Mapping the surface adsorption forces of nanomaterials in biological systems

Predicting adsorption of organic compounds onto graphene and black phosphorus by molecular dynamics and machine learning

With an increase in production and application of various engineering nanomaterials (ENMs), they will inevitably be released into the environment. Adsorption of various organic chemicals onto ENMs will impact on their environmental behavior and toxicology. It is unrealistic to experimentally determine adsorption equilibrium constants (K) for the vast number of organics and ENMs due to high cost in expenditure and time. Herein, appropriate molecular dynamics (MD) methods were evaluated and selected by comparing experimental K values of seven organics adsorbed onto graphene with the MD-calculated ones. Machine learning (ML) models on K of organics adsorption onto graphene and black phosphorus nanomaterials were constructed based on a benchmark data set from the MD simulations. Lasso models based on Mordred descriptors outperformed ML models built by support vector machine, random forest, k-nearest neighbor, and gradient boosting decision tree, in terms of cross-validation coefficients (Q2 > 0.90). The Lasso models also outperformed conventional poly-parameter linear free energy relationship models for predicting logK. Compared with previous models, the Lasso models considered more compounds with different functional groups and thus have broader applicability domains. This study provides a promising way to fill the data gap in logK for chemicals adsorbed onto the ENMs.

Basic and advanced spectrometric methods for complete nanoparticles characterization in bio/eco systems: current status and future prospects

The use of engineered nanoparticles in the environment and human life has increased in the last 20 years. The risk assessment concerning application of nanomaterials in biological systems requires their thorough characterization. Understanding the correlations between physicochemical properties of nanoparticles concerning not only the size, particle size distribution, number concentration, degree of aggregation, or agglomeration but also solubility, stability, binding affinity, surface activity, chemical composition, and nanoparticle synthesis yield allows their reliable characterization. Thus, to find the structure-function/property relationship of nanoparticles, multifaceted characterization approach based on more than one analytical technique is required. On the other hand, the increasing demand for identification and characterization of nanomaterials has contributed to the continuous development of spectrometric techniques which enables for their qualitative and quantitative analysis in complex matrices giving reproducible and reliable results. This review is aimed at providing a discussion concerning four main aspects of nanoparticle characterization: nanoparticle synthesis yield, particle size and number concentration, elemental and isotopic composition of nanoparticles, and their surface properties. The conventional and non-conventional spectrometric techniques such as spectrophotometry UV-Vis, mass spectrometric techniques working in conventional and single-particle mode, or those based on optical emission detection systems are described with special emphasis paid on their advantages and drawbacks. The application and recent advances of these methods are also comprehensively reviewed and critically discussed.

Interfacial Liquid Water on Graphite, Graphene, and 2D Materials

The optical, electronic, and mechanical properties of graphite, few-layer, and two-dimensional (2D) materials have prompted a considerable number of applications. Biosensing, energy storage, and water desalination illustrate applications that require a molecular-scale understanding of the interfacial water structure on 2D materials. This review introduces the most recent experimental and theoretical advances on the structure of interfacial liquid water on graphite-like and 2D materials surfaces. On pristine conditions, atomic-scale resolution experiments revealed the existence of 1-3 hydration layers. Those layers were separated by ∼0.3 nm. The experimental data were supported by molecular dynamics simulations. However, under standard working conditions, atomic-scale resolution experiments revealed the presence of 2-3 hydrocarbon layers. Those layers were separated by ∼0.5 nm. Linear alkanes were the dominant molecular specie within the hydrocarbon layers. Paradoxically, the interface of an aged 2D material surface immersed in water does not have water molecules on its vicinity. Free-energy considerations favored the replacement of water by alkanes.

The influence of nanotopography on cell behaviour through interactions with the extracellular matrix – A review

Nanotopography presents an effective physical approach for biomaterial cell manipulation mediated through material-extracellular matrix interactions. The extracellular matrix that exists in the cellular microenvironment is crucial for guiding cell behaviours, such as determination of integrin ligation and interaction with growth factors. These interactions with the extracellular matrix regulate downstream mechanotransductive pathways, such as rearrangements in the cytoskeleton and activation of signal cascades. Protein adsorption onto nanotopography strongly influences the conformation and distribution density of extracellular matrix and, therefore, subsequent cell responses. In this review, we first discuss the interactive mechanisms of protein physical adsorption on nanotopography. Secondly, we summarise advances in creating nanotopographical features to instruct desired cell behaviours. Lastly, we focus on the cellular mechanotransductive pathways initiated by nanotopography. This review provides an overview of the current state-of-the-art designs of nanotopography aiming to provide better biomedical materials for the future.

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A comprehensive overview of nanotopography fabrication, and nanotopography regulates various cell behaviours.

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The interactive physical adsorption between nanotopography and extracellular matrix.

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Nanotopography initiates the cellular mechanotransductive pathways and downstream signalling cascades.

