Role of Artificial Intelligence and Machine Learning in Nanosafety

Design rules applied to silver nanoparticles synthesis: A practical example of machine learning application

The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In this study, we manually extracted synthesis-related data from the literature and leveraged various machine learning algorithms. Data extraction included parameters such as reactant concentrations, experimental conditions, as well as physicochemical properties. The antibacterial efficiencies and toxicological profiles of the synthesized nanoparticles were also extracted. In a second step, based on data completeness, we employed regression algorithms to establish relationships between synthesis parameters and desired endpoints and to build predictive models. The models for core size and antibacterial efficiency were trained and validated using a cross-validation approach. Finally, the features' impact was evaluated via Shapley values to provide insights into the contribution of features to the predictions. Factors such as synthesis duration, scale of synthesis and the choice of capping agents emerged as the most significant predictors. This study demonstrated the potential of machine learning to aid in the rational design of synthesis process and paves the way for the safe-by-design principles development by providing insights into the optimization of the synthesis process to achieve the desired properties. Finally, this study provides a valuable dataset compiled from literature sources with significant time and effort from multiple researchers. Access to such datasets notably aids computational advances in the field of nanotechnology.

Advancements in nanomedicine: Precision delivery strategies for male pelvic malignancies - Spotlight on prostate and colorectal cancer

BACKGROUND

Pelvic malignancies consistently pose significant global health challenges, adversely affecting the well-being of the male population. It is anticipated that clinicians will continue to confront these cancers in their practice. Nanomedicine offers promising strategies that revolutionize the treatment of male pelvic malignancies by providing precise delivery methods that aim to improve the efficacy of therapeutic outcomes while minimizing side effects. Nanoparticles are designed to encapsulate therapeutic agents and selectively target cancer cells. They can also be loaded with theragnostic agents, enabling multifunctional capabilities.

OBJECTIVE

This review aims to summarize the latest nanomedicine research into clinical applications, focusing on nanotechnology-based treatment strategies for male pelvic malignancies, encompassing chemotherapy, radiotherapy, immunotherapy, and other cutting-edge therapies. The review is structured to assist physicians, particularly those with limited knowledge of biochemistry and bioengineering, in comprehending the functionalities and applications of nanomaterials.

METHODS

Multiple databases, including PubMed, the National Library of Medicine, and Embase, were utilized to locate and review recently published articles on advancements in nano-drug delivery for prostate and colorectal cancers.

CONCLUSION

Nanomedicine possesses considerable potential in improving therapeutic outcomes and reducing adverse effects for male pelvic malignancies. Through precision delivery methods, this emerging field presents innovative treatment modalities to address these challenging diseases. Nevertheless, the majority of current studies are in the preclinical phase, with a lack of sufficient evidence to fully understand the precise mechanisms of action, absence of comprehensive pharmacotoxicity profiles, and uncertainty surrounding long-term consequences.

Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size

A strong need exists for broadly applicable nano-QSARs, capable of predicting toxicological outcomes towards untested species and nanomaterials, under different environmental conditions. Existing nano-QSARs are generally limited to only a few species but the inclusion of species characteristics into models can aid in making them applicable to multiple species, even when toxicity data is not available for biological species. Species traits were used to create classification- and regression machine learning models to predict acute toxicity towards aquatic species for metallic nanomaterials. Afterwards, the individual classification- and regression models were stacked into a meta-model to improve performance. Additionally, the uncertainty and limitations of the models were assessed in detail (beyond the OECD principles) and it was investigated whether models would benefit from the addition of more data. Results showed a significant improvement in model performance following model stacking. Investigation of model uncertainties and limitations highlighted the discrepancy between the applicability domain and accuracy of predictions. Data points outside of the assessed chemical space did not have higher likelihoods of generating inadequate predictions or vice versa. It is therefore concluded that the applicability domain does not give complete insight into the uncertainty of predictions and instead the generation of prediction intervals can help in this regard. Furthermore, results indicated that an increase of the dataset size did not improve model performance. This implies that larger dataset sizes may not necessarily improve model performance while in turn also meaning that large datasets are not necessarily required for prediction of acute toxicity with nano-QSARs.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Perspectives on Protein-Nanoparticle Interactions at the In Vivo Level

The distinct features of nanoparticles have provided a vast opportunity of developing new diagnosis and therapy strategies for miscellaneous diseases. Although a few nanomedicines are available in the market or in the translation stage, many important issues are still unsolved. When entering the body, nanomaterials will be quickly coated by proteins from their surroundings, forming a corona on their surface, the so-called protein corona. Studies have shown that the protein corona has many important biological implications, particularly at the in vivo level. For example, they can promote the immune system to rapidly clear these outer materials and prevent nanoparticles from playing their designed role in therapy. In this Perspective, the available techniques for characterizing protein-nanoparticle interactions are critically summarized. Effects of nanoparticle properties and environmental factors on protein corona formation, which can further regulate the in vivo fate of nanoparticles, are highlighted and discussed. Moreover, recent progress on the biomedical application of protein corona-engineered nanoparticles is introduced, and future directions for this important yet challenging research area are also briefly discussed.

