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

A special issue of Nanomaterials (ISSN 2079-4991).

Deadline for manuscript submissions: 1 June 2020.

Special Issue Editors

Prof. Dario Greco
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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
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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

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
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
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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. Miguel A. Banares
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. Dario Greco
Dr. Antreas Afantitis
Dr. Georgia Melagraki
Prof. Iseult Lynch
Dr. Maria Dusinska
Prof. Miguel A. Bañares
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 2000 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 (5 papers)

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Research

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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 (registering DOI) - 23 May 2020
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
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
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
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
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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Open AccessReview
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 1
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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