Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Risk assessment of industrial chemicals towards salmon species amalgamating QSAR, q-RASAR, and ARKA framework

The extensive use of industrial chemicals poses a serious threat to aquatic species such as the salmon species, which, when consumed, can affect human beings via their dietary intake. Salmon fish is a vital source of protein for maintaining human health. The present study aims to estimate the toxicity of diverse chemicals using in silico-based global model involving three different salmon species: Salmo salar, Oncorhynchus kisutch, and Oncorhynchus tshawytscha encompassing the toxicity endpoint median lethal concentration (LC50). Primarily, a quantitative structure-activity relationship (QSAR) model is developed using molecular descriptors. QSAR model descriptors are integrated with the similarity and error-based measures of read-across to develop the read-across structure-activity relationship (RASAR) model. Another emerging dimensionality reduction modeling algorithm, arithmetic residuals in K-groups analysis (ARKA) is employed to enhance the model's degree of freedom. Model quality was improved by hybrid model development which combined the feature matrix of the QSAR model with those of the RASAR and ARKA descriptors. Finally, to attain more trustworthy results and address the limitations of individual models, a partial least square (PLS)-based stacking model is developed using the predicted response values of QSAR, RASAR, ARKA, and hybrid models as descriptors. The stacking model outperforms the quality of the individual models which is evident from the determination coefficient R2 (0.713), leave-one-out cross-validated correlation coefficient (Q2 LOO:0.697), predictive R2 (Q2 F1 : 0.797), Q2 F2 (0.795) and lower value of root mean square error of prediction RMSEp (0.652). Additionally, classification modelling was performed with the feature matrix of the QSAR model by employing both linear and non-linear approaches. The developed stacking model can thus be used in environmental risk assessment aiding in toxicity data-gap filling and design of safe and green chemicals.

AI-based nanotoxicity data extraction and prediction of nanotoxicity

With the growing use of nanomaterials (NMs), assessing their toxicity has become increasingly important. Among toxicity assessment methods, computational models for predicting nanotoxicity are emerging as alternatives to traditional in vitro and in vivo assays, which involve high costs and ethical concerns. As a result, the qualitative and quantitative importance of data is now widely recognized. However, collecting large, high-quality data is both time-consuming and labor-intensive. Artificial intelligence (AI)-based data extraction techniques hold significant potential for extracting and organizing information from unstructured text. However, the use of large language models (LLMs) and prompt engineering for nanotoxicity data extraction has not been widely studied. In this study, we developed an AI-based automated data extraction pipeline to facilitate efficient data collection. The automation process was implemented using Python-based LangChain. We used 216 nanotoxicity research articles as training data to refine prompts and evaluate LLM performance. Subsequently, the most suitable LLM with refined prompts was used to extract test data, from 605 research articles. As a result, data extraction performance on training data achieved F1D.E. (F1 score for Data Extraction) ranging from 84.6 % to 87.6 % across different LLMs. Furthermore, using the extracted dataset from test set, we constructed automated machine learning (AutoML) models that achieved F1N.P. (F1 score for Nanotoxicity Prediction) exceeding 86.1 % in predicting nanotoxicity. Additionally, we assessed the reliability and applicability of models by comparing them in terms of ground truth, size, and balance. This study highlights the potential of AI-based data extraction, representing a significant contribution to nanotoxicity research.

Nanoparticle Association with Brain Cells Is Augmented by Protein Coronas Formed in Cerebrospinal Fluid

Neuronanomedicine harnesses nanoparticle technology for the treatment of neurological disorders. An unavoidable consequence of nanoparticle delivery to biological systems is the formation of a protein corona on the nanoparticle surface. Despite the well-established influence of the protein corona on nanoparticle behavior and fate, as well as FDA approval of neuro-targeted nanotherapeutics, the effect of a physiologically relevant protein corona on nanoparticle-brain cell interactions is insufficiently explored. Indeed, less than 1% of protein corona studies have investigated protein coronas formed in cerebrospinal fluid (CSF), the fluid surrounding the brain. Herein, we utilize two clinically relevant polymeric nanoparticles (PLGA and PLGA-PEG) to evaluate the formation of serum and CSF protein coronas. LC-MS analysis revealed distinct protein compositions, with selective enrichment/depletion profiles. Enhanced association of CSF precoated particles with brain cells demonstrates the importance of selecting physiologically relevant biological fluids to more accurately study protein corona formation and subsequent nanoparticle-cell interactions, paving the way for improved nanoparticle engineering for in vivo applications.

