Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

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.

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.

Navigating Neurotoxicity and Safety Assessment of Nanocarriers for Brain Delivery: Strategies and Insights

Nanomedicine, an area that uses nanomaterials for theragnostic purposes, is advancing rapidly, particularly in the detection and treatment of neurodegenerative diseases. The design of nanocarriers can be optimized to enhance drug bioavailability and targeting to specific organs, improving therapeutic outcomes. However, clinical translation hinges on biocompatibility and safety. Nanocarriers can cross the blood-brain barrier (BBB), potentially causing neurotoxic effects through mechanisms such as oxidative stress, DNA damage, and neuroinflammation. Concerns about their accumulation and persistence in the brain make it imperative to carry out a nanotoxicological risk assessment. Generally, this involves identifying exposure sources and routes, characterizing physicochemical properties, and conducting cytotoxicity assays both in vitro and in vivo. The lack of a specialized regulatory framework creates substantial gaps, making it challenging to translate findings across development stages. Additionally, there is a pressing need for innovative testing methods due to constraints on animal use and the demand for high-throughput screening. This review examines the mechanisms of nanocarrier-induced neurotoxicity and the challenges in risk assessment, highlighting the impact of physicochemical properties and the advantages and limitations of current neurotoxicity evaluation models. Future perspectives are also discussed. Additional guidance is crucial to improve the safety of nanomaterials and reduce associated uncertainty. STATEMENT OF SIGNIFICANCE: Nanocarriers show tremendous potential for theragnostic purposes in neurological diseases, enhancing drug targeting to the brain, and improving biodistribution and pharmacokinetics. However, their neurotoxicity is still a major field to be explored, with only 5% of nanotechnology-related publications addressing this matter. This review focuses on the issue of neurotoxicity and safety assessment of nanocarriers for brain delivery. Neurotoxicity-relevant exposure sources, routes, and molecular mechanisms, along with the impact of the physicochemical properties of nanomaterials, are comprehensively described. Moreover, the different experimental models used for neurotoxicity evaluation are explored at length, including their main advantages and limitations. To conclude, we discuss current challenges and future perspectives for a better understanding of risk assessment of nanocarriers for neurobiomedical applications.

Toward Nano-Specific In Silico NAMs: How to Adjust Nano-QSAR to the Recent Advancements of Nanotoxicology?

The rapid development of engineered nanomaterials (ENMs) causes humans to become increasingly exposed to them. Therefore, a better understanding of the health impact of ENMs is highly demanded. Considering the 3Rs (Replacement, Reduction, and Refinement) principle, in vitro and computational methods are excellent alternatives for testing on animals. Among computational methods, nano-quantitative structure-activity relationship (nano-QSAR), which links the physicochemical and structural properties of EMNs with biological activities, is one of the leading method. The nature of toxicological experiments has evolved over the last decades; currently, one experiment can provide thousands of measurements of the organism's functioning at the molecular level. At the same time, the capacity of the in vitro systems to mimic the human organism is also improving significantly. Hence, the authors would like to discuss whether the nano-QSAR approach follows modern toxicological studies and takes full advantage of the opportunities offered by modern toxicological platforms. Challenges and possibilities for improving data integration are underlined narratively, including the need for a consensus built between the in vitro and the QSAR domains.

Toxicity prediction of nanoparticles using machine learning approaches

Nanoparticle toxicity analysis is critical for evaluating the safety of nanomaterials due to their potential harm to the biological system. However, traditional experimental methods for evaluating nanoparticle toxicity are expensive and time-consuming. As an alternative approach, machine learning offers a solution for predicting cellular responses to nanoparticles. This study focuses on developing ML models for nanoparticle toxicity prediction. The training dataset used for building these models includes the physicochemical properties of nanoparticles, exposure conditions, and cellular responses of different cell lines. The impact of each parameter on cell death was assessed using the Gini index. Five classifiers, namely Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network, were employed to predict toxicity. The models' performance was compared based on accuracy, sensitivity, specificity, area under the curve, F measure, K-fold validation, and classification error. The Gini index indicated that cell line, exposure dose, and tissue are the most influential factors in cell death. Among the models tested, Random Forest exhibited the highest performance in the given dataset. Other models demonstrated lower performance compared to Random Forest. Researchers can utilize the Random Forest model to predict nanoparticle toxicity, resulting in cost and time savings for toxicity analysis.

A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability

INTRODUCTION

The current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata.

METHODOLOGY

To address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms.

RESULTS

Universal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay. Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models.

CONCLUSIONS

This study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.

Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms

The current European (EU) policies, that is, the Green Deal, envisage safe and sustainable practices for chemicals, which include nanoforms (NFs), at the earliest stages of innovation. A theoretically safe and sustainable by design (SSbD) framework has been established from EU collaborative efforts toward the definition of quantitative criteria in each SSbD dimension, namely, the human and environmental safety dimension and the environmental, social, and economic sustainability dimensions. In this study, we target the safety dimension, and we demonstrate the journey toward quantitative intrinsic hazard criteria derived from findable, accessible, interoperable, and reusable data. Data were curated and merged for the development of new approach methodologies, that is, quantitative structure-activity relationship models based on regression and classification machine learning algorithms, with the intent to predict a hazard class. The models utilize system (i.e., hydrodynamic size and polydispersity index) and non-system (i.e., elemental composition and core size)-dependent nanoscale features in combination with biological in vitro attributes and experimental conditions for various silver NFs, functional antimicrobial textiles, and cosmetics applications. In a second step, interpretable rules (criteria) followed by a certainty factor were obtained by exploiting a Bayesian network structure crafted by expert reasoning. The probabilistic model shows a predictive capability of ≈78% (average accuracy across all hazard classes). In this work, we show how we shifted from the conceptualization of the SSbD framework toward the realistic implementation with pragmatic instances. This study reveals (i) quantitative intrinsic hazard criteria to be considered in the safety aspects during synthesis stage, (ii) the challenges within, and (iii) the future directions for the generation and distillation of such criteria that can feed SSbD paradigms. Specifically, the criteria can guide material engineers to synthesize NFs that are inherently safer from alternative nanoformulations, at the earliest stages of innovation, while the models enable a fast and cost-efficient in silico toxicological screening of previously synthesized and hypothetical scenarios of yet-to-be synthesized NFs.

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.

Quantitative adverse outcome pathway (qAOP) using bayesian network model on comparative toxicity of multi-walled carbon nanotubes (MWCNTs): safe-by-design approach

While the various physicochemical properties of engineered nanomaterials influence their toxicities, their understanding is still incomplete. A predictive framework is required to develop safe nanomaterials, and a Bayesian network (BN) model based on adverse outcome pathway (AOP) can be utilized for this purpose. In this study, to explore the applicability of the AOP-based BN model in the development of safe nanomaterials, a comparative study was conducted on the change in the probability of toxicity pathways in response to changes in the dimensions and surface functionalization of multi-walled carbon nanotubes (MWCNTs). Based on the results of our previous study, we developed an AOP leading to cell death, and the experimental results were collected in human liver cells (HepG2) and bronchial epithelium cells (Beas-2B). The BN model was trained on these data to identify probabilistic causal relationships between key events. The results indicated that dimensions were the main influencing factor for lung cells, whereas -OH or -COOH surface functionalization and aspect ratio were the main influencing factors for liver cells. Endoplasmic reticulum stress was found to be a more sensitive pathway for dimensional changes, and oxidative stress was a more sensitive pathway for surface functionalization. Overall, our results suggest that the AOP-based BN model can be used to provide a scientific basis for the development of safe nanomaterials.

Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment

The growing development and applications of nanomaterials lead to an increasing release of these materials in the environment. The adverse effects they may elicit on ecosystems or human health are not always fully characterized. Such potential toxicity must be carefully assessed with the underlying mechanisms elucidated. To that purpose, different approaches can be used. First, experimental toxicology consisting of conducting in vitro or in vivo experiments (including clinical studies) can be used to evaluate the nanomaterial hazard. It can rely on variable models (more or less complex), allowing the investigation of different biological endpoints. The respective advantages and limitations of in vitro and in vivo models are discussed as well as some issues associated with experimental nanotoxicology. Perspectives of future developments in the field are also proposed. Second, computational nanotoxicology, i.e., in silico approaches, can be used to predict nanomaterial toxicity. In this context, we describe the general principles, advantages, and limitations especially of quantitative structure–activity relationship (QSAR) models and grouping/read-across approaches. The aim of this review is to provide an overview of these different approaches based on examples and highlight their complementarity.

Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.

How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping to be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.

Nanotoxicology and Nanomedicine: The Yin and Yang of Nano-Bio Interactions for the New Decade

Nanotoxicology and nanomedicine are two sub-disciplines of nanotechnology focusing on the phenomena, mechanisms, and engineering at the nano-bio interface. For the better part of the past three decades, these two disciplines have been largely developing independently of each other. Yet recent breakthroughs in microbiome research and the current COVID-19 pandemic demonstrate that holistic approaches are crucial for solving grand challenges in global health. Here we show the Yin and Yang relationship between the two fields by highlighting their shared goals of making safer nanomaterials, improved cellular and organism models, as well as advanced methodologies. We focus on the transferable knowledge between the two fields as nanotoxicological research is moving from pristine to functional nanomaterials, while inorganic nanomaterials - the main subjects of nanotoxicology - have become an emerging source for the development of nanomedicines. We call for a close partnership between the two fields in the new decade, to harness the full potential of nanotechnology for benefiting human health and environmental safety.

