Leveraging large observational studies to discover genetic determinants of drug concentrations: A proof‐of‐concept study
Bibliographic record
Abstract
Abstract Large, observational genetic studies are commonly used to identify genetic factors associated with diseases and disease‐related traits. Such cohorts have not been commonly used to identify genetic predictors of drug dosing or concentrations, perhaps because of the heterogeneity in drug dosing and formulation, and the random timing of blood sampling. We hypothesized that large sample sizes relative to traditional pharmacokinetic studies would compensate for this variability and enable the identification of pharmacogenetic predictors of drug concentrations. We performed a cross‐sectional, proof‐of‐concept association study to replicate the well‐established association between metoprolol concentrations and CYP2D6 genotype‐inferred metabolizer phenotypes in participants from the Montreal Heart Institute Hospital Cohort undergoing metoprolol therapy. Plasma concentrations of metoprolol and α‐hydroxymetoprolol (α‐OH‐metoprolol) were measured in samples collected randomly regarding the previous metoprolol dose. A total of 999 individuals were included. The metoprolol daily dose ranged from 6.25 to 400 mg (mean 84.3 ± 57.1 mg). CYP2D6‐inferred phenotype was significantly associated with both metoprolol and α‐OH‐metoprolol in unadjusted and adjusted models (all p < 10 −14 ). Models for metoprolol daily dose showed consistent results. Our study suggests that randomly drawn blood samples from biobanks can serve as a new approach to discover genetic associations related to drug concentrations and dosing, with potentially broader implications for genomewide association studies on the pharmacogenomics of drug metabolism.
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 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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".