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Nat Mater. 2018 Apr;17(4):361-368. doi: 10.1038/s41563-017-0007-z. Epub 2018 Feb 5.

Quantitative self-assembly prediction yields targeted nanomedicines.

Author information

1
Memorial Sloan Kettering Cancer Center, New York, NY, USA.
2
Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, Israel.
3
Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
4
Department of Otolaryngology Head and Neck Surgery, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
5
Helmholtz-University Group "Cell Plasticity and Epigenetic Remodeling", German Cancer Research Center (DKFZ) & Institute of Pathology University Hospital, Heidelberg, Germany.
6
Department of Chemical Engineering, University of Rhode Island, Kingston, RI, 02881, USA.
7
Weill Cornell Medical College, Cornell University, New York, NY, USA.
8
Memorial Sloan Kettering Cancer Center, New York, NY, USA. hellerd@mskcc.org.
9
Weill Cornell Medical College, Cornell University, New York, NY, USA. hellerd@mskcc.org.

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.

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