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

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.

Rapid and comprehensive metabolites identification of 5-demethylnobiletin in rats using UPLC/Triple-TOF-MS/MS based on multiple mass defect filter and their neuroprotection against ferroptosis

5-Demethylnobiletin (5-deNOB) is a hydroxylated polymethoxyflavone (PMF) from Citrus plants known for its neurotrophic, anti-tumor, and antioxidant bioactivities. An ultra-high performance liquid chromatography coupled with triple-time of flight tandem mass spectrometry (UPLC/Triple-TOF-MS/MS) analysis combining with multiple mass defect filter (MMDF) and MetabolitePilot™ was employed to detect and characterize the metabolites of 5-deNOB in rats. A total of 130 metabolites were identified in rats, with 100, 25, 34, and 52 metabolites found in urine, plasma, bile, and feces, respectively. The major metabolic pathways involved demethylation, hydroxylation, dehydroxylation, glucuronidation, and methylation. In a bioassay of evaluating neuroprotection against ferroptosis in PC12 cells, most of the metabolites exhibited superior activity compared to 5-deNOB. These results provide valuable insights into the in vivo pharmacodynamic properties of 5-deNOB and offer potential active small molecules for neuroprotective therapy. Furthermore, the findings demonstrate the effectiveness of UPLC/Triple-TOF-MS/MS combined with MMDF and MetabolitePilot™ for rapid discovery and identification of the in vivo metabolites of natural products.

A mass balance study of [14C]SHR6390 (dalpiciclib), a selective and potent CDK4/6 inhibitor in humans

SHR6390 (dalpiciclib) is a selective and effective cyclin-dependent kinase (CDK) 4/6 inhibitor and an effective cancer therapeutic agent. On 31 December 2021, the new drug application was approved by National Medical Product Administration (NMPA). The metabolism, mass balance, and pharmacokinetics of SHR6390 in 6 healthy Chinese male subjects after a single oral dose of 150 mg [14C]SHR6390 (150 µCi) in this research. The Tmax of SHR6390 was 3.00 h. In plasma, the t1/2 of SHR6390 and its relative components was approximately 17.50 h. The radioactivity B/P (blood-to-plasma) AUC0-t ratio was 1.81, indicating the preferential distribution of drug-related substances in blood cells. At 312 h after administration, the average cumulative excretion of radioactivity was 94.63% of the dose, including 22.69% in urine and 71.93% in stool. Thirteen metabolites were identified. In plasma, because of the low level of radioactivity, only SHR6390 was detected in pooled AUC0-24 h plasma. Stool SHR6390 was the main component in urine and stool. Five metabolites were identified in urine, and 12 metabolites were identified in stool. Overall, faecal clearance is the main method of excretion.

Time-Series-Dependent Global Data Filtering Strategy for Mining and Profiling of Xenobiotic Metabolites in a Dynamic Complex Matrix: Application to Biotransformation of Flavonoids in the Extract of Ginkgo biloba by Gut Microbiota

Efficient characterization of xenobiotic metabolites and their dynamics in a changing complex matrix remains difficult. Herein, we proposed a time-series-dependent global data filtering strategy for the rapid and comprehensive characterization of xenobiotic metabolites and their dynamic variation based on metabolome data. A set of data preprocessing methods was used to screen potential xenobiotic metabolites, considering the differences between the treated and control groups and the fluctuations over time. To further identify metabolites of the target, an in-house accurate mass database was constructed by potential metabolic pathways and applied. Taking the extract of Ginkgo biloba (EGB) co-incubated with gut microbiota as an example, 107 compounds were identified as flavonoid-derived metabolites (including 67 original from EGB and 40 new) from 7468 ions. Their temporal metabolic profiles and regularities were also investigated. This study provided a systematic and feasible method to elucidate and profile xenobiotic metabolism.

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.

