Interfacial kinetics effects on transdermal drug delivery: a computer modeling
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Percutaneous permeation is a frequently used approach in drug delivery, but the detailed physics process in the patch--stratum corneum (SC)--viable epidermis system remains unclear: the influence of the interphases in the multilayered structure has been little studied. METHODS: This paper applied the finite-element method to develop a contact algorithm with an interphase element to account for the interphase barrier on drug diffusion and chemical absorption during a transdermal drug delivery process. A more realistic multilayer structure, including the patch, SC and viable epidermis, are incorporated into the algorithm. Both interphases between the patch and SC, and between SC and viable epidermis are considered. RESULTS: Our study confirms that the interphase transfer coefficients have a direct connection with drug concentration and flux distribution along the diffusion paths. The simulation results suggested a potential for the optimal control of drug diffusion. The partition coefficients and other interphase barrier factors can be incorporated into the model. CONCLUSIONS: The algorithm can deal with complicated geometrical conditions, which is difficult using classical analytical approaches. Furthermore, calibrated against experiments, the model may predict more realistically the drug delivery process and drug distribution profiles so as to assist in the patch and even drug design.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".