Prediction of protein corona on nanomaterials by machine learning using novel descriptors

Protein Adsorption on Solid Surfaces: Data Mining, Database, Molecular Surface-Derived Properties, and Semiempirical Relationships

Protein adsorption on solid surfaces is a process relevant to biological, medical, industrial, and environmental applications. Despite this wide interest and advancement in measurement techniques, the complexity of protein adsorption has frustrated its accurate prediction. To address this challenge, here, data regarding protein adsorption reported in the last four decades was collected, checked for completeness and correctness, organized, and archived in an upgraded, freely accessible Biomolecular Adsorption Database, which is equivalent to a large-scale, ad hoc, crowd-sourced multifactorial experiment. The shape and physicochemical properties of the proteins present in the database were quantified on their molecular surfaces using an in-house program (ProMS) operating as an add-on to the PyMol software. Machine learning-based analysis indicated that protein adsorption on hydrophobic and hydrophilic surfaces is modulated by different sets of operational, structural, and molecular surface-based physicochemical parameters. Separately, the adsorption data regarding four "benchmark" proteins, i.e., lysozyme, albumin, IgG, and fibrinogen, was processed by piecewise linear regression with the protein monolayer acting as breakpoint, using the linearization of the Langmuir isotherm formalism, resulting in semiempirical relationships predicting protein adsorption. These relationships, derived separately for hydrophilic and hydrophobic surfaces, described well the protein concentration on the surface as a function of the protein concentration in solution, adsorbing surface contact angle, ionic strength, pH, and temperature of the carrying fluid, and the difference between pH and the isoelectric point of the protein. When applying the semiempirical relationships derived for benchmark proteins to two other "test" proteins with known PDB structure, i.e., β-lactoglobulin and α-lactalbumin, the errors of this extrapolation were found to be in a linear relationship with the dissimilarity between the benchmark and the test proteins. The work presented here can be used for the estimation of operational parameters modulating protein adsorption for various applications such as diagnostic devices, pharmaceuticals, biomaterials, or the food industry.

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.

Artificial Intelligence and Computational Biology in Gene Therapy: A Review

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

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.

Unveiling the challenges of engineered protein corona from the proteins' perspective

It is well known that protein corona affects the "biological identity" of nanoparticles (NPs), which has been seen as both a challenge and an opportunity. Approaches have moved from avoiding protein adsorption to trying to direct it, taking advantage of the formation of a protein corona to favorably modify the pharmacokinetic parameters of NPs. Although promising, the results obtained with engineered NPs still need to be completely understood. While much effort has been put into understanding how the surface of nanomaterials affects protein absorption, less is known about how proteins can affect corona formation due to their specific physicochemical properties. This review addresses this knowledge gap, examining key protein factors influencing corona formation, highlighting current challenges in studying protein-protein interactions, and discussing future perspectives in the field.

In Vitro and In Vivo Evaluation of the Pathology and Safety Aspects of Three- and Four-Way Junction RNA Nanoparticles

RNA therapeutics has advanced into the third milestone in pharmaceutical drug development, following chemical and protein therapeutics. RNA itself can serve as therapeutics, carriers, regulators, or substrates in drug development. Due to RNA's motile, dynamic, and deformable properties, RNA nanoparticles have demonstrated spontaneous targeting and accumulation in cancer vasculature and fast excretion through the kidney glomerulus to urine to prevent possible interactions with healthy organs. Furthermore, the negatively charged phosphate backbone of RNA results in general repulsion from negatively charged lipid cell membranes for further avoidance of vital organs. Thus, RNA nanoparticles can spontaneously enrich tumor vasculature and efficiently enter tumor cells via specific targeting, while those not entering the tumor tissue will clear from the body quickly. These favorable parameters have led to the expectation that RNA has low or little toxicity. RNA nanoparticles have been well characterized for their anticancer efficacy; however, little detail on RNA nanoparticle pathology and safety is known. Here, we report the in vitro and in vivo assessment of the pathology and safety aspects of different RNA nanoparticles including RNA three-way junction (3WJ) harboring 2'-F modified pyrimidine, folic acid, and Survivin siRNA, as well as the RNA four-way junction (4WJ) harboring 2'-F modified pyrimidine and 24 copies of SN38. Both animal models and patient serum were investigated. In vitro studies include hemolysis, platelet aggregation, complement activation, plasma coagulation, and interferon induction. In vivo studies include hematoxylin and eosin (H&E) staining, hematological and biochemical analysis as the serum profiling, and animal organ weight study. No significant toxicity, side effect, or immune responses were detected during the extensive safety evaluations of RNA nanoparticles. These results further complement previous cancer inhibition studies and demonstrate RNA nanoparticles as an effective and safe drug delivery vehicle for future clinical translations.