Organic contaminants and atmospheric nitrogen at the graphene-water interface: a simulation study

Ordered nanoscale patterns have been observed by atomic force microscopy at graphene-water and graphite-water interfaces. The two dominant explanations for these patterns are that (i) they consist of self-assembled organic contaminants or (ii) they are dense layers formed from atmospheric gases (especially nitrogen). Here we apply molecular dynamics simulations to study the behavior of dinitrogen and possible organic contaminants at the graphene-water interface. Despite the high concentration of N₂ in ambient air, we find that its expected occupancy at the graphene-water interface is quite low. Although dense (disordered) aggregates of dinitrogen have been observed in previous simulations, our results suggest that they are stable only in the presence of supersaturated aqueous N₂ solutions and dissipate rapidly when they coexist with nitrogen gas near atmospheric pressure. On the other hand, although heavy alkanes are present at only trace concentrations (micrograms per cubic meter) in typical indoor air, we predict that such concentrations can be sufficient to form ordered monolayers that cover the graphene-water interface. For octadecane, grand canonical Monte Carlo suggests nucleation and growth of monolayers above an ambient concentration near 6 μg m^-3, which is less than some literature values for indoor air. The thermodynamics of the formation of these alkane monolayers includes contributions from the hydration free-energy (unfavorable), the free-energy of adsorption to the graphene-water interface (highly favorable), and integration into the alkane monolayer phase (highly favorable). Furthermore, the peak-to-peak distances in AFM force profiles perpendicular to the interface (0.43-0.53 nm), agree with the distances calculated in simulations for overlayers of alkane-like molecules, but not for molecules such as N₂, water, or aromatics. Taken together, these results suggest that ordered domains observed on graphene, graphite, and other hydrophobic materials in water are consistent with alkane-like molecules occupying the interface.

Extraction of Chlorobenzenes and PCBs from Water by ZnO Nanoparticles

Metal oxide nanoparticles have great potential for selective adsorption and catalytic degradation of contaminants from aqueous solutions. In this study, we employ mass spectrometry and molecular dynamics simulations to better understand the chemical and physical mechanisms determining the affinity of chlorobenzenes and polychlorinated biphenyls (PCBs) for zinc oxide nanoparticles (ZnO NPs). The experiments and simulations both demonstrate that the adsorption coefficients for chlorobenzenes increase steadily with the number of chlorine atoms, while, for PCBs, the relation is more complex. The simulations link this complexity to chlorine atoms at ortho positions hindering coplanar conformations. For a given number of chlorine atoms, the simulations predict decreasing adsorption affinity with increasing numbers of ortho substitutions. Consequently, the simulations predict that some of the highest adsorption affinities for ZnO NPs are exhibited by dioxin-like PCBs, suggesting the possibility of selective sequestration of these most acutely toxic PCBs. Remarkably, the experiments show that the PCB adsorption coefficients of ZnO NPs with diameters ≤ 80 nm exceed those of a soil sample by 5–7 orders of magnitude, meaning that a single gram of ZnO NPs could sequester low levels of PCB contamination from as much as a ton of soil.

Mechanistic insights into and modeling the effects of relative humidity on low-concentration VOCs adsorption on hyper-cross-linked polymeric resin by inverse gas chromatography

Water vapor is very common in contaminated streams, which has a great influence on the adsorption of low-concentration volatile organic compounds (VOCs) due to the competition between water and VOCs. Understanding adsorption mechanisms and predicting adsorption of VOCs under different relative humidity (RH) are of great importance to design effective adsorption unit. In this study, we comprehensively investigated the effects of RH on the surface properties of hyper-cross-linked polymeric resin (HPR) and adsorption of 18 VOCs at low concentration on HPR under five levels of RH using inverse gas chromatography (IGC). Further, a promising RH-dependent poly-parameter linear free energy relationships (PP-LFERs) model was developed. It was found that water vapor caused the decrease of surface free energy (γ_s^t) of HPR due to the occupation of active sites by water molecules, resulting in the decrease of adsorption partition coefficients (K). Moreover, the γ_s^t could accurately quantify the effects of RH on the surface properties of HPR. Therefore, the RH-dependent PP-LFERs model was established by correlating RH and γ_s^t. The developed model overcame the limited predictive ability of existing models only under a specific RH level, and excellently predicted the lnK values of VOCs (R² = 0.944, RMSEt = 0.36 and RMSEv = 0.47) under various RH.

Automated Grouping of Nanomaterials and Read-Across Prediction of Their Adverse Effects Based on Mathematical Optimization

In this study, a computational workflow is presented for grouping engineered nanomaterials (ENMs) and for predicting their toxicity-related end points. A mixed integer-linear optimization program (MILP) problem is formulated, which automatically filters out the noisy variables, defines the grouping boundaries, and develops specific to each group predictive models. The method is extended to the multidimensional space, by considering the ENM characterization categories (e.g., biological, physicochemical, biokinetics, image etc.) as different dimensions. The performance of the proposed method is illustrated through the application to benchmark data sets and comparison with alternative predictive modeling approaches. The trained models using the above data sets were made publicly available through a user-friendly web service.

An environmental ecocorona influences the formation and evolution of the biological corona on the surface of single-walled carbon nanotubes

Nanomaterials (NMs) taken up from the environment carry a complex ecocorona consisting of dissolved organic matter. An ecocorona is assumed to influence the interactions between NMs and endogenous biomolecules and consequently affects the formation of a biological corona (biocorona) and the biological fate of the NMs. This study shows that biomolecules in fish plasma attach immediately (within <5 min) to the surface of SWCNTs and the evolution of the biocorona is a size dependent phenomenon. Quantitative proteomics data revealed that the nanotube size also influences the plasma protein composition on the surface of SWCNTs. The presence of a pre-attached ecocorona on the surface of SWCNTs eliminated the influence of nanotube size on the formation and evolution of the biocorona. Over time, endogenous biomolecules from the plasma partially replaced the pre-attached ecocorona as measured using a fluorescently labelled ecocorona. The presence of an ecocorona offers a unique surface composition to each nanotube. This suggests that understanding the biological fate of NMs taken up from the environment by organisms to support the environmental risk assessment of NMs is a challenging task because each NM may have a unique surface composition in the body of an organism.

