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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1.

    Bobo, D., Robinson, K. J., Islam, J., Thurecht, K. J. & Corrie, S. R. Nanoparticle-based medicines: a review of FDA-approved materials and clinical trials to date. Pharm. Res. 33, 2373–2387 (2016).

    CAS  Article  Google Scholar 

  2. 2.

    Mirkin, C. A., Letsinger, R. L., Mucic, R. C. & Storhoff, J. J. A DNA-based method for rationally assembling nanoparticles into macroscopic materials. Nature 382, 607–609 (1996).

    CAS  Article  Google Scholar 

  3. 3.

    Cutler, J. I., Auyeung, E. & Mirkin, C. A. Spherical nucleic acids. J. Am. Chem. Soc. 134, 1376–1391 (2012).

    CAS  Article  Google Scholar 

  4. 4.

    Choi, C. H. J., Hao, L., Narayan, S. P., Auyeung, E. & Mirkin, C. A. Mechanism for the endocytosis of spherical nucleic acid nanoparticle conjugates. Proc. Natl Acad. Sci. USA 110, 7625–7630 (2013).

    CAS  Article  Google Scholar 

  5. 5.

    Radovic-Moreno, A. F. et al. Immunomodulatory spherical nucleic acids. Proc. Natl Acad. Sci. USA 112, 3892–3897 (2015).

    CAS  Article  Google Scholar 

  6. 6.

    Rosi, N. L. et al. Oligonucleotide-modified gold nanoparticles for intracellular gene regulation. Science 312, 1027–1030 (2006).

    CAS  Article  Google Scholar 

  7. 7.

    Seferos, D. S., Prigodich, A. E., Giljohann, D. A., Patel, P. C. & Mirkin, C. A. Polyvalent DNA nanoparticle conjugates stabilize nucleic acids. Nano Lett. 9, 308–311 (2009).

    CAS  Article  Google Scholar 

  8. 8.

    Li, J. et al. A review on phospholipids and their main applications in drug delivery systems. Asian J. Pharm. 10, 81–98 (2015).

    CAS  Google Scholar 

  9. 9.

    Schroit, A. J., Madsen, J. & Nayar, R. Liposome–cell interactions: in vitro discrimination of uptake mechanism and in vivo targeting strategies to mononuclear phagocytes. Chem. Phys. Lipids 40, 373–393 (1986).

    CAS  Article  Google Scholar 

  10. 10.

    Simoes, S., Slepushkin, V., Duzgunes, N. & Pedroso de Lima, M. C. On the mechanisms of internalization and intracellular delivery mediated by pH-sensitive liposomes. Biochim. Biophys. Acta 1515, 23–37 (2001).

    CAS  Article  Google Scholar 

  11. 11.

    McCluskie, M. J. & Davis, H. L. CpG DNA as mucosal adjuvant. Vaccine 18, 231–237 (1999).

    CAS  Article  Google Scholar 

  12. 12.

    Krieg, A. M. et al. CpG motifs in bacterial DNA trigger direct B-cell activation. Nature 374, 546–549 (1995).

    CAS  Article  Google Scholar 

  13. 13.

    Hemmi, H. et al. A Toll-like receptor recognizes bacterial DNA. Nature 408, 740–745 (2000).

    CAS  Article  Google Scholar 

  14. 14.

    Zhao, Q., Temsamani, J., Iadarola, P. L., Jiang, Z. & Agrawal, S. Effect of different chemically modified oligodeoxynucleotides on immune stimulation. Biochem. Pharmacol. 51, 173–182 (1996).

    CAS  Article  Google Scholar 

  15. 15.

    Giljohann, D. A. et al. Oligonucleotide loading determines cellular uptake of DNA-modified gold nanoparticles. Nano Lett. 7, 3818–3821 (2007).

    CAS  Article  Google Scholar 

  16. 16.

    Prigodich, A. E., Alhasan, A. H. & Mirkin, C. A. Selective enhancement of nucleases by polyvalent DNA-functionalized gold nanoparticles. J. Am. Chem. Soc. 133, 2120–2123 (2011).

    CAS  Article  Google Scholar 

  17. 17.

    Gendron, K. B., Rodriguez, A. & Sewell, D. A. Vaccination with human papillomavirus type 16 E7 peptide with CpG oligonucleotides for prevention of tumor growth in mice. Arch. Otolaryngol. Head Neck Surg. 132, 327–332 (2006).

    Article  Google Scholar 

  18. 18.

    Berns, E. J., Cabezas, M. D. & Mrksich, M. Cellular assays with a molecular endpoint measured by SAMDI mass spectrometry. Small. 12, 3811–3818 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    Min, D. H., Tang, W. J. & Mrksich, M. Chemical screening by mass spectrometry to identify inhibitors of anthrax lethal factor. Nat. Biotechnol. 22, 717–723 (2004).

