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

Overview of in vitro-in vivo extrapolation approaches for the risk assessment of nanomaterial toxicity

Nanomaterials are increasingly used in many applications due to their enhanced properties. To ensure their safety for humans and the environment, nanomaterials need to be evaluated for their potential risk. The risk assessment analysis on the nanomaterials based on animal or in vivo studies is accompanied by several concerns, including animal welfare, time and cost needed for the studies. Therefore, incorporating in vitro studies in the risk assessment process is increasingly considered. To be able to analyze the potential risk of nanomaterial to human health, there are factors to take into account. Utilizing in vitro data in the risk assessment analysis requires methods that can be used to translate in vitro data to predict in vivo phenomena (in vitro-in vivo extrapolation (IVIVE) methods) to be incorporated, to obtain a more accurate result. Apart from the experiments and species conversion (for example, translation between the cell culture, animal and human), the challenge also includes the unique properties of nanomaterials that might cause them to behave differently compared to the same materials in a bulk form. This overview presents the IVIVE techniques that are developed to extrapolate pharmacokinetics data or doses. A brief example of the IVIVE methods for chemicals is provided, followed by a more detailed summary of available IVIVE methods applied to nanomaterials. The IVIVE techniques discussed include the comparison between in vitro and in vivo studies, methods to rene the dose metric or the in vitro models, allometric approach, mechanistic modeling, Multiple-Path Particle Dosimetry (MPPD), methods using organ burden data and also approaches that are currently being developed.

Drug solubilization in dog intestinal fluids with and without administration of lipid-based formulations

The use of animal experiments can be minimized with computational models capable of reflecting the simulated environments. One such environment is intestinal fluid and the colloids formed in it. In this study we used molecular dynamics simulations to investigate solubilization patterns for three model drugs (carvedilol, felodipine and probucol) in dog intestinal fluid, a lipid-based formulation, and a mixture of both. We observed morphological transformations that lipids undergo due to the digestion process in the intestinal environment. Further, we evaluated the effect of bile salt concentration and observed the importance of interindividual variability. We applied two methods of estimating solubility enhancement based on the simulated data, of which one was in good qualitative agreement with the experimentally observed solubility enhancement. In addition to the computational simulations, we also measured solubility in i) aspirated dog intestinal fluid samples and ii) simulated canine intestinal fluid in the fasted state, and found there was no statistical difference between the two. Hence, a simplified dissolution medium suitable for in vitro studies provided physiologically relevant data for the systems explored. The computational protocol used in this study, coupled with in vitro studies using simulated intestinal fluids, can serve as a useful prescreening tool in the process of drug delivery strategies development.

Development of a bottom-up coarse-grained model for interactions of lipids with TiO 2 nanoparticles

Understanding interactions of inorganic nanoparticles with biomolecules is important in many biotechnology, nanomedicine, and toxicological research, however, the size of typical nanoparticles makes their direct modeling by atomistic simulations unfeasible. Here, we present a bottom-up coarse-graining approach for modeling titanium dioxide (TiO  2 ) nanomaterials in contact with phospholipids that uses the inverse Monte Carlo method to optimize the effective interactions from the structural data obtained in small-scale all-atom simulations of TiO  2 surfaces with lipids in aqueous solution. The resulting coarse-grained models are able to accurately reproduce the structural details of lipid adsorption on different titania surfaces without the use of an explicit solvent, enabling significant computational resource savings and favorable scaling. Our coarse-grained simulations show that small spherical TiO  2 nanoparticles ( r = 2 nm) can only be partially wrapped by a lipid bilayer with phosphoethanolamine headgroups, however, the lipid adsorption increases with the radius of the nanoparticle. The current approach can be used to study the effect of the size and shape of TiO  2 nanoparticles on their interactions with cell membrane lipids, which can be a determining factor in membrane wrapping as well as the recently discovered phenomenon of nanoquarantining, which involves the formation of layered nanomaterial-lipid structures.

Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size

A strong need exists for broadly applicable nano-QSARs, capable of predicting toxicological outcomes towards untested species and nanomaterials, under different environmental conditions. Existing nano-QSARs are generally limited to only a few species but the inclusion of species characteristics into models can aid in making them applicable to multiple species, even when toxicity data is not available for biological species. Species traits were used to create classification- and regression machine learning models to predict acute toxicity towards aquatic species for metallic nanomaterials. Afterwards, the individual classification- and regression models were stacked into a meta-model to improve performance. Additionally, the uncertainty and limitations of the models were assessed in detail (beyond the OECD principles) and it was investigated whether models would benefit from the addition of more data. Results showed a significant improvement in model performance following model stacking. Investigation of model uncertainties and limitations highlighted the discrepancy between the applicability domain and accuracy of predictions. Data points outside of the assessed chemical space did not have higher likelihoods of generating inadequate predictions or vice versa. It is therefore concluded that the applicability domain does not give complete insight into the uncertainty of predictions and instead the generation of prediction intervals can help in this regard. Furthermore, results indicated that an increase of the dataset size did not improve model performance. This implies that larger dataset sizes may not necessarily improve model performance while in turn also meaning that large datasets are not necessarily required for prediction of acute toxicity with nano-QSARs.

Big data and its impact on the 3Rs: a home cage monitoring oriented review

Undisturbed home cage recording of mouse activity and behavior has received increasing attention in recent years. In parallel, several technologies have been developed in a bid to automate data collection and interpretation. Thanks to these expanding technologies, massive datasets can be recorded and saved in the long term, providing a wealth of information concerning animal wellbeing, clinical status, baseline activity, and subsequent deviations in case of experimental interventions. Such large datasets can also serve as a long-term reservoir of scientific data that can be reanalyzed and repurposed upon need. In this review, we present how the impact of Big Data deriving from home cage monitoring (HCM) data acquisition, particularly through Digital Ventilated Cages (DVCs), can support the application of the 3Rs by enhancing Refinement, Reduction, and even Replacement of research in animals.

Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches

Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.

Quantitative Phase Imaging as Sensitive Screening Method for Nanoparticle-Induced Cytotoxicity Assessment

The assessment of nanoparticle cytotoxicity is challenging due to the lack of customized and standardized guidelines for nanoparticle testing. Nanoparticles, with their unique properties, can interfere with biochemical test methods, so multiple tests are required to fully assess their cellular effects. For a more reliable and comprehensive assessment, it is therefore imperative to include methods in nanoparticle testing routines that are not affected by particles and allow for the efficient integration of additional molecular techniques into the workflow. Digital holographic microscopy (DHM), an interferometric variant of quantitative phase imaging (QPI), has been demonstrated as a promising method for the label-free assessment of the cytotoxic potential of nanoparticles. Due to minimal interactions with the sample, DHM allows for further downstream analyses. In this study, we investigated the capabilities of DHM in a multimodal approach to assess cytotoxicity by directly comparing DHM-detected effects on the same cell population with two downstream biochemical assays. Therefore, the dry mass increase in RAW 264.7 macrophages and NIH-3T3 fibroblast populations measured by quantitative DHM phase contrast after incubation with poly(alkyl cyanoacrylate) nanoparticles for 24 h was compared to the cytotoxic control digitonin, and cell culture medium control. Viability was then determined using a metabolic activity assay (WST-8). Moreover, to determine cell death, supernatants were analyzed for the release of the enzyme lactate dehydrogenase (LDH assay). In a comparative analysis, in which the average half-maximal effective concentration (EC50) of the nanocarriers on the cells was determined, DHM was more sensitive to the effect of the nanoparticles on the used cell lines compared to the biochemical assays.

