Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries

Automated machine learning in nanotoxicity assessment: A comparative study of predictive model performance

Computational modeling has earned significant interest as an alternative to animal testing of toxicity assessment. However, the process of selecting an appropriate algorithm and fine-tuning hyperparameters for the developing of optimized models takes considerable time, expertise, and an intensive search. The recent emergence of automated machine learning (autoML) approaches, available as user-friendly platforms, has proven beneficial for individuals with limited knowledge in ML-based predictive model development. These autoML platforms automate crucial steps in model development, including data preprocessing, algorithm selection, and hyperparameter tuning. In this study, we used seven previously published and publicly available datasets for oxides and metals to develop nanotoxicity prediction models. AutoML platforms, namely Vertex AI, Azure, and Dataiku, were employed and performance measures such as accuracy, F1 score, precision, and recall for these autoML-based models were then compared with those of conventional ML-based models. The results demonstrated clearly that the autoML platforms produced more reliable nanotoxicity prediction models, outperforming those built with conventional ML algorithms. While none of the three autoML platforms significantly outperformed the others, distinctions exist among them in terms of the available options for choosing technical features throughout the model development steps. This allows users to select an autoML platform that aligns with their knowledge of predictive model development and its technical features. Additionally, prediction models constructed from datasets with better data quality displayed, enhanced performance than those built from datasets with lower data quality, indicating that future studies with high-quality datasets can further improve the performance of those autoML-based prediction models.

A review on the structural characterization of nanomaterials for nano-QSAR models

Quantitative structure-activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial's behavior, preparation, or test conditions) that indirectly reflect its structure.

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.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells

Nano-QSAR models can be effectively used for prediction of the biological activity of nanomaterials that have not been experimentally tested before. However, their use is associated with the need to have appropriate knowledge and skills in chemoinformatics. Thus, they are mainly aimed at specialists in the field. This significantly limits the potential group of recipients of the developed solutions. In this perspective, the purpose of the presented research was to develop an easily accessible and user-friendly web-based application that could enable the prediction of TiO₂-based multicomponent nanomaterials cytotoxicity toward Chinese Hamster Ovary (CHO-K1) cells. The graphical user interface is clear and intuitive and the only information required from the user is the type and concentration of the metals which will be modifying TiO₂-based nanomaterial. Thanks to this, the application will be easy to use not only by cheminformatics but also by specialists in the field of nanotechnology or toxicology, who will be able to quickly predict cytotoxicity of desired nanoclusters. We have performed case studies to demonstrate the features and utilities of developed application. The NanoMixHamster application is freely available at https://nanomixhamster.cloud.nanosolveit.eu/.

Particle Safety Assessment in Additive Manufacturing: From Exposure Risks to Advanced Toxicology Testing

Additive manufacturing (AM) or industrial three-dimensional (3D) printing drives a new spectrum of design and production possibilities; pushing the boundaries both in the application by production of sophisticated products as well as the development of next-generation materials. AM technologies apply a diversity of feedstocks, including plastic, metallic, and ceramic particle powders with distinct size, shape, and surface chemistry. In addition, powders are often reused, which may change the particles' physicochemical properties and by that alter their toxic potential. The AM production technology commonly relies on a laser or electron beam to selectively melt or sinter particle powders. Large energy input on feedstock powders generates several byproducts, including varying amounts of virgin microparticles, nanoparticles, spatter, and volatile chemicals that are emitted in the working environment; throughout the production and processing phases. The micro and nanoscale size may enable particles to interact with and to cross biological barriers, which could, in turn, give rise to unexpected adverse outcomes, including inflammation, oxidative stress, activation of signaling pathways, genotoxicity, and carcinogenicity. Another important aspect of AM-associated risks is emission/leakage of mono- and oligomers due to polymer breakdown and high temperature transformation of chemicals from polymeric particles, both during production, use, and in vivo, including in target cells. These chemicals are potential inducers of direct toxicity, genotoxicity, and endocrine disruption. Nevertheless, understanding whether AM particle powders and their byproducts may exert adverse effects in humans is largely lacking and urges comprehensive safety assessment across the entire AM lifecycle-spanning from virgin and reused to airborne particles. Therefore, this review will detail: 1) brief overview of the AM feedstock powders, impact of reuse on particle physicochemical properties, main exposure pathways and protective measures in AM industry, 2) role of particle biological identity and key toxicological endpoints in the particle safety assessment, and 3) next-generation toxicology approaches in nanosafety for safety assessment in AM. Altogether, the proposed testing approach will enable a deeper understanding of existing and emerging particle and chemical safety challenges and provide a strategy for the development of cutting-edge methodologies for hazard identification and risk assessment in the AM industry.

