From Enhancing Cannabidiol Delivery to Leveraging Data-driven Approaches with Self-emulsifying Drug Delivery Systems
Bibliographic record
Abstract
Advancing oral formulations of poorly water-soluble drugs to the clinic requires formulation strategies that can improve their oral bioavailability, such as self-emulsifying drug delivery systems (SEDDS). Interest in the therapeutic potential of the highly lipophilic and non-psychotropic drug cannabidiol (CBD) has grown in recent years, with the only clinical formulation containing predominantly sesame oil, a recognized allergen. This thesis aims to (1) develop a lipid-based SEDDS formulation of CBD, free of sesame oil, with a high drug-to-material ratio and good oral absorption, and (2) create a comprehensive SEDDS dataset that may be leveraged for future SEDDS formulation development. CBD SEDDS demonstrated comparable pharmacokinetics to the gold-standard clinical formulation, and increased plasma levels of CBD compared to a formulation representative of over-the-counter preparations. A SEDDS dataset curated from published literature revealed important patterns and provides a starting point that may inform future data-driven SEDDS development.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".