Personalizing Antidepressant Therapy: Integrating Pharmacogenomics, Therapeutic Drug Monitoring, and Digital Tools for Improved Depression Outcomes

Background: Major Depressive Disorder (MDD) is a leading global health concern, yet its pharmacological management is hampered by a «trial-and-error» approach, with a significant proportion of patients failing to achieve remission with initial therapy. This challenge stems from the disorder’s marked biological heterogeneity, which is poorly captured by current broad diagnostic categories. This literature review synthesizes the latest evidence across three complementary fields poised to revolutionize MDD treatment: pharmacogenetics testing (PGT), therapeutic drug monitoring (TDM), and artificial intelligence (AI). We hypothesize that integrating all three facilitates the transition from empirical prescribing to model-informed precision dosing (MIPD), enabling prediction of optimal antidepressant selection and dosage before the first dose is administered. The convergence of these technologies, supported by an interdisciplinary framework, has the potential to enhance current treatment strategies and contribute to more individualized psychiatric care. Conclusions: Antidepressant therapy for MDD may be further optimized through the combined use of TDM, PGT, and digital tools. However, the development of this field requires ongoing research and interdisciplinary work.