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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 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.

SUBMITTER: Yamankurt G

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

REPOSITORIES: biostudies-literature

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

Yamankurt Gokay G Berns Eric J EJ Xue Albert A Lee Andrew A Bagheri Neda N Mrksich Milan M Mirkin Chad A CA

Nature biomedical engineering 20190218 4

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 ...[more]

PMID: 30952978

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Dataset Information

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

Publications

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

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