Current cancer therapies, including chemotherapy and radiotherapy, are imprecise, non-specific, and are often administered at high dosages - resulting in side effects that severely impact the patient's overall well-being. A variety of multifunctional, cancer-targeted nanotheranostic systems that integrate therapy, imaging, and tumor targeting functionalities in a single platform have been developed to overcome the shortcomings of traditional drugs. Among the imaging modalities used, magnetic resonance imaging (MRI) provides high resolution imaging of structures deep within the body and, in combination with other imaging modalities, provides complementary diagnostic information for more accurate identification of tumor characteristics and precise guidance of anti-cancer therapy. This review article presents a comprehensive assessment of nanotheranostic systems that combine MRI-based imaging (T1 MRI, T2 MRI, and multimodal imaging) with therapy (chemo-, thermal-, gene- and combination therapy), connecting a range of topics including hybrid treatment options ( e.g. combined chemo-gene therapy), unique MRI-based imaging ( e.g. combined T1-T2 imaging, triple and quadruple multimodal imaging), novel targeting strategies ( e.g. dual magnetic-active targeting and nanoparticles carrying multiple ligands), and tumor microenvironment-responsive drug release ( e.g. redox and pH-responsive nanomaterials). With a special focus on systems that have been tested in vivo , this review is an essential summary of the most advanced developments in this rapidly evolving field.

Superparamagnetic iron-oxide nanoparticles (SPIONs) show great promise for multiple applications in biomedicine. While a number of studies have examined their safety profile, the toxicity of these particles on reproductive organs remains uncertain. The goal of this study was to evaluate the cytotoxicity of starch-coated, aminated, and PEGylated SPIONs on a cell line derived from Chinese Hamster ovaries (CHO-K1 cells). We evaluated the effect of particle diameter (50 and 100 nm) and polyethylene glycol (PEG) chain length (2k, 5k and 20k Da) on the cytotoxicity of SPIONs by investigating cell viability using the tetrazolium dye 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and sulforhodamine B (SRB) assays. The kinetics and extent of SPION uptake by CHO-K1 cells was also studied, as well as the resulting generation of intracellular reactive oxygen species (ROS). Cell toxicity profiles of SPIONs correlated strongly with their cellular uptake kinetics, which was strongly dependent on surface properties of the particles. PEGylation caused a decrease in both uptake and cytotoxicity compared to aminated SPIONs. Interestingly, 2k Da PEG-modifed SPIONs displayed the lowest cellular uptake and cytotoxicity among all studied particles. These results emphasize the importance of surface coatings when engineering nanoparticles for biomedical applications.

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Proteases play a key role in tumor biology, with high expression levels often correlating with poor prognosis for cancer patients - making them excellent disease markers for tumor diagnosis. Despite their significance, quantifying proteolytic activity in vivo remains a challenge. Nanoparticles, with their ability to serve as scaffolds having unique chemical, optical and magnetic properties, offer the promise of merging diagnostic medicine with material engineering. Such nanoparticles can interact preferentially with proteases enriched in tumors, providing the ability to assess disease state in a noninvasive and spatiotemporal manner. We review recent advances in the development of nanoparticles for imaging and quantification of proteolytic activity in tumor models, and prognosticate future advancements.

Realizing the full potential of magnetic nanoparticles (MNPs) in nanomedicine requires the optimization of their physical and chemical properties. Elucidation of the effects of these properties on clinical diagnostic or therapeutic properties, however, requires the synthesis or purification of homogenous samples, which has proved to be difficult. While initial simulations indicated that size-selective separation could be achieved by flowing magnetic nanoparticles through a magnetic field, subsequent in vitro experiments were unable to reproduce the predicted results. Magnetic field-flow fractionation, however, was found to be an effective method for the separation of polydisperse suspensions of iron oxide nanoparticles with diameters greater than 20 nm. While similar methods have been used to separate magnetic nanoparticles before, no previous work has been done with magnetic nanoparticles between 20 and 200 nm. Both transmission electron microscopy (TEM) and dynamic light scattering (DLS) analysis were used to confirm the size of the MNPs. Further development of this work could lead to MNPs with the narrow size distributions necessary for their in vitro and in vivo optimization.

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Superparamagnetic iron oxide nanoparticles (SPIONs) have been widely explored for use in many biomedical applications. Methods for synthesis of magnetic nanoparticle (MNP), however, typically yield multicore structures with broad size distribution, resulting in suboptimal and variable performance in vivo. In this study, a new method for sorting SPIONs by size, labeled diffusive magnetic fractionation (DMF), is introduced as an improvement over conventional magnetic field flow fractionation (MFFF). Unlike MFFF, which uses a constant magnetic field to capture particles, DMF utilizes a pulsed magnetic field approach that exploits size-dependent differences in the diffusivity and magnetic attractive force of SPIONs to yield more homogenous particle size distributions. To compare both methods, multicore SPIONs with a broad size distribution (polydispersity index (PdI) = 0.24 ± 0.05) were fractionated into nine different-sized SPION subpopulations, and the PdI values were compared. DMF provided significantly improved size separation compared to MFFF, with eight out of the nine fractionations having significantly lower PdI values (p value < 0.01). Additionally, the DMF method showed a high particle recovery (>95%), excellent reproducibility, and the potential for scale-up. Mathematical models were developed to enable optimization, and experimental results confirmed model predictions (R2 = 0.98).