Decoding Nanomaterial-Biosystem Interactions through Machine Learning

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

Does the surface charge of the nanoparticles drive nanoparticle-cell membrane interactions?

Classical Coulombic interaction, characterized by electrostatic interactions mediated through surface charges, is often regarded as the primary determinant in nanoparticles' (NPs) cellular association and internalization. However, the intricate physicochemical properties of particle surfaces, biomolecular coronas, and cell surfaces defy this oversimplified perspective. Moreover, the nanometrological techniques employed to characterize NPs in complex physiological fluids often exhibit limited accuracy and reproducibility. A more comprehensive understanding of nanoparticle-cell membrane interactions, extending beyond attractive forces between oppositely charged surfaces, necessitates the establishment of databases through rigorous physical, chemical, and biological characterization supported by nanoscale analytics. Additionally, computational approaches, such as in silico modeling and machine learning, play a crucial role in unraveling the complexities of these interactions.

Step-By-Step Standardization of the Bottom-Up Semi-Automated Nanocrystallization of Pharmaceuticals: A Quality By Design and Design of Experiments Joint Approach

Nanosized drug crystals have been reported with enhanced apparent solubility, bioavailability, and therapeutic efficacy compared to microcrystal materials, which are not suitable for parenteral administration. However, nanocrystal design and development by bottom-up approaches are challenging, especially considering the non-standardized process parameters in the injection step. This work aims to present a systematic step-by-step approach through Quality-by-Design (QbD) and Design of Experiments (DoE) for synthesizing drug nanocrystals by a semi-automated nanoprecipitation method. Curcumin is used as a drug model due to its well-known poor water solubility (0.6 µg mL-1 , 25 °C). Formal and informal risk assessment tools allow identifying the critical factors. A fractional factorial 24-1 screening design evaluates their impact on the average size and polydispersity of nanocrystals. The optimization of significant factors is done by a Central Composite Design. This response surface methodology supports the rational design of the nanocrystals, identifying and exploring the design space. The proposed joint approach leads to a reproducible, robust, and stable nanocrystalline preparation of 316 nm with a PdI of 0.217 in compliance with the quality profile. An orthogonal approach for particle size and polydispersity characterization allows discarding the formation of aggregates. Overall, the synergy between advanced data analysis and semi-automated standardized nanocrystallization of drugs is highlighted.

Revealing Academic Evolution and Frontier Pattern in the Field of Uveitis Using Bibliometric Analysis, Natural Language Processing, and Machine Learning

PURPOSE

Numerous uveitis articles were published in this century, underneath which hides valuable intelligence. We aimed to characterize the evolution and patterns in this field.

METHODS

We divided the 15,994 uveitis papers into four consecutive time periods for bibliometric analysis, and applied latent Dirichlet allocation topic modeling and machine learning techniques to the latest period. .

RESULTS

The yearly publication pattern fitted the curve: 1.21335x2 - 4,848.95282x + 4,844,935.58876 (R2 = 0.98311). The USA, the most productive country/region, focused on topics like ankylosing spondylitis and biologic therapy, whereas China (mainland) focused on topics like OCT and Behcet disease. The logistic regression showed the highest accuracy (71.6%) in the test set.

CONCLUSION

In this century, a growing number of countries/regions/authors/journals are involved in the uveitis study, promoting the scientific output and thematic evolution. Our pioneering study uncovers the evolving academic trends and frontier patterns in this field using bibliometric analysis and AI algorithms.

Averaging Strategy for Interpretable Machine Learning on Small Datasets to Understand Element Uptake after Seed Nanotreatment

Understanding plant uptake and translocation of nanomaterials is crucial for ensuring the successful and sustainable applications of seed nanotreatment. Here, we collect a dataset with 280 instances from experiments for predicting the relative metal/metalloid concentration (RMC) in maize seedlings after seed priming by various metal and metalloid oxide nanoparticles. To obtain unbiased predictions and explanations on small datasets, we present an averaging strategy and add a dimension for interpretable machine learning. The findings in post-hoc interpretations of sophisticated LightGBM models demonstrate that solubility is highly correlated with model performance. Surface area, concentration, zeta potential, and hydrodynamic diameter of nanoparticles and seedling part and relative weight of plants are dominant factors affecting RMC, and their effects and interactions are explained. Furthermore, self-interpretable models using the RuleFit algorithm are established to successfully predict RMC only based on six important features identified by post-hoc explanations. We then develop a visualization tool called RuleGrid to depict feature effects and interactions in numerous generated rules. Consistent parameter-RMC relationships are obtained by different methods. This study offers a promising interpretable data-driven approach to expand the knowledge of nanoparticle fate in plants and may profoundly contribute to the safety-by-design of nanomaterials in agricultural and environmental applications.

