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Record W3189812176 · doi:10.3390/biom11081134

Identification and Characterization of Cannabidiol as an OX1R Antagonist by Computational and In Vitro Functional Validation

2021· article· en· W3189812176 on OpenAlexaff
Rosa Maria Vitale, Fabio Arturo Iannotti, Aniello Schiano Moriello, Lea Tunisi, Fabiana Piscitelli, Ranjev Savopoulos, Luigia Cristino, Luciano De Petrocellis, Pietro Amodeo, Roy Gray, Vincenzo Di Marzo

Bibliographic record

VenueBiomolecules · 2021
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversité Laval
FundersGW Pharmaceuticals
KeywordsCannabidiolIdentification (biology)AntagonistComputational biologyCharacterization (materials science)In vitroComputer scienceChemistryBiologyPsychologyNanotechnologyMaterials scienceBiochemistryBotanyPsychotherapist

Abstract

fetched live from OpenAlex

plant, covers a wide range of neurological and psychiatric disorders, ranging from anxiety to pediatric epilepsy and drug addiction. However, the molecular targets responsible for these effects have been only partially identified. In this view, the involvement of the orexin system, the key regulator in arousal and the sleep/wake cycle, and in motivation and reward processes, including drug addiction, prompted us to explore, using computational and experimental approaches, the possibility that CBD could act as a ligand of orexin receptors, orexin 1 receptor of type 1 (OX1R) and type 2 (OX2R). Ligand-binding assays showed that CBD is a selective ligand of OX1R in the low micromolar range (Ki 1.58 ± 0.2 μM) while in vitro functional assays, carried out by intracellular calcium imaging and mobilization assays, showed that CBD acts as an antagonist at this receptor. Finally, the putative binding mode of CBD has been inferred by molecular docking and molecular dynamics simulations and its selectivity toward the OX1R subtype rationalized at the molecular level. This study provides the first evidence that CBD acts as an OX1R antagonist, supporting its potential use in addictive disorders and/or body weight regulation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.284
Teacher spread0.272 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations13
Published2021
Admission routes1
Has abstractyes

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