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.

An integrated strategy to study the combination mechanisms of Bupleurum chinense DC and Paeonia lactiflora Pall for treating depression based on correlation analysis between serum chemical components profiles and endogenous metabolites profiles

ETHNOPHARMACOLOGICAL RELEVANCE

Bupleurum chinense DC-Paeonia lactiflora Pall (BCD-PLP) is a common clinical herb pair in traditional Chinese medicine (TCM) prescriptions commonly used to treat depression. However, its combination mechanisms with its anti-depressive effects remain highly unclear.

AIM OF THE STUDY

Here, an effective strategy has been developed to study the combination mechanisms of Bupleurum chinense DC (BCD) and Paeonia lactiflora Pall (PLP) by integrating serum pharmacochemistry analysis, metabolomics technology, and molecular docking technology.

MATERIALS AND METHODS

First, the depression model rats were replicated by the chronic unpredictable mild stress (CUMS) procedure, and the difference in the chemical composition in vivo before and after the combination of BCD and PLP was analyzed by integrating background subtraction and multivariate statistical analysis techniques. Then, UPLC/HRMS-based serum metabolomics was performed to analyze the synergistic effect on metabolite regulation before and after the combination of BCD and PLP. Further, the correlation analysis between the differential exogenous chemical components and the differential endogenous metabolites before and after the combination was employed to dissect the combination mechanisms from a global perspective of combining metabolomics and serum pharmacochemistry. Finally, the molecular docking between the differential chemical components and the key metabolic enzymes was applied to verify the regulatory effect of the differential exogenous chemical components on the differential endogenous metabolites.

RESULTS

The serum pharmacochemistry analysis results demonstrated that the combination of BCD and PLP could significantly affect the content of 10 components in BCD (including 5 prototype components were significantly decreased and 5 metabolites were significantly increased) and 8 components in PLP (including 4 prototype components and 3 metabolites were significantly increased, 1 metabolite was significantly decreased), which indicated that the combination could enhance BCD prototype components' metabolism and the absorption of the PLP prototype components. Besides, metabolomics results indicated that the BCD-PLP herb pair group significantly reversed more metabolites (8) than BCD and PLP single herb group (5 & 4) and has a stronger regulatory effect on metabolite disorders caused by CUMS. Furthermore, the correlation analysis results suggested that saikogenin F and saikogenin G were significantly positively correlated with the endogenous metabolite itaconate, an endogenous anti-inflammatory metabolite; and benzoic acid was significantly positively correlated with D-serine, an endogenous metabolite with an antidepressant effect. Finally, the molecular docking results further confirmed that the combination of BCD and PLP could affect the activities of cis-aconitic acid decarboxylase and D-amino acid oxidase by increasing the in vivo concentration of saikogenin F and benzoic acid, which further enhances its anti-inflammatory activity and anti-depressive effect.

CONCLUSIONS

In this study, an effective strategy has been developed to study the combination mechanisms of BCD and PLP by integrating serum pharmacochemistry analysis, multivariate statistical analysis, metabolomics technology, and molecular docking technology. Based on this strategy, the present study indicated that the combination of BCD and PLP could affect the activities of cis-aconitic acid decarboxylase and D-amino acid oxidase by increasing the concentration of saikogenin F and benzoic acid in vivo, which further enhances its anti-depressive effect. In short, this strategy will provide a reliable method for elucidating the herb-herb compatibility mechanism of TCM.

Metabolite identification of iridin in rats by using UHPLC-MS/MS and pharmacokinetic study of its metabolite irigenin

Iridin, one of the main bioactive components isolated from Belamcanda chinensis (L.) DC, exerts various pharmacological activities, such as anti-inflammation, antioxidant, and antitumor. However, the metabolism and pharmacokinetics of iridin are still unknown. After 100 mg/kg administration of iridin orally, the plasma, urine, and fecal bio-samples from Sprague-Dawley (SD) rats were collected and detected by ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). The pharmacokinetics of the major metabolite irigenin (aglycon of iridin) and a total of thirteen metabolites of iridin were identified, including five metabolites in plasma, ten metabolites in urine, and six metabolites in feces. The most principal metabolic pathway of iridin was glucuronidation after demethylation and was mediated by UDP-glucuronosyltransferases (UGTs) 1A7, 1A8, 1A9 and 1A10. This study highlights the first-time investigation of the metabolism of iridin in vivo, and the pharmacokinetics of irigenin (the major metabolite of iridin) in rats. These results provide robust evidence for further research and clinical application of iridin.

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.