Multifunctional Theranostic Graphene Oxide Nanoflakes as MR Imaging Agents with Enhanced Photothermal and Radiosensitizing Properties.

The integration of multiple therapeutic and diagnostic functions into a single nanoplatform for image-guided cancer therapy has been an emerging trend in nanomedicine. We show here that multifunctional theranostic nanostructures consisting of superparamagnetic iron oxide (SPIO) and gold nanoparticles (AuNPs) scaffolded within graphene oxide nanoflakes (GO-SPIO-Au NFs) can be used for dual photo/radiotherapy by virtue of the near-infrared (NIR) absorbance of GO for photothermal therapy (PTT) and the Z element radiosensitization of AuNPs for enhanced radiation therapy (RT). At the same time, this nanoplatform can also be detected by magnetic resonance (MR) imaging because of the presence of SPIO NPs. Using a mouse carcinoma model, GO-SPIO-Au NF-mediated combined PTT/RT exhibited a 1.85-fold and 1.44-fold higher therapeutic efficacy compared to either NF-mediated PTT or RT alone, respectively, resulting in a complete eradication of tumors. As a sensitive multifunctional theranostic platform, GO-SPIO-Au NFs appear to be a promising nanomaterial for enhanced cancer imaging and therapy.

MRI-based numerical modeling strategy for simulation and treatment planning of nanoparticle-assisted photothermal therapy.

Nanoparticle-assisted photothermal therapy (NPTT) has recently emerged as a promising alternative to traditional thermal therapy methods. Computational modeling for simulation and treatment planning of NPTT seems to be essential for clinical translation of this modality. Non-invasive identification of nanoparticle distribution within the tissue is a key perquisite for accurate prediction of NPTT in real conditions. In the present study, we have developed a magnetic resonance imaging (MRI)-based numerical modeling strategy for simulation and treatment planning of NPTT. To this end, we have utilized the core-shell γ-Fe2O3@Au nanoparticle comprising a gold layer with plasmonic properties and a magnetic core that enables to track the location of this structure via MRI. The map of nanoparticle distribution in the tumor derived from T2-weighted MR image was imported into a finite element simulation software, and Pennes bioheat equation and Arrhenius damage model were applied to simulate the temperature and damage distributions, respectively. The validation of the model developed herein was assessed by monitoring the superficial and the central temperature variations of the tumor in experiment. Both the numerical modeling and experimental study proved that a localized heating and then a focused damage could be achieved due to nanoparticle inclusion. There is quite satisfactory agreement between the numerical and experimental results. The model developed in this study has a good capability to be used as a promising planning method for NPTT of cancer.

Simulation-guided photothermal therapy using MRI-traceable iron oxide-gold nanoparticle.

Despite the immense benefits of nanoparticle-assisted photothermal therapy (NPTT) in cancer treatment, the limited method and device for detecting temperature during heat operation significantly hinder its overall progress. Development of a pre-treatment planning tool for prediction of temperature distribution would greatly improve the accuracy and safety of heat delivery during NPTT. Reliable simulation of NPTT highly relies on accurate geometrical model description of tumor and determining the spatial location of nanoparticles within the tissue. The aim of this study is to develop a computational modeling method for simulation of NPTT by exploiting the theranostic potential of iron oxide­gold hybrid nanoparticles (IO@Au) that enable NPTT under magnetic resonance imaging (MRI) guidance. To this end, CT26 colon tumor-bearing mice were injected with IO@Au nanohybrid and underwent MR imaging. The geometrical model description of tumor and nanoparticle distribution map were obtained from MR image of the tumor and involved in finite element simulation of heat transfer process. The experimental measurement of tumor temperature confirmed the validity of the model to predict temperature distribution. The constructed model can help to predict temperature distribution during NPTT and then allows to optimize the heating protocol by adjusting the treatment parameters prior to the actual treatment operation.