An accurate-mass-based spectral-averaging isotope-pattern-filtering algorithm for extraction of drug metabolites possessing a distinct isotope pattern from LC-MS data

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).

Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS

Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.

MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS

To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS²). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC₅₀) values from the CompTox database and validated for 219 chemicals and 420 MS² spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC₅₀ values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC₅₀ values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or log K_OW.

Feasibility of establishing a veterinary marker to total residue in edible tissues with non-radiolabeled study using high-resolution mass spectrometry

Traditionally, in vivo metabolism and total residue studies in veterinary drug research were conducted using radiolabeled drug where information on metabolite profiles and marker residue to total residue ratio is obtained. The Veterinary International Conference on Harmonisation (VICH) guideline GL46 indicates that the metabolism and residue kinetics in food-producing animals may be documented by an alternative approach, one other than the traditional radiolabeled study. High-resolution mass spectrometry (HRMS) has been widely used in human pharmaceutical R&D from metabolite profiling and identification in early drug discovery to first-in-human (FIH) studies in development. Recent advances in data mining tools have greatly improved the metabolite profiling capability with HRMS. It is now routine to study metabolism using non-radiolabeled samples without missing any major metabolites. In the current paper, we explored the feasibility of conducting non-radiolabeled marker residue studies to obtain metabolism information using HRMS. Metabolite profiles of gamithromycin in edible tissues of sheep treated with 6 mg/kg body weight subcutaneous injections were obtained with HRMS. The semi-quantitative relationship between the level of gamithromycin and the total treatment-related residues was established by determining the percentages of extracted ion chromatograms for metabolites and parent compound residues in each tissue. Major components (gamithromycin and its metabolite, declad) were measured quantitatively using a validated liquid chromatography/tandem mass spectrometry (LC-MS/MS) method. Metabolite profiles in excreta were also obtained and the major components measured quantitatively with a LC-MS/MS method to ensure no major metabolite was missing. Combining previous knowledge of marker residue studies in cattle and swine, as well as an in vitro comparative metabolism study with metabolite data across various species, gamithromycin was designated as the marker residue in sheep edible tissues. The marker to total residue ratios were established using a combination of the semi-quantitative HRMS results and quantitative results with the major components: the marker residue and declad. The pros and cons of the HRMS method as well as the appropriate use of the method for marker residue studies are discussed.

Insight into chemical basis of traditional Chinese medicine based on the state-of-the-art techniques of liquid chromatography-mass spectrometry

Traditional Chinese medicine (TCM) has been an indispensable source of drugs for curing various human diseases. However, the inherent chemical diversity and complexity of TCM restricted the safety and efficacy of its usage. Over the past few decades, the combination of liquid chromatography with mass spectrometry has contributed greatly to the TCM qualitative analysis. And novel approaches have been continuously introduced to improve the analytical performance, including both the data acquisition methods to generate a large and informative dataset, and the data post-processing tools to extract the structure-related MS information. Furthermore, the fast-developing computer techniques and big data analytics have markedly enriched the data processing tools, bringing benefits of high efficiency and accuracy. To provide an up-to-date review of the latest techniques on the TCM qualitative analysis, multiple data-independent acquisition methods and data-dependent acquisition methods (precursor ion list, dynamic exclusion, mass tag, precursor ion scan, neutral loss scan, and multiple reaction monitoring) and post-processing techniques (mass defect filtering, diagnostic ion filtering, neutral loss filtering, mass spectral trees similarity filter, molecular networking, statistical analysis, database matching, etc.) were summarized and categorized. Applications of each technique and integrated analytical strategies were highlighted, discussion and future perspectives were proposed as well.

Capsaicin metabolites and GSH-associated detoxification and biotransformation pathways in human liver microsomes revealed by LC-HRMS/MS with data-mining tools

