Targeted Data-Independent Acquisition and Mining Strategy for Trace Drug Metabolite Identification Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Non-target metabolomics approach for the investigation of the hidden effects induced by atrazine and its degradation products on plant metabolism

Japanese radish (Raphanus sativus var. longipinnatus) plants grown under laboratory conditions were individually exposed to the same doses of atrazine (2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine, ATR) or its main degradation products: either 2-amino-4-chloro-6-isopropylamino-1,3,5-triazine (DEA) or 2-amino-4-chloro-6-ethylamino-1,3,5-triazine (DIA) or desethyl-desisopropyl-atrazine (DEDIA) or 4-(ethylamino)-2-hydroxy-6-(isopropylamino)-1,3,5-triazine (HA), respectively. One week after treatment in plants exposed to ATR, DIA, and DEA, their concentrations were 7.8 μg/g, 9.7 μg/g, and 14.5 μg/g, respectively, while those treated with DEDIA and HA did not contain these compounds. These results were correlated with plant amino acid profile obtained by suspect screening analysis and metabolomic "fingerprint" based on non-target analysis, obtained by liquid chromatography coupled with QTRAP triple quadrupole mass spectrometer. In all cases, both ATR and its by-products were found to interfere with the plant's amino acid profile and modify its metabolic "fingerprint". Therefore, we proved that the non-target metabolomics approach is an effective tool for investigating the hidden effects of pesticides and their transformation products, which is particularly important as these compounds may reduce the quality of edible plants.

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.

A mass defect filtering combined background subtraction strategy for rapid screening and identification of metabolites in rat plasma after oral administration of Yindan Xinnaotong soft capsule

The absorbed prototypes and metabolites of traditional Chinese medicines (TCMs) serves an important part in pharmacological action and clinical effects. However, the comprehensive characterization of which is facing actual or possible rigorous challenges due to the lack of data mining methods and the complexity of metabolite samples. Yindan Xinnaotong soft capsule (YDXNT), a typical traditional Chinese medicine prescription consisting of extracts from 8 herbal medicines, is widely used for the treatment of angina pectoris and ischemic stroke in the clinic. This study established a systematic data mining strategy based on ultra-high performance liquid chromatography tandem quadrupole-time-of-fight mass spectrometry (UHPLC-Q-TOF MS) for comprehensive metabolite profiling of YDXNT in rat plasma after oral administration. The multi-level feature ion filtration strategy was primarily conducted through the full scan MS data of plasma samples. All potential metabolites were rapidly fileted out from the endogenous background interference based on the background subtract and the chemical type specifically mass defect filter (MDF) windows including flavonoids, ginkgolides, phenolic acids, saponins, and tanshinones. As the MDF windows of certain types were overlapped, the screened-out potential metabolites were deeply characterized and identified according to their retention times (RT), integrating neutral loss filtering (NLF), diagnostic fragment ions filtering (DFIF), and further confirmed by reference standards. Thus, a total of 122 compounds, consisting of 29 prototype components (16 confirmed with reference standards) and 93 metabolites had been identified. This study provides a rapid and robust metabolite profiling method for researching complicated traditional Chinese medicine prescriptions.

Suspect screening strategy for pesticide application history based on characteristics of trace metabolites

Pesticides are widely used to protect crops but can also threaten public health as they can remain in the environment for a long time. Additionally, some transformation products (TPs) of unknown toxicity, stability, or bioaccumulation properties can further be formed from the hydrolysis, photolysis and biodegradation of pesticides. The identification and quantification of those TPs can be challenging for environmental regulation and risk assessment due to a limited understanding about them. In this study, a suspect screening strategy for pesticide application history was developed and then used to organic products (tea). Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) was used to screen and identify the TPs in crops and their toxicity was subsequently predicted with the open-source software (ECOSAR and admetSAR). Finally, the SIRIUS software was applied and 142 TPs from 20 pesticides were identified in tea plants based on the fragmentation-degradation relationship. Of these, standards (level 1) and 53 were considered as tentatively identified (levels 2a and 2b). Some TPs were also found to be present in tea plants and soil after 65 days, thus indicating higher persistency or stability than parent pesticides. While others from diafenthiuron and neonicotinoids had higher predicted toxicity of daphnid, and demonstrated positive for honeybee toxicity. Suspect screening is a powerful tool to screen pesticide TPs on the complex matrix of crops. Such screening can provide potential evidence of pesticide application, especially in cases of illegal practices in organic farming.

