Exploration of the Nanomedicine-Design Space with High-throughput Screening and Machine Learning*
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. This chapter reports a methodology for determining structure–activity relationships and design rules for spherical nucleic acids (SNAs) functioning as cancer-vaccine candidates. 1000 candidate SNAs are identified on the basis of reasonable ranges for 11 design parameters that can be systematically and independently varied to optimize SNA performance. The chapter develops a high-throughput method for making SNAs at the picomolar scale in a 384-well format, and uses a mass spectrometry assay to rapidly measure SNA immune activation. Machine learning is used to quantitatively model SNA immune activation and identify the minimum number of SNAs needed to capture optimum structure–activity relationships for a given SNA library.