Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Quantitative self-assembly prediction yields targeted nanomedicines

Nature Materials volume 17, pages 361–368 (2018)Cite this article

Subjects

Abstract

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Indocyanine-drug self-assembled nanoparticles.
Fig. 2: Computational prediction and analyses of indocyanine nanoparticle formation.
Fig. 3: Internalization of indocyanine nanoparticles in 2D and 3D cell culture.
Fig. 4: Indocyanine nanoparticle targeting and efficacy in MYC-driven autochthonous murine hepatic tumour model.
Fig. 5: Anti-tumour efficacy in HCT116 colon cancer model.

References

  1. Peer, D. et al. Nanocarriers as an emerging platform for cancer therapy. Nat. Nanotech. 2, 751–760 (2007).

    Article  Google Scholar 

  2. Schroeder, A. et al. Treating metastatic cancer with nanotechnology. Nat. Rev. Cancer 12, 39–50 (2012).

    Article  Google Scholar 

  3. Yaari, Z. et al. Theranostic barcoded nanoparticles for personalized cancer medicine. Nat. Commun. 7, 13325 (2016).

  4. Wilhelm, S. et al. Analysis of nanoparticle delivery to tumours. Nat. Rev. Mater. 1, 16014 (2016).

    Article  Google Scholar 

  5. Cheng, Z., Al Zaki, A., Hui, J. Z., Muzykantov, V. R. & Tsourkas, A. Multifunctional nanoparticles: Cost versus benefit of adding targeting and imaging capabilities. Science 338, 903–910 (2012).

    Article  Google Scholar 

  6. Lammers, T., Kiessling, F., Hennink, W. E. & Storm, G. Drug targeting to tumors: Principles, pitfalls and (pre-) clinical progress. J. Control. Release 161, 175–187 (2012).

    Article  Google Scholar 

  7. Shamay, Y. et al. P-Selectin is a nanotherapeutic delivery target in the tumor microenvironment. Sci. Transl. Med. 8, 345ra87 (2016).

    Article  Google Scholar 

  8. Mizrachi, A. et al. Tumour-specific Pi3k inhibition via nanoparticle-targeted delivery in head and neck squamous cell carcinoma. Nat. Commun. 8, 14292 (2017).

    Article  Google Scholar 

  9. Maojo, V. et al. Nanoinformatics: A new area of research in nanomedicine. Int. J. Nanomed. 7, 3867–3890 (2012).

    Article  Google Scholar 

  10. Irwin, J. J. et al. An aggregation advisor for ligand discovery. J. Med. Chem. 58, 7076–7087 (2015).

    Article  Google Scholar 

  11. Seidler, J., McGovern, S. L., Doman, T. N. & Shoichet, B. K. Identification and prediction of promiscuous aggregating inhibitors among known drugs. J. Med. Chem. 46, 4477–4486 (2003).

    Article  Google Scholar 

  12. Feng, B. Y., Shelat, A., Doman, T. N., Guy, R. K. & Shoichet, B. K. High-throughput assays for promiscuous inhibitors. Nat. Chem. Biol. 1, 146–148 (2005).

    Article  Google Scholar 

  13. Alskar, L. C., Porter, C. J. & Bergstrom, C. A. Tools for early prediction of drug loading in lipid-based formulations. Mol. Pharm. 13, 251–261 (2016).

    Article  Google Scholar 

  14. Fourches, D. et al. Quantitative nanostructure-activity relationship modeling. ACS Nano 4, 5703–5712 (2010).

    Article  Google Scholar 

  15. Puzyn, T. et al. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat. Nanotech. 6, 175–178 (2011).

    Article  Google Scholar 

  16. Zhang, Y. et al. Lipid-modified aminoglycoside derivatives for in vivo siRNA delivery. Adv. Mater. 25, 4641–4645 (2013).

