Transdermal Delivery of Cannabidiol for the Management of Acute Inflammatory Pain: A Comprehensive Review of the Literature
Bibliographic record
Abstract
The emerging field of nanotechnology has paved the way for revolutionary advancements in drug delivery systems, with nanosystems emerging as a promising avenue for enhancing the therapeutic potential and the stability of various bioactive compounds. Among these, cannabidiol (CBD), the non-psychotropic compound of the Cannabis sativa plant, has gained attention for its therapeutic properties. Consequently, researchers have devoted significant efforts to unlock the full potential of CBD’s clinical benefits, where various nanosystems and excipients have emerged to overcome challenges associated with its bioavailability, stability, and controlled release for its transdermal application. Therefore, this comprehensive review aims to explain CBD’s role in managing acute inflammatory pain and offers an overview of the state of the art of existing delivery systems and excipients for CBD. To summarize this review, a summary of the cannabinoids and therapeutical targets of CBD will be discussed, followed by its conventional modes of administration. The transdermal route of administration and the current topical and transdermal delivery systems will also be reviewed. This review will conclude with an overview of in vivo techniques that allow the evaluation of the anti-inflammatory and analgesic potentials of these systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".