Non-enzymatic electrochemical glucose sensing by Cu2O octahedrons: elucidating the protein adsorption signature

Developing an electrochemical biosensor based on Cu₂O octahedrons for rapid, sensitive and highly selective detection of glucose in real samples with an unprecedented analysis of their protein adsorption signature.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).

Adsorption of low-concentration VOCs on various adsorbents: Correlating partition coefficient with surface energy of adsorbent

Accurately evaluating the adsorption properties of various adsorbents by some parameter is of great significance to select an appropriate adsorbent and remove volatile organic compounds (VOCs) efficiently. In this study, we successfully found a new parameter as a common standard in selecting adsorbents. Six classical adsorbents containing three carbon materials and three porous polymeric resins were used, and their surface energy (γ_s^t) and corresponding gas-solid partition coefficients (K) of eleven VOCs were measured by inverse gas chromatography (IGC) at three different column temperatures of 343 K(or 353 K), 373 K and 403 K. Then, these values at 303 K were calculated according to the linear relationship between lnK and 1/T. It was found that surface energy was significantly correlated with K values for a specific VOC, and could be used as a common standard to well evaluate the adsorption properties of various adsorbents. Furthermore, we employed it to develop a model for predicting the adsorption properties of low-concentration VOCs on various adsorbents at 303 K. The developed model exhibited an excellent predictive ability by external validation. Moreover, the model showed wide applicability and predicted the lnK values of VOCs at 373 K and 403 K in R² of 0.910 and 0.889.

Adaptive methodology to determine hydrophobicity of nanomaterials in situ

The hydrophobicity of nanoparticles (NPs) is a key property determining environmental fate, biological partitioning and toxicity. However, methods to characterize surface hydrophobicity are not uniformly applied to NPs and cannot quantify surface changes in complex environments. Existing methods designed to evaluate the hydrophobicity of bulk solids, chemicals, and proteins have significant limitations when applied to NPs. In this study, we modified and evaluated two methods to determine the hydrophobicity of NPs, hydrophobic interaction chromatography (HIC) and dye adsorption, and compared them to the standard octanol-water partitioning protocol for chemicals. Gold, copper oxide, silica, and amine-functionalized silica NPs were used to evaluate methods based on their applicability to NPs that agglomerate and have surface coatings. The octanol water partitioning and HIC methods both measured Au NPs as hydrophilic, but despite having a small size and stable suspension, NPs could not be fully recovered from the HIC column. For the dye adsorption method, hydrophobic (Rose Bengal) and hydrophilic (Nile Blue) dyes were adsorbed to the NP surface, and linear isotherm parameters were used as a metric for hydrophobicity. CuO was determined to be slightly hydrophilic, while SiO₂ was hydrophilic and Ami-SiO₂ was hydrophobic. The advantages and limitations of each method are discussed, and the dye adsorption method is recommended as the most suitable for application across broad classes of nanomaterials. The dye assay method was further used to measure changes in the surface hydrophobicity of TiO₂ NPs after being suspended in natural water collected from the Alsea Rivers watershed in Oregon. TiO₂ NPs adsorbed Rose Bengal when suspended in ultrapure water, but adsorbed Nile Blue after being incubated in natural water samples, demonstrating a shift from hydrophobic to hydrophilic properties on the outer surface. The dye adsorption method can be applied to characterize surface hydrophobicity of NPs and quantify environmental transformations, potentially improving environmental fate models.

Nanotechnology-Based Strategies to Develop New Anticancer Therapies

The blooming of nanotechnology has made available a limitless landscape of solutions responding to crucial issues in many fields and, nowadays, a wide choice of nanotechnology-based strategies can be adopted to circumvent the limitations of conventional therapies for cancer. Herein, the current stage of nanotechnological applications for cancer management is summarized encompassing the core nanomaterials as well as the available chemical-physical approaches for their surface functionalization and drug ligands as possible therapeutic agents. The use of nanomaterials as vehicles to delivery various therapeutic substances is reported emphasizing advantages, such as the high drug loading, the enhancement of the pay-load half-life and bioavailability. Particular attention was dedicated to highlight the importance of nanomaterial intrinsic features. Indeed, the ability of combining the properties of the transported drug with the ones of the nano-sized carrier can lead to multifunctional theranostic tools. In this view, fluorescence of carbon quantum dots, optical properties of gold nanoparticle and superparamagnetism of iron oxide nanoparticles, are fundamental examples. Furthermore, smart anticancer devices can be developed by conjugating enzymes to nanoparticles, as in the case of bovine serum amine oxidase (BSAO) and gold nanoparticles. The present review is aimed at providing an overall vision on nanotechnological strategies to face the threat of human cancer, comprising opportunities and challenges.

