Search Results (12)

Nanomaterials (NMs) possess unique physicochemical features that set them apart from bulk counterparts. Their adjustable properties provide remarkable flexibility, giving rise to a wide array of variants. However, these attributes can also trigger complex biological interactions, particularly the generation of reactive oxygen species (ROS), which are central to nanomaterial-induced cytotoxicity. The ambivalent nature of ROS, essential for physiological signaling yet harmful when dysregulated, can lead to substantial health consequences. The scarcity of reliable toxicity and safety data, together with the inadequacies of conventional testing methods, highlights the urgent need for more effective strategies to assess nanomaterial-related hazards and risks. Given the intricate interplay between NMs and biological systems, computational approaches, particularly machine learning (ML), have emerged as powerful tools to model ROS dynamics, predict cytotoxic outcomes, and optimize nanomaterial design. This review highlights recent advances in applying ML to predict both the generation and neutralization of ROS by diverse NMs and to identify the critical determinants underlying ROS-mediated toxicity. These insights provide new opportunities for predictive nanotoxicology and the development of safer, application-tailored NMs. Full article

Currently, nanotechnology is the most promising science, engineering, and technology conducted at the nanoscale (nm), which is used in several sectors. Collectively, nanotechnology is causing a new industrial revolution, and nano-based products are becoming increasingly important for the global market and economy. The interest in nanomaterials has been strongly augmented during the last two decades, and this fact can be easily evaluated by considering the number of studies present in the literature. In November 2024, they accounted for 764,279 experimental studies developed in the years 2009–2024. During such a period, our group contributed to the field of applicative nanotechnology with several experimental and review articles, which we hope could have relevantly enhanced the knowledge of the scientific community. In this new publication, an exhaustive overview regarding the main types of developed nanomaterials, the characterization techniques, and their applications has been discussed. Particular attention has been paid to nanomaterials employed for the enhancement of bioavailability and delivery of bioactive molecules and to those used for ameliorating traditional food packaging. Then, we briefly reviewed our experimental studies on the development of nanoparticles (NPs), dendrimers, micelles, and liposomes for biomedical applications by collecting inherent details in a reader-friendly table. A brief excursus about our reviews on the topic has also been provided, followed by the stinging question of nanotoxicology. Indeed, although the application of nanotechnology translates into a great improvement in the properties of non-nanosized pristine materials, there may still be a not totally predictable risk for humans, animals, and the environment associated with an extensive application of NPs. Nanotoxicology is a science in rapid expansion, but several sneaky risks are not yet fully disclosed. So, the final part of this study discusses the pending issue related to the possible toxic effects of NPs and their impact on customers’ acceptance in a scenario of limited knowledge. Full article

Nanomaterials (NMs) solve specific problems with remarkable results in several industrial and scientific areas. Among NMs, silver nanoparticles (AgNPs) have been extensively employed as drug carriers, medical diagnostics, energy harvesting devices, sensors, lubricants, and bioremediation. Notably, they have shown excellent antimicrobial, anticancer, and antiviral properties in the biomedical field. The literature analysis shows a selective cytotoxic effect on cancer cells compared to healthy cells, making its potential application in cancer treatment evident, increasing the need to study the potential risk of their use to environmental and human health. A large battery of toxicity models, both in vitro and in vivo, have been established to predict the harmful effects of incorporating AgNPs in these numerous areas or those produced due to involuntary exposure. However, these models often report contradictory results due to their lack of standardization, generating controversy and slowing the advances in nanotoxicology research, fundamentally by generalizing the biological response produced by the AgNP formulations. This review summarizes the last ten years’ reports concerning AgNPs’ toxicity in cellular respiratory system models (e.g., mono-culture models, co-cultures, 3D cultures, ex vivo and in vivo). In turn, more complex cellular models represent in a better way the physical and chemical barriers of the body; however, results should be used carefully so as not to be misleading. The main objective of this work is to highlight current models with the highest physiological relevance, identifying the opportunity areas of lung nanotoxicology and contributing to the establishment and strengthening of specific regulations regarding health and the environment. Full article

The use of nanomaterials has been increasing in recent times, and they are widely used in industries such as cosmetics, drugs, food, water treatment, and agriculture. The rapid development of new nanomaterials demands a set of approaches to evaluate the potential toxicity and risks related to them. In this regard, nanosafety has been using and adapting already existing methods (toxicological approach), but the unique characteristics of nanomaterials demand new approaches (nanotoxicology) to fully understand the potential toxicity, immunotoxicity, and (epi)genotoxicity. In addition, new technologies, such as organs-on-chips and sophisticated sensors, are under development and/or adaptation. All the information generated is used to develop new in silico approaches trying to predict the potential effects of newly developed materials. The overall evaluation of nanomaterials from their production to their final disposal chain is completed using the life cycle assessment (LCA), which is becoming an important element of nanosafety considering sustainability and environmental impact. In this review, we give an overview of all these elements of nanosafety. Full article