Predicting environmental concentrations of nanomaterials for exposure assessment - a review

There have been major advances in the science to predict the likely environmental concentrations of nanomaterials, which is a key component of exposure and subsequent risk assessment. Considerable progress has been since the first Material Flow Analyses (MFAs) in 2008, which were based on very limited information, to more refined current tools that take into account engineered nanoparticle (ENP) size distribution, form, dynamic release, and better-informed release factors. These MFAs provide input for all environmental fate models (EFMs), that generate estimates of particle flows and concentrations in various environmental compartments. While MFA models provide valuable information on the magnitude of ENP release, they do not account for fate processes, such as homo- and heteroaggregation, transformations, dissolution, or corona formation. EFMs account for these processes in differing degrees. EFMs can be divided into multimedia compartment models (e.g., atmosphere, waterbodies and their sediments, soils in various landuses), of which there are currently a handful with varying degrees of complexity and process representation, and spatially-resolved watershed models which focus on the water and sediment compartments. Multimedia models have particular applications for considering predicted environmental concentrations (PECs) in particular regions, or for developing generic "fate factors" (i.e., overall persistence in a given compartment) for life-cycle assessment. Watershed models can track transport and eventual fate of emissions into a flowing river, from multiple sources along the waterway course, providing spatially and temporally resolved PECs. Both types of EFMs can be run with either continuous sources of emissions and environmental conditions, or with dynamic emissions (e.g., temporally varying for example as a new nanomaterial is introduced to the market, or with seasonal applications), to better understand the situations that may lead to peak PECs that are more likely to result in exceedance of a toxicological threshold. In addition, bioaccumulation models have been developed to predict the internal concentrations that may accumulate in exposed organisms, based on the PECs from EFMs. The main challenge for MFA and EFMs is a full validation against observed data. To date there have been no field studies that can provide the kind of dataset(s) needed for a true validation of the PECs. While EFMs have been evaluated against a few observations in a small number of locations, with results that indicate they are in the right order of magnitude, there is a great need for field data. Another major challenge is the input data for the MFAs, which depend on market data to estimate the production of ENPs. The current information has major gaps and large uncertainties. There is also a lack of robust analytical techniques for quantifying ENP properties in complex matrices; machine learning may be able to fill this gap. Nevertheless, there has been major progress in the tools for generating PECs. With the emergence of nano- and microplastics as a leading environmental concern, some EFMs have been adapted to these materials. However, caution is needed, since most nano- and microplastics are not engineered, therefore their characteristics are difficult to generalize, and there are new fate and transport processes to consider.

Unveiling protein corona composition: predicting with resampling embedding and machine learning

Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration. When nanoparticles (NPs) enter the living system, they quickly interact with proteins in the body fluid, forming the protein corona (PC). The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs. However, achieving accurate predictions remains a significant challenge. Although several machine learning (ML) models like Random Forest (RF) have been used for PC prediction, they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution. In this study, resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data. Various ML models were evaluated, and RF model was finally used for prediction, and good correlation coefficient (R2) and root-mean-square deviation (RMSE) values were obtained. Our ablation experiments demonstrated that the proposed method achieved an R2 of 0.68, indicating an improvement of approximately 10%, and an RMSE of 0.90, representing a reduction of approximately 10%. Furthermore, through the verification of label-free quantification of four NPs: hydroxyapatite (HA), titanium dioxide (TiO2), silicon dioxide (SiO2) and silver (Ag), and we achieved a prediction performance with an R2 value >0.70 using Random Oversampling. Additionally, the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration, PDI and surface modification.

The Hybrid Nano-Biointerface between Proteins/Peptides and Two-Dimensional Nanomaterials

In typical protein-nanoparticle surface interactions, the biomolecule surface binding and consequent conformational changes are intermingled with each other and are pivotal to the multiple functional properties of the resulting hybrid bioengineered nanomaterial. In this review, we focus on the peculiar properties of the layer formed when biomolecules, especially proteins and peptides, face two-dimensional (2D) nanomaterials, to provide an overview of the state-of-the-art knowledge and the current challenges concerning the biomolecule coronas and, in general, the 2D nano-biointerface established when peptides and proteins interact with the nanosheet surface. Specifically, this review includes both experimental and simulation studies, including some recent machine learning results of a wide range of nanomaterial and peptide/protein systems.

