High-Performance Liquid Chromatographic Analysis of CYP2CSCatalyzed Paclitaxel 6α-Hydroxylation
Bibliographic record
Abstract
CYP2C8 is a major CYP2C protein expressed in human liver 1 – 4 . Considerable interindividual differences (-20-fold) have been observed in hepatic CYP2C8 content ( 5 ) and a blmodal dlstrlbutlon in CYP2C8 protein amounts m a panel of human liver mlcrosomes has been reported ( 6 ). Experiments with primary cultures of human hepatocytes have indicated that CYP2C8 is subject to inductton by phenobarbital, dexamethasone, and rifampin (rifampicm) ( 4 ). Little is known about the function of CYP2C8, although studies with lmmunologically purified or cDNA-expressed CYP2C8 have indicated that this P450 catalyzes the metabohsm of retmol( 6 ), retinolc acid ( 6 ), arachldonic acid ( 7 , 8 ), carbamazepine ( 9 ) and paclitaxel ( 10 – 12 ). Oxldatlon of the anticancer drug paclitaxel to 6a-hydroxypachtaxel appears to be selectively catalyzed by CYP2C8 because cDNA-expressed human CYP2C8 is active in this reaction, whereas CYPlA2,2A6, 2B6,2C8, 2C9-Ile359, 2C9-Cys’44, 2C18, 2C19, 2D6, 2EI,3A3,3A4 and 3A5 are inactive ( 10 – 12 ). Paclitaxel Ga-hydroxylase actlvlty may therefore be a potentially useful dlagnostlc catalytic marker for human hepatic CYP2C8 see Note 1 . This chapter describes a high-performance liquid chromatographic (HPLC) assay for the determination of paclltaxel 6α-hydroxylase activity. 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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.003 |
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".