UNDERSTANDING THE IMPACT OF CYP2D6-MEDIATED VENLAFAXINE PHARMACOKINETICS ON TREATMENT OUTCOMES IN LATE-LIFE DEPRESSION
Bibliographic record
Abstract
Abstract Background Aging makes older adults more susceptible to antidepressant-induced side effects due to homeostatic reserve, comorbidity, poly-pharmacy, and age-related pharmacokinetic (PK) changes. Depression in older adults is often treated with venlafaxine, a serotonin-norepinephrine reuptake inhibitor metabolized by the enzyme CYP2D6. CYP2D6 is highly genetically polymorphic and thus might affect venlafaxine treatment outcomes by affecting venlafaxine PK. Aims and Objectives The study aims to investigate whether CYP2D6 metabolizers have different VEN- related PK parameters and examine the impact of these parameters on treatment outcomes in late-life depression. Method Data from participants from the Incomplete Response in Late-Life Depression: Getting to Remission study (IRL-GRey, NCT00892047) were analyzed in this study (N = 325) [1]. We used the software NONMEM to adapt a population PK analyses of VEN and its main metabolite O- desmethylvenlafaxine (ODV) [2]. The PK model was adjusted for CYP2D6 metabolizer status and age. The ANOVA was performed to identify differences in PK model-estimated PK parameters between CYP2D6 metabolizer groups. Treatment efficacy (measured using MADRS) and adverse effects (measured using UKU) were analyzed using regression models to see if they were associated with PK model-estimated drug exposure, followed by sex-stratified analyses for each outcome. Results CYP2D6 metabolizers had significantly different PK model-estimated VEN clearance, VEN exposure, and active moiety (venlafaxine plus ODV) exposure. None of the exposure was associated with treatment efficacy in either whole sample or sex-stratified analyses. The overall presence of adverse effects was associated with higher ODV exposure (OR = 1.5 [1.0, 2.2], p = 0.04) and higher AM exposure (OR = 1.7 [1.2, 2.5], p = 0.003). Only females showed a significant association between overall adverse effects and higher active moiety exposure (OR = 2.0 [1.3, 3.2], p = 0.004). Specifically, higher risk of nausea/vomiting was associated with higher venlafaxine exposure and higher active moiety exposure in both the whole sample (venlafaxine, OR = 1.1 [1.0, 1.2], p = 0.04; active moiety, OR = 2.0 [1.2, 3.4], p = 0.01) and females (venlafaxine, OR = 1.1 [1.0, 1.2], p = 0.02; active moiety, OR = 2.2 [1.2, 4.0], p = 0.01), but not in males. In the whole sample, orthostatic dizziness is associated with higher venlafaxine exposure (OR = 1.1 [1.0, 1.2], p = 0.02) and higher active moiety exposure (OR = 2.0 [1.2, 3.4], p = 0.01). For this adverse effect, females showed had association only with higher venlafaxine (OR = 1.1 [1.0, 1.2], p = 0.02), while males showed significant associations in higher ODV (OR = 5.2 [1.2, 23.0], p = 0.03) and higher active moiety exposure (OR = 5.5 [1.1, 26.8], p = 0.03). Discussion & Conclusions Our study indicated that CYP2D6 metabolizer groups had a significant impact on PK model-estimated VEN-related PK parameters. Higher venlafaxine-related exposure is associated with a higher risk of active effects, especially nausea/vomiting and orthostatic dizziness. Sex might also be an important factor in venlafaxine treatment outcomes. Overall, our study highlights the importance of personalized medicine and its clinical implications in older adults with depression. References 1.Lenze, E. J. et al. Efficacy, safety, and tolerability of augmentation pharmacotherapy with aripiprazole for treatment-resistant depression in late life: a randomised, double-blind, placebo- controlled trial. The Lancet 386, 2404–2412 (2015). 2.Lindauer, A. et al. Pharmacokinetic/pharmacodynamic modelling of venlafaxine: pupillary light reflex as a test system for noradrenergic effects. Clin. Pharmacokinet. 47, 721–731 (2008).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".