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.

Graphene oxide exposure alters gut microbial community composition and metabolism in an in vitro human model

Graphene oxide (GO) nanomaterials have unique physicochemical properties that make them highly promising for biomedical, environmental, and agricultural applications. There is growing interest in the use of GO and extensive in vitro and in vivo studies have been conducted to assess its nanotoxicity. Although it is known that GO can alter the composition of the gut microbiota in mice and zebrafish, studies on the potential impacts of GO on the human gut microbiome are largely lacking. This study addresses an important knowledge gap by investigating the impact of GO exposure- at low (25 mg/L) and high (250 mg/L) doses under both fed (nutrient rich) and fasted (nutrient deplete) conditions- on the gut microbial communitys' structure and function, using an in vitro model. This model includes simulated oral, gastric, small intestinal phase digestion of GO followed by incubation in a colon bioreactor. 16S rRNA amplicon sequencing revealed that GO exposure resulted in a restructuring of community composition. 25 mg/L GO induced a marked decrease in the Bacteroidota phylum and increased the ratio of Firmicutes to Bacteroidota (F/B). Untargeted metabolomics on the supernatants indicated that 25 mg/L GO impaired microbial utilization and metabolism of substrates (amino acids, carbohydrate metabolites) and reduced production of beneficial microbial metabolites such as 5-hydroxyindole-3-acetic acid and GABA. Exposure to 250 mg/L GO resulted in community composition and metabolome profiles that were very similar to the controls that lacked both GO and digestive enzymes. Differential abundance analyses revealed that 3 genera from the phylum Bacteroidota (Bacteroides, Dysgonomonas, and Parabacteroides) were more abundant after 250 mg/L GO exposure, irrespective of feed state. Integrative correlation network analysis indicated that the phylum Bacteroidota showed strong positive correlations to multiple microbial metabolites including GABA and 3-indoleacetic acid, are much larger number of correlations compared to other phyla. These results show that GO exposure has a significant impact on gut microbial community composition and metabolism at both low and high GO concentrations.

Effect of graphene-based nanomaterials on corneal wound healing in vitro

Graphene-based nanomaterials (GBNs) are widely used due to their chemical and physical properties for multiple commercial and environmental applications. From an occupational health perspective, there is concern regarding the effects of inhalation on the respiratory system, and many studies have been conducted to study inhalation impacts on lung. Similar to the respiratory system, the eyes may also be exposed to GBNs and thus impacted. In this study, immortalized human corneal epithelial (hTCEpi) cells and rabbit corneal fibroblasts (RCFs) were used to investigate the toxicity of eight types of GBN: graphene oxide (GO; 400 nm), GO (1 μm), partially reduced graphene oxide (PRGO; 400 nm), reduced graphene oxide (RGO; 400 nm), RGO (2 μm), graphene (110 nm), graphene (140 nm), and graphene (1 μm). We next examined the effects of these GBNs on hTCEpi cell migration. We also determined whether the expression of α-smooth muscle actin (αSMA), a myofibroblast marker, is altered by the GBNs using RCFs. We found that RGO (400 nm) and RGO (2 μm) were highly toxic to hTCEPi cells and RCFs meanwhile, PRGO (400 nm) was toxic only to hTCEpi cells. In addition, PRGO (400 nm), RGO (400 nm), and RGO (2 μm) inhibited hTCEpi cell migration and significantly increased αSMA mRNA expression. Further study in vivo is required to determine if RGO nanomaterials delay corneal epithelial healing and induce scar formation.

The protein corona from nanomedicine to environmental science

The protein corona spontaneously develops and evolves on the surface of nanoscale materials when they are exposed to biological environments, altering their physiochemical properties and affecting their subsequent interactions with biosystems. In this Review, we provide an overview of the current state of protein corona research in nanomedicine. We next discuss remaining challenges in the research methodology and characterization of the protein corona that slow the development of nanoparticle therapeutics and diagnostics, and we address how artificial intelligence can advance protein corona research as a complement to experimental research efforts. We then review emerging opportunities provided by the protein corona to address major issues in healthcare and environmental sciences. This Review details how mechanistic insights into nanoparticle protein corona formation can broadly address unmet clinical and environmental needs, as well as enhance the safety and efficacy of nanobiotechnology products.

