Rapastinel - an investigational NMDA-R modulator for major depressive disorder: evidence to date
Bibliographic record
Abstract
INTRODUCTION: Major depressive disorder (MDD) is a debilitating disorder with increasing prevalence globally. Despite the development of novel treatments for MDD, many patients present with treatment resistant depression (TRD), identified by treatment non-response following one or more adequate trials of an antidepressant. Rapastinel may prove to be a viable treatment for TRD; it has the potential to produce a rapid antidepressant response without serious adverse events and improve functional symptoms. Areas covered: We review the efficacy of rapastinel via completed and on-going clinical trials. The online databases Pubmed, clinicaltrials.gov and clinicaltrialsregister.eu were searched for rapastinel (GLYX-13) treatment in subjects with MDD. Nine clinical trials were identified. Expert opinion: Rapastinel is a novel and potentially transformative treatment for individuals with TRD. There is a limited number of clinical studies so far, but this compound has the potential to provide rapid, reliable and robust antidepressant effects without psychotomimetic and other unwanted side effects. Alternative formulations such as the oral formulation, provide the opportunity for rapastinel to be administered less frequently, i.e. once weekly. Furthermore, the beneficial effects on measures of cognition and suicidality so far, represent a tremendous advantage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".