Ketamine as an antidepressant: overview of its mechanisms of action and potential predictive biomarkers
Bibliographic record
Abstract
Ketamine, a drug introduced in the 1960s as an anesthetic agent and still used for that purpose, has garnered marked interest over the past two decades as an emerging treatment for major depressive disorder. With increasing evidence of its efficacy in treatment-resistant depression and its potential anti-suicidal action, a great deal of investigation has been conducted on elucidating ketamine’s effects on the brain. Of particular interest and therapeutic potential is the ability of ketamine to exert rapid antidepressant properties as early as several hours after administration. This is in stark contrast to the delayed effects observed with traditional antidepressants, often requiring several weeks of therapy for a clinical response. Furthermore, ketamine appears to have a unique mechanism of action involving glutamate modulation via actions at the N-methyl-D-aspartate (NMDA) and [Formula: see text]-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, as well as downstream activation of brain-derived neurotrophic factor (BDNF) and mechanistic target of rapamycin (mTOR) signaling pathways to potentiate synaptic plasticity. This paper provides a brief overview of ketamine with regard to pharmacology/pharmacokinetics, toxicology, the current state of clinical trials on depression, postulated antidepressant mechanisms and potential biomarkers (biochemical, inflammatory, metabolic, neuroimaging sleep-related and cognitive) for predicting response to and/or monitoring of therapeutic outcome with ketamine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".