2-Pyridine Carboxaldehyde for Semi-Automated Soft Spot Identification in Cyclic Peptides

Cyclic peptides are an attractive option as therapeutics due to their ability to disrupt crucial protein-protein interactions and their flexibility in display type screening strategies, but they come with their own bioanalytical challenges in metabolite identification. Initial amide hydrolysis of a cyclic peptide results in a ring opening event in which the sequence is linearized. Unfortunately, the mass of the singly hydrolyzed sequence is the same (M + 18.0106 Da) irrespective of the initial site of hydrolysis, or soft spot. Soft spot identification at this point typically requires time-consuming manual interpretation of the tandem mass spectra, resulting in a substantial bottleneck in the hit to lead process. To overcome this, derivatization using 2-pyridine carboxaldehyde, which shows high selectivity for the alpha amine on the N-terminus, was employed. This strategy results in moderate- to high-efficiency derivatization with a unique mass tag and diagnostic ions that serve to highlight the first amino acid in the newly linearized peptide. The derivatization method and analytical strategy are demonstrated on a whole cell lysate digest, and the soft spot identification strategy is shown with two commercially available cyclic peptides: JB1 and somatostatin. Effective utilization of the automated sample preparation and interpretation of the resulting spectra shown here will serve to reduce the hit-to-lead time for generating promising proteolytically stable peptide candidates.

Suspect screening and untargeted analysis of veterinary drugs in food by LC-HRMS: Application of background exclusion-dependent acquisition for retrospective analysis of unknown xenobiotics

The presence of veterinary drug and pesticide residues in food products pose considerable threats to human health. Monitoring of these residues in food is mainly carried out using targeted analysis by triple quadrupole mass spectrometry. However, these methods are not suitable for suspect screening and untargeted analysis of unknowns. The main objectives of this study were to develop a new high-resolution mass spectrometry (HRMS)-based analytical strategy for retrospective analysis of suspect and unknown xenobiotics and to evaluate its performance in the tentative identification of 48 veterinary drugs as "unknowns" spiked in a pork sample. In the analysis, a newly developed background exclusion data-dependent acquisition (BE-DDA) technique was employed to trigger the product ion (MS/MS) spectral acquisition of the "unknowns", and an in-house precise-and-thorough background-subtraction (PATBS) technique was applied to detect these "unknowns". Results showed that untargeted data mining of the acquired LC-MS dataset by PATBS was able to find all the 48 veterinary drugs and 46 of them were triggered by BE-DDA to generate accurate MS/MS spectra. The dataset of recorded accurate full-scan mass and MS/MS spectra of all the xenobiotics of the test pork sample is defined as the xenobiotics profile. Searching the xenobiotic profile of the test pork sample using mass spectral data of selected veterinary drugs (as suspects) from the mzCloud spectral library led to the correct hits. Searching against the mzCloud spectral library using the mass spectral data of selected individual veterinary drugs (as unknowns) from the xenobiotics profile tentatively confirmed their identities. In contrast, analysis of the same sample using ion intensity-data dependent acquisition only recorded the MS/MS spectra for 34 veterinary drugs. In addition, a data independent acquisition method enabled the acquisition of the fragment spectra for 44 veterinary drugs, but their spectral data displayed only one or a few true product ions of individual analytes of interest along with many fragments from coeluted biological components and background noises. This study demonstrates that this analytical strategy has a potential to become a practical tool for the retrospective suspect screening and untargeted analysis of unknown xenobiotics in a biological sample such as veterinary drugs and pesticides in food products.

Pharmacokinetics, mass balance, and metabolism of [14C]vicagrel, a novel irreversible P2Y12 inhibitor in humans

Vicagrel, a novel irreversible P2Y₁₂ receptor inhibitor, is undergoing phase III trials for the treatment of acute coronary syndromes in China. In this study, we evaluated the pharmacokinetics, mass balance, and metabolism of vicagrel in six healthy male Chinese subjects after a single oral dose of 20 mg [¹⁴C]vicagrel (120 µCi). Vicagrel absorption was fast (T_max = 0.625 h), and the mean t_1/2 of vicagrel-related components was ~38.0 h in both plasma and blood. The blood-to-plasma radioactivity AUC_inf ratio was 0.55, suggesting preferential distribution of drug-related material in plasma. At 168 h after oral administration, the mean cumulative excreted radioactivity was 96.71% of the dose, including 68.03% in urine and 28.67% in feces. A total of 22 metabolites were identified, and the parent vicagrel was not detected in plasma, urine, or feces. The most important metabolic spot of vicagrel was on the thiophene ring. In plasma pretreated with the derivatization reagent, M9-2, which is a methylated metabolite after thiophene ring opening, was the predominant drug-related component, accounting for 39.43% of the radioactivity in pooled AUC_0-8 h plasma. M4, a mono-oxidation metabolite upon ring-opening, was the most abundant metabolite in urine, accounting for 16.25% of the dose, followed by M3-1, accounting for 12.59% of the dose. By comparison, M21 was the major metabolite in feces, accounting for 6.81% of the dose. Overall, renal elimination plays a crucial role in vicagrel disposition, and the thiophene ring is the predominant metabolic site.

