Quantitative Structure-Permittivity Relationship Study of a Series of Polymers

Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.

A bibliometric analysis of the recent achievements in pulmonary safety of nanoparticles

Assessing research activity is an important step for planning future initiatives oriented toward filling the remaining gaps in a field. Therefore, the objective of the current study was to review recently published research on pulmonary toxicity caused by nanomaterials. However, here, instead of reviewing possible toxic effects and discussing their mode of action, the goal was to establish trends considering for example examined so far nanomaterials or used testing strategies. A total of 2316 related articles retrieved from the three most cited databases (PubMed Scopus, Web of Science), selected based on the title and abstract requirements, were used as the source of the review. Based on the bibliometric analysis, the nano-meter metal oxides, and carbon-based nanotubes were identified as the most frequently studied nanomaterials, while quantum dots, which might induce possible harmful effects, were not considered so far. The majority of testing of pulmonary safety is based on in vitro studies with observed growth of the contribution of novel testing strategies, such as 3D lung model, air-liquid interface system, or omic analysis.

An ancestral molecular response to nanomaterial particulates

The varied transcriptomic response to nanoparticles has hampered the understanding of the mechanism of action. Here, by performing a meta-analysis of a large collection of transcriptomics data from various engineered nanoparticle exposure studies, we identify common patterns of gene regulation that impact the transcriptomic response. Analysis identifies deregulation of immune functions as a prominent response across different exposure studies. Looking at the promoter regions of these genes, a set of binding sites for zinc finger transcription factors C2H2, involved in cell stress responses, protein misfolding and chromatin remodelling and immunomodulation, is identified. The model can be used to explain the outcomes of mechanism of action and is observed across a range of species indicating this is a conserved part of the innate immune system.

Core, Coating, or Corona? The Importance of Considering Protein Coronas in nano-QSPR Modeling of Zeta Potential

To control stability in a biological medium, several factors affecting the zeta potential (ζ) of nanoparticles (NPs) must be considered, including complex interactions between the nanostructure and the composition of the protein corona (PC). Effective in silico methods (based on machine learning and quantitative structure-property relationship (QSPR) models) could help predict and characterize the relationship between the physicochemical properties of NP and the formation of PC and biological outcomes in the medium at an early stage of the experiment. However, the models currently developed are limited to simple descriptors that do not represent the complex interactions between the core, the coating, and their PC fingerprints. To be useful, the models developed should be described as a function of both the structural properties determined by the core and coating of the NPs and the biological medium determined by the formation of the protein corona. We have developed a set of complex descriptors that describe the quantitative relationship between the value of the zeta potential (ζ), core, the coating of NPs, and their PC fingerprints (the so-called nano-QSPR model). The nano-QSPR model was developed based on a genetic algorithm using a partial least-squares regression method (GA-PLS), which is characterized by high external predictive power (Q²_EXT = 0.89). The GA-PLS model was developed using descriptors that describe (i) the core structure (determined by 7 different types of polymer-based NMs in the range of 20 different sizes), (ii) the coating structure with 7 different functional groups, and (iii) 80 different types of protein compositions adsorbed on the surface of the NPs. The presented study answers the question of how complex interactions between the corona and NP determine the zeta potential (ζ) of NP in a given medium. Moreover, our current study is a proof-of-concept that the zeta potential of NPs modeled on the original structure depends not only on the NPs themselves but also on the structure and properties determined by the NP core and coating, as well as the biological medium determined by the formation of the protein corona. On the basis of these results, our studies will be useful in determining the stability and mechanism of cell uptake, toxicity, and ability to predict the zeta potential of compounds not yet tested.

How Does the Study MD of pH-Dependent Exposure of Nanoparticles Affect Cellular Uptake of Anticancer Drugs?

