Leveraging machine learning to predict drug permeation: impact of menthol and limonene as enhancers.

Abstract

The present study aimed to develop robust machine learning (ML) models to predict the skin permeability of poorly water-soluble drugs in the presence of menthol and limonene as penetration enhancers (PEs). The ML models were also applied in virtual screening (VS) to identify hydrophobic drugs that exhibited better skin permeability in the presence of permeation enhancers i.e. menthol and limonene. The drugs identified through ML-based VS underwent experimental validation using in vitro skin penetration studies. The developed model predicted 80% probability of permeability enhancement for Sumatriptan Succinate (SS), Voriconazole (VCZ), and Pantoprazole Sodium (PS) with menthol and limonene. The in vitro release studies revealed that menthol increased penetration by approximately 2.49-fold, 2.25-fold, and 4.96-fold for SS, VCZ, and PS, respectively, while limonene enhanced permeability by approximately 1.32-fold, 2.27-fold, and 3.7-fold for SS, VCZ, and PS. The results from in silico and in vitro studies were positively correlated, indicating that the developed ML models could effectively reduce the need for extensive in vitro and in vivo experimentation.