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


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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Fig. 1: SNA architecture and design properties.
Fig. 2: SAMDI assay workflow.
Fig. 3: Trends in immune activation due to changes in the oligonucleotide properties of SNAs.
Fig. 4: Trends in immune activation due to changes in the peptide encapsulation of SNAs.
Fig. 5: Trends in immune activation due to hybridization.
Fig. 6: Analysis by machine learning.

Code availability

The custom codes used to generate the results reported in this manuscript are available from the corresponding authors upon reasonable request.

Data availability

The data that support the findings of this study are available within the paper and its Supplementary Information. All data generated in this study are available from the corresponding authors upon reasonable request.


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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.

Author information




G.Y., E.J.B., A.L., M.M. and C.A.M. designed the experiments. G.Y. and E.J.B. performed the experiments. E.J.B. and A.X. wrote the code for the data analysis. All authors analysed the data and wrote the manuscript.

Corresponding authors

Correspondence to Andrew Lee or Neda Bagheri or Milan Mrksich or Chad A. Mirkin.

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Competing interests

C.A.M. and M.M. own stock from Exicure, which has licensed the SNA technology. M.M. owns stock in SAMDI Tech—the company that has licensed the SAMDI technology.

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Yamankurt, G., Berns, E.J., Xue, A. et al. Exploration of the nanomedicine-design space with high-throughput screening and machine learning. Nat Biomed Eng 3, 318–327 (2019). https://doi.org/10.1038/s41551-019-0351-1

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