The synthetic cathinone α-pyrrolidinovalerophenone (α-PVP): pharmacokinetic and pharmacodynamic clinical and forensic aspects
Bibliographic record
Abstract
New psychoactive substances (NPS), often referred as 'legal highs' or 'designer drugs', are derivatives and analogs of existing psychoactive drugs that are introduced in the recreational market to circumvent existing legislation on drugs of abuse. This work aims to review the state-of-the-art regarding chemical, molecular pharmacology, and in vitro and in vivo data on toxicokinetics of the potent synthetic cathinone α-pyrrolidinovalerophenone (α-PVP or flakka or zombie drug). Chemical, pharmacological, toxicological, and clinical effects of α-PVP were searched in PubMed (U.S. National Library of Medicine) and governmental websites without limitation of the period. α-PVP is a wide spread and easy to get special type of synthetic cathinone with seemingly powerful cocaine-like stimulant effects, high brain penetration, high liability for abuse and with increased risk of adverse effects such as tachycardia, agitation, hypertension, hallucinations, delirium, mydriasis, self-injury, aggressive behavior, and suicidal ideations. α-PVP undergoes extensive metabolism via different pathways and the α-PVP itself or its metabolites β-hydroxy-α-PVP and α-PVP lactam represent the main targets for toxicological analysis in urine. There is a limited knowledge regarding the short- and long-term effects of α-PVP and metabolites, and pharmacogenetic influence, hence further clinical and forensic toxicological studies are required. Moreover, since α-PVP cannot be detected with classic routine analysis procedures, statements on the frequency of their consumption cannot be made.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".