Abietic Acid Enhances the Sedative Activity of Diazepam: In vivo Approach along with Receptor Binding Affinity and Molecular Interaction with the GABAergic System
Bibliographic record
Abstract
This study evaluated the sedative activity of abietic acid (AA) through a thiopental sodium (TS)‐induced sleep model in mice. AA (5, 10, and 20 mg/kg) and diazepam (DZP) (2 mg/kg) were provided, followed by TS (20 mg/kg) after 30 min to induce sleep. Sleep latency and total sleeping time were documented over a 4 h period. Additionally, molecular docking studies were conducted to examine the interactions of AA with GABA A (Protein Data Bank: 6X3X) receptors, which hold two subunits of α1 and β2, alongside pharmacokinetic and toxicity assessments. The results indicated that AA significantly ( p < 0.05) provided the fast onset of sleeping and extended sleeping time in a dose‐dependent manner. The combination of AA (20 mg/kg) with DZP further enhanced sedation, yielding a prolonged sleep duration and a reduced sleep latency, indicating a synergistic effect. In addition, in silico analysis expressed that AA exhibited a strong binding affinity for GABA A receptors (–7.9 kcal/mol), comparable to DZP (–8.4 kcal/mol). Furthermore, AA demonstrated favorable pharmacokinetic properties and drug‐likeness. Overall, these findings suggest that AA possesses potent sedative effects, likely mediated through interactions with the GABAergic system, warranting further investigation for its therapeutic potential in sleep disorders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".