Rational Design of Lipid Nanoparticles for Enhanced mRNA Vaccine Delivery via Machine Learning

Since the coronavirus pandemic, mRNA vaccines have revolutionized the field of vaccinology. Lipid nanoparticles (LNPs) are proposed to enhance mRNA delivery efficiency; however, their design is suboptimal. Here, a rational method for designing LNPs is explored, focusing on the ionizable lipid composition and structural optimization using machine learning (ML) techniques. A total of 213 LNPs are analyzed using random forest regression models trained with 314 features to predict the mRNA expression efficiency. The models, which predict mRNA expression levels post-administration of intradermal injection in mice, identify phenol as the dominant substructure affecting mRNA encapsulation and expression. The specific phospholipids used as components of the LNPs, as well as the N/P ratio and mass ratio, are found to affect the efficacy of mRNA delivery. Structural analysis highlights the impact of the carbon chain length on the encapsulation efficiency and LNP stability. This integrated approach offers a framework for designing advanced LNPs and has the potential to unlock the full potential of mRNA therapeutics.

Common issues of data science on the eco-environmental risks of emerging contaminants

Data-driven approaches (e.g., machine learning) are increasingly used to replace or assist laboratory studies in the study of emerging contaminants (ECs). In the past ten years, an increasing number of models or approaches have been applied to ECs, and the datasets used are continuously enriched. However, there are large knowledge gaps between what we have found and the natural eco-environmental meaning. For most published reviews, the contents are organized by the types of ECs, but the common issues of data science, regardless of the type of pollutant, are not sufficiently addressed. To close or narrow the knowledge gaps, we highlight the following issues ignored in the field of data-driven EC research. Complicated biological and ecological data and ensemble models revealing mechanisms and spatiotemporal trends with strong causal relationships and without data leakage deserve more attention in the future. In addition, the matrix influence, trace concentration, and complex scenario have often been ignored in previous works. Therefore, an integrated research framework related to natural fields, ecological systems, and large-scale environmental problems, rather than relying solely on laboratory data-related analysis, is urgently needed. Beyond the current prediction purposes, data science can inspire the discovery of scientific questions, and mutual inspiration among data science, process and mechanism models, and laboratory and field research is a critical direction. Focusing on the above urgent and common issues related to data, frameworks, and purposes, regardless of the type of pollutant, data science is expected to achieve great advancements in addressing the eco-environmental risks of ECs.

Nanotoxicology: developments and new insights

The use of nanoparticles (NPs) in treatment of diseases have increased exponentially recently, giving rise to the science of nanomedicine. The safety of these NPs in humans has also led to the science of nanotoxicology. Due to a dearth of both readily available models and precise bio-dispersion characterization techniques, nanotoxicological research has obviously been constrained. However, the ensuing years were notable for the emergence of improved synthesis methods and characterization tools. Major advances have been made in linking certain physical variables, paralleling improvements in characterization size, shape, or coating factors to the resulting physiological reactions. Although significant progress has been a contribution to the development of nanotoxicology, however, it faces numerous difficulties and technical constraints distinct from those of conventional toxicological assessment as it attempts to improve the therapeutic effects of medicines. Determining thorough characterization standards, standardizing dosimetry, assessing the kinetics of ions dissolving and enhancing the accuracy of in vitro-in vivo correlation efficiency, also defining restrictions on exposure protection are some of the most important and pressing concerns. This article will explore the past advancement and potential prospects of nanotoxicology, standard models, emphasizing significant findings from earlier studies and examining current challenges, giving insight on the way forward.

