<i>In vitro</i> activity of <i>Lycium barbarum</i> (Goji) against major human phase I metabolism enzymes
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Goji berry (Lycium barbarum) has been used as traditional Chinese medicine and a functional food in China. Goji tea may interact with drugs such as warfarin by inhibiting the cytochrome P450 (CYP) 2C9, and this study was undertaken to characterize the effect of Goji products on CYP2C9/19-, CYP2D6 *1/*10-, CYP3A4/5/7-, CYP19-, and flavin-containing monooxygenase (FMO) 3-mediated metabolism. METHODS: Goji juice, water, and ethanol extracts were examined for their effect on CYP2C9/19-, 2D6-, 3A4/5/7-, 4A11-, CYP19-, and FMO3-mediated metabolism by using in vitro bioassay. The mechanism-based inactivation (MBI) of Goji juice on CYP3A4 was also examined. RESULTS: Data indicates that both fresh juice and commercially available juice caused strong inhibition (over 75 %) of most of the major CYP450 enzymes and moderate inhibition of FMO3 (30-60 %). Compared to juice, the Goji cold/hot water extracts effected low inhibition (below 30 %) of these enzymes. Ethanol (80 %) extracts exhibit the strongest inhibition on CYP2C9 and 2C19 (over 90 %). The inhibition pattern of dried and fresh berry extract and high-performance liquid chromatography (HPLC)-UV fingerprints were similar. CONCLUSIONS: These findings suggest that Goji products (berries, tea, tincture, and juice) can inhibit phase I drug metabolism enzymes and have the potential to affect the safety and efficacy of therapeutic products.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 it