Stepwise Diagnostic Product Ions Filtering Strategy for Rapid Discovery of Diterpenoids in Scutellaria barbata Based on UHPLC-Q-Exactive-Orbitrap-MS

Diterpenoids are considered the major bioactive components in Scutellaria barbata to treat cancer and inflammation, but few comprehensive profiling studies of diterpenoids have been reported. Herein, a stepwise diagnostic product ions (DPIs) filtering strategy for efficient and targeted profiling of diterpenoids in Scutellaria barbata was developed using UHPLC-Q-Exactive-Orbitrap-MS. After UHPLC-HRMS/MS analysis of six diterpenoid reference standards, fragmentation behaviors of these references were studied to provide DPIs. Then, stepwise DPIs filtering aimed to reduce the potential interferences of matrix ions and achieve more chromatographic peaks was conducted to rapidly screen the diterpenoids. The results demonstrated that stepwise DPIs were capable of simplifying the workload in data post-processing and the effective acquisition of low abundance compounds. Subsequently, DPIs and MS/MS fragment patterns were adopted to identify the targeted diterpenoids. As a result, 381 diterpenoids were unambiguously or tentatively identified, while 141 of them with completely new molecular weights were potential new diterpenoids for Scutellaria barbata. These results demonstrate that the developed stepwise DPIs filtering method could be employed as an efficient, reliable, and valuable strategy to screen and identify the diterpenoid profile in Scutellaria barbata. This might accelerate and simplify target constituent profiling from traditional Chinese medicine (TCM) extracts.

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.

An Entire Process Optimization Strategy for Comprehensive In Vivo Metabolite Profiling of Prucalopride in Rats Based on Ultra-Performance Liquid Chromatography With Q-Exactive Hybrid Quadrupole-Orbitrap High-Resolution Mass Spectrometry

Prucalopride was widely used for chronic constipation, which is difficult to be adequately relieved by laxatives in adult patients in clinic. Due to the difficulty in metabolite identification, metabolic process of prucalopride had not been investigated in vivo. In this study, an efficient strategy was proposed for comprehensive metabolite profiling of prucalopride after oral administration in rat plasma, urine, and feces samples. This strategy was composed of five steps. First, the samples at multiple time points after oral administration were collected to increase the representativeness of the samples. Second, different sample preparation methods were investigated to obtain superior extraction efficiency. Third, the raw data of test sample and blank sample were acquired using ultra-performance liquid chromatography with Q-Exactive hybrid quadrupole–orbitrap high-resolution accurate mass spectrometry under the positive and negative full-scan/dd MS² mode. Fourth, combined mass defect filter with background subtraction model in soft of compound discovery, all peaks were constructed to filter potential metabolites after retention time alignment and ion filtration, which could remove large amounts of interference ions. Besides, it can predict potential biotransformation, promoting to understand how to metabolize the drug. This provides multiple possibilities and prevents us conjecturing the potential metabolites blindly. Finally, the verification procedure was implemented through exporting the structure and MS² spectrum to the analytical tool of Mass Frontier. The proposed strategy significantly improved the targeted detection and identification for metabolites in vivo. A total of 47 metabolites were tentatively characterized in the plasma, urine, and feces samples after oral administration of prucalopride. This study could provide a valuable reference for systematic metabolite profile of drug in vivo.

An integrated strategy for comprehensive characterization of metabolites and metabolic profiles of bufadienolides from Venenum Bufonis in rats

Comprehensive characterization of metabolites and metabolic profiles in plasma has considerable significance in determining the efficacy and safety of traditional Chinese medicine (TCM) in vivo. However, this process is usually hindered by the insufficient characteristic fragments of metabolites, ubiquitous matrix interference, and complicated screening and identification procedures for metabolites. In this study, an effective strategy was established to systematically characterize the metabolites, deduce the metabolic pathways, and describe the metabolic profiles of bufadienolides isolated from Venenum Bufonis in vivo. The strategy was divided into five steps. First, the blank and test plasma samples were injected into an ultra-high performance liquid chromatography/linear trap quadrupole-orbitrap-mass spectrometry (MS) system in the full scan mode continuously five times to screen for valid matrix compounds and metabolites. Second, an extension-mass defect filter model was established to obtain the targeted precursor ions of the list of bufadienolide metabolites, which reduced approximately 39% of the interfering ions. Third, an acquisition model was developed and used to trigger more tandem MS (MS/MS) fragments of precursor ions based on the targeted ion list. The acquisition mode enhanced the acquisition capability by approximately four times than that of the regular data-dependent acquisition mode. Fourth, the acquired data were imported into Compound Discoverer software for identification of metabolites with metabolic network prediction. The main in vivo metabolic pathways of bufadienolides were elucidated. A total of 147 metabolites were characterized, and the main biotransformation reactions of bufadienolides were hydroxylation, dihydroxylation, and isomerization. Finally, the main prototype bufadienolides in plasma at different time points were determined using LC-MS/MS, and the metabolic profiles were clearly identified. This strategy could be widely used to elucidate the metabolic profiles of TCM preparations or Chinese patent medicines in vivo and provide critical data for rational drug use.