Beyond the adverse effects of the systemic route: Exploiting nanocarriers for the topical treatment of skin cancers

Skin cancer is a heterogeneous disease that can be divided into two main groups, melanoma and nonmelanoma skin cancers. Conventional therapies for skin cancer have numerous systemic side effects and a high recurrence rate. Topical treatment is an alternative approach, but drug permeability remains a challenge. Therefore, nanocarriers appear as important nanotechnology tools that reduces both the side effects and improves clinical outcomes. This is why they are attracting growing interest. In this review, scientific articles on the use of nanocarriers for the topical treatment of skin cancer were collected. Despite the promising results of the presented nanocarriers and considering that some of them are already on the market, there is an urgent need for investment in the development of manufacturing methods, as well as of suitable toxicological and regulatory evaluations, since the conventional methods currently used to develop these nanocarriers-based products are more time-consuming and expensive than conventional products.

Curcumol metabolized by rat liver S9 fraction and orally administered in mouse suppressed the proliferation of colon cancer in vitro and in vivo

Following 3R (reduction, refinement, and replacement) principles, we employed the rat liver S9 fraction to mimic liver metabolism of curcumol having high in vitro IC50 on cancer cells. In HCT116 and HT29 colon cancer cells, the metabolites of curcumol by S9 fraction exerted more enhanced activity in inducing cell cycle arrest and apoptosis via regulating the expression of cyclin D1, CDK1, p21, PARP and Bcl-2 than curcumol. In addition, oral administration of curcumol at 4 mg/kg BW significantly suppressed the development of colon tumor induced by azoxymethane/dextran sulfate sodium, and induced cell cycle arrest and apoptosis in tumor tissues. In mass analysis, curcumenol and curzerene were identified as the metabolites of curcumol by S9 fraction metabolism. Taken together, curcumol metabolites showed the enhanced suppressive effect on colon cancer, suggesting that S9 fraction can be considered as simple, fast, and bio-mimicking platform for the screening of chemical libraries on different chronic diseases.

Selective Accumulation of Poly(Lactic-Co-Glycolic Acid) Nanoparticles in Endotheliocytes and Mesenchymal Stromal Cells Cultured as Mixed-Cell Spheroids

The use of drug-loaded nanoparticles is an actively developed approach in targeted cancer therapy. Prevascularized spheroids generated from mesenchymal stem cells and endotheliocytes are considered as a model to evaluate the tropism of therapeutic nanoparticles to a specific tissue. Nanoparticles based on co-polymer of lactic and glycolic acids (poly(lactic-co-glycolic acid; PLGA) labeled with cyanine dye (Cy5) were incubated with prevascularized spheroids, and the rate of their penetration and their distribution in the spheroid-forming cells were evaluated. Endotheliocytes more intensively accumulated nanoparticles than mesenchymal stem cells: the number of nanoparticles in mixed-cell spheroids of mesenchymal stem cells and endotheliocytes was greater than in spheroids built solely of mesenchymal stem cells by 5±1.2 times. The developed 3D in vitro cell model provides a low-cost way to assess tissue tropism of therapeutic nanoparticles under conditions closer to natural in comparison with 2D culture.

FDA Modernization Act 2.0: An insight from nondeveloping country

Animal testing is required in drug development research and is crucial for assessing the efficacy and safety of medications before they are commercialized. However, the newly furnished Food and Drug Administration Modernization Act 2.0 has given new insight into drug development. It opens a new door by offering an alternative testing method for developing a new drug without using animals. This newly proposed system may potentially significantly impact nondeveloped countries worldwide. In this study, we explore the alternative testing options such as in silico modeling, human tissue-on-chip engineering, animal-free recombinant antibodies, tissue engineering, and artificial intelligence presented by this act and discuss its implications for nondeveloped countries.

Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex Mixtures of Neutral Organic Compounds

The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.

