Toward a systematic exploration of nano-bio interactions

The influence of various polymer coatings on the in vitro and in vivo properties of PLGA nanoparticles: Comprehensive study

Nanoparticles based on poly(lactic-co-glycolic acid) (PLGA) with various surface chemistry are widely used in biomedicine for theranostic applications. The nature of the external coating of nanoparticles has a significant influence on their efficiency as drug carriers or visualization agents. However, information about the mechanisms of nanoparticle accumulation in tumors and the influence of their surface properties on biodistribution is scarce due to the lack of systematic evaluation. Here we investigate the effect of different polymer coatings of the surface on in vitro and in vivo properties of PLGA nanoparticles. Namely, cell binding efficiency, cytotoxicity, efficiency of fluorescent bioimaging, and tumor accumulation were tested. The highest binding efficiency in vitro and cytotoxicity were observed for positively charged polymers. Interestingly, in vivo fluorescent visualization of tumor-bearing mice and quantitative measurements of biodistribution of magnetite-loaded nanoparticles indicated different dependences of accumulation in tumors on the coating of PLGA nanoparticles. This means that nanoparticle surface properties can simultaneously enhance imaging efficiency and decrease quantitative accumulation in tumors. The obtained data demonstrate the complexity of the dependence of nanoparticles' effectiveness for theranostic applications on surface features. We believe that this study will contribute to the rational design of nanoparticles for effective cancer diagnostics and therapy.

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.

Principles of Nanoparticle Delivery to Solid Tumors

The effective treatment of patients with cancer hinges on the delivery of therapeutics to a tumor site. Nanoparticles provide an essential transport system. We present 5 principles to consider when designing nanoparticles for cancer targeting: (a) Nanoparticles acquire biological identity in vivo, (b) organs compete for nanoparticles in circulation, (c) nanoparticles must enter solid tumors to target tumor components, (d) nanoparticles must navigate the tumor microenvironment for cellular or organelle targeting, and (e) size, shape, surface chemistry, and other physicochemical properties of nanoparticles influence their transport process to the target. This review article describes these principles and their application for engineering nanoparticle delivery systems to carry therapeutics to tumors or other disease targets.

Structural parameters of nanoparticles affecting their toxicity for biomedical applications: a review

Rapidly growing interest in using nanoparticles (NPs) for biomedical applications has increased concerns about their safety and toxicity. In comparison with bulk materials, NPs are more chemically active and toxic due to the greater surface area and small size. Understanding the NPs' mechanism of toxicity, together with the factors influencing their behavior in biological environments, can help researchers to design NPs with reduced side effects and improved performance. After overviewing the classification and properties of NPs, this review article discusses their biomedical applications in molecular imaging and cell therapy, gene transfer, tissue engineering, targeted drug delivery, Anti-SARS-CoV-2 vaccines, cancer treatment, wound healing, and anti-bacterial applications. There are different mechanisms of toxicity of NPs, and their toxicity and behaviors depend on various factors, which are elaborated on in this article. More specifically, the mechanism of toxicity and their interactions with living components are discussed by considering the impact of different physiochemical parameters such as size, shape, structure, agglomeration state, surface charge, wettability, dose, and substance type. The toxicity of polymeric, silica-based, carbon-based, and metallic-based NPs (including plasmonic alloy NPs) have been considered separately.

The Promise of Emergent Nanobiotechnologies for In Vivo Applications and Implications for Safety and Security

Nanotechnology, the multidisciplinary field based on the exploitation of the unique physicochemical properties of nanoparticles (NPs) and nanoscale materials, has opened a new realm of possibilities for biological research and biomedical applications. The development and deployment of mRNA-NP vaccines for COVID-19, for example, may revolutionize vaccines and therapeutics. However, regulatory and ethical frameworks that protect the health and safety of the global community and environment are lagging, particularly for nanotechnology geared toward biological applications (ie, bionanotechnology). In this article, while not comprehensive, we attempt to illustrate the breadth and promise of bionanotechnology developments, and how they may present future safety and security challenges. Specifically, we address current advancements to streamline the development of engineered NPs for in vivo applications and provide discussion on nano–bio interactions, NP in vivo delivery, nanoenhancement of human performance, nanomedicine, and the impacts of NPs on human health and the environment.

Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives

Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods (e.g., omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future.

Probing the Effect of Rigidity on the Cellular Uptake of Core-Shell Nanoparticles: Stiffness Effects are Size Dependent

Nanoparticles are well established vectors for the delivery of a wide range of biomedically relevant cargoes. Numerous studies have investigated the impact of size, shape, charge, and surface functionality of nanoparticles on mammalian cellular uptake. Rigidity has been studied to a far lesser extent, and its effects are still unclear. Here, the importance of this property, and its interplay with particle size, is systematically explored using a library of core-shell spherical PEGylated nanoparticles synthesized by RAFT emulsion polymerization. Rigidity of these particles is controlled by altering the intrinsic glass transition temperature of their constituting polymers. Three polymeric core rigidities are tested: hard, medium, and soft using two particle sizes, 50 and 100 nm diameters. Cellular uptake studies indicate that softer particles are taken up faster and threefold more than harder nanoparticles with the larger 100 nm particles. In addition, the study indicates major differences in the cellular uptake pathway, with harder particles being internalized through clathrin- and caveolae-mediated endocytosis as well as macropinocytosis, while softer particles are taken up bycaveolae- and non-receptormediated endocytosis. However, 50 nm derivatives do not show any appreciable differences in uptake efficiency, suggesting that rigidity as a parameter in the biological regime may be size dependent.

Genotoxic assessment of cerium and magnesium nanoparticles and their ionic forms in Eisenia hortensis coelomocytes by alkaline comet assay

The present study aimed to evaluate the genotoxic potential of cerium oxide (CeO₂ ), magnesium oxide (MgO) nanoparticles and their ionic forms by alkaline comet assay. Eisenia hortensis were exposed to different series of concentrations (25, 50, 100, 200, and 400 μg/ml) of chemicals for 48 h to find LC₅₀ . The LC₅₀ for MgO and CeO₂ NPs were 70 and 80 μg/ml. Whereas, the LC₅₀ for their ionic forms were 50 and 70 μg/ml. To assess the potential DNA damage caused by the chosen chemicals, E. hortensis was further exposed for 48 h to the following concentrations, based on their respective LC_50s : LC_50/2 , LC⁵⁰ , and 2xLC₅₀ . Comet scores demonstrated the significant increase (p < 0.05) in DNA damage at all concentrations, both for NPs and ionic forms in a concentration-dependent manner. Findings of the present study revealed the genotoxic effects of CeO₂ NPs, MgO NPs and their ionic forms on E. hortensis.

Supervised learning model predicts protein adsorption to carbon nanotubes

Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)–based nanosensor and study the relationship between the protein’s amino acid–based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure–associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.

Supervised learning model predicts protein adsorption to carbon nanotubes

Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)–based nanosensor and study the relationship between the protein’s amino acid–based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure–associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.

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.

Obstacles and opportunities in a forward vision for cancer nanomedicine

Cancer nanomedicines were initially envisioned as magic bullets, travelling through the circulation to target tumours while sparing healthy tissues the toxicity of classic chemotherapy. While a limited number of nanomedicine therapies have resulted, the disappointing news is that major obstacles were overlooked in the nanoparticle's journey. However, some of these challenges may be turned into opportunities. Here, we discuss biological barriers to cancer nanomedicines and elaborate on two directions that the field is currently exploring to meet its initial expectations. The first strategy entails re-engineering cancer nanomedicines to prevent undesired interactions en route to the tumour. The second aims instead to leverage these obstacles into out-of-the-box diagnostic and therapeutic applications of nanomedicines, for cancer and beyond. Both paths require, among other developments, a deeper understanding of nano-bio interactions. We offer a forward look at how classic cancer nanomedicine may overcome its limitations while contributing to other areas of research.

