期刊
NATURE MATERIALS
卷 17, 期 4, 页码 361-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41563-017-0007-z
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资金
- NIH New Innovator Award [DP2-HD075698]
- National Cancer Institute [CA 013106]
- Cancer Center Support Grant [P30 CA008748]
- Expect Miracles Foundation - Financial Services Against Cancer
- Anna Fuller Fund
- Louis V. Gerstner Jr. Young Investigator's Fund
- Frank A. Howard Scholars Program
- Cycle for Survival
- Alan and Sandra Gerry Metastasis Research Initiative
- Mr. William H. Goodwin and Mrs. Alice Goodwin and the Commonwealth Foundation for Cancer Research
- Experimental Therapeutics Center
- Imaging & Radiation Sciences Program
- Center for Molecular Imaging and Nanotechnology of Memorial Sloan Kettering Cancer
- New York State Department of Health Fixed Term [DOH01-C30315GG-3450000]
- Center for Metastasis Research (CMR) Scholars Fellowship Program
- American Cancer Society - Roaring Fork Valley Postdoctoral Fellowship
- Tow Foundation Postdoctoral Fellowship, Center for Molecular Imaging and Nanotechnology at MSKCC
- National Science Foundation [TG-MCB-130013]
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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