Nanoparticle Skin Penetration: Depths and Routes Modeled In-Silico

Area of Science:

• Biomedical Engineering
• Dermatology
• Nanotechnology

Background:

• Nanoparticles (NPs) show promise for targeted delivery in skin.
• Predicting NP penetration depth and pathways is challenging due to complex interactions.

Purpose of the Study:

• To develop an in-silico model for predicting nanoparticle skin penetration.
• To identify key factors influencing NP penetration depth and pathways.

Main Methods:

• Developed an in-silico human skin model using 20 years of literature.
• Incorporated 19 parameters: NP properties, skin types, and experimental conditions.
• Utilized random forest analysis and Kennard-Stone sorting for prediction.

Main Results:

• Achieved 95% predictive accuracy for NP skin penetration.
• Hair follicle diameter identified as the most critical factor for NP penetration.
• Pig and rabbit skin models are suitable for human skin simulation.
• NP formulation (emulsion, oil-based, aqueous) dictates penetration route (intercellular/transappendageal vs. intracellular).

Conclusions:

• The in-silico model provides accurate predictions for NP skin penetration.
• Hair follicle size is a primary determinant of NP skin penetration.
• Formulation significantly influences NP transport pathways in the skin, guiding optimized delivery system design.