Development of Theoretical Descriptors for Cytotoxicity Evaluation of Metallic Nanoparticles

Biosynthesis and Mathematical Interpretation of Zero-Valent Iron NPs Using Nigella sativa Seed Tincture for Indemnification of Carcinogenic Metals Present in Industrial Effluents

Zero-valent iron nanoparticles (ZVI-NPs) are utilized for the indemnification of a wide range of environmental pollutants. Among the pollutants, heavy metal contamination is the major environmental concern due to their increasing prevalence and durability. In this study, heavy metal remediation capabilities are determined by the green synthesis of ZVI-NPs using aqueous seed extract of Nigella sativa which is a convenient, environmentally friendly, efficient, and cost-effective technique. The seed extract of Nigella sativa was utilized as a capping and reducing agent for the generation of ZVI-NPs. UV-visible spectrophotometry (UV-vis), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDX), and Fourier transform infrared spectroscopy (FTIR) was used to investigate the ZVI-NP composition, shape, elemental constitution, and perspective functional groups, respectively. The biosynthesized ZVI-NPs displayed a peak of plasmon resonance spectra at 340 nm. The synthesized NPs were cylindrical in shape, with a size of 2 nm and (-OH) hydroxyl, (C-H) alkanes and alkynes N-C, N=C, C-O, =CH functional groups attached to the surface of ZVI-NPs. Heavy metals were successfully remediated from industrial wastewater collected from the various tanneries of Kasur. During the reaction duration of 24 h, different concentrations of ZVI-NPs (10 μg, 20 μg and 30 μg) per 100 mL were utilized for the removal of heavy metals from industrial wastewater. The 30 μg/100 mL of ZVI-NPs proved the pre-eminent concentration of NPs as it removed >90% of heavy metals. The synthesized ZVI-NPs were analyzed for compatibility with the biological system resulting in 87.7% free radical scavenging, 96.16% inhibition of protein denaturation, 60.29% and 46.13% anti-cancerism against U87-MG and HEK 293 cell lines, respectively. The physiochemical and exposure mathematical models of ZVI-NPs represented them as stable and ecofriendly NPs. It proved that biologically synthesized NPs from a seed tincture of Nigella sativa have a strong potential to indemnify heavy metals found in industrial effluent samples.

Analyzing the TiO2surface reactivity based on oxygen vacancies computed by DFT and DFTB methods

Titanium dioxide is a key material in many fields, including technological, industrial and biomedical applications. Many of these applications are related to the surface reactivity of TiO₂and involve its reducibility properties. Recently titania has been banned as a food additive due to its (nano)toxicity, and the release of reactive oxygen species plays a crucial role in many toxicological mechanisms. Determining chemical descriptors that account for the extension of reduction is necessary to understand such processes and necessary for predicting the reactivity of an unknown system. In the present work, we compute a set of chemical descriptors for selected surfaces of anatase and rutile TiO₂. The aim is twofold: we want to provide chemically meaningful information on the surface reactivity, and benchmark the descriptors for twoab initioschemes. To do so, we compute the oxygen vacancy formation energy, and the corresponding electronic structure, in four slab models with two different computational schemes (DFT+Uand DFTB). In this way, we characterize the robustness of the dataset, with the purpose of scaling up to more realistic model systems such as nanoparticles or explicit solvent, which are too computationally demanding for state-of-the-art density functional theory approaches.

Importance of Surface Topography in Both Biological Activity and Catalysis of Nanomaterials: Can Catalysis by Design Guide Safe by Design?

It is acknowledged that the physicochemical properties of nanomaterials (NMs) have an impact on their toxicity and, eventually, their pathogenicity. These properties may include the NMs’ surface chemical composition, size, shape, surface charge, surface area, and surface coating with ligands (which can carry different functional groups as well as proteins). Nanotopography, defined as the specific surface features at the nanoscopic scale, is not widely acknowledged as an important physicochemical property. It is known that the size and shape of NMs determine their nanotopography which, in turn, determines their surface area and their active sites. Nanotopography may also influence the extent of dissolution of NMs and their ability to adsorb atoms and molecules such as proteins. Consequently, the surface atoms (due to their nanotopography) can influence the orientation of proteins as well as their denaturation. However, although it is of great importance, the role of surface topography (nanotopography) in nanotoxicity is not much considered. Many of the issues that relate to nanotopography have much in common with the fundamental principles underlying classic catalysis. Although these were developed over many decades, there have been recent important and remarkable improvements in the development and study of catalysts. These have been brought about by new techniques that have allowed for study at the nanoscopic scale. Furthermore, the issue of quantum confinement by nanosized particles is now seen as an important issue in studying nanoparticles (NPs). In catalysis, the manipulation of a surface to create active surface sites that enhance interactions with external molecules and atoms has much in common with the interaction of NP surfaces with proteins, viruses, and bacteria with the same active surface sites of NMs. By reviewing the role that surface nanotopography plays in defining many of the NMs’ surface properties, it reveals the need for its consideration as an important physicochemical property in descriptive and predictive toxicology. Through the manipulation of surface topography, and by using principles developed in catalysis, it may also be possible to make safe-by-design NMs with a reduction of the surface properties which contribute to their toxicity.

Impact of atomistic or crystallographic descriptors for classification of gold nanoparticles

Machine learning models are known to be sensitive to the features used to train them, but there is currently no way to predict the impact of using different features prior to feature extraction. This is particularly important to fields such as nanotechnology that are highly multi-disciplinary, and samples can be characterised many different ways depending on the preferences of individual researchers. Does it matter if nanomaterials are described using the interatomic coordinations or more complex order parameters? In this study we compare results of supervised and unsupervised learning on a single set of gold nanoparticles that has been characterised by two different descriptors, each with a unique feature space. We find that there are some consistencies, and model selection is descriptor-agnostic, but the level of detail and the type of information that can be extracted from the results is sensitive to the way the particles are described. Unsupervised clustering revealed that an atomistic descriptor provides a finer-grained interpretation and clusters that are sub-clusters of a more sophisticated crystallographic descriptor, which is consistent with both how the features were calculated, and how they are interpreted in the domain. A supervised classifier revealed that the types of features responsible for the separation are related to the bulk structure, regardless of the descriptor, but capture different types of information. For both the atomistic and crystallographic descriptor the gradient boosting decision tree classifier gave superior results of F1-scores of 0.96 and 0.98, respectively, with excellent precision and recall, even though the clustering presented a challenging multi-classification problem.

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.

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.

Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials

The present study is based on the application of a multivariate statistical analysis approach for the selection of optimal descriptors of nanomaterials with the objective of robust qualitative modeling of their toxicity. A novel data mining protocol has been developed for the selection of an optimal subset of descriptors of nanomaterials by using the well-known multivariate method principal component analysis (PCA). The selected subsets of descriptors were validated for qualitative modeling of the toxicity of nanomaterials in the PC space. The analysis and validation of the proposed schemes were based on five decisive nanomaterial toxicity data sets available in the published literature. Optimal descriptors were selected on the basis of the maximum loading criteria and using a threshold value of cumulative variance ≤90% on PC directions. A maximum inter-class separation(B) and the minimum intra-classes separation(A) were obtained for toxic vs. nontoxic nanomaterials in the PC space with the selected subsets of optimal descriptors compared to their other combinations for each of the datasets.