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. In this presentation, a methodology for determining structure–activity relationships and design rules for spherical nucleic acids (SNAs) functioning as cancer-vaccine candidates will be discussed. First, ~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 were identified. Second, a high-throughput method for making SNAs at the picomolar scale in a 384-well format was developed, and then a mass-spectrometry assay was used to rapidly measure SNA immune activation. Third, machine learning was 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. Our methodology is general, can reduce the number of nanoparticles that need to be tested by an order of magnitude, and could serve as a screening tool for the development of nanoparticle therapeutics.

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

2019 Fall National Meeting

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