Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review

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.

Review of machine learning for lipid nanoparticle formulation and process development

Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes of vaccines, and alternate modes of intracellular delivery for therapeutic proteins. In the pharmaceutical industry, establishing a robust formulation and process to achieve target product performance is a critical component of drug development. Fundamental understanding of the processes for making LNPs and their interactions with biological systems have advanced considerably in the wake of the COVID-19 pandemic. Nevertheless, LNP formulation research remains largely empirical and resource intensive due to the multitude of input parameters and the complex physical phenomena that govern the processes of nanoparticle precipitation, self-assembly, structure evolution, and stability. Increasingly, artificial intelligence and machine learning (AI/ML) are being applied to improve the efficiency of research activities through in silico models and predictions, and to drive deeper fundamental understanding of experimental inputs to functional outputs. This review will identify current challenges and opportunities in the development of robust LNP formulations of nucleic acids, review studies that apply machine learning methods to experimental datasets, and provide discussion on associated data science challenges to facilitate collaboration between formulation and data scientists, aiming to accelerate the advancement of AI/ML applied to LNP formulation and process optimization.

Multiplex Evaluation of Biointerface-Targeting Abilities and Affinity of Synthetized Nanoparticles-A Step Towards Improved Nanoplatforms for Biomedical Applications

To obtain versatile nanoplatforms comparable for various bio-applications, synthesis and functionalization of two inorganic nanoparticles (NPs), i.e., gold (AuNPs) and iron oxide (SPIONs), are described for different NP diameters. Chosen ligands have adapted chemical function to graft to the surfaces of the NPs (thiols and phosphonates, respectively) and the identical frequently used external carboxyl group for comparison of the NPs' material effect on their final behavior. To further evaluate molecular length effect, AuNPs are functionalized by different ligands. Numerous characterizations highlight the colloidal stability when grafting organic molecules on NPs. The potentiality of the functionalized NPs to react efficiently with a protein monolayer is finally evaluated by grafting them on a protein covered chip, characterized by atomic force microscopy. Comparison of the NPs' surface densities and measured heights enable observation of different NPs' reactivity and infer the influence of the inorganic core material, as well as the NPs' size and ligand length. AuNPs have higher affinities to biomolecules, especially when covered by shorter ligands. NP ligands should be chosen not only based on their length but also on their chemical chain, which affects proteic layer interactions. This original multiplex comparison method using AFM is of great interest to screen the effects of used NP materials and functionalization when developing theranostic nanoplatforms.

Recent advances in environmental toxicology: Exploring gene editing, organ-on-a-chip, chimeric animals, and in silico models

In our daily life, we are exposed to various environmental pollutants in multiple ways. At present, we mainly rely on animal models and two-dimensional cell culture models to evaluate the toxicity of environmental pollutants. Nevertheless, results in animal models do not always apply to humans because of differences between species, while two-dimensional cell culture models cannot replicate the in vivo microenvironments, making it difficult to predict the true toxic response of environmental pollutants in humans. The development of various high-end technologies in recent years has provided new opportunities for environmental toxicology research. The application of these high-end technologies in environmental toxicology can complement the limitations of traditional environmental toxicology screening and more accurately predict the toxicity of environmental pollutants. In this review, we first introduce the advantages and disadvantages of traditional environmental toxicology methods, then review the principles and development of four high-end technologies, such as gene editing, organ-on-a-chip, chimeric animals, and in silico models, summarize their application in toxicity testing, and finally emphasize their importance/potential in environmental toxicology.

Unlocking the potential of carbon dots in agriculture using data-driven approaches

The utilization of carbon dots (CDs) in agriculture to enhance plant growth has gained significant attention, but the data remains fractionated. Systematically integrating existing data is needed to identify the factors driving the interactions between CDs and plants and strategically guide future research. Articles reporting on CDs and their effects on plants were searched based on inclusion and exclusion criteria, resulting in the collection of 71 articles comprising a total of 2564 data points. The meta-analysis reveals that the soil and foliar application of red-emitting bio-derived CDs at a low concentration (<10 ppm) leads to the most beneficial effects on plant growth. Random forest and gradient boosting algorithms revealed that the size and dose of CDs were important factors in predicting plant responses across multiple aspects (CDs properties, plant properties, environmental factors, and experimental conditions). Specifically, smaller sizes are more favorable to growth indicators (GI) below 6 nm, nutrient and quality (NuQ) at 3-6 nm, photosynthesis (PSN) below 7 nm, and antioxidant responses (AR) below 5 nm. Overall, our analysis of existing data suggests that CDs applications can significantly improve plant responses (GI, NuQ, PSN, and AR) by 10-39 %. To unlock the full potential of CDs, customized synthesis techniques should be employed to meet the specific requirements of different crops and climate condition. For example, we recommend the synthesis of small CDs (<7 nm) with emission peak values falling within the range of 405-475 and 610-670 nm to enhance plant growth. The global prediction of plant responses to CDs application in future scenarios have shown significant improvements ranging from 17 to 58 %, suggesting that CDs have widespread applicability. This novel understanding of the impact of CDs on plant response provides valuable insights for optimizing the application of these nanomaterials in agriculture.

