St. John's wort extract with a high hyperforin content does not induce P‐glycoprotein activity at the human blood–brain barrier
Bibliographic record
Abstract
Abstract St. John's wort (SJW) extract, a herbal medicine with antidepressant effects, is a potent inducer of intestinal and/or hepatic cytochrome P450 (CYP) enzymes and P‐glycoprotein (P‐gp), which can cause clinically relevant drug interactions. It is currently not known whether SJW can also induce P‐gp activity at the human blood–brain barrier (BBB), which may potentially lead to decreased brain exposure and efficacy of certain central nervous system (CNS)‐targeted P‐gp substrate drugs. In this study, we used a combination of positron emission tomography (PET) imaging and cocktail phenotyping to gain a comprehensive picture on the effect of SJW on central and peripheral P‐gp and CYP activities. Before and after treatment of healthy volunteers (n = 10) with SJW extract with a high hyperforin content (3–6%) for 12–19 days (1800 mg/day), the activity of P‐gp at the BBB was assessed by means of PET imaging with the P‐gp substrate [11C]metoclopramide and the activity of peripheral P‐gp and CYPs was assessed by administering a low‐dose phenotyping cocktail (caffeine, omeprazole, dextromethorphan, and midazolam or fexofenadine). SJW significantly increased peripheral P‐gp, CYP3A, and CYP2C19 activity. Conversely, no significant changes in the peripheral metabolism, brain distribution, and P‐gp‐mediated efflux of [11C]metoclopramide across the BBB were observed following the treatment with SJW extract. Our data suggest that SJW does not lead to significant P‐gp induction at the human BBB despite its ability to induce peripheral P‐gp and CYPs. Simultaneous intake of SJW with CNS‐targeted P‐gp substrate drugs is not expected to lead to P‐gp‐mediated drug interactions at the BBB.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".