A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles

The emergence and rapid spread of multidrug-resistant bacteria strains are a public health concern. This emergence is caused by the overuse and misuse of antibiotics leading to the evolution of antibiotic-resistant strains. Nanoparticles (NPs) are objects with all three external dimensions in the nanoscale that varies from 1 to 100 nm. Research on NPs with enhanced antimicrobial activity as alternatives to antibiotics has grown due to the increased incidence of nosocomial and community acquired infections caused by pathogens. Machine learning (ML) tools have been used in the field of nanoinformatics with promising results. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. In this article, we present an ML tool that successfully predicts the antibacterial capacity of NPs while the model's validation demonstrates encouraging results (R2 = 0.78). The data were compiled after a literature review of 60 articles and consist of key physico-chemical (p-chem) properties and experimental conditions (exposure variables and bacterial clustering) from in vitro studies. Following data homogenization and pre-processing, we trained various regression algorithms and we validated them using diverse performance metrics. Finally, an important attribute evaluation, which ranks the attributes that are most important in predicting the outcome, was performed. The attribute importance revealed that NP core size, the exposure dose, and the species of bacterium are key variables in predicting the antibacterial effect of NPs. This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings. This concept also aids the safe-by-design paradigm by incorporating functionality tools.

Nanotechnology and artificial intelligence to enable sustainable and precision agriculture

Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.

NanoTox: Development of a Parsimonious In Silico Model for Toxicity Assessment of Metal-Oxide Nanoparticles Using Physicochemical Features

Metal-oxide nanoparticles find widespread applications in mundane life today, and cost-effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress. Machine learning models use existing experimental data and learn quantitative feature-toxicity relationships to yield predictive models. In this work, we adopted a principled approach to this problem by formulating a novel feature space based on intrinsic and extrinsic physicochemical properties, including periodic table properties but exclusive of in vitro characteristics such as cell line, cell type, and assay method. An optimal hypothesis space was developed by applying variance inflation analysis to the correlation structure of the features. Consequent to a stratified train-test split, the training dataset was balanced for the toxic outcomes and a mapping was then achieved from the normalized feature space to the toxicity class using various hyperparameter-tuned machine learning models, namely, logistic regression, random forest, support vector machines, and neural networks. Evaluation on an unseen test set yielded >96% balanced accuracy for the random forest, and neural network with one-hidden-layer models. The obtained cytotoxicity models are parsimonious, with intelligible inputs, and an embedded applicability check. Interpretability investigations of the models identified the key predictor variables of metal-oxide nanoparticle cytotoxicity. Our models could be applied on new, untested oxides, using a majority-voting ensemble classifier, NanoTox, that incorporates the best of the above models. NanoTox is the first open-source nanotoxicology pipeline, freely available under the GNU General Public License (https://github.com/NanoTox).

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.

Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.

Quantitative adverse outcome pathway (qAOP) models for toxicity prediction

The quantitative adverse outcome pathway (qAOP) concept is gaining interest due to its potential regulatory applications in chemical risk assessment. Even though an increasing number of qAOP models are being proposed as computational predictive tools, there is no framework to guide their development and assessment. As such, the objectives of this review were to: (i) analyse the definitions of qAOPs published in the scientific literature, (ii) define a set of common features of existing qAOP models derived from the published definitions, and (iii) identify and assess the existing published qAOP models and associated software tools. As a result, five probabilistic qAOPs and ten mechanistic qAOPs were evaluated against the common features. The review offers an overview of how the qAOP concept has advanced and how it can aid toxicity assessment in the future. Further efforts are required to achieve validation, harmonisation and regulatory acceptance of qAOP models.

Nanotoxicology data for in silico tools: a literature review

The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.

Practices and Trends of Machine Learning Application in Nanotoxicology

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

Machine learning prediction of nanoparticle in vitro toxicity: A comparative study of classifiers and ensemble-classifiers using the Copeland Index

Nano-Particles (NPs) are well established as important components across a broad range of products from cosmetics to electronics. Their utilization is increasing with their significant economic and societal potential yet to be fully realized. Inroads have been made in our understanding of the risks posed to human health and the environment by NPs but this area will require continuous research and monitoring. In recent years Machine Learning (ML) techniques have exploited large datasets and computation power to create breakthroughs in diverse fields from facial recognition to genomics. More recently, ML techniques have been applied to nanotoxicology with very encouraging results. In this study, categories of ML classifiers (rules, trees, lazy, functions and bayes) were compared using datasets from the Safe and Sustainable Nanotechnology (S2NANO) database to investigate their performance in predicting NPs in vitro toxicity. Physicochemical properties, toxicological and quantum-mechanical attributes and in vitro experimental conditions were used as input variables to predict the toxicity of NPs based on cell viability. Voting, an ensemble meta-classifier, was used to combine base models to optimize the classification prediction of toxicity. To facilitate inter-comparison, a Copeland Index was applied that ranks the classifiers according to their performance and suggested the optimal classifier. Neural Network (NN) and Random forest (RF) showed the best performance in the majority of the datasets used in this study. However, the combination of classifiers demonstrated an improved prediction resulting meta-classifier to have higher indices. This proposed Copeland Index can now be used by researchers to identify and clearly prioritize classifiers in order to achieve more accurate classification predictions for NP toxicity for a given dataset.