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      Predicting cytotoxicity of PAMAM dendrimers using molecular descriptors

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          Summary

          The use of data mining techniques in the field of nanomedicine has been very limited. In this paper we demonstrate that data mining techniques can be used for the development of predictive models of the cytotoxicity of poly(amido amine) (PAMAM) dendrimers using their chemical and structural properties. We present predictive models developed using 103 PAMAM dendrimer cytotoxicity values that were extracted from twelve cancer nanomedicine journal articles. The results indicate that data mining and machine learning can be effectively used to predict the cytotoxicity of PAMAM dendrimers on Caco-2 cells.

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          A study of cross-validation and bootstrap for accuracy estimation and model selection in

          R Kohavi, Kohavi, Ron Kohavi (1995)
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            Toxicology of nanoparticles.

            Andreas Elsaesser, C. Howard (2012)
            While nanotechnology and the production of nanoparticles are growing exponentially, research into the toxicological impact and possible hazard of nanoparticles to human health and the environment is still in its infancy. This review aims to give a comprehensive summary of what is known today about nanoparticle toxicology, the mechanisms at the cellular level, entry routes into the body and possible impacts to public health. Proper characterisation of the nanomaterial, as well as understanding processes happening on the nanoparticle surface when in contact with living systems, is crucial to understand possible toxicological effects. Dose as a key parameter is essential in hazard identification and risk assessment of nanotechnologies. Understanding nanoparticle pathways and entry routes into the body requires further research in order to inform policy makers and regulatory bodies about the nanotoxicological potential of certain nanomaterials. Copyright © 2011 Elsevier B.V. All rights reserved.
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              The influence of surface modification on the cytotoxicity of PAMAM dendrimers.

              R Jevprasesphant (2003)
              The influence of surface modification on the cytotoxicity of PAMAM dendrimers was examined using Caco-2 cells. Dendrimers were modified by conjugating either lauroyl chains or polyethylene glycol (PEG) 2000 onto the surface of cationic PAMAM dendrimers (G2, G3, G4). The cytotoxicity of unmodified dendrimers towards Caco-2 cells was appreciably higher for cationic (whole generation) compared with anionic (half generation) dendrimers and for both types increased with increasing size (generation) and concentration. A marked decrease in the cytotoxicity of cationic PAMAM dendrimers was noted when the surface was modified, with the addition of six lauroyl or four PEG chains being particularly effective in decreasing cytotoxicity. This decrease in cytotoxicity is thought to be due to a reduction/shielding of the positive charge on the dendrimer surface by the attached chains. The cytotoxicity of dendrimer-based delivery systems is likely to be very different from the parent dendrimer.
              Article 0 comments Cited 118 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Julio C Facelli
                Rong Liu: Role: Guest Editor
                Journal
                Journal ID (nlm-ta): Beilstein J Nanotechnol
                Journal ID (iso-abbrev): Beilstein J Nanotechnol
                Title: Beilstein Journal of Nanotechnology
                Publisher: Beilstein-Institut (Trakehner Str. 7-9, 60487 Frankfurt am Main, Germany )
                ISSN (Electronic): 2190-4286
                Publication date Collection: 2015
                Publication date (Electronic): 11 September 2015
                Volume: 6
                Pages: 1886-1896
                Affiliations
                [1 ]Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA
                [2 ]Department of Bioengineering, University of Utah, Salt Lake City, UT 84112, USA
                [3 ]Department of Pharmaceutics and Pharmaceutical Chemistry, University of Utah, Salt Lake City, UT 84112, USA,
                [4 ]Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA
                Article
                DOI: 10.3762/bjnano.6.192
                PMC ID: 4660915
                PubMed ID: 26665059
                SO-VID: 7fd07fd4-ac7b-49bd-b1d1-59fd40b5fc04
                Copyright © Copyright © 2015, Jones et al; licensee Beilstein-Institut.
                License:

                This is an Open Access article under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                The license is subject to the Beilstein Journal of Nanotechnology terms and conditions: ( http://www.beilstein-journals.org/bjnano)

                History
                Date received : 18 March 2015
                Date accepted : 20 August 2015
                Categories
                Subject: Full Research Paper
                Subject: Nanoscience
                Subject: Nanotechnology

                Keywords: data mining,machine learning,molecular descriptors,poly(amido amine) dendrimers (pamam)
                Data availability:
                Keywords: data mining, machine learning, molecular descriptors, poly(amido amine) dendrimers (pamam)

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