Nano(Q)SAR: Challenges, pitfalls and perspectives

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

Biomolecular Adsorption on Nanomaterials: Combining Molecular Simulations with Machine Learning

Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical machine learning approaches. Multiple unsupervised learning algorithms (principal component analysis, agglomerative clustering, and K-means) as well as supervised linear and nonlinear regression algorithms (linear regression, AdaBoost ensemble learning, artificial neural network) have been applied. As a result, a small set of biomolecules has been identified, knowledge of adsorption free energies of which to a specific nanomaterial can be used to predict, within the developed machine learning model, adsorption free energies of other biomolecules. Furthermore, the methodology of grouping of nanomaterials according to their interactions with biomolecules has been presented.

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

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

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.

Artificial intelligence aids in development of nanomedicines for cancer management

Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.

Precision Nanotoxicology in Drug Development: Current Trends and Challenges in Safety and Toxicity Implications of Customized Multifunctional Nanocarriers for Drug-Delivery Applications

The dire need for the assessment of human and environmental endangerments of nanoparticulate material has motivated the formulation of novel scientific tools and techniques to detect, quantify, and characterize these nanomaterials. Several of these paradigms possess enormous possibilities for applications in many of the realms of nanotoxicology. Furthermore, in a large number of cases, the limited capabilities to assess the environmental and human toxicological outcomes of customized and tailored multifunctional nanoparticles used for drug delivery have hindered their full exploitation in preclinical and clinical settings. With the ever-compounded availability of nanoparticulate materials in commercialized settings, an ever-arising popular debate has been egressing on whether the social, human, and environmental costs associated with the risks of nanomaterials outweigh their profits. Here we briefly review the various health, pharmaceutical, and regulatory aspects of nanotoxicology of engineered multifunctional nanoparticles in vitro and in vivo. Several aspects and issues encountered during the safety and toxicity assessments of these drug-delivery nanocarriers have also been summarized. Furthermore, recent trends implicated in the nanotoxicological evaluations of nanoparticulate matter in vitro and in vivo have also been discussed. Due to the absence of robust and rigid regulatory guidelines, researchers currently frequently encounter a larger number of challenges in the toxicology assessment of nanocarriers, which have also been briefly discussed here. Nanotoxicology has an appreciable and significant part in the clinical translational development as well as commercialization potential of nanocarriers; hence these aspects have also been touched upon. Finally, a brief overview has been provided regarding some of the nanocarrier-based medicines that are currently undergoing clinical trials, and some of those which have recently been commercialized and are available for patients. It is expected that this review will instigate an appreciable interest in the research community working in the arena of pharmaceutical drug development and nanoformulation-based drug delivery.

Immunotoxicity of nanomaterials in health and disease: Current challenges and emerging approaches for identifying immune modifiers in susceptible populations

Nanosafety assessment has experienced an intense era of research during the past decades driven by a vivid interest of regulators, industry, and society. Toxicological assays based on in vitro cellular models have undergone an evolution from experimentation using nanoparticulate systems on singular epithelial cell models to employing advanced complex models more realistically mimicking the respective body barriers for analyzing their capacity to alter the immune state of exposed individuals. During this phase, a number of lessons were learned. We have thus arrived at a state where the next chapters have to be opened, pursuing the following objectives: (1) to elucidate underlying mechanisms, (2) to address effects on vulnerable groups, (3) to test material mixtures, and (4) to use realistic doses on (5) sophisticated models. Moreover, data reproducibility has become a significant demand. In this context, we studied the emerging concept of adverse outcome pathways (AOPs) from the perspective of immune activation and modulation resulting in pro-inflammatory versus tolerogenic responses. When considering the interaction of nanomaterials with biological systems, protein corona formation represents the relevant molecular initiating event (e.g., by potential alterations of nanomaterial-adsorbed proteins). Using this as an example, we illustrate how integrated experimental-computational workflows combining in vitro assays with in silico models aid in data enrichment and upon comprehensive ontology-annotated (meta)data upload to online repositories assure FAIRness (Findability, Accessibility, Interoperability, Reusability). Such digital twinning may, in future, assist in early-stage decision-making during therapeutic development, and hence, promote safe-by-design innovation in nanomedicine. Moreover, it may, in combination with in silico-based exposure-relevant dose-finding, serve for risk monitoring in particularly loaded areas, for example, workplaces, taking into account pre-existing health conditions. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials.