Balancing the Functionality and Biocompatibility of Materials with a Deep-Learning-Based Inverse Design Framework

The rational design of molecules with the desired functionality presents a significant challenge in chemistry. Moreover, it is worth noting that making chemicals safe and sustainable is crucial to bringing them to the market. To address this, we propose a novel deep learning framework developed explicitly for inverse design of molecules with both functionality and biocompatibility. This innovative approach comprises two predictive models and one generative model, facilitating the targeted screening of novel molecules from created virtual chemical space. Our method's versatility is highlighted in the inverse design process, where it successfully generates molecules with specified motifs or composition, discovers synthetically accessible molecules, and jointly targets functional and safe properties beyond the training regime. The utility of this method is demonstrated in its ability to design ionic liquids (ILs) with enhanced antibacterial properties and reduced cytotoxicity, addressing the issue of balancing functionality and biocompatibility in molecular design.

Predicting zeta potential of liposomes from their structure: A nano-QSPR model for DOPE, DC-Chol, DOTAP, and EPC formulations

Liposomes, nanoscale spherical structures composed of amphiphilic lipids, hold great promise for various pharmaceutical applications, especially as nanocarriers in targeted drug delivery, due to their biocompatibility, biodegradability, and low immunogenicity. Understanding the factors influencing their physicochemical properties is crucial for designing and optimizing liposomes. In this study, we have presented the kernel-weighted local polynomial regression (KwLPR) nano-quantitative structure-property relationships (nano-QSPR) model to predict the zeta potential (ZP) based on the structure of 12 liposome formulations, including 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), 3ß-[N-(N',N'-dimethylaminoethane)-carbamoyl]cholesterol (DC-Chol), 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), and L-α-phosphatidylcholine (EPC). The developed model is well-fitted (R 2 = 0.96, RMSE C = 5.76), flexible (Q CVloo 2 = 0.83, RMSE CVloo = 10.77), and reliable (Q Ext 2 = 0.89 RMSE Ext = 5.17). Furthermore, we have established the formula for computing molecular nanodescriptors for liposomes, based on constituent lipids' molar fractions. Through the correlation matrix and principal component analysis (PCA), we have identified two key structural features affecting liposomes' zeta potential: hydrophilic-lipophilic balance (HLB) and enthalpy of formation. Lower HLB values, indicating a more lipophilic nature, are associated with a higher zeta potential, and thus stability. Higher enthalpy of formation reflects reduced zeta potential and decreased stability of liposomes. We have demonstrated that the nano-QSPR approach allows for a better understanding of how the composition and molecular structure of liposomes affect their zeta potential, filling a gap in ZP nano-QSPR modeling methodologies for nanomaterials (NMs). The proposed proof-of-concept study is the first step in developing a comprehensive and computationally based system for predicting the physicochemical properties of liposomes as one of the most important drug nano-vehicles.

De novo Design of Biocompatible Nanomaterials Using Quasi-SMILES and Recurrent Neural Networks

Screening nanomaterials (NMs) with desired properties from the extensive chemical space presents significant challenges. The potential toxicity of NMs further limits their applications in biological systems. Traditional methods struggle with these complexities, but generative models offer a possible solution to producing new molecules without prior knowledge. However, converting complex 3D nanostructures into computer-readable formats remains a critical prerequisite. To overcome these challenges, we proposed an innovative deep-learning framework for the de novo design of biocompatible NMs. This framework comprises two predictive models and a generative model, utilizing a Quasi-SMILES representation to encode three-dimensional structural information on NMs. Our generative model successfully created 289 new NMs not previously seen in the training set. The predictive models identified a particularly promising NM characterized by high cellular uptake and low toxicity. This NM was successfully synthesized, and its predicted properties were experimentally validated. Our approach advances the application of artificial intelligence in NM design and provides a practical solution for balancing functionality and toxicity in NMs.

Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers

The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, and more, each tailored to specific therapeutic applications. Despite significant achievements, the clinical translation of nanocarriers is limited, primarily due to the low efficiency of drug delivery and an incomplete understanding of nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration and a deep understanding of the nano-bio interface. To enhance nanocarrier design, scientists employ both physics-based and data-driven models. Physics-based models provide detailed insights into chemical reactions and interactions at atomic and molecular scales, while data-driven models leverage machine learning to analyze large datasets and uncover hidden mechanisms. The integration of these models presents challenges such as harmonizing different modeling approaches and ensuring model validation and generalization across biological systems. However, this integration is crucial for developing effective and targeted nanocarrier systems. By integrating these approaches with enhanced data infrastructure, explainable AI, computational advances, and machine learning potentials, researchers can develop innovative nanomedicine solutions, ultimately improving therapeutic outcomes.

Twenty years of in vitro nanotoxicology: how AI could make the difference

More than two decades ago, the advent of Nanotechnology has marked the onset of a new and critical field in science and technology, highlighting the importance of multidisciplinary approaches to assess and model the potential human hazard of newly developed advanced materials in the nanoscale, the nanomaterials (NMs). Nanotechnology is, by definition, a multidisciplinary field, that integrates knowledge and techniques from physics, chemistry, biology, materials science, and engineering to manipulate matter at the nanoscale, defined as anything comprised between 1 and 100 nm. The emergence of nanotechnology has undoubtedly led to significant innovations in many fields, from medical diagnostics and targeted drug delivery systems to advanced materials and energy solutions. However, the unique properties of nanomaterials, such as the increased surface to volume ratio, which provides increased reactivity and hence the ability to penetrate biological barriers, have been also considered as potential risk factors for unforeseen toxicological effects, stimulating the scientific community to investigate to which extent this new field of applications could pose a risk to human health and the environment.

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits

Modern nanotechnology has generated numerous datasets from in vitro and in vivo studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities of new nanomaterials. Moreover, a library of virtual nanostructures with their predicted properties and bioactivities is available, directing the synthesis of new nanomaterials. This platform provides a data-driven computational modeling platform for the nanoscience community, significantly aiding in the development of safe and effective nanomaterials.

Interpretable Causal System Optimization Framework for the Advancement of Biological Effect Prediction and Redesign of Nanoparticles

Nanomedicine has promising applications in disease treatment, given the remarkable safety concerns (e.g., nanotoxicity and inflammation) of nanomaterials, and realizing the trade-off between the immune response and organ burden of NPs and deeply understanding the interactions of the organism-nano systems are crucial to facilitate the biological applications of NPs. Here, we propose an interpretable causal system optimization (ICSO) framework and construct the upstream and downstream tasks of accurate prediction and intelligent NP optimization. ICSO framework screens the key drivers (recovery duration, specific surface area, and nanomaterial size) and potential causal information for immune responses and organ burden, revealing the hidden priming/constraint effects in bionano interactions. ICSO can be used to quantify the thresholds of biological responses to multiple properties (e.g., the specific surface area, diameter, and zeta potential). ICSO provides quantitative information and constraint conditions for the design of highly biocompatible and targeted organ delivery nanomaterials. For example, negative inflammation is reduced by 36.19%, and positive lung accumulation is promoted by 40.14% by optimizing the specific surface areas and shape and increasing the diameter-to-length ratio. ICSO overcomes the limitations of experience-dependent approaches and provides powerful and automated solutions for decision-makers during nanomaterial design.

Emerging trends and future challenges of advanced 2D nanomaterials for combating bacterial resistance

The number of multi-drug-resistant bacteria has increased over the last few decades, which has caused a detrimental impact on public health worldwide. In resolving antibiotic resistance development among different bacterial communities, new antimicrobial agents and nanoparticle-based strategies need to be designed foreseeing the slow discovery of new functioning antibiotics. Advanced research studies have revealed the significant disinfection potential of two-dimensional nanomaterials (2D NMs) to be severed as effective antibacterial agents due to their unique physicochemical properties. This review covers the current research progress of 2D NMs-based antibacterial strategies based on an inclusive explanation of 2D NMs' impact as antibacterial agents, including a detailed introduction to each possible well-known antibacterial mechanism. The impact of the physicochemical properties of 2D NMs on their antibacterial activities has been deliberated while explaining the toxic effects of 2D NMs and discussing their biomedical significance, dysbiosis, and cellular nanotoxicity. Adding to the challenges, we also discussed the major issues regarding the current quality and availability of nanotoxicity data. However, smart advancements are required to fabricate biocompatible 2D antibacterial NMs and exploit their potential to combat bacterial resistance clinically.

