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      Modelling the Transport of Nanoparticles under Blood Flow using an Agent-based Approach

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          Abstract

          Blood-mediated nanoparticle delivery is a new and growing field in the development of therapeutics and diagnostics. Nanoparticle properties such as size, shape and surface chemistry can be controlled to improve their performance in biological systems. This enables modulation of immune system interactions, blood clearance profile and interaction with target cells, thereby aiding effective delivery of cargo within cells or tissues. Their ability to target and enter tissues from the blood is highly dependent on their behaviour under blood flow. Here we have produced an agent-based model of nanoparticle behaviour under blood flow in capillaries. We demonstrate that red blood cells are highly important for effective nanoparticle distribution within capillaries. Furthermore, we use this model to demonstrate how nanoparticle size can selectively target tumour tissue over normal tissue. We demonstrate that the polydispersity of nanoparticle populations is an important consideration in achieving optimal specificity and to avoid off-target effects. In future this model could be used for informing new nanoparticle design and to predict general and specific uptake properties under blood flow.

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          Nanoparticle size and surface properties determine the protein corona with possible implications for biological impacts.

          Nanoparticles in a biological fluid (plasma, or otherwise) associate with a range of biopolymers, especially proteins, organized into the "protein corona" that is associated with the nanoparticle and continuously exchanging with the proteins in the environment. Methodologies to determine the corona and to understand its dependence on nanomaterial properties are likely to become important in bionanoscience. Here, we study the long-lived ("hard") protein corona formed from human plasma for a range of nanoparticles that differ in surface properties and size. Six different polystyrene nanoparticles were studied: three different surface chemistries (plain PS, carboxyl-modified, and amine-modified) and two sizes of each (50 and 100 nm), enabling us to perform systematic studies of the effect of surface properties and size on the detailed protein coronas. Proteins in the corona that are conserved and unique across the nanoparticle types were identified and classified according to the protein functional properties. Remarkably, both size and surface properties were found to play a very significant role in determining the nanoparticle coronas on the different particles of identical materials. We comment on the future need for scientific understanding, characterization, and possibly some additional emphasis on standards for the surfaces of nanoparticles.
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            Nanoparticles in medicine: therapeutic applications and developments.

            Nanotechnology is the understanding and control of matter generally in the 1-100 nm dimension range. The application of nanotechnology to medicine, known as nanomedicine, concerns the use of precisely engineered materials at this length scale to develop novel therapeutic and diagnostic modalities. Nanomaterials have unique physicochemical properties, such as ultra small size, large surface area to mass ratio, and high reactivity, which are different from bulk materials of the same composition. These properties can be used to overcome some of the limitations found in traditional therapeutic and diagnostic agents.
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              A Note on the Generation of Random Normal Deviates

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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                10 June 2015
                2015
                : 5
                : 10649
                Affiliations
                [1 ]Department of Chemistry, University College London , UK
                [2 ]MRC Centre for Molecular Virology, University College London , UK
                [3 ]Sheffield Institute for Translational Neuroscience, University of Sheffield , UK
                [4 ]Department of Computer Science, University of Sheffield , UK
                Author notes
                Article
                srep10649
                10.1038/srep10649
                4462051
                26058969
                4d17c6e7-0f27-49ba-b518-428a98a0bc2c
                Copyright © 2015, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 18 December 2014
                : 16 March 2015
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