Special Issue "From Nanoinformatics to Nanomaterials Risk Assessment and Governance"

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

Deadline for manuscript submissions: closed (1 September 2020).

Special Issue Editors

Prof. Dr. Dario Greco
E-Mail Website
Guest Editor
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 and Collections in MDPI journals
Dr. Antreas Afantitis
E-Mail Website1 Website2
Guest Editor
Department of Cheminformatics, NovaMechanics Ltd, Nicosia, Cyprus
Interests: cheminformatics; nanoinformatics; virtual screening; QSAR; computer aided drug design; computational toxicology; computational nanotoxicology
Special Issues and Collections in MDPI journals
Dr. Georgia Melagraki
E-Mail
Guest Editor
NovaMechanics Ltd, Nicosia, Cyprus
Interests: cheminformatics; nanoinformatics; predictive modeling; virtual screening; in silico risk assessment; nanosafety; computational toxicology; machine learning; artificial intelligence; image analysis
Prof. Dr. Iseult Lynch
E-Mail Website
Guest Editor
School of Geography, Earth & Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT Birmingham, United Kingdom
Interests: nanomaterial synthesis and characterisation; nanoparticle-biomolecule interactions (eco-corona); fate and behaviour of nanomaterials; green / benign-by-design nanomaterials; nanosafety assessment; nanosafety data management
Special Issues and Collections in MDPI journals
Dr. Maria Dusinska
E-Mail Website
Guest Editor
NILU - Norwegian Institute for Air Research, Kjeller, Norway
Interests: environment and health; nanosafety; risk governance of nanotechnology; risk assessment; genetic toxicology; regulatory toxicology; in vitro toxicology and alternative tests; molecular epidemiology; biomonitoring; biomarkers; DNA damage and repair; hazard and risk assessment; risk governance
Prof. Dr. Miguel A. Banares
E-Mail Website1 Website2
Co-Guest Editor
CSIC - Institute for Catalysis, Marie Curie 2, E-28049-Madrid, Spain
Interests: materials characterization and reactivity with relevance ranging from catalysis to toxicity

Special Issue Information

Dear Colleagues,

High-quality nanomaterials, with systematically varied properties that are retained in biological dispersions, are essential in order to definitively connect cause and effect and tease out nanomaterial specific drivers of toxicity or biological impacts from nanomaterials. However, the diversity of possible nanomaterial compositions in terms of core material(s), labelling, coatings, surface functionalisation and their physicochemical properties including size, shape, crystal structure, etc. mean than rigorous testing of each variant is not possible. To help to overcome this knowledge gap, nanoinformatics approaches are urgently needed and indeed are developing rapidly to facilitate prediction of properties from reduced characterisation information sets, to enable grouping of nanomaterials on the basis of their properties and effects, and read-across of knowledge from well-characterised nanomaterials to less extensively characterised ones based on similarities in their applications, exposure routes and expected toxicity. Integration of nanomaterials synthesis and nanoinformatics knowledge will ultimately lead to safer nanomaterials and indeed safer by design strategies.

This Special Issue will address the latest progress towards enhanced control over nanomaterials’ physical and chemical properties, including development of systematically varied nanomaterials libraries, development of reference nanomaterials, such as for agglomeration and dissolution in medium, for oxidative stress, genotoxicity and even as positive and negative control nanomaterials for -omics analyses. In parallel, developments in in silico nanosafety assessment and nanoinformatics, will be highlighted, as a means to drive cross-fertilisation of these two highly complementary research areas. The use of in silico approaches to support safe by design and benign by design nanomaterials, based on data collected from tens or even hundreds of nanomaterials of different compositions and/or properties, will revolutionise the design and optimisation of nanomaterials, by tailoring the material specifications to the application, and considering end-of-life considerations. This integration of nanoinformatics and nanomaterials design and synthesis will also form a key aspect of governance and regulation of nanomaterials in the future, where product developers will be asked to demonstrate the application of best-practice nanoinformatics approaches to ensure optimisation of function and safety at the earliest possible point in the product development.