A New Workflow for Drug Metabolite Profiling by Utilizing Advanced Tribrid Mass Spectrometry and Data-Processing Techniques

Drug metabolite profiling utilizes liquid chromatography with tandem mass spectrometry (LC/MS/MS) to acquire ample information for metabolite identification and structural elucidation. However, there are still challenges in detecting and characterizing all potential metabolites that can be masked by a high biological background, especially the unknown and uncommon ones. In this work, a novel metabolite profiling workflow was established on a platform using a state-of-the-art tribrid high-resolution mass spectrometry (HRMS) system. Primarily, an instrumental method was developed based on the novel design of the tribrid system that facilitates in-depth MSⁿ scans with two fragmentation devices. Additionally, different advanced data acquisition techniques were assessed and compared, and automatic background exclusion and deep-scan approaches were adopted to promote assay efficiency and metabolite coverage. Finally, different data-analysis techniques were explored to fully extract metabolite data from the information-rich MS/MS data sets. Overall, a workflow combining tribrid mass spectrometry and advanced acquisition methodology has been developed for metabolite characterization in drug discovery and development. It maximizes the tribrid HRMS platform's utility and enhances the coverage, efficiency, quality, and speed of metabolite profiling assays.

A High Throughput Three-step Ultra-performance Liquid Chromatography Tandem Mass Spectrometry Method to Study Metabolites of Atractylenolide-III

Atractylodes macrocephala Koidz (AMK) is a traditional Chinese medicine widely used in the treatment of various diseases, especially spleen deficiency. As the principle active constituents of AMK, however, the metabolites of Atractylenolide-III (A-lactone-III) have not been identified in rats yet. In this study, a three-step high throughput method based on UHPLC-Q-TOF-MS-MS was developed to profile and characterize the metabolites of A-lactone-III in rat feces, urine and plasma. The initial step was a full-scan that utilized a multiple mass defect filter (MMDF) combined with dynamic background subtraction (DBS). PeakView®1.2 and Metabolitepilot™1.5 software was then used to obtain data and seek possible metabolites. Finally, MS-MS spectra of the parent drug and possible metabolites were compared by the fragment ion peaks and retention times, which enabled metabolites to be identified. As a result, 53 metabolites were characterized in rats in vivo. The metabolic pathways of A-lactone-III were identified as including methylation, oxidation, hydroxylation, dihydroxylation, hydrogenation, glycosylation, sulfonation, and glucuronide, cysteine and N-acetylcysteine conjugation.

Metabolomics-based parallel discovery of xenobiotics and induced endogenous metabolic dysregulation in clinical toxicology

Intoxication by xenobiotics triggers the perturbation of metabolic fingerprints in biofluids, including the accumulation of xenobiotic compounds and the dysregulation of endogenous metabolites. In this work, an untargeted metabolomics workflow was developed to simultaneously profile both xenobiotic and endogenous metabolites for the identification of the xenobiotic origin and an in-depth understanding of the intoxication mechanism. This workflow was demonstrated in a real-world clinical case. Plasma samples were collected from four intoxicated children and another three healthy children. Untargeted metabolomics analysis was performed using ultraperformance liquid chromatography (UPLC) coupled to a high-resolution mass spectrometer (HRMS) with data-independent MS^E acquisition. LC-MS^E data was processed using an untargeted metabolomics data interpretation workflow, in which the identities of xenobiotics and altered endogenous metabolic features were determined via database searching. Five xenobiotic chemicals and 19 endogenous metabolites were found to be dysregulated. Combined with the clinical evidence, penfluridol was confirmed as the xenobiotic toxin. Furthermore, a mechanistic hypothesis was developed to explain the dysregulation of the four endogenous acyl-carnitines. This workflow can be readily applied to a wide range of clinical toxicology cases, offering a powerful and convenient means of simultaneous discovery of intoxication source and the understanding of intoxication mechanisms.

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 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.

MRM-based strategy for the homolog-focused detection of minor ginsenosides from notoginseng total saponins by ultra-performance liquid chromatography coupled with hybrid triple quadrupole-linear ion trap mass spectrometry

A validated MRM-based strategy was established for targeted detection of minor ginsenosides from NGTS by using a LC-Q-Trap/MS.