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Extension-mass defect filter model could reduce about 39% interfering ions.

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The optimized acquisition mode enhanced about 4 times acquisition capability than regular DDA mode.

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147 metabolites were characterized with metabolic network prediction, and the metabolic pathways were deduced in plasmas.

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The quantitative method of 14 prototypes was established by LC-MS/MS for metabolic profiles study.

Drug metabolite identification using ultrahigh-performance liquid chromatography-ultraviolet spectroscopy and parallelized scans on a tribrid Orbitrap mass spectrometer

RATIONALE

To capture all metabolites in metabolite identification studies, MS/MS information is required in both positive and negative ionization mode, usually involving several sample injections to gain all information about samples. A high-resolution and high mass accuracy quadrupole/linear trap/Orbitrap tribrid instrument was used to gain this information in a novel single injection 'capture-all' approach to metabolite identification.

METHODS

Diclofenac, a model compound, was incubated in human and rat hepatocytes. These incubated samples were run using an ultrahigh-performance liquid chromatography/ultraviolet (UHPLC-UV) system coupled to a Thermo Fusion tribrid mass spectrometer. Five parallel scans were used: positive and negative ion full scan, data-dependent MS/MS, both high energy dissociation and collision-induced dissociation, and data-independent all ion fragmentation (AIF) spectra were collected in positive and negative ion mode.

RESULTS

Nine metabolites were identified; a metabolite observed in the UV trace, but not positive ion full scan MS, was detected in the same sample injection by negative ion full scan MS. This was identified as a sulphate metabolite, and the corresponding negative ion AIF allowed for some structural elucidation. The use of a photo-diode array (PDA) detector allowed for spectral assessment in case of changes in absorbance spectra, and the subsequent semi-quantification of metabolites.

CONCLUSIONS

This method provided good-quality MS/MS data across the m/z range in both positive and negative ion mode. The addition of both negative ion full scan MS and negative ion MS/MS allowed for the detection and structural elucidation of metabolites not observed in positive ion mode. The use of the PDA detector allowed for the semi-quantification of metabolites.

Suspect and non-target screening of pesticides and pharmaceuticals transformation products in wastewater using QTOF-MS

Pesticides and pharmaceuticals are widely used in modern life and are discharged into wastewater after usage. However, a large number of transformation products (TPs) are formed through abiotic (hydrolysis/photolysis, etc.) and biotic (aerobic/anaerobic degradation by micro-organisms) wastewater treatment processes, and the structure and potential risk of TPs are still unclear. In this study, a suspect and non-target screening was performed to monitor these chemicals with HPLC-QTOF-MS. We identified 60 parent compounds by suspect screening in three Chinese wastewater treatment plants with the commercial database of pesticides and pharmaceuticals, and they were confirmed by authentic standards. Then, suspect and non-target screening strategies based on the predicted diagnostic fragment ions were used to screen TPs of the 60 parent compounds. We tentatively identified 50 TPs and confirmed thirteen of them with authentic standards. Among 13 quantified TPs, about 40% of them showed higher concentration than their parent compounds in effluent. Especially, cloquintocet, as a TP of cloquintocet-mexyl, had a concentration ratio TP/parent = 14,809 in effluent. Twenty-five TPs had higher predicted toxicity than the corresponding parent compounds by calculating their LC₅₀ values towards aquatic organisms using toxicity prediction software. Twenty identified TPs were firstly reported in this study. These results indicate the importance of TP analysis in environmental monitoring in wastewater.

Exploiting High-Resolution Mass Spectrometry for Targeted Metabolite Quantification and 13C-Labeling Metabolism Analysis

Quantification of targeted metabolites, especially trace metabolites and structural isomers, in complex biological materials is an ongoing challenge for metabolomics. In this chapter, we summarize high-resolution mass spectrometry-based approaches mainly used for targeted metabolite and metabolomics analysis, and then introduce an MS1/MS2-combined PRM workflow for quantification of central carbon metabolism intermediates, amino acids, and shikimate pathway-related metabolites. Major steps in the workflow, including cell culture, metabolite extraction, LC-MS analysis and data processing, are described. Furthermore, we adapt this new approach to a dynamic ¹³C-labeling experiment and demonstrate its unique advantage in capturing and correcting isotopomer labeling curves to facilitate nonstationary ¹³C-labeling metabolism analysis.