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.

Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions

The production of nanomaterials continues its rapid growth; however, newly manufactured nanomaterials' environmental and health safety are among the most significant concerns. A safety assessment is usually a lengthy and costly process, so computational studies are often used to complement experimental testing. One of the most time-efficient techniques is structure-activity relationships (SAR) modeling. In this project, we analyzed the Sustainable Nanotechnology (S2NANO) dataset that contains 574 experimental cell viability and toxicity datapoints for Al₂O₃, CuO, Fe₂O₃, Fe₃O₄, SiO₂, TiO₂, and ZnO measured in different conditions. We aimed to develop classification- and regression-based structure-activity relationship models using quasi-SMILES molecular representation. Introduced quasi-SMILES took into consideration all available information, including structural features of nanoparticles (molecular structure, core size, etc.) and related experimental parameters (cell line, dose, exposure time, assay, hydrodynamic size, surface charge, etc.). Resultant regression models demonstrated sufficient predictive power, while classification models demonstrated higher accuracy.

Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling

Zeta potential is usually measured to estimate the surface charge and the stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for a pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potential measurements in the context of environmental impact is an important challenge. The inconsistency of data measurements leads to inaccuracies in predictive modeling. In this article, we report a new curated dataset of zeta potentials measured for 208 silica- and metal oxide nanoparticles in different media. We discuss the data curation framework for zeta potentials designed to assess the quality and usefulness of the literature data for further computational modeling. We also provide an analysis of specific trends for the datapoints harvested from different literature sources. In addition to that, we present for the first time a structure-property relationship model for nanoparticles (nano-SPR) that predicts values of zeta potential values measured in different environmental conditions (i.e., biological media and pH).

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.

Biomedical nanomaterials: applications, toxicological concerns, and regulatory needs

Advances in cutting-edge technologies such as nano- and biotechnology have created an opportunity for re-engineering existing materials and generating new nano-scale products that can function beyond the limits of conventional ones. While the step change in the properties and functionalities of these new materials opens up new possibilities for a broad range of applications, it also calls for structural modifications to existing safety assessment processes that are primarily focused on bulk material properties. Decades after the need to modify existing risk management practices to include nano-specific behaviors and exposure pathways was recognized, relevant policies for evaluating, and controlling health risks of nano-enabled materials is still lacking. This review provides an overview of current progress in the field of nanobiotechnology rather than intentions and aspirations, summarizes long-recognized but still unresolved issues surrounding materials safety at the nanoscale, and discusses key barriers preventing generation and integration of reliable data in bio/nano-safety domain. Particular attention is given to nanostructured materials that are commonly used in biomedical applications.

Risk Management Framework for Nano-Biomaterials Used in Medical Devices and Advanced Therapy Medicinal Products

The convergence of nanotechnology and biotechnology has led to substantial advancements in nano-biomaterials (NBMs) used in medical devices (MD) and advanced therapy medicinal products (ATMP). However, there are concerns that applications of NBMs for medical diagnostics, therapeutics and regenerative medicine could also pose health and/or environmental risks since the current understanding of their safety is incomplete. A scientific strategy is therefore needed to assess all risks emerging along the life cycles of these products. To address this need, an overarching risk management framework (RMF) for NBMs used in MD and ATMP is presented in this paper, as a result of a collaborative effort of a team of experts within the EU Project BIORIMA and with relevant inputs from external stakeholders. The framework, in line with current regulatory requirements, is designed according to state-of-the-art approaches to risk assessment and management of both nanomaterials and biomaterials. The collection/generation of data for NBMs safety assessment is based on innovative integrated approaches to testing and assessment (IATA). The framework can support stakeholders (e.g., manufacturers, regulators, consultants) in systematically assessing not only patient safety but also occupational (including healthcare workers) and environmental risks along the life cycle of MD and ATMP. The outputs of the framework enable the user to identify suitable safe(r)-by-design alternatives and/or risk management measures and to compare the risks of NBMs to their (clinical) benefits, based on efficacy, quality and cost criteria, in order to inform robust risk management decision-making.

