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

Application of Electro-Activated Dissociation Fragmentation Technique to Identifying Glucuronidation and Oxidative Metabolism Sites of Vepdegestrant by Liquid Chromatography-High Resolution Mass Spectrometry

Drug metabolite identification is an integrated part of drug metabolism and pharmacokinetics studies in drug discovery and development. Definitive identification of metabolic modification sides of test compounds such as screening metabolic soft spots and supporting metabolite synthesis are often required. Currently, liquid chromatography-high resolution mass spectrometry is the dominant analytical platform for metabolite identification. However, the interpretation of product ion spectra generated by commonly used collision-induced disassociation (CID) and higher-energy collisional dissociation (HCD) often fails to identify locations of metabolic modifications, especially glucuronidation. Recently, a ZenoTOF 7600 mass spectrometer equipped with electron-activated dissociation (EAD-HRMS) was introduced. The primary objective of this study was to apply EAD-HRMS to identify metabolism sites of vepdegestrant (ARV-471), a model compound that consists of multiple functional groups. ARV-471 was incubated in dog liver microsomes and 12 phase I metabolites and glucuronides were detected. EAD generated unique product ions via orthogonal fragmentation, which allowed for accurately determining the metabolism sites of ARV-471, including phenol glucuronidation, piperazine N-dealkylation, glutarimide hydrolysis, piperidine oxidation, and piperidine lactam formation. In contrast, CID and HCD spectral interpretation failed to identify modification sites of three O-glucuronides and three phase I metabolites. The results demonstrated that EAD has significant advantages over CID and HCD in definitive structural elucidation of glucuronides and phase I metabolites although the utility of EAD-HRMS in identifying various types of drug metabolites remains to be further evaluated. SIGNIFICANCE STATEMENT: Definitive identification of metabolic modification sites by liquid chromatography-high resolution mass spectrometry is highly needed in drug metabolism research, such as screening metabolic soft spots and supporting metabolite synthesis. However, commonly used collision-induced dissociation (CID) and higher-energy collisional dissociation (HCD) fragmentation techniques often fail to provide critical information for definitive structural elucidation. In this study, the electron-activated dissociation (EAD) was applied to identifying glucuronidation and oxidative metabolism sites of vepdegestrant, which generated significantly better results than CID and HCD.

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.

Creation and filtering of a recurrent spectral library of CHO cell metabolites and media components

This paper reports the first implementation of a new type of mass spectral library for the analysis of Chinese hamster ovary (CHO) cell metabolites that allows users to quickly identify most compounds in any complex metabolite sample. We also describe an annotation methodology developed to filter out artifacts and low‐quality spectra from recurrent unidentified spectra of metabolites. CHO cells are commonly used to produce biological therapeutics. Metabolic profiles of CHO cells and media can be used to monitor process variability and look for markers that discriminate between batches of product. We have created a comprehensive library of both identified and unidentified metabolites derived from CHO cells that can be used in conjunction with tandem mass spectrometry to identify metabolites. In addition, we present a workflow that can be used for assigning confidence to a NIST MS/MS Library search match based on prior probability of general utility. The goal of our work is to annotate and identify (when possible), all liquid chromatography‐mass spectrometry generated metabolite ions as well as create automatable library building and identification pipelines for use by others in the field.

Automatic Analyte-Ion Recognition and Background Removal for Ambient Mass-Spectrometric Data Based on Cross-Correlation

