Spectrofluorometric Analysis of CYP2A6-Catalyzed Coumarin 7-Hydroxylation
Bibliographic record
Abstract
CYP2A6 has been isolated and purified to apparent homogeneity from human liver ( 1 , 2 ). The level of CYP2A6 protein expressed in liver is low (∼4% of total hepatic P450 content) ( 3 ), although substantial intermdividual variation exists ( 3 – 5 ). This P450 is primarily a hepatic protein, as suggested by its absence from several extrahepatic tissues, including adult human lung, colon, breast, kidney, and placenta microsomes ( 2 ). According to experiments with primary cultures of human hepatocytes, CYP2A6 is mducible by phenobarbital, dexamethasone, and rifampm (rifampicin) ( 6 ). Experiments with individual cDNA-expressed human P450 forms have indicated that CYP2A6 is a major catalyst of coumarm 7-hydroxylation ( 7 ). Among the other recombinant human P450s examined, CYPlAl, lA2, 2C8, 2C9, 2D6, 2E1, 3A3, 3A4, and 3A5 are catalytically inactive with respect to coumarm 7-hydroxylation, whereas CYP2B6 is active at only ∼5% the rate of CYP2A6 ( 7 ). Immunoinhibition studies have validated the use of coumarm 7-hydroxylase activity as a diagnostic catalytic marker for human hepatic CYP2A6. Antihuman CYP2A6 IgG inhibits coumarm 7-hydroxylase activity by >90% in human liver microsomes ( 2 ), indicating that CYP2A6 is the principal and perhaps the sole catalyst of human liver microsomal coumarin 7-hydroxylase activity. This activity can be inhibited by a variety of compounds, includmg 8-methoxypsoralen ( 8 ), tranylcypromme ( 8 ), α-naphthoflavone ( 8 – 11 ) and diethyldithiocarbamate ( 8 , 10 , 11 ). This chapter describes a modification ( 12 ) of a spectrofluorometric method ( 13 ) for the determination of coumarm 7-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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".