Nano-bio surface interactions, cellular internalisation in cancer cells and e-data portals of nanomaterials: A review

Nanomaterials (NMs) have abundant applications in areas such as electronics, energy, environment industries, biosensors, nano devices, theranostic platforms, etc. Nanoparticles can increase the solubility and stability of drug-loaded materials, enhance their internalisation, protect them from initial destruction in the biological system, and lengthen their circulation time. The biological interaction of proteins present in the body fluid with NMs can change the activity and natural surface properties of NMs. The size and charge of NMs, properties of the coated and uncoated NMs, nature of proteins, cellular interactions direct their internalisation pathway in the cellular system. Thus, the present review emphasises the impact of coated, uncoated NMs, size and charge, nature of proteins on nano-bio surface interactions and on internalisation with specific focus on cancer cells. The increased activity of NPs may also result in toxicity on health and environment, thus emphasis should be given to assess the toxicity of NMs in the medical field. The e-data sharing portals of NMs have also been discussed in this review that will be helpful in providing the information about the chemical, physical, biological properties and toxicity of NMs.

Hydroxyapatite Nanoparticles in Drug Delivery: Physicochemistry and Applications

Hydroxyapatite (HAP) has been the gold standard in the biomedical field due to its composition and similarity to human bone. Properties such as shape, size, morphology, and ionic substitution can be tailored through the use of different synthesis techniques and compounds. Regardless of the ability to determine its physicochemical properties, a conclusion for the correlation with the biological response it is yet to be found. Hence, a special focus on the most desirable properties for an appropriate biological response needs to be addressed. This review provides an overview of the fundamental properties of hydroxyapatite nanoparticles and the characterization of physicochemical properties involved in their biological response and role as a drug delivery system. A summary of the main chemical properties and applications of hydroxyapatite, the advantages of using nanoparticles, and the influence of shape, size, functional group, morphology, and crystalline phase in the biological response is presented. A special emphasis was placed on the analysis of chemical and physical interactions of the nanoparticles and the cargo, which was explained through the use of spectroscopic and physical techniques such as FTIR, Raman, XRD, SEM, DLS, and BET. We discuss the properties tailored for hydroxyapatite nanoparticles for a specific biomolecule based on the compilation of studies performed on proteins, peptides, drugs, and genetic material.

Brave new world revisited: Focus on nanomedicine

Nanomedicine is at a crossroads: with relatively few success stories in terms of clinical translation despite more and more research on increasingly sophisticated nanomaterials, it is important to consider whether we are on the right track. Indeed, it is crucial that we address the fact that while considerable efforts are being made to overcome barriers to translation from the bench to the clinic, scientists are still struggling to decipher fundamental aspects of nanomaterial interactions with biological systems. We believe that a key to the successful adoption of nanomedicines in oncology and beyond lies in a deeper understanding of underlying biological processes and in decoding interactions between engineered nanomaterials and biological systems. Here we provide an overview of progress in nanomedicine during the past 5 years.

Gold Nanoparticles as Radiosensitizers in Cancer Radiotherapy

The rapid development of nanotechnology offers a variety of potential therapeutic strategies for cancer treatment. High atomic element nanomaterials are often utilized as radiosensitizers due to their unique photoelectric decay characteristics. Among them, gold nanoparticles (GNPs) are one of the most widely investigated and are considered to be an ideal radiosensitizers for radiotherapy due to their high X-ray absorption and unique physicochemical properties. Over the last few decades, multi-disciplinary studies have focused on the design and optimization of GNPs to achieve greater dosing capability and higher therapeutic effects and highlight potential mechanisms for radiosensitization of GNPs. Although the radiosensitizing potential of GNPs has been widely recognized, its clinical translation still faces many challenges. This review analyses the different roles of GNPs as radiosensitizers in cancer radiotherapy and summarizes recent advances. In addition, the underlying mechanisms of GNP radiosensitization, including physical, chemical and biological mechanisms are discussed, which may provide new directions for the optimization and clinical transformation of next-generation GNPs.

Multiparametric Profiling of Engineered Nanomaterials: Unmasking the Surface Coating Effect

Despite considerable efforts, the properties that drive the cytotoxicity of engineered nanomaterials (ENMs) remain poorly understood. Here, the authors inverstigate a panel of 31 ENMs with different core chemistries and a variety of surface modifications using conventional in vitro assays coupled with omics-based approaches. Cytotoxicity screening and multiplex-based cytokine profiling reveals a good concordance between primary human monocyte-derived macrophages and the human monocyte-like cell line THP-1. Proteomics analysis following a low-dose exposure of cells suggests a nonspecific stress response to ENMs, while microarray-based profiling reveals significant changes in gene expression as a function of both surface modification and core chemistry. Pathway analysis highlights that the ENMs with cationic surfaces that are shown to elicit cytotoxicity downregulated DNA replication and cell cycle responses, while inflammatory responses are upregulated. These findings are validated using cell-based assays. Notably, certain small, PEGylated ENMs are found to be noncytotoxic yet they induce transcriptional responses reminiscent of viruses. In sum, using a multiparametric approach, it is shown that surface chemistry is a key determinant of cellular responses to ENMs. The data also reveal that cytotoxicity, determined by conventional in vitro assays, does not necessarily correlate with transcriptional effects of ENMs.