Ambient mass spectrometry is a powerful approach for rapid, high-throughput, and direct sample analysis. Due to the open-air desorption and ionization processes, random fluctuations of ambient conditions can lead to large variances in mass-spectral signals over time. The mass-spectral data also can be further complicated due to multiple analytes present in the sample, background-ion signals stemming from the desorption/ionization source itself, and other laboratory-specific conditions (e.g., ambient laboratory air, nearby hardware). Thus, background removal and analyte-ion recognition can be quite difficult, particularly in non-targeted analyses. Here, we demonstrate the use of a cross-correlation-based approach to exploit chemical information encoded in the time domain to group/categorize mass-spectral peaks from a single analysis dataset. Ions that originate from ambient (or other) background species were readily flagged and removed from spectra; the result was a decrease in mass-spectral complexity by over 70% due to the removal of these background ions. Meanwhile, analyte ions were differentiated and categorized based on their time-domain profiles. With sufficient mass resolving-power and mass-spectral acquisition rate, isolated mass spectra containing ions from the same species in a sample could be extracted, leading to a reduction in mass-spectral complexity by more than 98% in some cases. The cross-correlation approach was tested with different ionization sources as well as reproducible and irreproducible sample introduction. Software built in-house enabled fully automated data processing, which can be performed within a few seconds. Ultimately, this approach provides an additional dimension of analyte separation in ambient mass-spectrometric analyses with information that is already recorded throughout the analysis.

Metabolome analysis for investigating host-gut microbiota interactions

Dysbiosis of the gut microbiome is associated with host health conditions. Many diseases have shown to have correlations with imbalanced microbiota, including obesity, inflammatory bowel disease, cancer, and even neurodegeneration disorders. Metabolomics studies targeting small molecule metabolites that impact the host metabolome and their biochemical functions have shown promise for studying host-gut microbiota interactions. Metabolome analysis determines the metabolites being discussed for their biological implications in host-gut microbiota interactions. To facilitate understanding the critical aspects of metabolome analysis, this article reviewed (1) the sample types used in host-gut microbiome studies; (2) mass spectrometry (MS)-based analytical methods and (3) useful tools for MS-based data processing/analysis. In addition to the most frequently used sample type, feces, we also discussed others biosamples, such as urine, plasma/serum, saliva, cerebrospinal fluid, exhaled breaths, and tissues, to better understand gut metabolite systemic effects on the whole organism. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), three powerful tools that can be utilized to study host-gut microbiota interactions, are included with examples of their applications. After obtaining big data from MS-based instruments, noise removal, peak detection, missing value imputation, and data analysis are all important steps for acquiring valid results in host-gut microbiome research. The information provided in this review will help new researchers aiming to join this field by providing a global view of the analytical aspects involved in gut microbiota-related metabolomics studies.

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.

Exposure Marker Discovery of Phthalates Using Mass Spectrometry

Phthalates are chemicals widely used in industry and the consequences on human health caused by exposure to these agents are of significant interest currently. The urinary metabolites of phthalates can be measured and used as exposure markers for the assessment of the actual internal contamination of phthalates coming from different sources and absorbed by various ways. The purpose of this paper is to review the markers for exposure and risk assessment of phthalates such as di-methyl phthalate (DMP), di-ethyl phthalate (DEP), di-butyl phthalate (DBP), benzylbutyl phthalate (BBP), di-(2-ethylhexyl)phthalate (DEHP), di-(2-propylheptyl)phthalate (DPHP), di-iso-nonyl phthalate (DINP), di-n-octyl phthalate (DnOP) and di-iso-decyl phthalate (DIDP), and introduction of the analytical approach of three metabolomics data processing approaches that can be used for chemical exposure marker discovery in urine with high-resolution mass spectrometry (HRMS) data.

Chromatographic peak reconstruction algorithm to improve qualitative and quantitative analysis of trace pesticide residues

RATIONALE

In order to improve analysis of analytes in trace amounts in a complex matrix, we developed a novel post-processing method, named Chromatographic Peak Reconstruction (CPR), to process the recorded data from gas chromatography/time-of-flight mass spectrometry (GC/TOFMS).

METHODS

For a trace ion, the relative deviation (δ) between the adjacent scanned mass-to-charge ratios (m/z) was found to be inversely proportional to its MS peak intensity. Based on this relationship, the thresholds of δ value within the specified intensity segments were estimated by the CPR and used to screen out the suspicious scan-points in the extracted ion chromatographic (EIC) peak. Then, the intensities of these suspicious scan-points were calibrated to reconstruct a new EIC peak.

