Cytochrome P450 2B6 and UDP-Glucuronosyltransferase Enzyme-Mediated Clearance of Ciprofol (HSK3486) in Humans: The Role of Hepatic and Extrahepatic Metabolism.

Ciprofol (HSK3486) is a novel intravenous agent for general anesthesia. In humans, HSK3486 mainly undergoes glucuronidation to form M4 [fraction of clearance (fCL): 62.6%], followed by the formation of monohydroxylated metabolites that further undergo glucuronidation and sulfation to produce M5-1, M5-2, M5-3, and M3 (summed fCL: 35.2%). However, the complete metabolic pathways of HSK3486 in humans remain unclear. In this study, by comparison with chemically synthesized reference standards, three monohydroxylated metabolites [M7-1, 4-hydroxylation with an unbound intrinsic clearance (CLint,u) of 2211 µl/min/mg; M7-2, ω-hydroxylation with a CLint,u of 600 µl/min/mg; and M7-3, (ω-1)-hydroxylation with a CLint,u of 78.4 µl/min/mg] were identified in human liver microsomes, and CYP2B6 primarily catalyzed their formation. In humans, M7-1 was shown to undergo glucuronidation at the 4-position and 1-position by multiple UDP-glucuronosyltransferases (UGTs) to produce M5-1 and M5-3, respectively, or was metabolized to M3 by cytosolic sulfotransferases. M7-2 was glucuronidated at the ω position by UGT1A9, 2B4, and 2B7 to form M5-2. UGT1A9 predominantly catalyzed the glucuronidation of HSK3486 (M4). The CLint,u values for M4 formation in human liver and kidney microsomes were 1028 and 3407 µl/min/mg, respectively. In vitro to in vivo extrapolation analysis suggested that renal glucuronidation contributed approximately 31.4% of the combined clearance. In addition to HSK3486 glucuronidation (M4), 4-hydroxylation (M7-1) was identified as another crucial oxidative metabolic pathway (fCL: 34.5%). Further attention should be paid to the impact of CYP2B6- and UGT1A9-mediated drug interactions and gene polymorphisms on the exposure and efficacy of HSK3486. SIGNIFICANCE STATEMENT: This research elucidates the major oxidative metabolic pathways of HSK3486 (the formation of three monohydroxylated metabolites: M7-1, M7-2, M7-3) as well as definitive structures and formation pathways of these monohydroxylated metabolites and their glucuronides or sulfate in humans. This research also identifies major metabolizing enzymes responsible for the glucuronidation (UGT1A9) and oxidation (CYP2B6) of HSK3486 and characterizes the mechanism of extrahepatic metabolism. The above information is helpful in guiding the safe use of HSK3486 in the clinic.

Metabolite characterisation of the peptide-drug conjugate LN005 in liver S9s by UHPLC-Orbitrap-HRMS.

LN005 is a peptide-drug conjugate (PDC) targeting glucose-regulated protein 78 (GRP78) to treat several types of cancer, such as breast, colon, and prostate cancer.As a new drug modality, understanding its metabolism and elimination pathways will help us to have a whole picture of it. Currently, there are no metabolic studies on LN005; therefore, this study aimed to investigate the metabolism of LN005, clarify its metabolic profile in the liver S9s of different species, and identify the major metabolic pathways and differences between species.The incubation samples were measured by ultra-high performance liquid chromatography combined with orbitrap tandem mass spectrometry (UHPLC-Orbitrap-HRMS).The results showed that LN005 was metabolised by liver S9s, and four metabolites were identified. The main metabolic pathway of LN005 in liver S9s was oxidative deamination to ketone or hydrolysis. Similar metabolic profiles were observed in mouse, rat, dog, monkey, and human liver S9s, indicating no differences between these four animal species and humans.This study provides information for the structural modification and optimisation of LN005 and affords a reference for subsequent animal experiments and human metabolism of other PDCs.

Conference report for the 10th China Bioanalysis Forum annual meeting.

