<i>In</i> <i>silico</i> biophysical screening of surface ligands for enhanced nanoparticle mucopermeation

Abstract

The mucus layer, a physiologically relevant hydrogel found in the human gut, respiratory and genital tracts, and on ocular surfaces, forms a formidable physiological barrier with mucin proteins, creating its hygroscopic mesh that restricts the permeation of nanomedicines. Hydrophilic ligands such as polyethylene glycol, when grafted onto nanoparticles, are known to enhance mucopermeation, although the mechanisms remain unclear. To investigate this, 79 ligands were virtually selected from the ChEMBL and DrugBank databases based on their similarity to triethylene glycol (TEG), an analog of polyethylene glycol, and on Lipinski–Veber oral bioavailability parameters. The ligands were ranked by similarity score (SS), proposed here as a structural bioinformatic parameter for automated ligand-candidate prioritization. A final set of 15 ligands was selected and divided into terciles. The top tercile, which included triethylene glycol, had the highest SS values. These 15 ligands were docked with the equilibrated (unglycosylated) MUC5AC protein structure using the PatchDock and AutoDock Vina programs. For each ligand, the average docking or binding score (depending on the program used) and the average atomic contact energy (mean of the top five atomic contact energies) were calculated. The SS values correlated strongly with the average atomic contact energies and moderately with the average docking and binding scores, as well as with the Lipinski–Veber parameters. These trends were confirmed using 15 additional ligands docked on unglycosylated mucin, with the two top- and two bottom-ranked ligands and TEG further evaluated on glycosylated MUC5AC using PatchDock. Overall, the SS and the average atomic contact energy emerged as effective indicators for efficient and accurate surface-ligand screening.