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Cited by 328

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Polymerization-Induced Self-Assembly (PISA) – control over the morphology of nanoparticles for drug delivery applications

Karagoz, Bunyamin, Esser, Lars, Duong, Hien T., Basuki, Johan S., Boyer, Cyrille, Davis, Thomas P.·2014·Polym. Chem.

Abstract

In this paper, we describe the synthesis of asymmetric functional POEGMA-b-P(ST-co-VBA) copolymers in methanol, yielding in one-pot polymerization a range of nanoparticle morphologies, including spherical micelles, worm-like, rod-like micelles and vesicles. The presence of the aldehyde group was then exploited to form crosslinks or to conjugate chemotherapy compounds, such as doxorubicin, via pH-breakable bonds (Schiff base or imine) directly to the preformed nanoparticles. The influence of the nanoparticle morphologies on the MCF-7 breast cancer cell line uptake was investigated using flow cytometry and confocal microscopy. Finally, the IC50 of DOX, following nanoparticle delivery, was studied showing significant influence of the nanoparticle carrier morphology on therapeutic efficacy for breast cancer.

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Crossref · 10.1039/c3py01306e

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Metadata

Authors
Karagoz, B., Esser, L., Duong, H. T., Basuki, J. S., Boyer, C., & Davis, T. P.
Venue
Polym. Chem.
Published
2014
Volume
5
Issue
2
Pages
350-355
ISSN
  • 1759-9954 (ISSN-L)
  • 1759-9962
OpenAlex
W2064519977
Cited by
328
References
2
FWCI
9.82
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Polymerization-Induced Self-Assembly (PISA) – control over the morphology of nanoparticles for drug delivery applications. https://app.cochat.ai/papers/10.1039/c3py01306e
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Karagoz, B., Esser, L., Duong, H. T., Basuki, J. S., Boyer, C., & Davis, T. P. (2014). Polymerization-Induced Self-Assembly (PISA) – control over the morphology of nanoparticles for drug delivery applications. Polym. Chem. https://doi.org/10.1039/c3py01306e

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.