Significant inter-individual variability exists in antidepressant treatment response. Beyond genetic and clinical determinants, lifelong cumulative exposure to environmental chemical pollutants has emerged as an important exogenous factor. However, existing studies largely remain at the level of associations between environmental exposure and depression risk, lacking a systematic ex-planation of its impact on the antidepressant treatment process from an integrated pharmacokinetic-pharmacodynamic  perspective. This review aims to systematically summarize the mechanisms by which environmental chemical exposures influence antidepressant efficacy from a translational pharmacology perspective, and to explore their integration into predictive models for individualized treatment response. Existing evidence suggests that heavy metals, air pollutants, and persistent organic pollutants can induce oxidative stress and chronic inflammation, disrupt blood-brain barrier (BBB) integrity—particularly via endothelial gly-cocalyx damage—and modulate the activity of drug transporters (e.g., P-glycoprotein) and metabolizing enzymes (e.g., cytochrome P450). These alter-ations may affect drug disposition in the body, reduce central nervous system exposure, and lead to a decoupling between plasma drug concentration and therapeutic response. Such processes can be conceptualized as cumulative drug-environment interactions. Building on this framework, environmental exposure indicators may serve as novel predictive dimensions and can be integrated with pharmacogenomics, biomarkers, and clinical features using machine learning approaches (e.g., random forest, XGBoost) to develop individualized antidepressant efficacy prediction models. By proposing an integrated framework linking environmental exposure, BBB function, pharmacokinetics, and treatment outcomes, this review provides a novel perspective for understanding variability in antidepressant efficacy and offers potential pathways toward precision psychopharmacology. Future studies should address challenges related to exposure assessment standardization, causal re-lationship validation, and clinical translation.

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