Capsaicin (CAP) is a principal pungent ingredient in hot peppers, it is also employed as a common food additive, an efficient pharmaceutical component, or even a riot control agent. CAP exerts various pharmacological activities as well as associated adverse physiological responses and causes moderate toxicity if overused. A full screening and identification of CAP metabolites in combination with its main detoxification pathways are crucial for the clear demonstration on its pharmacological and toxicological significance. Here, we employed a post-acquisition data-mining metabolic screening approach to rapidly find and identify a broad range of CAP metabolites generated from in vitro human liver microsomes, based on an ultra-performance liquid chromatography-quadrupole orbitrap high resolution tandem mass spectrometric method. First, we collected full scan MS and MS/MS data sets by a data-dependent acquisition method in positive ion mode, and then we employed a modified mass defect filter and a diagnostic ion filter to screen and identify all the probable CAP metabolites, combining with information including retention time, accurate mass, characteristic fragments, and relevant drug biotransformation patterns. In comparison with the stable isotope-labeled CAP involved biotransformation products, we confirmed 19 functionalized metabolites and 13 glutathione (GSH) conjugates of CAP, in which 13 metabolites are reported for the first time. We then briefly depicted an overview metabolic pathway of CAP from the GSH detoxification viewpoint, revealed that various metabolites of CAP can be generated from single or multiple biotransformation and metabolic reactions. Both CAP and its reactive metabolites produced relevant GSH conjugates, which indicates a wide and important detoxification value of GSH conjugation way.

A tutorial in small molecule identification via electrospray ionization-mass spectrometry: The practical art of structural elucidation

The identification of unknown molecules has been one of the cornerstone applications of mass spectrometry for decades. This tutorial reviews the basics of the interpretation of electrospray ionization-based MS and MS/MS spectra in order to identify small-molecule analytes (typically below 2000 Da). Most of what is discussed in this tutorial also applies to other atmospheric pressure ionization methods like atmospheric pressure chemical/photoionization. We focus primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds, rather than describing strategies for large-scale identification in complex samples. We critically discuss topics like the detection of protonated and deprotonated ions ([M + H]+ and [M - H]- ) as well as other adduct ions, the determination of the molecular formula, and provide some basic rules on the interpretation of product ion spectra. Our tutorial focuses primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds (eg, contaminants in chemical production, pharmacological alteration of drugs), rather than describing strategies for large-scale identification in complex samples. This tutorial also discusses strategies to obtain useful orthogonal information (UV/Vis, H/D exchange, chemical derivatization, etc) and offers an overview of the different informatics tools and approaches that can be used for structural elucidation of small molecules. It is primarily intended for beginning mass spectrometrists and researchers from other mass spectrometry sub-disciplines that want to get acquainted with structural elucidation are interested in some practical tips and tricks.

The application of a novel high-resolution mass spectrometry-based analytical strategy to rapid metabolite profiling of a dual drug combination in humans

Metabolite profiling of combination drugs in complex matrix is a big challenge. Development of an effective data mining technique for simultaneously extracting metabolites of one parent drug from both background matrix and combined drug-related signals could be a solution. This study presented a novel high resolution mass spectrometry (HRMS)-based data-mining strategy to fast and comprehensive metabolite identification of combination drugs in human. The model drug combination was verapamil-irbesartan (VER-IRB), which is widely used in clinic to treat hypertension. First, mass defect filter (MDF), as a targeted data mining tool, worked effectively except for those metabolites with similar MDF values. Second, the accurate mass-based background subtraction (BS), as an untargeted data-mining tool, was able to recover all relevant metabolites of VER-IRB from the full-scan MS dataset except for trace metabolites buried in the background noise and/or combined drug-related signals. Third, the novel ring double bond (RDB; valence values of elements in structure) filter, could show rich structural information in more sensitive full-scan MS chromatograms; however, it had a low capability to remove background noise and was difficult to differentiate the metabolites with RDB coverage. Fourth, an integrated strategy, i.e., untargeted BS followed by RDB, was effective for metabolite identification of VER and IRB, which have different RDB values. Majority of matrix signals were firstly removed using BS. Metabolite ions for each parent drug were then isolated from remaining background matrix and combined drug-related signals by imposing of preset RDB values/ranges around the parent drug and selected core substructures. In parallel, MDF was used to recover potential metabolites with similar RDB. As a result, a total of 74 metabolites were found for VER-IRB in human plasma and urine, among which ten metabolites have not been previously reported in human. The results demonstrated that the combination of accurate mass-based multiple data-mining techniques, i.e., untargeted background subtraction followed by ring double bond filtering in parallel with targeted mass defect filtering, can be a valuable tool for rapid metabolite profiling of combination drug.

Post-acquisition data mining techniques for LC–MS/MS-acquired data in drug metabolite identification

Metabolite identification is a crucial part of the drug discovery process. LC–MS/MS-based metabolite identification has gained widespread use, but the data acquired by the LC–MS/MS instrument is complex, and thus the interpretation of data becomes troublesome. Fortunately, advancements in data mining techniques have simplified the process of data interpretation with improved mass accuracy and provide a potentially selective, sensitive, accurate and comprehensive way for metabolite identification. In this review, we have discussed the targeted (extracted ion chromatogram, mass defect filter, product ion filter, neutral loss filter and isotope pattern filter) and untargeted (control sample comparison, background subtraction and metabolomic approaches) post-acquisition data mining techniques, which facilitate the drug metabolite identification. We have also discussed the importance of integrated data mining strategy.