New Mechanistic Insights on Carbon Nanotubes' Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches

Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = -9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = -6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = -5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.

Toxicity of Carbon Nanotubes: Molecular Mechanisms, Signaling Cascades, and Remedies in Biomedical Applications

Carbon nanotubes (CNTs) are the most studied allotropic form of carbon. They can be used in various biomedical applications due to their novel physicochemical properties. In particular, the small size of CNTs, with a large surface area per unit volume, has a considerable impact on their toxicity. Despite of the use of CNTs in various applications, toxicity is a big problem that requires more research. In this Review, we discuss the toxicity of CNTs and the associated mechanisms. Physicochemical factors, such as metal impurities, length, size, solubilizing agents, CNTs functionalization, and agglomeration, that may lead to oxidative stress, toxic signaling pathways, and potential ways to control these mechanisms are also discussed. Moreover, with the latest mechanistic evidence described in this Review, we expect to give new insights into CNTs' toxicological effects at the molecular level and provide new clues for the mitigation of harmful effects emerging from exposure to CNTs.

15 Years of Small: Research Trends in Nanosafety

In the field of nano- and microscale science and technology, Small has become one of the worldwide leading journals since its initiation 15 years ago. Among all the topics covered in Small, "nanosafety" has received growing interest over the years, which accounts for a large proportion of the total publications of Small. Herein, inspired by its coming Special Issue "Rethinking Nanosafety," a general bibliometric overview of the nanosafety studies that have been published in Small is presented. Using the data derived from the Web of Science Core Collection, the annual publication growth, most influential countries/institutions as well as the visualized collaborations between different countries and institutions based on CiteSpace software are presented. A special emphasis on the impact of the previous Special Issue from Small that is related to nanosafety research is given and the research trend from the most highly cited papers during last 15 years is analyzed. Lastly, future research directions are also proposed.

Role of Artificial Intelligence and Machine Learning in Nanosafety

Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

QSAR modeling of the toxicity classification of superparamagnetic iron oxide nanoparticles (SPIONs) in stem-cell monitoring applications: an integrated study from data curation to model development

The use of in silico approaches for the prediction of biomedical properties of nano-biomaterials (NBMs) can play a significant role in guiding and reducing wetlab experiments. Computational methods, such as data mining and machine learning techniques, can increase the efficiency and reduce the time and cost required for hazard and risk assesment and for designing new safer NBMs. A major obstacle in developing accurate and well-validated in silico models such as Nano Quantitative Structure-Activity Relationships (Nano-QSARs) is that although the volume of data published in the literature is increasing, the data are fragmented in many different publications and are not sufficiently curated for modelling purposes. Moreover, NBMs exhibit high complexity and heterogeneity in their structures, making data collection and curation and QSAR model development more challenging compared to traditional small molecules. The aim of this study was to construct and fully validate a Nano-QSAR model for the prediction of toxicological properties of superparamagnetic iron oxide nanoparticles (SPIONs), focusing on their application as Magnetic Resonance Imaging (MRI) contrast agents for non-invasive stem cell labelling and tracking. To achieve this goal, we first performed an extensive search through the literature for collecting and curating relevant data and we developed a dataset containing both physicochemical and toxicological properties of SPIONs. The data were analysed next, using Automated machine learning (Auto-ML) approaches for optimising the development and validation of nanotoxicity classification QSAR models of SPIONs. Further analysis of relative attribute importances revealed that physicochemical properties such as the size and the magnetic core are the dominant attributes correlated to the toxicity of SPIONs. Our results suggest that as more systematic information from NBM experimental tests becomes available, computational tools could play an important role in supporting the safety-by-design (SbD) concept in regenerative medicine and disease therapeutics.

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.