The Nanomaterial Metabolite Corona Determined Using a Quantitative Metabolomics Approach: A Pilot Study

Nanomaterials (NMs) are promptly coated with biomolecules in biological systems leading to the formation of the so-called corona. To date, research has predominantly focused on the protein corona and how it affects NM uptake, distribution, and bioactivity by conferring a biological identity to NMs enabling interactions with receptors to mediate cellular responses. Thus, protein corona studies are now integral to nanosafety assessment. However, a larger class of molecules, the metabolites, which are orders of magnitude smaller than proteins (<1000 Da) and regulate metabolic pathways, has been largely overlooked. This hampers the understanding of the bio-nano interface, development of computational predictions of corona formation, and investigations into uptake or toxicity at the cellular level, including identification of molecular initiating events triggering adverse outcome pathways. Here, a capillary electrophoresis-mass spectrometry based metabolomics approach reveals that pure polar ionogenic metabolite standards differentially adsorb to a range of 6 NMs (SiO₂ , 3 TiO₂ with different surface chemistries, and naïve and carboxylated polystyrene NMs). The metabolite corona composition is quantitatively compared using protein-free and complete plasma samples, revealing that proteins in samples significantly change the composition of the metabolite corona. This key finding provides the basis to include the metabolite corona in future nanosafety endeavors.

Understanding and Controlling Food Protein Structure and Function in Foods: Perspectives from Experiments and Computer Simulations

The structure and interactions of proteins play a critical role in determining the quality attributes of many foods, beverages, and pharmaceutical products. Incorporating a multiscale understanding of the structure-function relationships of proteins can provide greater insight into, and control of, the relevant processes at play. Combining data from experimental measurements, human sensory panels, and computer simulations through machine learning allows the construction of statistical models relating nanoscale properties of proteins to the physicochemical properties, physiological outcomes, and tastes of foods. This review highlights several examples of advanced computer simulations at molecular, mesoscale, and multiscale levels that shed light on the mechanisms at play in foods, thereby facilitating their control. It includes a practical simulation toolbox for those new to in silico modeling.

Practices and Trends of Machine Learning Application in Nanotoxicology

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

Diagnosis of Metal Hypersensitivity in Total Knee Arthroplasty: A Case Report

Delayed type hypersensitivity (DTH) reactions are considered infrequent complications in arthroplasty, but have been recognized to be associated with devastating morbidity and substantial decrease in quality of life of affected patients. Chronic inflammation of artificial joints and associated loss of peri-implant bone often require revision surgery. Methods for the diagnosis of implant-related DTH are available but infrequently considered to the full extent. Sequential diagnostics based on exclusion of septic complications, local and systemic metal level determination, lymphocyte transformation testing (LTT), and local T cell subset analysis are required for an unequivocal DTH diagnosis. Here, we report on a patient with a history of chronic rheumatoid arthritis and an unfavorable outcome of unilateral knee arthroplasty. This case illustrates pitfalls and difficulties in the course of recurrent inflammation following joint replacement. In the early course, suspicion of low-grade bacterial infection led to three two-stage revisions. Afterwards, the joint was proven to be sterile. However, metal level quantification revealed release of especially cobalt and chromium from the joint, LTT indicated persisting cobalt and nickel sensitization and subset analysis of T cells from the synovium suggested DTH as a root cause for the inflammatory symptoms. This report aims to recommend the depicted diagnostic algorithm as an adequate tool for future DTH detection. Yet, systemic to local subset ratios for effector memory and regulatory T cells should be derived from sufficient patient numbers to establish it as a diagnostic marker. Moreover, future prospects regarding implant-related DTH diagnostics are discussed. Therapeutic options for the portrayed patient are proposed, considering pharmaceutical, cell-therapeutic and surgical aspects. Patients who experience peri-implant inflammation but do not have obvious mechanical or infectious problems remain a diagnostic challenge and are at high risk of being treated inadequately. Since potentially sensitizing materials are regularly used in arthroplasty, it is essential to detect cases of acute DTH-derived inflammation of an artificial joint at early postoperative stages. This would reduce the severity of inflammation-related long-term consequences for affected patients and may avoid unnecessary revision surgery.

Externally predictive quantum-mechanical models for the adsorption of aromatic organic compounds by graphene-oxide nanomaterials

Graphene oxide is most often chosen as an alternative to graphene in the applications of carbon-based nanomaterials where adsorption is the primary process. However, its adsorption properties are poorly understood. The existing reports on the adsorption mechanism of graphene oxide rely on the linear free-energy/solvation-energy relationship (LFER/LSER) models. This computational work explores the role of quantum mechanical descriptors in the adsorption of aromatic organic compounds by graphene-oxide. For this, externally predictive quantitative models based on quantum-mechanical descriptors are developed and compared with the existing LSERs for the prediction of adsorption coefficients of organic compounds at three different adsorbate concentrations. The predictivity of the models is assessed using an external prediction set of compounds not used for developing the models. Notably, the mean polarizability, but originating from the quantum mechanical exchange interactions (between electrons of parallel spin), is found to be the most significant factor in driving the adsorption on graphene oxide. The present work also proposes quantum-mechanical-LSER models based on a combination of quantum-mechanical and LSER descriptors, which are in fact found to be equally predictive as the existing LSERs. The quantum-mechanical models proposed in this work are further utilized for the prediction of adsorption coefficients of aliphatic compounds.