Microalgal ecotoxicity of nanoparticles: An updated review

Nowadays, nanotechnology and its related industries are becoming a rapidly explosive industry that offers many benefits to human life. However, along with the increased production and use of nanoparticles (NPs), their presence in the environment creates a high risk of increasing toxic effects on aquatic organisms. Therefore, a large number of studies focusing on the toxicity of these NPs to the aquatic organisms are carried out which used algal species as a common biological model. In this review, the influences of the physio-chemical properties of NPs and the response mechanisms of the algae on the toxicity of the NPs were discussed focusing on the "assay" studies. Besides, the specific algal toxicities of each type of NPs along with the NP-induced changes in algal cells of these NPs are also assessed. Almost all commonly-used NPs exhibit algal toxicity. Although the algae have similarities in the symptoms under NP exposure, the sensitivity and variability of each algae species to the inherent properties of each NPs are quite different. They depend strongly on the concentration, size, characteristics of NPs, and biochemical nature of algae. Through the assessment, the review identifies several gaps that need to be further studied to make an explicit understanding. The findings in the majority of studies are mostly in laboratory conditions and there are still uncertainties and contradictory/inconsistent results about the behavioral effects of NPs under field conditions. Besides, there remains unsureness about NP-uptake pathways of microalgae. Finally, the toxicity mechanisms of NPs need to be thoughtfully understood which is essential in risk assessment.

Understanding Nanomaterial Biotransformation: An Unmet Challenge to Achieving Predictive Nanotoxicology

More than a decade has passed since the first concepts of predictive nanotoxicology were formulated. During this time, many advancements have been achieved in multiple disciplines, including the success stories of the fiber paradigm and the oxidative stress paradigm. However, important knowledge gaps are slowing down the development of predictive nanotoxicology and require a mutidisciplinary effort to be overcome. Among these gaps, understanding, reproducing, and modeling of nanomaterial biotransformation in biological environments is a central challenge, both in vitro and in silico. This dynamic and complex process is still a challenge for today's bioanalytics. This work explores and discusses selected approaches of the multidisciplinary efforts taken in the last decade and the challenges that remain unmet, in particular concerning nanomaterial biotransformation. It highlights some future advancements that, together, can help to understand such complex processes and accelerate the development of predictive nanotoxicology.

Nanoinformatics and biomolecular nanomodeling: a novel move en route for effective cancer treatment

Empowering role of nanoinformatics in design and elucidation of nanoparticles for effective cancer treatment has made this field a fascinating area for researchers, inspiring them to enhance up the quality and efficacy of existing anticancer medicines. Theoretical and computational modeling is being seen as a forefront solution for problems related to surface chemistry, optimized geometry, or other properties in nanoparticle designing and drug delivery. The current review aims to acquaint with the insight story of the incubation of in silico tools and techniques in nanotechnology to develop better anticancer nanomedicines. The review also recapitulates the assets and liabilities of this field and present an outline of existing inventiveness and endeavors of nanoinformatics. We propose how nanoinformatics could hasten up the advancements in anticancer nanomedicines through use of computational tools, nanoparticles repositories & various modeling and simulation methods.

