[Current methods for predicting therapeutic response in patients with depressive disorders]

Area of Science:

• Psychiatry and Clinical Psychology
• Pharmacology and Therapeutics

Background:

• Depressive disorder is a leading cause of global disability and suicide.
• Up to 60% of patients exhibit inadequate response to standard antidepressant pharmacotherapy, termed non-responders.
• Current definitions of non-responders require a 4-week trial of multiple antidepressant classes, potentially delaying effective treatment.

Purpose of the Study:

• To review and summarize existing methods for predicting therapeutic response in patients with depressive disorders.
• To highlight the need for early identification of treatment resistance to optimize patient outcomes.

Main Methods:

• A comprehensive literature search was performed in PubMed, Scopus, and Google Scholar.
• Keywords included 'depression', 'antidepressant', 'outcome', 'predictor', '(bio)marker', 'treatment-resistant depression', and 'chronic depression'.
• The search focused on studies published between 2005 and 2020.

Main Results:

• The review synthesizes various predictive methods and biomarkers for antidepressant response.
• Identified predictors range from clinical factors to biological markers.
• The findings underscore the complexity of predicting treatment outcomes in depression.

Conclusions:

• Developing reliable prognostic methods for therapeutic resistance is essential for timely and effective depression management.
• Early prediction of non-response can prevent prolonged suffering and adverse consequences for patients.
• Further research into validated biomarkers and predictive models is warranted to improve personalized depression treatment.