An efficient data-filtering strategy for easy metabolite detection from the direct analysis of a biological fluid using Fourier transform mass spectrometry

RATIONALE

High-throughput analyses require an overall analytical workflow including not only a robust and high-speed technical platform, but also dedicated data-processing tools able to extract the relevant information. This work aimed at evaluating post-acquisition data-mining tools for selective extraction of metabolite species from direct introduction high-resolution mass spectrometry data.

METHODS

Investigations were performed on spectral data in which seven metabolites of vinclozolin, a dicarboximide fungicide containing two chloride atoms, were previously manually identified. The spectral data obtained from direct introduction (DI) and high-resolution mass spectrometry (HRMS) detection were post-processed by plotting the mass defect profiles and applying various data-filtering methods based on accurate mass values.

RESULTS

Exploration of mass defect profiles highlighted, in a specific plotting region, the presence of compounds containing common chemical elements and pairs of conjugated and non-conjugated metabolites resulting from classical metabolic pathways. Additionally, the judicious application of mass defect and/or isotope pattern filters removed many interfering ions from DI-HRMS data, greatly facilitating the detection of vinclozolin metabolites. Compared with previous results obtained by manual data treatment, three additional metabolites of vinclozolin were detected and putatively annotated.

CONCLUSIONS

Tracking simultaneously several specific species could be efficiently performed using data-mining tools based on accurate mass values. The selectivity of the data extraction was improved when the isotope filter was used for halogenated compounds, facilitating metabolite ion detection even for low-abundance species. Copyright © 2016 John Wiley & Sons, Ltd.

Trends in the application of high-resolution mass spectrometry for human biomonitoring: An analytical primer to studying the environmental chemical space of the human exposome

Global profiling of xenobiotics in human matrices in an untargeted mode is gaining attention for studying the environmental chemical space of the human exposome. Defined as the study of a comprehensive inclusion of environmental influences and associated biological responses, human exposome science is currently evolving out of the metabolomics science. In analogy to the latter, the development and applications of high resolution mass spectrometry (HRMS) has shown potential and promise to greatly expand our ability to capture the broad spectrum of environmental chemicals in exposome studies. HRMS can perform both untargeted and targeted analysis because of its capability of full- and/or tandem-mass spectrum acquisition at high mass accuracy with good sensitivity. The collected data from target, suspect and non-target screening can be used not only for the identification of environmental chemical contaminants in human matrices prospectively but also retrospectively. This review covers recent trends and advances in this field. We focus on advances and applications of HRMS in human biomonitoring studies, and data acquisition and mining. The acquired insights provide stepping stones to improve understanding of the human exposome by applying HRMS, and the challenges and prospects for future research.

In vivo drug metabolite identification in preclinical ADME studies by means of UPLC/TWIMS/high resolution-QTOF MS(E) and control comparison: cost and benefit of vehicle-dosed control samples

1. Liquid chromatography (LC)-high resolution mass spectrometry (HRMS) techniques proved to be well suited for the identification of predicted and unexpected drug metabolites in complex biological matrices. 2. To efficiently discriminate between drug-related and endogenous matrix compounds, however, sophisticated postacquisition data mining tools, such as control comparison techniques are needed. For preclinical absorption, distribution, metabolism and excretion (ADME) studies that usually lack a placebo-dosed control group, the question arises how high-quality control data can be yielded using only a minimum number of control animals. 3. In the present study, the combination of LC-traveling wave ion mobility separation (TWIMS)-HRMS(E) and multivariate data analysis was used to study the polymer patterns of the frequently used formulation constituents polyethylene glycol 400 and polysorbate 80 in rat plasma and urine after oral and intravenous administration, respectively. 4. Complex peak patterns of both constituents were identified underlining the general importance of a vehicle-dosed control group in ADME studies for control comparison. Furthermore, the detailed analysis of administration route, blood sampling time and gender influences on both vehicle peak pattern as well as endogenous matrix background revealed that high-quality control data is obtained when (i) control animals receive an intravenous dose of the vehicle, (ii) the blood sampling time point is the same for analyte and control sample and (iii) analyte and control samples of the same gender are compared.

