Pharmacokinetics of Psilocybin: A Systematic Review
Bibliographic record
Abstract
Background: Psilocybin has shown promise in therapeutic applications for mental disorders. Understanding the pharmacokinetics of psilocybin and its active metabolite psilocin is crucial for optimizing its clinical use and minimizing adverse effects. Methods: This systematic review involved a comprehensive search across MEDLINE, APA PsycINFO, and Embase databases, from inception to December 2024, identifying original studies that investigated the pharmacokinetics of psilocybin. Results: Fourteen studies met the inclusion criteria: eight laboratory-based and six clinical studies. Laboratory studies used animal models or in vitro systems, while clinical studies included 112 healthy human participants. Psilocybin is rapidly dephosphorylated to psilocin, which is absorbed with Tmax values ranging from 1.8 to 4 h following oral administration. Cmax varied dose-dependently, from 8.2 ± 2.8 ng/mL (plasma) to 871 ng/mL (urine). One study reported psilocin bioavailability at 52.7 ± 20%. The volume of distribution was extensive, ranging from 277 ± 92 L to 1016 L, suggesting significant tissue distribution. Psilocin metabolism is primarily mediated by CYP2D6 and CYP3A4, with secondary contributions from monoamine oxidase A. It undergoes further hepatic biotransformation into 4-hydroxyindole-3-acetic acid and 4-hydroxytryptophol. Elimination half-life varied across studies, ranging from 1.5 to 4 h. Conclusions: Psilocybin pharmacokinetics demonstrate significant variability based on dosage, route, and species. CYP enzymes play a critical role in its metabolism, highlighting the potential for drug–drug interactions. These findings underscore the importance of further research to elucidate psilocybin’s pharmacokinetic profile, which is assessed in vivo by its active metabolite psilocin.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".