Authors are invited to submit articles addressing one or more of the topics listed below:

  • Development of systematically varied nanomaterials libraries for nanosafety analysis;
  • Strategies to reduce batch-to-batch irreproducibility in nanomaterials synthesis;
  • Nanoscale reference materials for assessment of toxicity and as probes for adverse outcomes—beyond size standards in water;
  • Benign by design and safe by design nanomaterials synthesis strategies;
  • Strategies for recovery of nanomaterials from products or the environment;
  • Predictive toxicogenomics modelling using -omics data;
  • Predictive Nanoinformatics Modeling using state-of-the-art modelling methodologies;
  • Design of experiments for data gap filling;
  • Biokinetics and PBPK modelling for nanomaterials;
  • Nanomaterials multiscale simulations;
  • Sample provenance and data management for nanomaterials and nanosafety;
  • Integrating datasets and databases—best practice adapted for nanomaterials and nanosafety;
  • Best practice reporting for experimental and computational nanomaterials data;
  • Sustainability of data and modelling tools;
  • Facilitating use of research data in weight of evidence for nanomaterials regulatory dossiers;
  • Integrating models into safe-by-design or risk decision frameworks;
  • Semantic mapping of chemo- bio- and nano-informatics ontologies and taxonomies for translational research.

Prof. Dr. Dario Greco
Dr. Antreas Afantitis
Dr. Georgia Melagraki
Prof. Dr. Iseult Lynch
Dr. Maria Dusinska
Prof. Dr. Miguel A. Banares
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nanomaterials is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • safe by design
  • nanomaterials
  • -omics
  • predictive nanoinformatics modelling
  • predictive toxicogenomics modelling
  • machine learning
  • design of experiments
  • biokinetics
  • PBPK modelling
  • nanomaterials multiscale simulations
  • ontologies

Published Papers (17 papers)

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Editorial

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Open AccessEditorial
Editorial for the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance
Nanomaterials 2021, 11(1), 121; https://doi.org/10.3390/nano11010121 - 07 Jan 2021
Cited by 1 | Viewed by 432
Abstract
Ensuring the safe and responsible use of nanotechnologies and nanoscale materials is imperative to maximize consumer confidence and drive commercialization of nano-enabled products that underpin innovation and advances in every industrial sector [...] Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Research

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Open AccessArticle
Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?
Nanomaterials 2020, 10(12), 2493; https://doi.org/10.3390/nano10122493 - 11 Dec 2020
Cited by 1 | Viewed by 1492
Abstract
Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures [...] Read more.
Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a “nano” extension to the InChI standard. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessArticle
A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences
Nanomaterials 2020, 10(10), 2068; https://doi.org/10.3390/nano10102068 - 20 Oct 2020
Cited by 3 | Viewed by 983
Abstract
Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to [...] Read more.
Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessArticle
Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data
Nanomaterials 2020, 10(10), 2033; https://doi.org/10.3390/nano10102033 - 15 Oct 2020
Cited by 8 | Viewed by 1184
Abstract
The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need [...] Read more.
The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessArticle
Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform
Nanomaterials 2020, 10(10), 2017; https://doi.org/10.3390/nano10102017 - 13 Oct 2020
Cited by 6 | Viewed by 1391
Abstract
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 [...] Read more.
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). Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessArticle
In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles
Nanomaterials 2020, 10(10), 1967; https://doi.org/10.3390/nano10101967 - 04 Oct 2020
Cited by 1 | Viewed by 761
Abstract
The free energy of adsorption of proteins onto nanoparticles offers an insight into the biological activity of these particles in the body, but calculating these energies is challenging at the atomistic resolution. In addition, structural information of the proteins may not be readily [...] Read more.
The free energy of adsorption of proteins onto nanoparticles offers an insight into the biological activity of these particles in the body, but calculating these energies is challenging at the atomistic resolution. In addition, structural information of the proteins may not be readily available. In this work, we demonstrate how information about adsorption affinity of proteins onto nanoparticles can be obtained from first principles with minimum experimental input. We use a multiscale model of protein–nanoparticle interaction to evaluate adsorption energies for a set of 59 human blood serum proteins on gold and titanium dioxide (anatase) nanoparticles of various sizes. For each protein, we compare the results for 3D structures derived from experiments to those predicted computationally from amino acid sequences using the I-TASSER methodology and software. Based on these calculations and 2D and 3D protein descriptors, we develop statistical models for predicting the binding energy of proteins, enabling the rapid characterization of the affinity of nanoparticles to a wide range of proteins. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessArticle
Effect of the Albumin Corona on the Toxicity of Combined Graphene Oxide and Cadmium to Daphnia magna and Integration of the Datasets into the NanoCommons Knowledge Base
Nanomaterials 2020, 10(10), 1936; https://doi.org/10.3390/nano10101936 - 29 Sep 2020
Cited by 1 | Viewed by 968
Abstract
In this work, we evaluated the effect of protein corona formation on graphene oxide (GO) mixture toxicity testing (i.e., co-exposure) using the Daphnia magna model and assessing acute toxicity determined as immobilisation. Cadmium (Cd2+) and bovine serum albumin (BSA) were selected [...] Read more.
In this work, we evaluated the effect of protein corona formation on graphene oxide (GO) mixture toxicity testing (i.e., co-exposure) using the Daphnia magna model and assessing acute toxicity determined as immobilisation. Cadmium (Cd2+) and bovine serum albumin (BSA) were selected as co-pollutant and protein model system, respectively. Albumin corona formation on GO dramatically increased its colloidal stability (ca. 60%) and Cd2+ adsorption capacity (ca. 4.5 times) in reconstituted water (Daphnia medium). The acute toxicity values (48 h-EC50) observed were 0.18 mg L−1 for Cd2+-only and 0.29 and 0.61 mg L−1 following co-exposure of Cd2+ with GO and [email protected] materials, respectively, at a fixed non-toxic concentration of 1.0 mg L−1. After coronation of GO with BSA, a reduction in cadmium toxicity of 110 % and 238% was achieved when compared to bare GO and Cd2+-only, respectively. Integration of datasets associated with graphene-based materials, heavy metals and mixture toxicity is essential to enable re-use of the data and facilitate nanoinformatics approaches for design of safer nanomaterials for water quality monitoring and remediation technologies. Hence, all data from this work were annotated and integrated into the NanoCommons Knowledge Base, connecting the experimental data to nanoinformatics platforms under the FAIR data principles and making them interoperable with similar datasets. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessArticle
Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database
Nanomaterials 2020, 10(10), 1908; https://doi.org/10.3390/nano10101908 - 24 Sep 2020
Cited by 1 | Viewed by 942
Abstract
The field of nanoinformatics is rapidly developing and provides data driven solutions in the area of nanomaterials (NM) safety. Safe by Design approaches are encouraged and promoted through regulatory initiatives and multiple scientific projects. Experimental data is at the core of nanoinformatics processing [...] Read more.
The field of nanoinformatics is rapidly developing and provides data driven solutions in the area of nanomaterials (NM) safety. Safe by Design approaches are encouraged and promoted through regulatory initiatives and multiple scientific projects. Experimental data is at the core of nanoinformatics processing workflows for risk assessment. The nanosafety data is predominantly recorded in Excel spreadsheet files. Although the spreadsheets are quite convenient for the experimentalists, they also pose great challenges for the consequent processing into databases due to variability of the templates used, specific details provided by each laboratory and the need for proper metadata documentation and formatting. In this paper, we present a workflow to facilitate the conversion of spreadsheets into a FAIR (Findable, Accessible, Interoperable, and Reusable) database, with the pivotal aid of the NMDataParser tool, developed to streamline the mapping of the original file layout into the eNanoMapper semantic data model. The NMDataParser is an open source Java library and application, making use of a JSON configuration to define the mapping. We describe the JSON configuration syntax and the approaches applied for parsing different spreadsheet layouts used by the nanosafety community. Examples of using the NMDataParser tool in nanoinformatics workflows are given. Challenging cases are discussed and appropriate solutions are proposed. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessArticle
Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment
Nanomaterials 2020, 10(9), 1767; https://doi.org/10.3390/nano10091767 - 07 Sep 2020
Cited by 4 | Viewed by 826
Abstract
The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on [...] Read more.
The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on theoretical and empirical methods, but these methods produce inaccurate results. Recently, a machine learning model based on the combination of seven baselines, which is called the committee machine intelligent system (CMIS), was proposed to predict the viscosity of nanofluids. CMIS was applied on 3144 experimental data of relative viscosity of 42 different nanofluid systems based on five features (temperature, the viscosity of the base fluid, nanoparticle volume fraction, size, and density) and returned an average absolute relative error (AARE) of 4.036% on the test. In this work, eight models (on the same dataset as the one used in CMIS), including two multilayer perceptron (MLP), each with Nesterov accelerated adaptive moment (Nadam) optimizer; two MLP, each with three hidden layers and Adamax optimizer; a support vector regression (SVR) with radial basis function (RBF) kernel; a decision tree (DT); tree-based ensemble models, including random forest (RF) and extra tree (ET), were proposed. The performance of these models at different ranges of input variables was assessed and compared with the ones presented in the literature. Based on our result, all the eight suggested models outperformed the baselines used in the literature, and five of our presented models outperformed the CMIS, where two of them returned an AARE less than 3% on the test data. Besides, the physical validity of models was studied by examining the physically expected trends of nanofluid viscosity due to changing volume fraction. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessArticle
Reduction of Health Care-Associated Infections (HAIs) with Antimicrobial Inorganic Nanoparticles Incorporated in Medical Textiles: An Economic Assessment
Nanomaterials 2020, 10(5), 999; https://doi.org/10.3390/nano10050999 - 23 May 2020
Cited by 4 | Viewed by 1029
Abstract
Health care-associated infections (HAIs) affect millions of patients annually with up to 80,000 affected in Europe on any given day. This represents a significant societal and economic burden. Staff training, hand hygiene, patient identification and isolation and controlled antibiotic use are some of [...] Read more.
Health care-associated infections (HAIs) affect millions of patients annually with up to 80,000 affected in Europe on any given day. This represents a significant societal and economic burden. Staff training, hand hygiene, patient identification and isolation and controlled antibiotic use are some of the standard ways to reduce HAI incidence but this is time consuming and subject and subject to rigorous implementation. In addition, the lack of antimicrobial activity of some disinfectants against healthcare-associated pathogens may also affect the efficacy of disinfection practices. Textiles are an attractive substrate for pathogens because of contact with the human body with the attendant warmth and moisture. Textiles and surfaces coated with engineered nanomaterials (ENMs) have shown considerable promise in reducing the microbial burden on those surfaces. Studies have also shown that this antimicrobial affect can reduce the incidence of HAIs. For all of the promising research, there has been an absence of study on the economic effectiveness of ENM coated materials in a healthcare setting. This article examines the relative economic efficacy of ENM coated materials against an antiseptic approach. The goal is to establish the economic efficacy of the widespread usage of ENM coated materials in a healthcare setting. In the absence of detailed and segregated costs, benefits and control variables over at least cross sectional data or time series, an aggregated approach is warranted. This approach, while relying on some supposition allows for a comparison with similar data regarding standard treatment to reduce HAIs and provides a reasonable economic comparison. We find that while, relative to antiseptics, ENM coated textiles represent a significant clinical advantage, they can also offer considerable cost savings. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)

Review

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Open AccessReview
Genotoxicity of Nanomaterials: Advanced In Vitro Models and High Throughput Methods for Human Hazard Assessment—A Review
Nanomaterials 2020, 10(10), 1911; https://doi.org/10.3390/nano10101911 - 25 Sep 2020
Cited by 6 | Viewed by 970
Abstract
Changes in the genetic material can lead to serious human health defects, as mutations in somatic cells may cause cancer and can contribute to other chronic diseases. Genotoxic events can appear at both the DNA, chromosomal or (during mitosis) whole genome level. The [...] Read more.
Changes in the genetic material can lead to serious human health defects, as mutations in somatic cells may cause cancer and can contribute to other chronic diseases. Genotoxic events can appear at both the DNA, chromosomal or (during mitosis) whole genome level. The study of mechanisms leading to genotoxicity is crucially important, as well as the detection of potentially genotoxic compounds. We consider the current state of the art and describe here the main endpoints applied in standard human in vitro models as well as new advanced 3D models that are closer to the in vivo situation. We performed a literature review of in vitro studies published from 2000–2020 (August) dedicated to the genotoxicity of nanomaterials (NMs) in new models. Methods suitable for detection of genotoxicity of NMs will be presented with a focus on advances in miniaturization, organ-on-a-chip and high throughput methods. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessReview
Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials
Nanomaterials 2020, 10(7), 1267; https://doi.org/10.3390/nano10071267 - 29 Jun 2020
Cited by 2 | Viewed by 1165
Abstract
There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate [...] Read more.
There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (Pt) and partition coefficients (PCs) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
Open AccessReview
Translating Scientific Advances in the AOP Framework to Decision Making for Nanomaterials
Nanomaterials 2020, 10(6), 1229; https://doi.org/10.3390/nano10061229 - 24 Jun 2020
Cited by 6 | Viewed by 2015
Abstract
Much of the current innovation in advanced materials is occurring at the nanoscale, specifically in manufactured nanomaterials (MNs). MNs display unique attributes and behaviors, and may be biologically and physically unique, making them valuable across a wide range of applications. However, as the [...] Read more.
Much of the current innovation in advanced materials is occurring at the nanoscale, specifically in manufactured nanomaterials (MNs). MNs display unique attributes and behaviors, and may be biologically and physically unique, making them valuable across a wide range of applications. However, as the number, diversity and complexity of MNs coming to market continue to grow, assessing their health and environmental risks with traditional animal testing approaches is too time- and cost-intensive to be practical, and is undesirable for ethical reasons. New approaches are needed that meet current requirements for regulatory risk assessment while reducing reliance on animal testing and enabling safer-by-design product development strategies to be implemented. The adverse outcome pathway (AOP) framework presents a sound model for the advancement of MN decision making. Yet, there are currently gaps in technical and policy aspects of AOPs that hinder the adoption and use for MN risk assessment and regulatory decision making. This review outlines the current status and next steps for the development and use of the AOP framework in decision making regarding the safety of MNs. Opportunities and challenges are identified concerning the advancement and adoption of AOPs as part of an integrated approach to testing and assessing (IATA) MNs, as are specific actions proposed to advance the development, use and acceptance of the AOP framework and associated testing strategies for MN risk assessment and decision making. The intention of this review is to reflect the views of a diversity of stakeholders including experts, researchers, policymakers, regulators, risk assessors and industry representatives on the current status, needs and requirements to facilitate the future use of AOPs in MN risk assessment. It incorporates the views and feedback of experts that participated in two workshops hosted as part of an Organization for Economic Cooperation and Development (OECD) Working Party on Manufactured Nanomaterials (WPMN) project titled, “Advancing AOP Development for Nanomaterial Risk Assessment and Categorization”, as well as input from several EU-funded nanosafety research consortia. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessReview
Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data
Nanomaterials 2020, 10(5), 903; https://doi.org/10.3390/nano10050903 - 08 May 2020
Cited by 4 | Viewed by 1419
Abstract
Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough [...] Read more.
Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessReview
Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects
Nanomaterials 2020, 10(4), 750; https://doi.org/10.3390/nano10040750 - 15 Apr 2020
Cited by 5 | Viewed by 1699
Abstract
The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the [...] Read more.
The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms’ responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Open AccessReview
Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment
Nanomaterials 2020, 10(4), 708; https://doi.org/10.3390/nano10040708 - 08 Apr 2020
Cited by 7 | Viewed by 1835
Abstract
Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular [...] Read more.
Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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Practices and Trends of Machine Learning Application in Nanotoxicology
Nanomaterials 2020, 10(1), 116; https://doi.org/10.3390/nano10010116 - 08 Jan 2020
Cited by 13 | Viewed by 1565
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
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