Importance of Choosing Relevant Biological End Points To Predict Nanoparticle Toxicity with Computational Approaches for Human Health Risk Assessment

Because it is impossible to assess in vitro or in vivo the toxicity of all nanoparticles available on the market on a case-by-case basis, computational approaches have been proposed as useful alternatives to predict in silico the hazard potential of engineered nanoparticles. Despite promising results, a major issue associated with these mathematical models lies in the a priori choice of the physicochemical descriptors and the biological end points. We performed a thorough bibliographic survey on the biological end points used for nanotoxicology purposes and compared them between experimental and computational approaches. They were found to be disparate: while conventional in vitro nanotoxicology assays usually investigate a large array of biological effects using eukaryotic cells (cytotoxicity, pro-inflammatory response, oxidative stress, genotoxicity), computational studies mostly focus on cell viability and also include studies on prokaryotic cells. We may thus wonder the relevance of building complex mathematical models able to predict accurately a biological end point if this latter is not the most relevant to support human health risk assessment. The choice of biological end points clearly deserves to be more carefully discussed. This could bridge the gap between experimental and computational nanotoxicology studies and allow in silico predictive models to reach their full potential.

In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches

Rational nanomaterial design is urgently demanded for new nanomaterial development with desired properties. However, computational nanomaterial modeling and virtual nanomaterial screening are not applicable for this purpose due to the complexity of nanomaterial structures. To address this challenge, a new computational workflow is established in this study to virtually profile nanoparticles by (1) constructing a structurally diverse virtual gold nanoparticle (GNP) library and (2) developing novel universal nanodescriptors. The emphasis of this study is the second task by developing geometrical nanodescriptors that are suitable for the quantitative modeling of GNPs and virtual screening purposes. The feasibility, rigor and applicability of this novel computational method are validated by testing seven GNP datasets consisting of 191 unique GNPs of various nano-bioactivities and physicochemical properties. The high predictability of the developed GNP models suggests that this workflow can be used as a universal tool for nanomaterial profiling and rational nanomaterial design.

Comparison of conventional and advanced in vitro models in the toxicity testing of nanoparticles

Humans are exposed to a wide variety of nanoparticles (NPs) present in the environment, in consumer, health and medical products, and in food. Conventional cytotoxicity testing compared to animal testing is less expensive, faster and avoids ethical problems at the expense of a lower predictive value. New cellular models and exposure conditions have been developed to overcome the limitations of conventional cell culture and obtain more predictive data. The use of three-dimensional culture, co-culture and inclusion of mechanical stimulation can provide physiologically more relevant culture conditions. These systems are particularly relevant for oral, respiratory and intravenous exposure to NPs and it may be assumed that physiologically relevant application of the NPs can improve the predictive value of in vitro testing. Various groups have used advanced culture and exposure systems, but few direct comparisons between data from conventional cultures and from advanced systems exist. In silico models may present another option to predict human health risk by NPs without using animal studies. In the absence of validation, the question whether these alternative models provide more predictive data than conventional testing remains elusive.

Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources

A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.

Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells

Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R² for internal validation data sets: 0.60-0.80; R²_pred for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 μm; surface area, 11.3-380.0 m²/g; and dose, 0-200 ppm.

Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches

Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.

Nanomaterial libraries and model organisms for rapid high-content analysis of nanosafety

Safety analysis of engineered nanomaterials (ENMs) presents a formidable challenge regarding environmental health and safety, due to their complicated and diverse physicochemical properties. Although large amounts of data have been published regarding the potential hazards of these materials, we still lack a comprehensive strategy for their safety assessment, which generates a huge workload in decision-making. Thus, an integrated approach is urgently required by government, industry, academia and all others who deal with the safe implementation of nanomaterials on their way to the marketplace. The rapid emergence and sheer number of new nanomaterials with novel properties demands rapid and high-content screening (HCS), which could be performed on multiple materials to assess their safety and generate large data sets for integrated decision-making. With this approach, we have to consider reducing and replacing the commonly used rodent models, which are expensive, time-consuming, and not amenable to high-throughput screening and analysis. In this review, we present a ‘Library Integration Approach’ for high-content safety analysis relevant to the ENMs. We propose the integration of compositional and property-based ENM libraries for HCS of cells and biologically relevant organisms to be screened for mechanistic biomarkers that can be used to generate data for HCS and decision analysis. This systematic approach integrates the use of material and biological libraries, automated HCS and high-content data analysis to provide predictions about the environmental impact of large numbers of ENMs in various categories. This integrated approach also allows the safer design of ENMs, which is relevant to the implementation of nanotechnology solutions in the pharmaceutical industry.

