Evaluating the toxicity of TiO2-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach

A structure-activity approach towards the toxicity assessment of multicomponent metal oxide nanomaterials

The increase of human and environmental exposure to engineered nanomaterials (ENMs) due to the emergence of nanotechnology has raised concerns over their safety. The challenging nature of in vivo and in vitro toxicity assessment methods for ENMs, has led to emerging in silico techniques for ENM toxicity assessment, such as structure-activity relationship (SAR) models. Although such approaches have been extensively developed for the case of single-component nanomaterials, the case of multicomponent nanomaterials (MCNMs) has not been thoroughly addressed. In this paper, we present a SAR approach for the case metal and metal oxide MCNMs. The developed SAR framework is built using a dataset of 796 individual toxicity measurements for 340 different MCNMs, towards human cells, mammalian cells, and bacteria. The novelty of the approach lies in the multicomponent nature of the nanomaterials, as well as the size, diversity and heterogeneous nature of the dataset used. Furthermore, the approach used to calculate descriptors for surface loaded MCNMs, and the mechanistic insight provided by the model results can assist the understanding of MCNM toxicity. The developed models are able to correctly predict the toxic class of the MCNMs in the heterogeneous dataset, towards a wide range of human cells, mammalian cells and bacteria. Using the abovementioned approach, the principal toxicity pathways and mechanisms are identified, allowing a more holistic understanding of metal oxide MCNM toxicity.

Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning

Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q²  = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.

Synthesis of Ce and Sm Co-Doped TiO2 Nanoparticles with Enhanced Photocatalytic Activity for Rhodamine B Dye Degradation

One of the major concerns that receive global attention is the presence of organic pollutants (dyes, pharmaceuticals, pesticides, phenolic compounds, heavy metals, and so on), originating from various industries, in wastewater and water resources. Rhodamine B is widely used in the dyeing of paints, plastics, textiles, and other fabrics, as well as biological products. It is highly persistent, toxic, and carcinogenic to organisms and humans when directly released into the water supply. To avoid this hazard, several studies have been conducted in an attempt to remove Rhodamine B from wastewater. Metal oxide semiconducting materials have gained great interest because of their ability to decompose organic pollutants from wastewater. TiO2 is one of the most effective photocatalysts with a broad range of applications. Several attempts have been made to improve its photocatalytic activity. Accordingly, we have prepared in this work a series of cerium (Ce) and samarium (Sm) co-doped TiO2 nanoparticles (x = 0.00, 0.25, 0.50, 1.00, and 2.00%) using a sol–gel auto-combustion approach. The influence of Ce–Sm concentrations on the structural, morphology, electronic, and optical properties, as well as the photocatalytic activity, was investigated. Structure and elemental mapping analyses proved the presence of Ce and Sm in the compositions as well as the development of the TiO2 anatase phase with a tetragonal structure and crystallite size of 15.1–17.8 nm. Morphological observations confirmed the creation of spherical nanoparticles (NPs). The examination of the electronic structure properties using density functional theory (DFT) calculations and of the optical properties using a UV/Vis diffuse spectrophotometer showed a reduction in the bandgap energy upon Ce–Sm co-doping. The photocatalytic activity of the synthesized products was assessed on the degradation of Rhodamine B dye, and it was found that all Ce–Sm co-doped TiO2 nanoparticles have better photocatalytic activities than pristine TiO2 nanoparticles. Among all of the prepared nanoparticles, the sample with x = 0.50% demonstrated the best photocatalytic activity, with a degradation efficiency of 98% within 30 min and a reaction rate constant of about 0.0616 min−1. h+ and •O2− were determined to be the most important active species in the photocatalytic degradation process. Besides the high photocatalytic degradation efficiency, these photocatalysts are highly stable and could be easily recovered and reused, which indicates their potential for practical applications in the future.

NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells

Nano-QSAR models can be effectively used for prediction of the biological activity of nanomaterials that have not been experimentally tested before. However, their use is associated with the need to have appropriate knowledge and skills in chemoinformatics. Thus, they are mainly aimed at specialists in the field. This significantly limits the potential group of recipients of the developed solutions. In this perspective, the purpose of the presented research was to develop an easily accessible and user-friendly web-based application that could enable the prediction of TiO₂-based multicomponent nanomaterials cytotoxicity toward Chinese Hamster Ovary (CHO-K1) cells. The graphical user interface is clear and intuitive and the only information required from the user is the type and concentration of the metals which will be modifying TiO₂-based nanomaterial. Thanks to this, the application will be easy to use not only by cheminformatics but also by specialists in the field of nanotechnology or toxicology, who will be able to quickly predict cytotoxicity of desired nanoclusters. We have performed case studies to demonstrate the features and utilities of developed application. The NanoMixHamster application is freely available at https://nanomixhamster.cloud.nanosolveit.eu/.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions

The production of nanomaterials continues its rapid growth; however, newly manufactured nanomaterials' environmental and health safety are among the most significant concerns. A safety assessment is usually a lengthy and costly process, so computational studies are often used to complement experimental testing. One of the most time-efficient techniques is structure-activity relationships (SAR) modeling. In this project, we analyzed the Sustainable Nanotechnology (S2NANO) dataset that contains 574 experimental cell viability and toxicity datapoints for Al₂O₃, CuO, Fe₂O₃, Fe₃O₄, SiO₂, TiO₂, and ZnO measured in different conditions. We aimed to develop classification- and regression-based structure-activity relationship models using quasi-SMILES molecular representation. Introduced quasi-SMILES took into consideration all available information, including structural features of nanoparticles (molecular structure, core size, etc.) and related experimental parameters (cell line, dose, exposure time, assay, hydrodynamic size, surface charge, etc.). Resultant regression models demonstrated sufficient predictive power, while classification models demonstrated higher accuracy.

Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling

Zeta potential is usually measured to estimate the surface charge and the stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for a pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potential measurements in the context of environmental impact is an important challenge. The inconsistency of data measurements leads to inaccuracies in predictive modeling. In this article, we report a new curated dataset of zeta potentials measured for 208 silica- and metal oxide nanoparticles in different media. We discuss the data curation framework for zeta potentials designed to assess the quality and usefulness of the literature data for further computational modeling. We also provide an analysis of specific trends for the datapoints harvested from different literature sources. In addition to that, we present for the first time a structure-property relationship model for nanoparticles (nano-SPR) that predicts values of zeta potential values measured in different environmental conditions (i.e., biological media and pH).

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.

Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques

Presence of missing data points in datasets is among main challenges in handling the toxicological data for nanomaterials. As the processing of missing data is an important part of data analysis, we have introduced a read-across approach that uses a combination of supervised and unsupervised machine learning techniques to fill the missing values. A series of classification models (supervised learning) was developed to predict class label, and self-organizing map approach (unsupervised learning) was used to estimate relative distances between nanoparticles and refine results obtained during supervised learning. In this study, genotoxicity of 49 silicon and metal oxide nanoparticles in Ames and Comet tests. Collected literature data did not demonstrate significant variations related to the change of size including selected bulk materials. Genotoxicity-related features of nanomaterials were represented by ionic characteristics. General tendencies found in the current study were convincingly linked to known theories of genotoxic action at nano-level. Mechanisms of primary and secondary genotoxic effects were discussed in the context of developed models.

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.

A model of atomic compressibility and its application in QSAR domain for toxicological property prediction

A model for computing the atomic compressibility (β) based on two periodic descriptors, namely, absolute radius (r) and atomic electrophilicity index (ω), is proposed as[Formula: see text]The ansatz is invoked to compute compressibilities of atoms of 57 elements of the periodic table. The computed atomic data exhibits all sine qua non of periodic properties. Further, the concept group compressibility (Gβ) is also established invoking additivity property using some molecules with different functional groups and consequently utilized in correlating with molecular polarizability. Since toxicity prediction is an imperative need of the hour, chemical reactivity descriptors are of paramount importance in the study of toxicological behaviour along with a lot of other molecular reactivity studies within a Quantitative Structure-Activity Relationship (QSAR) context. Hence, this quantity is applied in the modelling of toxicological property through QSAR and a comprehensive study is performed in an effort to investigate and validate the application of compressibility in determining its toxicological power. Consequently, varied 209 organic molecules are selected for studying the toxic effect on Tetrahymena pyriformis. A QSAR model is constructed in terms of compressibility which offers a superior prediction of toxicity independently without adopting additional descriptors or properties as in some other QSAR studies. Graphical abstract.

Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment

Because it is impossible to assess in vitro or in vivo the toxicity of all nanoparticles available on the market on a case-by-case basis, computational approaches have been proposed as useful alternatives to predict in silico the hazard potential of engineered nanoparticles. Despite promising results, a major issue associated with these mathematical models lies in the a priori choice of the physicochemical descriptors and the biological end points. We performed a thorough bibliographic survey on the biological end points used for nanotoxicology purposes and compared them between experimental and computational approaches. They were found to be disparate: while conventional in vitro nanotoxicology assays usually investigate a large array of biological effects using eukaryotic cells (cytotoxicity, pro-inflammatory response, oxidative stress, genotoxicity), computational studies mostly focus on cell viability and also include studies on prokaryotic cells. We may thus wonder the relevance of building complex mathematical models able to predict accurately a biological end point if this latter is not the most relevant to support human health risk assessment. The choice of biological end points clearly deserves to be more carefully discussed. This could bridge the gap between experimental and computational nanotoxicology studies and allow in silico predictive models to reach their full potential.

