research published 2024-11-29 · by Demo I, Goes FS, Gould TD, Medeiros GC, Zarate CA Jr

Translational psychiatry · 2024 Nov 29

Abstract

Personalized use of ketamine and esketamine for treatment-resistant depression. A large and disproportionate portion of the burden associated with major depressive disorder (MDD) is due to treatment-resistant depression (TRD). Intravenous (R,S)-ketamine (ketamine) and intranasal (S)-ketamine (esketamine) are rapid-acting antidepressants that can effectively treat TRD. However, there is variability in response to ketamine/esketamine, and a personalized approach to their use will increase success rates in the treatment of TRD. There is a growing literature on the precision use of ketamine in TRD, and the body of evidence on esketamine is still relatively small. The identification of reliable predictors of response to ketamine/esketamine that are easily translatable to clinical practice is urgently needed. Potential clinical predictors of a robust response to ketamine include a pre-treatment positive family history of alcohol use disorder and a pre-treatment positive history of clinically significant childhood trauma. Pre-treatment versus post-treatment increases in gamma power in frontoparietal brain regions, observed in electroencephalogram (EEG) studies, is a promising brain-based biomarker of response to ketamine, given its time of onset and general applicability. Blood-based biomarkers have shown limited usefulness, with small-effect increases in brain-derived neurotrophic factor (BDNF) being the most consistent indicator of ketamine response. The severity of treatment-emergent dissociative symptoms is typically not associated with a response either to ketamine or esketamine. Future studies should ensure that biomarkers and clinical variables are obtained in a similar manner across studies to allow appropriate comparison across trials and to reduce the signal-to-noise ratio. Most predictors of response to ketamine/esketamine have modest effect sizes; therefore, the use of multivariate predictive models will be needed.

Neurotransmitters

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