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

INTRODUCTION

Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

AREAS COVERED

the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

EXPERT OPINION

The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

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.

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

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

Hemolytic Activity of Nanoparticles as a Marker of Their Hemocompatibility

The potential use of nanomaterials in medicine offers opportunities for novel therapeutic approaches to treating complex disorders. For that reason, a new branch of science, named nanotoxicology, which aims to study the dangerous effects of nanomaterials on human health and on the environment, has recently emerged. However, the toxicity and risk associated with nanomaterials are unclear or not completely understood. The development of an adequate experimental strategy for assessing the toxicity of nanomaterials may include a rapid/express method that will reliably, quickly, and cheaply make an initial assessment. One possibility is the characterization of the hemocompatibility of nanomaterials, which includes their hemolytic activity as a marker. In this review, we consider various factors affecting the hemolytic activity of nanomaterials and draw the reader's attention to the fact that the formation of a protein corona around a nanoparticle can significantly change its interaction with the red cell. This leads us to suggest that the nanomaterial hemolytic activity in the buffer does not reflect the situation in the blood plasma. As a recommendation, we propose studying the hemocompatibility of nanomaterials under more physiologically relevant conditions, in the presence of plasma proteins in the medium and under mechanical stress.

A Nanomedicine Structure-Activity Framework for Research, Development, and Regulation of Future Cancer Therapies

Despite their clinical success in drug delivery applications, the potential of theranostic nanomedicines is hampered by mechanistic uncertainty and a lack of science-informed regulatory guidance. Both the therapeutic efficacy and the toxicity of nanoformulations are tightly controlled by the complex interplay of the nanoparticle's physicochemical properties and the individual patient/tumor biology; however, it can be difficult to correlate such information with observed outcomes. Additionally, as nanomedicine research attempts to gradually move away from large-scale animal testing, the need for computer-assisted solutions for evaluation will increase. Such models will depend on a clear understanding of structure-activity relationships. This review provides a comprehensive overview of the field of cancer nanomedicine and provides a knowledge framework and foundational interaction maps that can facilitate future research, assessments, and regulation. By forming three complementary maps profiling nanobio interactions and pathways at different levels of biological complexity, a clear picture of a nanoparticle's journey through the body and the therapeutic and adverse consequences of each potential interaction are presented.

An In Vitro Dosimetry Tool for the Numerical Transport Modeling of Engineered Nanomaterials Powered by the Enalos RiskGONE Cloud Platform

A freely available "in vitro dosimetry" web application is presented enabling users to predict the concentration of nanomaterials reaching the cell surface, and therefore available for attachment and internalization, from initial dispersion concentrations. The web application is based on the distorted grid (DG) model for the dispersion of engineered nanoparticles (NPs) in culture medium used for in vitro cellular experiments, in accordance with previously published protocols for cellular dosimetry determination. A series of in vitro experiments for six different NPs, with Ag and Au cores, are performed to demonstrate the convenience of the web application for calculation of exposure concentrations of NPs. Our results show that the exposure concentrations at the cell surface can be more than 30 times higher compared to the nominal or dispersed concentrations, depending on the NPs' properties and their behavior in the cell culture medium. Therefore, the importance of calculating the exposure concentration at the bottom of the cell culture wells used for in vitro arrays, i.e., the particle concentration at the cell surface, is clearly presented, and the tool introduced here allows users easy access to such calculations. Widespread application of this web tool will increase the reliability of subsequent toxicity data, allowing improved correlation of the real exposure concentration with the observed toxicity, enabling the hazard potentials of different NPs to be compared on a more robust basis.

