Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms

Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches

The wide application of TiO₂-based engineered nanoparticles (nTiO₂) inevitably led to release into aquatic ecosystems. Importantly, increasing studies have emphasized the high risks of nTiO₂ to coastal environments. Bivalves, the representative benthic filter feeders in coastal zones, acted as important roles to assess and monitor the toxic effects of nanoparticles. Oxidative damage was one of the main toxic mechanisms of nTiO₂ on bivalves, but the experimental variables/nanomaterial characteristics were diverse and the toxicity mechanism was complex. Therefore, it was very necessary to develop machine learning model to characterize and predict the potential toxicity. In this study, thirty-six machine learning models were built by nanodescriptors combined with six machine learning algorithms. Among them, random forest (RF) - catalase (CAT), k-neighbors classifier (KNN) - glutathione peroxidase (GPx), neural networks - multilayer perceptron (ANN) - glutathione s-transferase (GST), random forest (RF) - malondialdehyde (MDA), random forest (RF) - reactive oxygen species (ROS), and extreme gradient boosting decision tree (XGB) - superoxide dismutase (SOD) models performed good with high accuracy and balanced accuracy for both training sets and external validation sets. Furthermore, the best model revealed the predominant factors (exposure concentration, exposure periods, and exposure matrix) influencing the oxidative stress induced by nTiO₂. These results showed that high exposure concentrations and short exposure-intervals tended to cause oxidative damage to bivalves. In addition, gills and digestive glands could be vulnerable to nTiO₂-induced oxidative damage as tissues/organs differences were the important factors controlling MDA activity. This study provided insights into important nano-features responsible for the different indicators of oxidative stress and thereby extended the application of machine learning approaches in toxicological assessment for nanoparticles.

Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review

Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.

Toxicity of Carbon Nanotubes: Molecular Mechanisms, Signaling Cascades, and Remedies in Biomedical Applications

Carbon nanotubes (CNTs) are the most studied allotropic form of carbon. They can be used in various biomedical applications due to their novel physicochemical properties. In particular, the small size of CNTs, with a large surface area per unit volume, has a considerable impact on their toxicity. Despite of the use of CNTs in various applications, toxicity is a big problem that requires more research. In this Review, we discuss the toxicity of CNTs and the associated mechanisms. Physicochemical factors, such as metal impurities, length, size, solubilizing agents, CNTs functionalization, and agglomeration, that may lead to oxidative stress, toxic signaling pathways, and potential ways to control these mechanisms are also discussed. Moreover, with the latest mechanistic evidence described in this Review, we expect to give new insights into CNTs' toxicological effects at the molecular level and provide new clues for the mitigation of harmful effects emerging from exposure to CNTs.

Effects of heavy metals and metal (oxide) nanoparticles on enhanced biological phosphorus removal

With the rapid growth of economics and nanotechnology, a significant portion of the anthropogenic emissions of heavy metals and nanoparticles (NPs) enters wastewater streams and discharges to wastewater treatment plants, thereby potentially posing a risk to the bacteria that facilitate the successful operation of the enhanced biological phosphorus (P) removal (EBPR) process. Although some efforts have been made to obtain detailed insights into the effects of heavy metals and metal (oxide) nanoparticles [Me(O)NPs], many unanswered questions remain. One question is whether the toxicity of Me(O)NPs originates from the released metal ions. This review aims to holistically evaluate the effects of heavy metals and Me(O)NPs. The interactions among extracellular polymeric substances, P, and heavy metals [Me(O)NPs] are presented and discussed for the first time. The potential mechanisms of the toxicity of heavy metals [Me(O)NPs] are summarized. Additionally, mathematical models of the toxicity and removal of P, heavy metals, and Me(O)NPs are overviewed. Finally, knowledge gaps and opportunities for further study are discussed to pave the way for fully understanding the inhibition of heavy metals [Me(O)NPs] and for reducing their inhibitory effect to maximize the reliability of the EBPR process.

