Nanoparticle discovery another step towards personalised medicine

1 August 2019:

A team led by the CNBP’s Dr Guozhen Liu has developed intelligent biodegradable polymer nanoparticles, which can help monitor a cell-signalling protein, or cytokine, widely expressed in cancer cells. The technique can help with earlier diagnostics and even treatment and represents another step towards personalised nanomedicine.

The research integrates a specific fluorogen – a molecule that generates fluorescence and can be used for protein monitoring – with PLGA nanoparticles for the first time.

The fluorogen in question is a so-called “aggregation-induced emission” fluorogen, known as an AIEgen. Aggregation-induced emission (AIE), has become an important area of research since its discovery around 20 years ago. It describes an abnormal phenomenon, in which some compounds show greater fluorescence as they aggregate than when in solution, as is more common. These AIEgens provide superior advantages for biosensing and bioimaging.

The integration of the nanoparticle and the AIEgen could become an important tool in the relatively new field of medicine known as “theranostics” – a combination of “therapy” and “diagnostics” made possible through the use of nanoparticles and an important transition towards personalised medicine.

Dr Liu’s discovery, for example, detects high levels of the cytokine VEGF-A found in tumor cells, and monitors simultaneous photothermal therapy (PTT), in which heat is used to kill cancer cells, and magnetic resonance imaging (MRI) as part of a whole package of early diagnostics and treatment of cancer cells.

It could be used in the future as a smart drug delivery system, with cancer drugs loaded in the nanoparticles for controlled and sustained release targeted precisely to a tumor.
In the future, Dr Liu believes it will be possible to develop the next generation of intelligent nanoparticles which can continually monitor cytokines and cytokine-triggered drug delivery while also carrying out deep tissue imaging.

Dr Liu is an ARC Future Fellow and Senior Lecturer at Graduate School of Biomedical Engineering at UNSW.

You can read the paper here.

Journal: Nanomedicine

Publication Title: AIEgen based poly(L-lactic-co-glycolic acid) magnetic nanoparticles to localize cytokine VEGF for early cancer diagnosis and photothermal therapy

Authors: Ma, K (Ma, Ke); Liu, GJ (Liu, Guo-Jun); Yan, LL (Yan, Lulin); Wen, SH (Wen, Shihui); Xu, B (Xu, Bin); Tian, WJ (Tian, Wenjing); Goldys, EM (Goldys, Ewa M.); Liu, GZ (Liu, Guozhen)

Abstract: Aim: We demonstrated a novel theranostic system for simultaneous photothermal therapy and magnetic resonance imaging applicable to early diagnostics and treatment of cancer cells. Materials & methods: Oleic acid-Fe3O4 and triphenylamine-divinylanthracene-dicyano were loaded to the poly(L-lactic-co-glycolic acid) nanoparticles (NPs) on which anti-VEGF antibodies were modified to form anti-VEGF/OA-Fe3O4/triphenylamine-divinylanthracene-dicyano@poly(L-lactic-co-glycolic acid) NPs. The 1H nuclear magnetic resonance (NMR), mass spectra, fluorescence, UV absorption, dynamic light scattering, transmission electron microscope and inductively coupled plasma mass spectrometry tests were used to characterize the NPs, and the bioimaging was illustrated by confocal laser scanning microscope (CLSM) and in vivo MRI animal experiment. Results: This system was capable to recognize the overexpressed VEGF-A as low as 68pg/ml in different cell lines with good selectivity and photothermal therapy effect. Conclusion: These ultrasensitive theranostic NPs were able to identify tumor cells by fluorescence imaging and MRI, and destroy tumors under near infrared illumination.

Keywords:
Author Keywords: AIEgen; cytokines; MRI; PDT; PLGA nanoparticle; PTT; theranostics

KeyWords Plus: ENDOTHELIAL GROWTH-FACTOR; IN-VIVO; ANGIOGENESIS; THERANOSTICS; NANOSPHERES; APTASENSOR; EXPRESSION; PROGNOSIS; MEDICINE; PROBE

Link: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000473676900008