Effect of Ginkgo biloba extract on oxidative metabolism of valproic acid in hepatic microsomes from donors with the<i>CYP2C9*1/*1</i>genotypeThis article is one of a selection of papers published in this special issue (part 1 of 2) on the Safety and Efficacy of Natural Health Products.
Bibliographic record
Abstract
We investigated the effect of Ginkgo biloba extracts and some of its individual constituents on the oxidative metabolism of valproic acid (VPA) in hepatic microsomes from donors with the CYP2C9*1/*1 genotype. G. biloba extract decreased 4-ene-VPA, 3-OH-VPA, 4-OH-VPA, and 5-OH-VPA formation with mean (+/- SE) IC50 values of 340 +/- 40 microg/mL, 370 +/- 100 microg/mL, 180 +/- 30 microg/mL, and 210 +/- 20 microg/mL, respectively. This was associated with inhibition of not only CYP2C9*1, but also CYP2A6 and CYP2B6. Bilobalide, ginkgolide A, ginkgolide B, ginkgolide C, ginkgolide J, quercetin-3-O-rutinoside, kaempferol-3-O-rutinoside, and isorhamnetin-3-O-rutinoside were not responsible for the inhibition of VPA metabolism by the extract. When analyzed as the sum of the aglycone and total glycosides present in the extract, quercetin decreased 4-ene-VPA, 4-OH-VPA, and 5-OH-VPA formation by 76%, 51%, and 70%, respectively, kaempferol decreased 4-ene-VPA, 4-OH-VPA, and 5-OH-VPA formation by 65%, 46%, and 49%, respectively, and isorhamnetin decreased 4-ene-VPA, 4-OH-VPA, and 5-OH-VPA formation by 29%, 26%, and 31%, respectively. The 3 aglycones did not affect 3-OH-VPA formation. In summary, G. biloba extract decreased hepatic microsomal formation of 4-ene-VPA, 4-OH-VPA, 5-OH-VPA, and 3-OH-VPA, but the effect was not due to the terpene trilactones or flavonol glycosides investigated in our study.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".