Risk assessments in nanotoxicology: bioinformatics and computational approaches

Nanotoxicology: developments and new insights

The use of nanoparticles (NPs) in treatment of diseases have increased exponentially recently, giving rise to the science of nanomedicine. The safety of these NPs in humans has also led to the science of nanotoxicology. Due to a dearth of both readily available models and precise bio-dispersion characterization techniques, nanotoxicological research has obviously been constrained. However, the ensuing years were notable for the emergence of improved synthesis methods and characterization tools. Major advances have been made in linking certain physical variables, paralleling improvements in characterization size, shape, or coating factors to the resulting physiological reactions. Although significant progress has been a contribution to the development of nanotoxicology, however, it faces numerous difficulties and technical constraints distinct from those of conventional toxicological assessment as it attempts to improve the therapeutic effects of medicines. Determining thorough characterization standards, standardizing dosimetry, assessing the kinetics of ions dissolving and enhancing the accuracy of in vitro-in vivo correlation efficiency, also defining restrictions on exposure protection are some of the most important and pressing concerns. This article will explore the past advancement and potential prospects of nanotoxicology, standard models, emphasizing significant findings from earlier studies and examining current challenges, giving insight on the way forward.

Metal-based nanomaterials in aquatic environments: What do we know so far about their ecotoxicity?

The wide range of applications of nanomaterials (NM) in different fields has led to both uncontrolled production and release into environmental compartments, such as aquatic systems, where final disposal occurs. Some efforts have been made to estimate their concentrations in environmental matrices; however, little is known about the actual effects of environmental NM concentrations on biota. The aims of the present review are to (i) expose the state of the art of the most applied NM and their actual concentrations regarding how much is being released to the aquatic environment and which are the predicted ones; (ii) analyze the current literature to elucidate if the aforementioned conditions were proven to cause deleterious effects on the associated organisms; and (iii) identify gaps in the knowledge regarding whether the actual NM concentrations are harmful to aquatic biota. These novel materials are expected to being released into the environment in the range of hundreds to thousands of tons per year, with Si- and Ti-based NM being the two most important. The estimated environmental NM concentrations are in the low range of ng to µg/L, except for Ti-based ones, which concentrations reach values on the order of mg/L. Empirical information regarding the ecotoxicity of environmental NM concentrations mainly focused on metal-based NM, however, it resulted poor and unbalanced in terms of materials and test species. Given its high predicted environmental concentration in comparison with the others, the ecotoxicity of Ti-based NM has been well assessed in algae and fish, while little is known regarding other NM types. While only a few marine species were addressed, the freshwater species Daphnia magna and Danio rerio accounted for the majority of studies on invertebrate and fish groups, respectively. Most of the reported responses are related to oxidative stress. Overall, we consider that invertebrate groups are the most vulnerable, with emphasis on microcrustaceans, as environmentally realistic metal-based NM concentration even caused mortality in some species. In the case of fish, we assumed that environmental concentrations of Ti-based NM represent a growing concern and threat; however, further studies should be carried out by employing other kinds of NM. Furthermore, more ecotoxicological information is needed in the case of carbon-based NM, as they are expected to considerably increase in terms of released amounts and applications in the near future.

Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms

The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.

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.

Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity

Engineered nanomaterials (ENMs) are ubiquitous nowadays, finding their application in different fields of technology and various consumer products. Virtually any chemical can be manipulated at the nano-scale to display unique characteristics which makes them appealing over larger sized materials. As the production and development of ENMs have increased considerably over time, so too have concerns regarding their adverse effects and environmental impacts. It is unfeasible to assess the risks associated with every single ENM through in vivo or in vitro experiments. As an alternative, in silico methods can be employed to evaluate ENMs. To perform such an evaluation, we collected data from databases and literature to create classification models based on machine learning algorithms in accordance with the principles laid out by the OECD for the creation of QSARs. The aim was to investigate the performance of various machine learning algorithms towards predicting a well-defined in vivo toxicity endpoint (Daphnia magna immobilization) and also to identify which features are important drivers of D. magna in vivo nanotoxicity. Results indicated highly comparable model performance between all algorithms and predictive performance exceeding ∼0.7 for all evaluated metrics (e.g. accuracy, sensitivity, specificity, balanced accuracy, Matthews correlation coefficient, area under the receiver operator characteristic curve). The random forest, artificial neural network, and k-nearest neighbor models displayed the best performance but this was only marginally better compared to the other models. Furthermore, the variable importance analysis indicated that molecular descriptors and physicochemical properties were generally important within most models, while features related to the exposure conditions produced slightly conflicting results. Lastly, results also indicate that reliable and robust machine learning models can be generated for in vivo endpoints with smaller datasets.

