An integrated strategy for in vivo metabolite profiling using high-resolution mass spectrometry based data processing techniques

An ongoing challenge of drug metabolite profiling is to detect and identify unknown or low-level metabolites in complex biological matrices. Here we present a generic strategy for metabolite detection using multiple accurate-mass-based data processing tools via the analysis of rat samples of two model drug candidates, AZD6280 and AZ12488024. First, the function of isotopic pattern recognition was proved to be highly effective in the detection of metabolites derived from [¹⁴C]-AZD6280 that possesses a distinct isotopic pattern. The metabolites revealed using this approach were in excellent qualitative correlation to those observed in radiochromatograms. Second, the effectiveness of accurate mass based untargeted data mining tools such as background subtraction, mass defect filtering, or a data mining package (MZmine) used for metabolomic analysis in detection of metabolites of [¹⁴C]-AZ12488024 in rat urine, feces, bile and plasma samples was examined and a total of 33 metabolites of AZ12488024 were detected. Among them, at least 16 metabolites were only detected by the aid of the data mining packages and not via radiochromatograms. New metabolic pathways such as S-oxidation and thiomethylation reactions occurring on the thiazole ring were proposed based on the processed data. The results of these experiments also demonstrated that accurate mass-based mass defect filtering (MDF) and data mining techniques used in metabolomics are complementary and can be valuable tools for delineating low-level metabolites in complex matrices. Furthermore, the application of distinct multiple data-mining algorithms in parallel, or in tandem, can be effective for rapidly profiling in vivo drug metabolites.     

An algorithm for thorough background subtraction from high-resolution LC/MS data: application to the detection of troglitazone metabolites in rat plasma, bile, and urine

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 (c) 2008 John Wiley & Sons, Ltd.     

Identification of urinary metabolites of imperatorin with a single run on an LC/Triple TOF system based on multiple mass defect filter data acquisition and multiple data mining techniques

The detection of drug metabolites, especially for minor metabolites, continues to be a challenge because of the complexity of biological samples. Imperatorin (IMP) is an active natural furocoumarin component originating from many traditional Chinese herbal medicines and is expected to be pursued as a new vasorelaxant agent. In the present study, a generic and efficient approach was developed for the in vivo screening and identification of IMP metabolites using liquid chromatography-Triple TOF mass spectrometry. In this approach, a novel on-line data acquisition method mutiple mass defect filter (MMDF) combined with dynamic background subtraction was developed to trace all probable urinary metabolites of IMP. Comparing with the traditionally intensity-dependent data acquisition method, MMDF method could give the information of low-level metabolites masked by background noise and endogenous components. Thus, the minor metabolites in complex biological matrices could be detected. Then, the sensitive and specific multiple data-mining techniques extracted ion chromatography, mass defect filter, product ion filter, and neutral loss filter were used for the discovery of IMP metabolites. Based on the proposed strategy, 44 phase I and 7 phase II metabolites were identified in rat urine after oral administration of IMP. The results indicated that oxidization was the main metabolic pathway and that different oxidized substituent positions had a significant influence on the fragmentation of the metabolites. Two types of characteristic ions at m/z 203 and 219 can be observed in the MS/MS spectra. This is the first study of IMP metabolism in vivo. The interpretation of the MS/MS spectra of these metabolites and the proposed metabolite pathway provide essential data for further pharmacological studies of other linear-type furocoumarins.     

Untargeted screening of unknown xenobiotics and potential toxins in plasma of poisoned patients using high-resolution mass spectrometry: Generation of xenobiotic fingerprint using background subtraction

