Objective: This study aimed to establish a UPLC-MS/MS method for the simultaneous quantification of five antidepressants: venlafaxine (VEN) and its metabolite O-desmethylvenlafaxine (ODV), mirtazapine (MIR), sertraline (SER), escitalopram (ESC), and vortioxetine (VTX) in human plasma and saliva. By analyzing real-world therapeutic drug monitoring (TDM) data, this study aimed to identify key factors influencing drug concentrations thereby optimizing personalized treatment strategies for patients with depression and advancing precision medicine.
Methods: Following liquid-liquid extraction for plasma and protein precipitation for saliva, analyte concentrations were determined using a fully validated UPLC-MS/MS method. Validation included assessments of selectivity, linearity, accuracy, precision, extraction recovery, matrix effects, stability, and dilution integrity. The established method was applied to clinical samples, with further investigation into how clinical factors, including age, BMI, renal function (as measured by GFR), total protein (TP), albumin levels, and concomitant medications, influenced the concentration-to-dose ratio (CDR).
Results: The method demonstrated excellent linearity (5-500 ng/mL) with all validation parameters meeting acceptance criteria. The established method was successfully applied to analyze 566 plasma and 39 saliva samples. TDM revealed significant variations in target attainment rates among different antidepressants, along with varying degrees of dose-concentration correlations. Multivariate analysis demonstrated that the CDR of VEN + ODV was primarily influenced by age and GFR, while the CDR of MIR showed a significant association with BMI. The CDR of SER was affected by both BMI and TP levels. The CDR of ESC was modulated by age, concomitant medications, and renal function.
Conclusion: This study demonstrated that TDM-based individualized medication strategies can support the optimization of antidepressant efficacy. Saliva monitoring requires further validation. Clinicians should adopt dynamic, patient-specific monitoring to enhance precision medicine outcomes in depression management.
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