Effect of <i>Ginkgo biloba</i> extract on rat hepatic microsomal CYP1A activity: role of ginkgolides, bilobalide, and flavonols
Bibliographic record
Abstract
The present study investigated the in vitro effect of Ginkgo biloba extracts and some of the individual constituents (ginkgolides, bilobalide, and flavonols such as kaempferol, quercetin, isorhamnetin, and their glycosides) on CYP1A-mediated 7-ethoxyresorufin O-dealkylation in hepatic microsomes isolated from rats induced with beta-naphthoflavone. G. biloba extract competitively inhibited CYP1A activity, with an apparent Ki value of 1.6 +/- 0.4 microg/mL (mean +/- SE). At the concentrations present in the G. biloba extracts, ginkgolides A, B, C, and J and bilobalide did not affect CYP1A activity, whereas kaempferol (IC50 = 0.006 +/- 0.001 microg/mL, mean +/- SE), isorhamnetin (0.007 +/- 0.001 microg/mL), and quercetin (0.050 +/- 0.003 microg/mL) decreased this activity. The monoglycosides (1 and 10 microg/mL) and diglycosides (10 microg/mL) of kaempferol and quercetin but not those of isorhamnetin also inhibited CYP1A activity. The order of inhibitory potency was kaempferol approximately equal to isorhamnetin > quercetin, and for each of these flavonols the order of potency was aglycone >> monoglycoside > diglycoside. In summary, G. biloba extract competitively inhibited rat hepatic microsomal CYP1A activity, but the effect was not due to ginkgolides A, B, C, or J, bilobalide, kaempferol, quercetin, isorhamnetin, or the respective flavonol monoglycosides or diglycosides.
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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 | 0.000 |
| 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 | 0.000 | 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".