A novel analytical workflow was developed and applied for the detection and identification of unknown xenobiotics in biological samples. High-resolution mass spectrometry (HRMS)-based data-independent MS^E acquisition was employed to record full scan MS and fragment spectral datasets of test and control samples. Then, an untargeted data-mining technique, background subtraction, was utilized to find xenobiotics present only in test samples. Structural elucidation of the detected xenobiotics was accomplished by database search, spectral interpretation, and/or comparison with reference standards. Application of the workflow to analysis of unknown xenobiotics in plasma samples collected from four poisoned patients led to generation of xenobiotic profiles, which were regarded as xenobiotic fingerprints of the individual samples. Among 19 xenobiotics detected, 11 xenobiotics existed in a majority of the patients' plasma samples, thus were considered as potential toxins. The follow-up database search led to the tentative identification of azithromycin (X5), α-chaconine (X9) and penfluridol (X12). The identity of X12 was further confirmed with its reference standard. In addition, one xenobiotic component (Y5) was tentatively identified as a penfluridol metabolite. The remaining unidentified xenobiotics listed in the xenobiotic fingerprints can be further characterized or identified in retrospective analyses after their spectral data and/or reference compounds are available. This HRMS-based workflow may have broad applications in the detection and identification of unknown xenobiotics in individual biological samples, such as forensic and toxicological analysis and sport enhancement drug screening.     

Rapid detection and structural characterization of verapamil metabolites in rats by UPLC–MSE and UNIFI platform

High-resolution mass spectrometry (HRMS) is an important technology for studying biotransformations of drugs in biological systems. In order to process complex HRMS data, bioinformatics, including data-mining techniques for identifying drug metabolites from liquid chromatography/high-resolution mass spectrometry (LC/HRMS) or multistage mass spectrometry (MSⁿ) datasets as well as elucidating the detected metabolites’ structure by spectral interpretation software, are important tools. Data-mining technologies have widely been used in drug metabolite identification, including mass defect filters, product ion filters, neutral-loss filters, control sample comparisons and extracted ion chromatographic analysis. However, the metabolites identified by current different technologies are not the same, indicating the importance of technique integration for efficient and complete identification of metabolic products. In this study, a universal, high-throughput workflow for identifying and verifying metabolites by applying the drug metabolite identification software UNIFI is reported, to study the biotransformation of verapamil in rats. A total of 71 verapamil metabolites were found in rat plasma, urine and faeces, including two metabolites that have not been reported in the literature. Phase I metabolites of verapamil were identified as N-demethylation, O-demethylation, N-dealkylation and oxidation and dehydrogenation metabolites; phase II metabolites were mainly glucuronidation and sulfate conjugates, indicating that UNIFI software could be effective and valuable in identifying drug metabolites.     

Mass defect filter technique and its applications to drug metabolite identification by high‐resolution mass spectrometry

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.     

Urinary exposure marker discovery for toxicants using ultra-high pressure liquid chromatography coupled with Orbitrap high resolution mass spectrometry and three untargeted metabolomics approaches

Human biomonitoring is the assessment of actual internal contamination of chemicals by measuring exposure markers, chemicals or their metabolites, in human urine, blood, serum, and other body fluids. However, the metabolism of chemicals within an organism is extremely complex. Therefore, the identification of metabolites is often difficult and laborious. Several untargeted metabolomics methods have been developed to perform objective searching/filtering of accurate-mass-based LC-MS data to facilitate metabolite identification. In this study, three metabolomics data processing approaches were used for chemical exposure marker discovery in urine with an LTQ-Orbitrap high-resolution mass spectrometry (HRMS) dataset; di-isononyl phthalate (DINP) was used as an example. The data processing techniques included the SMAIT, mass defect filtering (MDF), and XCMS Online. Sixteen, 83, and 139 probable DINP metabolite signals were obtained using the SMAIT, MDF, and XCMS procedures, respectively. Fourteen probable metabolite signals mined simultaneously by the three metabolomics approaches were confirmed as DINP metabolites by structural information provided by LC-MS/MS. Among them, 13 probable metabolite signals were validated as exposure-related markers in a rat model. Six (m/z 319.155, 361.127, 373.126, 389.157, 437.112 and 443.130) of the 13 exposure-related DINP metabolite signals have not previously been reported in the literature. Our data indicate that SMAIT provided an efficient method to discover effectively and systematically urinary exposure markers of toxicant. The DINP metabolism information can provide valuable information for further investigations of DINP toxicity, toxicokinetics, exposure assessment, and human health effects.     