Use of metal/metal oxide spherical cluster and hydroxyl metal coordination complex for descriptor calculation in development of nanoparticle cytotoxicity classification model

Computational approaches have been suggested as an informative tool for risk assessment of nanomaterials. Nano (quantitative) structure-activity relationship, nano-(Q)SAR, models have been developed to predict toxicity of metal oxide (MOx) nanoparticles (NPs); however, the packing structure and cluster of nanoparticle have been included for the descriptor calculation in only two studies. This study proposed spherical cluster and hydroxyl metal coordination complex to calculate descriptors for development of nanoparticle cytotoxicity classification model. The model cluster was generated from metal (M) or MOx crystal structure to calculate physicochemical properties of M/MOx NPs and the hydroxyl metal coordination complex was used to calculate the properties of the metal cation in an aqueous environment. Data were collected for 2 M and 19 MOx NPs in human bronchial epithelial cell lines and murine myeloid cell lines at 100 μg/ml after 24 hours exposure. The model was developed with scaled HOMO energy of the model cluster and polarizability of the hydroxyl metal coordination complex, as reactivity of the particles and the cations explained cause of cytotoxic action by M/MOx NPs. As the developed model achieved 90.31% accuracy, the classification model in this work can be used for virtual screening of toxic action of M/MOx NPs.

Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory

Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological systems and their ecosystems. Toxicity testing is an essential step for assessing the potential risks of the NPs, but the experimental assays are often very expensive and usually too slow to flag the number of NPs that may cause adverse effects. In silico models centered on quantitative structure-activity/toxicity relationships (QSAR/QSTR) are alternative tools that have become valuable supports to risk assessment, rationalizing the search for safer NPs. In this work, we develop a unified QSTR-perturbation model based on artificial neural networks, aimed at simultaneously predicting general toxicity profiles of NPs under diverse experimental conditions. The model is derived from 54,371 NP-NP pair cases generated by applying the perturbation theory to a set of 260 unique NPs, and showed an accuracy higher than 97% in both training and validation sets. Physicochemical interpretation of the different descriptors in the model are additionally provided. The QSTR-perturbation model is then employed to predict the toxic effects of several NPs not included in the original dataset. The theoretical results obtained for this independent set are strongly consistent with the experimental evidence found in the literature, suggesting that the present QSTR-perturbation model can be viewed as a promising and reliable computational tool for probing the toxicity of NPs.

Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology

Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles.

A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials

Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs). The extension of conventional (quantitative) structure-activity relationships ((Q)SARs) approach to nanotoxicology, i.e., nano-(Q)SARs, is a possible solution. The preliminary attempts of correlating ENMs' characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (Q)SARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(Q)SARs for metallic ENMs, on the aspects of data sources, reported nano-(Q)SARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.

Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity

Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.

QSAR-Based Studies of Nanomaterials in the Environment

Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the environment. In recent years, the development of nanotechnology has led to the design and large-scale production of many new materials and devices with a vast range of applications. However, along with the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible health-risks and environmental impacts. This chapter provides an overview of the Quantitative Structure-Activity Relationships (QSAR) studies performed so far towards predicting nanoparticles' environmental toxicity. Recent progresses on the application of these modeling studies are additionally pointed out. Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling are discussed.

Comparing the CORAL and Random Forest Approaches for Modelling the In Vitro Cytotoxicity of Silica Nanomaterials

Nanotechnology is one of the most important technological developments of the 21st century. In silico methods to predict toxicity, such as quantitative structure–activity relationships (QSARs), promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials were investigated, to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called ‘nano-QSAR’ models. ‘External’ leave-one-out cross-validation (LOO) analysis was performed, to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken, in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R2) for training and for LOO (0.83 and 0.65 for training and LOO, respectively), compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. With regard to the physicochemical properties of the nanomaterials, the aspect ratio and zeta potential were found to be the two most important variables for Random Forest, and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the data set: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends.

