Development of a highly reproducible system to evaluate inhibition of cytochrome P450 3A4 activity by natural medicines
Bibliographic record
Abstract
PURPOSE: In recent years, a number of natural medicines have been reported to have inductive or inhibitive effects on the activity of drug metabolizing enzymes, upon co-administration with prescribed medicines. However, information regarding natural medicine-drug interactions that influence drug metabolism is limited owing to the lack of efficient screening method for such interactions. Therefore, to understand whether P450 activity is affected by natural medicine in small intestines, we have established frozen recombinant P450-expressing cells infected with human CYP3A4 expressing adenovirus (Ad-CYP3A4) to evaluate the effect of natural medicines on CYP3A4 activity. METHODS: Ad-CYP3A4 cells were created by infecting HepG2 cells with Ad-CYP3A4 at 10 multiplicity of infection (MOI) and these cells were stored using cryopreservation medium (fAd-CYP3A4 cells) to obtain long-term consistent data and stable supplies of cells expressing a constant level of CYP3A4 activity. RESULTS: The CYP3A4 activity in fAd-CYP3A4 cells remained unaffected at the end of each frozen period (0, 1, 2, and 6 months). Inhibitory effect on CYP3A4 activity by typical inhibitors (ketoconazole, hyperforin) and natural medicines (Cat's Claw, Devil's Claw, Feverfew, Peppermint Oil, Red Clover, and Siberian Eleuthero) were evaluated. The inhibitors had nearly equal IC50 values in fAd-CYP3A4 cells, Ad-CYP3A4 cells and recombinant CYP3A4 microsomes. Cat's Claw, Peppermint Oil and Siberian Eleuthero inhibited CYP3A4 activity more potently than 0.1 μM ketoconazole in fAd-CYP3A4 cells. CONCLUSIONS: In the present study, we have successfully developed a highly reproducible system to evaluate CYP3A4 inhibition in small intestines by natural medicines.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".