Machine learning-informed timescale dependent modes of nanoparticle diffusion through human mucus

Abstract

Modes of diffusion and transport of nanomaterials through biological gels, like mucus, is of critical importance to many applied areas of research (e.g., drug delivery). However, it is often challenging to interpret the dynamics of nanoparticles within biological gel networks due to the complexity and inherent heterogeneity of their microstructure and biochemistry. In this study, we measured the diffusion of densely PEGylated, muco-inert nanoparticles (NP) in human airway mucus using multiple particle tracking and utilized machine learning to classify NP movement as either traditional Brownian motion (BM) or two different models of anomalous diffusion; fractional Brownian motion (FBM) and continuous time random walk (CTRW).