The 10th China Bioanalysis Forum annual conference was held in Suzhou between 9 and 11 June 2023. This year a full range of bioanalytical topics were discussed such as new technology and bioanalytical approaches for biotherapeutics and biomarkers, particularly in the areas of gene and cell therapy. Another research area covered extensively at the conference was drug metabolism, including new drug metabolism and pharmacokinetic methods; absorption, distribution, metabolism and excretion of new modality drugs, recent regulatory guidance such as human mass balance study and preclinical study of antibody-drug conjugates and case studies of drug metabolism support to newly approved drugs.

Global Xenobiotic Profiling of Rat Plasma Using Untargeted Metabolomics and Background Subtraction-Based Approaches: Method Evaluation and Comparison.

BACKGROUND: Global xenobiotic profiling (GXP) is to detect and structurally characterize all xenobiotics in biological samples using mainly liquid chromatography-high resolution mass spectrometry (LC-HRMS) based methods. GXP is highly needed in drug metabolism study, food safety testing, forensic chemical analysis, and exposome research. For detecting known or predictable xenobiotics, targeted LC-HRMS data processing methods based on molecular weights, mass defects and fragmentations of analytes are routinely employed. For profiling unknown xenobiotics, untargeted and LC-HRMS based metabolomics and background subtraction-based approaches are required. OBJECTIVE: This study aimed to evaluate the effectiveness of untargeted metabolomics and the precise and thorough background subtraction (PATBS) in GXP of rat plasma. METHODS: Rat plasma samples collected from an oral administration of nefazodone (NEF) or Glycyrrhizae Radix et Rhizoma (Gancao, GC) were analyzed by LC-HRMS. NEF metabolites and GC components in rat plasma were thoroughly searched and characterized via processing LC-HRMS datasets using targeted and untargeted methods. RESULTS: PATBS detected 68 NEF metabolites and 63 GC components, while the metabolomic approach (MS-DIAL) found 67 NEF metabolites and 60 GC components in rat plasma. The two methods found 79 NEF metabolites and 80 GC components with 96% and 91% successful rates, respectively. CONCLUSION: Metabolomics methods are capable of GXP and measuring alternations of endogenous metabolites in a group of biological samples, while PATBS is more suited for sensitive GXP of a single biological sample. A combination of metabolomics and PATBS approaches can generate better results in the untargeted profiling of unknown xenobiotics.

Detection and Characterization of In Vitro Payload-Containing Catabolites of Noncleavable Antibody-Drug Conjugates by High-Resolution Mass Spectrometry and Multiple Data Mining Tools.

The formation and accumulation of payload-containing catabolites (PCCs) from a noncleavable antibody-drug conjugate (ADC) in targeted and normal tissues are directly associated with the therapeutic effect and toxicity of the ADC, respectively. Understanding the PCC formation is important for supporting the payload design and facilitating preclinical evaluation of ADCs. However, detection and identification of PCCs of a noncleavable ADC are challenging due to their low concentrations and unknown structures. The main objective of this study was to develop and apply a generic liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method for profiling PCCs in vitro. Noncleavable ADCs, ado-trastuzumab emtansine (T-DM1) and ADC-1, were incubated in liver lysosomes, liver S9, and/or cancer cells followed by data acquisition using LC-HRMS. Profiling PCCs mainly relied on processing LC-HRMS datasets using untargeted precise and thorough background subtraction (PATBS) processing and targeted product ion filtering (PIF). As a result, 12 PCCs of T-DM1 were detected and structurally characterized in human liver lysosomal incubation, a majority of which consisted of 4-[N-maleimidomethyl]cyclohexane-1-carboxylate (MCC)-DM1 and a few amino acids. Additionally, the incubation of ADC-1 in human, rat, and monkey liver S9 and cancer cells generated one major and three very minor PCCs, verifying the payload design. The results demonstrate that PATBS enabled the comprehensive profiling of PCCs regardless of their molecular weights, charge states, and fragmentations. As a complementary tool, PIF detected specific PCCs with superior sensitivity. The combination of the in vitro metabolism systems and the LC-HRMS method is a useful approach to profiling in vitro PCCs of noncleavable ADCs in support of drug discovery programs. SIGNIFICANCE STATEMENT: Profiling in vitro payload-containing catabolites (PCCs) of a noncleavable antibody-drug conjugate (ADC) is important for optimization of the payload design and preclinical evaluation of ADC. However, currently used analytical approaches often fail to quickly provide reliable PCC profiling results. The work introduces a new liquid chromatography high resolution mass spectrometry method for comprehensive and rapid detection and characterization of PCCs released from a noncleavable ADC in liver lysosomes and S9 incubations.

High-Throughput Metabolic Soft-Spot Identification in Liver Microsomes by LC/UV/MS: Application of a Single Variable Incubation Time Approach.

CYP-mediated fast metabolism may lead to poor bioavailability, fast drug clearance and significant drug interaction. Thus, metabolic stability screening in human liver microsomes (HLM) followed by metabolic soft-spot identification (MSSID) is routinely conducted in drug discovery. Liver microsomal incubations of testing compounds with fixed single or multiple incubation time(s) and quantitative and qualitative analysis of metabolites using high-resolution mass spectrometry are routinely employed in MSSID assays. The major objective of this study was to develop and validate a simple, effective, and high-throughput assay for determining metabolic soft-spots of testing compounds in liver microsomes using a single variable incubation time and LC/UV/MS. Model compounds (verapamil, dextromethorphan, buspirone, mirtazapine, saquinavir, midazolam, amodiaquine) were incubated at 3 or 5 µM with HLM for a single variable incubation time between 1 and 60 min based on predetermined metabolic stability data. As a result, disappearances of the parents were around 20-40%, and only one or a few primary metabolites were generated as major metabolite(s) without notable formation of secondary metabolites. The unique metabolite profiles generated from the optimal incubation conditions enabled LC/UV to perform direct quantitative estimation for identifying major metabolites. Consequently, structural characterization by LC/MS focused on one or a few major primary metabolite(s) rather than many metabolites including secondary metabolites. Furthermore, generic data-dependent acquisition methods were utilized to enable Q-TOF and Qtrap to continuously record full MS and MS/MS spectral data of major metabolites for post-acquisition data-mining and interpretation. Results from analyzing metabolic soft-spots of the seven model compounds demonstrated that the novel MSSID assay can substantially simplify metabolic soft-spot identification and is well suited for high-throughput analysis in lead optimization.

Exploration of Q-Marker of Rhubarb Based on Intelligent Data Processing Techniques and the AUC Pooled Method.

Rhubarb, as a traditional Chinese medicine, has several positive therapeutic effects, such as purging and attacking accumulation, clearing heat and purging fire, cooling blood, and detoxification. Recently, Rhubarb has been used in prescriptions for the prevention and treatment of COVID-19, with good efficacy. However, the exploration of effective quantitative approach to ensure the consistency of rhubarb's therapeutic efficacy remains a challenge. In this case, this study aims to use non-targeted and targeted data mining technologies for its exploration and has comprehensively identified 72 rhubarb-related components in human plasma for the first time. In details, the area under the time-concentration curve (AUC)-pooled method was used to quickly screen the components with high exposure, and the main components were analyzed using Pearson correlation and other statistical analyses. Interestingly, the prototype component (rhein) with high exposure could be selected out as a Q-marker, which could also reflect the metabolic status changes of rhubarb anthraquinone in human. Furthermore, after comparing the metabolism of different species, mice were selected as model animals to verify the pharmacodynamics of rhein. The in vivo experimental results showed that rhein has a positive therapeutic effect on pneumonia, significantly reducing the concentration of pro-inflammatory factors [interleukin (IL)-6 and IL-1ß] and improving lung disease. In short, based on the perspective of human exposure, this study comprehensively used intelligent data post-processing technologies and the AUC-pooled method to establish that rhein can be chosen as a Q-marker for rhubarb, whose content needs to be monitored individually.

Application of physiologically based pharmacokinetic modelling for the prediction of drug-drug interactions involving anlotinib as a perpetrator of cytochrome P450 enzymes.

Anlotinib is a small molecule of novel tyrosine kinase inhibitor initially approved to treat non-small cell lung cancer in China. Drug-drug interaction (DDI) is an extrinsic factor important for the appropriate use of anlotinib in clinical practice. In vitro experiments demonstrated that anlotinib is a substrate of cytochrome P450 (CYP) enzymes and moderate inhibitor of several common ones; however, no clinical DDI studies have been performed to investigate inhibitory effects of anlotinib on these CYP enzymes. Thus, its drug label recommends avoiding co-administration with substrates of these enzymes, which have narrow therapeutic windows. In this study, we performed a CYP450 inhibition study, followed by gathering in vitro and clinical pharmacokinetic data to build the first physiologically based pharmacokinetic (PBPK) model of anlotinib. The verified model was subsequently used to predict the DDI mediated by anlotinib. As a result, the marginal plasma exposure changes of typical CYP3A and CYP2C9 substrates were less than the bioequivalence threshold, indicating that anlotinib has a very low potential of causing clinically meaningful DDI through the inhibition of several major CYP enzymes. According to the FDA's latest guideline on DDI, the established model with the simulation results may support the revision of anlotinib labelling without further clinical studies, lifting unnecessary restrictions on anlotinib regimens.

Recent Progress on Mass Spectrum Based Approaches for Absorption, Distribution, Metabolism, and Excretion Characterization of Traditional Chinese Medicine.

BACKGROUND: The absorption, distribution, metabolism, and excretion (ADME) of traditional Chinese medicine (TCM) components are closely related to their therapeutic efficacy, toxic effects, and drug interactions. Based on the study of the whole process of ADME in TCM, it is important to screen out the key pharmacokinetic index components (pharmacokinetics PK/toxicokinetics TK makers), which can be beneficial for their clinical application or drug development. Although the detection of traditional small molecular drug's in vivo ADME process can be achieved by radioisotope methods, this approach might not be useful for the case of TCM. In detail, it is very difficult to label and trace each component in complex Chinese medicine, while it is also difficult to accurately follow the position of tracer in the whole in vivo process. In short, it is a tough task to obtain the ADME information of Chinese medicine, especially in the case of a clinical study. METHODS: We searched several scientific databases, including Pubmed, ACS, ScienceDirect, Springer, Wiley, etc., by using "Chinese medicine" and "in vivo metabolism" as keywords. By summarizing the current reports as well as our recent progress in this field, this review aims to summarize current research methods and strategies for ADME study of TCM based on high-resolution mass spectrometry-based data acquisition and data mining technology which is an important approach but has not been systematically reviewed. RESULTS: With the development of various hybrid tandem high-resolution mass spectrometry (Q-TOF, LTQ FT, Q-Exactive), liquid chromatography-high resolution mass spectrometry (LC-HRMS) has become the mainstream method in studying ADME process of TCM. This review aims to comprehensively summarize current research technologies and strategies based on high-resolution mass spectrometry, with emphasis on the following three aspects: (1) comprehensive and automatic acquisition technologies for the analysis of in vivo TCM components (i.e., BEDDA); (2) quick and comprehensive identification techniques for analyzing in vivo chemical substances and metabolites of TCM (i.e., PATBS or metabolomic analysis); (3) efficient correlation determination between in vivo or in vitro compounds and their metabolic transformation (i.e., MTSF). CONCLUSION: To the best of our knowledge, this is a pioneering review for systematically summarizing the analysis methods and strategies of ADME in TCM, which can help understand the whole ADME process, therapeutic molecular basis, or toxic substances of TCM. Furthermore, this review can also provide a feasible strategy to screen out PK/TK markers of TCM, while this information can be helpful to elucidate the pharmacodynamics or toxicity mechanisms of Chinese medicines and be useful in their future drug development. At the same time, we also hope that this review can provide ideas for further improvement of TCM analysis methods and help rational clinical use of TCM and the development of new drugs.

