Frequencies of Genetic Polymorphisms of Clinically Relevant Gene-Drug Pairs in a German Psychiatric Inpatient Population
Bibliographic record
Abstract
INTRODUCTION: Genetic variation is known to affect enzymatic activities allowing differentiating various metabolizer types (e. g., slow or rapid metabolizers), in particular CYP2C19 and CYP2D6. METHODS: PGx-testing was conducted in adult major depressive disorder inpatients admitted to the Vitos Klinik Eichberg between 11/2016 and 7/2017 (n=108, 57% female). We conducted a two-sided Z-Test (p=0.05) to analyze and compare frequencies of CYP2D6, CYP2C19, CYP3A4, CYP3A5 and CYP2C9 metabolizer groups with other European and psychiatric inpatient cohorts. The HLA-A and -B genes were also analyzed. RESULTS: Non-normal metabolizer status of CYP2D6 were present in 47%. More specifically, 35 % were intermediate, 7% poor and 4% ultra-rapid metabolizers. 68% were CYP2C19 non-normal metabolizers. 8% were ultra-rapid and 31% rapid metabolizers. Notably, only 13% were NM for CYP2C19 and NM for CYP2D6 (activity score of 1 or more). For CYP2C9 we found 16% to be intermediate metabolizers, 1.0% poor metabolizer. CYP3A4 and CYP3A5 genetic polymorphisms were present in 25% and 19% respectively. HLA-B TAG- SNPs for *15:01 was positive in 25 patients, showing the need for different Tag-SNPs in Caucasians. HLA-B *57:01 TAG-SNP was positive in 8% of the patients, HLA-A TAG-SNP for *31:01 in Caucasians was positive in 9%. Z-Test showed statistical significance for our results. DISCUSSION: Our results suggest that our psychiatric inpatients were enriched with genotypes consistent with non-normal drug metabolism compared to reference populations. We therefore conclude that pharmacogenetic testing should be implemented in clinical practice to guide drug therapy.
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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 | 0.001 | 0.001 |
| 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".