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.

Glyphosate Separating and Sensing for Precision Agriculture and Environmental Protection in the Era of Smart Materials

The present article critically and comprehensively reviews the most recent reports on smart sensors for determining glyphosate (GLP), an active agent of GLP-based herbicides (GBHs) traditionally used in agriculture over the past decades. Commercialized in 1974, GBHs have now reached 350 million hectares of crops in over 140 countries with an annual turnover of 11 billion USD worldwide. However, rolling exploitation of GLP and GBHs in the last decades has led to environmental pollution, animal intoxication, bacterial resistance, and sustained occupational exposure of the herbicide of farm and companies' workers. Intoxication with these herbicides dysregulates the microbiome-gut-brain axis, cholinergic neurotransmission, and endocrine system, causing paralytic ileus, hyperkalemia, oliguria, pulmonary edema, and cardiogenic shock. Precision agriculture, i.e., an (information technology)-enhanced approach to crop management, including a site-specific determination of agrochemicals, derives from the benefits of smart materials (SMs), data science, and nanosensors. Those typically feature fluorescent molecularly imprinted polymers or immunochemical aptamer artificial receptors integrated with electrochemical transducers. Fabricated as portable or wearable lab-on-chips, smartphones, and soft robotics and connected with SM-based devices that provide machine learning algorithms and online databases, they integrate, process, analyze, and interpret massive amounts of spatiotemporal data in a user-friendly and decision-making manner. Exploited for the ultrasensitive determination of toxins, including GLP, they will become practical tools in farmlands and point-of-care testing. Expectedly, smart sensors can be used for personalized diagnostics, real-time water, food, soil, and air quality monitoring, site-specific herbicide management, and crop control.

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.

Ethical and legal challenges in nanomedical innovations: a scoping review

Background: Rapid advancements in research and development related to nanomedical technology raise various ethical and legal challenges in areas relevant to disease detection, diagnosis, and treatment. This study aims to outline the existing literature, covering issues associated with emerging nanomedicine and related clinical research, and identify implications for the responsible advancement and integration of nanomedicine and nanomedical technology throughout medical networks in the future. Methods: A scoping review, designed to cover scientific, ethical, and legal literature associated with nanomedical technology, was conducted, generating and analyzing 27 peer-reviewed articles published between 2007-2020. Results: Results indicate that articles referencing ethical and legal issues related to nanomedical technology were concerned with six key areas: 1) harm exposure and potential risks to health, 2) consent to nano-research, 3) privacy, 4) access to nanomedical technology and potential nanomedical therapies, 5) classification of nanomedical products in relation to the research and development of nanomedical technology, and 6) the precautionary principle as it relates to the research and development of nanomedical technology. Conclusion: This review of the literature suggests that few practical solutions are comprehensive enough to allay the ethical and legal concerns surrounding research and development in fields related to nanomedical technology, especially as it continues to evolve and contribute to future innovations in medicine. It is also clearly apparent that a more coordinated approach is required to ensure global standards of practice governing the study and development of nanomedical technology, especially as discussions surrounding the regulation of nanomedical research throughout the literature are mainly confined to systems of governance in the United States.

Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery

Research and development (R&D) of nanodrugs is a long, complex and uncertain process. Since the 1960s, computing has been used as an auxiliary tool in the field of drug discovery. Many cases have proven the practicability and efficiency of computing in drug discovery. Over the past decade, computing, especially model prediction and molecular simulation, has been gradually applied to nanodrug R&D, providing substantive solutions to many problems. Computing has made important contributions to promoting data-driven decision-making and reducing failure rates and time costs in discovery and development of nanodrugs. However, there are still a few articles to examine, and it is necessary to summarize the development of the research direction. In the review, we summarize application of computing in various stages of nanodrug R&D, including physicochemical properties and biological activities prediction, pharmacokinetics analysis, toxicological assessment and other related applications. Moreover, current challenges and future perspectives of the computing methods are also discussed, with a view to help computing become a high-practicability and -efficiency auxiliary tool in nanodrugs discovery and development.

