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

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

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)318-327
Number of pages10
JournalNature Biomedical Engineering
Volume3
Issue number4
DOIs
StatePublished - Apr 1 2019

Fingerprint

Nanomedicine
Medical nanotechnology
Nucleic acids
Nucleic Acids
Learning systems
Screening
Throughput
Nanoparticles
Chemical activation
Cancer Vaccines
Vaccines
Machine Learning
Libraries
Mass spectrometry
Assays
Mass Spectrometry

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

Cite this

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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.",
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Exploration of the nanomedicine-design space with high-throughput screening and machine learning. / Yamankurt, Gokay; Berns, Eric Jason; Xue, Albert; Lee, Andrew; Bagheri, Neda; Mrksich, Milan; Mirkin, Chad A.

In: Nature Biomedical Engineering, Vol. 3, No. 4, 01.04.2019, p. 318-327.

Research output: Contribution to journalArticle

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