Investigating Novel Syntheses of a Series of Unique Hybrid PLGA-Chitosan Polymers for Potential Therapeutic Delivery Applications

Discovering new materials to aid in the therapeutic delivery of drugs is in high demand. PLGA, a FDA approved polymer, is well known in the literature to form films or nanoparticles that can load, protect, and deliver drug molecules; however, its incompatibility with certain drugs (due to hydrophilicity or charge repulsion interactions) limits its use. Combining PLGA or other polymers such as polycaprolactone with other safe and positively-charged molecules, such as chitosan, has been sought after to make hybrid systems that are more flexible in terms of loading ability, but often the reactions for polymer coupling use harsh conditions, films, unpurified products, or create a single unoptimized product. In this work, we aimed to investigate possible innovative improvements regarding two synthetic procedures. Two methods were attempted and analytically compared using nuclear magnetic resonance (NMR), fourier-transform infrared spectroscopy (FT-IR), and dynamic scanning calorimetry (DSC) to furnish pure, homogenous, and tunable PLGA-chitosan hybrid polymers. These were fully characterized by analytical methods. A series of hybrids was produced that could be used to increase the suitability of PLGA with previously non-compatible drug molecules.

Analysis of model PM2.5-induced inflammation and cytotoxicity by the combination of a virtual carbon nanoparticle library and computational modeling

Health risks induced by PM2.5 have become one of the major concerns among living populations, especially in regions facing serious pollution such as China and India. Furthermore, the composition of PM2.5 is complex and it also varies with time and locations. To facilitate our understanding of PM2.5-induced toxicity, a predictive modeling framework was developed in the present study. The core of this study was 1) to construct a virtual carbon nanoparticle library based on the experimental data to simulate the PM2.5 structures; 2) to quantify the nanoparticle structures by novel nanodescriptors; and 3) to perform computational modeling for critical toxicity endpoints. The virtual carbon nanoparticle library was developed to represent the nanostructures of 20 carbon nanoparticles, which were synthesized to simulate PM2.5 structures and tested for potential health risks. Based on the calculated nanodescriptors from virtual carbon nanoparticles, quantitative nanostructure-activity relationship (QNAR) models were developed to predict cytotoxicity and four different inflammatory responses induced by model PM2.5. The high predictability (R² > 0.65 for leave-one-out validations) of the resulted consensus models indicated that this approach could be a universal tool to predict and analyze the potential toxicity of model PM2.5, ultimately understanding and evaluating the ambient PM2.5-induced toxicity.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

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.

Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints

BACKGROUND

Quantitative and qualitative structure–activity relationships (QSARs) have been used to understand chemical behavior for almost a century. The main source of QSAR models is the scientific literature, but the open question is how well these models are documented.

OBJECTIVES

The main aim of this study was to critically analyze the publication practices of QSARs with regard to transparency, potential reproducibility, and independent verification. The focus was on the level of technical completeness of the published QSARs.

METHODS

A total of 1,533 QSAR articles reporting 79 individual endpoints, mostly in environmental and health science, were reviewed. The QSAR parameters required for technical completeness were grouped into five categories: chemical structures, experimental endpoint values, descriptor values, mathematical representation of the model, and predicted endpoint values. The data were summarized and discussed using Circos plots.

RESULTS

Altogether, 42.5% of the reviewed articles were found to be potentially reproducible. The potential reproducibility for different endpoint groups varied; the respective rates were 39% for physical and chemical properties, 52% for ecotoxicity, 56% for environmental fate, 30% for human health, and 32% for toxicokinetics. The reproducibility of QSARs is discussed and placed in the context of the reproducibility of the experimental methods. Included are 65 references to open QSAR datasets as examples of models restored from scientific articles.

DISCUSSION

Strikingly poor documentation of QSARs was observed, which reduces the transparency, availability, and consequently, the application of research results in scientific, industrial, and regulatory areas. A list of the components needed to ensure the best practices for QSAR reporting is provided, allowing long-term use and preservation of the models. This list also allows an assessment of the reproducibility of models by interested parties such as journal editors, reviewers, regulators, evaluators, and potential users. https://doi.org/10.1289/EHP3264.

