From Measurements to Predictive Models: Recent Advancements in Nanosafety Research

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Biology and Medicines".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 10287

Special Issue Editors

Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Republic of Korea
Interests: nanosafety; nanoinformatics; nanotoxicology; nanomedicine; analytical techniques for nanomaterial characterization; predictive models of nanotoxicity; adverse-outcome pathway
School of Geography, Earth and Environmental Science, University of Birmingham, Birmingham B15 2TT, UK
Interests: nanomaterial properties; reactivity; toxicity; solubility; bio-nano interactions
Special Issues, Collections and Topics in MDPI journals
Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
Interests: toxicogenomics; pharmacogenomics; predictive pharmacology; bioinformatics; cheminformatics; nanosafety; multi-omics; gene networks; machine learning; biomarkers
Special Issues, Collections and Topics in MDPI journals
Hanyang University, Seoul, South Korea
Interests: predictive models of nanotoxicity; nanomedicine; cancer biology; molecular biology; adverse-outcome pathway; flow-cytometry analysis; natural product chemistry
Hanyang University, Seoul, South Korea
Interests: physicochemical characterizations of nanomaterials; toxicity assessments; development of novel nanomaterials; analytical techniques for nanomaterial characterization

Special Issue Information

Dear Colleagues,

Understanding the interactions of engineered nanomaterials with biological systems and the environment is becoming increasingly important due to the rapid growth of the nano-industry, such as biomedical applications of nanomaterials for therapeutics and diagnosis. Conventional measurement methods for nanomaterials’ physicochemical properties and in vitro toxicity assessment have been widely applied, and their outcomes have been used for the development of predictive models of nanotoxicity. However, the limitations of these conventional methods, such as interference from cell–nanoparticle interactions and an inability to probe the complex heterogeneities of both nanoparticles and biological systems, prevent the development of better-performing predictive models of nanotoxicity and underlying mechanisms. In this Special Issue, we invite reviews, research articles and communications on recent advancements in nanosafety research, particularly on novel measurement methods for the physicochemical and biological properties of nanomaterials as well as advanced models developed with novel algorithms and/or high-dimensional datasets collected with high-content and high-throughput measurement methods. The potential topics for this Special Issue include but are not limited to:

  1. Advanced characterization methods for nanomaterials and nanoproducts (e.g., nanoparticle-tracking analysis, single-particle ICP-MS etc.);
  2. Novel assessment methods with single-cell resolution for probing the heterogeneities of nanoparticles interacting with complex biological systems (e.g., mass cytometry, single-cell RNAseq etc.);
  3. Advanced models developed with novel algorithms and/or high-dimensional datasets collected with high-content and high-throughput assay methods;
  4. Physicochemical characterization, toxicity assessment and predictive-model development for novel nanomaterials (e.g., upconversion nanoparticles (UCNPs), 2D nanomaterials etc.).

Prof. Dr. Tae-Hyun Yoon
Prof. Dr. Eugenia Valsami-Jones
Prof. Dr. Dario Greco
Dr. Antreas Afantitis
Dr. Haribalan Perumalsamy
Dr. Zayakhuu Gerelkhuu
Guest Editors

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Keywords

  • nanosafety
  • nanomaterials
  • advanced materials
  • predictive models
  • physicochemical properties
  • toxicity assays
  • QNTR (quantitative nanostructure toxicity relationship)

Published Papers (5 papers)

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Research

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14 pages, 3131 KiB  
Article
An In Vitro Dosimetry Tool for the Numerical Transport Modeling of Engineered Nanomaterials Powered by the Enalos RiskGONE Cloud Platform
Nanomaterials 2022, 12(22), 3935; https://doi.org/10.3390/nano12223935 - 08 Nov 2022
Cited by 2 | Viewed by 1625
Abstract
A freely available “in vitro dosimetry” web application is presented enabling users to predict the concentration of nanomaterials reaching the cell surface, and therefore available for attachment and internalization, from initial dispersion concentrations. The web application is based on the distorted grid (DG) [...] Read more.
A freely available “in vitro dosimetry” web application is presented enabling users to predict the concentration of nanomaterials reaching the cell surface, and therefore available for attachment and internalization, from initial dispersion concentrations. The web application is based on the distorted grid (DG) model for the dispersion of engineered nanoparticles (NPs) in culture medium used for in vitro cellular experiments, in accordance with previously published protocols for cellular dosimetry determination. A series of in vitro experiments for six different NPs, with Ag and Au cores, are performed to demonstrate the convenience of the web application for calculation of exposure concentrations of NPs. Our results show that the exposure concentrations at the cell surface can be more than 30 times higher compared to the nominal or dispersed concentrations, depending on the NPs’ properties and their behavior in the cell culture medium. Therefore, the importance of calculating the exposure concentration at the bottom of the cell culture wells used for in vitro arrays, i.e., the particle concentration at the cell surface, is clearly presented, and the tool introduced here allows users easy access to such calculations. Widespread application of this web tool will increase the reliability of subsequent toxicity data, allowing improved correlation of the real exposure concentration with the observed toxicity, enabling the hazard potentials of different NPs to be compared on a more robust basis. Full article
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12 pages, 2567 KiB  
Article
Quantitative Estimation of Cell-Associated Silver Nanoparticles using the Normalized Side Scattering Intensities of Flow Cytometry
Nanomaterials 2021, 11(11), 3079; https://doi.org/10.3390/nano11113079 - 15 Nov 2021
Cited by 4 | Viewed by 1506
Abstract
Quantification of cellular nanoparticles (NPs) is one of the most important steps in studying NP–cell interactions. Here, a simple method for the estimation of cell-associated silver (Ag) NPs in lung cancer cells (A549) is proposed based on their side scattering (SSC) intensities measured [...] Read more.
Quantification of cellular nanoparticles (NPs) is one of the most important steps in studying NP–cell interactions. Here, a simple method for the estimation of cell-associated silver (Ag) NPs in lung cancer cells (A549) is proposed based on their side scattering (SSC) intensities measured by flow cytometry (FCM). To estimate cellular Ag NPs associated with A549 cells over a broad range of experimental conditions, we measured the normalized SSC intensities (nSSC) of A549 cells treated with Ag NPs with five different core sizes (i.e., 40–200 nm, positively charged) under various exposure conditions that reflect different situations of agglomeration, diffusion, and sedimentation in cell culture media, such as upright and inverted configurations with different media heights. Then, we correlated these nSSC values with the numbers of cellular Ag NPs determined by inductively coupled plasma mass spectrometry (ICPMS) as a well-established cross-validation method. The different core sizes of Ag NPs and the various exposure conditions tested in this study confirmed that the FCM-SSC intensities are highly correlated with their core sizes as well as the amount of cellular Ag NPs over a linear range up to ~80,000 Ag NPs/cell and ~23 nSSC, which is significantly broader than those of previous studies. Full article
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Review

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15 pages, 1299 KiB  
Review
Upconversion Nanomaterials in Bioimaging and Biosensor Applications and Their Biological Response
Nanomaterials 2022, 12(19), 3470; https://doi.org/10.3390/nano12193470 - 04 Oct 2022
Cited by 4 | Viewed by 1904
Abstract
In recent decades, upconversion nanomaterials (UCNMs) have attracted considerable research interest because of their unique optical properties, such as large anti-Stokes shifts, sharp emissions, non-photobleaching, and long lifetime. These unique properties make them ideal candidates for unified applications in biomedical fields, including drug [...] Read more.
In recent decades, upconversion nanomaterials (UCNMs) have attracted considerable research interest because of their unique optical properties, such as large anti-Stokes shifts, sharp emissions, non-photobleaching, and long lifetime. These unique properties make them ideal candidates for unified applications in biomedical fields, including drug delivery, bioimaging, biosensing, and photodynamic therapy for specific cancers. This review describes the general mechanisms of upconversion, synthesis methods, and potential applications in biology and their biological responses. Additionally, the biological toxicity of UCNMs is explained and summarized with the associated intracellular association mechanisms. Finally, the prospects and future challenges of UCNMs at the clinical level in biological applications are described, along with a summary of opportunity for biological as well as clinical applications of UCNMs. Full article
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16 pages, 2467 KiB  
Review
Analysis of Nanotoxicity with Integrated Omics and Mechanobiology
Nanomaterials 2021, 11(9), 2385; https://doi.org/10.3390/nano11092385 - 13 Sep 2021
Cited by 23 | Viewed by 3054
Abstract
Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses [...] Read more.
Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity. Full article
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26 pages, 1866 KiB  
Review
Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity
Nanomaterials 2021, 11(1), 124; https://doi.org/10.3390/nano11010124 - 07 Jan 2021
Cited by 12 | Viewed by 3102
Abstract
Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based [...] Read more.
Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models. Full article
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