Alternative Testing Strategies for Nanomaterials: State of the Science and Considerations for Risk Analysis

The rapid growth of the nanotechnology industry has warranted equal progress in the nanotoxicology and risk assessment fields. In vivo models have traditionally been used to determine human and environmental risk for chemicals; however, the use of these tests has limitations, and there are global appeals to develop reliable alternatives to animal testing. Many have investigated the use of alternative (nonanimal) testing methods and strategies have quickly developed and resulted in the generation of large toxicological data sets for numerous nanomaterials (NMs). Due to the novel physicochemical properties of NMs that are related to surface characteristics, the approach toward toxicity test development has distinct considerations from traditional chemicals, bringing new requirements for adapting these approaches for NMs. The methodical development of strategies that combine multiple alternative tests can be useful for predictive NM risk assessment and help screening-level decision making. This article provides an overview of the main developments in alternative methods and strategies for reducing uncertainty in NM risk assessment, including advantages and disadvantages of in vitro, ex vivo, and in silico methods, and examples of existing comprehensive strategies. In addition, knowledge gaps are identified toward improvements for experimental and strategy design, specifically highlighting the need to represent realistic exposure scenarios and to consider NM-specific concerns such as characterization, assay interferences, and standardization. Overall, this article aims to improve the reliability and utility of alternative testing methods and strategies for risk assessment of manufactured NMs.

A computational framework for interspecies pharmacokinetics, exposure and toxicity assessment of gold nanoparticles

Aim: To develop a comprehensive computational framework to simulate tissue distribution of gold nanoparticles (AuNP) across several species. Materials & methods: This framework was built on physiologically based pharmacokinetic modeling, calibrated and evaluated with multiple independent datasets. Results: Rats and pigs seem to be more appropriate models than mice in animal-to-human extrapolation of AuNP pharmacokinetics and that the dose and age should be considered. Incorporation of in vitro and/or in vivo cellular uptake and toxicity data into the model improved toxicity assessment of AuNP. Conclusion: These results partially explain the current low translation rate of nanotechnology-based drug delivery systems from mice to humans. This simulation approach may be applied to other nanomaterials and provides guidance to design future translational studies.

Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties

Cellular association of nanoparticles (NPs) in biological fluids is affected by proteins adsorbed onto the NP surface, forming a "protein corona", thereby impacting cellular bioactivity. Here we investigate, based on an extensive gold NPs protein corona dataset, the relationships between NP-cell association and protein corona fingerprints (PCFs) as well as NP physicochemical properties. Accordingly, quantitative structure-activity relationships (QSARs) were developed based on both linear and non-linear support vector regression (SVR) models making use of a sequential forward floating selection of descriptors. The SVR model with only 6 serum proteins and zeta potential had higher accuracy (R(2) = 0.895) relative to the linear model (R(2) = 0.850) with 11 PCFs. Considering the initial pool of 148 descriptors, the APOB, A1AT, ANT3, and PLMN serum proteins along with NP zeta potential were identified as most significant to correlating NP-cell association. The present study suggests that QSARs exploration of NP-cell association data, considering the role of both NP protein corona and physicochemical properties, can support the planning and interpretation of toxicity studies and guide the design of NPs for biomedical applications.

Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions

The Monte Carlo technique has been used to build up quantitative structure-activity relationships (QSARs) for prediction of dark cytotoxicity and photo-induced cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of lethal concentration for 50% bacteria pLC50, LC50 in mol/L). The representation of nanoparticles include (i) in the case of the dark cytotoxicity a simplified molecular input-line entry system (SMILES), and (ii) in the case of photo-induced cytotoxicity a SMILES plus symbol '^'. The predictability of the approach is checked up with six random distributions of available data into the visible training and calibration sets, and invisible validation set. The statistical characteristics of these models are correlation coefficient 0.90-0.94 (training set) and 0.73-0.98 (validation set).

