Trajectory features as surrogate measures of the nanoparticle-microenvironment interaction space

The use of nanoparticle platforms in the field of medicine as diagnostic tools and therapeutic agents has been a long-promised hope. Despite some successes, including FDA approval of micelle and liposomal formulations for the delivery of strongly hydrophobic anticancer drugs, the translation of these technologies has slowed; in 1995, the ratio of animal studies to FDA-approved drugs was 19:1 and the ratio of animal studies to clinical studies was 7:1. Fifteen years later in 2010, the ratio of animal studies to FDA-approved drugs was 4000:1, and the ratio of animal studies to clinical studies was 120:1. There are many factors contributing to the slow in translation of nanotherapeutic technologies including bottlenecks in funding and the number of researchers trained in translational and clinical studies. But there are some upstream causes that need to be addressed prior to consideration of clinical trials. In this dissertation, I focus on two in particular: a limited knowledge of nanoparticle-environment interactions at the cell, tissue, and organ levels; and the slowness of existing screening tools to identify potentially successful nanoformulations. This dissertation presents a number of tools that can be used to inform systems-level models of drug-environment interactions and improve the screening of drug candidates. Using in vitro and ex vivo models we demonstrate that nanoparticle colloidal stability can be used as a screening tool for nanoparticle therapeutics to the brain. We also demonstrate the use of trajectory datasets collected via fluorescent microscopy and multiple particle tracking to distinguish nuanced aspects of the nanoparticle-microenvironment interaction space using neural networks, including protein adhesion and nanoparticle cell uptake. We apply similar methods to investigate the role of key nanoparticle features (surface functionality, PEG grafting density, PEG chain length) in determining in vitro and ex vivo transport behavior. This method of analysis highlights the potential of nanoparticles to be used both as pre-clinical diagnostic probes without the use of complex chemistries and to guide nanoparticle design for therapeutic interventions.

Author dc:creator

• Curtis, Chad Daniel

Advisor dc:contributor.advisor

• Nance, Elizabeth

• Machine learning
• Microscopy
• Nanoparticles
• Particle tracking
• Systems biology
• Nanoscience
• Neurosciences
• Nanotechnology

Statement dc:rights

• CC BY

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1773/44740

OAI identifier oai:identifier

oai:digital.lib.washington.edu:1773/44740