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Record W4384264283 · doi:10.3390/pharmaceutics15071941

Therapeutic Potential and Predictive Pharmaceutical Modeling of Stilbenes in Cannabis sativa

2023· review· en· W4384264283 on OpenAlexafffund
Conor O’Croinin, Andrés Garcia Guerra, Michael R. Doschak, Raimar Löbenberg, Neal M. Davies

Bibliographic record

VenuePharmaceutics · 2023
Typereview
Languageen
FieldMedicine
TopicBiological and pharmacological studies of plants
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsCannabisCannabis sativaIn silicoChemistryPharmacologyComputational biologyBiologyMedicineBiochemistryGeneBotany

Abstract

fetched live from OpenAlex

is a plant used for recreational and therapeutic purposes; however, many of the secondary metabolites in the plant have not been thoroughly investigated. Stilbenes are a class of compounds with demonstrated anti-inflammatory and antioxidant properties and are present in cannabis. Many stilbenes present in cannabis have been investigated for their therapeutic effects. Fourteen stilbenes have been identified to be present in cannabis, all of which are structurally dihydrostilbenoids, with half possessing a prenylated moiety. The stilbenes summarized in this analysis show varying degrees of therapeutic benefits ranging from anti-inflammatory, antiviral, and anti-cancer to antioxidant effects. Many of the identified stilbenes have been researched to a limited extent for potential health benefits. In addition, predictive in silico modeling was performed on the fourteen identified cannabis-derived stilbenes. This modeling provides prospective activity, pharmacokinetic, metabolism, and permeability data, setting the groundwork for further investigation into these poorly characterized compounds.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.290
GPT teacher head0.466
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations23
Published2023
Admission routes2
Has abstractyes

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