A chemoinformatics approach for the characterization of hybrid nanomaterials: safer and efficient design perspective

In this study, photocatalytic properties and in vitro cytotoxicity of 29 TiO2-based multi-component nanomaterials (i.e., hybrids of more than two composition types of nanoparticles) were evaluated using a combination of the experimental testing and supervised machine learning modeling. TiO2-based multi-component nanomaterials with metal clusters of silver, and their mixtures with gold, palladium, and platinum were successfully synthesized. Two activities, photocatalytic activity and cytotoxicity, were studied. A novel cheminformatic approach was developed and applied for the computational representation of the photocatalytic activity and cytotoxicity effect. In this approach, features of investigated TiO2-based hybrid nanomaterials were reflected by a series of novel additive descriptors for hybrid and hybrid nanostructures (denoted as "hybrid nanosctructure descriptors"). These descriptors are based on quantum chemical calculations and the Smoluchowski equation. The obtained experimental data and calculated hybrid-nanostructure descriptors were used to develop novel predictive Quantitative Structure-Activity Relationship computational models (called "nano-QSARmix"). The proposed modeling approach is an initial step in the understanding of the relationships between physicochemical properties of hybrid nanoparticles, their toxicity, and photochemical activity under UV-vis irradiation. Acquired knowledge supports the safe-by-design approaches relevant to the development of efficient hybrid nanomaterials with reduced hazardous effects.

Universal nanohydrophobicity predictions using virtual nanoparticle library

To facilitate the development of new nanomaterials, especially nanomedicines, a novel computational approach was developed to precisely predict the hydrophobicity of gold nanoparticles (GNPs). The core of this study was to develop a large virtual gold nanoparticle (vGNP) library with computational nanostructure simulations. Based on the vGNP library, a nanohydrophobicity model was developed and then validated against externally synthesized and tested GNPs. This approach and resulted model is an efficient and effective universal tool to visualize and predict critical physicochemical properties of new nanomaterials before synthesis, guiding nanomaterial design.

An assessment of applicability of existing approaches to predicting the bioaccumulation of conventional substances in nanomaterials

The experimental determination of bioaccumulation is challenging, and a number of approaches have been developed for its prediction. It is important to assess the applicability of these predictive approaches to nanomaterials (NMs), which have been shown to bioaccumulate. The octanol/water partition coefficient (K_OW ) may not be applicable to some NMs that are not found in either the octanol or water phases but rather are found at the interface. Thus the K_OW values obtained for certain NMs are shown not to correlate well with the experimentally determined bioaccumulation. Implementation of quantitative structure-activity relationships (QSARs) for NMs is also challenging because the bioaccumulation of NMs depends on nano-specific properties such as shape, size, and surface area. Thus there is a need to develop new QSAR models based on these new nanodescriptors; current efforts appear to focus on digital processing of NM images as well as the conversion of surface chemistry parameters into adsorption indices. Water solubility can be used as a screening tool for the exclusion of NMs with short half-lives. Adaptation of fugacity/aquivalence models, which include physicochemical properties, may give some insights into the bioaccumulation potential of NMs, especially with the addition of a biota component. The use of kinetic models, including physiologically based pharmacokinetic models, appears to be the most suitable approach for predicting bioaccumulation of NMs. Furthermore, because bioaccumulation of NMs depends on a number of biotic and abiotic factors, it is important to take these factors into account when one is modeling bioaccumulation and interpreting bioaccumulation results. Environ Toxicol Chem 2018;37:2972-2988. © 2018 SETAC.