Systematic and Quantitative Structure-Property Relationships of Polymeric Medical Nanomaterials: From Systematic Synthesis and Characterization to Computer Modeling and Nano-Bio Interaction and Toxicity

Nanomaterials allow designing targeted therapies, facilitate molecular diagnostics, and are therefore enabling platforms for personalized medicine. A systematic science and a predictive understanding of molecular/supramolecular structure relationships and nanoparticle structure/biological property relationships are needed for rational design and clinical progress but are hampered by the anecdotal nature, nonsystematic and nonrepresentative nanomaterial assortment, and oligo-disciplinary approach of many publications. Here, we find that a systematic and comprehensive multidisciplinary approach to production and exploration of molecular-structure/nanostructure relationship and nano-bio structure/function relationship of medical nanomaterials can be achieved by combining systematic chemical synthesis, thorough physicochemical analysis, computer modeling, and biological experiments, as shown in a nanomaterial family of amphiphilic, micelle-forming oxazoline/siloxane block copolymers suited for the clinical application. This comprehensive interdisciplinary approach leads to improved understanding of nanomaterial structures, allows good insights into binding modes for the nanomaterial protein corona, induces the design of minimal cell-binding materials, and yields rational strategies to avoid toxicity. Thus, this work contributes to a systematic and scientific basis for rational design of medical nanomaterials.

Systematic Study of Perfluorocarbon Nanoemulsions Stabilized by Polymer Amphiphiles

Perfluorocarbon (PFC) nanoemulsions, droplets of fluorous solvent stabilized by surfactants dispersed in water, are simple yet versatile nanomaterials. The orthogonal nature of the fluorous phase promotes the formation of nanoemulsions through a simple, self-assembly process while simultaneously encapsulating fluorous-tagged payloads for various applications. The size, stability, and surface chemistry of PFC nanoemulsions are controlled by the surfactant. Here, we systematically study the effect of the hydrophilic portion of polymer surfactants on PFC nanoemulsions. We find that the hydrophilic block length and identity, the overall polymer hydrophilic/lipophilic balance, and the polymer architecture are all important factors. The ability to modulate these parameters enables control over initial size, stability, payload retention, cellular internalization, and protein adsorption of PFC nanoemulsions. With the insight obtained from this systematic study of polymer amphiphiles stabilizing PFC nanoemulsions, design features required for the optimal material are obtained.

Role of Artificial Intelligence and Machine Learning in Nanosafety

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

The Current Understanding of Autophagy in Nanomaterial Toxicity and Its Implementation in Safety Assessment-Related Alternative Testing Strategies

Nanotechnology has rapidly promoted the development of a new generation of industrial and commercial products; however, it has also raised some concerns about human health and safety. To evaluate the toxicity of the great diversity of nanomaterials (NMs) in the traditional manner, a tremendous number of safety assessments and a very large number of animals would be required. For this reason, it is necessary to consider the use of alternative testing strategies or methods that reduce, refine, or replace (3Rs) the use of animals for assessing the toxicity of NMs. Autophagy is considered an early indicator of NM interactions with cells and has been recently recognized as an important form of cell death in nanoparticle-induced toxicity. Impairment of autophagy is related to the accelerated pathogenesis of diseases. By using mechanism-based high-throughput screening in vitro, we can predict the NMs that may lead to the generation of disease outcomes in vivo. Thus, a tiered testing strategy is suggested that includes a set of standardized assays in relevant human cell lines followed by critical validation studies carried out in animals or whole organism models such as C. elegans (Caenorhabditis elegans), zebrafish (Danio rerio), and Drosophila (Drosophila melanogaster)for improved screening of NM safety. A thorough understanding of the mechanisms by which NMs perturb biological systems, including autophagy induction, is critical for a more comprehensive elucidation of nanotoxicity. A more profound understanding of toxicity mechanisms will also facilitate the development of prevention and intervention policies against adverse outcomes induced by NMs. The development of a tiered testing strategy for NM hazard assessment not only promotes a more widespread adoption of non-rodent or 3R principles but also makes nanotoxicology testing more ethical, relevant, and cost- and time-efficient.

