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

Shaymaa A Abd-Algaleel; Hend M Abdel-Bar; Abdelkader A Metwally; Rania M Hathout

doi:10.3390/ph14070645

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

Pharmaceuticals (Basel). 2021 Jul 5;14(7):645. doi: 10.3390/ph14070645.

Authors

Shaymaa A Abd-Algaleel¹, Hend M Abdel-Bar², Abdelkader A Metwally^{3

4}, Rania M Hathout³

Affiliations

¹ Department of Pharmaceutics, Egyptian Drug Authority, Cairo 12618, Egypt.
² Department of Pharmaceutics, Faculty of Pharmacy, University of Sadat City, Sadat 32897, Egypt.
³ Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo 11566, Egypt.
⁴ Health Sciences Center, Department of Pharmaceutics, Faculty of Pharmacy, Kuwait University, Safat, Kuwait 13110, Kuwait.

Abstract

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.

Keywords: docking; in-silico; lipid; machine learning; polymer; simulations.

Publication types

Review