Mass defect profiles of biological matrices and the general applicability of mass defect filtering for metabolite detection

Recent examples have demonstrated that the high-resolution liquid chromatography/mass spectrometry (LC/MS)-based mass defect filtering (MDF) technique was effective in selectively detecting drug metabolites regardless of their molecular weights or fragmentation patterns. The main objective of the current study was to evaluate the general applicability of MDF for drug metabolite detection in typical biological matrices. Mass defect profiles of commonly used biological matrices including plasma, urine, bile, and feces were obtained using an LTQ FT mass spectrometer and were compared with those of 115 commonly prescribed drugs. The mass defect profiles were presented as two-dimensional Y-X plots with the determined mass defects of components on the y-axis versus the corresponding m/z values on the x-axis. The mass defect profiles of the matrices appeared to be similar for each type of matrix across species, yet marked differences were apparent between matrices of a given species. The mass defect profiles of components in plasma, bile, and feces showed significant separation from most of the 115 drugs. The mass defect profiles of urine did not show such clean separation from that of the 115 drugs. The results suggest that MDF has a broad applicability for selective detection of drug metabolites in plasma, bile and feces although the selectivity for detecting urinary drug metabolites is not as good as in the other matrices. In addition, the mass defect profiles of the biological matrices allow for prediction of the effectiveness of MDF for certain applications, and for designing specific MDF windows for selective detection of drug metabolites.     

Nontargeted screening of veterinary drugs and their metabolites in milk based on mass defect filtering using liquid chromatography–high-resolution mass spectrometry

The development of nontargeted screening strategy for veterinary drugs and their metabolites is very important for food safety. In this study, a nontargeted screening strategy was developed to find the potentially hazardous substances based on mass defect filtering (MDF) using liquid chromatography–high-resolution mass spectrometry. First, the drug metabolites of 112 veterinary drugs from seven classes of antimicrobials were predicted. Second, three MDF models were established, including the traditional rectangular MDF, the enhanced parallelogram MDF, and the polygonal MDF. Finally, the strategy was applied to nontargeted screening of veterinary drugs in 36 milk samples. The polygonal MDF model based on the distribution area of parent drugs and their metabolites showed a better filtering effect. After removing food components and performing MDF, about 10% of the substances remained, and four veterinary drugs and six drug metabolites were discovered and identified, showing the effectiveness of this strategy. The nontargeted screening strategy can rapidly remove interfering substances and find the suspected compounds. It can also be used for nontargeted screening of veterinary drugs and their metabolites in other food matrices.     

Development of the first metabolite-based LC-MS<Superscript><Emphasis Type="Italic">n</Emphasis></Superscript> urine drug screening procedure-exemplified for antidepressants

In contrast to GC-MS libraries, currently available LC-MS libraries for toxicological detection contain besides parent drugs only some main metabolites limiting their applicability for urine screening. Therefore, a metabolite-based LC-MS ⁿ screening procedure was developed and exemplified for antidepressants. The library was built up with MS² and MS³ wideband spectra using an LXQ linear ion trap with electrospray ionization in the positive mode and full-scan information-dependent acquisition. Pure substance spectra were recorded in methanolic solution and metabolite spectra in urine from rats after administration of the corresponding drugs. After identification, the metabolite spectra were added to the library. Various drugs and metabolites could be sufficiently separated. Recovery, process efficiency, matrix effects, and limits of detection for selected drugs were determined using protein precipitation. Automatic data evaluation was performed using ToxID and SmileMS software. The library consists of over 700 parent compounds including 45 antidepressants, over 1,600 metabolites, and artifacts. Protein precipitation led to sufficient results for sample preparation. ToxID and SmileMS were both suitable for target screening with some pros and cons. In our study, only SmileMS was suitable for untargeted screening being not limited to precursor selection. The LC-MS ⁿ method was suitable for urine screening as exemplified for antidepressants. It also allowed detecting unknown compounds based on known fragment structures. As ion suppression can never be excluded, it is advantageous to have several targets per drug. Furthermore, the detection of metabolites confirms the body passage. The presented LC-MS ⁿ method complements established GC-MS or LC-MS procedures in the authors’ lab.     

