Quantitative self-assembly prediction yields targeted nanomedicines Journal Article


Authors: Shamay, Y.; Shah, J.; Işık, M.; Mizrachi, A.; Leibold, J.; Tschaharganeh, D. F.; Roxbury, D.; Budhathoki-Uprety, J.; Nawaly, K.; Sugarman, J. L.; Baut, E.; Neiman, M. R.; Dacek, M.; Ganesh, K. S.; Johnson, D. C.; Sridharan, R.; Chu, K. L.; Rajasekhar, V. K.; Lowe, S. W.; Chodera, J. D.; Heller, D. A.
Article Title: Quantitative self-assembly prediction yields targeted nanomedicines
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. © 2018 The Author(s).
Keywords: forecasting; nanoparticles; diseases; chemical modification; drug delivery; self assembly; targeted drug delivery; controlled drug delivery; targeted nanoparticle; targeted drug delivery systems; computational design; molecular descriptors; nanoparticle assemblies; predictive indicators; quantitative structures; supramolecular self-assemblies
Journal Title: Nature Materials
Volume: 17
Issue: 4
ISSN: 1476-1122
Publisher: Nature Publishing Group  
Date Published: 2018-04-01
Start Page: 361
End Page: 368
Language: English
DOI: 10.1038/s41563-017-0007-z
PROVIDER: scopus
PMCID: PMC5930166
PUBMED: 29403054
DOI/URL:
Notes: Article -- Export Date: 1 May 2018 -- Source: Scopus
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