Improved detection of reactive metabolites with a bromine‐containing glutathione analog using mass defect and isotope pattern matching
Bibliographic record
Abstract
Drug bioactivation leading to the formation of reactive species capable of covalent binding to proteins represents an important cause of drug-induced toxicity. Reactive metabolite detection using in vitro microsomal incubations is a crucial step in assessing potential toxicity of pharmaceutical compounds. The most common method for screening the formation of these unstable, electrophilic species is by trapping them with glutathione (GSH) followed by liquid chromatography/mass spectrometry (LC/MS) analysis. The present work describes the use of a brominated analog of glutathione, N-(2-bromocarbobenzyloxy)-GSH (GSH-Br), for the in vitro screening of reactive metabolites by LC/MS. This novel trapping agent was tested with four drug compounds known to form reactive metabolites, acetaminophen, fipexide, trimethoprim and clozapine. In vitro rat microsomal incubations were performed with GSH and GSH-Br for each drug with subsequent analysis by liquid chromatography/high-resolution mass spectrometry on an electrospray time-of-flight (ESI-TOF) instrument. A generic LC/MS method was used for data acquisition, followed by drug-specific processing of accurate mass data based on mass defect filtering and isotope pattern matching. GSH and GSH-Br incubations were compared to control samples using differential analysis (Mass Profiler) software to identify adducts formed via the formation of reactive metabolites. In all four cases, GSH-Br yielded improved results, with a decreased false positive rate, increased sensitivity and new adducts being identified in contrast to GSH alone. The combination of using this novel trapping agent with powerful processing routines for filtering accurate mass data and differential analysis represents a very reliable method for the identification of reactive metabolites formed in microsomal incubations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".