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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
Published on: September 20, 2022
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Modified two-dose difference with stable isotope tracing workflow using mass shift defect filtering substantially
Sheu-Fang Lo1, Yi-Shiou Chiou2, Jie-Huei Wang3
1Department of Obstetrics and Gynecology, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, 60002, Taiwan.
Analytica Chimica Acta
|October 10, 2025
Summary
Improving drug-metabolite identification, this study enhanced a two-dose-difference platform with stable isotope tracing (SIT) and a mass shift defect filter. This significantly boosted the true-positive rate for identifying drug metabolites, reducing false positives in early drug development.
Area of Science:
- Pharmacology and Toxicology
- Analytical Chemistry
- Biochemistry
Background:
- Untargeted drug-metabolite identification faces challenges balancing comprehensive coverage with high false-positive rates.
- Previous two-dose-difference platform with stable isotope tracing (SIT) offered good coverage but a high false-positive burden.
- Developing strategies to reduce false positives is critical for reliable metabolite identification.
Purpose of the Study:
- To evaluate and enhance data-processing workflows for untargeted drug-metabolite identification.
- To reduce the false-positive rate in metabolite detection without compromising coverage.
- To optimize the two-dose-difference platform combined with stable isotope tracing (SIT).
Main Methods:
- Benchmarking four data-processing workflows: original and modified two-dose-difference + SIT, dose-response + SIT, and mass defect filtering + SIT.
- Utilizing nifedipine (NIF) as a probe and acquiring UPLC-MS data under three incubation setups: coincubation, separate incubations, and postreaction mixing.
- Employing targeted MS/MS validation to confirm identified metabolites.
Main Results:
- The modified two-dose-difference + SIT workflow, incorporating a mass shift defect filter, significantly improved the true-positive rate from 36.9% to 71.0% for NIF metabolites in the coincubation setup.
- This modified workflow identified all 65 putative NIF metabolites, including three previously reported, without sacrificing metabolite coverage.
- Separate incubations yielded a more comprehensive metabolite profile (56 features) compared to coincubation (44) or mixed supernatants (38).
Conclusions:
- Incorporating a mass shift defect filter into the two-dose-difference + SIT workflow effectively doubled the true-positive discovery rate for drug metabolites.
- This streamlined, dose-independent method reduces false positives and accelerates reliable metabolite identification.
- The enhanced platform offers a practical and resource-efficient solution for early-stage drug metabolism studies and mechanistic pharmacology.
