<scp>CYP2D6</scp> Phenotype Influences Pharmacokinetic Parameters of Venlafaxine: Results from a Population Pharmacokinetic Model in Older Adults with Depression
Bibliographic record
Abstract
In this study, we aimed to improve upon a published population pharmacokinetic (PK) model for venlafaxine (VEN) in the treatment of depression in older adults, then investigate whether CYP2D6 metabolizer status affected model-estimated PK parameters of VEN and its active metabolite O-desmethylvenlafaxine. The model included 325 participants from a clinical trial in which older adults with depression were treated with open-label VEN (maximum 300 mg/day) for 12 weeks and plasma levels of VEN and O-desmethylvenlafaxine were assessed at weeks 4 and 12. We fitted a nonlinear mixed-effect PK model using NONMEM to estimate PK parameters for VEN and O-desmethylvenlafaxine adjusted for CYP2D6 metabolizer status and age. At both lower doses (up to 150 mg/day) and higher doses (up to 300 mg/day), CYP2D6 metabolizers impacted PK model-estimated VEN clearance, VEN exposure, and active moiety (VEN + O-desmethylvenlafaxine) exposure. Specifically, compared with CYP2D6 normal metabolizers, (i) CYP2D6 ultra-rapid metabolizers had higher VEN clearance; (ii) CYP2D6 intermediate metabolizers had lower VEN clearance; (iii) CYP2D6 poor metabolizers had lower VEN clearance, higher VEN exposure, and higher active moiety exposure. Overall, our study showed that including a pharmacogenetic factor in a population PK model could increase model fit, and this improved model demonstrated how CYP2D6 metabolizer status affected VEN-related PK parameters, highlighting the importance of genetic factors in personalized medicine.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".