Integrating structure annotation and machine learning approaches to develop graphene toxicity models

Modern nanotechnology provides efficient and cost-effective nanomaterials (NMs). The increasing usage of NMs arises great concerns regarding nanotoxicity in humans. Traditional animal testing of nanotoxicity is expensive and time-consuming. Modeling studies using machine learning (ML) approaches are promising alternatives to direct evaluation of nanotoxicity based on nanostructure features. However, NMs, including two-dimensional nanomaterials (2DNMs) such as graphenes, have complex structures making them difficult to annotate and quantify the nanostructures for modeling purposes. To address this issue, we constructed a virtual graphenes library using nanostructure annotation techniques. The irregular graphene structures were generated by modifying virtual nanosheets. The nanostructures were digitalized from the annotated graphenes. Based on the annotated nanostructures, geometrical nanodescriptors were computed using Delaunay tessellation approach for ML modeling. The partial least square regression (PLSR) models for the graphenes were built and validated using a leave-one-out cross-validation (LOOCV) procedure. The resulted models showed good predictivity in four toxicity-related endpoints with the coefficient of determination (R2) ranging from 0.558 to 0.822. This study provides a novel nanostructure annotation strategy that can be applied to generate high-quality nanodescriptors for ML model developments, which can be widely applied to nanoinformatics studies of graphenes and other NMs.

Construction of Covalent Organic Frameworks via Multicomponent Reactions

Multicomponent reactions (MCRs) combine at least three reactants to afford the desired product in a highly atom-economic way and are therefore viewed as efficient one-pot combinatorial synthesis tools allowing one to significantly boost molecular complexity and diversity. Nowadays, MCRs are no longer confined to organic synthesis and have found applications in materials chemistry. In particular, MCRs can be used to prepare covalent organic frameworks (COFs), which are crystalline porous materials assembled from organic monomers and exhibit a broad range of properties and applications. This synthetic approach retains the advantages of small-molecule MCRs, not only strengthening the skeletal robustness of COFs, but also providing additional driving forces for their crystallization, and has been used to prepare a series of robust COFs with diverse applications. The present perspective article provides the general background for MCRs, discusses the types of MCRs employed for COF synthesis to date, and addresses the related critical challenges and future perspectives to inspire the MCR-based design of new robust COFs and promote further progress in this emerging field.

Advancing intestinal organoid technology to decipher nano-intestine interactions and treat intestinal disease

With research burgeoning in nanoscience and nanotechnology, there is an urgent need to develop new biological models that can simulate native structure, function, and genetic properties of tissues to evaluate the adverse or beneficial effects of nanomaterials on a host. Among the current biological models, three-dimensional (3D) organoids have developed as powerful tools in the study of nanomaterial-biology (nano-bio) interactions, since these models can overcome many of the limitations of cell and animal models. A deep understanding of organoid techniques will facilitate the development of more efficient nanomedicines and further the fields of tissue engineering and personalized medicine. Herein, we summarize the recent progress in intestinal organoids culture systems with a focus on our understanding of the nature and influencing factors of intestinal organoid growth. We also discuss biomimetic extracellular matrices (ECMs) coupled with nanotechnology. In particular, we analyze the application prospects for intestinal organoids in investigating nano-intestine interactions. By integrating nanotechnology and organoid technology, this recently developed model will fill the gaps left due to the deficiencies of traditional cell and animal models, thus accelerating both our understanding of intestine-related nanotoxicity and the development of nanomedicines.

Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity

Engineered nanomaterials (ENMs) are ubiquitous nowadays, finding their application in different fields of technology and various consumer products. Virtually any chemical can be manipulated at the nano-scale to display unique characteristics which makes them appealing over larger sized materials. As the production and development of ENMs have increased considerably over time, so too have concerns regarding their adverse effects and environmental impacts. It is unfeasible to assess the risks associated with every single ENM through in vivo or in vitro experiments. As an alternative, in silico methods can be employed to evaluate ENMs. To perform such an evaluation, we collected data from databases and literature to create classification models based on machine learning algorithms in accordance with the principles laid out by the OECD for the creation of QSARs. The aim was to investigate the performance of various machine learning algorithms towards predicting a well-defined in vivo toxicity endpoint (Daphnia magna immobilization) and also to identify which features are important drivers of D. magna in vivo nanotoxicity. Results indicated highly comparable model performance between all algorithms and predictive performance exceeding ∼0.7 for all evaluated metrics (e.g. accuracy, sensitivity, specificity, balanced accuracy, Matthews correlation coefficient, area under the receiver operator characteristic curve). The random forest, artificial neural network, and k-nearest neighbor models displayed the best performance but this was only marginally better compared to the other models. Furthermore, the variable importance analysis indicated that molecular descriptors and physicochemical properties were generally important within most models, while features related to the exposure conditions produced slightly conflicting results. Lastly, results also indicate that reliable and robust machine learning models can be generated for in vivo endpoints with smaller datasets.