A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints

The increasing use of nanoparticles (NPs) in a wide range of consumer and industrial applications has necessitated significant effort to address the challenge of characterizing and quantifying the underlying nanostructure - biological response relationships to ensure that these novel materials can be exploited responsibly and safely. Such efforts demand reliable experimental data not only in terms of the biological dose-response, but also regarding the physicochemical properties of the NPs and their interaction with the biological environment. The latter has not been extensively studied, as a large surface to bind biological macromolecules is a unique feature of NPs that is not relevant for chemicals or pharmaceuticals, and thus only limited data have been reported in the literature quantifying the protein corona formed when NPs interact with a biological medium and linking this with NP cellular association/uptake. In this work we report the development of a predictive model for the assessment of the biological response (cellular association, which can include both internalized NPs and those attached to the cell surface) of surface-modified gold NPs, based on their physicochemical properties and protein corona fingerprints, utilizing a dataset of 105 unique NPs. Cellular association was chosen as the end-point for the original experimental study due to its relevance to inflammatory responses, biodistribution, and toxicity in vivo. The validated predictive model is freely available online through the Enalos Cloud Platform ( http://enalos.insilicotox.com/NanoProteinCorona/ ) to be used as part of a regulatory or NP safe-by-design decision support system. This online tool will allow the virtual screening of NPs, based on a list of the significant NP descriptors, identifying those NPs that would warrant further toxicity testing on the basis of predicted NP cellular association.

Towards the Development of Global Nano-Quantitative Structure-Property Relationship Models: Zeta Potentials of Metal Oxide Nanoparticles

Zeta potential indirectly reflects a charge of the surface of nanoparticles in solutions and could be used to represent the stability of the colloidal solution. As processes of synthesis, testing and evaluation of new nanomaterials are expensive and time-consuming, so it would be helpful to estimate an approximate range of properties for untested nanomaterials using computational modeling. We collected the largest dataset of zeta potential measurements of bare metal oxide nanoparticles in water (87 data points). The dataset was used to develop quantitative structure–property relationship (QSPR) models. Essential features of nanoparticles were represented using a modified simplified molecular input line entry system (SMILES). SMILES strings reflected the size-dependent behavior of zeta potentials, as the considered quasi-SMILES modification included information about both chemical composition and the size of the nanoparticles. Three mathematical models were generated using the Monte Carlo method, and their statistical quality was evaluated (R² for the training set varied from 0.71 to 0.87; for the validation set, from 0.67 to 0.82; root mean square errors for both training and validation sets ranged from 11.3 to 17.2 mV). The developed models were analyzed and linked to aggregation effects in aqueous solutions.

How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach

Application of predictive modeling approaches can solve the problem of missing data. Numerous studies have investigated the effects of missing values on qualitative or quantitative modeling, but only a few studies have discussed it for the case of applications in nanotechnology-related data. The present study is aimed at the development of a multi-nano-read-across modeling technique that helps in predicting the toxicity of different species such as bacteria, algae, protozoa, and mammalian cell lines. Herein, the experimental toxicity of 184 metal and silica oxide (30 unique chemical types) nanoparticles from 15 datasets is analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis is developed. In the first step, hidden patterns of toxicity among nanoparticles are identified using a combination of methods. Subsequently, the developed model based on categorization of the toxicity of the metal oxide nanoparticle outcomes is evaluated via the combination of supervised and unsupervised machine learning techniques to determine the underlying factors responsible for the toxicity.

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.

