<i>CYP2D6</i> genotype and smoking influence fluvoxamine steady-state concentration in Japanese psychiatric patients: lessons for genotype–phenotype association study design in translational pharmacogenetics
Bibliographic record
Abstract
The CYP2D6 enzyme is a capacity-limited high-affinity drug elimination pathway that metabolizes numerous psychiatric medicines. The capacity-limited nature of this enzyme suggests that drug dose may serve as an important factor that influence genotype-phenotype associations. However, dose dependency of CYP2D6 genotype contributions to drug elimination, and its interaction with environmental factors (e.g., smoking) did not receive adequate attention in translational study designs. Fluvoxamine is a selective serotonin reuptake inhibitor antidepressant. Fluvoxamine concentration is one of the factors previously linked to clinical remission in moderate to severe depression. We investigated the joint effect of smoking (an inducer of CYP1A2) and CYP2D6 genotype on interindividual variability in fluvoxamine steady-state concentration. Fluvoxamine concentration was measured in 87 patients treated with 50, 100, 150 or 200 mg/d. While CYP2D6 genotype significantly influenced fluvoxamine concentration in all four dose groups (p < 0.05), the percentage variance explained (R²) by CYP2D6 decreased as the dose of fluvoxamine increased. Smoking status (nonsmokers vs. smoking 20 or more cigarettes/d) significantly affected fluvoxamine concentration in the 50 mg/d group only (p = 0.005). Together, CYP2D6 genotype and smoking status explained 23% of the variance in fluvoxamine concentration but only at the low 50 mg/d dose group. These findings contribute to evidence-based and personalized choice of fluvoxamine dose using smoking status and CYP2D6 genetic variation. Additionally, these data lend evidence for drug dose as an important variable in translational pharmacogenetic study design and pharmaceutical phenotype associations with capacity-limited drug metabolism pathways such as CYP2D6.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".