Proteomic responses to silver nanoparticles vary with the fungal ecotype

Enhanced commercial application of silver nanoparticles (AgNPs) is increasing the chance of their release into aquatic environments, potentially putting non-target microorganisms at risk. Impacts of AgNPs and Ag⁺ on two fungal ecotypes of Articulospora tetracladia, collected from a metal-polluted (At61) and a non-polluted (At72) stream, were assessed based on antioxidant enzymatic and proteomic responses. At61 showed more tolerance to AgNPs than At72 (EC₂₀, 158.9 vs 7.5 µg L^-1, respectively). Antioxidant enzyme activities were induced by AgNPs or Ag⁺ in both fungal ecotypes. Proteomic responses to AgNPs or Ag⁺ revealed that 41.3% of the total altered proteins were common in At72, while 27.3% were common in At61. In At72, gene ontology enrichment analyses indicated that Ag⁺ increased mainly the content of proteins involved in proteostasis and decreased the content of those related to vesicle-mediated transport; whereas the key group of proteins induced by AgNPs had functions in DNA repair and energy production. In At61, AgNPs induced proteins involved in energy production and protein biosynthesis, while both Ag forms induced proteins related to cell-redox and protein homeostasis, ascospore formation, fatty acid biosynthesis and nucleic acids metabolism. Both Ag forms induced stress-responsive proteins, and this was consistent with the responses of antioxidant enzymes. The negligible quantity of Ag⁺ released from AgNPs (<0.2 μg L^-1) supported a minor role of dissolved ionic form in AgNP-induced toxicity to both fungal ecotypes. Overall, results unraveled distinct mechanisms of toxicity and cellular targets of nanoparticulate and ionic silver in aquatic fungi with different environmental background, and constitutes a proof of concept that toxicants induce adaptive responses in microbes to face emergent contaminants.

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

Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

Combinatorial Use of Chitosan Nanoparticles, Reversine, and Ionising Radiation on Breast Cancer Cells Associated with Mitosis Deregulation

Breast cancer is the most commonly occurring cancer in women worldwide and the second most common cancer overall. The development of new therapies to treat this devastating malignancy is needed urgently. Nanoparticles are one class of nanomaterial with multiple applications in medicine, ranging from their use as drug delivery systems and the promotion of changes in cell morphology to the control of gene transcription. Nanoparticles made of the natural polymer chitosan are easy to produce, have a very low immunogenic profile, and diffuse easily into cells. One hallmark feature of cancer, including breast tumours, is the genome instability caused by defects in the spindle-assembly checkpoint (SAC), the molecular signalling mechanism that ensures the timely and high-fidelity transmission of the genetic material to an offspring. In recent years, the use of nanoparticles to treat cancer cells has gained momentum. This is in part because nanoparticles made of different materials can sensitise cancer cells to chemotherapy and radiotherapy. These advances prompted us to study the potential sensitising effect of chitosan-based nanoparticles on breast cancer cells treated with reversine, which is a small molecule inhibitor of Mps1 and Aurora B that induces premature exit from mitosis, aneuploidy, and cell death, before and after exposure of the cancer cells to X-ray irradiation. Our measurements of metabolic activity as an indicator of cell viability, DNA damage by alkaline comet assay, and immunofluorescence using anti-P-H3 as a mitotic biomarker indicate that chitosan nanoparticles elicit cellular responses that affect mitosis and cell viability and can sensitise breast cancer cells to X-ray radiation (2Gy). We also show that such a sensitisation effect is not caused by direct damage to the DNA by the nanoparticles. Taken together, our data indicates that chitosan nanoparticles have potential application for the treatment of breast cancer as adjunct to radiotherapy.