The effect of AgNPS bio-functionalization on the cytotoxicity of the yeast Saccharomyces cerevisiae

This work used Sedum praealtum leaf extract to synthesize silver nanoparticles (AgNPs) in a single step. The cytotoxicity of AgNPs was studied with the yeast Saccharomyces cerevisiae W303-1. In addition, the antioxidant activity of the DPPH radical was studied both in the extract of S. praealtum and in the AgNPs. UV-Vis spectroscopy determined the presence of AgNPs by the location of the surface plasmon resonance (SPR) band at 434 nm. TEM and XRD analyzes show AgNPs with fcc structure and hemispherical morphology. Also, AgNPs range in size from 5 to 25 nm and have an average size of 14 nm. 1H NMR, FTIR, and UV-Vis spectroscopy techniques agreed that glycosidic compounds were the main phytochemical components responsible for the reduction and stabilization of AgNPs. In addition, AgNPs presented a maximum of 12% toxicity in yeast attributed to the generation of ROS. Consequently, there was low bioactivity because glycoside compounds cover the biosynthesized AgNPs from S. praealtum. These findings allow applications of AgNPs involving contact with mammals and higher organisms.

Valorization of agro-industrial biowaste to green nanomaterials for wastewater treatment: Approaching green chemistry and circular economy principles

Water pollution is one of the most critical issues worldwide and is a priority in all scientific agendas. Green nanotechnology presents a plethora of promising avenues for wastewater treatment. This review discusses the current trends in the valorization of zero-cost, biodegradable, and readily available agro-industrial biowaste to produce green bio-nanocatalysts and bio-nanosorbents for wastewater treatment. The promising roles of green bio-nanocatalysts and bio-nanosorbents in removing organic and inorganic water contaminants are discussed. The potent antimicrobial activity of bio-derived nanodisinfectants against water-borne pathogenic microbes is reviewed. The bioactive molecules involved in the chelation and tailoring of green synthesized nanomaterials are highlighted along with the mechanisms involved. Furthermore, this review emphasizes how the valorization of agro-industrial biowaste to green nanomaterials for wastewater treatment adheres to the fundamental principles of green chemistry, circular economy, nexus thinking, and zero-waste manufacturing. The potential economic, environmental, and health impacts of valorizing agro-industrial biowaste to green nanomaterials are highlighted. The challenges and future outlooks for the management of agro-industrial biowaste and safe application of green nanomaterials for wastewater treatment are summarized.

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.

Development of a method for cadmium ion removal from the water using nano γ-alumina/β-cyclodextrin

Cadmium is one of the heavy metals, which is harmful to humans and animals. The toxicity of this metal in the body has caused many studies to remove it in water and soil. Because according to WHO, the maximum concentration of cadmium in drinking water is 3 μg/L. In this study, trace amount of Cd ion or Cd(II) in water and in the industrial effluent sample were determined via the solid phase extraction approach based on the γ-Alumina/β-Cyclodextrin as a sorbent followed by flame atomic absorption spectrometry. The effects of various parameters such as pH, the Cd(II) concentration, amount of sorbent, and type and concentration of the eluting agents were determined on the removal efficiency. Maximum removal of Cd(II) was obtained at pH 7. The limit of detection (LOD) and repeatability (RSD%) values (0.389) obtained were found to be in the ranges of 6.77-6.81 μg/L. The results showed adsorbed cadmium ions are recovered on the nano γ- alumina/β-cyclodextrin surface with an optimum amount of 16 mL of 0.3 M nitric acid as eluting agent at pH 7.