Nanotechnology in sustainable agriculture: A double-edged sword

Nanotechnology is a rapidly developing discipline that has the potential to transform the way we approach problems in a variety of fields, including agriculture. The use of nanotechnology in sustainable agriculture has gained popularity in recent years. It has various applications in agriculture, such as the development of nanoscale materials and devices to boost agricultural productivity, enhance food quality and safety, improve the efficiency of water and nutrient usage, and reduce environmental pollution. Nanotechnology has proven to be very beneficial in this field, particularly in the development of nanoscale delivery systems for agrochemicals such as pesticides, fertilizers, and growth regulators. These nanoscale delivery technologies offer various benefits over conventional delivery systems, including better penetration and distribution, enhanced efficacy, and lower environmental impact. Encapsulating agrochemicals in nanoscale particles enables direct delivery to the targeted site in the plant, thereby reducing waste and minimizing off-target effects. Plants are fundamental building blocks of all ecosystems and evaluating the interaction between nanoparticles (NPs) and plants is a crucial aspect of risk assessment. This critical review therefore aims to provide an overview of the latest advances regarding the positive and negative effects of nanotechnology in agriculture. It also explores potential future research directions focused on ensuring the safe utilization of NPs in this field, which could lead to sustainable development. © 2024 Society of Chemical Industry.

Advances in AI-assisted biochip technology for biomedicine

The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.

De novo design of a nanoregulator for the dynamic restoration of ovarian tissue in cryopreservation and transplantation

The cryopreservation and transplantation of ovarian tissue underscore its paramount importance in safeguarding reproductive capacity and ameliorating reproductive disorders. However, challenges persist in ovarian tissue cryopreservation and transplantation (OTC-T), including the risk of tissue damage and dysfunction. Consequently, there has been a compelling exploration into the realm of nanoregulators to refine and enhance these procedures. This review embarks on a meticulous examination of the intricate anatomical structure of the ovary and its microenvironment, thereby establishing a robust groundwork for the development of nanomodulators. It systematically categorizes nanoregulators and delves deeply into their functions and mechanisms, meticulously tailored for optimizing ovarian tissue cryopreservation and transplantation. Furthermore, the review imparts valuable insights into the practical applications and obstacles encountered in clinical settings associated with OTC-T. Moreover, the review advocates for the utilization of microbially derived nanomodulators as a potent therapeutic intervention in ovarian tissue cryopreservation. The progression of these approaches holds the promise of seamlessly integrating nanoregulators into OTC-T practices, thereby heralding a new era of expansive applications and auspicious prospects in this pivotal domain.

Bioinformatics and machine learning to support nanomaterial grouping

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

Decoding Nanomaterial-Biosystem Interactions through Machine Learning

The interactions between biosystems and nanomaterials regulate most of their theranostic and nanomedicine applications. These nanomaterial-biosystem interactions are highly complex and influenced by a number of entangled factors, including but not limited to the physicochemical features of nanomaterials, the types and characteristics of the interacting biosystems, and the properties of the surrounding microenvironments. Over the years, different experimental approaches coupled with computational modeling have revealed important insights into these interactions, although many outstanding questions remain unanswered. The emergence of machine learning has provided a timely and unique opportunity to revisit nanomaterial-biosystem interactions and to further push the boundary of this field. This minireview highlights the development and use of machine learning to decode nanomaterial-biosystem interactions and provides our perspectives on the current challenges and potential opportunities in this field.

Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint

Assessment and prediction for the ecotoxicity of engineered nanoparticles (ENPs) at the community or ecosystem levels represents a critical step toward a comprehensive understanding of the ecological risks of ENPs. Current studies on predicting the ecotoxicity of ENPs primarily focus on the cellular and individual levels, with limited exploration at the community or ecosystem levels. Herein, we present the first of the reports for the direct prediction of aquatic ecological risk for ENPs at the community level using machine learning (ML) approaches in the field of computational toxicology. Specifically, we extensively collected the threshold concentrations of twelve ENPs including metal- and carbon-based nanoparticles for aquatic species, i.e., hazardous concentrations at which 5% of species are harmed (HC5), established by a species sensitivity distribution. Afterwards, we used eight supervised ML methods including Adaboost, artificial neural network, C4.5 decision tree, K-nearest neighbor, logistic regression, Naive Bayes, random forest, and support vector machine to develop nine classification models and four regression models, respectively, for the qualitative and quantitative prediction of HC5. The evaluation of model performance yielded the internal validation accuracy of all classification models ranging from 71.4 to 100%, and the determination coefficient of regression models ranging from 0.702 to 0.999, indicating that the developed models showed good performance. By using a cross-validation method and an application domain characterization, the selected models were further validated to have powerful predictive ability. Furthermore, the incorporation of three nanostructural descriptors (metal oxide sublimation enthalpy, zeta potential, and specific surface area) linked to toxicity mechanisms (the release of metal ions, the stability of dispersions of particles in aqueous suspensions, and the surface properties of the material) effectively enhanced the prediction power and mechanistic interpretability of the selected models. These findings would not only be beneficial in the screening of ENPs with potential high ecological risks that need to be tested as a priority but also contribute to the development of environmental regulations and standards for ENPs.

Point biserial correlation symbiotic organism search nanoengineering based drug delivery for tumor diagnosis

Nanoparticulate systems have the prospect of accounting for a new making of drug delivery systems. Nanotechnology is manifested to traverse the hurdle of both physical and biological sciences by implementing nanostructures indistinct fields of science, particularly in nano-based drug delivery. The low delivery efficiency of nanoparticles is a critical obstacle in the field of tumor diagnosis. Several nano-based drug delivery studies are focused on for tumor diagnosis. But, the nano-based drug delivery efficiency was not increased for tumor diagnosis. This work proposes a method called point biserial correlation symbiotic organism search nanoengineering-based drug delivery (PBC-SOSN). The objective and aim of the PBC-SOSN method is to achieve higher drug delivery efficiency and lesser drug delivery time for tumor diagnosis. The contribution of the PBC-SOSN is to optimized nanonengineering-based drug delivery with higher r drug delivery detection rate and smaller drug delivery error detection rate. Initially, raw data acquired from the nano-tumor dataset, and nano-drugs for glioblastoma dataset, overhead improved preprocessed samples are evolved using nano variational model decomposition-based preprocessing. After that, the preprocessed samples as input are subjected to variance analysis and point biserial correlation-based feature selection model. Finally, the preprocessed samples and features selected are subjected to symbiotic organism search nanoengineering (SOSN) to corroborate the objective. Based on these findings, point biserial correlation-based feature selection and a symbiotic organism search nanoengineering were tested for their modeling performance with a nano-tumor dataset and nano-drugs for glioblastoma dataset, finding the latter the better algorithm. Incorporated into the method is the potential to adjust the drug delivery detection rate and drug delivery error detection rate of the learned method based on selected features determined by nano variational model decomposition for efficient drug delivery.

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure-activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.

Package delivered: folate receptor-mediated transporters in cancer therapy and diagnosis

Neoplasias pose a significant threat to aging society, underscoring the urgent need to overcome the limitations of traditional chemotherapy through pioneering strategies. Targeted drug delivery is an evolving frontier in cancer therapy, aiming to enhance treatment efficacy while mitigating undesirable side effects. One promising avenue utilizes cell membrane receptors like the folate receptor to guide drug transporters precisely to malignant cells. Based on the cellular folate receptor as a cancer cell hallmark, targeted nanocarriers and small molecule-drug conjugates have been developed that comprise different (bio) chemistries and/or mechanical properties with individual advantages and challenges. Such modern folic acid-conjugated stimuli-responsive drug transporters provide systemic drug delivery and controlled release, enabling reduced dosages, circumvention of drug resistance, and diminished adverse effects. Since the drug transporters' structure-based de novo design is increasingly relevant for precision cancer remediation and diagnosis, this review seeks to collect and debate the recent approaches to deliver therapeutics or diagnostics based on folic acid conjugated Trojan Horses and to facilitate the understanding of the relevant chemistry and biochemical pathways. Focusing exemplarily on brain and breast cancer, recent advances spanning 2017 to 2023 in conjugated nanocarriers and small molecule drug conjugates were considered, evaluating the chemical and biological aspects in order to improve accessibility to the field and to bridge chemical and biomedical points of view ultimately guiding future research in FR-targeted cancer therapy and diagnosis.

