CYP2D6 and DRD2 genes differentially impact pharmacodynamic sensitivity and time course of prolactin response to perphenazine
Bibliographic record
Abstract
OBJECTIVES: We observed that CYP2D6 contributes to pharmacodynamic tissue sensitivity to perphenazine as measured by the areas under the curve (AUCs) expressed as a ratio (prolactin-AUC0-6/perphenazine-AUC0-6) in Chinese Canadians [Pharmacogenetics and Genomics 2007; 17:339-347]. As genetic heterogeneity in drug targets can influence drug response, we sought to further evaluate the contribution of CYP2D6 to pharmacodynamic sensitivity in our previous study sample in tandem with DRD2, the primary molecular target for perphenazine. METHODS: Genotyping for DRD2 Taq1A, -141C ins/del and Ser311Cys functional polymorphisms was performed using PCR-restriction-fragment length polymorphism methods. RESULTS: After controlling for DRD2 polymorphisms, CYP2D6 was a significant predictor of pituitary pharmacodynamic tissue sensitivity to perphenazine (P=0.024; power=80.4%). Taq1A polymorphism significantly influenced the time course of prolactin response (P=0.039; power=70%). A1/A1 genotype displayed a higher prolactin elevation 2 h after perphenazine administration (P=0.02). Patients with -141C ins/ins genotype showed a strong trend toward a 38% larger prolactin AUC compared with the -141C ins/del genotypic group (P=0.07). CONCLUSIONS: CYP2D6 seems to be an independent contributor to pituitary pharmacodynamic tissue sensitivity to perphenazine after accounting for DRD2 functional polymorphisms. The A1 allele of the Taq1A polymorphism was previously shown to decrease D2 receptor density in vitro and in neuroimaging studies in vivo. At a given antipsychotic dose, individuals with A1 allele might thus achieve a higher DRD2 antipsychotic occupancy, which is consistent with an increased prolactin elevation in the A1/A1 genotype in this study. These findings provide a basis for further studies on the endogenous substrates of CYP2D6 and the rational selection of candidate genes for long-term consequences of antipsychotic-induced hyperprolactinemia (e.g. susceptibility to breast and prostate cancers).
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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 | 0.001 |
| 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".