NLC-Based Sunscreen Formulations with Optimized Proportion of Encapsulated and Free Filters Exhibit Enhanced UVA and UVB Photoprotection
Bibliographic record
Abstract
The topical use of sunscreens is recommended for avoiding the damaging effects of UV radiation. However, improvements are still needed in the existing products to enhance their photoprotection effectiveness and safety. This involves minimizing the use of chemical UV filters while providing enhanced and prolonged photoprotection. This work investigated novel sunscreen formulations and their UV protection effects by encapsulating Uvinul® A, Tinosorb® S, and Uvinul® T150 into nanostructured lipid carriers (NLCs) based on bacuri butter and raspberry seed oil. First, the impact of critical formulation and process parameters on NLCs’ particle size was evaluated using a 22 Face Centered Central Composite Design. Then, formulations were evaluated in terms of critical quality factors, in vitro skin permeation, and in vitro and in vivo photoprotection activities. The developed NLCs-containing formulations exhibited appropriate size (122–135 nm), PdI (<0.3), encapsulation efficiency (>90%), and drug content (>80%), which were preserved for at least 90 days under different stability conditions. Moreover, these NLCs-based formulations had equivalent skin permeation to emulsion-based controls, and the addition of NLCs into sunscreen cream bases in the optimum proportion of 20% (w/w) resulted in enhanced UVA and UVB photoprotection levels, despite a 10% reduction in the total filters content. Altogether, these results describe the application of nanoencapsulated organic UV filters in innovative sunscreen formulations to achieve superior photoprotection and cosmeceutical properties.
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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) | 0.001 | 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".