Enhancing Intradermal Delivery of Lidocaine by Dissolving Microneedles: Comparison between Hyaluronic Acid and Poly(Vinyl Pyrrolidone) Backbone Polymers
Bibliographic record
Abstract
Lidocaine hydrochloride (LiH), an amide-type local anesthetic agent, is commonly used in dermatological procedures. LiH is categorized as a BCS (biopharmaceutics classification system) class III group, which has high solubility and poor permeability. It should be noted that, in this context, LiH is intended as a local anesthetic, so the level of LiH in systemic circulation should be minimized to avoid toxicity and unwanted side effects such as hypotension and bradycardia. This study aimed to formulate and evaluate LiH-loaded dissolving microneedles (DMNs) with different polymer bases. Moreover, an in vitro permeation study using Franz diffusion cells and in vivo study were also performed. LiH-loaded DMNs were prepared using polymer groups of poly(vinyl pyrrolidone) (PVP-K30) and hyaluronic acid (HA). DMNs were created using the micro-molding method with centrifugation. The formulations selected based on the evaluation were F3 (HA 10%) and F5 (PVP-K30 25%). Based on the in vitro permeation study, the amount of drug permeated and deposited in the skin at F3 (HA 10%) was 247.1 ± 41.85 and 98.35 ± 12.86 μg, respectively. On the other hand, the amount of drug permeated and deposited in the skin at F5 (PVP-K30 25%) was 277.7 ± 55.88 and 59.46 ± 9.25 μg, respectively. Our in vivo drug-permeation study showed that only one rat from the PVP-K30 polymer group-with a concentration of 150.32 ng/mL-was detected on rat plasma. Therefore, LiH can be formulated into a DMN and can be deposited in the skin with a safe concentration of the drug permeating into systemic circulation.
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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".