The lack of knowledge about the uptake of NPs by biological cells poses a significant problem for drug delivery. For this reason, designing an appropriate model is the main challenge for modelers. To address this problem, molecular modeling studies that can describe the mechanism of cellular uptake of drug-loaded nanoparticles have been conducted in recent decades. In this context, we developed three different models for the amphipathic nature of drug-loaded nanoparticles (MTX-SS-γ-PGA), whose cellular uptake mechanism was predicted by molecular dynamics studies. Many factors affect nanoparticle uptake, including nanoparticle physicochemical properties, protein-particle interactions, and subsequent agglomeration, diffusion, and sedimentation. Therefore, the scientific community needs to understand how these factors can be controlled and the NP uptake of nanoparticles. Based on these considerations, in this study, we investigated for the first time the effects of the selected physicochemical properties of the anticancer drug methotrexate (MTX) grafted with hydrophilic-γ-polyglutamic acid (MTX-SS-γ-PGA) on its cellular uptake at different pH values. To answer this question, we developed three theoretical models describing drug-loaded nanoparticles (MTX-SS-γ-PGA) at three different pH values, such as (1) pH 7.0 (the so-called neutral pH model), (2) pH 6.4 (the so-called tumor pH model), and (3) pH 2.0 (the so-called stomach pH model). Exceptionally, the electron density profile shows that the tumor model interacts more strongly with the head groups of the lipid bilayer than the other models due to charge fluctuations. Hydrogen bonding and RDF analyses provide information about the solution of the NPs with water and their interaction with the lipid bilayer. Finally, dipole moment and HOMO-LUMO analysis showed the free energy of the solution in the water phase and chemical reactivity, which are particularly useful for determining the cellular uptake of the NPs. The proposed study provides fundamental insights into molecular dynamics (MD) that will allow researchers to determine the influence of pH, structure, charge, and energetics of NPs on the cellular uptake of anticancer drugs. We believe that our current study will be useful in developing a new model for drug delivery to cancer cells with a much more efficient and less time-consuming model.

Predicting electrophoretic mobility of TiO2, ZnO, and CeO2 nanoparticles in natural waters: The importance of environment descriptors in nanoinformatics models

Natural and engineered nanoparticles (NPs) entering the environment are influenced by many physicochemical processes and show various behavior in different systems (e.g., natural waters showing different characteristics). Determining the physicochemical characteristics and predicting the behavior of nanoparticles ending up in the natural aquatic environment are key aspects of their risk assessment. Here, we show that the quantitative structure-property relationship modeling method used in nanoinformatics (nano-QSPR) can be successfully applied to predict environmental fate-relevant properties (electrophoretic mobility) of TiO₂, ZnO, and CeO₂ nanoparticles. However, in contrast to the previous works, we postulate to use, in parallel: (i) the nanoparticles' structure descriptors (S-descriptors) and (ii) the environment descriptors (E-descriptors) as the input variables. Thus, the method should be abbreviated more precisely as nano-QSEPR ("E" stands for the "environment"). As a proof-of-the-concept, we have developed a group of models (including MLR, GA-PLS, PCR, and Meta-Consensus models) with high predictive capabilities (Q_EXT² = 0.931 for the GA-PLS model), where the S-descriptors are represented by the core-shell model descriptor and the E-descriptors - by different ambient water features (including ions concentration and the ionic strength). The newly proposed nano-QSEPR modeling scheme can be efficiently used to design safe and sustainable nanomaterials.

Representing and describing nanomaterials in predictive nanoinformatics

Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.

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/.

Integrated Approach to Interaction Studies of Pyrene Derivatives with Bovine Serum Albumin: Insights from Theory and Experiment

This work aimed to investigate the interaction of bovine serum albumin with newly synthesized potent new pyrene derivatives (PS1 and PS2), which might prove useful to have a better antibacterial character as found for similar compounds in the previous report [Low et al. Bioconjugate Chemistry2014, 12, 2269−2284]. However, to date, binding studies with plasma protein are still unknown. Steady-state fluorescence spectroscopy and lifetime fluorescence studies show that the static interaction binding mode and binding constants of PS1 and PS2 are 7.39 and 7.81 [K_b × 10⁵ (M^–1)], respectively. The experimental results suggest that hydrophobic forces play a crucial role in interacting pyrene derivatives with BSA protein. To verify this, molecular docking and molecular dynamics simulations were performed to predict the nature of the interaction and the dynamic behavior of the two compounds in the BSA complex, PS1 and PS2, under physiological conditions of pH = 7.1. In addition, the free energies of binding for the BSA-PS1 and BSA-PS2 complexes were estimated at 300 K based on the molecular mechanics of the Poisson–Boltzmann surface (MMPBSA) with the Gromacs package. PS2 was found to have a higher binding affinity than PS1. To determine the behavior of the orbital transitions in the ground state geometry, we found that both compounds have similar orbital transitions from HOMO–LUMO via π → π* and HOMO–1–LUMO+1 via n → π*, which was included in the FMO analysis. A cytotoxicity study was performed to determine the toxicity of the compounds. Based on the MD study, the stability of the compounds with BSA and the dynamic binding modes were further revealed, as well as the nature of the binding force components involved and the important residues involved in the binding process. From the binding energy analysis, it can be assumed that PS2 may be more active than PS1.

