabolite identification because it allows the elimination of false positives and the determination of non-trivial metabolites in a much faster throughput environment than any other standard current methodology available to this field. By improving the chromatographic resolution, increased peak capacity can be achieved with a reduction in the number of co-eluting species leading to superior separations. The overall enhancement in the chromatographic resolution and peak capacity is transferred into a net reduction in ion suppression leading to an improvement in the MS sensitivity. To investigate this, a number of in vitro samples were analyzed using an ultra-performance liquid chromatography (UPLC) system, with columns packed with porous 1.7,,m particles, coupled to a hybrid quadrupole time-of-flight (ToF) mass spectrometer. This technique showed very clear examples for fundamental gains in sensitivity, chromatographic resolution and speed of analysis, which are all important factors for the demands of today's HTS in discovery. Copyright © 2005 John Wiley & Sons, Ltd. [source] SyGMa: Combining Expert Knowledge and Empirical Scoring in the Prediction of MetabolitesCHEMMEDCHEM, Issue 5 2008Lars Ridder Dr. Abstract Predictions of potential metabolites based on chemical structure are becoming increasingly important in drug discovery to guide medicinal chemistry efforts that address metabolic issues and to support experimental metabolite screening and identification. Herein we present a novel rule-based method, SyGMa (Systematic Generation of potential Metabolites), to predict the potential metabolites of a given parent structure. A set of reaction rules covering a broad range of phase,1 and phase,2 metabolism has been derived from metabolic reactions reported in the Metabolite Database to occur in humans. An empirical probability score is assigned to each rule representing the fraction of correctly predicted metabolites in the training database. This score is used to refine the rules and to rank predicted metabolites. The current rule set of SyGMa covers approximately 70,% of biotransformation reactions observed in humans. Evaluation of the rule-based predictions demonstrated a significant enrichment of true metabolites in the top of the ranking list: while in total, 68,% of all observed metabolites in an independent test set were reproduced by SyGMa, a large part, 30,% of the observed metabolites, were identified among the top three predictions. From a subset of cytochrome P450 specific metabolites, 84,% were reproduced overall, with 66,% in the top three predicted phase,1 metabolites. A similarity analysis of the reactions present in the database was performed to obtain an overview of the metabolic reactions predicted by SyGMa and to support ongoing efforts to extend the rules. Specific examples demonstrate the use of SyGMa in experimental metabolite identification and the application of SyGMa to suggest chemical modifications that improve the metabolic stability of compounds. 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