RNA Combined with Nanoformulation to Advance Therapeutic Technologies

Nucleic acid-based therapies have the potential to address numerous diseases that pose significant challenges to more traditional methods. RNA-based therapies have emerged as a promising avenue, utilizing nanoformulation treatments to target a range of pathologies. Nanoformulation offers several advantages compared to other treatment modalities, including targeted delivery, low toxicity, and bioactivity suitable for drug loading. At present, various types of nanoformulations are available, such as liposomes, polymeric nanoparticles (NPs), magnetic NPs, nanoshells, and solid lipid nanoparticles (SLNs). RNA-based therapy utilizes intracellular gene nanoparticles with messenger RNA (mRNA) emerging prominently in cancer therapy and immunotechnology against infectious diseases. The approval of mRNA-based technology opens doors for future technological advancements, particularly self-amplifying replicon RNA (repRNA). RepRNA is a novel platform in gene therapy, comprising viral RNA with a unique molecular property that enables the amplification of all encoded genetic information countless times. As a result, repRNA-based therapies have achieved significant levels of gene expression. In this context, the primary objective of this study is to furnish a comprehensive review of repRNA and its applications in nanoformulation treatments, with a specific focus on encapsulated nanoparticles. The overarching goal is to provide an extensive overview of the use of repRNA in conjunction with nanoformulations across a range of treatments and therapies.

Artificial augmented dataset for the enhancement of nano-QSARs models. A methodology based on topological projections

Nanoinformatics demands accurate predictive models to assess the potential hazards of nanomaterials (NMs). However, limited data availability and the diverse nature of NMs physicochemical properties and their interaction with biological media, hinder the development of robust nano-Quantitative Structure-Activity Relationship (QSAR) models. This article proposes an approach that combines artificially data generation techniques and topological projections to address the challenges of insufficient dataset sizes and their limited representativeness of the chemical space. By leveraging the rich information embedded in the topological features, this methodology enhances the representation of the chemical space, enabling a more an exploration of the structure-activity relationships. We demonstrate the efficacy of our approach through extensive experiments, employing various machine learning regression algorithms to validate the methodology. Finally, we compare two different resampling approaches based on different modeling scenarios. The results showcase a significant improved predictive performance of QSAR models demonstrating a promising strategy to overcome the limitations of small datasets in the field of nanoinformatics. The proposed approach offers noteworthy potential for advancing nanoinformatics research within the nanosafety domain by enabling the development of more accurate predictive models for assessing the potential hazards associated with NMs.

Deimos: A novel automated methodology for optimal grouping. Application to nanoinformatics case studies

In this study we present deimos, a computational methodology for optimal grouping, applied on the read-across prediction of engineered nanomaterials' (ENMs) toxicity-related properties. The method is based on the formulation and the solution of a mixed-integer optimization program (MILP) problem that automatically and simultaneously performs feature selection, defines the grouping boundaries according to the response variable and develops linear regression models in each group. For each group/region, the characteristic centroid is defined in order to allocate untested ENMs to the groups. The deimos MILP problem is integrated in a broader optimization workflow that selects the best performing methodology between the standard multiple linear regression (MLR), the least absolute shrinkage and selection operator (LASSO) models and the proposed deimos multiple-region model. The performance of the suggested methodology is demonstrated through the application to benchmark ENMs datasets and comparison with other predictive modelling approaches. However, the proposed method can be applied to property prediction of other than ENM chemical entities and it is not limited to ENMs toxicity prediction.

Editorial of Special Issue: The Toxicity of Nanomaterials and Legacy Contaminants: Risks to the Environment and Human Health

Nanotechnology and the incorporation of nanomaterials (NM) into everyday products help to solve problems in society and improve the quality of life, allowing for major advances in the technological, industrial, and medical fields [...].

