Determining protein adducts of fipexide: mass spectrometry based assay for confirming the involvement of its reactive metabolite in covalent binding
Bibliographic record
Abstract
Fipexide is a nootropic drug, withdrawn from the market due to its idiosyncratic drug reactions causing adverse effects in man. Previous work on its metabolites has identified several potential reactive metabolites which could be implicated in protein binding. Here, we investigated the formation of these metabolites in rat and human hepatocytes. Based on these results, the o-quinone of fipexide (FIP), formed via the demethylenation reaction through a catechol intermediate, was chosen for further investigation. Studies were then pursued in order to relate this metabolite to protein binding, and thus better understand potential mechanisms for the toxicity of the parent compound. An assay was developed for determining the fipexide catechol-cysteine adduct in the microsomal protein fractions following in vitro incubations. This method digests the entire protein fraction into amino acids, followed by the detection of the Cys-metabolite adduct by liquid chromatography/mass spectrometry (LC/MS). We have designed a strategy where drug metabolism taking place in microsomal incubations and involved in protein binding can be assessed after the proteins have been digested, with the detection of the specific amino acid adduct. In this study, the structure of the fipexide adduct was hypothesized using knowledge previously gained in glutathione and N-acetylcysteine trapping experiments. Acetaminophen was used as a positive control for detecting a drug metabolite-cysteine adduct by LC/MS. This approach has the potential to be applicable as a protein-binding assay in early drug discovery without the need for radioactive compounds.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".