Surface Functionalization of Graphene-Based Materials: Biological Behavior, Toxicology, and Safe-By-Design Aspects

The increasing exploitation of graphene-based materials (GBMs) is driven by their unique properties and structures, which ignite the imagination of scientists and engineers. At the same time, the very properties that make them so useful for applications lead to growing concerns regarding their potential impacts on human health and the environment. Since GBMs are inert to reaction, various attempts of surface functionalization are made to make them reactive. Herein, surface functionalization of GBMs, including those intentionally designed for specific applications, as well as those unintentionally acquired (e.g., protein corona formation) from the environment and biota, are reviewed through the lenses of nanotoxicity and design of safe materials (safe-by-design). Uptake and toxicity of functionalized GBMs and the underlying mechanisms are discussed and linked with the surface functionalization. Computational tools that can predict the interaction of GBMs behavior with their toxicity are discussed. A concise framing of current knowledge and key features of GBMs to be controlled for safe and sustainable applications are provided for the community.

New Multi-Walled carbon nanotube of industrial interest induce cell death in murine fibroblast cells

The search for new nanomaterials has brought to the multifactorial industry several opportunities for use and applications for existing materials. Carbon nanotubes (CNT), for example, present excellent properties which allow us to assume a series of applications, however there is concern in the industrial scope about possible adverse health effects related to constant exposure for inhalation or direct skin contact. Thus, using cell models is the fastest and safest way to assess the effects of a new material. The aim of this study was to investigate the cytotoxic profile in LA9 murine fibroblast lineage, of a new multi-walled carbon nanotube (MWCNT) that was functionalized with tetraethylenepentamine (TEPA) to obtain better physical-chemical characteristics for industrial use. The modifications presented in the CNT cause concern, as they can change its initial characteristics, making this nanomaterial harmful. HR-TEM, FE-SEM and zeta potential were used for the characterization. Cytotoxicity and cell proliferation tests, oxidative and nitrosative stress analyzes and inflammatory cytokine assay (TNF-α) were performed. The main findings demonstrated a reduction in cell viability, increased release of intracellular ROS, accompanied by an increase in TNF-α, indicating an important inflammatory profile. Confirmation of the data was performed by flow cytometry and ImageXpress with apoptosis/necrosis markers. These data provide initial evidence that OCNT-TEPA has a cytotoxic profile dependent on the concentration of LA9 fibroblasts, since there was an increase in free radicals, inflammation induction and cell death, suggesting that continuous exposure to this nanoparticle can cause damage to different tissues in the organism.

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.

Too small to matter? Physicochemical transformation and toxicity of engineered nTiO2, nSiO2, nZnO, carbon nanotubes, and nAg

Engineered nanomaterials (ENMs) refer to a relatively novel class of materials that are increasingly prevalent in various consumer products and industrial applications - most notably for their superlative physicochemical properties when compared with conventional materials. However, consumer products inevitably degrade over the course of their lifetime, releasing ENMs into the environment. These ENMs undergo physicochemical transformations and subsequently accumulate in the environment, possibly leading to various toxic effects. As a result, a significant number of studies have focused on identifying the possible transformations and environmental risks of ENMs, with the objective of ensuring a safe and responsible application of ENMs in consumer products. This review aims to consolidate the results from previous studies related to each stage of the pathway of ENMs from being embodied in a product to disintegration/transformation in the environment. The scope of this work was defined to include the five most prevalent ENMs based on recent projected production market data, namely: nTiO₂, nSiO₂, nZnO, carbon nanotubes, and nAg. The review focuses on: (i) models developed to estimate environmental concentrations of ENMs; (ii) the possible physicochemical transformations; (iii) cytotoxicity and genotoxicity effects specific to each ENM selected; and (iv) a discussion to identify potential gaps in the studies conducted and recommend areas where further investigation is warranted.