Design strategies, advances and future perspectives of colon-targeted delivery systems for the treatment of inflammatory bowel disease

Inflammatory bowel diseases (IBD) significantly contribute to high mortality globally and negatively affect patients' qualifications of life. The gastrointestinal tract has unique anatomical characteristics and physiological environment limitations. Moreover, certain natural or synthetic anti-inflammatory drugs are associated with poor targeting, low drug accumulation at the lesion site, and other side effects, hindering them from exerting their therapeutic effects. Colon-targeted drug delivery systems represent attractive alternatives as novel carriers for IBD treatment. This review mainly discusses the treatment status of IBD, obstacles to drug delivery, design strategies of colon-targeted delivery systems, and perspectives on the existing complementary therapies. Moreover, based on recent reports, we summarized the therapeutic mechanism of colon-targeted drug delivery. Finally, we addressed the challenges and future directions to facilitate the exploitation of advanced nanomedicine for IBD therapy.

Deep learning-assisted flavonoid-based fluorescent sensor array for the nondestructive detection of meat freshness

Gas sensors containing indicators have been widely used in meat freshness testing. However, concerns about the toxicity of indicators have prevented their commercialization. Here, we prepared three fluorescent sensors by complexing each flavonoid (fisetin, puerarin, daidzein) with a flexible film, forming a fluorescent sensor array. The fluorescent sensor array was used as a freshness indication label for packaged meat. Then, the images of the indication labels on the packaged meat under different freshness levels were collected by smartphones. A deep convolutional neural network (DCNN) model was built using the collected indicator label images and freshness labels as the dataset. Finally, the model was used to detect the freshness of meat samples, and the overall accuracy of the prediction model was as high as 97.1%. Unlike the TVB-N measurement, this method provides a nondestructive, real-time measurement of meat freshness.

Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size

A strong need exists for broadly applicable nano-QSARs, capable of predicting toxicological outcomes towards untested species and nanomaterials, under different environmental conditions. Existing nano-QSARs are generally limited to only a few species but the inclusion of species characteristics into models can aid in making them applicable to multiple species, even when toxicity data is not available for biological species. Species traits were used to create classification- and regression machine learning models to predict acute toxicity towards aquatic species for metallic nanomaterials. Afterwards, the individual classification- and regression models were stacked into a meta-model to improve performance. Additionally, the uncertainty and limitations of the models were assessed in detail (beyond the OECD principles) and it was investigated whether models would benefit from the addition of more data. Results showed a significant improvement in model performance following model stacking. Investigation of model uncertainties and limitations highlighted the discrepancy between the applicability domain and accuracy of predictions. Data points outside of the assessed chemical space did not have higher likelihoods of generating inadequate predictions or vice versa. It is therefore concluded that the applicability domain does not give complete insight into the uncertainty of predictions and instead the generation of prediction intervals can help in this regard. Furthermore, results indicated that an increase of the dataset size did not improve model performance. This implies that larger dataset sizes may not necessarily improve model performance while in turn also meaning that large datasets are not necessarily required for prediction of acute toxicity with nano-QSARs.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Nanoparticles traversing the extracellular matrix induce biophysical perturbation of fibronectin depicted by surface chemistry

