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

Nanostrategies synergize with locoregional interventional therapies for boosting antitumor immunity

Compared with traditional surgical resection, systemic chemotherapy, or radiotherapy, locoregional interventional therapies (LITs) possess their own advantages of minimally invasive procedure and immunomodulatory effects in cancer treatment. Local ablation and intravascular interventional therapy represent excellent LIT candidate to combine with immunotherapy. Diverse nanomaterials with excellent biocompatibility show promises in modulating antitumor immunity. In this review, we summarized several immune-LIT combinations, discussed the following immunomodulatory effects, and presented the novel nanostrategies for synergizing with the combination therapy. With continuous optimization, further promotion of clinical translation will ultimately benefit patients with personalized and tailored cancer strategy.

Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both in vitro and in vivo, leveraging physicochemical properties and experimental conditions. A curated in vitro cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for in vitro settings. To enable organ-specific toxicity predictions in vivo, we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation.

A model for screening the fate and behaviour of the engineered nanoparticles in aquatic systems using semi-quantitative analysis and rule-based system

Concerns over the possible adverse effects of engineered nanoparticles (ENPs) on aquatic organisms have grown due to their continuous emission into aquatic systems. Consequently, to safeguard these aquatic life forms and support the sustainable use of ENPs, the characterisation of their exposure is necessary. Currently, despite the great amount of work reported to elucidate the exposure and risks of ENPs, cost-effective and easy-to-use exposure characterisation models are lacking and scarce. This study describes the use of semi-quantitative analysis (SQA) integrated with a rule-based system to evaluate ENP exposure in aquatic systems. The performance of the model was illustrated using case studies of nZnO, nTiO2, and nAg and theoretical examples that simulate natural systems. The results demonstrate that our proposed model can be highly valuable as an alternative approach for the preliminary screening of the exposure and possible environmental impact of ENPs in aquatic systems. The SQA application is relatively cost-effective and easy to use, since no software or mathematical computations are required. In addition, non-experts can easily understand the hierarchical nature, Boolean logic, and visual representations of simple rules using decision trees; which is highly valuable given that testing each variation of ENPs is tedious and associated with high cost.

Unleashing the Potential of Metal Ions in cGAS-STING Activation: Advancing Nanomaterial-Based Tumor Immunotherapy

Immunotherapy is a critical modality in cancer treatment with diverse activation pathways. In recent years, the stimulator of interferon genes (STING) signaling pathway has exhibited significant potential in tumor immunotherapy. This pathway exerts notable antitumor effects by activating innate and adaptive immunity and regulating the tumor immune microenvironment. Various metal ions have been identified as effective activators of the STING pathway and, through the design and synthesis of nanodelivery platforms, have been applied in immunotherapy as well as in combination therapies, such as chemotherapy, chemodynamic therapy, photodynamic therapy, and cancer vaccines. Metal nanomaterials showcase unique advantages in immunotherapy; however, there are still aspects that require optimization. This review systematically examines existing metal-based nanomaterials, elaborates on the mechanisms by which different metal ions activate the STING pathway, and discusses their application models in tumor combination therapies. We also provide a comparative analysis of the advantages of metal nanomaterials over other treatment methods. Our exploration highlights the broad application prospects of metal nanomaterials in cancer treatment, offering new insights and directions for the advancement of tumor immunotherapy.

Multimodal feature fusion machine learning for predicting chronic injury induced by engineered nanomaterials

Concerns regarding chronic injuries (e.g., fibrosis and carcinogenesis) induced by nanoparticles raised public health concerns and need to be rapidly assessed in hazard identification. Although in silico analysis is commonly used for risk assessment of chemicals, predicting chronic in vivo nanotoxicity remains challenging due to the intricate interactions at multiple interfaces like nano-biofluids and nano-subcellular organelles. Herein, we develop a multimodal feature fusion analysis framework to predict the fibrogenic potential of metal oxide nanoparticles (MeONPs) in female mice. Treating each nano-bio interface as an independent entity, eighty-seven features derived from MeONP-lung interactions are used to develop a machine learning-based predictive framework for lung fibrosis. We identify cell damage and cytokine (IL-1β and TGF-β1) production in macrophages and epithelial cells as key events closely associated with particle size, surface charge, and lysosome interactions. Experimental validations show that the developed in silico model has 85% accuracy. Our findings demonstrate the potential usefulness of this predictive model for risk assessment of nanomaterials and in assisting regulatory decision-making. While the model is developed based on 52 MeONPs, further validation using a larger nanoparticle library is necessary to confirm its broader applicability.

Machine Learning Approach to Investigating Macrophage Polarization on Various Titanium Surface Characteristics

Objective: Current laboratory studies on the effect of biomaterial properties on immune reactions are incomplete and based on a single or a few combination features of the biomaterial design. This study utilizes intelligent prediction models to explore the key features of titanium implant materials in macrophage polarization. Impact Statement: This pilot study provided some insights into the great potential of machine learning in exploring bone immunomodulatory biomaterials. Introduction: Titanium materials are commonly utilized as bone replacement materials to treat missing teeth and bone defects. The immune response caused by implant materials after implantation in the body has a double-edged sword effect on osseointegration. Macrophage polarization has been extensively explored to understand early material-mediated immunomodulation. However, understanding of implant material surface properties and immunoregulations remains limited due to current experimental settings, which are based on trial-by-trial approaches. Artificial intelligence, with its capacity to analyze large datasets, can help explore complex material-cell interactions. Methods: In this study, the effect of titanium surface properties on macrophage polarization was analyzed using intelligent prediction models, including random forest, extreme gradient boosting, and multilayer perceptron. Additionally, data extracted from the newly published literature were further input into the trained models to validate their performance. Results: The analysis identified "cell seeding density", "contact angle", and "roughness" as the most important features regulating interleukin 10 and tumor necrosis factor α secretion. Additionally, the predicted interleukin 10 levels closely matched the experimental results from newly published literature, while the tumor necrosis factor α predictions exhibited consistent trends. Conclusion: The polarization response of macrophages seeded on titanium materials is influenced by multiple factors, and artificial intelligence can assist in extracting the key features of implant materials for immunoregulation.

