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Researchers Design Protein-Based Contrast Agent

By MedImaging International staff writers
Posted on 17 Jul 2017
Image: Researchers have developed a tumor-targeting MRI contrast agent that is based on human protein (Photo courtesy of UMMS).
Image: Researchers have developed a tumor-targeting MRI contrast agent that is based on human protein (Photo courtesy of UMMS).
A team of researchers has designed a human protein-based MRI contrast agent for detecting tumors that is easily cleared by the body.

Gadolinium-based Magnetic Resonance Imaging (MRI) contrast agents are commonly used to detect, stage and monitor tumor malignancy without the need for ionizing radiation or radionuclides. While gadolinium contrast agents do not provoke an immune response in cells, they are retained in the human body.

Researchers from the University of Massachusetts Medical School (UMMS) and the Second Hospital of Tianjin Medical University (Heping Qu, China) developed the agent. The research results were published in the June 26, 2017, issue of the journal Nano Letters, ACS Publications. The new tumor-targeting MRI contrast agent could be used for detecting early stage tumors.

In the paper, the researchers explain how they were able to mimic a natural process occurring in the human body, and use human transferrin (Tf) proteins to create gadolinium bio-mineralized human transferrin protein-based (Gd@TfNP) MRI contrast nanoprobes.

During their study, the researchers looked for naturally-occurring proteins that could be used as alternatives to gadolinium-based contrast agents, and managed to demonstrate the effectiveness of metal-based nanoparticulate contrast agents enclosed in protein scaffolds. One example of such a protein-bound nanoparticle called Abraxane, is already being used to treat metastatic breast cancer.

First author of the paper, Yang Zhao, MD, PhD, from the Second Hospital of Tianjin Medical University, said, "Such probes can immediately leave the tumor sites after delivery and we could track the overall process by MRI. Such a technique might be useful not only for visualizing tumor therapies, but for optimizing drug dose and evaluating clinical results."

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