Back

An EGF-modified PLGA-lanthanide nano platform for combined NIR-II cancer imaging and targeted drug delivery

He, Y.; Yu, z.; Schomann, T.; Zhang, H.; Eich, C.; Cruz, L. j.

2023-06-21 cancer biology
10.1101/2023.06.18.545497 bioRxiv
Show abstract

The use of multifunctional nanoplatforms for synergistic therapy and imaging is a promising approach in cancer treatment. In this study, we exploited the imaging properties of lanthanides by encapsulating CaF2:Y, Nd along with the chemotherapeutic drug doxorubicin (DOX) into poly (D,L-lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) to prepare a nanoplatform suitable for imaging in the second near-infrared (NIR-II) window and simultaneous anti-cancer therapy. To facilitate the accumulation of CaF2:Y, Nd+DOX@PLGA NPs in breast cancer cells, we modified the NPs with EGF. The diameter of the obtained CaF2:Y, Nd+DOX@PLGA/PEG/EGF NPs was approximately 150 nm, with a nearly round shape and homogeneous size distribution. In addition, analysis of the drug release behaviour showed that DOX was released more readily and had a longer release time in acidic environments. Accordingly, MTS results indicated that DOX-loaded NPs were significantly cytotoxic. Furthermore, fluorescence microscopy and flow cytometry studies revealed that CaF2:Y, Nd+DOX@PLGA/PEG and CaF2:Y, Nd+DOX@PLGA/PEG/EGF NPs were gradually taken up by 4T1 breast cancer cells over time, and EGF-coated Nd+DOX@PLGA NPs exhibited increased uptake rates after 72 h. Moreover, we found that EGF increased the solubility of Nd+DOX@PLGA NPs in water by comparing the aqueous solutions of the different NPs formulations. Finally, NIR imaging demonstrated strong fluorescence of PLGA NPs carrying CaF2:Y, Nd NPs at 900-1200 nm under 808 nm laser excitation. In conclusion, the developed CaF2:Y, Nd+DOX@PLGA/PEG/EGF NPs could be monitored for an extended period of time, and co-encapsulated DOX could be efficiently released to kill breast cancer cells.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.