Exploration of the Nanomedicine-Design Space with High-throughput Screening and Machine Learning

Gokay Yamankurt, Eric J. Berns, Albert Xue, Andrew Lee, Neda Bagheri, Milan Mrksich, Chad A. Mirkin

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations

Abstract

1688Only 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 ~1000 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.

Original languageEnglish (US)
Title of host publicationSpherical Nucleic Acids
Subtitle of host publicationVolume 4
PublisherJenny Stanford Publishing
Pages1687-1716
Number of pages30
Volume4
ISBN (Electronic)9781000092493
ISBN (Print)9789814877244
DOIs
StatePublished - Jan 1 2021

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • General Engineering
  • General Chemistry

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