Metabolic N-Dealkylation and N-Oxidation as Elucidators of the Role of Alkylamino Moieties in Drugs Acting at Various Receptors

Metabolic reactions that occur at alkylamino moieties may provide insight into the roles of these moieties when they are parts of drug molecules that act at different receptors. N-dealkylation of N,N-dialkylamino moieties has been associated with retaining, attenuation or loss of pharmacologic activities of metabolites compared to their parent drugs. Further, N-dealkylation has resulted in clinically used drugs, activation of prodrugs, change of receptor selectivity, and providing potential for developing fully-fledged drugs. While both secondary and tertiary alkylamino moieties (open chain aliphatic or heterocyclic) are metabolized by CYP450 isozymes oxidative N-dealkylation, only tertiary alkylamino moieties are subject to metabolic N-oxidation by Flavin-containing monooxygenase (FMO) to give N-oxide products. In this review, two aspects will be examined after surveying the metabolism of representative alkylamino-moieties-containing drugs that act at various receptors (i) the pharmacologic activities and relevant physicochemical properties (basicity and polarity) of the metabolites with respect to their parent drugs and (ii) the role of alkylamino moieties on the molecular docking of drugs in receptors. Such information is illuminative in structure-based drug design considering that fully-fledged metabolite drugs and metabolite prodrugs have been, respectively, developed from N-desalkyl and N-oxide metabolites.     

Metabolite identification of the antimalarial naphthoquine using liquid chromatography–tandem high‐resolution mass spectrometry in combination with multiple data‐mining tools

Naphthoquine (NQ) is one of important partner drugs of artemisinin‐based combination therapy (ACT), which is recommended for the treatment of uncomplicated Plasmodium falciparum. NQ shows a high cure rate after a single oral administration. It is absorbed quickly (time to peak concentration 2–4 h) and has a long elimination half‐life (255 h). However, the metabolism of NQ has not been clarified. In this work, the metabolite profiling of NQ was studied in six liver microsomal incubates (human, cynomolgus monkey, beagle dog, mini pig, rat and CD1 mouse), seven recombinant CYP enzymes (1A2, 2B6, 2C8, 2C9, 2C19, 2D6 and 3A4) and rat (plasma, urine, bile and feces) using liquid chromatography tandem high‐resolution LTQ‐Orbitrap mass spectrometry (HRMSⁿ) in conjunction with online hydrogen/deuterium exchange. The biological samples were pretreated by protein precipitation and solid‐phase extraction. For data processing, multiple data‐mining tools were applied in tandem, i.e. background subtraction and followed by mass defect filter. NQ metabolites were characterized by accurate MS/MS fragmentation characteristics, the hydrogen/deuterium exchange data and cLogP simulation. As a result, five phase I metabolites (M1–M5) of NQ were characterized for the first time. Two metabolic pathways were involved: hydroxylation and N‐oxidation. This study demonstrates that LC‐HRMSⁿ in combination with multiple data‐mining tools in tandem can be a valuable analytical strategy for rapid metabolite profiling of drugs.     

A retention‐time‐shift‐tolerant background subtraction and noise reduction algorithm (BgS‐NoRA) for extraction of drug metabolites in liquid chromatography/mass spectrometry data from biological matrices

