Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

This review describes different trials to model and predict drug payload in lipid and polymeric nanocarriers. It traces the evolution of the field from the earliest attempts when numerous solubility and Flory-Huggins models were applied, to the emergence of molecular dynamic simulations and docking studies, until the exciting practically successful era of artificial intelligence and machine learning. Going through matching and poorly matching studies with the wet lab-dry lab results, many key aspects were reviewed and addressed in the form of sequential examples that highlighted both cases.

Automated summary

This review summarizes different trials for modeling and predicting drug payload in lipid and polymeric nanocarriers. It discusses the evolution of the field from early attempts using solubility and Flory-Huggins models to the emergence of molecular dynamic simulations and docking studies, and finally to the successful era of artificial intelligence and machine learning. Key aspects are reviewed through sequential examples that highlight both matching and poorly matching studies with wet lab-dry lab results.