Toxicological Assessment of Cellulose Nanomaterials: Oral Exposure

Cellulose nanomaterials (CNMs) have emerged recently as an important group of sustainable bio-based nanomaterials (NMs) with potential applications in multiple sectors, including the food, food packaging, and biomedical fields. The widening of these applications leads to increased human oral exposure to these NMs and, potentially, to adverse health outcomes. Presently, the potential hazards regarding oral exposure to CNMs are insufficiently characterised. There is a need to understand and manage the potential adverse effects that might result from the ingestion of CNMs before products using CNMs reach commercialisation. This work reviews the potential applications of CNMs in the food and biomedical sectors along with the existing toxicological in vitro and in vivo studies, while also identifying current knowledge gaps. Relevant considerations when performing toxicological studies following oral exposure to CNMs are highlighted. An increasing number of studies have been published in the last years, overall showing that ingested CNMs are not toxic to the gastrointestinal tract (GIT), suggestive of the biocompatibility of the majority of the tested CNMs. However, in vitro and in vivo genotoxicity studies, as well as long-term carcinogenic or reproductive toxicity studies, are not yet available. These studies are needed to support a wider use of CNMs in applications that can lead to human oral ingestion, thereby promoting a safe and sustainable-by-design approach.

Smoothies Reduce the “Bioaccessibility” of TiO2 (E 171) in the Model of the In Vitro Gastrointestinal Tract

The food colorant E171 (TiO₂) containing nano fractions can cause potential health problems. In the presented work, we used a “gastrointestinal tract” model (oral→large intestine) to “digest” a fruit smoothie in the presence of TiO₂ nanoparticles and the Lactiplantibacillus plantarum B strain. The TiO₂ migration was measured using the microfiltration membrane (0.2 µm; model of “TiO₂ bioacessability”). We observed that the addition of the smoothie reduced the Ti content in the microfiltrate (reduced “bioacessability”) at the “mouth”, “stomach” and “large intestine” stages, probably due to the entrapment of Ti by the smoothie components. A significant decrease in Ti “bioaccessibility” at the “gastric” stage may have resulted from the agglomeration of nanoparticles at a low pH. Additionally, the presence of bacterial cells reduced the “bioaccessibility” at the “large intestine” stage. Microscopic imaging (SEM) revealed clear morphological changes to the bacterial cells in the presence of TiO₂ (altered topography, shrunk-deformed cells with collapsed walls due to leakage of the content, indentations). Additionally, TiO₂ significantly reduced the growth of the tested bacteria. It can be stated that the interactions (most probably entrapment) of TiO₂ in the food matrix can occur during the digestion. This can influence the physicochemical properties, bioavailability and in vivo effect of TiO₂. Research aimed at understanding the interactions between TiO₂ and food components is in progress.

Biological Impacts of Reduced Graphene Oxide Affected by Protein Corona Formation

Applications of reduced graphene oxide (rGO) in many different areas have been gradually increasing owing to its unique physicochemical characteristics, demanding more understanding of their biological impacts. Herein, we assessed the toxicological effects of rGO in mammary epithelial cells. Because the as-synthesized rGO was dissolved in sodium cholate to maintain a stable aqueous dispersion, we hypothesize that changing the cholate concentration in the dispersion may alter the surface property of rGO and subsequently affect its cellular toxicity. Thus, four types of rGO were prepared and compared: rGO dispersed in 4 and 2 mg/mL sodium cholate, labeled as rGO and concentrated-rGO (c-rGO), respectively, and rGO and c-rGO coated with a protein corona through 1 h incubation in culture media, correspondingly named pro-rGO and pro-c-rGO. Notably, c-rGO and pro-c-rGO exhibited higher toxicity than rGO and pro-rGO and also caused higher reactive oxygen species production, more lipid membrane peroxidation, and more significant disruption of mitochondrial-based ATP synthesis. In all toxicological assessments, pro-c-rGO induced more severe adverse impacts than c-rGO. Further examination of the material surface, protein adsorption, and cellular uptake showed that the surface of c-rGO was coated with a lower content of surfactant and adsorbed more proteins, which may result in the higher cellular uptake observed with pro-c-rGO than pro-rGO. Several proteins involved in cellular redox mediation were also more enriched in pro-c-rGO. These results support the strong correlation between dispersant coating and corona formation and their subsequent cellular impacts. Future studies in this direction could reveal a deeper understanding of the correlation and the specific cellular pathways involved and help gain knowledge on how the toxicity of rGO could be modulated through surface modification, guiding the sustainable applications of rGO.

