Awakn plans line of psychedelics to treat AUD; Nutt joins as chief research officer
Bibliographic record
Abstract
A “pipeline of new psychedelic medicines” to be used in treating addiction is underway, with the most recent announcement from Canada‐based Awakn Life Sciences, which on June 24 named David Nutt its chief research officer. Many of Awakn's candidate medications were acquired in March from Nutt's Equasy Enterprises. Also included in Awakn's list of promising drugs: the only ketamine‐assisted psychotherapy trial for alcohol use disorder (AUD). Finally, Awakn has a trial underway for MDMA‐assisted psychotherapy for AUD. Investors are interested in these companies. Nutt is also chair of Awakn's scientific advisory board. “I am delighted to take on the role of Chief Research Officer in this exciting new biotechnology company that promises to revolutionise the treatment of addictions. Awakn's combination of pre‐clinical and clinical research uniquely positions Awakn to solve some of the biggest societal problems,” said Nutt. Anthony Tennyson, Awakn's CEO, added, “Our ambition is to fully integrate effective psychedelic‐based treatments into mainstream health care to better treat addiction.”
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.127 | 0.029 |
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".