An updated database of human maximum skin fluxes and epidermal permeability coefficients for drugs, xenobiotics, and other solutes applied as aqueous solutions
Bibliographic record
Abstract
The dataset represented in this article is referred to by the review article entitled “Topical drug delivery: history, percutaneous absorption, and product development” (MS Roberts et al., 2021) [1]. The dataset contains maximal flux (Jmax), and permeability coefficient (kp) values collated from In Vitro human skin Permeation Test (IVPT) reports published to date for various drugs, xenobiotics, and other solutes applied to human epidermis from aqueous solutions. Also included are each solute's physicochemical properties and the experimental conditions, such as temperature, skin thickness, and skin integrity, under which the data was generated. This database is limited to diluted or saturated aqueous solutions of solutes applied on human epidermal membranes or isolated stratum corneum in large volumes so that there was minimal change in the donor phase concentration. Included in this paper are univariate Quantitative Structure-epidermal Permeability Relationships (QSPR) in which the solute epidermal permeation parameters (kp, and Jmax) are related to potential individual solute physicochemical properties, such as molecular weight (MW), log octanol-water partition coefficient (log P), melting point (MP), hydrogen bonding (acceptor - Ha, donor – Hd), by scatter plots. This data was used in the associated review article to externally validate existing QSPR regression equations used to forecast the kp and Jmax for new therapeutic agents and chemicals. The data may also be useful in developing new QSPRs that may aid in: (1) drug choice and (2) product design for both topical and transdermal delivery, as well as (3) characterizing the potential skin exposure of hazardous substances.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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".