Recent Advances in Nano-Enabled Seed Treatment Strategies for Sustainable Agriculture: Challenges, Risk Assessment, and Future Perspectives

Agro seeds are vulnerable to environmental stressors, adversely affecting seed vigor, crop growth, and crop productivity. Different agrochemical-based seed treatments enhance seed germination, but they can also cause damage to the environment; therefore, sustainable technologies such as nano-based agrochemicals are urgently needed. Nanoagrochemicals can reduce the dose-dependent toxicity of seed treatment, thereby improving seed viability and ensuring the controlled release of nanoagrochemical active ingredients However, the applications of nanoagrochemicals to plants in the field raise concerns about nanomaterial safety, exposure levels, and toxicological implications to the environment and human health. In the present comprehensive review, the development, scope, challenges, and risk assessments of nanoagrochemicals on seed treatment are discussed. Moreover, the implementation obstacles for nanoagrochemicals use in seed treatments, their commercialization potential, and the need for policy regulations to assess possible risks are also discussed. Based on our knowledge, this is the first time that we have presented legendary literature to readers in order to help them gain a deeper understanding of upcoming nanotechnologies that may enable the development of future generation seed treatment agrochemical formulations, their scope, and potential risks associated with seed treatment.

Experimental Methods for the Biological Evaluation of Nanoparticle-Based Drug Delivery Risks

Many novel medical therapies use nanoparticle-based drug delivery systems, including nanomaterials through drug delivery systems, diagnostics, or physiologically active medicinal products. The approval of nanoparticles with advanced therapeutic and diagnostic potentials for applications in medication and immunization depends strongly on their synthesizing procedure, efficiency of functionalization, and biological safety and biocompatibility. Nanoparticle biodistribution, absorption, bioavailability, passage across biological barriers, and biodistribution are frequently assessed using bespoke and biological models. These methods largely rely on in vitro cell-based evaluations that cannot predict the complexity involved in preclinical and clinical studies. Therefore, assessing the nanoparticle risk has to involve pharmacokinetics, organ toxicity, and drug interactions manifested at multiple cellular levels. At the same time, there is a need for novel approaches to examine nanoparticle safety risks due to increased constraints on animal exploitation and the demand for high-throughput testing. We focus here on biological evaluation methodologies that provide access to nanoparticle interactions with the organism (positive or negative via toxicity). This work aimed to provide a perception regarding the risks associated with the utilization of nanoparticle-based formulations with a particular focus on assays applied to assess the cytotoxicity of nanomaterials.

In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES

A simulation of the effect of metal nano-oxides at various concentrations (25, 50, 100, and 200 milligrams per millilitre) on cell viability in THP-1 cells (%) based on data on the molecular structure of the oxide and its concentration is proposed. We used a simplified molecular input-line entry system (SMILES) to represent the molecular structure. So-called quasi-SMILES extends usual SMILES with special codes for experimental conditions (concentration). The approach based on building up models using quasi-SMILES is self-consistent, i.e., the predictive potential of the model group obtained by random splits into training and validation sets is stable. The Monte Carlo method was used as a basis for building up the above groups of models. The CORAL software was applied to building the Monte Carlo calculations. The average determination coefficient for the five different validation sets was R² = 0.806 ± 0.061.

Alternative Methods as Tools for Obesity Research: In Vitro and In Silico Approaches

The study of adipogenesis is essential for understanding and treating obesity, a multifactorial problem related to body fat accumulation that leads to several life-threatening diseases, becoming one of the most critical public health problems worldwide. In this review, we propose to provide the highlights of the adipogenesis study based on in vitro differentiation of human mesenchymal stem cells (hMSCs). We list in silico methods, such as molecular docking for identification of molecular targets, and in vitro approaches, from 2D, more straightforward and applied for screening large libraries of substances, to more representative physiological models, such as 3D and bioprinting models. We also describe the development of physiological models based on microfluidic systems applied to investigate adipogenesis in vitro. We intend to identify the main alternative models for adipogenesis evaluation, contributing to the direction of preclinical research in obesity. Future directions indicate the association of in silico and in vitro techniques to bring a clear picture of alternative methods based on adipogenesis as a tool for obesity research.

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.