Optical and x-ray technology synergies enabling diagnostic and therapeutic applications in medicine

X-ray and optical technologies are the two central pillars for human imaging and therapy. The strengths of x-rays are deep tissue penetration, effective cytotoxicity, and the ability to image with robust projection and computed-tomography methods. The major limitations of x-ray use are the lack of molecular specificity and the carcinogenic risk. In comparison, optical interactions with tissue are strongly scatter dominated, leading to limited tissue penetration, making imaging and therapy largely restricted to superficial or endoscopically directed tissues. However, optical photon energies are comparable with molecular energy levels, thereby providing the strength of intrinsic molecular specificity. Additionally, optical technologies are highly advanced and diversified, being ubiquitously used throughout medicine as the single largest technology sector. Both have dominant spatial localization value, achieved with optical surface scanning or x-ray internal visualization, where one often is used with the other. Therapeutic delivery can also be enhanced by their synergy, where radio-optical and optical-radio interactions can inform about dose or amplify the clinical therapeutic value. An emerging trend is the integration of nanoparticles to serve as molecular intermediates or energy transducers for imaging and therapy, requiring careful design for the interaction either by scintillation or Cherenkov light, and the nanoscale design is impacted by the choices of optical interaction mechanism. The enhancement of optical molecular sensing or sensitization of tissue using x-rays as the energy source is an important emerging field combining x-ray tissue penetration in radiation oncology with the molecular specificity and packaging of optical probes or molecular localization. The ways in which x-rays can enable optical procedures, or optics can enable x-ray procedures, provide a range of new opportunities in both diagnostic and therapeutic medicine. Taken together, these two technologies form the basis for the vast majority of diagnostics and therapeutics in use in clinical medicine.

Comparison of conventional and advanced in vitro models in the toxicity testing of nanoparticles

Humans are exposed to a wide variety of nanoparticles (NPs) present in the environment, in consumer, health and medical products, and in food. Conventional cytotoxicity testing compared to animal testing is less expensive, faster and avoids ethical problems at the expense of a lower predictive value. New cellular models and exposure conditions have been developed to overcome the limitations of conventional cell culture and obtain more predictive data. The use of three-dimensional culture, co-culture and inclusion of mechanical stimulation can provide physiologically more relevant culture conditions. These systems are particularly relevant for oral, respiratory and intravenous exposure to NPs and it may be assumed that physiologically relevant application of the NPs can improve the predictive value of in vitro testing. Various groups have used advanced culture and exposure systems, but few direct comparisons between data from conventional cultures and from advanced systems exist. In silico models may present another option to predict human health risk by NPs without using animal studies. In the absence of validation, the question whether these alternative models provide more predictive data than conventional testing remains elusive.

Towards the Development of Global Nano-Quantitative Structure-Property Relationship Models: Zeta Potentials of Metal Oxide Nanoparticles

Zeta potential indirectly reflects a charge of the surface of nanoparticles in solutions and could be used to represent the stability of the colloidal solution. As processes of synthesis, testing and evaluation of new nanomaterials are expensive and time-consuming, so it would be helpful to estimate an approximate range of properties for untested nanomaterials using computational modeling. We collected the largest dataset of zeta potential measurements of bare metal oxide nanoparticles in water (87 data points). The dataset was used to develop quantitative structure–property relationship (QSPR) models. Essential features of nanoparticles were represented using a modified simplified molecular input line entry system (SMILES). SMILES strings reflected the size-dependent behavior of zeta potentials, as the considered quasi-SMILES modification included information about both chemical composition and the size of the nanoparticles. Three mathematical models were generated using the Monte Carlo method, and their statistical quality was evaluated (R² for the training set varied from 0.71 to 0.87; for the validation set, from 0.67 to 0.82; root mean square errors for both training and validation sets ranged from 11.3 to 17.2 mV). The developed models were analyzed and linked to aggregation effects in aqueous solutions.

Modeling of Interactions between the Zebrafish Hatching Enzyme ZHE1 and A Series of Metal Oxide Nanoparticles: Nano-QSAR and Causal Analysis of Inactivation Mechanisms

The quantitative relationships between the activity of zebrafish ZHE1 enzyme and a series of experimental and physicochemical features of 24 metal oxide nanoparticles were revealed. Vital characteristics of the nanoparticles’ structure were reflected using both experimental and theoretical descriptors. The developed quantitative structure–activity relationship model for nanoparticles (nano-QSAR) was capable of predicting the enzyme inactivation based on four descriptors: the hydrodynamic radius, mass density, the Wigner–Seitz radius, and the covalent index. The nano-QSAR model was calculated using the non-linear regression tree M5P algorithm. The developed model is characterized by high robustness R²_bagging = 0.90 and external predictivity Q²_EXT = 0.93. This model is in agreement with modern theories of aquatic toxicity. Dissolution and size-dependent characteristics are among the key driving forces for enzyme inactivation. It was proven that ZnO, CuO, Cr₂O₃, and NiO nanoparticles demonstrated strong inhibitory effects because of their solubility. The proposed approach could be used as a non-experimental alternative to animal testing. Additionally, methods of causal discovery were applied to shed light on the mechanisms and modes of action.