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Quantitative Self-Assembly Prediction Yields Targeted Nanomedicines

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. Until recently, these processes were generally difficult to predict, execute, and control. We describe herein a targeted drug delivery sy...

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Detalles Bibliográficos
Autores principales: Shamay, Yosi, Shah, Janki, Işık, Mehtap, Mizrachi, Aviram, Leibold, Josef, Tschaharganeh, Darjus F., Roxbury, Daniel, Budhathoki-Uprety, Januka, Nawaly, Karla, Sugarman, James L., Baut, Emily, Neiman, Michelle R., Dacek, Megan, Ganesh, Kripa S., Johnson, Darren C., Sridharan, Ramya, Chu, Karen L., Rajasekhar, Vinagolu K., Lowe, Scott W., Chodera, John D., Heller, Daniel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930166/
https://www.ncbi.nlm.nih.gov/pubmed/29403054
http://dx.doi.org/10.1038/s41563-017-0007-z
Descripción
Sumario:Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. Until recently, these processes were generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system which 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 ultra-high drug loadings of up to 90%. Using quantitative structure-nanoparticle assembly prediction (QSNAP) calculations, we identified and validated 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 a computational design of nanomedicines based on quantitative models for drug payload selection.