Algorithm-driven high-throughput screening of colloidal nanoparticles under simulated physiological and therapeutic conditions

Abstract

Colloidal nanoparticles have shown tremendous potential as cancer drug carriers and as phototherapeutics. However, the stability of nanoparticles under physiological and phototherapeutic conditions is a daunting issue, which needs to be addressed in order to ensure a successful clinical translation. The design, development and implementation of unique algorithms are described herein for high-throughput hydrodynamic size measurements of colloidal nanoparticles. The data obtained from such measurements provide clinically-relevant particle size distribution assessments that are directly related to the stability and aggregation profiles of the nanoparticles under putative physiological and phototherapeutic conditions; those profiles are not only dependent on the size and surface coating of the nanoparticles, but also on their composition. Uncoated nanoparticles showed varying degrees of association with bovine serum albumin, whereas PEGylated nanoparticles did not exhibit significant association with the protein. The algorithm-driven, high-throughput size screening method described in this report provides highly meaningful size measurement patterns stemming from the association of colloidal particles with bovine serum albumin used as a protein model. Noteworthy is that this algorithm-based high-throughput method can accomplish sophisticated hydrodynamic size measurement protocols within days instead of years it would take conventional hydrodynamic size measurement techniques to achieve a similar task.