A strategy for fast screening and identification of sulfur derivatives in medicinal Pueraria species based on the fine isotopic pattern filtering method using ultra-high-resolution mass spectrometry

Sulfurous compounds are commonly present in plants, fungi, and animals. Most of them were reported to possess various bioactivities. Isotopic pattern filter (IPF) is a powerful tool for screening compounds with distinct isotope pattern. Over the past decades, the IPF was used mainly to study Cl- and Br-containing compounds. To our knowledge, the algorithm was scarcely used to screen S-containing compounds, especially when combined with chromatography analyses, because the (34)S isotopic ion is drastically affected by (13)C2 and (18)O. Thus, we present a new method for a fine isotopic pattern filter (FIPF) based on the separated M + 2 ions ((12)C(x)(1)H(y)(16)O(z)(32)S(13)C2(18)O, (12)C(x+2)(1)H(y)(16)O(z+1)(34)S, tentatively named M + 2OC and M + 2S) with an ultra-high-resolution mass (100,000 FWHM @ 400 m/z) to screen sulfur derivatives in traditional Chinese medicines (TCM).This finer algorithm operates through convenient filters, including an accurate mass shift of M + 2OC and M + 2S from M and their relative intensity compared to M. The method was validated at various mass resolutions, mass accuracies, and screening thresholds of flexible elemental compositions. Using the established FIPF method, twelve S-derivatives were found in the popular medicinal used Pueraria species, and 9 of them were tentatively identified by high-resolution multiple stage mass spectrometry (HRMS(n)). The compounds were used to evaluate the sulfurous compounds' situation in commercially purchased Pueraria products. The strategy presented here provides a promising application of the IPF method in a new field.

High-resolution twin-ion metabolite extraction (HiTIME) mass spectrometry: nontargeted detection of unknown drug metabolites by isotope labeling, liquid chromatography mass spectrometry, and automated high-performance computing

The metabolic fate of a compound can often determine the success of a new drug lead. Thus, significant effort is directed toward identifying the metabolites formed from a given molecule. Here, an automated and nontargeted procedure is introduced for detecting drug metabolites without authentic metabolite standards via the use of stable isotope labeling, liquid chromatography mass spectrometry (LC/MS), and high-performance computing. LC/MS of blood plasma extracts from rats that were administered a 1:1 mixture of acetaminophen (APAP) and (13)C6-APAP resulted in mass spectra that contained "twin" ions for drug metabolites that were not detected in control spectra (i.e., no APAP administered). Because of the development of a program (high-resolution twin-ion metabolite extraction; HiTIME) that can identify twin-ions in high-resolution mass spectra without centroiding (i.e., reduction of mass spectral peaks to single data points), 9 doublets corresponding to APAP metabolites were identified. This is nearly twice that obtained by use of existing programs that make use of centroiding to reduce computational cost under these conditions with a quadrupole time-of-flight mass spectrometer. By a manual search for all reported APAP metabolite ions, no additional twin-ion signals were assigned. These data indicate that all the major metabolites of APAP and multiple low-abundance metabolites (e.g., acetaminophen hydroxy- and methoxysulfate) that are rarely reported were detected. This methodology can be used to detect drug metabolites without prior knowledge of their identity. HiTIME is freely available from https://github.com/bjpop/HiTIME .

ChelomEx: Isotope-assisted discovery of metal chelates in complex media using high-resolution LC-MS

Chelating agents can control the speciation and reactivity of trace metals in biological, environmental, and laboratory-derived media. A large number of trace metals (including Fe, Cu, Zn, Hg, and others) show characteristic isotopic fingerprints that can be exploited for the discovery of known and unknown organic metal complexes and related chelating ligands in very complex sample matrices using high-resolution liquid chromatography mass spectrometry (LC-MS). However, there is currently no free open-source software available for this purpose. We present a novel software tool, ChelomEx, which identifies isotope pattern-matched chromatographic features associated with metal complexes along with free ligands and other related adducts in high-resolution LC-MS data. High sensitivity and exclusion of false positives are achieved by evaluation of the chromatographic coherence of the isotope pattern within chromatographic features, which we demonstrate through the analysis of bacterial culture media. A built-in graphical user interface and compound library aid in identification and efficient evaluation of results. ChelomEx is implemented in MatLab. The source code, binaries for MS Windows and MAC OS X as well as test LC-MS data are available for download at SourceForge ( http://sourceforge.net/projects/chelomex ).