Differential modulation of endothelial cytoplasmic protrusions after exposure to graphene-family nanomaterials

Engineered nanomaterials offer the benefit of having systematically tunable physicochemical characteristics (e.g., size, dimensionality, and surface chemistry) that highly dictate the biological activity of a material. Among the most promising engineered nanomaterials to date are graphene-family nanomaterials (GFNs), which are 2-D nanomaterials (2DNMs) with unique electrical and mechanical properties. Beyond engineering new nanomaterial properties, employing safety-by-design through considering the consequences of cell-material interactions is essential for exploring their applicability in the biomedical realm. In this study, we asked the effect of GFNs on the endothelial barrier function and cellular architecture of vascular endothelial cells. Using micropatterned cell pairs as a reductionist in vitro model of the endothelium, the progression of cytoskeletal reorganization as a function of GFN surface chemistry and time was quantitatively monitored. Here, we show that the surface oxidation of GFNs (graphene, reduced graphene oxide, partially reduced graphene oxide, and graphene oxide) differentially affect the endothelial barrier at multiple scales; from the biochemical pathways that influence the development of cellular protrusions to endothelial barrier integrity. More oxidized GFNs induce higher endothelial permeability and the increased formation of cytoplasmic protrusions such as filopodia. We found that these changes in cytoskeletal organization, along with barrier function, can be potentiated by the effect of GFNs on the Rho/Rho-associated kinase (ROCK) pathway. Specifically, GFNs with higher surface oxidation elicit stronger ROCK2 inhibitory behavior as compared to pristine graphene sheets. Overall, findings from these studies offer a new perspective towards systematically controlling the surface-dependent effects of GFNs on cytoskeletal organization via ROCK2 inhibition, providing insight for implementing safety-by-design principles in GFN manufacturing towards their targeted biomedical applications.

Investigation of interaction between MXene nanosheets and human plasma and protein corona composition

The composition of protein corona affects the behavior and fate of nanoparticles in biological systems, which strongly relates to the intrinsic properties of nanoparticles and proteins. Here, three types of MXene Ti₃C₂T_x nanosheets are prepared by different etching methods, and certain physicochemical characteristics of the nanosheets before and after exposure to human plasma (HP) are characterized. The Ti₃C₂T_x nanosheets with protein coronas suffer more easily from aggregation than pristine Ti₃C₂T_x. The composition of protein coronas by LC-MS/MS-based label-free proteomic analysis reveals a high overlap of protein types and functions but a significant difference in relative protein abundance for the three Ti₃C₂T_x. Immunoglobulins and coagulation proteins are highly enriched while albumin is depleted in the coronas compared with their abundance in original HP. The random forest classification model predicts that the main driving forces for the adsorption of HP proteins on Ti₃C₂T_x are hydrogen bonding, steric hindrance, and hydrophobic interaction. This study provides insights into the colloidal stability of Ti₃C₂T_x nanosheets and their interaction with human plasma proteins.

Influence of surface chemistry and morphology of nanoparticles on protein corona formation

Nanomaterials offer promising solutions as drug delivery systems and imaging agents in response to the demand for better therapeutics and diagnostics. However, the limited understanding of the interaction between nanoparticles and biological entities is currently hampering the development of new systems and their applications in clinical settings. Proteins and lipids in biological fluids are known to complex with nanoparticles to form a "biomolecular corona". This has been shown to affect particles' morphology and behavior in biological systems and their interactions with cells. Hence, understanding how nanomaterials' physicochemical properties affect the formation and composition of this biocorona is a crucial step. This work evaluates existing literature on how morphology (size and shape), and surface chemistry (charge and hydrophobicity) of nanoparticles influence the formation of protein corona. The latest evidence suggest that although surface charge promotes the interaction with proteins and lipids, surface chemistry plays a leading role in determining the affinity of the nanoparticle for biomolecules and, ultimately, the composition of the corona. More recently the study of additional nanoparticles' properties like shape and surface chirality have demonstrated a significant effect on protein corona architecture, providing new tools to tailor biomolecular corona formation. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials.

