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