Machine learning models for quantitatively prediction of toxicity in macrophages induced by metal oxide nanoparticles

As nanotechnology advances, metal oxide nanoparticles (MeONPs) increasingly come into contact with humans. The inhaled MeONPs cannot be effectively cleared by cilia or lung mucus. In the last decade, potential immune toxicity arising from exposure to MeONPs has been extensively debated, as lung macrophage is the main pathway for cleaning inhaled exogenous particles. However, their toxicity on lung macrophages has rarely been quantitatively predicted in silico due to the complexity of responses in macrophages and the intricate properties of MeONPs. Here, machine learning (ML) methods were used to establish models for quantitatively predicting the toxicity of MeONPs in macrophages. A multidimensional dataset including 240 data points covering the lethality, biochemical behaviors, and physicochemical properties of 30 MeONPs was obtained. ML models based on different algorithms with high prediction accuracy were constructed by addressing the issue of class imbalance during the training process. The models were verified by 10-fold cross-validation and external validation. The best-performed model has an R2 of 0.85 and 0.90 in the 10-fold cross-validation and external test set, respectively; and Q2 of 0.88 and 0.90 in the 10-fold cross-validation and test set, respectively. Five parameters that impact toxicity were identified and the toxicity mechanisms were elucidated by ML analysis. The prediction results can be used to fill the data gap in the risk assessment of nanomaterials. The framework offers valuable insights for designing and utilizing safe nanoparticles, as well as aiding in decision-making processes aimed at protecting the environment and public health.

In Vitro-to-In Vivo Extrapolation on Lung Toxicity Induced by Metal Oxide Nanoparticles via Data-Mining

While in silico analyses are commonly employed for chemical risk assessments, predicting chronic lung toxicity induced by engineered nanoparticles (ENMs) in vivo still faces many challenges due to complex interactions at multiple nanobio interfaces. In this study, we developed a rigorous method to compile published evidence on the in vivo lung toxicity of metal oxide nanoparticles (MeONPs) and revealed previously overlooked in vitro-to-in vivo extrapolation (IVIVE) relationships. A comprehensive multidimensional data set containing 1102 in vivo data points, 75 pulmonary toxicological biomarkers, and 20 features (covering in vitro effects, physicochemical properties, and exposure conditions) was constructed. An IVIVE approach that related effects at the cellular level to in vivo lung toxicity in rodent model was established with prediction accuracy reaching 89 and 80% in training and test sets. Experimental validation was conducted by testing chronic lung fibrosis of 8 new MeONPs in 32 independent mice, with prediction accuracy reaching 88%. The IVIVE model indicated that the proinflammatory cytokine IL-1β in THP-1 cells could serve as an in vitro marker to predict lung toxicity. The IVIVE model showed great promise for minimizing unnecessary animal tests and understanding toxicological mechanisms.

Integrating machine learning and nano-QSAR models to predict the oxidative stress potential caused by single and mixed carbon nanomaterials in algal cells

In silico methods are increasingly important in predicting the ecotoxicity of engineered nanomaterials (ENMs), encompassing both individual and mixture toxicity predictions. It is widely recognized that ENMs trigger oxidative stress effects by generating intracellular reactive oxygen species (ROS), serving as a key mechanism in their cytotoxicity studies. However, existing in silico methods still face significant challenges in predicting the oxidative stress effects induced by ENMs. Herein, we utilized laboratory-derived toxicity data and machine learning methods to develop quantitative nanostructure-activity relationship (nano-QSAR) classification and regression models, aiming to predict the oxidative stress effects of five carbon nanomaterials (fullerene, graphene, graphene oxide, single-walled carbon nanotubes, and multi-walled carbon nanotubes) and their binary mixtures on Scenedesmus obliquus cells. We constructed five nano-QSAR classification models by combining zeta potential (ζP) with the C4.5 decision tree, support vector machine, artificial neural network, naive Bayes, and K-nearest neighbor algorithms. Moreover, we constructed three classification models by integrating the features including ζP, hydrodynamic diameter (DH), and specific surface area (SSA) with the logistic regression, random forest, and Adaboost algorithms. The Accuracy, Recall, Precision and harmonic mean of Precision and Recall (F1-score) values of these models were all higher than 0.600, indicating an excellent performance in distinguishing whether CNMs have the potential to generate ROS. In addition, using the ζP, DH, and SSA descriptors, we combined decision tree regression, random forest regression, gradient boosting, and the Adaboost algorithm, and successfully constructed four nano-QSAR regression models with applicable application domains (all training and testing data points lie within 95% confidence intervals), goodness-of-fit (Rtrain2 ≥ 0.850), and robustness (cross-validation R2 ≥ 0.650) as well as predictive power (Rtest2 ≥ 0.610). The method developed would establish a fundamental basis for more precise evaluations of ecological risks posed by these materials from a mechanistic standpoint.

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.