Nanoparticle-protein corona complex: understanding multiple interactions between environmental factors, corona formation, and biological activity

The surfaces of pristine nanoparticles become rapidly coated by proteins in biological fluids, forming the so-called protein corona. The corona modifies key physicochemical characteristics of nanoparticle surfaces that modulate its biological and pharmacokinetic activity, biodistribution, and safety. In the two decades since the protein corona was identified, the importance of nanoparticles surface properties in regulating biological responses have been recognized. However, there is still a lack of clarity about the relationships between physiological conditions and corona composition over time, and how this controls biological activities/interactions. Here we review recent progress in characterizing the structure and composition of protein corona as a function of biological fluid and time. We summarize the influence of nanoparticle characteristics on protein corona composition and discuss the relevance of protein corona to the biological activity and fate of nanoparticles. The aim is to provide a critical summary of the key factors that affect protein corona formation (e.g. characteristics of nanoparticles and biological environment) and how the corona modulates biological activity, cellular uptake, biodistribution, and drug delivery. In addition to a discussion on the importance of the characterization of protein corona adsorbed on nanoparticle surfaces under conditions that mimic relevant physiological environment, we discuss the unresolved technical issues related to the characterization of nanoparticle-protein corona complexes during their journey in the body. Lastly, the paper offers a perspective on how the existing nanomaterial toxicity data obtained from in vitro studies should be reconsidered in the light of the presence of a protein corona, and how recent advances in fields, such as proteomics and machine learning can be integrated into the quantitative analysis of protein corona components.

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.

Printer center nanoparticles alter the DNA repair capacity of human bronchial airway epithelial cells

Nano-enabled, toner-based printing equipment emit nanoparticles during operation. The bioactivity of these nanoparticles as documented in a plethora of published toxicological studies raises concerns about their potential health effects. These include pro-inflammatory effects that can lead to adverse epigenetic alterations and cardiovascular disorders in rats. At the same time, their potential to alter DNA repair pathways at realistic doses remains unclear. In this study, size-fractionated, airborne particles from a printer center in Singapore were sampled and characterized. The PM_0.1 size fraction (particles with an aerodynamic diameter less than 100 nm) of printer center particles (PCP) were then administered to human lung adenocarcinoma (Calu-3) or lymphoblastoid (TK6) cells. We evaluated plasma membrane integrity, mitochondrial activity, and intracellular reactive oxygen species (ROS) generation. Moreover, we quantified DNA damage and alterations in the cells' capacity to repair 6 distinct types of DNA lesions. Results show that PCP altered the ability of Calu-3 cells to repair 8oxoG:C lesions and perform nucleotide excision repair, in the absence of acute cytotoxicity or DNA damage. Alterations in DNA repair capacity have been correlated with the risk of various diseases, including cancer, therefore further genotoxicity studies are needed to assess the potential risks of PCP exposure, at both occupational settings and at the end-consumer level.

Bioinformatics-Based Approaches to Study Virus-Host Interactions During SARS-CoV-2 Infection

As the knowledge of biomolecules is increasing from the last decades, it is helping the researchers to understand the unsolved issues regarding virology. Recent technologies in high-throughput sequencing are providing the swift generation of SARS-CoV-2 genomic data with the basic inside of viral infection. Owing to various virus-host protein interactions, high-throughput technologies are unable to provide complete details of viral pathogenesis. Identifying the virus-host protein interactions using bioinformatics approaches can assist in understanding the mechanism of SARS-CoV-2 infection and pathogenesis. In this chapter, recent integrative bioinformatics approaches are discussed to help the virologists and computational biologists in the identification of structurally similar proteins of human and SARS-CoV-2 virus, and to predict the potential of virus-host interactions. Considering experimental and time limitations for effective viral drug development, computational aided drug design (CADD) can reduce the gap between drug prediction and development. More research with respect to evolutionary solutions could be helpful to make a new pipeline for virus-host protein-protein interactions and provide more understanding to disclose the cases of host switch, and also expand the virulence of the pathogen and host range in developing viral infections.

Biotransformations and cytotoxicity of eleven graphene and inorganic two-dimensional nanomaterials using simulated digestions coupled with a triculture in vitro model of the human gastrointestinal epithelium

Background

Engineered nanomaterials (ENMs) have already made their way into myriad applications and products across multiple industries. However, the potential health risks of exposure to ENMs remain poorly understood. This is particularly true for the emerging class of ENMs know as 2-dimensional nanomaterials (2DNMs), with a thickness of one or a few layers of atoms arranged in a planar structure.

Methods

The present study assesses the biotransformations and in vitro cytotoxicity in the gastrointestinal tract of 11 2DNMs, namely graphene, graphene oxide (GO), partially reduced graphene oxide (prGO), reduced graphene oxide (rGO), hexagonal boron nitride (h-BN), molybdenum disulphide (MoS2), and tungsten disulphide (WS2). The evaluated pristine materials were either readily dispersed in water or dispersed with the use of a surfactant (Na-cholate or PF108). Materials dispersed in a fasting food model (FFM, water) were subjected to simulated 3-phase (oral, gastric, and small intestinal) digestion to replicate the biotransformations that would occur in the GIT after ingestion. A triculture model of small intestinal epithelium was used to assess the effects of the digested products (digestas) on epithelial layer integrity, cytotoxicity, viability, oxidative stress, and initiation of apoptosis.

