Return of the lysergamides. Part II: Analytical and behavioural characterization of <i>N</i><sup>6</sup>‐allyl‐6‐norlysergic acid diethylamide (AL‐LAD) and (2’<i>S</i>,4’<i>S</i>)‐lysergic acid 2,4‐dimethylazetidide (LSZ)
Bibliographic record
Abstract
Lysergic acid N,N‐diethylamide (LSD) is perhaps one of the most intriguing psychoactive substances known and numerous analogs have been explored to varying extents in previous decades. In 2013, N6‐allyl‐6‐norlysergic acid diethylamide (AL‐LAD) and (2’S,4’S)‐lysergic acid 2,4‐dimethylazetidide (LSZ) appeared on the ‘research chemicals’/new psychoactive substances (NPS) market in both powdered and blotter form. This study reports the analytical characterization of powdered AL‐LAD and LSZ tartrate samples and their semi‐quantitative determination on blotter paper. Included in this study was the use of nuclear magnetic resonance (NMR) spectroscopy, gas chromatography‐mass spectrometry (GC‐MS), low and high mass accuracy electrospray MS(/MS), high performance liquid chromatography diode array detection and GC solid‐state infrared analysis. One feature shared by serotonergic psychedelics, such as LSD, is the ability to mediate behavioural responses via activation of 5‐HT2A receptors. Both AL‐LAD and LSZ displayed LSD‐like responses in male C57BL/6 J mice when employing the head‐twitch response (HTR) assay. AL‐LAD and LSZ produced nearly identical inverted‐U‐shaped dose‐dependent effects, with the maximal responses occurring at 200 µg/kg. Analysis of the dose responses by nonlinear regression confirmed that LSZ (ED50 = 114.2 nmol/kg) was equipotent to LSD (ED50 = 132.8 nmol/kg) in mice, whereas AL‐LAD was slightly less potent (ED50 = 174.9 nmol/kg). The extent to which a comparison in potency can be translated directly to humans requires further investigation. Chemical and pharmacological data obtained from NPS may assist research communities that are interested in various aspects related to substance use and forensic identification. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".