Physicochemical Properties Can Be Key Determinants of Mesoporous Silica Nanoparticle Potency in Vitro

Nanoforms of mesoporous silica (mSiNPs) are increasingly applied in medicine, imaging, energy storage, catalysis, biosensors, and bioremediation. The impact of their physicochemical properties on health and the environment remain to be elucidated. In this work, newly synthesized mesoporous silica (sizes: 25, 70, 100, 170, and 600 nm; surface functionalization: pristine, C3-, and C11-COOH moieties) were assessed for cytotoxicity and induction of inflammatory responses in vitro (A549, THP-1, J774A.1 cells). All toxicity end points were integrated to obtain simple descriptors of biological potencies of these mSiNPs. The findings indicate that mSiNPs are less bioactive than the nonporous reference SiNP used in this study. The C3-COOH-modified mSiNPs were generally less cytotoxic than their pristine and C11-modified counterparts in the nanorange (≤100 nm). Carboxyl-modified mSiNPs affected inflammatory marker release across all sizes with cell-type specificity, suggesting a potential for immunomodulatory effects. Surface area, size, extent of agglomeration, ζ-potential, and surface modification appeared to be important determinants of cytotoxicity of mSiNPs based on association tests. Pathway analysis identified particle and cell-type-specific alteration of cellular pathways and functions by mSiNPs. The integration of exposure-related biological responses of multiple cell lines to mSiNPs allowed for a comprehensive evaluation of the impact of physicochemical factors on their toxicity characteristics. The integrated multilevel toxicity assessment approach can be valuable as a hazard screening tool for safety evaluations of emerging nanomaterials for regulatory purpose.

Characterization and toxicity of nanoscale fragments in wastewater treatment plant effluent

Much attention has been paid to extracting and isolating specific and well-known nanoparticles (especially for engineered nanomaterials) from complex environmental matrices. However, such research may not provide global information on actual contamination because nanoscale fragments exist as mixtures of various elements and matrices in the real environment. The present work first isolated and characterized nanoscale fragments in effluents from municipal wastewater treatment plants (WWTPs). The nanoscale fragments were found to be composed of 70-85% carbon and low amounts of oxygen, heavy metals and other elements and exhibited nanosheet topographies (approximately 0.87-1.31 nm thickness and 68-187 nm lateral length). Because the isolated nanoscale fragments were mixtures rather than one specific type of nanoparticle, they were present at high concentrations ranging from 0.07 to 0.55 mg/L. It was also found that the accumulation of nanoscale fragments in rice reached 0.59 mg/g under exposure to environmentally relevant concentrations, leading to marked phytotoxicity (e.g., ultrastructural damage to chloroplasts and mitochondria). Metabolic analysis revealed the toxicological mechanisms to be related to disorders of carbohydrate, amino acid and fatty acid metabolism. This study is the first to characterize the properties and analyze the toxicity of nanoscale fragments in the effluents of WWTPs. Given that WWTP effluents containing nanoscale fragments are continuously discharged to the soil, surface water and seas, nanoscale fragment materials deserve considerable attention in future work compared with the few widely studied engineered nanoparticles.

Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties

Proteins encountered in biological and environmental systems bind to engineered nanomaterials (ENMs) to form a protein corona (PC) that alters the surface chemistry, reactivity, and fate of the ENMs. Complexities such as the diversity of the PC and variation with ENM properties and reaction conditions make the PC population difficult to predict. Here, we support the development of predictive models for PC populations by relating biophysicochemical characteristics of proteins, ENMs, and solution conditions to PC formation using random forest classification. The resulting model offers a predictive analysis into the population of PC proteins in Ag ENM systems of various ENM size and surface coatings. With an area under the receiver operating characteristic curve of 0.83 and F1-score of 0.81, a model with strong performance has been constructed based upon experimental data. The weighted contribution of each variable provides recommendations for mechanistic models based upon protein enrichment classification results. Protein biophysical properties such as pI and weight are weighted heavily. Yet, ENM size, surface charge, and solution ionic strength also proved essential to an accurate model. The model can be readily modified and applied to other ENM PC populations. The model presented here represents the first step toward robust predictions of PC fingerprints.

Nanomaterial–microbe cross-talk: physicochemical principles and (patho)biological consequences

NPs’ characteristics impact their spontaneous binding to microbes, which may affect the (patho)biological identity of both NP and microbes.

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.