Biological and environmental surface interactions of nanomaterials: characterization, modeling, and prediction

Advanced Review

Ran Chen, Jim E. Riviere

Published Online: Nov 08 2016

DOI: 10.1002/wnan.1440

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The understanding of nano‐bio interactions is deemed essential in the design, application, and safe handling of nanomaterials. Proper characterization of the intrinsic physicochemical properties, including their size, surface charge, shape, and functionalization, is needed to consider the fate or impact of nanomaterials in biological and environmental systems. The characterizations of their interactions with surrounding chemical species are often hindered by the complexity of biological or environmental systems, and the drastically different surface physicochemical properties among a large population of nanomaterials. The complexity of these interactions is also due to the diverse ligands of different chemical properties present in most biomacromolecules, and multiple conformations they can assume at different conditions to minimize their conformational free energy. Often these interactions are collectively determined by multiple physical or chemical forces, including electrostatic forces, hydrogen bonding, and hydrophobic forces, and calls for multidimensional characterization strategies, both experimentally and computationally. Through these characterizations, the understanding of the roles surface physicochemical properties of nanomaterials and their surface interactions with biomacromolecules can play in their applications in biomedical and environmental fields can be obtained. To quantitatively decipher these physicochemical surface interactions, computational methods, including physical, statistical, and pharmacokinetic models, can be used for either analyses of large amounts of experimental characterization data, or theoretical prediction of the interactions, and consequent biological behavior in the body after administration. These computational methods include molecular dynamics simulation, structure–activity relationship models such as biological surface adsorption index, and physiologically‐based pharmacokinetic models. WIREs Nanomed Nanobiotechnol 2017, 9:e1440. doi: 10.1002/wnan.1440 This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials

Results of polynomial biological surface adsorption index modeling for select nanoparticles based on adsorption data of 34 pesticides from C₆₀. 20% of the total observations were randomly chosen as a testing set, the rest were used to build the model (a). ANN improves the predictive capability of the model with lower R² values, comparison between multivariate linear regression (b) and ANN modeling (c). ANN, artificial neural network. (Reprinted with permission from Ref . Copyright 2016 Taylor & Francis)

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(a) The five nanodescriptors [r, p, a, b, v] measured by the biological surface adsorption index approach, representing the five major molecular interactions in nanoparticle adsorption processes. Positive values indicate that the nanoparticle surfaces have stronger interaction potentials with the chemicals or biomolecules, and negative values indicate the molecular interactions are stronger in the aqueous phase. (b) Predicted versus measured log k values for MWNT–OH (R² = 0.86, = 0.75, = 0.79). (c) Nanoparticle scattering plot by two principal components. (Reprinted with permission from Ref . Copyright 2014 American Chemical Society; Ref . Copyright 2011 American Chemical Society)

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(a) Eight binding sites of C₆₀(OH)₂₀ on tubulin dimer (R subunit on the left side and β subunit on the right side) identified from docking simulations. The GTP/GDP binding E‐site in the β subunit and the N‐site in the R subunit are indicated by arrows and highlighted in blue. No binding was observed near the E‐site. GTP: Guanosine‐5′‐triphosphate, GDP: guanosine diphosphate. (b) Model for molecular dynamics simulation of adsorption of small molecules onto the surface of multiwall carbon nanotubes. The relatively large multiwall nanotubes are modeled as several flat graphene sheets, which, through periodic boundary conditions, form an effectively infinite surface in the xy plane. The atoms of the graphene sheets and an exemplary adsorbate (3‐bromophenol) are shown as spheres, with hydrogen, carbon, oxygen, and bromine atoms shown in white, green, red, and crimson. Here, for clarity, the water is indicated by a translucent cyan surface; however, in the simulations, water molecules were represented explicitly. (c) Calculated free energy as a function of distance between the first graphene sheet and the center of mass of the adsorbate (the coordinate z) for exemplary adsorbates. (d) Comparison of the logarithm of the adsorption equilibrium constant measured in experiment and the same quantity calculated in simulation for all 29 adsorbates. The full names of the chemicals can be found in the referenced publication. (Reprinted with permission from Ref . Copyright 2011 American Chemical Society; Ref . Copyright 2015 American Chemical Society)

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(a) Selectivity of the proteins bound to positively charged (+AuNP) versus negatively charged (−AuNP) particles identified using MS. Venn diagrams show proteins identified in the protein corona around +AuNP and −AuNP from (left) normal OSE cell lysates and (right) malignant OV167 cell lysates. The figure clearly depicts the preferential enrichment of low abundance proteins by engineered nanoparticles that were not detectable in the lysates by proteomics analysis. (b) ITC Titration of HSA into solutions of 70 nm nanoparticles with 50:50 (left) and 85:15 (right) NIPAM/BAM in 10 mM Hepes/NaOH, 0.15 M NaCl, 1 mM EDTA, pH 7.5, is shown. (Upper) Raw data. (Lower) Integrated heats in each injection versus molar ratio of protein to nanoparticle together with a fit using a one site binding model. (Inset) Size comparison of albumin and particles of 70 or 200 nm diameter. AuNP, gold nanoparticle; HSA, human serum albumin; ITC, isothermal titration calorimetry; MS, mass spectrometry; NIPAM/BAM, N‐isopropylacrylamide: N‐tert‐butylacrylamide copolymer; OSE, ovarian surface epithelial. (Reprinted with permission from Ref . Copyright 2012 Plos One; Ref . Copyright 2007 United States National Academy of Sciences)

