Effect of Cumulative Exposure on the Efficacy of Paroxetine: A Population Pharmacokinetic-Pharmacodynamic and Machine Learning Analyses.

Selective serotonin reuptake inhibitors (SSRIs) are widely used in depression treatment. However, the relationship between treatment efficacy and plasma concentrations remains unclear. We assessed whether the anti-depressive response can be predicted based on the pharmacokinetic (PK) data of paroxetine, a frequently used SSRI. During treatment, we measured the plasma paroxetine concentrations in 179 paroxetine-treated patients with major depressive disorder. Of these patients, 50 patients had received a pre-treatment personality assessment using the Temperament and Character Inventory at baseline, and their depression severity was assessed using the Montgomery-Asberg Depression Rating Scale (MADRS) at baseline and 1, 2, 4, and 6 weeks after treatment initiation. We conducted population PK modeling followed by a population PK-pharmacodynamic (popPK/PD) model to analyze the enhancement in depression severity until 6 weeks of paroxetine treatment using nonlinear mixed-effects modeling. Additionally, we developed machine learning models to predict the likelihood of remission after 6 weeks. The contribution of each feature to the prediction was explained using SHapley Additive exPlanations (SHAP) values. The area under the plasma paroxetine concentration-time curve during the first week (AUC0-1week) and MADRS score after 1 week of treatment (MADRSW1) were incorporated into the popPK/PD model. The SHAP values indicated that the AUC0-1week and MADRSW1 were the significant predictors of remission. Our results indicate that therapeutic responsiveness to paroxetine can be anticipated from its cumulative exposure, highlighting the clinical relevance of assessing SSRI blood concentrations.