Preparation and Characterization of Nanostructured Lipid Carriers (NLCs) Containing Glycyrrhiza glabra Extract for the Treatment of Skin Hyperpigmentation
Bibliographic record
Abstract
Purpose: This study aimed to prepare, characterize, and in vitro and in vivo evaluate a novel nanostructured lipid carriers (NLCs) formulation containing two fractions of Glycyrrhiza glabra L. (licorice) extract for the treatment of hyperpigmentation. Methods: Two fractions, one enriched with glabridin (FEG) and the other enriched with liquiritin (FEL), were obtained by partitioning the methanol (MeOH) extract of licorice roots with ethyl acetate (EtOAc) and partitioning the EtOAc fraction with butanol (n-BuOH) and water. The quantities of glabridin (Glab) and liquiritin (LQ) in the fractions were determined by high-performance liquid chromatography (HPLC). FEG and FEL were loaded in different NLC formulations, and surface characterization and long-term stability were studied using Dynamic Light Scattering (DLS). The best formulation was chosen for further surface characterization, including Transmission Electron Microscopy (TEM), Differential Scanning Calorimetry (DSC), and Fouriertransform infrared (FTIR) spectroscopy. Moreover, entrapment efficiency percentage (EE%), in vitro drug release, in vivo skin penetration, cytotoxicity on B16F10 melanoma cells, effect on melanin production, and anti- tyrosinase activity were tested for the selected formulation. Results: Based on HPLC results, FEG contained 34.501 mg/g of Glab, and FEL contained 31.714 mg/g of LQ. Among 20 different formulations, NLC 20 (LG-NLCs) showed desirable DLS results with a Z-average size of 185.3 ± 1.08 nm, polydispersity index (PDI) of 0.229 ± 0.35, and zeta potential of -16.2 ± 1.13 mV. It indicated good spherical shape, high EE% (79.01% for Glab and 69.27% for LQ), two-stage release pattern (an initial burst release followed by sustained release), efficient in vivo skin penetration, and strong anti-tyrosinase activity. LG-NLCs had acceptable physiochemical stability for up to 9 months and were non-cytotoxic. Conclusion: The LG-NLC formulation has revealed desirable surface characterization, good physiochemical stability, efficient drug release pattern and in vivo penetration, and high EE%. Therefore, it can be a suitable nanosystem for the delivery of licorice extract in the treatment of hyperpigmentation.
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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) | 0.000 | 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) | 0.000 | 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".