Metabolomic signatures of drug response phenotypes for ketamine and esketamine in subjects with refractory major depressive disorder: new mechanistic insights for rapid acting antidepressants

Abstract

Ketamine, at sub-anesthetic doses, is reported to rapidly decrease depression symptoms in patients with treatment-resistant major depressive disorder (MDD). Many patients do not respond to currently available antidepressants, (for example, serotonin reuptake inhibitors), making ketamine and its enantiomer, esketamine, potentially attractive options for treatment-resistant MDD. Although mechanisms by which ketamine/esketamine may produce antidepressant effects have been hypothesized on the basis of preclinical data, the neurobiological correlates of the rapid therapeutic response observed in patients receiving treatment have not been established. Here we use a pharmacometabolomics approach to map global metabolic effects of these compounds in treatment-refractory MDD patients upon 2 h from infusion with ketamine (n=33) or its S-enantiomer, esketamine (n=20). The effects of esketamine on metabolism were retested in the same subjects following a second exposure administered 4 days later. Two complementary metabolomics platforms were used to provide broad biochemical coverage. In addition, we investigated whether changes in particular metabolites correlated with treatment outcome. Both drugs altered metabolites related to tryptophan metabolism (for example, indole-3-acetate and methionine) and/or the urea cycle (for example, citrulline, arginine and ornithine) at 2 h post infusion (q<0.25). In addition, we observed changes in glutamate and circulating phospholipids that were significantly associated with decreases in depression severity. These data provide new insights into the mechanism underlying the rapid antidepressant effects of ketamine and esketamine, and constitute some of the first detailed metabolomics mapping for these promising therapies.

Figure 1

Hierarchical clustering of GC-TOF metabolites upon exposure to ketamine (q<0.25). Colors represent positively correlated (red) to negatively correlated (blue) metabolites. Some unknown metabolites are highly correlated with known metabolites, which may provide insight into their underlying function such as, methionine and 299, and also cholesterol, gamma-tocopherol, alpha-tocopherol and 54. GC-TOF, gas chromatography–time-of-flight mass spectrometry.

Figure 2

Heatmap displaying correlations of the changing metabolites from pre-treatment to post-treatment ketamine on the Biocrates platform. Distinct clusters of phosphatidylcholine and sphyngomyelin classes of metabolites show highly correlated changes from pre- to post-treatment ketamine.