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Published on: June 6, 2025
Construction and Validation of a Model for Predicting Antidepressant Treatment Outcomes in Patients with Depression
Xiaoya Wen1, Chengxiang Hou2, Yin Yao1
1Department of Pharmacy, The Fourth People's Hospital of Guiyang, Guiyang, China.
Background:
Paroxetine efficacy cannot be accurately predicted through TDM alone. Given SIRT1's linkage to drug response, we explored the predictive value of combined TDM and SIRT1 detection.Methods: In total, 27 treatment-naive depressed patients receiving 8-week standard paroxetine therapy were stratified into drug-sensitive (reduction rate ≥50%) and drug-resistant groups (reduction rate <50%) according to Hamilton Depression Rating Scale score reduction rates. Peripheral blood samples were collected at baseline (pretreatment) and 2 weeks and 8 weeks post-treatment. Plasma paroxetine concentrations were measured using UPLC-MS/MS, and SIRT1 mRNA levels in peripheral blood mononuclear cells were determined using qPCR. The CYP2D6 genotype was identified by PCR-SNP assay.
Results:
After 2 weeks of treatment, SIRT1 mRNA expression was significantly reduced in the resistant group, with the decrease rate (>30%) being significantly higher than that in the sensitive group (<10%; P < 0.001). This difference persisted until week 8. The SIRT1 mRNA decrease rate correlated positively with excessive plasma paroxetine concentrations (>65 ng/mL) (r = 0.68, P < 0.001). The XGBoost-based efficacy prediction model (incorporating SIRT1 mRNA change rate, plasma drug concentration, and CYP2D6*10 genotype) outperformed the single TDM model in predicting insufficient 8-week efficacy (AUC = 0.91 vs. AUC = 0.75; P < 0.01). Combined monitoring-guided dose adjustment reduced the excessive plasma drug concentration rate by 63% and increased the 8-week treatment response rate by 22% (95% CI, 15-29).
Conclusions:
Combined SIRT1 mRNA and plasma paroxetine monitoring predicted treatment outcomes 14 days early, improving predictive accuracy and dose precision. SIRT1 may regulate paroxetine metabolism through CYP2D6 acetylation, supporting the role of the "SIRT1-CYP2D6 acetylation axis" in precision treatment. This new model highlights the value of multiple biological indicators.
