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How Can We Predict Behavior of Nanoparticles In Vivo ?
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How Can We Predict Behavior of Nanoparticles In Vivo ?
How Can We Predict Behavior of Nanoparticles In Vivo ?
Journal Article

How Can We Predict Behavior of Nanoparticles In Vivo ?

2016
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Overview
The main reason for this remarkable change in nanomaterial behavior is its enormous surface-to-volume ratio, which provides a very large interfacial surface area as driving force for enhanced interaction of nanomaterial with surrounding it molecules. [...]upon intravenous administration, nanoparticles inevitably form layers of adsorbed biomolecules (mainly proteins) known as a ‘protein corona’ (1,2). The biological identity of nanoparticles, which could be significantly different from their original synthetic identity, determines the physiological behavior of nanoparticles influencing their colloidal stability, targeting capability, kinetics of circulation, transport, cellular uptake and organ accumulation, degradation, drug release, signaling and toxicity (4). Because the relative quantities of the adsorbed proteins on the surface of nanoparticles do not necessarily correlate with their abundance in blood plasma, the composition of protein corona for each particular type of nanomaterial is unique and influenced by multiple factors. [...]particle analysis technologies that work more effectively in complex fluids like blood and serum under little dilution. Using the quantitative structure-activity relationships that will link the information obtained by the most advanced colloidal surface analysis techniques with the protein corona composition data (originated from ideal in vivo conditions) and corresponding biological outcomes, the researchers will be able to make more accurate predictions of nanoparticles association with various relevant cells types, such as circulating immune cells, endothelial cells (lining the lumen of the blood vessels) and organ-related hepatocytes and splenocytes. Because the nanoparticle-cell interactions influence multiple downstream cellular responses, the predictions could be extended to characterize signaling pathways, cytokine secretion, gene expression and toxicity.