Investigation of a broad‐spectrum micronutrient formulation as a possible precipitant of pharmacokinetic micronutrient–drug interactions
Bibliographic record
Abstract
Aims Daily broad‐spectrum micronutrients are being used by the general public and formulations are receiving research interest in mental health settings. Despite concerns about combining medicines and broad‐spectrum micronutrients in mental health care, there have not been any formal evaluations of potential interactions. Our objective was to evaluate a broad‐spectrum micronutrient formula as a potential precipitant of pharmacokinetic drug–drug interactions through inhibition or induction of cytochrome P450 (CYP) enzymes. Methods This was a single‐centre pharmacokinetic study. Twelve healthy participants received broad‐spectrum micronutrients for 14 days (Days 1–14). Participants were administered a ‘cocktail’ of selective CYP probes midazolam 2 mg (CYP3A), dextromethorphan 30 mg (CYP2D6), losartan 25 mg (CYP2C9), omeprazole 20 mg (CYP2CI9) and caffeine 100 mg (CYP1A2) on Day 0 and Day 14, before taking and while taking broad‐spectrum micronutrients. Plasma drug concentrations were measured at baseline and for 8 h following cocktail administration. AUC, C max and T max were compared before and after broad spectrum micronutrient administration using paired t tests. Results Pre‐ and post‐micronutrient geometric means (SD) for AUC 0‐8h (μg*h/L) were: midazolam 25 (13) and 26 (14), P = 0.60; dextromethorphan 25 (99) and 19 (110), P = 0.46; losartan 219 (105) and 205 (76), P = 0.20; omeprazole 474 (394) and 402 (342), P = 016; and caffeine 13 800 (5400) and 12 800 (3500), P = 0.79. There were no statistically significant changes in geometric means of probe C max , or T max for any of the study drugs. Conclusions Broad‐spectrum micronutrients are unlikely to be a major precipitant of pharmacokinetic drug–drug interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".