Improving our understanding of metal implant failures: Multiscale chemical imaging of exogenous metals in ex-vivo biological tissues

Biological exposures to micro- and nano-scale exogenous metal particles generated as a consequence of in-service degradation of orthopaedic prosthetics can result in severe adverse tissues reactions. However, individual reactions are highly variable and are not easily predicted, due to in part a lack of understanding of the speciation of the metal-stimuli which dictates cellular interactions and toxicity. Investigating the chemistry of implant derived metallic particles in biological tissue samples is complicated by small feature sizes, low concentrations and often a heterogeneous speciation and distribution. These challenges were addressed by developing a multi-scale two-dimensional X-ray absorption spectroscopic (XAS) mapping approach to discriminate sub-micron changes in particulate chemistry within ex-vivo tissues associated with failed CoCrMo total hip replacements (THRs). As a result, in the context of THRs, we demonstrate much greater variation in Cr chemistry within tissues compared with previous reports. Cr compounds including phosphate, hydroxide, oxide, metal and organic complexes were observed and correlated with Co and Mo distributions. This variability may help explain the lack of agreement between biological responses observed in experimental exposure models and clinical outcomes. The multi-scale 2D XAS mapping approach presents an essential tool in discriminating the chemistry in dilute biological systems where speciation heterogeneity is expected. SIGNIFICANCE: Metal implants are routinely used in healthcare but may fail following degradation in the body. Although specific implants can be identified as 'high-risk', our analysis of failures is limited by a lack of understanding of the chemistry of implant metals within the peri-prosthetic milieu. A new approach to identify the speciation and variability in speciation at sub-micron resolution, of dilute exogenous metals within biological tissues is reported; applied to understanding the failure of metallic (CoCrMo) total-hip-replacements widely used in orthopedic surgery. Much greater variation in Cr chemistry was observed compared with previous reports and included phosphate, hydroxide, oxide, metal and organic complexes. This variability may explain lack of agreement between biological responses observed in experimental exposure models and clinical outcomes.

Fe-Doped ZnO nanoparticle toxicity: assessment by a new generation of nanodescriptors

In the search for novel tools to combat cancer, nanoparticles (NPs) have attracted a lot of attention. Recently, the controlled release of cancer-cell-killing metal ions from doped NPs has shown promise, but fine tuning of dissolution kinetics is required to ensure specificity and minimize undesirable toxic side-effects. Theoretical tools to help in reaching a proper understanding and finally be able to control the dissolution kinetics by NP design have not been available until now. Here, we present a novel set of true nanodescriptors to analyze the charge distribution, the effect of doping and surface coating of whole metal oxide NP structures. The polarizable model of oxygen atoms enables light to be shed on the charge distribution on the NP surface, allowing the in detail study of the factors influencing the release of metal ions from NPs. The descriptors and their capabilities are demonstrated on a Fe-doped ZnO nanoparticle system, a system with practical outlook and available experimental data.

Structural and Kinetic Visualization of the Protein Corona on Bioceramic Nanoparticles

Bioceramic nanoparticles exhibit excellent features that enable them to function as an ideal material for hard tissue engineering. However, to fully understand their behavior, it is of crucial importance to understand their behavior within the fluids of the human body. To achieve this goal, we have studied the interaction between hydroxyapatite nanorods (HA) and bovine serum albumin (BSA). First, we describe the surface morphology of the nanoparticle. Then, the main characteristics of the physiological interplay of BSA and the hydroxyapatite nanoparticle are presented by using a battery of techniques: ITC, zeta potential, UV-vis, fluorescence, and CD. Experimental data was analyzed by developing specific approaches to determining important parameters such as rates, affinities, and stochiometries of protein associated with the nanoparticles. ITC has been confirmed as a powerful technique for determining the affinity, binding, and thermodynamics of BSA-nanoparticle interactions. Careful quantitative assessment of the kinetic properties of the adsorption were revealed by UV-vis and fluorescence measurements. Finally, CD measurements highlight the important role of protein flexibility in these kinds of systems.

Current Application of Capillary Electrophoresis in Nanomaterial Characterisation and Its Potential to Characterise the Protein and Small Molecule Corona

Due to the increasing use and production of nanomaterials (NMs), the ability to characterise their physical/chemical properties quickly and reliably has never been so important. Proper characterisation allows a thorough understanding of the material and its stability, and is critical to establishing dose-response curves to ascertain risks to human and environmental health. Traditionally, methods such as Transmission Electron Microscopy (TEM), Field Flow Fractionation (FFF) and Dynamic Light Scattering (DLS) have been favoured for size characterisation, due to their wide-availability and well-established protocols. Capillary Electrophoresis (CE) offers a faster and more cost-effective solution for complex dispersions including polydisperse or non-spherical NMs. CE has been used to rapidly separate NMs of varying sizes, shapes, surface modifications and compositions. This review will discuss the literature surrounding the CE separation techniques, detection and NM characteristics used for the analysis of a wide range of NMs. The potential of combining CE with mass spectrometry (CE-MS) will also be explored to further expand the characterisation of NMs, including the layer of biomolecules adsorbed to the surface of NMs in biological or environmental compartments, termed the acquired biomolecule corona. CE offers the opportunity to uncover new/poorly characterised low abundance and polar protein classes due to the high ionisation efficiency of CE-MS. Furthermore, the possibility of using CE-MS to characterise the poorly researched small molecule interactions within the NM corona is discussed.