    CAS  Article  Google Scholar 

  20. 20.

    Mrksich, M. Mass spectrometry of self-assembled monolayers: a new tool for molecular surface science. ACS Nano 2, 7–18 (2008).

    CAS  Article  Google Scholar 

  21. 21.

    Su, J. & Mrksich, M. Using mass spectrometry to characterize self-assembled monolayers presenting peptides, proteins, and carbohydrates. Angew. Chem. Int. Ed. Engl. 41, 4715–4718 (2002).

    CAS  Article  Google Scholar 

  22. 22.

    Su, J., Rajapaksha, T. W., Peter, M. E. & Mrksich, M. Assays of endogenous caspase activities: a comparison of mass spectrometry and fluorescence formats. Anal. Chem. 78, 4945–4951 (2006).

    CAS  Article  Google Scholar 

  23. 23.

    Humerickhouse, R., Lohrbach, K., Li, L., Bosron, W. F. & Dolan, M. E. Characterization of CPT-11 hydrolysis by human liver carboxylesterase isoforms hCE-1 and hCE-2. Cancer Res. 60, 1189–1192 (2000).

    CAS  PubMed  Google Scholar 

  24. 24.

    Li, Y. et al. Free cholesterol-loaded macrophages are an abundant source of tumor necrosis factor-α and interleukin-6: model of NF-κB and MAP kinase-dependent inflammation in advanced atherosclerosis. J. Biol. Chem. 280, 21763–21772 (2005).

    CAS  Article  Google Scholar 

  25. 25.

    Yu, D., Zhao, Q., Kandimalla, E. R. & Agrawal, S. Accessible 5′-end of CpG-containing phosphorothioate oligodeoxynucleotides is essential for immunostimulatory activity. Bioorg. Med. Chem. Lett. 10, 2585–2588 (2000).

    CAS  Article  Google Scholar 

  26. 26.

    Kandimalla, E. R. et al. Conjugation of ligands at the 5′-end of CpG DNA affects immunostimulatory activity. Bioconjug. Chem. 13, 966–974 (2002).

    CAS  Article  Google Scholar 

  27. 27.

    De Clercq, E., Eckstein, E. & Merigan, T. C. Interferon induction increased through chemical modification of a synthetic polyribonucleotide. Science 165, 1137–1139 (1969).

    CAS  Article  Google Scholar 

  28. 28.

    Roberts, T. L., Sweet, M. J., Hume, D. A. & Stacey, K. J. Cutting edge: species-specific TLR9-mediated recognition of CpG and non-CpG phosphorothioate-modified oligonucleotides. J. Immunol. 174, 605–608 (2005).

    CAS  Article  Google Scholar 

  29. 29.

    Flierl, U. et al. Phosphorothioate backbone modifications of nucleotide-based drugs are potent platelet activators. J. Exp. Med. 212, 129–137 (2015).

    CAS  Article  Google Scholar 

  30. 30.

    Henry, S. P. et al. Complement activation is responsible for acute toxicities in rhesus monkeys treated with a phosphorothioate oligodeoxynucleotide. Int. Immunopharmacol. 2, 1657–1666 (2002).

    CAS  Article  Google Scholar 

  31. 31.

    Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (ACM, 2016).

  32. 32.

    Menard, S. Applied Logistic Regression Analysis Vol. 106 (Sage, Thousand Oaks, 2002).

  33. 33.

    Schuurmann, G., Ebert, R. U., Chen, J., Wang, B. & Kuhne, R. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean. J. Chem. Inf. Model. 48, 2140–2145 (2008).

    Article  Google Scholar 

  34. 34.

    Golbraikh, A. & Tropsha, A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J. Comput. Aided. Mol. Des. 16, 357–369 (2002).

    CAS  Article  Google Scholar 

  35. 35.

    Akinc, A. et al. A combinatorial library of lipid-like materials for delivery of RNAi therapeutics. Nat. Biotechnol. 26, 561–569 (2008).

    CAS  Article  Google Scholar 

  36. 36.

    Anderson, D. G., Lynn, D. M. & Langer, R. Semi-automated synthesis and screening of a large library of degradable cationic polymers for gene delivery. Angew. Chem. Int. Ed. Engl. 42, 3153–3158 (2003).

    CAS  Article  Google Scholar 

  37. 37.

    Banga, R. J., Chernyak, N., Narayan, S. P., Nguyen, S. T. & Mirkin, C. A. Liposomal spherical nucleic acids. J. Am. Chem. Soc. 136, 9866–9869 (2014).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

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

Affiliations

Authors

Contributions

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.

Ethics declarations

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.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information

Supplementary figures and tables.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing