Identification and Characterization of Cannabidiol as an OX1R Antagonist by Computational and In Vitro Functional Validation
Bibliographic record
Abstract
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".