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Record W4402620626 · doi:10.1016/j.pbi.2024.102626

New frontiers in the biosynthesis of psychoactive specialized metabolites

2024· review· en· W4402620626 on OpenAlexafffund
Ginny Li, Peter J. Facchini

Bibliographic record

VenueCurrent Opinion in Plant Biology · 2024
Typereview
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Alberta
KeywordsPsilocybinMescalineLysergic acid diethylamideLysergic acidHallucinogenBiologyTryptaminePharmacologyBiochemistrySerotonin

Abstract

fetched live from OpenAlex

The recent relaxation of psychedelic drug regulations has prompted extensive clinical investigation into their potential use to treat diverse mental health conditions including anxiety, depression, post-traumatic stress, and substance-abuse disorders. Most clinical trials have relied on a small number of known molecules found in nature, such as psilocybin, or long-known synthetic analogs of natural metabolites, including lysergic acid diethylamide (LSD). Elucidation of biosynthetic pathways leading to several psychedelic compounds has established an opportunity to use synthetic biology as a complement to synthetic chemistry for the preparation of novel derivatives with potentially superior pharmacological properties compared with known drugs. Herein we review the metabolic biochemistry of pathways from plants, fungi and animals that yield the medicinally important hallucinogenic specialized metabolites ibogaine, mescaline, psilocybin, lysergic acid, and N,N-dimethyltryptamine (DMT). We also summarize the reconstitution of these pathways in microorganisms and comment on the integration of native and non-native enzymes to prepare novel derivatives.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.794
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.183
GPT teacher head0.461
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2024
Admission routes2
Has abstractyes

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