Development of a metabolomics-based data analysis approach for identifying drug metabolites based on high-resolution mass spectrometry

Ting, Hsiao-Hsien; Chiou, Yi‐Shiou; Chang, Tien-Yi; Lin, Guosheng; Li, Pei-Jhen; Shih, Chia-Lung

doi:10.38212/2224-6614.3451

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Therefore, the number of ROS metabolites should be similar to that of PIO metabolites. In our previous study, we used the same UPLC-MS system but different metabolomics-based data processing approach (a time-course experiment combined with SIT) for PIO metabolite identification ( Ting et al, 2023 ). However, that study identified a relatively small number of PIO metabolites (n = 20) ( Ting et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

“…In our previous study, we used the same UPLC-MS system but different metabolomics-based data processing approach (a time-course experiment combined with SIT) for PIO metabolite identification ( Ting et al, 2023 ). However, that study identified a relatively small number of PIO metabolites (n = 20) ( Ting et al, 2023 ). In the present study, we conducted time-course experiments to validate the identified features, and many features were validated.…”

Section: Discussionmentioning

confidence: 99%

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Dose-response technique combined with stable isotope tracing for drug metabolite profiling by using high-resolution mass spectrometry

Lin,

Chuang,

Shih

2023

Front. Pharmacol.

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Background: Mass spectrometry metabolomics-based data-processing approaches have been developed for drug metabolite profiling. However, existing approaches cannot be used to comprehensively identify drug metabolites with high efficacy.Methods: Herein, we propose a two-stage data-processing approach for effective and comprehensive drug metabolite identification. The approach combines dose-response experiments with stable isotope tracing (SIT). Rosiglitazone (ROS), commonly used to treat type 2 diabetes, was employed as a model drug.Results: In the first stage of data processing, 1,071 features exhibited a dose-response relationship among 22,597 features investigated. In the second stage, these 1,071 features were screened for isotope pairs, and 200 features with isotope pairs were identified. In time-course experiments, a large proportion of the identified features (69.5%: 137 out of 200 features) were confirmed to be possible ROS metabolites. We compared the validated features identified using our approach with those identified using a previously reported approach [the mass defect filter (MDF) combined with SIT] and discovered that most of the validated features (37 out of 42) identified using the MDF-SIT combination were also successfully identified using our approach. Of the 143 validated features identified by both approaches, 74 had a proposed structure of an ROS-structure-related metabolite; the other 34 features that contained a specific fragment of ROS metabolites were considered possible ROS metabolites. Interestingly, numerous ROS-structure-related metabolites were identified in this study, most of which were novel.Conclusion: The results reveal that the proposed approach can effectively and comprehensively identify ROS metabolites.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%