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. Full article

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. Full article

A comparative, fully parallel study of nanoparticles (NPs) toxicity by in vitro cell viability is shown looking for reliable comparability of nanotoxicological results, a well-recognized bottleneck in the context. This procedure is suitable to compare toxicity of similar NPs, as well as the influence on toxicity of the size, surface, and other characteristics. As a case of study, semiconductor (SQDs) and graphitic-carbon quantum dots (CQDs) with identical surface groups and size were evaluated. All experiments were conducted at same conditions, involving two types of cells (mouse fibroblasts (3T3-L1) and carcinoma human hepatocellular cells (HepG2)) and different extracellular components (in the absence or presence of fetal bovine serum (FBS)). Cell viability demonstrated the excellent biocompatibility of CQDs compared to SQDs, which caused higher percentage of cell death at lower concentrations, as predicted but never clearly demonstrated. However, our comparative studies established that the toxicity of SQDs and CQDs are cellular type-dependent, and the absence or presence of serum proteins reduces the minimal concentration necessary of NPs to produce toxicity. Full article

Metallic nanoparticles (NPs), as iron oxide NPs, accumulate in organs, cross the blood-brain barrier and placenta, and have the potential to elicit developmental neurotoxicity (DNT). Human stem cell-derived in vitro models may provide more realistic platforms to study NPs effects on neural cells, and to obtain relevant information on the potential for early or late DNT effects in humans. Primary neuronal-like cells (hNLCs) were generated from mesenchymal stem cells derived from human umbilical cord lining and the effects caused by magnetite (Fe₃O₄NPs, 1–50 μg/mL) evaluated. Neuronal differentiation process was divided into stages: undifferentiated, early, mid- and fully-differentiated (from day-2 to 8 of induction) based on different neuronal markers and morphological changes over time. Reduction in neuronal differentiation induction after NP exposure was observed associated with NP uptake: β-tubulin III (β-Tub III), microtubule-associated protein 2 (MAP-2), enolase (NSE) and nestin were downregulated (10–40%), starting from 25 μg/mL at the early stage. Effects were exacerbated at higher concentrations and persisted up to 8 days without cell morphology alterations. Adenosine triphosphate (ATP) and caspase-3/7 activity data indicated Fe₃O₄NPs-induced cell mortality in a concentration-dependent manner and increases of apoptosis: effects appeared early (from day-3), started at low concentrations (≥5 μg/mL) and persisted. This new human cell-based model allows different stages of hNLCs to be cultured, exposed to NPs/chemicals, and analyzed for different endpoints at early or later developmental stage. Full article

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models. Full article

Nanotechnology has rapidly promoted the development of a new generation of industrial and commercial products; however, it has also raised some concerns about human health and safety. To evaluate the toxicity of the great diversity of nanomaterials (NMs) in the traditional manner, a tremendous number of safety assessments and a very large number of animals would be required. For this reason, it is necessary to consider the use of alternative testing strategies or methods that reduce, refine, or replace (3Rs) the use of animals for assessing the toxicity of NMs. Autophagy is considered an early indicator of NM interactions with cells and has been recently recognized as an important form of cell death in nanoparticle-induced toxicity. Impairment of autophagy is related to the accelerated pathogenesis of diseases. By using mechanism-based high-throughput screening in vitro, we can predict the NMs that may lead to the generation of disease outcomes in vivo. Thus, a tiered testing strategy is suggested that includes a set of standardized assays in relevant human cell lines followed by critical validation studies carried out in animals or whole organism models such as C. elegans (Caenorhabditis elegans), zebrafish (Danio rerio), and Drosophila (Drosophila melanogaster)for improved screening of NM safety. A thorough understanding of the mechanisms by which NMs perturb biological systems, including autophagy induction, is critical for a more comprehensive elucidation of nanotoxicity. A more profound understanding of toxicity mechanisms will also facilitate the development of prevention and intervention policies against adverse outcomes induced by NMs. The development of a tiered testing strategy for NM hazard assessment not only promotes a more widespread adoption of non-rodent or 3R principles but also makes nanotoxicology testing more ethical, relevant, and cost- and time-efficient. Full article

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications. Full article

The wide employment of iron nanoparticles in environmental and occupational settings underlines their potential to enter the brain. Human cell-based systems are recommended as relevant models to reduce uncertainty and to improve prediction of human toxicity. This study aimed at demonstrating the in vitro differentiation of the human umbilical cord lining-derived-mesenchymal stem cells (hCL-MSCs) into neuron-like cells (hNLCs) and the benefit of using them as an ideal primary cell source of human origin for the neuronal toxicity of Fe₃O₄NPs (magnetite-nanoparticles). Neuron-like phenotype was confirmed by: live morphology; Nissl body staining; protein expression of different neuronal-specific markers (immunofluorescent staining), at different maturation stages (i.e., day-3-early and day-8-full differentiated), namely β-tubulin III, MAP-2, enolase (NSE), glial protein, and almost no nestin and SOX-2 expression. Synaptic makers (SYN, GAP43, and PSD95) were also expressed. Fe₃O₄NPs determined a concentration- and time-dependent reduction of hNLCs viability (by ATP and the Trypan Blue test). Cell density decreased (20–50%) and apoptotic effects were detected at ≥10 μg/mL in both types of differentiated hNLCs. Three-day-differentiated hNLCs were more susceptible (toxicity appeared early and lasted for up to 48 h) than 8-day-differentiated cells (delayed effects). The study demonstrated that (i) hCL-MSCs easily differentiated into neuronal-like cells; (ii) the hNCLs susceptibility to Fe₃O₄NPs; and (iii) human primary cultures of neurons are new in vitro model for NP evaluation. Full article