Tumor Microenvironment-Based Stimuli-Responsive Nanoparticles for Controlled Release of Drugs in Cancer Therapy

With the development of nanomedicine technology, stimuli-responsive nanocarriers play an increasingly important role in antitumor therapy. Compared with the normal physiological environment, the tumor microenvironment (TME) possesses several unique properties, including acidity, high glutathione (GSH) concentration, hypoxia, over-expressed enzymes and excessive reactive oxygen species (ROS), which are closely related to the occurrence and development of tumors. However, on the other hand, these properties could also be harnessed for smart drug delivery systems to release drugs specifically in tumor tissues. Stimuli-responsive nanoparticles (srNPs) can maintain stability at physiological conditions, while they could be triggered rapidly to release drugs by specific stimuli to prolong blood circulation and enhance cancer cellular uptake, thus achieving excellent therapeutic performance and improved biosafety. This review focuses on the design of srNPs based on several stimuli in the TME for the delivery of antitumor drugs. In addition, the challenges and prospects for the development of srNPs are discussed, which can possibly inspire researchers to develop srNPs for clinical applications in the future.

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

Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.

Representing and describing nanomaterials in predictive nanoinformatics

Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.

Safety-by-design using forward and inverse multi-target machine learning

The economic and social future of nanotechnology depends on our ability and manufacture nanomaterials that avoid potential toxicity, by identifying them before they are made, used and released into the environment. Safety-by-design is a framework for including these issues at an early stage of the development process, but balancing multiple nanoparticle properties and selection criteria remains challenging. Based on a synthetic data set of over 19,000 possible sunscreen product specifications, we have used multi-target machine learning to predict the corresponding size, shape, concentration and polytype of titania nanoparticle additives. The study considers the optical properties responsible for the sun protection factor and product transparency, including the extinction coefficients for ultra violet and visible light, and the potential for toxicity due to the generation of reactive oxygen species from the photocatalytically active facets of both anatase and rutile nanoparticles, as a function of the size and shape. We predict a number of conventional forward structure/property and property/product relationships, but show that a direct structure/product relationship provides superior performance when predicting multiple properties or product specifications simultaneously. These models are then inverted, re-optimized and re-trained to provide focused, high performing inverse design models that do not require additional optimization, and are capable of identifying nanoparticle configurations outside of the training set. The ability to directly predict suitable nanoparticle structures that conform to prerequisite sun protection, transparently and potential toxicity thresholds represents a new approach to safety-by-design that can be applied to other products and materials where multiple design criteria must be met at the same time.

Emerging trends in the nanomedicine applications of functionalized magnetic nanoparticles as novel therapies for acute and chronic diseases

High-quality point-of-care is critical for timely decision of disease diagnosis and healthcare management. In this regard, biosensors have revolutionized the field of rapid testing and screening, however, are confounded by several technical challenges including material cost, half-life, stability, site-specific targeting, analytes specificity, and detection sensitivity that affect the overall diagnostic potential and therapeutic profile. Despite their advances in point-of-care testing, very few classical biosensors have proven effective and commercially viable in situations of healthcare emergency including the recent COVID-19 pandemic. To overcome these challenges functionalized magnetic nanoparticles (MNPs) have emerged as key players in advancing the biomedical and healthcare sector with promising applications during the ongoing healthcare crises. This critical review focus on understanding recent developments in theranostic applications of functionalized magnetic nanoparticles (MNPs). Given the profound global economic and health burden, we discuss the therapeutic impact of functionalized MNPs in acute and chronic diseases like small RNA therapeutics, vascular diseases, neurological disorders, and cancer, as well as for COVID-19 testing. Lastly, we culminate with a futuristic perspective on the scope of this field and provide an insight into the emerging opportunities whose impact is anticipated to disrupt the healthcare industry.

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

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.

Protein adsorption onto nanomaterials engineered for theranostic applications

The key role of biomolecule adsorption onto engineered nanomaterials for therapeutic and diagnostic purposes has been well recognized by the nanobiotechnology community, and our mechanistic understanding of nano-bio interactions has greatly advanced over the past decades. Attention has recently shifted to gaining active control of nano-bio interactions, so as to enhance the efficacy of nanomaterials in biomedical applications. In this review, we summarize progress in this field and outline directions for future development. First, we briefly review fundamental knowledge about the intricate interactions between proteins and nanomaterials, as unraveled by a large number of mechanistic studies. Then, we give a systematic overview of the ways that protein-nanomaterial interactions have been exploited in biomedical applications, including the control of protein adsorption for enhancing the targeting efficiency of nanomedicines, the design of specific protein adsorption layers on the surfaces of nanomaterials for use as drug carriers, and the development of novel nanoparticle array-based sensors based on nano-bio interactions. We will focus on particularly relevant and recent examples within these areas. Finally, we conclude this topical review with an outlook on future developments in this fascinating research field.

Key knowledge gaps for One Health approach to mitigate nanoplastic risks

There are increasing concerns over the threat of nanoplastics to environmental and human health. However, multidisciplinary barriers persist between the communities assessing the risks to environmental and human health. As a result, the hazards and risks of nanoplastics remain uncertain. Here, we identify key knowledge gaps by evaluating the exposure of nanoplastics in the environment, assessing their bio-nano interactions, and examining their potential risks to humans and the environment. We suggest considering nanoplastics a complex and dynamic mixture of polymers, additives, and contaminants, with interconnected risks to environmental and human health. We call for comprehensive integration of One Health approach to produce robust multidisciplinary evidence to nanoplastics threats at the planetary level. Although there are many challenges, this holistic approach incorporates the relevance of environmental exposure and multi-sectoral responses, which provide the opportunity to identify the risk mitigation strategies of nanoplastics to build resilient health systems.

Current understandings and clinical translation of nanomedicines for breast cancer therapy

Breast cancer is one of the most frequently diagnosed cancers that is threatening women's life. Current clinical treatment regimens for breast cancer often involve neoadjuvant and adjuvant systemic therapies, which somewhat are associated with unfavorable features. Also, the heterogeneous nature of breast cancers requires precision medicine that cannot be fulfilled by a single type of systemically administered drug. Taking advantage of the nanocarriers, nanomedicines emerge as promising therapeutic agents for breast cancer that could resolve the defects of drugs and achieve precise drug delivery to almost all sites of primary and metastatic breast tumors (e.g. tumor vasculature, tumor stroma components, breast cancer cells, and some immune cells). Seven nanomedicines as represented by Doxil® have been approved for breast cancer clinical treatment so far. More nanomedicines including both non-targeting and active targeting nanomedicines are being evaluated in the clinical trials. However, we have to realize that the translation of nanomedicines, particularly the active targeting nanomedicines is not as successful as people have expected. This review provides a comprehensive landscape of the nanomedicines for breast cancer treatment, from laboratory investigations to clinical applications. We also highlight the key advances in the understanding of the biological fate and the targeting strategies of breast cancer nanomedicine and the implications to clinical translation.

Metal/metal oxide nanoparticles: Toxicity concerns associated with their physical state and remediation for biomedical applications

Metal/metal oxide nanoparticles show promise for various applications, including diagnosis, treatment, theranostics, sensors, cosmetics, etc. Their altered chemical, optical, magnetic, and structural properties have differential toxicity profiles. Depending upon their physical state, these NPs can also change their properties due to alteration in pH, interaction with proteins, lipids, blood cells, and genetic material. Metallic nanomaterials (comprised of a single metal element) tend to be relatively stable and do not readily undergo dissolution. Contrarily, metal oxide and metal alloy-based nanomaterials tend to exhibit a lower degree of stability and are more susceptible to dissolution and ion release when introduced to a biological milieu, leading to reactive oxygen species production and oxidative stress to cells. Since NPs have considerable mobility in various biological tissues, the investigation related to their adverse effects is a critical issue and required to be appropriately addressed before their biomedical applications. Short and long-term toxicity assessment of metal/metal oxide nanoparticles or their nano-formulations is of paramount importance to ensure the global biome's safety; otherwise, to face a fiasco. This article provides a comprehensive introspection regarding the effects of metal/metal oxides' physical state, their surface properties, the possible mechanism of actions along with the potential future strategy for remediation of their toxic effects.

Ferroptosis in cancer therapeutics: a materials chemistry perspective

Ferroptosis, distinct from apoptosis, is a regulated form of cell death caused by lipid peroxidation that has attracted extensive research interest since it was first defined in 2012. Over the past five years, an increasing number of studies have revealed the close relationship between ferroptosis and materials chemistry, in particular nanobiotechnology, and have concluded that nanotechnology-triggered ferroptosis is an efficient and promising antitumor strategy that provides an alternative therapeutic approach, especially for apoptosis-resistant tumors. In this review, we summarize recent advances in ferroptosis-induced tumor therapy at the intersection of materials chemistry, redox biology, and tumor biology. The biological features and molecular mechanisms of ferroptosis are first outlined, followed by a summary of the feasible strategies to induce ferroptosis using nanomaterials and the applications of ferroptosis in combined tumor therapy. Finally, the existing challenges and future development directions in this emerging field are discussed, with the aim of promoting the progress of ferroptosis-based oncotherapy in materials science and nanoscience and enriching the antitumor arsenal.