Analysis of Nanomaterials on Biological and Environmental Systems and New Analytical Methods for Improved Detection

The advancing field of nanoscience has produced lower mass, smaller size, and expanded chemical composition nanoparticles over recent years. These new nanoparticles have challenged traditional analytical methods of qualification and quantification. Such advancements in nanoparticles and nanomaterials have captured the attention of toxicologists with concerns regarding the environment and human health impacts. Given that nanoparticles are only limited by size (1–100 nm), their chemical and physical characteristics can drastically change and thus alter their overall nanotoxicity in unpredictable ways. A significant limitation to the development of nanomaterials is that traditional regulatory and scientific methods used to assess the biological and environmental toxicity of chemicals do not generally apply to the assessment of nanomaterials. Significant research effort has been initiated, but much more is still needed to develop new and improved analytical measurement methods for detecting and quantitating nanomaterials in biological and environmental systems.

Synthesis, Toxicity Assessment, Environmental and Biomedical Applications of MXenes: A Review

MXenes are a family of two-dimensional (2D) composite materials based on transition metal carbides, nitrides and carbonitrides that have been attracting attention since 2011. Combination of electrical and mechanical properties with hydrophilicity makes them promising materials for biomedical applications. This review briefly discusses methods for the synthesis of MXenes, their potential applications in medicine, ranging from sensors and antibacterial agents to targeted drug delivery, cancer photo/chemotherapy, tissue engineering, bioimaging, and environmental applications such as sensors and adsorbents. We focus on in vitro and in vivo toxicity and possible mechanisms. We discuss the toxicity analogies of MXenes and other 2D materials such as graphene, mentioning the greater biocompatibility of MXenes. We identify existing barriers that hinder the formation of objective knowledge about the toxicity of MXenes. The most important of these barriers are the differences in the methods of synthesis of MXenes, their composition and structure, including the level of oxidation, the number of layers and flake size; functionalization, test concentrations, duration of exposure, and individual characteristics of biological test objects Finally, we discuss key areas for further research that need to involve new methods of nanotoxicology, including predictive computational methods. Such studies will bring closer the prospect of widespread industrial production and safe use of MXene-based products.

Investigation of potential descriptors of chemical compounds on prevention of nephrotoxicity via QSAR approach

Drug-induced nephrotoxicity remains a common problem after exposure to medications and diagnostic agents, which may be heightened in the kidney microenvironment and deteriorate kidney function. In this study, the toxic effects of fourteen marked drugs with the individual chemical structure were evaluated in kidney cells. The quantitative structure-activity relationship (QSAR) approach was employed to investigate the potential structural descriptors of each drug-related to their toxic effects. The most reasonable equation of the QSAR model displayed that the estimated regression coefficients such as the number of ring assemblies, three-membered rings, and six-membered rings were strongly related to toxic effects on renal cells. Meanwhile, the chemical properties of the tested compounds including carbon atoms, bridge bonds, H-bond donors, negative atoms, and rotatable bonds were favored properties and promote the toxic effects on renal cells. Particularly, more numbers of rotatable bonds were positively correlated with strong toxic effects that displayed on the most toxic compound. The useful information discovered from our regression QSAR models may help to identify potential hazardous moiety to avoid nephrotoxicity in renal preventive medicine.

Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment

The growing development and applications of nanomaterials lead to an increasing release of these materials in the environment. The adverse effects they may elicit on ecosystems or human health are not always fully characterized. Such potential toxicity must be carefully assessed with the underlying mechanisms elucidated. To that purpose, different approaches can be used. First, experimental toxicology consisting of conducting in vitro or in vivo experiments (including clinical studies) can be used to evaluate the nanomaterial hazard. It can rely on variable models (more or less complex), allowing the investigation of different biological endpoints. The respective advantages and limitations of in vitro and in vivo models are discussed as well as some issues associated with experimental nanotoxicology. Perspectives of future developments in the field are also proposed. Second, computational nanotoxicology, i.e., in silico approaches, can be used to predict nanomaterial toxicity. In this context, we describe the general principles, advantages, and limitations especially of quantitative structure–activity relationship (QSAR) models and grouping/read-across approaches. The aim of this review is to provide an overview of these different approaches based on examples and highlight their complementarity.

