Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning
Benjamin R Kingston, Abdullah Muhammad Syed, Jessica Ngai, Shrey Sindhwani, Warren CW Chan
PNAS July 23, 2019 116 (30) 14937-14946 | DOI:10.1073/pnas.1907646116
Successful delivery of therapeutic agents to metastatic tumors is critical for controlling their growth and improving cancer patient survival. It is challenging to design drug carriers that target metastases because of the limitations of current techniques for analyzing drug carrier interactions with metastatic tumors. We overcome this problem by developing a new imaging and image analysis workflow that enables us to track nanoparticle delivery, penetration, and distribution within micrometastases. More importantly, we can predict the delivery of nanoparticles to the micrometastases based on their physiology using a machine learning algorithm. This allows us to predict the micrometastases pathophysiology that can be targeted more effectively.