Experimental characterization and simulation of amino acid and peptide interactions with inorganic materials

Inspired by nature, many applications and new materials benefit from the interplay of inorganic materials and biomolecules. A fundamental understanding of complex organic-inorganic interactions would improve the controlled production of nanomaterials and biosensors to the development of biocompatible implants for the human body. Although widely exploited in applications, the interaction of amino acids and peptides with most inorganic surfaces is not fully understood. To date, precisely characterizing complex surfaces of inorganic materials and analyzing surface-biomolecule interactions remain challenging both experimentally and computationally. This article reviews several approaches to characterizing biomolecule-surface interactions and illustrates the advantages and disadvantages of the methods presented. First, we explain how the adsorption mechanism of amino acids/peptides to inorganic surfaces can be determined and how thermodynamic and kinetic process constants can be obtained. Second, we demonstrate how this data can be used to develop models for peptide-surface interactions. The understanding and simulation of such interactions constitute a basis for developing molecules with high affinity binding domains in proteins for bioprocess engineering and future biomedical technologies.

Unveiling Adsorption Mechanisms of Organic Pollutants onto Carbon Nanomaterials by Density Functional Theory Computations and Linear Free Energy Relationship Modeling

Predicting adsorption of organic pollutants onto carbon nanomaterials (CNMs) and understanding the adsorption mechanisms are of great importance to assess the environmental behavior and ecological risks of organic pollutants and CNMs. By means of density functional theory (DFT) computations, we investigated the adsorption of 38 organic molecules (aliphatic hydrocarbons, benzene and its derivatives, and polycyclic aromatic hydrocarbons) onto pristine graphene in both gaseous and aqueous phases. Polyparameter linear free energy relationships (pp-LFERs) were developed, which can be employed to predict adsorption energies of aliphatic and aromatic hydrocarbons on graphene. Based on the pp-LFERs, contributions of different interactions to the overall adsorption were estimated. As suggested by the pp-LFERs, the gaseous adsorption energies are mainly governed by dispersion and electrostatic interactions, while the aqueous adsorption energies are mainly determined by dispersion and hydrophobic interactions. It was also revealed that curvature of single-walled carbon nanotubes (SWNTs) exhibits more significant effects than the electronic properties (metallic or semiconducting) on gaseous adsorption energies, and graphene has stronger adsorption abilities than SWNTs. The developed models may pave a promising way for predicting adsorption of environmental chemicals onto CNMs with in silico techniques.

Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory

The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: (SWCNT-VDAC2-Danio rerio) > (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio rerio when compared with VDAC1-Mus musculus and VDAC1-Homo sapiens (p < 0.05). In addition, a significant correlation (0.66 > r² > 0.97) was observed between n-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73–98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R² of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available at https://doi.org/10.6084/m9.figshare.4802320.v2.

NanoEHS beyond Toxicity - Focusing on Biocorona

The first phase of environmental health and safety of nanomaterials (nanoEHS) studies has been mainly focused on evidence-based investigations that probe the impact of nanoparticles, nanomaterials and nano-enabled products on biological and ecological systems. The integration of multiple disciplines, including colloidal science, nanomaterial science, chemistry, toxicology/immunology and environmental science, is necessary to understand the implications of nanotechnology for both human health and the environment. While strides have been made in connecting the physicochemical properties of nanomaterials with their hazard potential in tiered models, fundamental understanding of nano-biomolecular interactions and their implications for nanoEHS is largely absent from the literature. Research on nano-biomolecular interactions within the context of natural systems not only provides important clues for deciphering nanotoxicity and nanoparticle-induced pathology, but also presents vast new opportunities for screening beneficial material properties and designing greener products from bottom up. This review highlights new opportunities concerning nano-biomolecular interactions beyond the scope of toxicity.

Biological Surface Adsorption Index of Nanomaterials: Modelling Surface Interactions of Nanomaterials with Biomolecules

Quantitative analysis of the interactions between nanomaterials and their surrounding environment is crucial for safety evaluation in the application of nanotechnology as well as its development and standardization. In this chapter, we demonstrate the importance of the adsorption of surrounding molecules onto the surface of nanomaterials by forming biocorona and thus impact the bio-identity and fate of those materials. We illustrate the key factors including various physical forces in determining the interaction happening at bio-nano interfaces. We further discuss the mathematical endeavors in explaining and predicting the adsorption phenomena, and propose a new statistics-based surface adsorption model, the Biological Surface Adsorption Index (BSAI), to quantitatively analyze the interaction profile of surface adsorption of a large group of small organic molecules onto nanomaterials with varying surface physicochemical properties, first employing five descriptors representing the surface energy profile of the nanomaterials, then further incorporating traditional semi-empirical adsorption models to address concentration effects of solutes. These Advancements in surface adsorption modelling showed a promising development in the application of quantitative predictive models in biological applications, nanomedicine, and environmental safety assessment of nanomaterials.

Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity

Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.

Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project

The particular properties of nanomaterials have led to their rapidly increasing use in diverse fields of application. However, safety assessment is not keeping pace and there are still gaps in the understanding of their hazards. Computational models predicting nanotoxicity, such as (quantitative) structure-activity relationships ((Q)SARs), can contribute to safety evaluation, in line with general efforts to apply alternative methods in chemical risk assessment. Their development is highly dependent on the availability of reliable and high quality experimental data, both regarding the compounds' properties as well as the measured toxic effects. In particular, "nano-QSARs" should take the nano-specific characteristics into account. The information compiled needs to be well organized, quality controlled and standardized. Integrating the data in an overarching, structured data collection aims to (a) organize the data in a way to support modelling, (b) make (meta)data necessary for modelling available, and (c) add value by making a comparison between data from different sources possible.Based on the available data, specific descriptors can be derived to parameterize the nanomaterial-specific structure and physico-chemical properties appropriately. Furthermore, the interactions between nanoparticles and biological systems as well as small molecules, which can lead to modifications of the structure of the active nanoparticles, need to be described and taken into account in the development of models to predict the biological activity and toxicity of nanoparticles. The EU NanoPUZZLES project was part of a global cooperative effort to advance data availability and modelling approaches supporting the characterization and evaluation of nanomaterials.

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

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 For further resources related to this article, please visit the WIREs website.

Quantum-mechanical parameters for the risk assessment of multi-walled carbon-nanotubes: A study using adsorption of probe compounds and its application to biomolecules

This work forwards new insights into the risk-assessment of multi-walled carbon-nanotubes (MWCNTs) while analysing the role of quantum-mechanical interactions between the electrons in the adsorption of probe compounds and biomolecules by MWCNTs. For this, the quantitative models are developed using quantum-chemical descriptors and their electron-correlation contribution. The major quantum-chemical factors contributing to the adsorption are found to be mean polarizability, electron-correlation energy, and electron-correlation contribution to the absolute electronegativity and LUMO energy. The proposed models, based on only three quantum-chemical factors, are found to be even more robust and predictive than the previously known five or four factors based linear free-energy and solvation-energy relationships. The proposed models are employed to predict the adsorption of biomolecules including steroid hormones and DNA bases. The steroid hormones are predicted to be strongly adsorbed by the MWCNTs, with the order: hydrocortisone > aldosterone > progesterone > ethinyl-oestradiol > testosterone > oestradiol, whereas the DNA bases are found to be relatively less adsorbed but follow the order as: guanine > adenine > thymine > cytosine > uracil. Besides these, the developed electron-correlation based models predict several insecticides, pesticides, herbicides, fungicides, plasticizers and antimicrobial agents in cosmetics, to be strongly adsorbed by the carbon-nanotubes. The present study proposes that the instantaneous inter-electronic interactions may be quite significant in various physico-chemical processes involving MWCNTs, and can be used as a reliable predictor for their risk assessment.

Quantification of nanoparticle pesticide adsorption: computational approaches based on experimental data

Quantitative analysis of the interactions between nanomaterials and environmental contamINANts, such as pesticides, in natural water systems and food residuals is crucial for the application of nanomaterials-based tools for the detection of the presence of toxic substances, monitoring pollution levels and environmental remediation. Previously, the Biological Surface Adsorption Index (BSAI) has demonstrated promising capabilities of interaction characterization and prediction based on experimental data from small organic molecules. In this article, the first attempt of the application of such quantitative measures toward environmental endpoints by analyzing the interactions of a selected group of nanomaterials with a variety of pesticides was made. Statistical modeling was conducted on the experimental obtained adsorption data based on polynomial BSAI models, as well as models with the incorporation of artificial neural network methodologies. Finally, clustering analyzes were performed for the categorization of nanomaterials based on surface physicochemical properties using both polynomial indices and physical adsorption modeling parameters. These quantitative computational approaches support the application of BSAI modeling in the area of environmental contamINANt detection and remediation.

Extrapolating between toxicity endpoints of metal oxide nanoparticles: Predicting toxicity to Escherichia coli and human keratinocyte cell line (HaCaT) with Nano-QTTR

Synthesis of novel nanoparticles should always be accompanied by a comprehensive assessment of risk to human health and to ecosystem. Application of in silico models is encouraged by regulatory authorities to fill the data gaps related to the properties of nanoparticles affecting the environment and human health. Interspecies toxicity correlations provide a tool for estimation of contaminant's sensitivity with known levels of uncertainty for a diverse pool of species. We propose here first interspecies cytotoxicity correlation models between Escherichia coli (prokaryotic system) and human keratinocyte cell line (HaCaT) (eukaryotic system) to assess the discriminatory features for cytotoxicity of metal oxide nanoparticles. The nano-QTTR models can be employed for extrapolating cytotoxicity to E. coli and human keratinocyte cell line (HaCaT) for metal nanoparticles when the data for the other species are available. Informative illustrations of the contributing mechanisms of toxic action of the metal oxide nanoparticles to the HaCaT cell line as well as to the E. coli are identified from the developed nano quantitative toxicity-toxicity relationship (nano-QTTR) models.

Biological potential of nanomaterials strongly depends on the suspension media: experimental data on the effects of fullerene C₆₀ on membranes

Fullerenes (C60) are some of the most promising carbon nanomaterials to be used for medical applications as drug delivery agents. Computational and experimental studies have proposed their ability to enter cells by penetrating lipid bilayers. The aim of our study was to provide experimental evidence on whether pristine C60 in physiological media could penetrate cell membranes. The effect was tested on phospholipid vesicles (liposomes) composed of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine, and validated on isolated human red blood cells (RBCs). We incubated the liposomes in an aqueous suspension of C60 and dissolved the lipids and C60 together in chloroform and subsequently formatted the liposomes. By differential scanning calorimetry measurements, we assessed the effect of C60 on the phospholipid thermal profile. The latter was not affected after the incubation of liposomes in the C60 suspension; also, a shape transformation of RBCs did not occur. Differently, by dispersing both C60 and the phospholipids in chloroform, we confirmed the possible interaction of C60 with the bilayer. We provide experimental data suggesting that the suspension medium is an important factor in determining the C60-membrane interaction, which is not always included in computational studies. Since the primary particle size is not the only crucial parameter in C60-membrane interactions, it is important to determine the most relevant characteristics of their effects on membranes.

