Investigating the <i>in vitro</i> metabolism of fipexide: characterization of reactive metabolites using liquid chromatography/mass spectrometry
Bibliographic record
Abstract
The in vitro metabolism of the nootropic drug fipexide was studied using different liquid chromatography/mass spectrometry (LC/MS) techniques. This drug has been withdrawn from the market due to toxic effects. No previous reports have investigated the possible involvement of reactive metabolites in the toxicity of fipexide. The hydrolysis of this drug leads to the formation of two potentially toxic species, 3,4-methylenedioxybenzylpiperazine (MDBP) and 4-chlorophenoxyacetic acid (4-CPA). Here, we investigate the in vitro metabolism of fipexide in human, rat, mouse and dog, as well as of MDBP and 4-CPA in human and rat, while focusing on the formation of reactive metabolites. A combination of LC/MS analyses on a hybrid quadrupole-linear ion trap instrument and accurate mass data from QqTOF measurements was employed for the characterization of these metabolites. Microsomal metabolites of fipexide were MDBP, 4-CPA, fipexide N-oxide or hydroxyl, demethylenated fipexide and other minor ones, all of which were investigated by tandem mass spectrometry. Reactive metabolites were detected using several trapping procedures with small molecules such as glutathione, its ethyl ester derivative and N-acetylcysteine. The demethylenated metabolite, a catechol, formed its corresponding ortho-quinone, which readily reacts with these nucleophiles. MDBP was studied in a similar manner, due to its ability to form an analogous catechol. Because of its acidic nature, 4-CPA was assessed for possible acylglucuronide and acyl-CoA thioester metabolites, which could also be involved in bioactivation pathways. Several important metabolites were identified as potential mediators of toxicity via protein binding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".