Exploration of the nanomedicine-design space with high-throughput screening and machine learning.

ABSTRACT: Only a tiny fraction of the nanomedicine-design space has been explored, owing to the structural complexity of nanomedicines and the lack of relevant high-throughput synthesis and analysis methods. Here, we report a methodology for determining structure-activity relationships and design rules for spherical nucleic acids (SNAs) functioning as cancer-vaccine candidates. First, we identified ~1,000 candidate SNAs on the basis of reasonable ranges for 11 design parameters that can be systematically and independently varied to optimize SNA performance. Second, we developed a high-throughput method for making SNAs at the picomolar scale in a 384-well format, and used a mass spectrometry assay to rapidly measure SNA immune activation. Third, we used machine learning to quantitatively model SNA im

SUBMITTER: Yamankurt G 

PROVIDER: S-EPMC6452897 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature