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

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood-brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

The critical role of nanoparticle sizes in the interactions between gold nanoparticles and ABC transporters in zebrafish embryos

Despite the increasing evidences for adenosine triphosphate-binding cassette (ABC transporters)-mediated efflux of nanoparticles, the universality of these phenomena and the determining factors for the process remained to be clarified. This paper aimed to systemically investigate the role of nanoparticle size in the interactions between adenosine triphosphate-binding cassette (ABC transporters) and gold nanoparticles (AuNPs, 3 nm, 19 nm, and 84 nm, named as Au-3, Au-19, and Au-84) in zebrafish embryos. The results showed that all the three AuNPs induced significant toxicity as reflected by delayed hatching of embryos, decreased glutathione (GSH) contents, and increased reactive oxygen species (ROS) levels. Under the hindrance of embryo chorions, smaller AuNPs could more easily accumulate in the embryos, causing higher toxicity. Addition of transporter inhibitors enhanced the accumulation and toxicity of Au-3 and Au-19, and these nanoparticles induced the expressions of abcc2 and abcb4, indicating a fact that Au-3 and Au-19 were the potential substrates of ABC transporters, but these phenomena were barely found for Au-84. On the contrary, Au-84 suppressed the gene expressions of various ABC transporters like abcc1, abcg5, and abcg8. With specific suppressors, transcription factors like nuclear factor-erythroid 2-related factor-2 (Nrf2) and pregnane X receptor (Pxr) were found to be important in the induction of ABC transporters by AuNPs. After all, these results revealed a vital role of nanoparticle sizes in the interactions between ABC transporters and AuNPs in zebrafish embryos, and the critical size could be around 19 nm. Such information would be beneficial in assessing the environmental risk of nanoparticles, as well as their interactions with other chemical toxicants.

Neutralization of ionic interactions by dextran-based single-chain nanoparticles improves tobramycin diffusion into a mature biofilm

The extracellular matrix protects biofilm cells by reducing diffusion of antimicrobials. Tobramycin is an antibiotic used extensively to treat P. aeruginosa biofilms, but it is sequestered in the biofilm periphery by the extracellular negative charge matrix and loses its efficacy significantly. Dispersal of the biofilm extracellular matrix with enzymes such as DNase I is another promising therapy that enhances antibiotic diffusion into the biofilm. Here, we combine the charge neutralization of tobramycin provided by dextran-based single-chain polymer nanoparticles (SCPNs) together with DNase I to break the biofilm matrix. Our study demonstrates that the SCPNs improve the activity of tobramycin and DNase I by neutralizing the ionic interactions that keep this antibiotic in the biofilm periphery. Moreover, the detailed effects and interactions of nanoformulations with extracellular matrix components were revealed through time-lapse imaging of the P. aeruginosa biofilms by laser scanning confocal microscopy with specific labeling of the different biofilm components.

Towards machine learning discovery of dual antibacterial drug-nanoparticle systems

Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL^-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.

Molecular toxicity of Benzo(a)pyrene mediated by elicited oxidative stress infer skeletal deformities and apoptosis in embryonic zebrafish

Benzo(a)pyrene (BaP) has become an integral component of disposed of plastic waste, organic pollutants, and remnants of combustible materials in the aquatic environment due to their persistent nature. The accumulation and integration of these polycyclic aromatic hydrocarbons (PAHs) have raised concern to human health and ecological safety. This study assessed the BaP-induced in vivo molecular toxicity with embryonic zebrafish inferred by oxidative stress and apoptosis. BaP was found to induce morphological and physiological abnormalities like delayed hatching (p < 0.05). Computational analysis demonstrated the high-affinity interaction of BaP with the zebrafish hatching enzyme (ZHE1) with Arg, Cys, Ala, Tyr, and Phe located at the active site revealing the influence of BaP on delayed hatching due to alteration of the enzyme structure. RT-PCR analysis revealed significant down-regulation of the skeletal genes Sox9a, SPP1/OPN, and Col1a1 (p < 0.05) genes. The cellular investigations unraveled that the toxicity of BaP extends to the skeletal regions of zebrafish (head, backbone, and tail) because of the elicited oxidative stress leading to apoptosis. The study extended the horizon of understanding of BaP toxicity at the molecular level which will enhance the indulgent and designing of techniques for better ecological sustainability.

Nanostructured Ceria: Biomolecular Templates and (Bio)applications

Ceria (CeO2) nanostructures are well-known in catalysis for energy and environmental preservation and remediation. Recently, they have also been gaining momentum for biological applications in virtue of their unique redox properties that make them antioxidant or pro-oxidant, depending on the experimental conditions and ceria nanomorphology. In particular, interest has grown in the use of biotemplates to exert control over ceria morphology and reactivity. However, only a handful of reports exist on the use of specific biomolecules to template ceria nucleation and growth into defined nanostructures. This review focusses on the latest advancements in the area of biomolecular templates for ceria nanostructures and existing opportunities for their (bio)applications.

