A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials

A review on the structural characterization of nanomaterials for nano-QSAR models

Quantitative structure-activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial's behavior, preparation, or test conditions) that indirectly reflect its structure.

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.

Nano-QTTR development for interspecies aquatic toxicity of silver nanoparticles between daphnia and fish

Limited studies of quantitative toxicity-toxicity relationship (QTTR) modeling have been conducted to predict interspecies toxicity of engineered nanomaterials (ENMs) between aquatic test species. A meta-analysis of 66 publications providing acute toxicity data of silver nanoparticles (AgNPs) to daphnia and fish was performed, and the toxicity data, physicochemical properties, and experimental conditions were collected and curated. Based on Euclidean distance (ED) grouping, a meaningful correlation of logarithmic lethal concentrations between daphnia and fish was derived for bare (R²_bare = 0.47) and coated AgNPs (R²_coated = 0.48) when a distance of 10 was applied. The correlation of coated AgNPs was improved (R²_coated = 0.55) by the inclusion of descriptors of the coating materials. The correlations were further improved by R²_bare = 0.57 and R²_coated = 0.81 after additionally considering particle size only, and by R²_bare = 0.59 and R²_coated = 0.92 after considering particle size and zeta potential simultaneously. The developed ED-based nano-QTTR model demonstrated that inclusion of the coating material descriptors and physicochemical properties improved the goodness-of-fit to predict interspecies aquatic toxicity of AgNPs between daphnia and fish. This study provides insight for future in silico research on QTTR model development in ENM toxicology.

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time-consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long-term reproductive toxicity assays over multiple generations.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

Comparative developmental toxicity of iron oxide nanoparticles and ferric chloride to zebrafish (Danio rerio) after static and semi-static exposure

Iron oxide nanoparticles (IONPs) are used in several medical and environmental applications, but their mechanism of action and hazardous effects to early developmental stages of fish remain unknown. Thus, the present study aimed to assess the developmental toxicity of citrate-functionalized IONPs (γ-Fe₂O₃ NPs), in comparison with its dissolved counterpart, in zebrafish (Danio rerio) after static and semi-static exposure. Embryos were exposed to environmental concentrations of both iron forms (0.3, 0.6, 1.25, 2.5, 5 and 10 mg L^-1) during 144 h, jointly with negative control group. The interaction and distribution of both Fe forms on the external chorion and larvae surface were measured, following by multiple biomarker assessment (mortality, hatching rate, neurotoxicity, cardiotoxicity, morphological alterations and 12 morphometrics parameters). Results showed that IONPs were mainly accumulated on the zebrafish chorion, and in the digestive system and liver of the larvae. Although the IONPs induced low embryotoxicity compared to iron ions in both exposure conditions, these nanomaterials induced sublethal effects, mainly cardiotoxic effects (reduced heartbeat, blood accumulation in the heart and pericardial edema). The semi-static exposure to both iron forms induced high embryotoxicity compared to static exposure, indicating that the nanotoxicity to early developmental stages of fish depends on the exposure system. This is the first study concerning the role of the exposure condition on the developmental toxicity of IONPs on fish species.

Determination and characterization of metal nanoparticles in clams and oysters

With the extensive application of nanotechnology, metal nanoparticles (MNPs) have been widely used, thus are universally detected in the environment. This has caused increasingly concerns due to their toxicity and the potential health risks they pose to humans. In this work, the concentrations and particle size distributions of MNPs and concentrations of associated metal ionic species in shellfish seafood (clams and oysters) were investigated using single particle inductively coupled plasma mass spectrometry (sp-ICP-MS) and inductively coupled plasma mass spectrometry (ICP-MS). The MNPs in the clam and oyster tissues were extracted via an alkaline digestion method with a recovery rate of 95.9% (for gold nanoparticles (AuNPs)). Then total concentrations of 41 metal elements were measured in the two types of seafood, of which 20 were selected for sp-ICP-MS analysis. The results showed that 5 types of MNPs were detectable in clams (Y, La, Ce, Pr, Gd) and 5 types of MNPs were detectable in oysters (Y, La, Ce, Pr, Nd). Size distributions of MNPs in clams and oysters were in the range of 35-55 nm and 30-65 nm, respectively. Nanoparticle concentrations in clams and oysters ranged from 0.6 to 37.7 ng/g and 4.2-19.7 ng/g, and accounted for 3.4%-50% and 5.5%-46% of the total metal content, respectively. Based on this analysis, the health risks of metals in the two kinds of seafood were evaluated by comparing the Provisional Tolerable Weekly Intake (PTWI) with limits recommended by the World Health Organization (WHO)/Food and Agriculture Organization (FAO). These results provide important information about the presence of metal nanoparticles in seafood and, to the best of our knowledge, this is the first time that the nanoparticles of rare earth elements have been detected and reported in bivalve mollusc tissues.

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

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

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

Cytotoxicity, genotoxicity, transplacental transfer and tissue disposition in pregnant rats mediated by nanoparticles: the case of magnetic core mesoporous silica nanoparticles

Whether in the cosmetic or as therapeutic, the use of nanoparticles has been increasing and taking on global proportion. However, there are few studies about the physical potential of long-term use or use in special conditions such as chronic, AIDS, pregnant women and other special health circumstances. In this context, the study of the mutagenicity and the transplacental passage represents an important and reliable model for the primary evaluation of potential health risks, especially maternal and child health. In this study we performed mutagenicity, cytotoxic and transplacental evaluation of magnetic core mesoporous silica nanoparticles, radiolabeled with ^99mTc for determination of toxicogenic and embryonic/fetuses potential risk in animal model. Magnetic core mesoporous silica nanoparticles were produced and characterized by obtaining nanoparticles with a size of (58.9 ± 8.1 nm) in spherical shape and with intact magnetic core. The 99 m Tc radiolabeling process demonstrated high efficacy and stability in 98% yield over a period of 8 hours of stability. Mutagenicity assays were performed using Salmonella enteric serovar Typhimurium standard strains TA98, TA100 and TA102. Cytotoxicity assays were performed using WST-1. The transplacental evaluation assays were performed using the in vivo model with rats in two periods: embryonic and fetal stage. The results of both analyzes corroborate that the nanoparticles can i) generate DNA damage; ii) generate cytotoxic potential and iii) cross the transplantation barrier in both stages and bioaccumulates in both embryos and fetuses. The results suggest that complementary evaluations should be conducted in order to attest safety, efficacy and quality of nanoparticles before unrestricted approval of their use.