<i>CYP2C19</i> genotype predicts steady state escitalopram concentration in GENDEP
Bibliographic record
Abstract
In vitro work shows CYP2C19 and CYP2D6 contribute to the metabolism of escitalopram to its primary metabolite, N-desmethylescitalopram. We report the effect of CYP2C19 and CYP2D6 genotypes on steady state morning concentrations of escitalopram and N-desmethylescitalopram and the ratio of this metabolite to the parent drug in 196 adult patients with depression in GENDEP, a clinical pharmacogenomic trial. Subjects who had one CYP2D6 allele associated with intermediate metabolizer phenotype and one associated with poor metabolizer (i.e. IM/PM genotypic category) had a higher mean logarithm escitalopram concentration than CYP2D6 extensive metabolizers (EMs) (p = 0.004). Older age was also associated with higher concentrations of escitalopram. Covarying for CYP2D6 and age, we found those homozygous for the CYP2C19*17 allele associated with ultrarapid metabolizer (UM) phenotype had a significantly lower mean escitalopram concentration (2-fold, p = 0.0001) and a higher mean metabolic ratio (p = 0.0003) than EMs, while those homozygous for alleles conferring the PM phenotype had a higher mean escitalopram concentration than EMs (1.55-fold, p = 0.008). There was a significant overall association between CYP2C19 genotypic category and escitalopram concentration (p = 0.0003; p = 0.0012 Bonferroni corrected). In conclusion, we have demonstrated an association between CYP2C19 genotype, including the CYP2C19*17 allele, and steady state escitalopram concentration.
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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) | 0.002 | 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".