Frequencies of <i>CYP2C19</i> and <i>CYP2D6</i> gene variants in a German inpatient sample with mood and anxiety disorders
Bibliographic record
Abstract
Objectives: Previous results demonstrated that CYP2D6 and CYP2C19 gene variants affect serum concentrations of antidepressants. We implemented a PGx service determining gene variants in CYP2D6 and CYP2C19 in our clinical routine care and report on our first patient cohort.Methods: We analyzed CYP2D6 and CYP2C19 allele, genotype, and phenotype frequencies, and actionable pharmacogenetic variants in this German psychiatric inpatient cohort. Two-tailed z-test was used to investigate for differences in CYP2D6 and CYP2C19 phenotypes and actionable/non-actionable genetic variant frequencies between our cohort and reference cohorts.Results: Out of the 154 patients included, 44.8% of patients were classified as CYP2D6 normal metabolizer, 38.3% as intermediate metabolizers, 8.4% as poor metabolizers, and 2.6% as ultrarapid metabolizers. As for CYP2C19, 40.9% of patients were classified as normal metabolizers, 19.5% as intermediate metabolizers, 2.6% as poor metabolizers, 31.2% as rapid metabolizers, and 5.8% as ultrarapid metabolizers. Approximately 80% of patients had at least one actionable PGx variant.Conclusion: There is a high prevalence of actionable PGx variants in psychiatric inpatients which may affect treatment response. Physicians should refer to PGx-informed dosing guidelines in carriers of these variants. Pre-emptive PGx testing in general may facilitate precision medicine also for other drugs metabolized by CYP2D6 and/or CYP2C19.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".