Females present higher dose‐adjusted drug concentrations of metoprolol and allopurinol/oxypurinol than males
Bibliographic record
Abstract
Abstract Females present a higher risk of adverse drug reactions. Sex‐related differences in drug concentrations may contribute to these observations but they remain understudied given the underrepresentation of females in clinical trials. The aim of this study was to investigate whether anthropometric and socioeconomic factors and comorbidities could explain sex‐related differences in concentrations and dosing for metoprolol and oxypurinol, the active metabolite of allopurinol. We conducted an analysis of two cross‐sectional studies. Participants were self‐described “White” adults taking metoprolol or allopurinol selected from the Montreal Heart Institute Hospital Cohort. A total of 1007 participants were included in the metoprolol subpopulation and 459 participants in the allopurinol subpopulation; 73% and 86% of the participants from the metoprolol and allopurinol subpopulations were males, respectively. Females presented higher age‐ and dose‐adjusted concentrations of both metoprolol and oxypurinol (both p < 0.03). Accordingly, females presented higher unadjusted and age‐adjusted concentration:dose ratio of both metoprolol and allopurinol/oxypurinol compared to males (all p < 3.0 × 10−4). Sex remained an independent predictor of metoprolol concentrations (p < 0.01), but not of oxypurinol concentrations, after adjusting for other predictors. In addition to sex, age, daily dose, use of moderate to strong CYP2D6 inhibitors, weight, and CYP2D6 genotype‐inferred phenotype were associated with concentrations of metoprolol (all p < 0.01). Daily dose, weight, estimated glomerular filtration rate (eGFR), and employment status were associated with oxypurinol concentrations (all p < 0.01). Females present higher dose‐adjusted concentrations of metoprolol and oxypurinol than males. This suggests the need for sex‐specific dosing requirements for these drugs, although this hypothesis should be validated in prospective studies.
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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 | 0.001 |
| 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) | 0.008 | 0.001 |
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".