Frequencies of Genetic Polymorphisms of Clinically Relevant Gene-Drug Pairs in a German Psychiatric Inpatient Population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it