Evaluating the toxicity of TiO2-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach

Titania-supported palladium, gold and bimetallic nanoparticles (second-generation nanoparticles) demonstrate promising photocatalytic properties. However, due to unusual reactivity, second-generation nanoparticles can be hazardous for living organisms. Considering the ever-growing number of new types of nanoparticles that can potentially contaminate the environment, a determination of their toxicity is extremely important. The main aim of presented study was to investigate the cytotoxic effect of surface modified TiO₂-based nanoparticles, to model their quantitative nanostructure-toxicity relationships and to reveal the toxicity mechanism. In this context, toxicity tests for surface-modified TiO₂-based nanoparticles were performed in vitro, using Gram-negative bacteria Escherichia coli and Chinese hamster ovary (CHO-K1) cells. The obtained cytotoxicity data were analyzed by means of computational methods (quantitative structure-activity relationships, QSAR approach). Based on a combined experimental and computational approach, predictive models were developed, and relationships between cytotoxicity, size, and specific surface area (Brunauer-Emmett-Teller surface, BET) of nanoparticles were discussed.

Modeling of Interactions between the Zebrafish Hatching Enzyme ZHE1 and A Series of Metal Oxide Nanoparticles: Nano-QSAR and Causal Analysis of Inactivation Mechanisms

The quantitative relationships between the activity of zebrafish ZHE1 enzyme and a series of experimental and physicochemical features of 24 metal oxide nanoparticles were revealed. Vital characteristics of the nanoparticles’ structure were reflected using both experimental and theoretical descriptors. The developed quantitative structure–activity relationship model for nanoparticles (nano-QSAR) was capable of predicting the enzyme inactivation based on four descriptors: the hydrodynamic radius, mass density, the Wigner–Seitz radius, and the covalent index. The nano-QSAR model was calculated using the non-linear regression tree M5P algorithm. The developed model is characterized by high robustness R²_bagging = 0.90 and external predictivity Q²_EXT = 0.93. This model is in agreement with modern theories of aquatic toxicity. Dissolution and size-dependent characteristics are among the key driving forces for enzyme inactivation. It was proven that ZnO, CuO, Cr₂O₃, and NiO nanoparticles demonstrated strong inhibitory effects because of their solubility. The proposed approach could be used as a non-experimental alternative to animal testing. Additionally, methods of causal discovery were applied to shed light on the mechanisms and modes of action.

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.

What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps

Over the past decade, computational nanotoxicology, in particular Quantitative Structure-Activity Relationship models (Nano-QSAR) that help in assessing the biological effects of nanomaterials, have received much attention. In effect, a solid basis for uncovering the relationships between the structure and property/activity of nanoparticles has been created. Nonetheless, six years after the first pioneering computational studies focusing on the investigation of nanotoxicity were commenced, these computational methods still suffer from many limitations. These are mainly related to the paucity of widely available, systematically varied, libraries of experimental data necessary for the development and validation of such models. This results in the still-low acceptance of these methods as valuable research tools for nanosafety and raises the query as to whether these methods could gain wide acceptance of regulatory bodies as alternatives for traditional in vitro methods. This study aimed to give an answer to the following question: How to remedy the paucity of experimental nanotoxicity data and thereby, overcome key roadblock that hinders the development of approaches for data-driven modeling of nanoparticle properties and toxicities? Here, a simple and transparent read-across algorithm for a pre-screening hazard assessment of nanomaterials that provides reasonably accurate results by making the best use of existing limited set of observations will be introduced.

Experimental-Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov-Shannon Entropy Invariants