Teratogenic and Neurotoxic Effects of n-Butanol on Zebrafish Development

In recent years, n-butanol, a type of alcohol, has been widely used from the chemical industry to the food industry. In this study, toxic effects of n-butanol's different concentrations (10, 50, 250, 500, 750, 1,000, and 1,250 mg/L) in Zebrafish Danio rerio embryos and larvae were investigated. For this purpose, Zebrafish embryos were exposed to n-butanol in acute semistatic applications. Teratogenic effects such as cardiac edema, scoliosis, lordosis, head development abnormality, yolk sac edema, and tail abnormality were determined at different time intervals (24, 48, 72, 96, and 120 h). Additionally, histopathological abnormalities such as vacuole formation in brain tissue and necrosis in liver tissue were observed at high doses (500, 750, and 1,000 mg/L) in all treatment groups at 96 h. It was determined that heart rate decreased at 48, 72, and 96 h due to an increase in concentration. In addition, alcohol-induced eye size reduction (microphthalmia) and single eye formation (cyclopia) are also among the effects observed in our research findings. In conclusion, n-butanol has been observed to cause intense neurotoxic, teratogenic, and cardiotoxic effects in Zebrafish embryos and larvae.

IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks

Nanoparticles are useful antimicrobial drug-release systems, but some nanoparticles also exhibit antibacterial activity. However, investigation of their antibacterial activity is a difficult and slow process due to the numerous combinations of nanoparticle size, shape, and composition vs. biological tests, assay organisms, and multiple activity parameters to be measured. Additionally, the overuse of antibiotics has led to the emergence of resistant bacterial strains with different metabolic networks. Computational models may speed up this process, but the models reported to date do not to consider all the previous factors, and the data sources are dispersed and not curated. Thus, herein, we used an information fusion, perturbation-theory machine learning (IFPTML) approach, which is introduced by us for the first time, to fit a model for the discovery of antibacterial nanoparticles. The dataset studied had 15 classes of nanoparticles (1-100 nm) with most cases in the range of 1-50 nm vs. >20 pathogenic bacteria species with different metabolic networks. The nanoparticles studied included metal nanoparticles of Au, Ag, and Cu; oxide nanoparticles of Zn, Cu, La, Al, Fe, Sn, Ti, Cd, and Si; and metal salt nanoparticles of CuI and CdS. We used the SOFT.PTML software (our own application) with a user-friendly interface for the IFPTML calculations and a control statistics package. Using SOFT.PTML, we found a linear logistic regression equation that could model 4 biological activity parameters using only 8 variables with χ2 = 2265.75, p-level <0.05, sensitivity, Sn = 79.4, and specificity, Sp = 99.3, for 3213 cases (nanoparticle-bacteria pairs) in the training series. The model had Sn = 80.8 and Sp = 99.3 for 2114 cases in the external validation series. We also developed a random forest non-linear model with higher values of Sn and Sp = 98-99% in the training/validation series, although it was more complicated to use. SOFT.PTML has been demonstrated to be a useful tool for the analysis of complex data in nanotechnology. We also introduced a new anabolism-catabolism unbalance index of metabolic networks to reveal the biological connotation of the IFPTML predictions for antibacterial nanoparticles. These new models open a new door for the discovery of NPs vs. new bacterial species and strains with different topological structures of their metabolic networks.

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.

Toward comprehension of multiple human cells uptake of engineered nano metal oxides: quantitative inter cell line uptake specificity (QICLUS) modeling

To address the nanomaterial exposure threat, it is imperative to understand how nanomaterials are recognized, internalized, and distributed within diverse cell systems. Targeting of nanomaterials to a specific cell type is generally attained through the modification of the nanoparticle (NP) surface leading to required cellular uptake. The enhanced cellular uptake to normal cells can direct to the higher interaction of NPs with subcellular organelles resulting the provocation of various signaling pathways. The successes of NPs rely on the prospect for the synthesis of functionalized NPs with necessary properties and their enhanced potential for cellular uptake for specific targeting. In the present study, we have modeled the cellular uptake of 109 surface modifiers of metal oxide nanoparticles (MNPs) for three different cell lines: HUVEC (Human endothelial cells), U937 (human macrophage cells), and PaCa2 (cancer cell lines). Along with the quantitative structure-activity relationship (QSAR) models, for the very first time we have developed and performed quantitative inter cell line uptake specificity (QICLUS) modeling to identify the physicochemical properties, as well as majorly structural fragments responsible for cellular uptake differences between two specific cell lines. The present work provides a comprehensive understanding of the cellular uptake of MNPs and the underlying structural parameters controlling the nano-cellular interactions. This phenomenon has also been analyzed from the QSAR and QICLUS models that concluded the functional groups of surface modifiers like amine, anhydride, halogen atoms, nitro group, acids have the dominating roles for the uptake of MNPs into the cell lines. Thus, the developed models may be used for designing of novel surface modifiers of MNPs of desired characteristics for proper cell-NPs interactions, as well as in the context of virtual screening aspect. Moreover, the MNP-cell interactions can give some idea about the toxicity for target-specific drug delivery treatment as higher cellular uptake is required for specific cells to treat the disease and lower uptake to the neighboring cells for lower toxicity.