Nanosafety: An Evolving Concept to Bring the Safest Possible Nanomaterials to Society and Environment

Abstract

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.

Keywords: advanced in vitro models; epigenetics; genotoxicity; immunotoxicity; in silico; life cycle assessment; nanomaterials; nanotoxicology.

Figure 1

General view of possible interactions, routes of exposure, and adverse outcomes that can be triggered by exposure of humans and the environment to nanomaterials.

Figure 2

The main target organs studied in the context of (nano)toxicology. A PubMed search for “particulate matter toxicology” or “nanomaterial toxicology” followed by the organ identified on the X-axis was performed in April 2022. No other filters were applied. Of note, using the words “pulmonary”, “cardiovascular”, and “gastrointestinal” instead of the specific organ yields even higher numbers for the 3 categories, but lung-related toxicology still prevails by far.

Figure 3

Predominant mechanisms of nanomaterial-induced toxicity identified so far and their presumed interaction.

Figure 4

Exposure to nanomaterials activates the immune surveillance system. Nanomaterials used for industrial and biomedical applications or present in the environment can have a major impact on human, animal, and plant health. If nanoparticles penetrate anatomical barriers, cells of the innate immune system (e.g., macrophages, monocytes), found in circulation or locally in different tissues, recognize them. This may lead to nanoparticle degradation/elimination or modulate the body towards beneficial or detrimental responses. (Servier Medical Art, smart.servier.com).

Figure 5

Nanomaterial-induced genotoxicity in various model systems (the figure is generated with the numbers of published papers appearing in PubMed database with specific keyword search; epidemiology mainly represents “occupational exposure”-related studies; ecotoxicology model species include mainly fish species, drosophila, bivalve mollusks, C. elegans, white worms, yeast, etc.).

Figure 6

The nanomaterial-induced alterations in different epigenetic biomarkers based on various model systems (the figure is generated with the numbers of published papers appearing in the PubMed database with a specific keyword search; epidemiology mainly represents “occupational exposure”-related studies; in ecotoxicology, model species include mainly zebrafish, yeast, and C. elegans).

Figure 7

Graphical network of the 19 articles that meet the criteria to be included in Table 1. (a) Network with all the connections between the types of nanomaterials, their relevant structural characteristics (descriptors), and the QSAR models used to predict a defined endpoint. (b) Most relevant connections of metal oxide class. (c) Most relevant connections of optimal quasi-SMILES-based descriptors and the closely related Monte Carlo (MC) algorithm. ANN: artificial neural network; DecTrees: decision trees; GBR: gradient boosting regressor; KNN: k-nearest neighbors; LinReg: linear regression; LogReg: logistic regression; MC: Monte Carlo; MLR: multiple linear regression; PLS: partial least squares; RF: random forest; SVM: support vector machine; XGBoost: extreme gradient boosting.

Figure 8

General conceptual framework of LCA representing the four main phases. Phase I: Cradle-to-cradle is one of the system boundaries that could be established in this stage; phase II: during the LCI, for each of the stages of the life cycle, all the inputs (e.g., natural resources) and outputs (e.g., emissions) within the system boundaries are accounted for; phase III in the LCIA, different models are used to translate the quantity of each emission in order to evaluate the potential impacts; phase IV: interpretation of the LCA results. EoL: end-of-life.

Figure 9

LCA application in different nanotechnology fields, including the nanomaterials studied. ENMs: engineered nanomaterials; PCP: personal care products; other metals and alloys: some of the most studied were Zn-based and Cu-based; other ENMs: some of the most studied were tungsten-based and zirconia-based.

Figure 10

Number of publications related to the LCA in nanotechnology accounting for conventional impacts and also for those impacts linked to the releases of nanomaterials.