Beyond the 5-HT<sub>2A</sub>Receptor: Classic and Nonclassic Targets in Psychedelic Drug Action
Bibliographic record
Abstract
Serotonergic psychedelics, such as psilocybin and LSD, have garnered significant attention in recent years for their potential therapeutic effects and unique mechanisms of action. These compounds exert their primary effects through activating serotonin 5-HT2Areceptors, found predominantly in cortical regions. By interacting with these receptors, serotonergic psychedelics induce alterations in perception, cognition, and emotions, leading to the characteristic psychedelic experience. One of the most crucial aspects of serotonergic psychedelics is their ability to promote neuroplasticity, the formation of new neural connections, and rewire neuronal networks. This neuroplasticity is believed to underlie their therapeutic potential for various mental health conditions, including depression, anxiety, and substance use disorders. In this mini-review, we will discuss how the 5-HT2Areceptor activation is just one facet of the complex mechanisms of action of serotonergic psychedelics. They also interact with other serotonin receptor subtypes, such as 5-HT1Aand 5-HT2Creceptors, and with neurotrophin receptors (e.g., tropomyosin receptor kinase B). These interactions contribute to the complexity of their effects on perception, mood, and cognition. Moreover, as psychedelic research advances, there is an increasing interest in developing nonhallucinogenic derivatives of these drugs to create safer and more targeted medications for psychiatric disorders by removing the hallucinogenic properties while retaining the potential therapeutic benefits. These nonhallucinogenic derivatives would offer patients therapeutic advantages without the intense psychedelic experience, potentially reducing the risks of adverse reactions. Finally, we discuss the potential of psychedelics as substrates for post-translational modification of proteins as part of their mechanism of action.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".