Demystifying the Antidepressant Mechanism of Action of Stinels, a Novel Class of Neuroplastogens: Positive Allosteric Modulators of the NMDA Receptor
Bibliographic record
Abstract
Plastogens are a class of therapeutics that function by rapidly promoting changes in neuroplasticity. A notable example, ketamine, is receiving great attention due to its combined rapid and long-term antidepressant effects. Ketamine is an N-methyl-D-aspartate receptor (NMDAR) antagonist, and, in addition to its therapeutic activity, it is associated with psychotomimetic and dissociative side effects. Stinels-rapastinel, apimostinel, and zelquistinel-are also plastogens not only with rapid and long-term antidepressant effects but also with improved safety and tolerability profiles compared to ketamine. Previous descriptions of the mechanism by which stinels modulate NMDAR activity have been inconsistent and, at times, contradictory. The purpose of this review is to clarify the mechanism of action and contextualize stinels within a broader class of NMDAR-targeting therapeutics. In this review, we present the rationale behind targeting NMDARs for treatment-resistant depression and other psychiatric conditions, describe the various mechanisms by which NMDAR activity is regulated by different classes of therapeutics, and present evidence for the stinel mechanism. In contrast with previous descriptions of glycine-like NMDAR partial agonists, we define stinels as positive allosteric modulators of NMDAR activity with a novel regulatory binding site.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".