Inhibitory Effects of Eight Green Tea Catechins on Cytochrome P450 1A2, 2C9, 2D6, and 3A4 Activities
Bibliographic record
Abstract
PURPOSE: Green tea is a traditional beverage that has been enjoyed by the Japanese to this day. Recently, there has been an increase in the consumption of green tea beverage having high concentrations of catechins, such as (-)-epigallocatechin-3-O-gallate (EGCG). Many people tend to ingest large amounts of catechins through the frequent consumption of green tea beverage, and this dietary habit may lead to unwanted interactions between the catechins in green tea and medicinal drug. METHODS: The inhibitory effects of eight green tea catechins on drug metabolizing enzymes, cytochrome P450 (CYP) 1A2, 2C9, 2D6, and 3A4, were investigated in human liver microsomes. Incubation was initiated by the addition of cocktail probe drugs that served as specific substrates for each CYP, and the resulting metabolites were analyzed by LC-MS. RESULTS: From a comparison of the fifty percent inhibitory concentration (IC50) values of the eight green tea catechins, it was found that non-gallated catechins did not inhibit CYPs, whereas gallated catechins inhibited all CYPs except CYP2D6. Among them, CYP2C9 was most strongly inhibited by (-)-catechin-3-O-gallate (CG) (7.60 µM), and CYP1A2 was most strongly inhibited by EGCG (8.93 µM). Catechin gallate exhibited non-competitive inhibition of CYP2C9, and its Ki value was 9.76 ± 0.47µM. The present study is the first to report the inhibitory effect of CG on CYP2C9. In contrast, EGCG showed competitive inhibition of CYP1A2, and its Ki value was 14.3 ± 0.09 µM. CONCLUSION: Previous reports had predicted that plasma EGCG concentration reached 7.4 µM after ingesting green tea having high concentrations of catechins. That concentration of EGCG is equivalent to one-half to one-third of its Ki value for CYP1A2 and CYP3A4 in this study. The ingestion of beverages containing large amounts of green tea catechins together with drugs that are metabolized by CYP1A2, CYP2C9, and CYP3A4 should be avoided. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.001 |
| 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) | 0.001 | 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".