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(a) Raman spectra of algal exudate‐graphene/graphene oxide system (1×: stock, 1/10×: ten‐time diluted). (b) Raman spectra of AuMN‐DTTC and control probes in water. AuMN‐DTTC has a distinctive SERS signature, which is absent in the control probes. (c) FTIR Spectra of BSA (upper spectra) and BSA‐GNPs (lower spectra). (d) LSPR spectra of cit‐AuNPs in the absence and presence of different lysozyme (LYZ) concentrations, as gathered in the figure, in 0.01 M sodium phosphate buffer at pH 7.4 (left). Effect of LYZ concentration on the maximum wavelength of the LSPR band of citAuNPs (right). The cit‐AuNP concentration is 2 nM. AuMN, gold nanoparticles deposited onto (dextran‐coated) superparamagnetic iron oxide nanoparticles; AuNP, gold nanoparticle; BSA, bovine serum albumin; DTTC, 3,3'‐diethylthiatricarbocyanine iodide; FTIR, fourier transform infrared spectroscopy; LSPR, localized surface plasmon resonance; SERS, surface‐enhanced Raman spectroscopy. (Reprinted with permission from: Ref . Copyright 2013 Nature Publishing Group; Ref . Copyright 2001 American Chemical Society; Ref . Copyright 2016 Elsevier; Ref . Copyright 2014 American Chemical Society)

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(a, b) Fluorescence of HSA quenched by AuNCs, showing concentration‐dependent quenching rate. (c, d) Representative fluorescence emission spectra of noncross‐linked FRET‐based nanoparticles in the presence of human plasma, showing time‐dependent FRET efficiency indicating the release of drug load. AuNC, gold nanocluster; DCMN1 (are the names given to the nano complexes in the original paper, please refer to the original publication for structures of those nanomaterials); FLU, fluorescence; FRET, Förster resonance energy transfer; HSA, human serum albumin; NCMN1 (are the names given to the nano complexes in the original paper, please refer to the original publication for structures of those nanomaterials). (Reprinted with permission from: Ref . Copyright 2011 John Wiley and Sons; Ref . Copyright 2012 American Chemical Society)

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(a) DLS measurements of (top) G1/fullerenol and (bottom) G4/fullerenol complexes. An abrupt increase in the hydrodynamic size of the complexes was observed for both G1/fullerenol and G4/fullerenol mixtures at a ratio of number of primary amines of dendrimer/fullerenol ≈2. (b) Hydrodynamic radius of bAgNP– lysozyme versus temperature, displaying a hysteresis between heating and cooling (top) and hydrodynamic radius of bAgNP–lysozyme versus time (bottom). (c) Time evolutions of the hydrodynamic diameters of hIAPP (19 × 10⁻⁶ M). (d) Normalized number‐weighted particle size distributions of aminated silica NPs in Tris buffer. (e) Characterization of Tf‐coated NPs in PBS at room temperature. Differential centrifugal sedimentation results for: (left) 100 nm PSOSO3H NPs and [email protected] NPs; and (right) 100 nm PSCOOH NPs and [email protected] NPs. AgNP, silver NP; DLS, dynamic light scattering; hIAPP, human islet amyloid polypeptide; NP, nanoparticle; TRPS, tunable resistive pulse sensing. (Reprinted with permission from: Ref . Copyright 2012 American Chemical Society; Ref . Copyright 2014 The Royal Society of Chemistry; Ref . Copyright 2016 John Wiley and Sons; Ref . Copyright 2012 Plos One)

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Illustration of the physicochemical properties of nanoparticles (shape, size, density, charge solubility, and hydrophobicity), their defects, surface modifications, decorations, or formation of nanoscale complexes. Also indicated in the illustration is their behavior in aqueous suspension, including aggregation and dissolution, as well as interactions with different molecules and native species in physiological or environmental surroundings. These interactions usually affect their surface modifications, formation of biocorona, and artificial molecular loading for delivery. The interactions between nanomaterials and biological or environmental molecules can all be characterized and predicted using both experimental and computational tools. BSAI, biological surface adsorption index; DLS, dynamic light scattering; FRET, Förster resonance energy transfer; FS, fluorescence spectroscopy; ICP‐MS, Inductively coupled plasma mass spectrometry; ITC, isothermal titration calorimetry; MD, molecular dynamics; SPR, surface plasmon resonance; STM, scanning tunneling microscope

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