    Article  Google Scholar 

  17. Roxbury, D., Jagota, A. & Mittal, J. Sequence-specific self-stitching motif of short single-stranded DNA on a single-walled carbon nanotube. J. Am. Chem. Soc. 133, 13545–13550 (2011).

    Article  Google Scholar 

  18. Lee, O. S., Stupp, S. I. & Schatz, G. C. Atomistic molecular dynamics simulations of peptide amphiphile self-assembly into cylindrical nanofibers. J. Am. Chem. Soc. 133, 3677–3683 (2011).

    Article  Google Scholar 

  19. Frederix, P. W. et al. Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels. Nat. Chem. 7, 30–37 (2015).

    Article  Google Scholar 

  20. Shi, C. et al. Drug-specific nanocarrier design for efficient anticancer therapy. Nat. Commun. 6, 7449 (2015).

    Article  Google Scholar 

  21. Alphandery, E., Grand-Dewyse, P., Lefevre, R., Mandawala, C. & Durand-Dubief, M. Cancer therapy using nanoformulated substances: Scientific, regulatory and financial Aspects. Expert. Rev. Anticancer. Ther. 15, 1233–1255 (2015).

    Article  Google Scholar 

  22. Agarwal, A., Lvov, Y., Sawant, R. & Torchilin, V. Stable nanocolloids of poorly soluble drugs with high drug content prepared using the combination of sonication and layer-by-layer lechnology. J. Control. Release 128, 255–260 (2008).

    Article  Google Scholar 

  23. Muller, R. H. & Keck, C. M. Challenges and solutions for the delivery of biotech drugs—a review of drug nanocrystal technology and lipid nanoparticles. J. Biotech. 113, 151–170 (2004).

    Article  Google Scholar 

  24. McLaughlin, C. K. et al. Stable colloidal drug aggregates catch and release active enzymes. ACS Chem. Biol. 11, 992–1000 (2016).

    Article  Google Scholar 

  25. Shi, C., Wu, J. B. & Pan, D. Review on near-infrared heptamethine cyanine dyes as theranostic agents for tumor imaging, targeting, and photodynamic therapy. J. Biomed. Opt. 21, 50901 (2016).

    Article  Google Scholar 

  26. Yang, X. et al. Near Ir heptamethine cyanine dye-mediated cancer imaging. Clin. Cancer Res. 16, 2833–2844 (2010).

    Article  Google Scholar 

  27. McArthur, E. A., Godbe, J. M., Tice, D. B. & Weiss, E. A. A study of the binding of cyanine dyes to colloidal quantum dots using spectral signatures of dye aggregation. J. Phys. Chem. 116, 6136–6142 (2012).

    Google Scholar 

  28. Slavnova, T. D., Gorner, H. & Chibisov, A. K. Cyanine-based J-aggregates as a chirality-sensing supramolecular system. J. Phys. Chem. B 115, 3379–3384 (2011).

    Article  Google Scholar 

  29. Fofang, N. T., Grady, N. K., Fan, Z., Govorov, A. O. & Halas, N. J. Plexciton dynamics: exciton–plasmon coupling in a J-aggregate–Au nanoshell complex provides a mechanism for nonlinearity. Nano Lett. 11, 1556–1560 (2011).

    Article  Google Scholar 

  30. Todeschini, R. & Consonni, V. Handbook of Molecular Descriptors Vol. 11 (eds Mannhold, R., Kubinyi, H. & Timmerman, H.) 285–286 (Wiley, New York, NY, 2008).

  31. Kier, L. B. & Hall, L. H. An electrotopological-state index for atoms in molecules. Pharm. Res. 7, 801–807 (1990).

    Article  Google Scholar 

  32. Consonni, V., Todeschini, R. & Pavan, M. Structure/response correlations and similarity/diversity analysis by Getaway descriptors. 1. Theory of the novel 3d molecular descriptors. J. Chem., Inf. Comput. Sci. 42, 682–692 (2002).