A retention‐time‐shift‐tolerant background subtraction and noise reduction algorithm (BgS‐NoRA) is implemented using the statistical programming language R to remove non‐drug‐related ion signals from accurate mass liquid chromatography/mass spectrometry (LC/MS) data. The background‐subtraction part of the algorithm is similar to a previously published procedure (Zhang H and Yang Y. J. Mass Spectrom. 2008, 43: 1181–1190). The noise reduction algorithm (NoRA) is an add‐on feature to help further clean up the residual matrix ion noises after background subtraction. It functions by removing ion signals that are not consistent across many adjacent scans. The effectiveness of BgS‐NoRA was examined in biological matrices by spiking blank plasma extract, bile and urine with diclofenac and ibuprofen that have been pre‐metabolized by microsomal incubation. Efficient removal of background ions permitted the detection of drug‐related ions in in vivo samples (plasma, bile, urine and feces) obtained from rats orally dosed with ¹⁴C‐loratadine with minimal interference. Results from these experiments demonstrate that BgS‐NoRA is more effective in removing analyte‐unrelated ions than background subtraction alone. NoRA is shown to be particularly effective in the early retention region for urine samples and middle retention region for bile samples, where the matrix ion signals still dominate the total ion chromatograms (TICs) after background subtraction. In most cases, the TICs after BgS‐NoRA are in excellent qualitative correlation to the radiochromatograms. BgS‐NoRA will be a very useful tool in metabolite detection and identification work, especially in first‐in‐human (FIH) studies and multiple dose toxicology studies where non‐radio‐labeled drugs are administered. Data from these types of studies are critical to meet the latest FDA guidance on Metabolite in Safety Testing (MIST). Copyright © 2009 John Wiley & Sons, Ltd.     

Metabolite profiling to discriminate different species and genus from thistles in Korea using liquid chromatography with quadrupole time‐of‐flight mass spectrometry

This study was designed to classify and identify closely related thistle species in the genus Cirsium, as well as Carduus and Cephalonoplos species, which are also thistles. The comprehensive and untargeted metabolite profiles of nine Korean thistles were determined using ultra high performance liquid chromatography combined with hybrid quadrupole time‐of‐flight mass spectrometry. The difference in metabolite profiles among species was explored using principal component analysis and hierarchical clustering analysis. The significantly different metabolites (Bonferroni‐corrected P‐value < 0.001) were used to construct a partial least squares discriminant analysis model to predict the species of thistle. Nine species were successfully classified using a partial least squares discriminant analysis model and confirmed using a cross‐validation method. Species with similar features were grouped based on unique patterns in variable clusters. The present study suggests that liquid chromatography with quadrupole time‐of‐flight mass spectrometry untargeted metabolomic profiling with chemometric analysis is an efficient and powerful tool for discriminating between different species of medicinal herbs.     

A strategy for identification of drug metabolites from dried blood spots using triple-quadrupole/linear ion trap hybrid mass spectrometry

Discovery stage studies that address issues of absorption, distribution, metabolism and excretion (ADME) are vital for lead optimization resulting in new drug candidates. Often pharmacokinetics (PK) is assessed in these experiments without regard for the metabolism of the compound or the potential for metabolites to circulate in vivo. This work presents a strategy for drug level determination and detection of metabolites using dried blood spots for sample collection. Initially, metabolites are detected from microsomal incubations and characterized using tandem mass spectrometry. Data dependent enhanced MS and enhanced product ion (EMS-EPI) scanning with dynamic background subtraction was used on a hybrid quadruple linear ion trap mass spectrometer. On-the-fly background subtraction greatly improved the detection of metabolites. These data were used to build a multiple reaction monitoring (MRM) method for the parent and metabolites. MRM-EPI scanning was used to analyze the extracted dried blood spots from the PK study. Circulating metabolites were detected using MRM and their identities confirmed on the basis of fragment ion spectra collected simultaneously. The use of dried blood spots provides a means for re-analysis of PK samples for metabolite identification without the need for complex sample storage and preparation. Both parent compound and metabolite information can be collected in these studies, resulting in a savings of time and resources.     