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.

Identifying potential anti-COVID-19 pharmacological components of traditional Chinese medicine Lianhuaqingwen capsule based on human exposure and ACE2 biochromatography screening.

Lianhuaqingwen (LHQW) capsule, a herb medicine product, has been clinically proved to be effective in coronavirus disease 2019 (COVID-19) pneumonia treatment. However, human exposure to LHQW components and their pharmacological effects remain largely unknown. Hence, this study aimed to determine human exposure to LHQW components and their anti-COVID-19 pharmacological activities. Analysis of LHQW component profiles in human plasma and urine after repeated therapeutic dosing was conducted using a combination of HRMS and an untargeted data-mining approach, leading to detection of 132 LHQW prototype and metabolite components, which were absorbed via the gastrointestinal tract and formed via biotransformation in human, respectively. Together with data from screening by comprehensive 2D angiotensin-converting enzyme 2 (ACE2) biochromatography, 8 components in LHQW that were exposed to human and had potential ACE2 targeting ability were identified for further pharmacodynamic evaluation. Results show that rhein, forsythoside A, forsythoside I, neochlorogenic acid and its isomers exhibited high inhibitory effect on ACE2. For the first time, this study provides chemical and biochemical evidence for exploring molecular mechanisms of therapeutic effects of LHQW capsule for the treatment of COVID-19 patients based on the components exposed to human. It also demonstrates the utility of the human exposure-based approach to identify pharmaceutically active components in Chinese herb medicines.

Metabolic disposition of [14 C]-abivertinib, an epidermal growth factor receptor tyrosine kinase inhibitor: Role of glutathione conjugation.

AIMS: To determine the absorption, distribution, metabolism and excretion of abivertinib, a third-generation epidermal growth factor receptor tyrosine kinase inhibitor, in patients with advanced non-small cell lung cancer (NSCLC). METHODS: Seven patients with advanced NSCLC were given a single 200 mg/83 µCi oral suspension of [14 C]-abivertinib. Blood, urine and faeces were collected. Mass balance of radioactivity, the pharmacokinetics of abivertinib, and the total radioactivity were determined. Metabolite profiling and characterisation were performed. RESULTS: The mean recovery was 82.16%, with 2.38 and 79.78% of the radioactive dose excreted in urine and faeces, respectively. The unchanged abivertinib was the major radioactive component detected in plasma within the first 24 hours after dosing, accounting for 59.17% of the total drug-related radioactivity. Abivertinib in urine accounted for only 0.96% of the administered dose, whereas in faeces it accounted for 33.36%. Eight metabolites were detected and characterised in plasma, among which MII-7, a product of cysteine glycine conjugate, was the only circulating metabolite, accounting for approximate 10.6% of the total drug-related exposure. MII-2 (an abivertinib cysteine-glycine adduct) and M7 (a reduced product of abivertinib) were the 2 major metabolites in the excreta, accounting for 20.0 and 12.4%, respectively, of the drug-related radioactivity in faeces. CONCLUSION: Following a single oral administration, the unchanged abivertinib was the predominant drug-related material in plasma, urine and faeces. The drug-related materials were primarily eliminated via the faecal route. Direct glutathione conjugation of abivertinib played a significant role in the metabolic clearance and metabolite exposure of abivertinib.

Software-aided detection and structural characterization of cyclic peptide metabolites in biological matrix by high-resolution mass spectrometry.

