Effect of St. John's Wort administration on CYP2C8 mediated rosiglitazone metabolism
Bibliographic record
Abstract
Background/Aims The objective of this study was to investigate the effect of St. John's wort (SJW) administration on rosiglitazone metabolism in healthy subjects genotyped for CYP2C8 polymorphisms. Methods This fixed-sequence design study involved twenty-seven subjects sequentially selected with the following CYP2C8 genotypes: CYP2C8*1/*1 (n= 8), *1/*2 (n= 4), *1/*3 (n= 7), *1/*4 (n= 6), *2/*2 (n= 1), and *3/*3 (n= 1). The pharmacokinetics of single dose rosiglitazone (8 mg) was evaluated in the absence and presence of SJW 900 mg daily. Results SJW administration induced rosiglitazone metabolism as the area under the plasma concentration-time curve (AUC) decreased by 26%, from 3190± 641 μg×h/L to 2375± 537 μg×h/L (p< 0.0001, 90% Confidence Interval[CI] for mean ratio, 71% - 78%) and apparent oral clearance (CL/F) increased by 35% from 2.6± 0.6 L/h to 3.5± 0.7 L/h (p< 0.0001, 90% CI, 128% - 142%). The CYP2C8 genotype did not influence the magnitude of induction as the rosiglitazone AUC ratio (SJW/Control) was not significantly different (p= 0.96) between the subjects genotyped as CYP2C8*1/*1 and the heterozygous carriers of CYP2C8 variant alleles. Conclusions Administration of SJW significantly increases the CYP2C8 mediated clearance of rosiglitazone after single dose administration; the magnitude of induction by SJW is not influenced by CYP2C8 genotype. SJW use should be monitored when patients are administered CYP2C8 substrates. Clinical Pharmacology & Therapeutics (2005) 77, P35–P35; doi: 10.1016/j.clpt.2004.12.026
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".