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.

Fingerprinting Metabolic Activity and Tissue Integrity of 3D Lung Cancer Spheroids under Gold Nanowire Treatment

Inadequacy of most animal models for drug efficacy assessments has led to the development of improved in vitro models capable of mimicking in vivo exposure scenarios. Among others, 3D multicellular spheroid technology is considered to be one of the promising alternatives in the pharmaceutical drug discovery process. In addition to its physiological relevance, this method fulfills high-throughput and low-cost requirements for preclinical cell-based assays. Despite the increasing applications of spheroid technology in pharmaceutical screening, its application, in nanotoxicity testing is still in its infancy due to the limited penetration and uptake rates into 3D-cell assemblies. To gain a better understanding of gold nanowires (AuNWs) interactions with 3D spheroids, a comparative study of 2D monolayer cultures and 3D multicellular spheroids was conducted using two lung cancer cell lines (A549 and PC9). Cell apoptosis (live/dead assay), metabolic activity, and spheroid integrity were evaluated following exposure to AuNWs at different dose-time manners. Results revealed a distinct different cellular response between 2D and 3D cell cultures during AuNWs treatment including metabolic rates, cell viability, dose-response curves and, uptake rates. Our data also highlighted further need for more physiologically relevant tissue models to investigate in depth nanomaterial-biology interactions. It is important to note that higher concentrations of AuNWs with lower exposure times and lower concentrations of AuNWs with higher exposure times of 3 days resulted in the loss of spheroid integrity by disrupting cell-cell contacts. These findings could help to increase the understanding of AuNWs-induced toxicity on tissue levels and also contribute to the establishment of new analytical approaches for toxicological and drug screening studies.

Use of an in silico knowledge discovery approach to determine mechanistic studies of silver nanoparticles-induced toxicity from in vitro to in vivo

BACKGROUND

Silver nanoparticles (AgNPs) are considered a double-edged sword that demonstrates beneficial and harmful effects depending on their dimensions and surface coating types. However, mechanistic understanding of the size- and coating-dependent effects of AgNPs in vitro and in vivo remains elusive. We adopted an in silico decision tree-based knowledge-discovery-in-databases process to prioritize the factors affecting the toxic potential of AgNPs, which included exposure dose, cell type and AgNP type (i.e., size and surface coating), and exposure time. This approach also contributed to effective knowledge integration between cell-based phenomenological observations and in vitro/in vivo mechanistic explorations.

RESULTS

The consolidated cell viability assessment results were used to create a tree model for generalizing cytotoxic behavior of the four AgNP types: SCS, LCS, SAS, and LAS. The model ranked the toxicity-related parameters in the following order of importance: exposure dose > cell type > particle size > exposure time ≥ surface coating. Mechanistically, larger AgNPs appeared to provoke greater levels of autophagy in vitro, which occurred during the earlier phase of both subcytotoxic and cytotoxic exposures. Furthermore, apoptosis rather than necrosis majorly accounted for compromised cell survival over the above dosage range. Intriguingly, exposure to non-cytotoxic doses of AgNPs induced G2/M cell cycle arrest and senescence instead. At the organismal level, SCS following a single intraperitoneal injection was found more toxic to BALB/c mice as compared to SAS. Both particles could be deposited in various target organs (e.g., spleen, liver, and kidneys). Morphological observation, along with serum biochemical and histological analyses, indicated that AgNPs could produce pancreatic toxicity, apart from leading to hepatic inflammation.

CONCLUSIONS

Our integrated in vitro, in silico, and in vivo study revealed that AgNPs exerted toxicity in dose-, cell/organ type- and particle type-dependent manners. More importantly, a single injection of lethal-dose AgNPs (i.e., SCS and SAS) could incur severe damage to pancreas and raise blood glucose levels at the early phase of exposure.