Radiofrequency driving antitumor effect of graphene oxide-based nanocomposites: a Hill model analysis

Aim: This report proposes using the Hill model to assess the benchmark dose, the 50% lethal dose, the cooperativity and the dissociation constant while analyzing cell viability data using nanomaterials to evaluate the antitumor potential while combined with radiofrequency therapy. Materials & methods: A nanocomposite was synthesized (graphene oxide-polyethyleneimine-gold) and the viability was evaluated using two tumor cell lines, namely LLC-WRC-256 and B16-F10. Results: Our findings demonstrated that while the nanocomposite is biocompatible against the LLC-WRC-256 and B16-F10 cancer cell lines in the absence of radiofrequency, the application of radiofrequency enhances the cell toxicity by orders of magnitude. Conclusion: This result points to prospective studies with the tested cell lines using tumor animal models.

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.

A structure-activity approach towards the toxicity assessment of multicomponent metal oxide nanomaterials

The increase of human and environmental exposure to engineered nanomaterials (ENMs) due to the emergence of nanotechnology has raised concerns over their safety. The challenging nature of in vivo and in vitro toxicity assessment methods for ENMs, has led to emerging in silico techniques for ENM toxicity assessment, such as structure-activity relationship (SAR) models. Although such approaches have been extensively developed for the case of single-component nanomaterials, the case of multicomponent nanomaterials (MCNMs) has not been thoroughly addressed. In this paper, we present a SAR approach for the case metal and metal oxide MCNMs. The developed SAR framework is built using a dataset of 796 individual toxicity measurements for 340 different MCNMs, towards human cells, mammalian cells, and bacteria. The novelty of the approach lies in the multicomponent nature of the nanomaterials, as well as the size, diversity and heterogeneous nature of the dataset used. Furthermore, the approach used to calculate descriptors for surface loaded MCNMs, and the mechanistic insight provided by the model results can assist the understanding of MCNM toxicity. The developed models are able to correctly predict the toxic class of the MCNMs in the heterogeneous dataset, towards a wide range of human cells, mammalian cells and bacteria. Using the abovementioned approach, the principal toxicity pathways and mechanisms are identified, allowing a more holistic understanding of metal oxide MCNM toxicity.

Predicting cytotoxicity of engineered nanoparticles using regularized regression models: an in silico approach

The widespread application of engineered nanoparticles (NPs) in various industries has demonstrated their effectiveness over the years. However, modifications to NPs' physicochemical properties can lead to toxicological effects. Therefore, understanding the toxicity behaviour of NPs is crucial. In this paper, regularized regression models, such as ridge, LASSO, and elastic net, were constructed to predict the cytotoxicity of various engineered NPs. The dataset utilized in this study was compiled from several journals published between 2010 and 2022. Data exploration revealed missing values, which were addressed through listwise deletion and kNN imputation, resulting in two complete datasets. The ridge, LASSO, and elastic net models achieved F1 scores ranging from 91.81% to 92.65% during internal validation and 92.89% to 93.63% during external validation on Dataset 1. On Dataset 2, the models attained F1 scores between 92.16% and 92.43% during internal validation and 92% and 92.6% during external validation. These results indicate that the developed models effectively generalize to unseen data and demonstrate high accuracy in classifying cytotoxicity levels. Furthermore, the cell type, material, cell source, cell tissue, synthesis method, and coat or functional group were identified as the most important descriptors by the three models across both datasets.

A review on accelerated development of skin-like MXene electrodes: from experimental to machine learning

Foreshadowing future needs has catapulted the progress of skin-like electronic devices for human-machine interactions. These devices possess human skin-like properties such as stretchability, self-healability, transparency, biocompatibility, and wearability. This review highlights the recent progress in a promising material, MXenes, to realize soft, deformable, skin-like electrodes. Various structural designs, fabrication strategies, and rational guidelines adopted to realize MXene-based skin-like electrodes are outlined. We explicitly discussed machine learning-based material informatics to understand and predict the properties of MXenes. Finally, an outlook on the existing challenges and the future roadmap to realize soft skin-like MXene electrodes to facilitate technological advances in the next-generation human-machine interactions has been described.

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.

Toxicity of metal-based nanoparticles: Challenges in the nano era

With the rapid progress of nanotechnology, various nanoparticles (NPs) have been applicated in our daily life. In the field of nanotechnology, metal-based NPs are an important component of engineered NPs, including metal and metal oxide NPs, with a variety of biomedical applications. However, the unique physicochemical properties of metal-based NPs confer not only promising biological effects but also pose unexpected toxic threats to human body at the same time. For safer application of metal-based NPs in humans, we should have a comprehensive understanding of NP toxicity. In this review, we summarize our current knowledge about metal-based NPs, including the physicochemical properties affecting their toxicity, mechanisms of their toxicity, their toxicological assessment, the potential strategies to mitigate their toxicity and current status of regulatory movement on their toxicity. Hopefully, in the near future, through the convergence of related disciplines, the development of nanotoxicity research will be significantly promoted, thereby making the application of metal-based NPs in humans much safer.