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).

Structure-activity prediction networks (SAPNets): a step beyond Nano-QSAR for effective implementation of the safe-by-design concept

A significant number of experimental studies are supported by computational methods such as quantitative structure-activity relationship modeling of nanoparticles (Nano-QSAR). This is especially so in research focused on design and synthesis of new, safer nanomaterials using safe-by-design concepts. However, Nano-QSAR has a number of important limitations. For example, it is not clear which descriptors that describe the nanoparticle physicochemical and structural properties are essential and can be adjusted to alter the target properties. This limitation can be overcome with the use of the Structure-Activity Prediction Network (SAPNet) presented in this paper. There are three main phases of building the SAPNet. First, information about the structural characterization of a nanomaterial, its physical and chemical properties and toxicity is compiled. Then, the most relevant properties (intrinsic/extrinsic) likely to influence the ENM toxicity are identified by developing "meta-models". Finally, these "meta-models" describing the dependencies between the most relevant properties of the ENMs and their adverse biological properties are developed. In this way, the network is built layer by layer from the endpoint (e.g. toxicity or other properties of interest) to descriptors that describe the particle structure. Therefore, SAPNets go beyond the current standards and provide sufficient information on what structural features should be altered to obtain a material with desired properties.

Experimental and theoretical investigations of the influence of carbon on a Ho3+-TiO2 photocatalyst with Vis response

Due to their photon up-converting capability, lanthanide ions are ideal candidates dopants for semiconductors for developing visible light-driven photocatalytic activity. Of particular relevance, the low luminescence efficiency of Ln-based nanoparticles is one of the main factors that limits their further applications. Carbon, which is present on the surface of all TiO₂ photocatalysts, can be responsible for luminescence quenching processes and, thus, decreasing the photocatalytic activity of TiO₂. This article presents a systematic experimental and theoretical study of the effects of carbon on the photocatalytic performance of Ho³⁺-modified TiO₂. Ho³⁺-TiO₂ photocatalysts modified with various carbon contents (from 0.5 to 20 mol.%) were successfully prepared using a simple hydrothermal method. As-obtained samples were characterized by UV-Vis diffuse reflectance spectroscopy (DRS/UV-Vis), X-ray diffraction (XRD), X-ray photoelectron emission spectroscopy (XPS), N₂ adsorption measurements, photoluminescent spectroscopy (PL), field-emission scanning electron microscopy (FE-SEM) and scanning transmission microscopy (STEM). The photodegradation efficiency of phenol was estimated for visible light (λ > 420 nm and λ > 455 nm). The XPS and XRD analyses and theoretical calculations revealed that the substitutional doping of holmium and carbon in the TiO₂ anatase structure resulted in the appearance of a new sub-band-gap. Changes in the material texture, BET surface area and pore volume can be easily controlled via carbon content in samples. Doping of the Ho³⁺-TiO₂ photocatalysts with carbon resulted in quenching of the emission of Ho³⁺ and, thus, the photodegradation of phenol, was observed in samples containing smaller amounts of carbon. Sixty minutes of irradiation resulted in 89% of phenol degradation under visible light (λ > 420 nm).

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.

How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach

Application of predictive modeling approaches can solve the problem of missing data. Numerous studies have investigated the effects of missing values on qualitative or quantitative modeling, but only a few studies have discussed it for the case of applications in nanotechnology-related data. The present study is aimed at the development of a multi-nano-read-across modeling technique that helps in predicting the toxicity of different species such as bacteria, algae, protozoa, and mammalian cell lines. Herein, the experimental toxicity of 184 metal and silica oxide (30 unique chemical types) nanoparticles from 15 datasets is analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis is developed. In the first step, hidden patterns of toxicity among nanoparticles are identified using a combination of methods. Subsequently, the developed model based on categorization of the toxicity of the metal oxide nanoparticle outcomes is evaluated via the combination of supervised and unsupervised machine learning techniques to determine the underlying factors responsible for the toxicity.

