Mechanistic Skin Modeling of Plasma Concentrations of Sunscreen Active Ingredients Following Facial Application
Bibliographic record
Abstract
Sunscreen products constitute two distinct categories. Recreational sunscreens protect against high-intensity, episodic sun exposure, often applied over the entire body. In contrast, facial sunscreen products are designed for sub-erythemal, low-intensity daily sun exposure. Such different exposures necessitate distinctive product safety assessments. Building on earlier methods for predicting dermal disposition, a mechanistic model was developed to simulate plasma concentrations of seven organic sunscreen active ingredients: avobenzone, ensulizole, homosalate, octinoxate, octisalate, octocrylene, and oxybenzone, following facial application. In vitro permeation testing (IVPT) was performed with two different vehicles using a subset of the UV filters. These IVPT results, in addition to previously published IVPT data and published in vivo Maximal Usage Trial (MUsT) data for the UV filters, were used to train the mechanistic dermal model via a Bayesian Markov chain Monte Carlo (MCMC) method. An external validation of the trained model with real-world in vivo datasets demonstrated that the model's predicted UV filter plasma concentrations align well with experimental measurements and capture the observed inter-individual variability. Predictions of steady-state UV filter plasma concentrations under facial application scenarios at 5% concentration and at the maximal allowable concentrations were then generated by the trained model. Oxybenzone had the greatest predicted plasma concentration following facial application. Homosalate and octisalate predictions had high uncertainty associated with the absence of data. Several application scenarios pertaining to avobenzone, ensulizole, octocrylene and octinoxate were identified in which median plasma concentration levels were at 0.5 ng/ml or below when applied in the recreational or facial product. Model limitations include uncertainty in vehicle/water partitioning, formulation metamorphosis, and UV filter systemic clearance, all of which can be refined with additional data. For UV filters, limiting exposure to facial application reduces human safety concerns based on FDA established thresholds.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".