Simple and signal-off electrochemiluminescence immunosensor for alpha fetoprotein based on gold nanoparticle-modified graphite-like carbon nitride nanosheet nanohybrids

A simple, signal-off electrochemiluminescence immunosensor for sensitive and selective detection of alpha fetoprotein was developed based on gold nanoparticle modified graphite-like carbon nitride nanosheet nanohybrids.

Predicting Adsorption Affinities of Small Molecules on Carbon Nanotubes Using Molecular Dynamics Simulation

Computational techniques have the potential to accelerate the design and optimization of nanomaterials for applications such as drug delivery and contaminant removal; however, the success of such techniques requires reliable models of nanomaterial surfaces as well as accurate descriptions of their interactions with relevant solutes. In the present work, we evaluate the ability of selected models of naked and hydroxylated carbon nanotubes to predict adsorption equilibrium constants for about 30 small aromatic compounds with a variety of functional groups. The equilibrium constants determined using molecular dynamics coupled with free-energy calculation techniques are directly compared to those derived from experimental measurements. The calculations are highly predictive of the relative adsorption affinities of the compounds, with excellent correlation (r ≥ 0.9) between calculated and measured values of the logarithm of the adsorption equilibrium constant. Moreover, the agreement in absolute terms is also reasonable, with average errors of less than one decade. We also explore possible effects of surface loading, although we demonstrate that they are negligible for the experimental conditions considered. Given the degree of reliability demonstrated, we move on to employing the in silico techniques in the design of nanomaterials, using the optimization of adsorption affinity for the herbacide atrazine as an example. Our simulations suggest that, compared to other modifications of graphenic carbon, polyvinylpyrrolidone conjugation gives the highest affinity for atrazine-substantially greater than that of graphenic carbon alone-and may be useful as a nanomaterial for delivery or sequestration of atrazine.

In Vivo Biomolecule Corona around Blood-Circulating, Clinically Used and Antibody-Targeted Lipid Bilayer Nanoscale Vesicles

The adsorption of proteins and their layering onto nanoparticle surfaces has been called the "protein corona". This dynamic process of protein adsorption has been extensively studied following in vitro incubation of many different nanoparticles with plasma proteins. However, the formation of protein corona under dynamic, in vivo conditions remains largely unexplored. Extrapolation of in vitro formed protein coronas to predict the fate and possible toxicological burden from nanoparticles in vivo is of great interest. However, complete lack of such direct comparisons for clinically used nanoparticles makes the study of in vitro and in vivo formed protein coronas of great importance. Our aim was to study the in vivo protein corona formed onto intravenously injected, clinically used liposomes, based on the composition of the PEGylated liposomal formulation that constitutes the anticancer agent Doxil. The formation of in vivo protein corona was determined after the recovery of the liposomes from the blood circulation of CD-1 mice 10 min postinjection. In comparison, in vitro protein corona was formed by the incubation of liposomes in CD-1 mouse plasma. In vivo and in vitro formed protein coronas were compared in terms of morphology, composition and cellular internalization. The protein coronas on bare (non-PEGylated) and monoclonal antibody (IgG) targeted liposomes of the same lipid composition were also comparatively investigated. A network of linear fibrillary structures constituted the in vitro formed protein corona, whereas the in vivo corona had a different morphology but did not appear to coat the liposome surface entirely. Even though the total amount of protein attached on circulating liposomes correlated with that observed from in vitro incubations, the variety of molecular species in the in vivo corona were considerably wider. Both in vitro and in vivo formed protein coronas were found to significantly reduce receptor binding and cellular internalization of antibody-conjugated liposomes; however, the in vivo corona formation did not lead to complete ablation of their targeting capability.

Sperm exposure to carbon-based nanomaterials causes abnormalities in early development of purple sea urchin (Paracentrotus lividus)

We examined egg fertilisation in purple sea urchin (Paracentrotus lividus) after sperm exposure to carbon-based nanomaterials, carbon black (CB) and graphene oxide (GO), from 0.0001 mg/L to 1.0mg/L. Gastrula stage embryos were investigated for acetylcholinesterase and propionylcholinesterase activities, and their morphological characteristics. Plutei were analysed for morphological abnormalities, with emphasis on skeletal rod formation. Egg fertilisation was significantly affected by CB, at all concentrations tested. Loss of cell adhesion at the gastrula surface was observed in eggs fertilised with sperm treated with CB. However, concentration-dependent morphological anomalies were observed in the gastrulae and plutei formed after sperm exposure to either CB or GO. The activities of both cholinesterases decreased in the gastrulae, although not in a concentration-dependent manner. These effects appear to arise from physical interactions between these carbon-based nanomaterials and the sperm, whereby nanomaterials attached to the sperm surface interfere with fertilisation, which leads to disturbances in the signalling pathways of early embryonic development. Reduced cholinesterase activity in gastrulae from eggs fertilised with nanomaterial-treated sperm confirms involvement of the cholinergic system in early sea urchin development, including skeletogenesis.