2020
DOI: 10.1039/d0tb00924e
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A library of aminoglycoside-derived lipopolymer nanoparticles for delivery of small molecules and nucleic acids

Abstract: Simultaneous delivery of small molecules and nucleic acids using a single vehicle can lead to novel combination treatments and multifunctional carriers for a variety of diseases. In this study, we...

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Cited by 4 publications
(2 citation statements)
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“…[ 151 ] Further, a series of alkanoyl was modified on the PAE polymers to facilitate their self‐assembly, and the resulting nanoparticles demonstrated great promise in the co‐delivery of small‐molecule drugs and plasmid DNA. [ 152 ]…”
Section: Aminoglycoside‐based Biomaterialsmentioning
confidence: 99%
“…[ 151 ] Further, a series of alkanoyl was modified on the PAE polymers to facilitate their self‐assembly, and the resulting nanoparticles demonstrated great promise in the co‐delivery of small‐molecule drugs and plasmid DNA. [ 152 ]…”
Section: Aminoglycoside‐based Biomaterialsmentioning
confidence: 99%
“…Computational chemistry tools, such as density functional theory, are used to calculate molecular descriptors or fingerprint descriptors of the repeat units of the polymers, by breaking each representative chemical block into constituent atomic and molecular fragments. , Thus, any polymer in the chemical space can be parametrized by a series of numbers, which can subsequently be correlated with material properties, such as ζ-potential, p K a , and hydrophobicity, as well as cellular responses, such as nucleic acid binding, cytotoxicity, and transfection efficiency. Using this cheminformatic workflow, Rege and co-workers were able to discover aminoglycoside polymers that mediated high levels of transgene expression. Algorithms, such as support vector regression and partial least-squares regression quantified the contribution of hydrophobicity, lipid substitution, and molecular weight, allowing them to understand the role of each physicochemical descriptor (Figure A). Despite the molecular understanding and predictive accuracy achieved from forward-QSPR models, inverse-QSPR or algorithmic design of polymeric vectors was not attempted.…”
Section: Data-driven Design Of Polymeric Vectorsmentioning
confidence: 99%