Use of dissociation degree in lysosomes to predict metal oxide nanoparticle toxicity in immune cells: Machine learning boosts nano-safety assessment

In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation

The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.

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.

Ferroptosis Induced by Pollutants: An Emerging Mechanism in Environmental Toxicology

Environmental pollutants have been recognized for their ability to induce various adverse outcomes in both the environment and human health, including inflammation, apoptosis, necrosis, pyroptosis, and autophagy. Understanding these biological mechanisms has played a crucial role in risk assessment and management efforts. However, the recent identification of ferroptosis as a form of programmed cell death has emerged as a critical mechanism underlying pollutant-induced toxicity. Numerous studies have demonstrated that fine particulates, heavy metals, and organic substances can trigger ferroptosis, which is closely intertwined with lipid, iron, and amino acid metabolism. Given the growing evidence linking ferroptosis to severe diseases such as heart failure, chronic obstructive pulmonary disease, liver injury, Parkinson's disease, Alzheimer's disease, and cancer, it is imperative to investigate the role of pollutant-induced ferroptosis. In this review, we comprehensively analyze various pollutant-induced ferroptosis pathways and intricate signaling molecules and elucidate their integration into the driving and braking axes. Furthermore, we discuss the potential hazards associated with pollutant-induced ferroptosis in various organs and four representative animal models. Finally, we provide an outlook on future research directions and strategies aimed at preventing pollutant-induced ferroptosis. By enhancing our understanding of this novel form of cell death and developing effective preventive measures, we can mitigate the adverse effects of environmental pollutants and safeguard human and environmental health.

A review on chitosan/metal oxide nanocomposites for applications in environmental remediation

A cleaner and safer environment is one of the most important requirements in the future. It has become increasingly urgent and important to fabricate novel environmentally-friendly materials to remove various hazardous pollutants. Compared with traditional materials, chitosan is a more environmentally friendly material due to its abundance, biocompatibility, biodegradability, film-forming ability and hydrophilicity. As an abundant of -NH2 and -OH groups on chitosan molecular chain could chelate with all kinds of metal ions efficiently, chitosan-based materials hold great potential as a versatile supporting matrix for metal oxide nanomaterials (MONMs) (TiO2, ZnO, SnO2, Fe3O4, etc.). Recently, many chitosan/metal oxide nanomaterials (CS/MONMs) have been reported as adsorbents, photocatalysts, heterogeneous Fenton-like agents, and sensors for potential and practical applications in environmental remediation and monitoring. This review analyzed and summarized the recent advances in CS/MONMs composites, which will provide plentiful and meaningful information on the preparation and application of CS/MONMs composites for wastewater treatment and help researchers to better understand the potential of CS/MONMs composites for environmental remediation and monitoring. In addition, the challenges of CS/MONM have been proposed.

Application of Machine Learning in Material Synthesis and Property Prediction

Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.

Machine learning assisted-nanomedicine using magnetic nanoparticles for central nervous system diseases

Magnetic nanoparticles possess unique properties distinct from other types of nanoparticles developed for biomedical applications. Their unique magnetic properties and multifunctionalities are especially beneficial for central nervous system (CNS) disease therapy and diagnostics, as well as targeted and personalized applications using image-guided therapy and theranostics. This review discusses the recent development of magnetic nanoparticles for CNS applications, including Alzheimer's disease, Parkinson's disease, epilepsy, multiple sclerosis, and drug addiction. Machine learning (ML) methods are increasingly applied towards the processing, optimization and development of nanomaterials. By using data-driven approach, ML has the potential to bridge the gap between basic research and clinical research. We review ML approaches used within the various stages of nanomedicine development, from nanoparticle synthesis and characterization to performance prediction and disease diagnosis.

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.

Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery

Research and development (R&D) of nanodrugs is a long, complex and uncertain process. Since the 1960s, computing has been used as an auxiliary tool in the field of drug discovery. Many cases have proven the practicability and efficiency of computing in drug discovery. Over the past decade, computing, especially model prediction and molecular simulation, has been gradually applied to nanodrug R&D, providing substantive solutions to many problems. Computing has made important contributions to promoting data-driven decision-making and reducing failure rates and time costs in discovery and development of nanodrugs. However, there are still a few articles to examine, and it is necessary to summarize the development of the research direction. In the review, we summarize application of computing in various stages of nanodrug R&D, including physicochemical properties and biological activities prediction, pharmacokinetics analysis, toxicological assessment and other related applications. Moreover, current challenges and future perspectives of the computing methods are also discussed, with a view to help computing become a high-practicability and -efficiency auxiliary tool in nanodrugs discovery and development.

Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review

Nanoparticles have been used extensively in different scientific fields. Due to the possible destructive effects of nanoparticles on the environment or the biological systems, their toxicity evaluation is a crucial phase for studying nanomaterial safety. In the meantime, experimental approaches for toxicity assessment of various nanoparticles are expensive and time-consuming. Thus, an alternative technique, such as artificial intelligence (AI), could be valuable for predicting nanoparticle toxicity. Therefore, in this review, the AI tools were investigated for the toxicity assessment of nanomaterials. To this end, a systematic search was performed on PubMed, Web of Science, and Scopus databases. Articles were included or excluded based on pre-defined inclusion and exclusion criteria, and duplicate studies were excluded. Finally, twenty-six studies were included. The majority of the studies were conducted on metal oxide and metallic nanoparticles. In addition, Random Forest (RF) and Support Vector Machine (SVM) had the most frequency in the included studies. Most of the models demonstrated acceptable performance. Overall, AI could provide a robust, fast, and low-cost tool for the evaluation of nanoparticle toxicity.

Insights into eco-corona formation and its role in the biological effects of nanomaterials from a molecular mechanisms perspective

Broad application of nanotechnology inevitably results in the release of nanomaterials (NMs) into the aquatic environment, and the negative effects of NMs on aquatic organisms have received much attention. Notably, in the natural aquatic environment, ubiquitous ecological macromolecules (i.e., natural organic matter, extracellular polymeric substances, proteins, and metabolites) can easily adsorb onto the surfaces of NMs and form an "eco-corona". As most NMs have such an eco-corona modification, the properties of their eco-corona significantly determine the fate and ecotoxicity of NMs in the natural aquatic ecosystem. Therefore, it is of great importance to understand the role of the eco-corona to evaluate the environmental risks NMs pose. However, studies on the mechanism of eco-corona formation and its resulting nanotoxicity on aquatic organisms, especially at molecular levels, are rare. This review systemically summarizes the mechanisms of eco-corona formation by several typical ecological macromolecules. In addition, the similarities and differences in nanotoxicity between pristine and corona-coated NMs to aquatic organisms at different trophic levels were compared. Finally, recent findings about potential mechanisms on how NM coronas act on aquatic organisms are discussed, including cellular internalization, oxidative stress, and genotoxicity. The literature shows that 1) the formation of an eco-corona on NMs and its biological effect highly depend on both the composition and conformation of macromolecules; 2) both feeding behavior and body size of aquatic organisms at different trophic levels result in different responses to corona-coated NMs; 3) genotoxicity can be used as a promising biological endpoint for evaluating the role of eco-coronas in natural waters. This review provides informative insight for a better understanding of the role of eco-corona plays in the nanotoxicity of NMs to aquatic organisms which will aid the safe use of NMs.

In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES

A simulation of the effect of metal nano-oxides at various concentrations (25, 50, 100, and 200 milligrams per millilitre) on cell viability in THP-1 cells (%) based on data on the molecular structure of the oxide and its concentration is proposed. We used a simplified molecular input-line entry system (SMILES) to represent the molecular structure. So-called quasi-SMILES extends usual SMILES with special codes for experimental conditions (concentration). The approach based on building up models using quasi-SMILES is self-consistent, i.e., the predictive potential of the model group obtained by random splits into training and validation sets is stable. The Monte Carlo method was used as a basis for building up the above groups of models. The CORAL software was applied to building the Monte Carlo calculations. The average determination coefficient for the five different validation sets was R² = 0.806 ± 0.061.