Determination of CYP2CSGatalyzed Diclofenac 4'-Hydroxylation by High-Performance Liquid Chromatography
Bibliographic record
Abstract
CYP2C9 is a major CYP2C enzyme expressed in human liver ( 1 – 3 ). Recent experiments with primary cultures of human hepatocytes have suggested that CYP2C9 protein expression can be increased in cells treated with a P450 inducer such as phenobarbital, dexamethasone, or rlfampin (rifampicin) ( 3 ). This is consistent with the clmical finding that rlfampin increases the total body clearance of tolbutamide ( 4 ), which is metabolized almost entirely by hepatic CYP2C9 ( 5 ). Other substrates for CYP2C9 include phenytom ( 5 , 6 ), warfarin ( 7 ) and diclofenac ( 8 ). Sulfaphenazole is a CYP2C9-selective chemical mhlbitor ( 9 – 12 ) and inhibition experiments with this sulfur-containing compound have suggested that human liver microsomal diclofenac 4′-hydroxylatlon IS selectively catalyzed by CYP2C9 ( 8 ). This chapter describes a high-performance liquid chromatographic (HPLC) assay for the determination of diclofenac 4′-hydroxylase activity. Methods for other CYP2C9 assays such as tolbutamide methylhydroxylase ( 13 ) and (S)-warfarin 7-hydroxylase ( 14 , 15 ) can be found in the cited references. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".