Predicting nanotoxicity by an integrated machine learning and metabolomics approach

Predicting the biological responses to engineered nanoparticles (ENPs) is critical to their environmental health assessment. The disturbances of metabolic pathways reflect the global profile of biological responses to ENPs but are difficult to predict due to the highly heterogeneous data from complicated biological systems and various ENP properties. Herein, integrating multiple machine learning models and metabolomics enabled accurate prediction of the disturbance of metabolic pathways induced by 33 ENPs. Screening nine typical properties of ENPs identified type and size as the top features determining the effects on metabolic pathways. Similarity network analysis and decision tree models overcame the highly heterogeneous data sources to visualize and judge the occurrence of metabolic pathways depending on the sorting priority features. The model accuracy was verified by animal experiments and reached 75%-100%, even for the prediction of ENPs outside of databases. The models also predicted metabolic pathway-related histopathology. This work provides an approach for the quick assessment of environmental health risks induced by known and unknown ENPs.

Toxicity of Carbon, Silicon, and Metal-Based Nanoparticles to Sea Urchin Strongylocentrotus Intermedius

With the increasing annual production of nanoparticles (NPs), the risks of their harmful influence on the environment and human health are rising. However, our knowledge about the mechanisms of interaction between NPs and living organisms is limited. Prior studies have shown that echinoderms, and especially sea urchins, represent one of the most suitable models for risk assessment in environmental nanotoxicology. To the best of the authors’ knowledge, the sea urchin Strongylocentrotus intermedius has not been used for testing the toxicity of NPs. The present study was designed to determine the effect of 10 types of common NPs on spermatozoa activity, egg fertilization, and early stage of embryo development of the sea urchin S. intermedius. In this research, we used two types of multiwalled carbon nanotubes (CNT-1 and CNT-2), two types of carbon nanofibers (CNF-1 and CNF-2), two types of silicon nanotubes (SNT-1 and SNT-2), nanocrystals of cadmium and zinc sulfides (CdS and ZnS), gold NPs (Au), and titanium dioxide NPs (TiO₂). The results of the embryotoxicity test showed the following trend in the toxicity level of used NPs: Au > SNT-2 > SNT-1 > CdS > ZnS > CNF-2 > CNF-1 > TiO₂ > CNT-1 > CNT-2. This research confirmed that the sea urchin S. intermedius can be considered as a sensitive and stable test model in marine nanotoxicology.

Aquatic toxicity and mode of action of CdS and ZnS nanoparticles in four microalgae species

This study reports the differences in toxic action between cadmium sulfide (CdS) and zinc sulfide (ZnS) nanoparticles (NPs) prepared by recently developed xanthate-mediated method. The aquatic toxicity of the synthesized NPs on four marine microalgae species was explored. Growth rate, esterase activity, membrane potential, and morphological changes of microalgae cells were evaluated using flow cytometry and optical microscopy. CdS and ZnS NPs demonstrated similar level of general toxicity and growth-rate inhibition to all used microalgae species, except the red algae P. purpureum. More specifically, CdS NPs caused higher inhibition of growth rate for C. muelleri and P. purpureum, while ZnS NPs were more toxic for A. ussuriensis and H. akashiwo species. Our findings suggest that the sensitivity of different microalgae species to CdS and ZnS NPs depends on the chemical composition of NPs and their ability to interact with the components of microalgal cell-wall. The red microalga was highly resistant to ZnS NPs most likely due to the presence of phycoerythrin proteins in the outer membrane bound Zn²⁺ cations defending their cells from further toxic influence. The treatment with CdS NPs caused morphological changes and biochemical disorder in all tested microalgae species. The toxicity of CdS NPs is based on their higher photoactivity under visible light irradiation and lower dissociation in water, which allows them to generate more reactive oxygen species and create a higher risk of oxidative stress to aquatic organisms. The results of this study contribute to our understanding of the parameters affecting the aquatic toxicity of semiconductor NPs and provide a basis for further investigations.