Compared to their linear counterparts, cyclic peptides show better biological activities, such as antibacterial, immunosuppressive, and anti-tumor activities, and pharmaceutical properties due to their conformational rigidity. However, cyclic peptides could form numerous putative metabolites from potential hydrolytic cleavages and their fragments are very difficult to interpret. These characteristics pose a great challenge when analyzing metabolites of cyclic peptides by mass spectrometry. This study was to assess and apply a software-aided analytical workflow for the detection and structural characterization of cyclic peptide metabolites. Insulin and atrial natriuretic peptide (ANP) as model cyclic peptides were incubated with trypsin/chymotrypsin and/or rat liver S9, followed by data acquisition using TripleTOF® 5600. Resultant full-scan MS and MS/MS datasets were automatically processed through a combination of targeted and untargeted peak finding strategies. MS/MS spectra of predicted metabolites were interrogated against putative metabolite sequences, in light of a, b, y and internal fragment series. The resulting fragment assignments led to the confirmation and ranking of the metabolite sequences and identification of metabolic modification. As a result, 29 metabolites with linear or cyclic structures were detected in the insulin incubation with the hydrolytic enzymes. Sequences of twenty insulin metabolites were further determined, which were consistent with the hydrolytic sites of these enzymes. In the same manner, multiple metabolites of insulin and ANP formed in rat liver S9 incubation were detected and structurally characterized, some of which have not been previously reported. The results demonstrated the utility of software-aided data processing tool in detection and identification of cyclic peptide metabolites.

Artificial intelligence and network pharmacology based investigation of pharmacological mechanism and substance basis of Xiaokewan in treating diabetes.

Xiaokewan is a typical Traditional Chinese medicine (TCM) for diabetes and contains various natural chemicals, such as lignans, flavonoids, saponins, polysaccharides, and western medicine glibenclamide. In the current study, a highly efficient system for screening hypoglycemic efficacy constituents of Xiaokewan has been developed with the integration of intelligent data acquisition, data mining, network pharmacology, and computer assisted target fishing. With the combination of background exclusion data dependent acquisition (BE-DDA) and non-targeted precise-and-thorough background-subtraction (PATBS) techniques, a novel workflow has been established for the non-targeted recognition and identification of TCM constituents in vivo, and has been applied to the exposure study of Xiaokewan in rat. In this case, an interesting correlation among drug, target, and disease can be established, by combining the screening or characterization results with the strategy of network pharmacology and multiple computer assisted techniques. Consequently, five main constituents (puerarin, daidzein, formononetin, deoxyschizandrin and glibenclamide) exposed in vivo have been selected as effective hypoglycemic components. Meanwhile, the network pharmacology experimental results showed that these five constituents could act on various drug targets, such as PI3K, PTP1B, MAPK, AKT, TNF, and NF-κB. These five constituents might be involved in the regulation of ß-cell function or exhibit inflammation inhibition ability to relieve the pathophysiological process of disease from multiple links. Furthermore, the pharmacological effects of these five constituents have been verified by diabetic zebrafish model. The zebrafish model results showed that the TCM monomer mixture without glibenclamide exhibited similar hypoglycemic activity with Xiaokewan. Although the monomer mixture with glibenclamide showed better activity than Xiaokewan only, the deoxyschizandrin (TCM constituent of Xiaokewan) exhibited best hypoglycemic performance. In summary, the above results indicated that the application of both intelligent recognition technology in mass spectrometry dataset and computerized network pharmacology might provide a pioneering approach for investigating the substance basis of TCM and searching lead compounds from natural sources.

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

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

Renal Excretion of Dabigatran: The Potential Role of Multidrug and Toxin Extrusion (MATE) Proteins.

