Advancing paroxetine treatment in depression: predicting remission and plasma concentration, and validating and updating therapeutic reference ranges
Bibliographic record
Abstract
Optimizing paroxetine therapy for major depressive disorder (MDD) requires effective prediction models for treatment efficacy and therapeutic drug monitoring (TDM). This study aimed to develop prediction models for treatment remission and steady-state concentration (Css) of paroxetine, elucidate the role of CYP2D6 activity score (AS) in predicting Css, establish associations between adverse drug reactions (ADRs) and Css, and validate and update the therapeutic reference range (TRR) for patients with MDD in the Han Chinese population. We conducted a post-hoc analysis of an 8-week multicenter prospective cohort study involving 530 Han Chinese patients with MDD. Logistic regression models were developed to predict treatment remission at the eighth week and Css as a binary variable (within/outside TRR of 20-65 ng/ml). The model for predicting treatment remission demonstrated an AUC of 0.707, while the model for Css achieved an AUC of 0.615. Associations between ADRs and Css were assessed using logistic regression, adjusted for sex and age. Patients with Css within 20-65 ng/ml were more likely to achieve remission (OR = 1.655, 95% CI: 1.109-2.489) and less likely to experience ADRs (OR = 0.460, 95% CI: 0.203-0.961). Additionally, those with lower AS were more likely to maintain Css within this range (OR = 0.638, 95% CI: 0.461-0.878). ROC analysis further established an updated TRR of 20.8-52.5 ng/ml considering both treatment remission and ADRs. Our findings enhance paroxetine treatment and monitoring, underscoring the potential of CYP2D6 AS and Css as predictors for Css and treatment remission, respectively.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".