Toxicity of Carbon, Silicon, and Metal-Based Nanoparticles to the Hemocytes of Three Marine Bivalves

Simple Summary

The growing nanotechnology industry disposes of a variety of nanoparticles with different physiochemical properties in everyday life. However, the dependence of the safety and toxicity of nanoparticles on their physicochemical properties remains unclear. Bivalve molluscs represent an efficient model for the investigation of nanoparticle toxicity owing to their filtrating ability and feeding on particles suspended in the water. Moreover, the blood cells of bivalve molluscs, the hemocytes, have been suggested as a good analog test-object to mammalian immune cells, phagocytes. In this study, we used hemocytes of three marine bivalve species, namely, Crenomytilus grayanus, Modiolus modiolus, and Arca boucardi, to evaluate and compare the toxic effects of 10 different types of nanoparticles. We gave short-term exposure of the nanoparticles to the hemocytes and registered viability and changes in their cell membrane polarization by employing flow cytometry. Metal-based nanoparticles were the most toxic to the cells of all three tested bivalve mollusc species. However, the sensitivity to different nanoparticle types varied between species. Moreover, the registered cell membrane depolarization indicated an early toxic response and raised concern that chronic long-term exposure of nanoparticles (even if they were previously declared as safe) is a serious threat for aquatic organisms.

Abstract

Nanoparticles (NPs) have broad applications in medicine, cosmetics, optics, catalysis, environmental purification, and other areas nowadays. With increasing annual production of NPs, the risks of their harmful influence on the environment and human health are also increasing. Currently, our knowledge about the mechanisms of the interaction between NPs and living organisms is limited. The marine species and their habitat environment are under continuous stress owing to the anthropogenic activities, which result in the release of NPs in the aquatic environment. We used a bioassay model with hemocytes of three bivalve mollusc species, namely, Crenomytilus grayanus, Modiolus modiolus, and Arca boucardi, to evaluate the toxicity of 10 different types of NPs. Specifically, we compared the cytotoxic effects and cell-membrane polarization changes in the hemocytes exposed to carbon nanotubes, carbon nanofibers, silicon nanotubes, cadmium and zinc sulfides, Au-NPs, and TiO₂ NPs. Viability and the changes in hemocyte membrane polarization were measured by the flow cytometry method. The highest aquatic toxicity was registered for metal-based NPs, which caused cytotoxicity to the hemocytes of all the studied bivalve species. Our results also highlighted different sensitivities of the used tested mollusc species to specific NPs.

Comparison of the Level and Mechanisms of Toxicity of Carbon Nanotubes, Carbon Nanofibers, and Silicon Nanotubes in Bioassay with Four Marine Microalgae

Nanoparticles (NPs) have various applications in medicine, cosmetics, optics, catalysis, environmental purification, and other areas nowadays. With an increasing annual production of NPs, the risks of their harmful influence to the environment and human health is rising. Currently, our knowledge about the mechanisms of interaction between NPs and living organisms is limited. Additionally, poor understanding of how physical and chemical characteristic and different conditions influence the toxicity of NPs restrict our attempts to develop the standards and regulations which might allow us to maintain safe living conditions. The marine species and their habitat environment are under continuous stress due to anthropogenic activities which result in the appearance of NPs in the aquatic environment. Our study aimed to evaluate and compare biochemical effects caused by the influence of different types of carbon nanotubes, carbon nanofibers, and silica nanotubes on four marine microalgae species. We evaluated the changes in growth-rate, esterase activity, membrane polarization, and size changes of microalgae cells using flow cytometry method. Our results demonstrated that toxic effects caused by the carbon nanotubes strongly correlated with the content of heavy metal impurities in the NPs. More hydrophobic carbon NPs with less ordered structure had a higher impact on the red microalgae P. purpureum because of higher adherence between the particles and mucous covering of the algae. Silica NPs caused significant inhibition of microalgae growth-rate predominantly produced by mechanical influence.