While the filtering and accumulation effects of the extracellular matrix (ECM) on nanoparticles (NPs) have been experimentally observed, the detailed interactions between NPs and specific biomolecules within the ECM remain poorly understood and pose challenges for in vivo molecular-level investigations. Herein, we adopt molecular dynamics simulations to elucidate the impacts of methyl-, hydroxy-, amine-, and carboxyl-modified gold NPs on the cell-binding domains of fibronectin (Fn), an indispensable component of the ECM for cell attachment and signaling. Simulation results show that NPs can specifically bind to distinct Fn domains, and the strength of these interactions depends on the physicochemical properties of NPs. NP-NH3+ exhibits the highest affinity to domains rich in acidic residues, leading to strong electrostatic interactions that induce severe deformation, potentially disrupting the normal functioning of Fn. NP-CH3 and NP-COO- selectively occupy the RGD/PHSRN motifs, which may hinder their recognition by integrins on the cell surface. Additionally, NPs can disrupt the dimerization of Fn through competing for residues at the dimer interface or by diminishing the shape complementarity between dimerized proteins. The mechanical stretching of Fn, crucial for ECM fibrillogenesis, is suppressed by NPs due to their local rigidifying effect. These results provide valuable molecular-level insights into the impacts of various NPs on the ECM, holding significant implications for advancing nanomedicine and nanosafety evaluation.

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.

Nanoplastics induce neuroexcitatory symptoms in zebrafish (Danio rerio) larvae through a manner contrary to Parkinsonian's way in proteomics

Although nanoplastics (NPs) can penetrate the blood-brain barrier and accumulate in the brain, the neurotoxicity of these particles and the mechanisms associated with their unique physio-chemical properties have yet to be sufficiently ascertained. In this study, we assessed the neuroexcitatory symptoms of zebrafish (Danio rerio) larvae treated with polystyrene (PS) NPs based on an examination of locomotory behaviour, dopamine levels, and acetylcholinesterase activity. We found that PS NPs caused oxidative stress and inhibited atoh1a expression in the cerebellum of Tg(atoh1a:dTomato) transgenic zebrafish larvae, thereby indicating damage to the central nervous system. In contrast to the Parkinson's disease (PD) like effects induced by most types of nanoparticles, such as graphene oxide, we established that PS NPs influenced the neuronal proteomic profiles of zebrafish larvae in a manner contrary to the molecular pathways characteristic of PD-like effects, which could be explained by the molecular dynamic simulation. Unlike graphene oxide nanoparticles that promote significant change in the internal structure of neuroproteins, the complex macromolecular polymers of PS NPs promoted the coalescence and increased expression of neuroproteins, thereby plausibly contributing to the neuroexcitatory symptoms observed in treated zebrafish larvae. Consequently, compared with traditional nanoparticles, we believe that the unique physio-chemical properties of NPs could be a potential factor contributing to their toxicity.

Heterogeneous aggregation of carbon and silicon nanoparticles with benzo[a]pyrene modulates their impacts on the pulmonary surfactant film

Inhaled nanoparticles (NPs) can deposit in alveoli where they interact with the pulmonary surfactant (PS) and potentially induce toxicity. Although nano-bio interactions are influenced by the physicochemical properties of NPs, isolated NPs used in previous studies cannot accurately represent those found in atmosphere. Here we used molecular dynamics simulations to investigate the interplay between two types of NPs associated with benzo[a]pyrene (BaP) at the PS film. Silicon NPs (SiNPs), regardless of aggregation and adsorption, directly penetrated through the PS film with minimal disturbance. Meanwhile, BaPs adsorbed on SiNPs were rapidly solubilized by PS, increasing the BaP's bioaccessibility in alveoli. Carbon NPs (CNPs) showed aggregation and adsorption-dependent effects on the PS film. Compared to isolated CNPs, which extracted PS to form biomolecular coronas, aggregated CNPs caused more pronounced PS disruption, especially around irregularly shaped edges. SiNPs in mixture exacerbated the PS perturbation by piercing PS film around the site of CNP interactions. BaPs adsorbed on CNPs were less solubilized and suppressed PS extraction, but aggravated biophysical inhibition by prompting film collapse under compression. These results suggest that for proper assessment of inhalation toxicity of airborne NPs, it is imperative to consider their heterogeneous aggregation and adsorption of pollutants under atmospheric conditions.