A Survey of Orbitrap All Ion Fragmentation Analysis Assessed by an R MetaboList Package to Study Small-Molecule Metabolites

Leukemia cell and melanoma tumor tissue extracts were studied for small (mostly m/z?<?250) polar metabolites by LC-ESI-HRMSⁿ analysis powered by a hybrid Quadrupole-Orbitrap. MS data were simultaneously acquired in fast polarity switching mode operating in MS¹ and MS/MS (All Ion Fragmentation, AIF) full-scan analyses at high mass resolution. Positive metabolite assignments were achieved by AIF analysis considering at least two characteristic transitions. Targeted metabolite profiling was achieved by the relative quantification of 18 metabolites through spiking of their respective deuterated counterparts. Manual data processing of MS¹ and AIF scans were compared for the accurate determination of natural metabolites and their deuterated analogs by chromatographic alignment and peak area integration. Evaluation of manual and automated (MetaboList R package) AIF data processing yielded comparable results. The versatility of AIF analysis also enabled the untargeted metabolite profiling of leukemia and melanoma samples in which 22 and 53 compounds were, respectively, identified outside those studied by labeling. The main limitation of this method was that low abundance metabolites with scan rates below 8 scans/peak could not be accurately quantified by AIF analysis. The combination of AIF analysis with MetaboList R package represents an opportunity to move towards automated, faster, and more global metabolomics approaches supported by an entirely flexible open source data processing platform freely available from Comprehensive R Archive Network (CRAN, https://CRAN.R-project.org/package=MetaboList).     

In silico and in vitro metabolism studies support identification of designer drugs in human urine by liquid chromatography/quadrupole-time-of-flight mass spectrometry

Human phase I metabolism of four designer drugs, 2-desoxypipradrol (2-DPMP), 3,4-dimethylmethcathinone (3,4-DMMC), α-pyrrolidinovalerophenone (α-PVP), and methiopropamine (MPA), was studied using in silico and in vitro metabolite prediction. The metabolites were identified in drug abusers’ urine samples using liquid chromatography/quadrupole-time-of-flight mass spectrometry (LC/Q-TOF/MS). The aim of the study was to evaluate the ability of the in silico and in vitro methods to generate the main urinary metabolites found in vivo. Meteor 14.0.0 software (Lhasa Limited) was used for in silico metabolite prediction, and in vitro metabolites were produced in human liver microsomes (HLMs). 2-DPMP was metabolized by hydroxylation, dehydrogenation, and oxidation, resulting in six phase I metabolites. Six metabolites were identified for 3,4-DMMC formed via N-demethylation, reduction, hydroxylation, and oxidation reactions. α-PVP was found to undergo reduction, hydroxylation, dehydrogenation, and oxidation reactions, as well as degradation of the pyrrolidine ring, and seven phase I metabolites were identified. For MPA, the nor-MPA metabolite was detected. Meteor software predicted the main human urinary phase I metabolites of 3,4-DMMC, α-PVP, and MPA and two of the four main metabolites of 2-DPMP. It assisted in the identification of the previously unreported metabolic reactions for α-PVP. Eight of the 12 most abundant in vivo phase I metabolites were detected in the in vitro HLM experiments. In vitro tests serve as material for exploitation of in silico data when an authentic urine sample is not available. In silico and in vitro designer drug metabolism studies with LC/Q-TOF/MS produced sufficient metabolic information to support identification of the parent compound in vivo.

Evaluation of ion mobility in capillary electrophoresis coupled to mass spectrometry for the identification in metabolomics

Currently, feature annotation remains one of the main challenges in untargeted metabolomics. In this context, the information provided by high-resolution mass spectrometry (HRMS) in addition to accurate mass can improve the quality of metabolite annotation, and MS/MS fragmentation patterns are widely used. Accurate mass and a separation index, such as retention time or effective mobility (μ_eff), in chromatographic and electrophoretic approaches, respectively, must be used for unequivocal metabolite identification. The possibility of measuring collision cross-section (CCS) values by using ion mobility (IM) is becoming increasingly popular in metabolomic studies thanks to the new generation of IM mass spectrometers. Based on their similar separation mechanisms involving electric field and the size of the compounds, the complementarity of ^DTCCS_N2 and μ_eff needs to be evaluated. In this study, a comparison of ^DTCCS_N2 and μ_eff was achieved in the context of feature identification ability in untargeted metabolomics by capillary zone electrophoresis (CZE) coupled with HRMS. This study confirms the high correlation of ^DTCCS_N2 with the mass of the studied metabolites as well as the orthogonality between accurate mass and μ_eff, making this combination particularly interesting for the identification of several endogenous metabolites. The use of IM-MS remains of great interest for facilitating the annotation of neutral metabolites present in the electroosmotic flow (EOF) that are poorly or not separated by CZE.     