RESULTS

In the qualitative analysis of 118 pesticides, 107 out of the test pesticides can be confirmed. The corrected response ratios of the qualitative ion (q) over the quantitative ion (Q), q/Q, became closer to their references. In the quantitative analysis of 10 test pesticides at 5 ppb, the relative errors of the calculated concentrations after using the CPR were below ±1.55%, down from ±2.29% without using the CPR.

CONCLUSIONS

The developed CPR showed great potential in the analysis of trace analytes in complex matrices. It was proved to be a helpful data processing method for the monitoring of trace pesticide residues. Copyright © 2016 John Wiley & Sons, Ltd.

An accelerated background subtraction algorithm for processing high-resolution MS data and its application to metabolite identification

Background: Metabolite identification without radiolabeled compound is often challenging because of interference of matrix-related components. Results: A novel and an effective background subtraction algorithm (A-BgS) has been developed to process high-resolution mass spectral data that can selectively remove matrix-related components. The use of a graphics processing unit with a multicore central processing unit enhanced processing speed several 1000-fold compared with a single central processing unit. A-BgS algorithm effectively removes background peaks from the mass spectra of biological matrices as demonstrated by the identification of metabolites of delavirdine and metoclopramide. Conclusion: The A-BgS algorithm is fast, user friendly and provides reliable removal of matrix-related ions from biological samples, and thus can be very helpful in detection and identification of in vivo and in vitro metabolites.

An Integrated Approach for Studying Exposure, Metabolism, and Disposition of Multiple Component Herbal Medicines Using High-Resolution Mass Spectrometry and Multiple Data Processing Tools

A typical prescription of traditional Chinese medicine (TCM) contains up to a few hundred prototype components. Studying their absorption, metabolism, distribution, and elimination (ADME) presents great challenges. The objective of this study was to develop a practical approach for investigating ADME of individual prototypes in TCM. An active fraction of Xiao-Xu-Ming decoction (AF-XXMD) as a model TCM prescription was orally administered to rats. AF-XXMD-related components in plasma, urine, bile, and feces were detected using high-resolution mass spectrometry and background subtraction, an untargeted data-mining tool. Components were then structurally characterized on the basis of MS(n) spectral data. Connection of detected AF-XXMD metabolites to their precursor species, either prototypes or upstream metabolites, were determined on the basis of mass spectral similarity and the matching of biotransformation reactions. As a result, 247 AF-XXMD-related components were detected and structurally characterized in rats, 134 of which were metabolites. Among 198 AF-XXMD prototypes dosed, 65 were fully or partially absorbed and 13 prototypes and 34 metabolites were found in the circulation. Glucuronidation, isomerization, and deglycosylation followed by biliary and urinary excretions and direct elimination of prototypes via kidney and liver were the major clearance pathways of AF-XXMD prototypes. As an example, the ADME profile of H56, the single major AF-XXMD component in rat plasma, was elucidated on the basis of profiles of H56-related components in plasma and excreta. The results demonstrate that the new analytical approach is a useful tool for rapid and comprehensive detection and characterization of TCM components in biologic matrix in a TCM ADME study.

Metabolic differences in ripening of Solanum lycopersicum 'Ailsa Craig' and three monogenic mutants

Application of mass spectrometry enables the detection of metabolic differences between groups of related organisms. Differences in the metabolic fingerprints of wild-type Solanum lycopersicum and three monogenic mutants, ripening inhibitor (rin), non-ripening (nor) and Colourless non-ripening (Cnr), of tomato are captured with regard to ripening behaviour. A high-resolution tandem mass spectrometry system coupled to liquid chromatography produced a time series of the ripening behaviour at discrete intervals with a focus on changes post-anthesis. Internal standards and quality controls were used to ensure system stability. The raw data of the samples and reference compounds including study protocols have been deposited in the open metabolomics database MetaboLights via the metadata annotation tool Isatab to enable efficient re-use of the datasets, such as in metabolomics cross-study comparisons or data fusion exercises.

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.

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 [(14)C]-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 [(14)C]-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.