Current situation on the availability of nanostructure-biological activity data

Recent developments in nanotechnology have not only increased the number of nanoproducts on the market, but also raised concerns about the safety of engineered nanomaterials (ENMs) for human health and the environment. As the production and use of ENMs increase, we are approaching the point at which it is impossible to individually assess the toxicity of a vast number of ENMs. Therefore, it is desirable to use time-effective computational methods, such as the quantitative structure-activity relationship (QSAR) models, to predict the toxicity of ENMs. However, the accuracy of the nano-(Q)SARs is directly tied to the quality of the data from which the model is estimated. Although the amount of available nanotoxicity data is insufficient for generating robust nano-(Q)SAR models in most cases, there are a handful of studies that provide appropriate experimental data for (Q)SAR-like modelling investigations. The aim of this study is to review the available literature data that are particularly suitable for nano-(Q)SAR modelling. We hope that this paper can serve as a starting point for those who would like to know more about the current availability of experimental data on the health effects of ENMs for future modelling purposes.

Toxicity of metal oxide nanoparticles: mechanisms, characterization, and avoiding experimental artefacts

Metal oxide nanomaterials are widely used in practical applications and represent a class of nanomaterials with the highest global annual production. Many of those, such as TiO2 and ZnO, are generally considered non-toxic due to the lack of toxicity of the bulk material. However, these materials typically exhibit toxicity to bacteria and fungi, and there have been emerging concerns about their ecotoxicity effects. The understanding of the toxicity mechanisms is incomplete, with different studies often reporting contradictory results. The relationship between the material properties and toxicity appears to be complex and diifficult to understand, which is partly due to incomplete characterization of the nanomaterial, and possibly due to experimental artefacts in the characterization of the nanomaterial and/or its interactions with living organisms. This review discusses the comprehensive characterization of metal oxide nanomaterials and the mechanisms of their toxicity.

The nanoparticle biomolecule corona: lessons learned – challenge accepted?

Besides the wide use of engineered nanomaterials (NMs) in technical products, their applications are not only increasing in biotechnology and biomedicine, but also in the environmental field.

Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions

Nanomaterials have revolutionized modern science and technology due to their multiple applications in engineering, physics, chemistry, and biomedicine. Nevertheless, the use and manipulation of nanoparticles (NPs) can bring serious damages to living organisms and their ecosystems. For this reason, ecotoxicity and cytotoxicity assays are of special interest in order to determine the potential harmful effects of NPs. Processes based on ecotoxicity and cytotoxicity tests can significantly consume time and financial resources. In this sense, alternative approaches such as quantitative structure-activity/toxicity relationships (QSAR/QSTR) modeling have provided important insights for the better understanding of the biological behavior of NPs that may be responsible for causing toxicity. Until now, QSAR/QSTR models have predicted ecotoxicity or cytotoxicity separately against only one organism (bioindicator species or cell line) and have not reported information regarding the quantitative influence of characteristics other than composition or size. In this work, we developed a unified QSTR-perturbation model to simultaneously probe ecotoxicity and cytotoxicity of NPs under different experimental conditions, including diverse measures of toxicities, multiple biological targets, compositions, sizes and conditions to measure those sizes, shapes, times during which the biological targets were exposed to NPs, and coating agents. The model was created from 36488 cases (NP-NP pairs) and exhibited accuracies higher than 98% in both training and prediction sets. The model was used to predict toxicities of several NPs that were not included in the original data set. The results of the predictions suggest that the present QSTR-perturbation model can be employed as a highly promising tool for the fast and efficient assessment of ecotoxicity and cytotoxicity of NPs.

Association rule mining of cellular responses induced by metal and metal oxide nanoparticles

Relationships among fourteen different biological responses (including ten signaling pathway activities and four cytotoxicity effects) of murine macrophage (RAW264.7) and bronchial epithelial (BEAS-2B) cells exposed to six metal and metal oxide nanoparticles (NPs) were analyzed using both statistical and data mining approaches. Both the pathway activities and cytotoxicity effects were assessed using high-throughput screening (HTS) over an exposure period of up to 24 h and concentration range of 0.39-200 mg L(-1). HTS data were processed by outlier removal, normalization, and hit-identification (for significantly regulated cellular responses) to arrive at reliable multiparametric bioactivity profiles for the NPs. Association rule mining was then applied to the bioactivity profiles followed by a pruning process to remove redundant rules. The non-redundant association rules indicated that "significant regulation" of one or more cellular responses implies regulation of other (associated) cellular response types. Pairwise correlation analysis (via Pearson's χ(2) test) and self-organizing map clustering of the different cellular response types indicated consistency with the identified non-redundant association rules. Furthermore, in order to explore the potential use of association rules as a tool for data-driven hypothesis generation, specific pathway activity experiments were carried out for ZnO NPs. The experimental results confirmed the association rule identified for the p53 pathway and mitochondrial superoxide levels (via MitoSox reagent) and further revealed that blocking of the transcriptional activity of p53 lowered the MitoSox signal. The present approach of using association rule mining for data-driven hypothesis generation has important implications for streamlining multi-parameter HTS assays, improving the understanding of NP toxicity mechanisms, and selection of endpoints for the development of nanomaterial structure-activity relationships.