Generic dealkylation: a tool for increasing the hit‐rate of metabolite rationalization,and automatic customization of mass defect filters

The use of exact mass liquid chromatography/mass spectrometry (LC/MS) for drug metabolism studies has increased significantly in recent years. Firstly, exact mass measurements facilitate identification of standard biotransformations through the use of narrow window extracted ion chromatograms, which are typically highly selective relative to signals from matrix or dosing components. Secondly, novel metabolites can be characterized via elemental formula calculations and high‐resolution product ion spectra. Furthermore, biological background ions can be removed by the use of mass defect filters (MDFs) which filter out ions based on the decimal component of their m/z value. Here, we describe an approach which we term ‘generic dealkylation’ that in association with other data interpretation tools adds significant value to the assignment process. Generic dealkylation uses a simple strategy to identify those bonds which have the potential to be cleaved by metabolism. In combination with standard phase 1 and phase 2 biotransformations, this allows creation of a chemically intelligent MDF which balances the need to remove matrix background with the requirement of avoiding filtering true metabolites. Secondly, generic dealkylation increases the hit‐rate at which non‐trivial (i.e. not covered by simple phase 1 oxidations or direct phase 2 conjugations) metabolites can be directly rationalized. The value of the generic dealkylation approach is illustrated by its application to determination of in vitro metabolic routes for two commercial drugs, nefazodone and indinavir. Copyright © 2009 John Wiley & Sons, Ltd.     

Detection and Identification of Dizocilpine and its Major Urinary Metabolites in the Horse: A Preliminary Report

Dizocilpine ([+]-10,11-dihydro-5-methyl-5H-dibenzo[a,d]cyclohepten-5,10-imine), is a potent and selective NMDA (N-methyl-D-aspartate) receptor antagonist, which acts by blocking receptor ion channels. Dizocilpine is pharmacologically related to ketamine and phencyclidine; as such, it has the potential to affect behavior and performance in horses, with particular efficacy at lower concentrations. We now report development of a sensitive method for the detection of dizocilpine and preliminary characterization of its urinary metabolites in the horse. Dizocilpine (MW 221) readily produces a protonated species [M+H]⁺ in formic acid, and yields a m/z 205 product ion in Multiple Reaction Monitoring (MRM), allowing highly sensitive detection of parent drug. The 17 AMU loss most likely represents an unusual loss of CH₅ from the exocyclic methyl group. No unchanged dizocilpine was identified in unhydrolysed urine, and the presence of hydroxymethyl and carboxydizocilpine glucuronide metabolites were supported by observation of m/z 414→238 and 428→235 transitions. Urine samples from horses dosed with dizocilpine (0.0132 and 0.0656 mg kg^?1, iv) were hydrolysed with glucuronidase and were found to contain dizocilpine and OH-dizocilpine. Tentatively identified phase I post-hydrolysis compounds include dizocilpine itself, an hydroxymethyl metabolite, two ring-hydroxylated metabolites, a di-hydroxy metabolite, and a carboxy-dizocilpine metabolite. Corresponding Phase II glucuronidated metabolites were also identified as well as a number of combination metabolites and a posssible n-glucuronide metabolite for a total of at least six identifiable urinary glucuronide metabolites. Among the phase I metabolites, the hydroxymethyl metabolite apparently predominated, especially at the 0.0132 mg kg^?1 dose. The goal of this research was to identify a target analyte for dizocilpine in post-administration equine urine, so that work may begin on development of a forensically validated qualitative method for this target analyte. Given the likelihood that the doses of dizocilpine used in attempts to influence the behavior or performance of horses, either alone or in combination with other agents, are expected to be in the order of 0.02 mg kg^?1 or less, these results suggest selection of the phase I hydroxymethyl metabolite of dizocilpine as the optimal target analyte for regulatory control of dizocilpine in performance horses.