TY - JOUR
T1 - Exploration of the nanomedicine-design space with high-throughput screening and machine learning
AU - Yamankurt, Gokay
AU - Berns, Eric J.
AU - Xue, Albert
AU - Lee, Andrew
AU - Bagheri, Neda
AU - Mrksich, Milan
AU - Mirkin, Chad A.
N1 - Funding Information:
Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award number U54CA199091. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work made use of the IMSERC at Northwestern University, which has received support from Northwestern University and the State of Illinois.
Publisher Copyright:
© 2019, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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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U2 - 10.1038/s41551-019-0351-1
DO - 10.1038/s41551-019-0351-1
M3 - Article
C2 - 30952978
AN - SCOPUS:85061731935
SN - 2157-846X
VL - 3
SP - 318
EP - 327
JO - Nature Biomedical Engineering
JF - Nature Biomedical Engineering
IS - 4
ER -