Untargeted profiling of tracer-derived metabolites using stable isotopic labeling and fast polarity-switching LC-ESI-HRMS

An untargeted metabolomics workflow for the detection of metabolites derived from endogenous or exogenous tracer substances is presented. To this end, a recently developed stable isotope-assisted LC-HRMS-based metabolomics workflow for the global annotation of biological samples has been further developed and extended. For untargeted detection of metabolites arising from labeled tracer substances, isotope pattern recognition has been adjusted to account for nonlabeled moieties conjugated to the native and labeled tracer molecules. Furthermore, the workflow has been extended by (i) an optional ion intensity ratio check, (ii) the automated combination of positive and negative ionization mode mass spectra derived from fast polarity switching, and (iii) metabolic feature annotation. These extensions enable the automated, unbiased, and global detection of tracer-derived metabolites in complex biological samples. The workflow is demonstrated with the metabolism of (13)C9-phenylalanine in wheat cell suspension cultures in the presence of the mycotoxin deoxynivalenol (DON). In total, 341 metabolic features (150 in positive and 191 in negative ionization mode) corresponding to 139 metabolites were detected. The benefit of fast polarity switching was evident, with 32 and 58 of these metabolites having exclusively been detected in the positive and negative modes, respectively. Moreover, for 19 of the remaining 49 phenylalanine-derived metabolites, the assignment of ion species and, thus, molecular weight was possible only by the use of complementary features of the two ion polarity modes. Statistical evaluation showed that treatment with DON increased or decreased the abundances of many detected metabolites.

An integrated approach for detection and characterization of the trace impurities in levofloxacin using liquid chromatography-tandem mass spectrometry

RATIONALE

Impurity analysis plays an important role to guarantee the quality and safety of pharmaceuticals. However, identification of impurities remains challenging, especially for those unknown or at trace levels. We present an integrated approach to detect and characterize the trace impurities in drugs.

METHODS

Based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), an approach integrating automatic impurity screening method using multiple mass defect filters (MMDFs) and background subtraction (BS) was developed. This approach was used to acquire the structural and semi-quantitative information in a single sample run, and even to discover the impurity signals submerged by background and drug ions. This approach was illustrated by the comprehensive impurity analysis of levofloxacin.

RESULTS

This approach was sensitive to detect impurities at the level of 0.02% with respect to levofloxacin concentration. Nineteen impurities were detected, fourteen of which were structurally characterized and eight impurities were reported for the first time. Impurity profiles of levofloxacin drug substances and degradation samples were obtained reliably. A plausible degradation pathway of levofloxacin was proposed including descarboxyl reaction under acid, piperazinyl ring cleavage degradation under light, and N-oxidation under oxidative condition.

CONCLUSIONS

The generic approach integrating LC-MS/MS and an automatic impurity screening method was developed for the detection, characterization and monitoring of impurities, especially those unknown or at trace levels. This approach was demonstrated to be rapid, sensitive and automatic for impurity profiling of drugs.

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.

A new strategy for the discovery of epimedium metabolites using high-performance liquid chromatography with high resolution mass spectrometry

In this paper, a new strategy of drug metabolite discovery and identification was established using high-performance liquid chromatography with high resolution mass spectrometry (HPLC-HRMS) and a mass spectral trees similarity filter (MTSF) technique. The MTSF technique was developed as a means to rapidly discover comprehensive metabolites from multiple active components in a complicated biological matrix. Using full-scan mass spectra as the stem and data-dependent subsequent stage mass spectra to form branches, the HRMS and multiple-stage mass spectrometric data from detected compounds were converted to mass spectral trees data. Potential metabolites were discovered based on the similarity between their mass spectral trees and that known compounds or metabolites in a mass spectra trees library. The threshold value for match similarity scores was set at above 200, allowing approximately 80% of interference to be filtered out. A total of 115 metabolites of five flavonoid monomers (epimedin A, epimedin B, epimedin C, icariin, and baohuoside I) and herbal extract of epimedium were discovered and identified in rats via this new strategy. As a result, a metabolic profile for epimedium was obtained and a metabolic pathway was proposed. In addition, comparing to the widely used neutral loss filter (NLF), product ion filter (PIF), and mass defect filter (MDF) techniques, the MTSF technique was shown superior efficiency and selectivity for discovering and identifying metabolites in traditional Chinese medicine (TCM).

