Strategic identification of <i>in vitro</i> metabolites of 13‐desmethyl spirolide C using liquid chromatography/high‐resolution mass spectrometry
Bibliographic record
Abstract
A strategy to identify metabolites of a marine biotoxin, 13-desmethyl spirolide C, has been developed using liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS). Metabolites were generated in vitro through incubation with human liver microsomes. A list of metabolites was established by selecting precursor ions of a common fragment ion characteristic of the spirolide toxin which was known to contain a cyclic imine ring. Accurate mass measurements were subsequently used to confirm the molecular formula of each biotransformation product. Using this approach, a total of nine phase I metabolites was successfully identified with deviations of mass accuracy less than 2 ppm. The biotransformations observed included hydroxylation, dihydroxylation, oxidation of a quaternary methyl group to hydroxymethyl or carboxylic acid groups, dehydrogenation and hydroxylation, as well as demethylation and dihydroxylation reactions. In a second step, tandem mass spectrometry (MS/MS) was performed to elucidate structures of the metabolites. Using the unique fragment ions in the spectra, the structures of the three major metabolites, 13,19-didesmethyl-19-carboxy spirolide C, 13,19-didesmethyl-19-hydroxymethyl spirolide C and 13-desmethyl-17-hydroxy spirolide C, were assigned. Levels of 13-desmethyl spirolide C and its metabolites were monitored at selected time points over a 32-h incubation period with human liver microsomes. It was determined that 13,19-didesmethyl-19-carboxy spirolide C became the predominant metabolite after 2 h of incubation. The stability plot of 13-desmethyl spirolide C showed first-order kinetics for its metabolism and the intrinsic clearance was calculated to be 41 μL/min/mg, suggesting first-pass metabolism may contribute to limiting oral toxicity of 13-desmethyl spirolide C.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".