Recent Advancements in the Nanomaterial Application in Concrete and Its Ecological Impact

At present, nanotechnology is a significant research area in different countries, owing to its immense ability along with its economic impact. Nanotechnology is the scientific study, development, manufacturing, and processing of structures and materials on a nanoscale level. It has tremendous application in different industries such as construction. This study discusses the various progressive uses of nanomaterials in concrete, as well as their related health risks and environmental impacts. Nanomaterials such as nanosilica, nano-TiO₂, carbon nanotubes (CNTs), ferric oxides, polycarboxylates, and nanocellulose have the capability to increase the durability of buildings by improving their mechanical and thermal properties. This could cause an indirect reduction in energy usage and total expenses in the concrete industry. However, due to the uncertainties and irregularities in size, shape, and chemical compositions, some nanosized materials might have harmful effects on the environment and human health. Acknowledgement of the possible beneficial impacts and inadvertent dangers of these nanosized materials to the environment will be extremely important when pursuing progress in the upcoming years. This research paper is expected to bring proper attention to the probable effects of construction waste, together with the importance of proper regulations, on the final disposal of the construction waste.

Safe-by-Design part II: A strategy for balancing safety and functionality in the different stages of the innovation process

Manufactured nanomaterials have the potential to impact an exceedingly wide number of industries and markets ranging from energy storage, electronic and optical devices, light-weight construction to innovative medical approaches for diagnostics and therapy. In order to foster the development of safer nanomaterial-containing products, two main aspects are of major interest: their functional performance as well as their safety towards human health and the environment. In this paper a first proposal for a strategy is presented to link the functionality of nanomaterials with safety aspects. This strategy first combines information on the functionality and safety early during the innovation process and onwards, and then identifies Safe-by-Design (SbD) actions that allow for optimisation of both aspects throughout the innovation process. The strategy encompasses suggestions for the type of information needed to balance functionality and safety to support decision making in the innovation process. The applicability of the strategy is illustrated using a literature-based case study on carbon nanotube-based transparent conductive films. This is a first attempt to identify information that can be used for balancing functionality and safety in a structured way during innovation processes.

Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity.

Surface Functionalization of Graphene-Based Materials: Biological Behavior, Toxicology, and Safe-By-Design Aspects

The increasing exploitation of graphene-based materials (GBMs) is driven by their unique properties and structures, which ignite the imagination of scientists and engineers. At the same time, the very properties that make them so useful for applications lead to growing concerns regarding their potential impacts on human health and the environment. Since GBMs are inert to reaction, various attempts of surface functionalization are made to make them reactive. Herein, surface functionalization of GBMs, including those intentionally designed for specific applications, as well as those unintentionally acquired (e.g., protein corona formation) from the environment and biota, are reviewed through the lenses of nanotoxicity and design of safe materials (safe-by-design). Uptake and toxicity of functionalized GBMs and the underlying mechanisms are discussed and linked with the surface functionalization. Computational tools that can predict the interaction of GBMs behavior with their toxicity are discussed. A concise framing of current knowledge and key features of GBMs to be controlled for safe and sustainable applications are provided for the community.

Impact of atomistic or crystallographic descriptors for classification of gold nanoparticles

Machine learning models are known to be sensitive to the features used to train them, but there is currently no way to predict the impact of using different features prior to feature extraction. This is particularly important to fields such as nanotechnology that are highly multi-disciplinary, and samples can be characterised many different ways depending on the preferences of individual researchers. Does it matter if nanomaterials are described using the interatomic coordinations or more complex order parameters? In this study we compare results of supervised and unsupervised learning on a single set of gold nanoparticles that has been characterised by two different descriptors, each with a unique feature space. We find that there are some consistencies, and model selection is descriptor-agnostic, but the level of detail and the type of information that can be extracted from the results is sensitive to the way the particles are described. Unsupervised clustering revealed that an atomistic descriptor provides a finer-grained interpretation and clusters that are sub-clusters of a more sophisticated crystallographic descriptor, which is consistent with both how the features were calculated, and how they are interpreted in the domain. A supervised classifier revealed that the types of features responsible for the separation are related to the bulk structure, regardless of the descriptor, but capture different types of information. For both the atomistic and crystallographic descriptor the gradient boosting decision tree classifier gave superior results of F1-scores of 0.96 and 0.98, respectively, with excellent precision and recall, even though the clustering presented a challenging multi-classification problem.