The study of selective toxicity of carbon nanotubes (CNTs) on mitochondria (CNT-mitotoxicity) is of major interest for future biomedical applications. In the current work, the mitochondrial oxygen consumption (E3) is measured under three experimental conditions by exposure to pristine and oxidized CNTs (hydroxylated and carboxylated). Respiratory functional assays showed that the information on the CNT Raman spectroscopy could be useful to predict structural parameters of mitotoxicity induced by CNTs. The in vitro functional assays show that the mitochondrial oxidative phosphorylation by ATP-synthase (or state V3 of respiration) was not perturbed in isolated rat-liver mitochondria. For the first time a star graph (SG) transform of the CNT Raman spectra is proposed in order to obtain the raw information for a nano-QSPR model. Box-Jenkins and perturbation theory operators are used for the SG Shannon entropies. A modified RRegrs methodology is employed to test four regression methods such as multiple linear regression (LM), partial least squares regression (PLS), neural networks regression (NN), and random forest (RF). RF provides the best models to predict the mitochondrial oxygen consumption in the presence of specific CNTs with R² of 0.998-0.999 and RMSE of 0.0068-0.0133 (training and test subsets). This work is aimed at demonstrating that the SG transform of Raman spectra is useful to encode CNT information, similarly to the SG transform of the blood proteome spectra in cancer or electroencephalograms in epilepsy and also as a prospective chemoinformatics tool for nanorisk assessment. All data files and R object models are available at https://dx.doi.org/10.6084/m9.figshare.3472349 .

What can nanosafety learn from drug development? The feasibility of "safety by design"

"Safety by design" (SbD) is an intuitively appealing concept that is on the rise within nanotoxicology and nanosafety research, as well as within nanotechnology research policy. It leans on principles established within drug discovery and development (DDD) and seeks to address safety early, as well as throughout product development. However, it remains unclear what the concept of SbD exactly entails for engineered nanomaterials (ENMs) or how it is envisioned to be implemented. Here, we review the concept as it is emerging in European research and compare its resemblance with the safety testing and assessment practices in DDD. From this comparison, it is clear that "safety" is not obtained through DDD, and that SbD should be considered a starting point rather than an end, meaning that products will still need to progress through thorough safety evaluations and regulation. We conclude that although risk reduction is clearly desirable, the way SbD is currently communicated tends to treat safety as an inherent material property and that this is fundamentally problematic as it represents a recasting and reduction of societal issues into technical problems. SbD therefore faces a multitude of challenges, from practical implementation to unrealistic stakeholder expectations.

Aquatic Ecotoxicity Testing of Nanoparticles-The Quest To Disclose Nanoparticle Effects

The number of products on the market containing engineered nanoparticles (ENPs) has increased significantly, and concerns have been raised regarding their ecotoxicological effects. Environmental safety assessments as well as relevant and reliable ecotoxicological data are required for the safe and sustainable use of ENPs. Although the number of publications on the ecotoxicological effects and uptake of ENPs is rapidly expanding, the applicability of the reported data for hazard assessment is questionable. A major knowledge gap is whether nanoparticle effects occur when test organisms are exposed to ENPs in aquatic test systems. Filling this gap is not straightforward, because of the broad range of ENPs and the different behavior of ENPs compared to "ordinary" (dissolved) chemicals in the ecotoxicity test systems. The risk of generating false negatives, and false positives, in the currently used tests is high, and in most cases difficult to assess. This Review outlines some of the pitfalls in the aquatic toxicity testing of ENPs which may lead to misinterpretation of test results. Response types are also proposed to reveal potential nanoparticle effects in the aquatic test organisms.

Unmet needs: Research helps regulators do their jobs

A plethora of innovative new medical products along with the need to apply modern technologies to medical-product evaluation has spurred seminal opportunities in regulatory sciences. Here, we provide eight examples of regulatory science research for diverse products. Opportunities abound, particularly in data science and precision health.

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.

Approaches to Develop Alternative Testing Strategies to Inform Human Health Risk Assessment of Nanomaterials