A Review on Conventional and Advanced Methods for Nanotoxicology Evaluation of Engineered Nanomaterials

Nanotechnology can be defined as the field of science and technology that studies material at nanoscale (1-100 nm). These nanomaterials, especially carbon nanostructure-based composites and biopolymer-based nanocomposites, exhibit excellent chemical, physical, mechanical, electrical, and many other properties beneficial for their application in many consumer products (e.g., industrial, food, pharmaceutical, and medical). The current literature reports that the increased exposure of humans to nanomaterials could toxicologically affect their environment. Hence, this paper aims to present a review on the possible nanotoxicology assays that can be used to evaluate the toxicity of engineered nanomaterials. The different ways humans are exposed to nanomaterials are discussed, and the recent toxicity evaluation approaches of these nanomaterials are critically assessed.

Nanobiotechnological advancements in agriculture and food industry: Applications, nanotoxicity, and future perspectives

The high demand for sufficient and safe food, and continuous damage of environment by conventional agriculture are major challenges facing the globe. The necessity of smart alternatives and more sustainable practices in food production is crucial to confront the steady increase in human population and careless depletion of global resources. Nanotechnology implementation in agriculture offers smart delivery systems of nutrients, pesticides, and genetic materials for enhanced soil fertility and protection, along with improved traits for better stress tolerance. Additionally, nano-based sensors are the ideal approach towards precision farming for monitoring all factors that impact on agricultural productivity. Furthermore, nanotechnology can play a significant role in post-harvest food processing and packaging to reduce food contamination and wastage. In this review, nanotechnology applications in the agriculture and food sector are reviewed. Implementations of nanotechnology in agriculture have included nano- remediation of wastewater for land irrigation, nanofertilizers, nanopesticides, and nanosensors, while the beneficial effects of nanomaterials (NMs) in promoting genetic traits, germination, and stress tolerance of plants are discussed. Furthermore, the article highlights the efficiency of nanoparticles (NPs) and nanozymes in food processing and packaging. To this end, the potential risks and impacts of NMs on soil, plants, and human tissues and organs are emphasized in order to unravel the complex bio-nano interactions. Finally, the strengths, weaknesses, opportunities, and threats of nanotechnology are evaluated and discussed to provide a broad and clear view of the nanotechnology potentials, as well as future directions for nano-based agri-food applications towards sustainability.

Organoid Technology: A Reliable Developmental Biology Tool for Organ-Specific Nanotoxicity Evaluation

Engineered nanomaterials are bestowed with certain inherent physicochemical properties unlike their parent materials, rendering them suitable for the multifaceted needs of state-of-the-art biomedical, and pharmaceutical applications. The log-phase development of nano-science along with improved "bench to beside" conversion carries an enhanced probability of human exposure with numerous nanoparticles. Thus, toxicity assessment of these novel nanoscale materials holds a key to ensuring the safety aspects or else the global biome will certainly face a debacle. The toxicity may span from health hazards due to direct exposure to indirect means through food chain contamination or environmental pollution, even causing genotoxicity. Multiple ways of nanotoxicity evaluation include several in vitro and in vivo methods, with in vitro methods occupying the bulk of the "experimental space." The underlying reason may be multiple, but ethical constraints in in vivo animal experiments are a significant one. Two-dimensional (2D) monoculture is undoubtedly the most exploited in vitro method providing advantages in terms of cost-effectiveness, high throughput, and reproducibility. However, it often fails to mimic a tissue or organ which possesses a defined three-dimensional structure (3D) along with intercellular communication machinery. Instead, microtissues such as spheroids or organoids having a precise 3D architecture and proximate in vivo tissue-like behavior can provide a more realistic evaluation than 2D monocultures. Recent developments in microfluidics and bioreactor-based organoid synthesis have eased the difficulties to prosper nano-toxicological analysis in organoid models surpassing the obstacle of ethical issues. The present review will enlighten applications of organoids in nanotoxicological evaluation, their advantages, and prospects toward securing commonplace nano-interventions.