Development and pharmacokinetic evaluation of a self-nanoemulsifying drug delivery system for the oral delivery of cannabidiol
Bibliographic record
Abstract
The number of lipophilic drug candidates in pharmaceutical discovery pipelines has increased in recent years. These drugs often possess physicochemical properties that result in poor oral bioavailability, and their clinical potential may be limited without adequate formulation strategies. Cannabidiol (CBD) is an excellent example of a highly lipophilic compound with poor oral bioavailability, due to low water solubility and extensive first-pass metabolism. An approach that may overcome these limitations is formulation of the drug in self-nanoemulsifying drug delivery systems (SNEDDS). Herein, CBD-SNEDDS formulations were prepared and evaluated in vitro. Promising formulations (F2, F4) were administered to healthy female Sprague-Dawley rats via oral gavage (20 mg/kg CBD). Resulting pharmacokinetic parameters of CBD were compared to those obtained following administration of CBD in two oil-based formulations: a medium-chain triglyceride oil vehicle (MCT-CBD), and a sesame oil-based formulation similar in composition to an FDA-approved formulation of CBD, Epidiolex® (SO-CBD). Compared to MCT-CBD, administration of the SNEDDS formulations led to more rapid absorption of CBD (median Tmax values: 0.5 h (F2), 1 h (F4), 6 h (MCT-CBD)). Administration of F2 and F4 formulations also improved the systemic exposure to CBD by 2.2 and 2.8-fold compared to MCT-CBD; however, no improvement was found compared to SO-CBD.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".