The development of alternative testing strategies (ATS) for hazard assessment of new and emerging materials is high on the agenda of scientists, funders, and regulators. The relatively large number of nanomaterials on the market and under development means that an increasing emphasis will be placed on the use of reliable, predictive ATS when assessing their safety. We have provided recommendations as to how ATS development for assessment of nanomaterial hazard may be accelerated. Predefined search terms were used to identify the quantity and distribution of peer-reviewed publications for nanomaterial hazard assessment following inhalation, ingestion, or dermal absorption. A summary of knowledge gaps relating to nanomaterial hazard is provided to identify future research priorities and areas in which a rich data set might exist to allow ATS identification. Consultation with stakeholders (e.g., academia, industry, regulators) was critical to ensure that current expert opinion was reflected. The gap analysis revealed an abundance of studies that assessed the local and systemic impacts of inhaled particles, and so ATS are available for immediate use. Development of ATS for assessment of the dermal toxicity of chemicals is already relatively advanced, and these models should be applied to nanomaterials as relatively few studies have assessed the dermal toxicity of nanomaterials to date. Limited studies have investigated the local and systemic impacts of ingested nanomaterials. If the recommendations for research prioritization proposed are adopted, it is envisioned that a comprehensive battery of ATS can be developed to support the risk assessment process for nanomaterials. Some alternative models are available for immediate implementation, while others require more developmental work to become widely adopted. Case studies are included that can be used to inform the selection of alternative models and end points when assessing the pathogenicity of fibers and mode of action of nanomaterial toxicity.

A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles

This article presents a comprehensive review of applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles of medical interest. The papers reviewed here present the results of research using these techniques to predict the biological fate and properties of a variety of nanoparticles relevant to their biomedical applications. These include the influence of particle physicochemical properties on cellular uptake, cytotoxicity, molecular loading, and molecular release in addition to manufacturing properties like nanoparticle size, and polydispersity. Overall, the results are encouraging and suggest that as more systematic data from nanoparticles becomes available, machine learning and data mining would become a powerful aid in the design of nanoparticles for biomedical applications. There is however the challenge of great heterogeneity in nanoparticles, which will make these discoveries more challenging than for traditional small molecule drug design.

Development of nanostructure–activity relationships assisting the nanomaterial hazard categorization for risk assessment and regulatory decision-making

Developed nano-SARs based on the state-of-art of ecotoxicity testing of metallic nanomaterials.

The MARINA Risk Assessment Strategy: A Flexible Strategy for Efficient Information Collection and Risk Assessment of Nanomaterials

An engineered nanomaterial (ENM) may actually consist of a population of primary particles, aggregates and agglomerates of various sizes. Furthermore, their physico-chemical characteristics may change during the various life-cycle stages. It will probably not be feasible to test all varieties of all ENMs for possible health and environmental risks. There is therefore a need to further develop the approaches for risk assessment of ENMs. Within the EU FP7 project Managing Risks of Nanoparticles (MARINA) a two-phase risk assessment strategy has been developed. In Phase 1 (Problem framing) a base set of information is considered, relevant exposure scenarios (RESs) are identified and the scope for Phase 2 (Risk assessment) is established. The relevance of an RES is indicated by information on exposure, fate/kinetics and/or hazard; these three domains are included as separate pillars that contain specific tools. Phase 2 consists of an iterative process of risk characterization, identification of data needs and integrated collection and evaluation of data on the three domains, until sufficient information is obtained to conclude on possible risks in a RES. Only data are generated that are considered to be needed for the purpose of risk assessment. A fourth pillar, risk characterization, is defined and it contains risk assessment tools. This strategy describes a flexible and efficient approach for data collection and risk assessment which is essential to ensure safety of ENMs. Further developments are needed to provide guidance and make the MARINA Risk Assessment Strategy operational. Case studies will be needed to refine the strategy.

Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms

Metal oxide nanomaterials are widely used in various areas; however, the divergent published toxicology data makes it difficult to determine whether there is a risk associated with exposure to metal oxide nanomaterials. The application of quantitative structure activity relationship (QSAR) modeling in metal oxide nanomaterials toxicity studies can reduce the need for time-consuming and resource-intensive nanotoxicity tests. The nanostructure and inorganic composition of metal oxide nanomaterials makes this approach different from classical QSAR study; this review lists and classifies some structural descriptors, such as size, cation charge, and band gap energy, in recent metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) studies and discusses the mechanism of metal oxide nanomaterials toxicity based on these descriptors and traditional nanotoxicity tests.

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.