Zebrafish (<scp><i>Danio rerio</i></scp>) water tank model for the investigation of drug metabolism: Progress, outlook, and challenges
Bibliographic record
Abstract
Zebrafish (Danio rerio) water tank (ZWT) approach was investigated as an alternative model for metabolism studies based on six different experiments with four model compounds. Sibutramine was applied for the multivariate optimization of ZWT conditions, also for the comparison of the metabolism among ZWT, humans and mice, beyond for the role of CYP2B6 in ZWT. After the optimization, 18 fish and 168 hours of experiments is the minimum requirement for a relevant panel of biotransformation products. A comparison among the species resulted in the observation of the same hydroxylated metabolites, with differences in metabolites concentration ratio. However, the ZWT allowed tuning of the conditions to obtain a specific metabolic profile, depending on the need. In addition, by utilizing CYP2B6 inhibition, a relevant ZWT pathway for the demethylation of drugs was determined. The stereospecificity of the ZWT metabolism was investigated using selegiline and no racemization or inversion transformations were observed. Moreover, the investigation of metabolism of cannabimimetics was performed using JWH-073 and the metabolites observed are the same described for humans, except for the hydroxylation at the indol group, which was explained by the absence of CYP2C9 orthologs in zebrafish. Finally, hexarelin was used as a model to evaluate studies by ZWT for drugs with low stability. As a result, hexarelin displays a very fast metabolization in ZWT conditions and all the metabolites described for human were observed in ZWT. Therefore, the appropriate conditions, merits, and relevant limitations to conduct ZWT experiments for the investigation of drug metabolism are described.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".