Post-acquisition data processing for the screening of transformation products of different organic contaminants. Two-year monitoring of river water using LC-ESI-QTOF-MS and GCxGC-EI-TOF-MS

This study describes a comprehensive strategy for detecting and elucidating the chemical structures of expected and unexpected transformation products (TPs) from chemicals found in river water and effluent wastewater samples, using liquid chromatography coupled to electrospray ionization quadrupole-time-of-flight mass spectrometer (LC-ESI-QTOF-MS), with post-acquisition data processing and an automated search using an in-house database. The efficacy of the mass defect filtering (MDF) approach to screen metabolites from common biotransformation pathways was tested, and it was shown to be sufficiently sensitive and applicable for detecting metabolites in environmental samples. Four omeprazole metabolites and two venlafaxine metabolites were identified in river water samples. This paper reports the analytical results obtained during 2 years of monitoring, carried out at eight sampling points along the Henares River (Spain). Multiresidue monitoring, for targeted analysis, includes a group of 122 chemicals, amongst which are pharmaceuticals, personal care products, pesticides and PAHs. For this purpose, two analytical methods were used based on direct injection with a LC-ESI-QTOF-MS system and stir bar sorptive extraction (SBSE) with bi-dimensional gas chromatography coupled with a time-of-flight spectrometer (GCxGC-EI-TOF-MS).

Data acquisition and data mining techniques for metabolite identification using LC coupled to high-resolution MS

Metabolite identification plays a pivotal role through all stages of drug discovery and development. The task of detecting and characterizing drug metabolites in complex biological matrices is very challenging, due in part to the co-existence of drug-related material with a large excess of endogenous material. Deciphering information on drug metabolites in these complex biological systems requires not only sophisticated LC-MS systems, but also software that can help differentiate drug-related compounds from endogenous material in the MS data. Fortunately, there have been considerable advances in high-resolution MS technologies with improved mass accuracy. The high resolution and mass accuracy capabilities have necessitated and augmented the development of integrated data acquisition methods, which have significantly facilitated metabolite detection and identification. In this review, we discuss various data-dependent and -independent acquisition methods in combination with accurate mass-based data mining tools for metabolite identification in drug discovery and development.

Comparison of unit resolution SRM and TOF-MS at 12,000 mass resolution for quantitative bioanalysis of 11 steroids from human plasma

BACKGROUND

The use of high-resolution MS systems for quantitative bioanalysis is a growing field, even though a clear majority of bioanalytical methods are still based on MS/MS with triple quadrupole (QqQ) instrumentation. The recent advances in TOF-MS technology have provided increased linear range and a high selectivity of detection by increased mass resolution and mass accuracy, making these instruments attractive for quantitative analysis due to lack of a need for compound-specific detection reaction optimization and their capability to collect data for a high number of compounds by sensitive wide mass range data acquisition.

MATERIALS & METHODS

Here, 11 steroids spiked to human plasma were analyzed by LC-MS using both a QqQ MS system and a TOF instrument operating at 12,000 mass resolution. Sample preparation was performed by hybrid SPE technology.

RESULTS

The LOD were 0.5-5 and 0.5-20 ng/ml in plasma for all analytes with QqQ and TOF-MS detection, respectively.

CONCLUSION

Although the results show wider linear range and slightly better sensitivity for most of the compounds with QqQ in comparison to TOF, acceptable performance was obtained for most of the compounds within the range of LOD to 2000 ng/ml (in plasma), this was also the case with LC-TOF-MS analysis. The main problem in TOF-MS analysis at 12,000 mass resolution from plasma was selectivity rather than sensitivity or linear range.

Bioanalytical aspects of clinical mass balance studies in oncology

Clinical mass balance studies aim to investigate the absorption, distribution, metabolism and excretion (ADME) of a(n) (often radiolabeled) drug, following a single administration to humans. They are perfectly suited to determine the disposition and major metabolic pathways of a drug, the exposure to the parent drug and its metabolites, and the rate and route of elimination. A mass balance study, however, poses interesting challenges to the analysis of parent drug and metabolites in different biological matrices. Using recent clinical mass balance studies in oncology as an example, this review focuses on the aspects of mass balance studies, from bioanalytical assay development, analysis of clinical samples to reporting of study results. Along the way, it discusses bioanalytical problems and practical solutions.