It is time for a paradigm shift in drug discovery bioanalysis: from SRM to HRMS

It can be argued that the last true paradigm shift in the bioanalytical (BA) arena was the shift from high-performance liquid chromatography (HPLC) with ultraviolet (UV) detection to HPLC with tandem mass spectrometry (MS/MS) detection after the commercialization of the triple quadrupole mass spectrometer in the 1990s. HPLC-MS/MS analysis based on selected reaction monitoring (SRM) has become the gold standard for BA assays and is used by all the major pharmaceutical companies for the quantitative analysis of new drug entities (NCEs) as part of the new drug discovery and development process. While LC-MS/MS continues to be the best tool for drug discovery bioanalysis, a new paradigm involving high-resolution mass spectrometry (HRMS) and ultrahigh-pressure liquid chromatography (uHPLC) is starting to make inroads into the pharmaceutical industry. The ability to collect full scan spectra, with excellent mass accuracy, mass resolution, 10-250 ms scan speeds and no NCE-related MS parameter optimization, makes the uHPLC-HRMS techniques suitable for quantitative analysis of NCEs while preserving maximum qualitative information about other drug-related and endogenous components such as metabolites, degradants, biomarkers and formulation materials. In this perspective article, we provide some insight into the evolution of the hybrid quadrupole-time-of-flight (Qq-TOF) mass spectrometer and propose some of the desirable specifications that such HRMS systems should have to be integrated into the drug discovery bioanalytical workflow for performing integrated qualitative and quantitative bioanalysis of drugs and related components.

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.

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.

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.

Analytical strategies for the screening and evaluation of chemically reactive drug metabolites

BACKGROUND

Metabolic activation leading to formation of chemically reactive drug metabolites is a long-standing issue for drug development inasmuch as some, but not all, reactive intermediates play a role as mediators of drug-induced toxicities. The risk assessment profile/decision-making guide requires a comprehensive understanding of bioactivation mechanism(s), quantitative magnitude and cellular consequences of this principal and continued safety attrition.

OBJECTIVE

To evaluate analytical methodologies with improved sensitivity, selectivity and throughput for the analysis of reactive metabolites.

CONCLUSIONS

Identification and quantification of short-lived electrophilic intermediates through appropriate trapping experiments have become relatively straightforward. Minimizing the bioactivation potential of drug candidates during the discovery/lead optimization phase has been adopted as a default strategy. Together with advances of proteomics, metabolomics and toxicogenomics, an integrated multitier approach possibly provides a deeper insight into mechanistic aspects of drug-induced toxicities, and contributes to bridging the relationships between metabolic activation, drug-protein adduct formation and their toxicological consequences.

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 (14)C-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).

Algorithm for thorough background subtraction of high-resolution LC/MS data: application to obtain clean product ion spectra from nonselective collision-induced dissociation experiments

Nonselective collision-induced dissociation (CID) is a technique for producing fragmentation products for all ions generated in an ion source. It is typical of liquid chromatography/mass spectrometry (LC/MS) analysis of complex samples that matrix-related components may contribute to the resulting product ion spectra and confound the usefulness of this technique for structure interpretation. In this proof-of-principle study, a high-resolution LC/MS-based background subtraction algorithm was used to process the nonselective CID data to obtain clean product ion spectra for metabolites in human plasma samples. With buspirone and clozapine metabolites in human plasma as examples, this approach allowed for not only facile detection of metabolites of interest but also generation of their respective product ion spectra that were clean and free of matrix-related interferences. This was demonstrated with both an MS(E) technique (where E represents collision energy) with a quadrupole time-of-flight (QTOF) instrument and an in-source fragmentation technique with an LTQ Orbitrap instrument. The combined nonselective CID and background subtraction approach should allow for detection and structural interpretation of other types of sample analyses where control samples are obtained.

Liquid chromatography-mass spectrometry in in vitro drug metabolite screening

A combination of high performance liquid chromatography (HPLC) and mass spectrometry (LC/MS) has proven its status as the most powerful analytical tool for screening and identifying drug metabolites in modern drug discovery. These techniques have become irreplaceable for drug metabolism laboratories, providing high amounts of information from a wide variety of samples. This review focuses on the most common and useful applications of these techniques when working on in vitro metabolism, more specifically with screening and identification of chemically stable or reactive metabolites formed via biotransformation reactions. Matching specific tasks and suitable instruments is a recurring consideration; for many reasons, the time-of-flight or orbitrap mass spectrometry provides clearly increased efficiency in metabolite profiling compared to other types of mass spectrometry.