Protein corona fingerprinting predicts the cellular interaction of gold and silver nanoparticles

Using quantitative models to predict the biological interactions of nanoparticles will accelerate the translation of nanotechnology. Here, we characterized the serum protein corona 'fingerprint' formed around a library of 105 surface-modified gold nanoparticles. Applying a bioinformatics-inspired approach, we developed a multivariate model that uses the protein corona fingerprint to predict cell association 50% more accurately than a model that uses parameters describing nanoparticle size, aggregation state, and surface charge. Our model implicates a set of hyaluronan-binding proteins as mediators of nanoparticle-cell interactions. This study establishes a framework for developing a comprehensive database of protein corona fingerprints and biological responses for multiple nanoparticle types. Such a database can be used to develop quantitative relationships that predict the biological responses to nanoparticles and will aid in uncovering the fundamental mechanisms of nano-bio interactions.

Physicochemical signatures of nanoparticle-dependent complement activation

Nanoparticles are potentially powerful therapeutic tools that have the capacity to target drug payloads and imaging agents. However, some nanoparticles can activate complement, a branch of the innate immune system, and cause adverse side-effects. Recently, we employed an in vitro hemolysis assay to measure the serum complement activity of perfluorocarbon nanoparticles that differed by size, surface charge, and surface chemistry, quantifying the nanoparticle-dependent complement activity using a metric called Residual Hemolytic Activity (RHA). In the present work, we have used a decision tree learning algorithm to derive the rules for estimating nanoparticle-dependent complement response based on the data generated from the hemolytic assay studies. Our results indicate that physicochemical properties of nanoparticles, namely, size, polydispersity index, zeta potential, and mole percentage of the active surface ligand of a nanoparticle, can serve as good descriptors for prediction of nanoparticle-dependent complement activation in the decision tree modeling framework.

Enalos InSilicoNano platform: an online decision support tool for the design and virtual screening of nanoparticles

A QNAR model, available online through Enalos InSilicoNano platform, has been developed and validated for the risk assessment of nanoparticles (NPs).

Implementation of alternative test strategies for the safety assessment of engineered nanomaterials

Nanotechnology introduces a new field that requires novel approaches and methods for hazard and risk assessment. For an appropriate scientific platform for safety assessment, nanoscale properties and functions of engineered nanomaterials (ENMs), including how the physicochemical properties of the materials relate to mechanisms of injury at the nano-bio interface, must be considered. Moreover, this rapidly advancing new field requires novel test strategies that allow multiple toxicants to be screened in robust, mechanism-based assays in which the bulk of the investigation can be carried out at the cellular and biomolecular level whilst maintaining limited animal use and is based on the contribution of toxicological pathways to the pathophysiology of disease. First, a predictive toxicological approach for the safety assessment of ENMs will be discussed against the background of a '21st-century vision' for using alternative test strategies (ATSs) to perform toxicological assessment of large numbers of untested chemicals, thereby reducing a backlog that could otherwise become a problem for nanotechnology. An ATS is defined here as an alternative to animal experiments or refinement/reduction alternative to traditional animal testing. Secondly, the approach of selecting pathways of toxicity to screen for the pulmonary hazard potential of carbon nanotubes and metal oxides will be discussed, as well as how to use these pathways to perform high-content or high-throughput testing and how the data can be used for hazard ranking, risk assessment, regulatory decision-making and 'safer-by-design' strategies. Finally, the utility and disadvantages of this predictive toxicological approach to ENM safety assessment, and how it can assist the 21st-century vision, will be addressed.

