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We assessed whether the anti\u2010depressive 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\u2010treated patients with major depressive disorder. Of these patients, 50 patients had received a pre\u2010treatment personality assessment using the Temperament and Character Inventory at baseline, and their depression severity was assessed using the Montgomery\u2010Asberg Depression Rating Scale (MADRS) at baseline and 1, 2, 4, and 6\u2009weeks after treatment initiation. We conducted population PK modeling followed by a population PK\u2010pharmacodynamic (popPK\/PD) model to analyze the enhancement in depression severity until 6\u2009weeks of paroxetine treatment using nonlinear mixed\u2010effects modeling. Additionally, we developed machine learning models to predict the likelihood of remission after 6\u2009weeks. The contribution of each feature to the prediction was explained using SHapley Additive exPlanations (SHAP) values. The area under the plasma paroxetine concentration\u2010time curve during the first week (AUC<jats:sub>0\u20131week<\/jats:sub>) and MADRS score after 1\u2009week of treatment (MADRS<jats:sub>W1<\/jats:sub>) were incorporated into the popPK\/PD model. The SHAP values indicated that the AUC<jats:sub>0\u20131week<\/jats:sub> and MADRS<jats:sub>W1<\/jats:sub> 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.<\/jats:p>","DOI":"10.1002\/psp4.70032","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T12:51:25Z","timestamp":1747140685000},"page":"1119-1127","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Effect of Cumulative Exposure on the Efficacy of Paroxetine: A Population Pharmacokinetic\u2010Pharmacodynamic and Machine Learning Analyses"],"prefix":"10.1002","volume":"14","author":[{"given":"Keiichi","family":"Shigetome","sequence":"first","affiliation":[{"name":"Division of Pharmacology and Therapeutics Graduate School of Pharmaceutical Sciences, Kumamoto University  Kumamoto Japan"},{"name":"Department of Psychiatry School of Medicine, Dokkyo Medical University  Tochigi 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