Zn2+ leakage and photo-induced reactive oxidative species do not explain the full toxicity of ZnO core Quantum Dots

Metal oxide nanoparticles (NPs), and among them metal oxides Quantum Dots (QDs), exhibit a multifactorial toxicity combining metal leaching, oxidative stress and possibly direct deleterious interactions, the relative contribution of each varying according to the NP composition and surface chemistry. Their wide use in public and industrial domains requires a good understanding and even a good control of their toxicity. To address this question, we engineered ZnO QDs with different surface chemistries, expecting that they would exhibit different photo-induced reactivities and possibly different levels of interaction with biological materials. No photo-induced toxicity could be detected on whole bacterial cell toxicity assays, indicating that ROS-dependent damages, albeit real, are hidden behind a stronger source of toxicity, which was comforted by the fact that the different ZnO QDs displayed the same level of cell toxicity. However, using in vitro DNA damage assays based on quantitative PCR, significant photo-induced reactivity could be measured precisely, showing that different NPs exhibiting similar inhibitory effects on whole bacteria could differ dramatically in terms of ROS-generated damages on biomolecules. We propose that direct interactions between NPs and bacterial cell surfaces prime over any kind of intracellular damages to explain the ZnO QDs toxicity on whole bacterial cells.

Fe-Doped ZnO nanoparticle toxicity: assessment by a new generation of nanodescriptors

In the search for novel tools to combat cancer, nanoparticles (NPs) have attracted a lot of attention. Recently, the controlled release of cancer-cell-killing metal ions from doped NPs has shown promise, but fine tuning of dissolution kinetics is required to ensure specificity and minimize undesirable toxic side-effects. Theoretical tools to help in reaching a proper understanding and finally be able to control the dissolution kinetics by NP design have not been available until now. Here, we present a novel set of true nanodescriptors to analyze the charge distribution, the effect of doping and surface coating of whole metal oxide NP structures. The polarizable model of oxygen atoms enables light to be shed on the charge distribution on the NP surface, allowing the in detail study of the factors influencing the release of metal ions from NPs. The descriptors and their capabilities are demonstrated on a Fe-doped ZnO nanoparticle system, a system with practical outlook and available experimental data.

CORAL: Predictive models for cytotoxicity of functionalized nanozeolites based on quasi-SMILES

Unlike the well-known simplified molecular input-line entry system (SMILES), the so-called quasi-SMILES contains information related to physicochemical and biochemical conditions by a special additional symbols (codes), each standing for different conditions (time exposure, concentration, type of cell, etc.). Thus, quasi-SMILES can be used to build up models for cytotoxicity of functionalized nanozeolites using a mathematical function of eclectic information. These calculations were done with the Monte Carlo CORAL software. The statistical quality of models based on quasi-SMILES was usually considerably better than the statistical quality of models based on traditional SMILES.

Toxicity Assessment in the Nanoparticle Era

The wide use of engineered nanomaterials in many fields, ranging from biomedical, agriculture, environment, cosmetic, urged the scientific community to understand the processes behind their potential toxicity, in order to develop new strategies for human safety. As a matter of fact, there is a big discrepancy between the increased classes of nanoparticles and the consequent applications versus their toxicity assessment. Nanotoxicology is defined as the science that studies the effects of engineered nanodevices and nanostructures in living organisms. This chapter analyzes the physico-chemical properties of the most used nanoparticles, the way they enter the living organism and their cytoxicity mechanisms at cellular exposure level. Moreover, the current state of nanoparticles risk assessment is reported and analyzed.

CORAL and Nano-QFAR: Quantitative feature - Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2)

Quantitative feature - activity relationships (QFAR) approach was applied to prediction of bioavailability of metal oxide nanoparticles. ZnO, CuO, Co₃O₄, and TiO₂ nanoxides were considered. The computational model for bioavailability of investigated species is asserted. The model was calculated using the Monte Carlo method. The CORAL free software (http://www.insilico.eu/coral) was used in this study. The developed model was tested by application of three different splits of data into the training and validation sets. So-called, quasi-SMILES are used to represent the conditions of action of metal oxide nanoparticles. A new paradigm of building up predictive models of endpoints related to nanomaterials is suggested. The paradigm is the following "An endpoint is a mathematical function of available eclectic data (conditions)". Recently, the paradigm has been checked up with endpoints related to metal oxide nanoparticles, fullerenes, and multi-walled carbon-nanotubes.

Comparing the CORAL and Random Forest Approaches for Modelling the In Vitro Cytotoxicity of Silica Nanomaterials

Nanotechnology is one of the most important technological developments of the 21st century. In silico methods to predict toxicity, such as quantitative structure–activity relationships (QSARs), promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials were investigated, to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called ‘nano-QSAR’ models. ‘External’ leave-one-out cross-validation (LOO) analysis was performed, to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken, in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R2) for training and for LOO (0.83 and 0.65 for training and LOO, respectively), compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. With regard to the physicochemical properties of the nanomaterials, the aspect ratio and zeta potential were found to be the two most important variables for Random Forest, and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the data set: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends.