Nano-SAR Development for Bioactivity of Nanoparticles with Considerations of Decision Boundaries

Authors

  • Rong Liu,

    1. Center for the Environmental Implications of Nanotechnology, California Nanosystems Institute, University of California, Los Angeles, CA 90095, USA
    2. Chemical and Biomolecular Engineering Department, University of California, Los Angeles, CA 90095, USA
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  • Robert Rallo,

    1. Center for the Environmental Implications of Nanotechnology, California Nanosystems Institute, University of California, Los Angeles, CA 90095, USA
    2. Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av. Paisos Catalans 26, 43007 Tarragona, Catalunya, Spain
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  • Ralph Weissleder,

    1. Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114 and Harvard Medical School, Boston, MA 02115, USA
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  • Carlos Tassa,

    1. Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114 and Harvard Medical School, Boston, MA 02115, USA
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  • Stanley Shaw,

    1. Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114 and Harvard Medical School, Boston, MA 02115, USA
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  • Yoram Cohen

    Corresponding author
    1. Center for the Environmental Implications of Nanotechnology, California Nanosystems Institute, University of California, Los Angeles, CA 90095, USA
    2. Chemical and Biomolecular Engineering Department, University of California, Los Angeles, CA 90095, USA
    • Center for the Environmental Implications of Nanotechnology, California Nanosystems Institute, University of California, Los Angeles, CA 90095, USA.
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Abstract

The development of classification nano-structure–activity Relationships (nano-SARs) of nanoparticle (NP) bioactivity is presented with the aim of demonstrating the integration of multiparametric toxicity/bioactivity assays to arrive at statistically meaningful class definitions (i.e., bioactivity/inactivity endpoints), as well as the implications of nano-SAR applicability domains and decision boundaries. Nano-SARs are constructed based on a dataset of 44 iron oxide core nanoparticles (NPs), used in molecular imaging and nano-sensing, containing bioactivity profiles for four cell types and four different assays. Class definitions are developed on the basis of ‘hit’ (i.e., significant bioactivity) identification analysis and self-organizing map based consensus clustering; these class definitions enable construction of nano-SARs of a high classification accuracy (>78%) with different NP descriptor combinations that include primary size, spin-lattice and spin-spin relaxivities, and zeta potentials. Analysis of the nano-SAR performance for different class definitions suggests that H4 (i.e., class with at least four hits) is a reasonable endpoint (from a ‘regulatory’ viewpoint) for keeping the level of false negatives (i.e., incorrect labeling of bioactive NPs as inactive) low. The establishment of a quantitative nano-SAR applicability domain is demonstrated, making use of a probability density with the H4 class definition and naive Bayesian classifier (NBC) model (with spin-lattice relaxivity and zeta potential as descriptors). Decision boundaries are determined for the above H4/NBC nano-SAR for different acceptance levels of false negative to false positive predictions, illustrating a practical approach that may assist in regulatory decision making with a consideration of reducing the likelihood of identifying bioactive NPs as being inactive.

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