    Article  Google Scholar 

  33. Wishart, D. S. et al. Drugbank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, D668–672 (2006).

    Article  Google Scholar 

  34. Rhee, Y. M. & Pande, V. S. Multiplexed-replica exchange molecular dynamics method for protein folding simulation. Biophys. J. 84, 775–786 (2003).

    Article  Google Scholar 

  35. Zhou, R. in Protein Folding Protocols (eds Bai, Y. & Nussinov, R.) 205–223 (Springer, New York, NY, 2007).

  36. Voigt, J., Christensen, J. & Shastri, V. P. Differential uptake of nanoparticles by endothelial cells through polyelectrolytes with affinity for caveolae. Proc. Natl Acad. Sci. USA 111, 2942–2947 (2014).

  37. Jena, P. V. et al. Photoluminescent carbon nanotubes interrogate the permeability of multicellular tumor spheroids. Carbon 97, 99–109 (2016).

    Article  Google Scholar 

  38. Wang, Z., Tiruppathi, C., Cho, J., Minshall, R. D. & Malik, A. B. Delivery of nanoparticle: Complexed drugs across the vascular endothelial barrier via caveolae. IUBMB Life 63, 659–667 (2011).

    Article  Google Scholar 

  39. Chrastina, A., Massey, K. A. & Schnitzer, J. E. Overcoming in vivo barriers to targeted nanodelivery. WIREs. Nanomed. Nanobiotechnol. 3, 421–437 (2011).

    Article  Google Scholar 

  40. Chen, X. & Calvisi, D. F. Hydrodynamic transfection for generation of novel mouse models for liver cancer research. Am. J. Path. 184, 912–923 (2014).

    Article  Google Scholar 

  41. O’Donnell, K. A. et al. A Sleeping Beauty mutagenesis screen reveals a tumor suppressor role for Ncoa2/Src-2 in liver cancer. Proc. Natl Acad. Sci. USA 109, E1377–1386 (2012).

  42. Nicholls, A. Confidence limits, error bars and method comparison in molecular modeling. Part 1: the calculation of confidence intervals. J. Comput. Aided Mol. Des. 28, 887–918 (2014).

    Article  Google Scholar 

  43. Schomburg, K. T., Wetzer, L. & Rarey, M. Interactive design of generic chemical patterns. Drug Discovery Today 18, 651–658 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the NIH New Innovator Award (DP2-HD075698), National Cancer Institute (CA 013106), and Cancer Center Support Grant (P30 CA008748), the Expect Miracles Foundation - Financial Services Against Cancer, the Anna Fuller Fund, the Louis V. Gerstner Jr. Young Investigator’s Fund, the Frank A. Howard Scholars Program, Cycle for Survival, the Alan and Sandra Gerry Metastasis Research Initiative, Mr. William H. Goodwin and Mrs. Alice Goodwin and the Commonwealth Foundation for Cancer Research, the Experimental Therapeutics Center, the Imaging & Radiation Sciences Program, and the Center for Molecular Imaging and Nanotechnology of Memorial Sloan Kettering Cancer. This work is supported in part by a New York State Department of Health Fixed Term Agreement (Contract# DOH01-C30315GG-3450000). The opinions, results, findings and/or interpretations of data contained therein are the responsibility of the contractor and do not necessarily represent the opinions, interpretations or policy of the state or, if funded with federal funds, the applicable federal funding agency. Y.S. was supported by the Center for Metastasis Research (CMR) Scholars Fellowship Program. D.R. was supported by an American Cancer Society – Roaring Fork Valley Postdoctoral Fellowship. S.W.L. is a Howard Hughes Medical Institute Investigator and the Geoffrey Beene Chair for Cancer Biology (MSKCC). J.B. was supported by the Tow Foundation Postdoctoral Fellowship, Center for Molecular Imaging and Nanotechnology at MSKCC. We would like to thank the following facilities at MSKCC: Molecular Cytology Core Facility, Small Animal Imaging, Anti-tumor Assessment, and Electron Microscopy. The molecular dynamics work used the Extreme Science and Engineering Discovery Environment (XSEDE), supported by National Science Foundation grant number TG-MCB-130013. We would like to thank N. Lampen for assistance with electron microscopy. We would also like to thank P.V. Jena, R. Williams, J. Harvey, T. Galassi, J. Humm and C. Horoszko for helpful discussions.

