Predicting in situ nanoparticle behavior using multiple particle tracking and artificial neural networks.

Predictive models of nanoparticle transport can drive design of nanotherapeutic platforms to overcome biological barriers and achieve localized delivery. In this paper, we demonstrate the ability of artificial neural networks to predict both nanoparticle properties, such as size and protein adsorption, and aspects of the brain microenvironment, such as cell internalization, viscosity, and brain region by using large (>100 000) trajectory datasets collected via multiple particle tracking in in vitro gel models of the brain and cultured organotypic brain slices. Our neural network achieved a 0.

Colloidal stability as a determinant of nanoparticle behavior in the brain.

Drug delivery to the brain is challenging due to a highly regulated blood-brain barrier (BBB) and a complex brain microenvironment. Nanoparticles, due to their tailorability, provide promising platforms to enhance therapeutic delivery and achieve controlled release and disease-specific localization in the brain. However, we have yet to fully understand the complex interactions between nanoparticles and the biological environments in which they operate.