A multi-stakeholder perspective on the use of alternative test strategies for nanomaterial safety assessment

There has been a conceptual shift in toxicological studies from describing what happens to explaining how the adverse outcome occurs, thereby enabling a deeper and improved understanding of how biomolecular and mechanistic profiling can inform hazard identification and improve risk assessment. Compared to traditional toxicology methods, which have a heavy reliance on animals, new approaches to generate toxicological data are becoming available for the safety assessment of chemicals, including high-throughput and high-content screening (HTS, HCS). With the emergence of nanotechnology, the exponential increase in the total number of engineered nanomaterials (ENMs) in research, development, and commercialization requires a robust scientific approach to screen ENM safety in humans and the environment rapidly and efficiently. Spurred by the developments in chemical testing, a promising new toxicological paradigm for ENMs is to use alternative test strategies (ATS), which reduce reliance on animal testing through the use of in vitro and in silico methods such as HTS, HCS, and computational modeling. Furthermore, this allows for the comparative analysis of large numbers of ENMs simultaneously and for hazard assessment at various stages of the product development process and overall life cycle. Using carbon nanotubes as a case study, a workshop bringing together national and international leaders from government, industry, and academia was convened at the University of California, Los Angeles, to discuss the utility of ATS for decision-making analyses of ENMs. After lively discussions, a short list of generally shared viewpoints on this topic was generated, including a general view that ATS approaches for ENMs can significantly benefit chemical safety analysis.

Development of structure-activity relationship for metal oxide nanoparticles

Nanomaterial structure-activity relationships (nano-SARs) for metal oxide nanoparticles (NPs) toxicity were investigated using metrics based on dose-response analysis and consensus self-organizing map clustering. The NP cellular toxicity dataset included toxicity profiles consisting of seven different assays for human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells, over a concentration range of 0.39-100 mg L(-1) and exposure time up to 24 h, for twenty-four different metal oxide NPs. Various nano-SAR building models were evaluated, based on an initial pool of thirty NP descriptors. The conduction band energy and ionic index (often correlated with the hydration enthalpy) were identified as suitable NP descriptors that are consistent with suggested toxicity mechanisms for metal oxide NPs and metal ions. The best performing nano-SAR with the above two descriptors, built with support vector machine (SVM) model and of validated robustness, had a balanced classification accuracy of ~94%. An applicability domain for the present data was established with a reasonable confidence level of 80%. Given the potential role of nano-SARs in decision making, regarding the environmental impact of NPs, the class probabilities provided by the SVM nano-SAR enabled the construction of decision boundaries with respect to toxicity classification under different acceptance levels of false negative relative to false positive predictions.

Nanoinformatics workshop report: Current resources, community needs, and the proposal of a collaborative framework for data sharing and information integration

The quantity of information on nanomaterial properties and behavior continues to grow rapidly. Without a concerted effort to collect, organize and mine disparate information coming out of current research efforts, the value and effective use of this information will be limited at best. Data will not be translated to knowledge. At worst, erroneous conclusions will be drawn and future research may be misdirected. Nanoinformatics can be a powerful approach to enhance the value of global information in nanoscience and nanotechnology. Much progress has been made through grassroots efforts in nanoinformatics resulting in a multitude of resources and tools for nanoscience researchers. In 2012, the nanoinformatics community believed it was important to critically evaluate and refine currently available nanoinformatics approaches in order to best inform the science and support the future of predictive nanotechnology. The Greener Nano 2012: Nanoinformatics Tools and Resources Workshop brought together informatics groups with materials scientists active in nanoscience research to evaluate and reflect on the tools and resources that have recently emerged in support of predictive nanotechnology. The workshop goals were to establish a better understanding of current nanoinformatics approaches and to clearly define immediate and projected informatics infrastructure needs of the nanotechnology community. The theme of nanotechnology environmental health and safety (nanoEHS) was used to provide real-world, concrete examples on how informatics can be utilized to advance our knowledge and guide nanoscience. The benefit here is that the same properties that impact the performance of products could also be the properties that inform EHS. From a decision management standpoint, the dual use of such data should be considered a priority. Key outcomes include a proposed collaborative framework for data collection, data sharing and information integration.