Author information

Authors and Affiliations

  1. Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yosi Shamay, Janki Shah, Mehtap Işık, Aviram Mizrachi, Josef Leibold, Januka Budhathoki-Uprety, Karla Nawaly, James L. Sugarman, Emily Baut, Michelle R. Neiman, Megan Dacek, Kripa S. Ganesh, Darren C. Johnson, Ramya Sridharan, Karen L. Chu, Vinagolu K. Rajasekhar, Scott W. Lowe, John D. Chodera & Daniel A. Heller

  2. Faculty of Biomedical Engineering, Technion—Israel Institute of Technology, Haifa, Israel

    Yosi Shamay

  3. Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Mehtap Işık & Darren C. Johnson

  4. Department of Otolaryngology Head and Neck Surgery, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Aviram Mizrachi

  5. Helmholtz-University Group “Cell Plasticity and Epigenetic Remodeling”, German Cancer Research Center (DKFZ) & Institute of Pathology University Hospital, Heidelberg, Germany

    Darjus F. Tschaharganeh

  6. Department of Chemical Engineering, University of Rhode Island, Kingston, RI, 02881, USA

    Daniel Roxbury

  7. Weill Cornell Medical College, Cornell University, New York, NY, USA

    Emily Baut, Megan Dacek, Kripa S. Ganesh, Ramya Sridharan, Karen L. Chu & Daniel A. Heller

Authors
  1. Yosi Shamay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Janki Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehtap Işık
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aviram Mizrachi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Josef Leibold
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Darjus F. Tschaharganeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daniel Roxbury
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Januka Budhathoki-Uprety
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Karla Nawaly
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. James L. Sugarman
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Emily Baut
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Michelle R. Neiman
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Megan Dacek
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Kripa S. Ganesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Darren C. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ramya Sridharan
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Karen L. Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Vinagolu K. Rajasekhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Scott W. Lowe
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. John D. Chodera
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Daniel A. Heller
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.S. and D.A.H. conceived the project and designed experiments. Y.S. analysed data, designed, and conducted the self-assembly experiments. D.R. performed MD experiments and analysis. D.F.T. and J.L. performed the in vivo liver cancer model. Y.S., V.K.R., A.M. and J.S. performed all other in vivo experiments, electron microscopy and tissue staining. Y.S., V.K.R., K.N., J.L.S., M.R.N., K.C. and J.S. performed in vitro experiments. Y.S. developed the tumour spheroid model. E.B., J.L.S., K.N., R.S., M.R.N., K.C., K.S.G., M.D. and D.C.J. performed experiments for the computational drug screening and nanoparticle self-assembly validation experiments. J.B. performed F-NMR studies for drug biodistribution. M.I. performed self-assembly categorization of DrugBank small molecule drugs. M.I. and J.D.C. conducted statistical analysis of QSNAP descriptors. Y.S. and D.A.H. wrote the paper. D.A.H., S.W.L. and J.D.C. supervised the research.

Corresponding author

Correspondence to Daniel A. Heller.

Ethics declarations

Competing interests

J. D. C. is a member of the Scientific Advisory Board for Schrödinger, LLC.

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 Discussion, Supplementary Figures 1–24, Supplementary Tables 1–6

Life Sciences Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shamay, Y., Shah, J., Işık, M. et al. Quantitative self-assembly prediction yields targeted nanomedicines. Nature Mater 17, 361–368 (2018). https://doi.org/10.1038/s41563-017-0007-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41563-017-0007-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research