Development and pharmacokinetic evaluation of a self-nanoemulsifying drug delivery system for the oral delivery of cannabidiol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 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 it