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

Titania-supported palladium, gold and bimetallic nanoparticles (second-generation nanoparticles) demonstrate promising photocatalytic properties. However, due to unusual reactivity, second-generation nanoparticles can be hazardous for living organisms. Considering the ever-growing number of new types of nanoparticles that can potentially contaminate the environment, a determination of their toxicity is extremely important. The main aim of presented study was to investigate the cytotoxic effect of surface modified TiO₂-based nanoparticles, to model their quantitative nanostructure-toxicity relationships and to reveal the toxicity mechanism. In this context, toxicity tests for surface-modified TiO₂-based nanoparticles were performed in vitro, using Gram-negative bacteria Escherichia coli and Chinese hamster ovary (CHO-K1) cells. The obtained cytotoxicity data were analyzed by means of computational methods (quantitative structure-activity relationships, QSAR approach). Based on a combined experimental and computational approach, predictive models were developed, and relationships between cytotoxicity, size, and specific surface area (Brunauer-Emmett-Teller surface, BET) of nanoparticles were discussed.

Development of a novel in silico model of zeta potential for metal oxide nanoparticles: a nano-QSPR approach

Once released into the aquatic environment, nanoparticles (NPs) are expected to interact (e.g. dissolve, agglomerate/aggregate, settle), with important consequences for NP fate and toxicity. A clear understanding of how internal and environmental factors influence the NP toxicity and fate in the environment is still in its infancy. In this study, a quantitative structure-property relationship (QSPR) approach was employed to systematically explore factors that affect surface charge (zeta potential) under environmentally realistic conditions. The nano-QSPR model developed with multiple linear regression (MLR) was characterized by high robustness [Formula: see text] and external predictivity [Formula: see text] The results clearly showed that zeta potential values varied markedly as functions of the ionic radius of the metal atom in the metal oxides, confirming that agglomeration and the extent of release of free MexOy largely depend on their intrinsic properties. A developed nano-QSPR model was successfully applied to predict zeta potential in an ionized solution of NPs for which experimentally determined values of response have been unavailable. Hence, the application of our model is possible when the values of zeta potential in the ionized solution for metal oxide nanoparticles are undetermined, without the necessity of performing more time consuming and expensive experiments. We believe that our studies will be helpful in predicting the conditions under which MexOy is likely to become problematic for the environment and human health.

Aggressive, solid variant of alveolar rhabdomyosarcoma with cutaneous involvement in a juvenile labrador retriever

An 8.5-month-old male Labrador retriever presented with a cutaneous mass in the right maxillofacial region and swelling of the gingiva. The dog received antibiotic and anti-inflammatory treatment. After 3 weeks the dog returned, presenting with disseminated cutaneous tumours on the neck, trunk and groin. One of the nodules was resected and a cutaneous round cell tumour was diagnosed on microscopical examination. The dog was humanely destroyed. Necropsy examination revealed disseminated tumours in the skin, internal organs and skeletal muscles. Microscopically, all of the tumours were composed of small round cells, arranged in nests. Immunohistochemically, the neoplastic cells expressed vimentin, desmin, MyoD1, myogenin and smooth muscle actin, but were negative for CD3, CD18, CD79αcy, cytokeratin AE1/AE3, chromogranin A, class II molecules of the major histocompatibility complex, neuron-specific enolase and S100. The average Ki67 index was 89.5%. The final diagnosis was a solid variant of alveolar rhabdomyosarcoma (ARMS). This is the first report of the cutaneous multifocal form of ARMS in veterinary oncology.

Role of the surface and volume of palatal relief chambers in retention of maxillary complete dentures

Maxillary complete dentures were made with thickened denture bases for 10 patients. The retentive force was measured with a dynamometer. Acrylic resin was then removed with a bur from the mucosal side of the denture base, thus progressively enlarging the palatal chamber. Each enlargement was followed by measurement of the surface and volume of the chambers and the retention force of the dentures. The mean force of the denture retention diminished progressively from 57 N to 13.5 N as the chambers enlarged. An analysis of the results showed that the retention of maxillary complete dentures does not depend on the surface of the palatal chambers but is inversely proportionate to their volume.