The early estimation of circulating drug metabolites in humans

INTRODUCTION

An evolution in bioanalytical methodologies to identify and quantify drug metabolites has led to a wealth of biotransformation information during preclinical and early clinical testing phases. However, this abundance of metabolism data has not clarified how to select the most important circulating human metabolites for safety assessment. Consequently, more stringent regulatory expectations for a comprehensive approach to human metabolism have led pharmaceutical sponsors to employ a variety of novel methods to estimate circulating drug metabolites in humans and animals.

AREAS COVERED

This review provides context for 'why' human circulating metabolites must be qualified for safety in animals. A detailed overview is also presented concerning 'where,' 'how' and 'when' to conduct these assessments during drug development.

EXPERT OPINION

A human metabolite qualification strategy is now a required element of the drug safety package submitted with a new drug application (NDA). The important question is whether or not this additional information, about metabolite safety, is making human drugs any safer. Currently, this is a debatable issue, especially because stand-alone metabolite testing is fraught with its own challenges. As drug development moves into the twenty-first century, there is a pressing need for more sophisticated methodologies to address human drug and metabolite safety.

Recent advances in metabolite identification and quantitative bioanalysis by LC–Q-TOF MS

The need for rapid, sensitive and effective identification and quantitation of drugs and metabolites to accelerate drug discovery and development has given MS its central position in drug metabolism and pharmacokinetic research. This review attempts to orient the readers with respect to hybrid Q-TOF MS, which enables accurate mass measurement and generates information-rich datasets. The key properties of the Q-TOF MS system, including mass accuracy, resolution, scan speed and dynamic range, are herein discussed. Developments on tandem separation techniques (e.g., UHPLC® and ion mobility spectrometry), data acquisition and data-mining methods (e.g., mass defect, product/neutral loss, isotope pattern filters and background subtraction) that facilitate qualitative and quantitative analysis are then examined. The performance and versatility of LC–Q-TOF MS are thoroughly illustrated by its applications in metabolite identification and quantitative bioanalysis. Future perspectives are also discussed.

Identifying metabolite ions of peptide drugs in the presence of an in vivo matrix background

Background: Peptides represent a growing class of potential drugs. Information on metabolic clearance can be valuable for peptide drug development but different challenges are encountered with the identification of peptide metabolites in comparison to the process used for small-molecule therapeutics. Results: Enfuvirtide was selected as a test compound and dosed intravenously at 2 mg/kg to rats. Plasma samples were collected and analyzed on two different quadrupole-TOF instruments in positive and negative ion modes. Different post-acquisition processing tools were evaluated to identify the metabolites of a peptide drug in the presence of an in vivo matrix. Charge state filtering and ion mobility extraction were applied to reduce the matrix background and combined with more comprehensive software tools generally used for large molecule analyses as well as tools designed for small-molecule metabolite identification work. Conclusion: Both ion mobility spectrometry and charge state filtration proved to be successful in extracting peptide ions and significantly reducing background signals. Both small- and large-molecule software tools contain specific capabilities that could be usefully combined in a single package for peptide metabolite identification.

Application of LC–high-resolution MS with ‘intelligent’ data mining tools for screening reactive drug metabolites

Biotransformation of chemically stable compounds to reactive metabolites that can bind covalently to macromolecules (such as proteins and DNA) is considered an undesirable property of drug candidates. Due to the possible link, which has not yet been conclusively demonstrated, between reactive metabolites and adverse drug reactions, screening for metabolic activation of lead compounds through in vitro chemical trapping experiments has become an integral part of the drug discovery process in many laboratories. In this review, we provide an overview of the recent advances in the application of high-resolution MS. These advances facilitated the development of accurate-mass-based data mining tools for high-throughput screening of reactive drug metabolites in drug discovery.

Organochlorine isotopic pattern-enhanced detection and quantification of triclosan and its metabolites in human serum by ultra-high-performance liquid chromatography/quadrupole time-of-flight/mass spectrometry

Organochlorines possess special isotopic patterns that obey the chlorine rule. In the case of triclosan (TCS), which contains three chlorine atoms, the isotopic patterns are composed of seven obvious peaks with the calculated masses ranging from 286.9435 to 292.9350 in negative ion mode and with specific isotopic abundance ratios of 100:13.1:97.1:12.6:31.8:4.1:3.6. In this study, mass differences between the calculated and observed m/z values for all isotopic peaks of TCS were less than 3.5 ppm in the analyses of the serum samples by ultra-high-performance liquid chromatography/quadrupole time-of-flight/mass spectrometry (UHPLC-Q-TOF/MS). Combining the characteristics described above, four metabolites were identified as sulfonated TCS, glucuronidated TCS and hydroxylated sulfonated TCS. Several novel MS techniques were applied to improve the sensitivity of quantification of TCS. The limit of detection for TCS in a 250 μL serum sample was 0.05 ng/mL, which was over twenty times lower than values obtained by the LC/triple quadrupole-MS/MS method reported in the literature. The concentration of total TCS (free and conjugated) was quantified to range from 0.15 to 217 ng/mL, whereas free TCS ranged from 0.15 to 10 ng/mL. To the best of our knowledge, this is the first report on the identification of TCS and metabolites in human serum, and it also provides the most sensitive LC/MS approach for the quantification of TCS.

Use of relative 12C/14C isotope ratios to estimate metabolite concentrations in the absence of authentic standards

Background: There is considerable interest in the determination of relative abundances of human metabolites in plasma (and potentially excreta) with reasonable accuracy early on in the drug development process in order to make scientifically sound decisions with regard to the presence of potentially active or toxic disproportionate metabolites. At this point, authentic metabolite standards are generally not available. Results: A new methodology is proposed for the estimation of metabolite concentrations in the absence of authentic standards. A reference sample containing radiolabeled metabolites of interest is produced by incubating the 14C-labeled drug in vitro, and mixed with a sample to be quantitated containing the unlabeled metabolites. The 12C/14C isotope ratio is measured with high-resolution ESI–MS for each metabolite, and used as a basis for quantitation of the cold metabolite based on the concentration of radioactive metabolite, determined from independent analysis of the radioactive sample with LC-radiochemical detection. The 14C-labeled metabolite serves as an isotopically labeled internal standard, which corrects for any variations in injection volume, sample preparation, MS intensity drift, matrix effects and/or saturation of electrospray ionization. The approach was validated by the analysis of solutions containing variable amounts of the analyte with a fixed amount of radioactive standard on a QToF Synapt® G2 MS system. The same methodology was also successfully applied to first-in-human plasma samples analyzed on a LTQ-Orbitrap®. Conclusion: The metabolite abundances obtained by 12C/14C isotope ratio measurements showed suitable accuracy and precision and were very close to those obtained with matrix mixing. The parent drug concentrations also corresponded well with the bioanalytical results obtained with a validated LC–MS/MS method.

DETECTING LOW-LEVEL SYNTHESIS IMPURITIES IN MODIFIED PHOSPHOROTHIOATE OLIGONUCLEOTIDES USING LIQUID CHROMATOGRAPHY - HIGH RESOLUTION MASS SPECTROMETRY

An LC-MS method based on the use of high resolution Fourier transform ion cyclotron resonance mass spectrometry (FTIRCMS) for profiling oligonucleotides synthesis impurities is described.Oligonucleotide phosphorothioatediesters (phosphorothioate oligonucleotides), in which one of the non-bridging oxygen atoms at each phosphorus center is replaced by a sulfur atom, are now one of the most popular oligonucleotide modifications due to their ease of chemical synthesis and advantageous pharmacokinetic properties. Despite significant progress in the solid-phase oligomerization chemistry used in the manufacturing of these oligonucleotides, multiple classes of low-level impurities always accompany synthetic oligonucleotides. Liquid chromatography-mass spectrometry has emerged as a powerful technique for the identification of these synthesis impurities. However, impurity profiling, where the entire complement of low-level synthetic impurities is identified in a single analysis, is more challenging. Here we present an LC-MS method based the use of high resolution-mass spectrometry, specifically Fourier transform ion cyclotron resonance mass spectrometry (FTIRCMS or FTMS). The optimal LC-FTMS conditions, including the stationary phase and mobile phases for the separation and identification of phosphorothioate oligonucleotides, were found. The characteristics of FTMS enable charge state determination from single m/z values of low-level impurities. Charge state information then enables more accurate modeling of the detected isotopic distribution for identification of the chemical composition of the detected impurity. Using this approach, a number of phosphorothioate impurities can be detected by LC-FTMS including failure sequences carrying 3'-terminal phosphate monoester and 3'-terminal phosphorothioate monoester, incomplete backbone sulfurization and desulfurization products, high molecular weight impurities, and chloral, isobutyryl, and N(3) (2-cyanoethyl) adducts of the full length product. When compared with low resolution LC-MS, ~60% more impurities can be identified when charge state and isotopic distribution information is available and used for impurity profiling.

