Drug delivery through the skin: molecular simulations of barrier lipids to design more effective noninvasive dermal and transdermal delivery systems for small molecules, biologics, and cosmetics
Bibliographic record
Abstract
The delivery of drugs through the skin provides a convenient route of administration that is often preferable to injection because it is noninvasive and can typically be self-administered. These two factors alone result in a significant reduction of medical complications and improvement in patient compliance. Unfortunately, a significant obstacle to dermal and transdermal drug delivery alike is the resilient barrier that the epidermal layers of the skin, primarily the stratum corneum, presents for the diffusion of exogenous chemical agents. Further advancement of transdermal drug delivery requires the development of novel delivery systems that are suitable for modern, macromolecular protein and nucleotide therapeutic agents. Significant effort has already been devoted to obtain a functional understanding of the physical barrier properties imparted by the epidermis, specifically the membrane structures of the stratum corneum. However, structural observations of membrane systems are often hindered by low resolutions, making it difficult to resolve the molecular mechanisms related to interactions between lipids found within the stratum corneum. Several models describing the molecular diffusion of drug molecules through the stratum corneum have now been postulated, where chemical permeation enhancers are thought to disrupt the underlying lipid structure, resulting in enhanced permeability. Recent investigations using biphasic vesicles also suggested a possibility for novel mechanisms involving the formation of complex polymorphic lipid phases. In this review, we discuss the advantages and limitations of permeation-enhancing strategies and how computational simulations, at the atomic scale, coupled with physical observations can provide insight into the mechanisms of diffusion through the stratum corneum.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 teacher head, 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".