True ion pick (TIPick): a denoising and peak picking algorithm to extract ion signals from liquid chromatography/mass spectrometry data

Liquid Chromatography-Time of Flight Mass Spectrometry has become an important technique for toxicological screening and metabolomics. We describe TIPick a novel algorithm that accurately and sensitively detects target compounds in biological samples. TIPick comprises two main steps: background subtraction and peak picking. By subtracting a blank chromatogram, TIPick eliminates chemical signals of blank injections and reduces false positive results. TIPick detects peaks by calculating the S(CC(INI)) values of extracted ion chromatograms (EICs) without considering peak shapes, and it is able to detect tailing and fronting peaks. TIPick also uses duplicate injections to enhance the signals of the peaks and thus improve the peak detection power. Commonly seen split peaks caused by either saturation of the mass spectrometer detector or a mathematical background subtraction algorithm can be resolved by adjusting the mass error tolerance of the EICs and by comparing the EICs before and after background subtraction. The performance of TIPick was tested in a data set containing 297 standard mixtures; the recall, precision and F-score were 0.99, 0.97 and 0.98, respectively. TIPick was successfully used to construct and analyze the NTU MetaCore metabolomics chemical standards library, and it was applied for toxicological screening and metabolomics studies.

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.

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.

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.

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.

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.

Novel MS solutions inspired by MIST

To improve patient safety and to help avoid costly late-stage failures, the pharmaceutical industry, along with the US FDA and International Committee on Harmonization (ICH), recommends the identification of differences in drug metabolism between animals used in nonclinical safety assessments and humans as early as possible during the drug-development process. LC–MS is the technique of choice for detection and characterization of metabolites, however, the widely different LC–MS response observed for a new chemical entity (NCE) and its structurally related metabolites limits the direct use of LC–MS responses for quantitative determination of NCEs and metabolites. While no method provides completely accurate universal response, UV, corona charged aerosol detection (CAD), radioactivity, NMR and low-flow (<20 µl/min) nanospray approaches provide opportunities to quantify metabolites in the absence of reference standards or radiolabeled material with enough precision to meet the needs of early clinical development.

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

Detection and identification (ID) of all drug metabolites following liquid chromatography (LC)/mass spectrometry (MS) analysis of complex biological matrixes are not trivial. To facilitate detection of drug-derived materials that possess highly diagnostic isotopic patterns (e.g., chlorine- and bromine-containing compounds), we report an accurate-mass-based spectral-averaging isotope-pattern-filtering (AMSA-IPF) algorithm developed in the computational language R. The AMSA-IPF algorithm offers three significant improvements over the traditional isotope filtering method often provided by instrument vendors. First, spectral averaging is performed before the IPF to reduce scan-to-scan variability of ion intensities. Second, the IPF process is strictly based on accurate mass typically obtained on high resolution mass spectrometers. The designated isotopic ion-pairs (e.g., M + 2:M or M + 1:M, where M is the molecular ion and M + 1 and M + 2 are the isotopic ions) must fall into the predefined accurate mass tolerance window (e.g., 5 ppm) and at the same time satisfy the predefined relative abundance criteria. Third, both M + 1:M and M + 2:M ion pairs are inspected in the filtering process. The inclusion of M + 1:M ion pair enhanced the specificity of this algorithm by removing background ions that form M:M + 2 ion pairs within predefined isotope ratios by coincidence. The algorithm demonstrated excellent effectiveness in detecting drug-related ions in in vivo samples (plasma, bile, urine and feces) obtained from rats orally dosed with 14C-loratadine. The ion chromatograms of the filtered LC-MS data files showed near perfect qualitative correlation with the corresponding radioprofiles. AMSA-IPF will be another great tool to facilitate detection and ID of drug metabolites in complex LC-MS data without the help of radiolabels. The AMSA-IPF algorithm is applicable to not only compounds containing distinct natural isotopes (such as Cl and Br) but also compounds that contain synthetically incorporated isotopes (13C, 15N, etc) generating a distinct isotope pattern. The ability to detect and identify metabolites from nonradiolabeled studies will be extremely beneficial to achieve compliance with FDA's most recent guidance on metabolites in safety testing (MIST).

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.