Nanotechnology and artificial intelligence to enable sustainable and precision agriculture

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

Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging

Two-dimensional (2D) materials have emerged as an important class of nanomaterials for technological innovation due to their remarkable physicochemical properties, including sheet-like morphology and minimal thickness, high surface area, tuneable chemical composition, and surface functionalization. These materials are being proposed for new applications in energy, health, and the environment; these are all strategic society sectors toward sustainable development. Specifically, 2D materials for nano-imaging have shown exciting opportunities in in vitro and in vivo models, providing novel molecular imaging techniques such as computed tomography, magnetic resonance imaging, fluorescence and luminescence optical imaging and others. Therefore, given the growing interest in 2D materials, it is mandatory to evaluate their impact on the immune system in a broader sense, because it is responsible for detecting and eliminating foreign agents in living organisms. This mini-review presents an overview on the frontier of research involving 2D materials applications, nano-imaging and their immunosafety aspects. Finally, we highlight the importance of nanoinformatics approaches and computational modeling for a deeper understanding of the links between nanomaterial physicochemical properties and biological responses (immunotoxicity/biocompatibility) towards enabling immunosafety-by-design 2D materials.

Enhancing Clinical Translation of Cancer Using Nanoinformatics

Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial intelligence (AI) can be significantly beneficial for optimization of nanomedicine and combinatorial nanotherapy. AI has become a tool for researchers to manage complicated and big data, ranging from achieving complementary results to routine statistical analyses. AI enhances the prediction precision of treatment impact in cancer patients and specify estimation outcomes. Application of AI in nanotechnology leads to a new field of study, i.e., nanoinformatics. Besides, AI can be coupled with nanorobots, as an emerging technology, to develop targeted drug delivery systems. Furthermore, by the advancements in the nanomedicine field, AI-based combination therapy can facilitate the understanding of diagnosis and therapy of the cancer patients. The main objectives of this review are to discuss the current developments, possibilities, and future visions in naoinformatics, for providing more effective treatment for cancer patients.

Lipid-Based Nanocarriers for Ophthalmic Administration: Towards Experimental Design Implementation

Nanotherapeutics based on biocompatible lipid matrices allow for enhanced solubility of poorly soluble compounds in the treatment of ophthalmic diseases, overcoming the anatomical and physiological barriers present in the eye, which, despite the ease of access, remains strongly protected. Micro-/nanoemulsions, solid lipid nanoparticles (SLN) or nanostructured lipid carriers (NLC) combine liquid and/or solid lipids with surfactants, improving drug stability and ocular bioavailability. Current research and development approaches based on try-and-error methodologies are unable to easily fine-tune nanoparticle populations in order to overcome the numerous constraints of ocular administration routes, which is believed to hamper easy approval from regulatory agencies for these systems. The predictable quality and specifications of the product can be achieved through quality-by-design (QbD) implementation in both research and industrial environments, in contrast to the current quality-by-testing (QbT) framework. Mathematical modelling of the expected final nanoparticle characteristics by variation of operator-controllable variables of the process can be achieved through adequate statistical design-of-experiments (DoE) application. This multivariate approach allows for optimisation of drug delivery platforms, reducing research costs and time, while maximising the understanding of the production process. This review aims to highlight the latest efforts in implementing the design of experiments to produce optimised lipid-based nanocarriers intended for ophthalmic administration. A useful background and an overview of the different possible approaches are presented, serving as a starting point to introduce the design of experiments in current nanoparticle research.

RNA m6A Modification Alteration by Black Phosphorus Quantum Dots Regulates Cell Ferroptosis: Implications for Nanotoxicological Assessment

Nanosafety is a major concern for nanotechnology development. Evaluation of the transcriptome and the DNA methylome is proposed for nanosafety assessments. RNA m6A modification plays a crucial role in development, disease, and cell fate determination through regulating RNA stability and decay. Here, since black phosphorus quantum dots (BPQDs), among many other types of QDs, increase the global m6A level and decrease the demethylase ALKBH5 level in lung cells, the epitranscriptome is taken into consideration for the first time to evaluate nanosafety. Both the transcriptome and m6A epitranscriptome analyses show that BPQDs alter many biological processes, such as the response to selenium ions and the lipoxygenase pathway, indicating possible ferroptosis activation. The results further show that BPQDs cause lipid peroxidation, mitochondrial dysfunction, and iron overload. Recognition of these modified mRNAs by YTHDF2 leads to mRNAs' decay and eventually ferroptosis. This study shows that RNA m6A modification not only is a more sophisticated indicator for nanosafety assessment but also provides novel insight into the role of RNA m6A in regulating BPQD-induced ferroptosis, which may be broadly applicable to understanding the functions of RNA m6A under stress.

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.

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).