Differential role of ginkgolide A and bilobalide in CYP3A and CYP2B6 induction by Ginkgo biloba extract in primary cultures of human hepatocytes
Bibliographic record
Abstract
Ginkgo biloba extract and ginkgolide A induce CYP3A and CYP2B6 in human hepatocytes, but conflicting data exist for the effect of bilobalide on CYP3A expression. Also, it is unknown which chemicals contribute to these effects by the extract. We assessed in human hepatocytes the role of ginkgolide A and bilobalide in the modulation of CYP3A and CYP2B6 expression by G. biloba extract, and compared the effect of the extract and chemicals on these enzymes. As analyzed by HPLC, LC‐MS, and real‐time PCR, control analysis indicated that rifampicin increased CYP3A‐mediated testosterone 6β‐hydroxylation and CYP3A4 mRNA levels, and CITCO increased CYP2B6‐mediated bupropion hydroxylation and CYP2B6 mRNA levels. G. biloba extract (25–200 μg/ml) induced CYP3A to a greater extent than CYP2B6. At a level present in 100 μg/ml of extract, ginkgolide A (1.1 μg/ml) increased CYP3A but not CYP2B6 expression, whereas bilobalide (2.8 μg/ml) had no effect. Ginkgolide A (1.1–10 μg/ml) induced CYP3A to a greater extent than CYP2B6, but bilobalide (5–20 μg/ml) induced both enzymes to a similar extent. CYP3A induction by bilobalide was less than that by ginkgolide A. In summary, G. biloba extract, ginkgolide A, and bilobalide differentially induced CYP3A and CYP2B6. Among the two chemicals, only ginkgolide A contributed to CYP3A induction by the extract, whereas neither of them accounted for CYP2B6 induction. [Supported by CIHR and MSFHR]
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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) | 0.000 | 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".