Development of an Effective Tumor-Targeted Contrast Agent for Magnetic Resonance Imaging Based on Mn/H-Ferritin Nanocomplexes.
Chiara TullioLucia SalvioniMichela BelliniAnna DegrassiLuisa FiandraMassimiliano D'ArienzoStefania GarbujoRany RotemFilippo TestaDavide ProsperiMiriam ColomboPublished in: ACS applied bio materials (2021)
Magnetic resonance imaging (MRI) is one of the most sophisticated diagnostic tools that is routinely used in clinical practice. Contrast agents (CAs) are commonly exploited to afford much clearer images of detectable organs and to reduce the risk of misdiagnosis caused by limited MRI sensitivity. Currently, only a few gadolinium-based CAs are approved for clinical use. Concerns about their toxicity remain, and their administration is approved only under strict controls. Here, we report the synthesis and validation of a manganese-based CA, namely, Mn@HFn-RT. Manganese is an endogenous paramagnetic metal able to produce a positive contrast like gadolinium, but it is thought to result in less toxicity for the human body. Mn ions were efficiently loaded inside the shell of a recombinant H-ferritin (HFn), which is selectively recognized by the majority of human cancer cells through their transferrin receptor 1. Mn@HFn-RT was characterized, showing excellent colloidal stability, superior relaxivity, and a good safety profile. In vitro experiments confirmed the ability of Mn@HFn-RT to efficiently and selectively target breast cancer cells. In vivo, Mn@HFn-RT allowed the direct detection of tumors by positive contrast enhancement in a breast cancer murine model, using very low metal dosages and exhibiting rapid clearance after diagnosis. Hence, Mn@HFn-RT is proposed as a promising CA candidate to be developed for MRI.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- magnetic resonance
- room temperature
- transition metal
- computed tomography
- diffusion weighted imaging
- metal organic framework
- endothelial cells
- clinical practice
- crispr cas
- oxidative stress
- breast cancer cells
- genome editing
- deep learning
- cancer therapy
- drug delivery
- induced pluripotent stem cells
- loop mediated isothermal amplification
- label free
- machine learning
- convolutional neural network
- binding protein
- oxide nanoparticles