Characterization of nanoparticle-based contrast agents for molecular magnetic resonance imaging
Source abstract
Abstract
The development of molecular imaging agents is currently undergoing a dramatic expansion. As of October 2011, ~4,800 newly developed agents have been synthesized and characterized in vitro and in animal models of human disease. Despite this rapid progress, the transfer of these agents to clinical practice is rather slow. To address this issue, the National Institutes of Health launched the Molecular Imaging and Contrast Agents Database (MICAD) in 2005 to provide freely accessible online information regarding molecular imaging probes and contrast agents for the imaging community. While compiling information regarding imaging agents published in peer-reviewed journals, the MICAD editors have observed that some important information regarding the characterization of a contrast agent is not consistently reported. This makes it difficult for investigators to evaluate and meta-analyze data generated from different studies of imaging agents, especially for the agents based on nanoparticles. This article is intended to serve as a guideline for new investigators for the characterization of preclinical studies performed with nanoparticle-based MRI contrast agents. The common characterization parameters are summarized into seven categories: contrast agent designation, physicochemical properties, magnetic properties, in vitro studies, animal studies, MRI studies, and toxicity. Although no single set of parameters is suitable to define the properties of the various types of contrast agents, it is essential to ensure that these agents meet certain quality control parameters at the preclinical stage, so that they can be used without delay for clinical studies.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nanoparticle-Based Drug Delivery · Lanthanide and Transition Metal Complexes · Nanoplatforms for cancer theranostics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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