Identification and screening of cardiac glycosides in Streptocaulon griffithii using an integrated data mining strategy based on high resolution mass spectrometry

The present study was designed to develop a practical strategy to tackle the problem of lacking standard compounds and limited references for identifying structure-related compounds in Streptocaulon griffithii Hook. f., especially those in trace concentrations, with a focus on antitumor activity. The cardiac glycosides (CGs)-enriched part was determined using in vitro bioactive assays in three cancer cell lines and then isolated using macroporous resins. The MS and MS/MS data were acquired using a high performance liquid chromatography coupled with hybrid quadrupole-time of flight (HPLC-Q-TOF-MS) system. To acquire data of trace compound in the extract, a multiple segment program was applied to modify the HPLC-Q-TOF-MS method. A mass defect filter (MDF) approach was employed to make a primary MS data filtration. Utilizing a MATLAB program, the redundant peaks obtained by imprecise MDF template calculated with limited references were excluded by fragment ion classification, which was based on the ion occurrence number in the MDF-filtered total ion chromatograms (TIC). Additionally, the complete cleavage pathways of CG aglycones were proposed to assist the structural identification of 29 common fragment ions (CFIs, ion occurrence number ≥ 5) and diagnostic fragment ions (DFIs, ion occurrence number < 5). As a result, 30 CGs were filtered out from the MDF results, among which 23 were identified. This newly developed strategy may provide a rapid and effective tool for identifying structure-related compounds in herbal medicines.

An enhanced strategy integrating offline two-dimensional separation and step-wise precursor ion list-based raster-mass defect filter: Characterization of indole alkaloids in five botanical origins of Uncariae Ramulus Cum Unicis as an exemplary application

Comprehensive chemical profiling is of great significance for understanding the therapeutic material basis and quality control of herbal medicines, which is challenging due to its inherent chemical diversity and complexity, as well as wide concentration range. In this study, we introduced an enhanced strategy integrating offline two-dimensional (2D) separation and the step-wise precursor ion list-based raster-mass defect filter (step-wise PIL-based raster-MDF) scan by tandem LTQ-Orbitrap mass spectrometer. A comprehensive analysis of indole alkaloids in five botanical origins of Uncariae Ramulus Cum Unicis (Gou-Teng) was used as an exemplary application. A positively charged reversed phase (PR) × conventional RP LC system in different pH conditions was constructed with the orthogonality of 74%. A theoretical step-wise PIL among 310-950 Da with the step-size of 2 Da was developed to selectively trigger fragmentations and extend the coverage of potential indole alkaloids. Simultaneously, by defining parent mass width (PMW) of the step-wise PIL to ±55 mDa, a raster-MDF screening was achieved in the acquisition process. Additionally, subtype classification and structural elucidation were facilitated by a four-step interpretation strategy. As a result, a total of 1227 indole alkaloids were efficiently exposed and characterized from five botanical origins of Gou-Teng, which showed high chemical diversity. A systematic comparison among five species was first performed and only 66 indole alkaloids were common. For method validation, three new alkaloid N-oxides were isolated and unambiguously identified by NMR. The present study provides a novel data-dependent acquisition method with improved target coverage and high selectivity. The integrated strategy is practical to efficiently expose and comprehensively characterize complex components in herbal medicines.

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.

Global profiling and rapid matching of natural products using diagnostic product ion network and in silico analogue database: Gastrodia elata as a case study

Rapid discovery of novel compounds of a traditional herbal medicine is of vital significance for pharmaceutical industry and plant metabolic pathway analysis. However, discovery of unknown or trace natural products is an ongoing challenge. This study presents a universal targeted data-independent acquisition and mining strategy to globally profile and effectively match novel natural product analogues from an herbal extract. The famous medical plant Gastrodia elata was selected as an example. This strategy consists of three steps: (i) acquisition of accurate parent and adduct ions (PAIs) and the product ions data of all eluting compounds by untargeted full-scan MS(E) mode; (ii) rapid compound screening using diagnostic product ions (DPIs) network and in silico analogue database with SUMPRODUCT function to find novel candidates; and (iii) identification and isomerism discrimination of multiple types of compounds using ClogP and ions fragment behavior analyses. Using above data mining methods, a total of 152 compounds were characterized, and 70 were discovered for the first time, including series of phospholipids and novel gastroxyl derivatives. Furthermore, a number of gastronucleosides and phase II metabolites of gastrodin and parishins were discovered, including glutathionylated, cysteinylglycinated and cysteinated compounds, and phosphatidylserine analogues. This study extended the application of classical DPIs filter strategy and developed a structure-based screening approach with the potential for significant increase of efficiency for discovery and identification of trace novel natural products.

Urinary antihypertensive drug metabolite screening using molecular networking coupled to high-resolution mass spectrometry fragmentation

INTRODUCTION

Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry in combination with MS/MS gas-phase experiments has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectral files pose challenges to downstream analysis, given their complexity and size.

OBJECTIVES

This study aims to detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments based on the spectral similarity of their fragmentation spectra. Furthermore, spectral clusters of endogenous metabolites were also examined.

METHODS

Here we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. The mass spectrometry data was collected on a Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments.

RESULTS

In total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlodipine. The molecular networking approach also generated clusters of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline.

CONCLUSIONS

The approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome.