Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

A Weight of Evidence approach to classify nanomaterials according to the EU Classification, Labelling and Packaging Regulation criteria

In the context of the European Union (EU) Horizon 2020 GRACIOUS project (Grouping, Read-Across, Characterisation and classification framework for regulatory risk assessment of manufactured nanomaterials and Safer design of nano-enabled products), we proposed a quantitative Weight of Evidence (WoE) approach for hazard classification of nanomaterials (NMs). This approach is based on the requirements of the European Regulation on Classification, Labelling and Packaging of Substances and Mixtures (the CLP regulation), which implements the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (UN GHS) in the European Union. The goal of this WoE methodology is to facilitate classification of NMs according to CLP criteria, following the decision trees defined in ECHA's CLP regulatory guidance. In the WoE, results from heterogeneous studies are weighted according to data quality and completeness criteria, integrated, and then evaluated by expert judgment to obtain a hazard classification, resulting in a coherent and justifiable methodology. Moreover, the probabilistic nature of the proposed approach enables highlighting the uncertainty in the analysis. The proposed methodology involves the following stages: (1) collection of data for different NMs related to the endpoint of interest: each study related to each NM is referred as a Line of Evidence (LoE); (2) computation of weighted scores for each LoE: each LoE is weighted by a score calculated based on data quality and completeness criteria defined in the GRACIOUS project; (3) comparison and integration of the weighed LoEs for each NM: A Monte Carlo resampling approach is adopted to quantitatively and probabilistically integrate the weighted evidence; and (4) assignment of each NM to a hazard class: according to the results, each NM is assigned to one of the classes defined by the CLP regulation. Furthermore, to facilitate the integration and the classification of the weighted LoEs, an online R tool was developed. Finally, the approach was tested against an endpoint relevant to CLP (Aquatic Toxicity) using data retrieved from the eNanoMapper database, results obtained were consistent to results in REACH registration dossiers and in recent literature.

Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms

In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.

Predicting and investigating cytotoxicity of nanoparticles by translucent machine learning

Safety concerns of engineered nanoparticles (ENPs) hamper their applications and commercialization in many potential fields. Machine learning has been proved as a great tool to understand the complex ENP-organism-environment relationship. However, good-performance machine learning models usually exist as black boxes, which may be difficult to build trust and whose ways of expressing knowledge rarely directly map to forms familiar to scientists. Here, we present an approach for uncovering causal structure in nanotoxicity datasets by mutual-validated and model-agnostic interpretation methods. Model predictions can be explained from feature importance, feature effects, and feature interactions. The utility of this approach is demonstrated through two case studies, the cytotoxicity of cadmium-containing quantum dots and metal oxide nanoparticles. Further, these case studies indicate the efficacy and impacts at two scales: (i) model interpretation, where the most relevant features for correlating cytotoxicity are identified and their influence on model predictions and interactions with other features are then explained, and (ii) model validation, where the difference among interpretation results of different methods (or the difference between interpretation results and well-known toxicity mechanisms) may reflect some inherent problems in the used dataset (or the developed models). Our approach of integrating machine learning models and interpretation methods provides a roadmap for predicting the toxicity of ENPs in a translucent way.

Salivary Leucocytes as In Vitro Model to Evaluate Nanoparticle-Induced DNA Damage

Metal oxide nanoparticles (NPs) have a wide variety of applications in many consumer products and biomedical practices. As a result, human exposure to these nanomaterials is highly frequent, becoming an issue of concern to public health. Recently, human salivary leucocytes have been proposed as an adequate non-invasive alternative to peripheral blood leucocytes to evaluate genotoxicity in vitro. The present study focused on proving the suitability of salivary leucocytes as a biomatrix in the comet assay for in vitro nanogenotoxicity studies, by testing some of the metal oxide NPs most frequently present in consumer products, namely, titanium dioxide (TiO2), zinc oxide (ZnO), and cerium dioxide (CeO2) NPs. Primary and oxidative DNA damage were evaluated by alkaline and hOGG1-modified comet assay, respectively. Any possible interference of the NPs with the methodological procedure or the hOGG1 activity was addressed before performing genotoxicity evaluation. Results obtained showed an increase of both primary and oxidative damage after NPs treatments. These data support the use of salivary leucocytes as a proper and sensitive biological sample for in vitro nanogenotoxicity studies, and contribute to increase the knowledge on the impact of metal oxide NPs on human health, reinforcing the need for a specific regulation of the nanomaterials use.

The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union

BACKGROUND

Machine Learning (ML) has experienced an increasing use, given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need for efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in the European Union and the role of ML in the authorization process.

METHODS

In terms of methodology, a dogmatic study of the European regulation and the guidance of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated in the regulations.

RESULTS

As a result, we present a discussion of the information that must be provided for every case for simulation methods. The results show a favorable and flexible position for the development of the use of predictive models to complement the applicant's information.

CONCLUSION

It is concluded that Machine Learning has the capacity to help improve the application of nanotechnology medicine products regulation. Future regulations should promote this kind of information given the advanced state of the art in terms of algorithms that are able to build accurate predictive models. This especially applies to methods, such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development (OECD), European Union regulations, and European Authority Medicine. To our best knowledge, this is the first study focused on nanotechnology medicine products and machine learning used to support technical European public assessment reports (EPAR) for complementary information.

Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling

Zeta potential is usually measured to estimate the surface charge and the stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for a pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potential measurements in the context of environmental impact is an important challenge. The inconsistency of data measurements leads to inaccuracies in predictive modeling. In this article, we report a new curated dataset of zeta potentials measured for 208 silica- and metal oxide nanoparticles in different media. We discuss the data curation framework for zeta potentials designed to assess the quality and usefulness of the literature data for further computational modeling. We also provide an analysis of specific trends for the datapoints harvested from different literature sources. In addition to that, we present for the first time a structure-property relationship model for nanoparticles (nano-SPR) that predicts values of zeta potential values measured in different environmental conditions (i.e., biological media and pH).

Engineering bioactive surfaces on nanoparticles and their biological interactions

The successful integration of nanoparticles into biomedical applications requires modulation of their surface properties so that the interaction with biological systems is regulated to minimize toxicity for biological function. In the present work, we have engineered bioactive surfaces on gold (Au) and silver (Ag) nanoparticles and subsequently evaluated their interaction with mouse skin fibroblasts and macrophages. The Au and Ag nanoparticles were synthesized using tyrosine, tryptophan, isonicotinylhydrazide, epigallocatechin gallate, and curcumin as reducing and stabilizing agents. The nanoparticles thus prepared showed surface corona and exhibited free radical scavenging and enzyme activities with limited cytotoxicity and genotoxicity. We have thus developed avenues for engineering the surface of nanoparticles for biological applications.

Thermodynamic Parameters at Bio-Nano Interface and Nanomaterial Toxicity: A Case Study on BSA Interaction with ZnO, SiO2, and TiO2

Understanding nanomaterial (NM)-protein interactions is a key issue in defining the bioreactivity of NMs with great impact for nanosafety. In the present work, the complex phenomena occurring at the bio/nano interface were evaluated in a simple case study focusing on NM-protein binding thermodynamics and protein stability for three representative metal oxide NMs, namely, zinc oxide (ZnO; NM-110), titanium dioxide (TiO₂; NM-101), and silica (SiO₂; NM-203). The thermodynamic signature associated with the NM interaction with an abundant protein occurring in most cell culture media, bovine serum albumin (BSA), has been investigated by isothermal titration and differential scanning calorimetry. Circular dichroism spectroscopy offers additional information concerning adsorption-induced protein conformational changes. The BSA adsorption onto NMs is enthalpy-controlled, with the enthalpic character (favorable interaction) decreasing as follows: ZnO (NM-110) > SiO₂ (NM-203) > TiO₂ (NM-101). The binding of BSA is spontaneous, as revealed by the negative free energy, ΔG, for all systems. The structural stability of the protein decreased as follows: TiO₂ (NM-101) > SiO₂ (NM-203) > ZnO (NM-110). As protein binding may alter NM reactivity and thus the toxicity, we furthermore assessed its putative influence on DNA damage, as well as on the expression of target genes for cell death (RIPK1, FAS) and oxidative stress (SOD1, SOD2, CAT, GSTK1) in the A549 human alveolar basal epithelial cell line. The enthalpic component of the BSA-NM interaction, corroborated with BSA structural stability, matched the ranking for the biological alterations, i.e., DNA strand breaks, oxidized DNA lesions, cell-death, and antioxidant gene expression in A549 cells. The relative and total content of BSA in the protein corona was determined using mass-spectrometry-based proteomics. For the present case study, the thermodynamic parameters at bio/nano interface emerge as key descriptors for the dominant contributions determining the adsorption processes and NMs toxicological effect.

A unified in silico model based on perturbation theory for assessing the genotoxicity of metal oxide nanoparticles

Nanomaterials (NMs) are an ever-increasing field of interest, due to their wide range of applications in science and technology. However, despite providing solutions to many societal problems and challenges, NMs are associated with adverse effects with potential severe damages towards biological species and their ecosystems. Particularly, it has been confirmed that NMs may induce serious genotoxic effects on various biological targets. Given the difficulties of experimental assays for estimating the genotoxicity of many NMs on diverse biological targets, development of alternative methodologies is crucial to establish their level of safety. In silico modelling approaches, such as Quantitative Structure-Toxicity Relationships (QSTR), are now considered a promising solution for such purpose. In this work, a perturbation theory machine learning (PTML) based QSTR approach is proposed for predicting the genotoxicity of metal oxide NMs under various experimental assay conditions. The application of such perturbation approach to 6084 NM-NM pair cases, set up from 78 unique NMs, afforded a final PTML-QSTR model with an accuracy better than 96% for both training and test sets. This model was then used to predict the genotoxicity of some NMs not included in the modelling dataset. The results for this independent data set were in excellent agreement with the experimental ones. Overall, that thus suggests that the derived PTML-QSTR model is a reliable in silico tool to rapidly and cost-efficiently assess the genotoxicity of metal oxide NMs. Finally, and most importantly, the model provides important insights regarding the mechanism of the genotoxicity triggered by these NMs.

Fluorescein sodium loaded by polyethyleneimine for fundus fluorescein angiography improves adhesion

Aim: To improve the retention of fluorescein sodium (FS) as a kind of clinical contrast agent for fundus fluorescein angiography (FFA). Materials & methods: Polyethyleneimine (PEI) was designed to synthesize PEI–NHAc–FS nanoparticles (NPs), and the formed NPs were characterized by both physicochemical properties and their effects on FFA. Results: Compared with free FS, PEI–NHAc–FS NPs showed similar optical performance, and could obviously reduce cellular adsorption and uptake both in vitro and in vivo, which could promote the metabolism of NPs in ocular blood vessels. Conclusion: PEI–NHAc–FS NPs represent a smart nanosize fluorescence contrast agent, which hold promising potential for clinical FFA diagnosis, therapy and research work.

Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment

Because it is impossible to assess in vitro or in vivo the toxicity of all nanoparticles available on the market on a case-by-case basis, computational approaches have been proposed as useful alternatives to predict in silico the hazard potential of engineered nanoparticles. Despite promising results, a major issue associated with these mathematical models lies in the a priori choice of the physicochemical descriptors and the biological end points. We performed a thorough bibliographic survey on the biological end points used for nanotoxicology purposes and compared them between experimental and computational approaches. They were found to be disparate: while conventional in vitro nanotoxicology assays usually investigate a large array of biological effects using eukaryotic cells (cytotoxicity, pro-inflammatory response, oxidative stress, genotoxicity), computational studies mostly focus on cell viability and also include studies on prokaryotic cells. We may thus wonder the relevance of building complex mathematical models able to predict accurately a biological end point if this latter is not the most relevant to support human health risk assessment. The choice of biological end points clearly deserves to be more carefully discussed. This could bridge the gap between experimental and computational nanotoxicology studies and allow in silico predictive models to reach their full potential.

Engineered nanomaterials and oxidative stress: current understanding and future challenges

Engineered nanomaterials (ENMs) are being incorporated at an unprecedented rate into consumer and biomedical products. This increased usage will ultimately lead to increased human exposure; therefore, understanding ENM safety is an important concern to the public. Although ENMs may exert toxicity through multiple mechanisms, one common mechanism of toxicity recognized across a range of ENMs with varying physicochemical properties is oxidative stress. Further, it is recognized that several key physicochemical properties of ENMs including size, material composition, surface chemistry, band gap, and level of ionic dissolution for example contribute to ENM driven oxidative stress. While it has been shown that exposure of cells to ENMs at high acute doses produce reactive oxygen species at a toxic level often leading to cytotoxicity, there is little research looking at oxidative stress caused by ENM exposure at more relevant low or non-toxic doses. Although the former can lead to apoptosis, genotoxicity, and inflammation, the latter can potentially be damaging as chronic changes to the intracellular redox state leads to cellular reprogramming, resulting in disease initiation and progression among other systemic damage. This current opinions article will review the physicochemical properties and mechanisms associated with ENM-driven oxidative stress and will discuss the need for research investigating effects on the redox proteome that may lead to cellular dysfunction at low or chronic doses of ENMs.