Drug metabolite profiling and identification by high-resolution mass spectrometry

Mass spectrometry plays a key role in drug metabolite identification, an integral part of drug discovery and development. The development of high-resolution (HR) MS instrumentation with improved accuracy and stability, along with new data processing techniques, has improved the quality and productivity of metabolite identification processes. In this minireview, HR-MS-based targeted and non-targeted acquisition methods and data mining techniques (e.g. mass defect, product ion, and isotope pattern filters and background subtraction) that facilitate metabolite identification are examined. Methods are presented that enable multiple metabolite identification tasks with a single LC/HR-MS platform and/or analysis. Also, application of HR-MS-based strategies to key metabolite identification activities and future developments in the field are discussed.

Approaches for the rapid identification of drug metabolites in early clinical studies

Understanding the metabolism of a novel drug candidate in drug discovery and drug development is as important today as it was 30 years ago. What has changed in this period is the technology available for proficient metabolite characterization from complex biological sources. High-efficiency chromatography, sensitive MS and information-rich NMR spectroscopy are approaches that are now commonplace in the modern laboratory. These advancements in analytical technology have led to unequivocal metabolite identification often being performed at the earliest opportunity, following the first dose to man. For this reason an alternative approach is to shift from predicting and extrapolating possible human metabolism from in silico and nonclinical sources, to actual characterization at steady state within early clinical trials. This review provides an overview of modern approaches for characterizing drug metabolites in these early clinical studies. Since much of this progress has come from technology development over the years, the review is concluded with a forward-looking perspective on how this progression may continue into the next decade.

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.

Looking back through the MIST: a perspective of evolving strategies and key focus areas for metabolite safety analysis

The publication of the US FDA MIST guidance document in 2008 reignited the debate around the most appropriate strategies to underwrite metabolite safety for novel compounds. Whilst some organizations have suggested that the guidelines necessitate a paradigm shift to more thorough metabolite analysis during early development, an evaluation of historical practices shows that the principles of the guidelines have always largely underpinned metabolism studies within the pharmaceutical industry. Therefore, it is argued that existing practices, when coupled to appropriate emerging analytical tools and a case-by-case consideration of the relevance of the generated metabolism data in terms of structure, physicochemisty, abundance and activity, represent a fit-for-purpose approach to metabolite-safety assessments.

Overview of metabolite safety testing from an industry perspective

Regulatory guidelines on MIST were initially established in 2005 and finalized in 2008 by the US FDA and this has led to much discussion and debate on how to apply these recommendations in today’s resource-constrained pharmaceutical environment. There are four aspects of MIST that impact on the field of bioanalysis: definition of a disproportionate human metabolite, establishment of nonclinical (animal) safety coverage for important human metabolites, degree of rigor in validation of bioanalytical methods to quantify metabolites when synthetic standards are available, and semiquantitation of metabolites when synthetic standards are not available. In this manuscript, each of these points has been addressed from a pharmaceutical industry standpoint, including a perspective on the necessary convergence of the fields of metabolite safety testing and bioanalysis.

Determination and identification of estrogenic compounds generated with biosynthetic enzymes using hyphenated screening assays, high resolution mass spectrometry and off-line NMR

This paper describes the determination and identification of active and inactive estrogenic compounds produced by biosynthetic methods. A hyphenated screening assay towards the human estrogen receptor ligand binding domain (hER)alpha and hERbeta integrating target-ligand interactions and liquid chromatography-high resolution mass spectrometry was used. With this approach, information on both biologic activity and structure identity of compounds produced by bacterial mutants of cytochrome P450s was obtained in parallel. Initial structure identification was achieved by high resolution MS/MS, while for full structure determination, P450 incubations were scaled up and the produced entities were purified using preparative liquid chromatography with automated fraction collection. NMR spectroscopy was performed on all fractions for 3D structure analysis; this included 1D-(1)H, 2D-COSY, 2D-NOESY, and (1)H-(13)C-HSQC experiments. This multidimensional screening approach enabled the detection of low abundant biotransformation products which were not suitable for detection in either one of its single components. In total, the analytical scale biosynthesis produced over 85 compounds from 6 different starting templates. Inter- and intra-day variation of the biochemical signals in the dual receptor affinity detection system was less than 5%. The multi-target screening approach combined with full structure characterization based on high resolution MS(/MS) and NMR spectroscopy demonstrated in this paper can generally be applied to e.g. metabolism studies and compound-library screening.