Following oral administration, dabigatran etexilate (DABE) is rapidly hydrolyzed to its active form, dabigatran. DABE, but not dabigatran, presents as a P-glycoprotein (P-gp) substrate and has increasingly been used as a probe drug. Therefore, although dosed as DABE, a P-gp drug-drug interaction (DDI) is reported as a dabigatran plasma concentration ratio (perpetrator versus placebo). Because the majority of a DABE dose (80 to 85%) is recovered in urine as unchanged dabigatran (renal active secretion is ∼25% of total clearance), dabigatran was evaluated in vitro as a substrate of various human renal transporters. Active (pyrimethamine-sensitive) dabigatran uptake was observed with human embryonic kidney (HEK) 293 cells expressing multidrug and toxin extrusion protein 1 (MATE1) and 2K (MATE2K), with Michaelis-Menten constant (Km) values of 4.0 and 8.0 µM, respectively. By comparison, no uptake of 2 µM dabigatran (versus mock-transfected HEK293 cells) was evident with HEK293 cells transfected with organic cation transporters (OCT1 and OCT2) and organic anion transporters (OAT1, 2, 3, and 4). The efflux ratios of dabigatran across P-gp- and BCRP (breast cancer resistance protein)-MDCK (Madin-Darby canine kidney) cell monolayers were 1.5 and 2.0 (versus mock-MDCK cell monolayers), suggesting dabigatran is a relatively poor P-gp and BCRP substrate. Three of five drugs (verapamil, ketoconazole, and quinidine) known to interact clinically with dabigatran, as P-gp inhibitors, presented as MATE inhibitors in vitro (IC50 = 1.0 to 25.2 µM). Taken together, although no basolateral transporter was identified for dabigatran, the results suggest that apical MATE1 and MATE2K could play an important role in its renal clearance. MATE-mediated renal secretion of dabigatran needs to be considered when interpreting the results of P-gp DDI studies following DABE administration.

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 MSE acquisition. LC-MSE 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.

Biotransformation of Ilaprazole in Human Liver Microsomes and Human: Role of CYP3A4 in Ilaprazole Clearance and Drug-Drug Interaction.

Ilaprazole is a new proton pump inhibitor and is currently marketed in China and South Korea for the treatment of gastric and duodenal ulcer. Ilaprazole has a favorable long half-life and minimal pharmacokinetic variability associated with CYP2C19 polymorphism. Sulfoxide oxidation of ilaprazole is catalyzed mainly by CYP3A4. Thus, it has been widely accepted that CYP3A4 plays a major role in the clearance of ilaprazole in humans. However, absorption, distribution, metabolism, and excretion data of radiolabeled ilaprazole in humans are not available. The primary goal of this study was to determine if sulfoxide oxidation is a major metabolic pathway of ilaprazole in humans. Metabolite profiles of ilaprazole, ilaprazole sulfide, and ilaprazole sulfone in human liver microsomes (HLMs) were characterized and quantitively analyzed by liquid chromatography (LC)/UV/high-resolution mass spectrometry (HRMS). Moreover, metabolites of ilaprazole in human urine and feces were detected and identified by LC-HRMS. The results revealed that sulfoxide reduction to ilaprazole sulfide rather than sulfoxide oxidation was the major biotransformation pathway in HLMs. Sulfoxide reduction also occurred in HLMs without NADPH or in deactivated HLMs. Ilaprazole sulfide and its multiple oxidative metabolites were major drug-related components in human urine and feces, where there were no ilaprazole sulfone and its metabolites. A small amount of the parent drug was found in feces. Thus, we propose that nonenzymatic sulfoxide reduction rather than CYP3A4-medidated sulfoxide oxidation is the major metabolic clearance pathway of ilaprazole in humans. Consequently, it is predicted that ilaprazole has no significant drug-drug interaction via CYP3A4 inhibition or induction by a coadministered drug.

LC-MS Differential Analysis for Fast and Sensitive Determination of Biotransformation of Therapeutic Proteins.

Therapeutic biologics have become a fast-growing segment within the pharmaceutical industry during the past 3 decades. Although the metabolism of biologics is more predictable than small molecule drugs, biotransformation can significantly affect the activity of biologics. Unfortunately, there are only a limited number of published studies on the biotransformation of biologics, most of which are focused on one or a few types of modifications. In this study, an untargeted LC-MS-based differential analysis approach was developed to rapidly and precisely determine the universal biotransformation profile of biologics with the assistance of bioinformatic tools. A human monoclonal antibody (mAb) was treated with t-butyl hydroperoxide and compared with control mAb using a bottom-up proteomics approach. Thirty-seven types of post-translational modifications were identified, and 38 peptides were significantly changed. Moreover, although all modifications were screened and detected, only the ones related to the treatment process were revealed by differential analysis. Other modifications that coexist in both groups were filtered out. This novel analytical strategy can be effectively applied to study biotransformation-mediated protein modifications, which will streamline the process of biologic drug discovery and development.