Results

Physicochemical characterization of the 2DNMs in FFM dispersions and in small intestinal digestas revealed significant agglomeration by all materials during digestion, most prominently by graphene, which was likely caused by interactions with digestive proteins. Also, MoS2 had dissolved by ~75% by the end of simulated digestion. Other than a low but statistically significant increase in cytotoxicity observed with all inorganic materials and graphene dispersed in PF108, no adverse effects were observed in the exposed tricultures.

Conclusions

Our results suggest that occasional ingestion of small quantities of 2DNMs may not be highly cytotoxic in a physiologically relevant in vitro model of the intestinal epithelium. Still, their inflammatory or genotoxic potential after short- or long-term ingestion remains unclear and needs to be studied in future in vitro and in vivo studies. These would include studies of effects on co-ingested nutrient digestion and absorption, which have been documented for numerous ingested ENMs, as well as effects on the gut microbiome, which can have important health implications.

Transcorneal delivery of topically applied silver nanoparticles does not delay epithelial wound healing

Silver nanoparticles (AgNPs) are a common antimicrobial additive for a variety of applications, including wound care. However, AgNPs often undergo dissolution resulting in release of silver ions, with subsequent toxicity to mammalian cells. The cornea is a primary exposure site to topically administered AgNPs in and around the eye but their impact on corneal wound healing is understudied. Thus, the purpose of this study was to determine in vitro toxicity of AgNPs on corneal epithelial cells and fibroblasts as well as their effects on corneal epithelial wound healing utilizing an in vivo rabbit model. Non-coated 20 nm sized AgNP (AgNP-20) as well as 1% and 10% silver silica NPs (AgSiO₂NPs) were tested at concentrations ranging from 0.05-250 μg/mL. Immortalized human corneal epithelial (hTCEpi) cells and primary rabbit corneal fibroblasts (RCFs) were incubated for 24 h with AgNPs and cell viability was tested. Additionally, a round wound healing assay was performed to determine hTCEpi cell migration. Quantitative real-time PCR and western blot analysis was performed to determine α-smooth muscle actin (α-SMA, a myofibroblast marker) mRNA and protein expression, respectively, in RCFs treated with 50 μg/mL of AgNPs. Corneal epithelial wound healing was evaluated with 1%-AgSiO₂NPs (10 and 250 μg/mL) using an in vivo rabbit model. Rabbits were subsequently euthanized, and histologic sections of the enucleated globes were used to determine corneal penetration of 1%-AgSiO₂NPs with autometallography and hyperspectral darkfield microscopy. Cell viability of both the hTCEpi cells and fibroblasts was significantly decreased by the three AgNPs in a dose dependent manner. Migration of hTCEpi cells was significantly inhibited by the three AgNPs. Alpha-SMA mRNA expression was significantly inhibited with three AgNPs, but only the 1%-AgSiO₂NPs inhibited protein expression of α-SMA. In vivo epithelial wound closure did not significantly differ between groups treated with 10 or 250 μg/mL of 1%-AgSiO₂NPs or vehicle control. The 1%-AgSiO₂NPs penetrated throughout all corneal layers and into the anterior chamber in all treated eyes with no histopathological changes observed. In conclusion, the 1%-AgSiO₂NPs are safe and have potential therapeutic applications through its efficacy of the corneal penetration and reduced scar formation during corneal wound healing.

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.

Environmental dimensions of the protein corona

The adsorption of biomolecules to the surface of engineered nanomaterials, known as corona formation, defines their biological identity by altering their surface properties and transforming the physical, chemical and biological characteristics of the particles. In the first decade since the term protein corona was coined, studies have focused primarily on biomedical applications and human toxicity. The relevance of the environmental dimensions of the protein corona is still emerging. Often referred to as the eco-corona, a biomolecular coating forms upon nanomaterials as they enter the environment and may include proteins, as well as a diverse array of other biomolecules such as metabolites from cellular activity and/or natural organic matter. Proteins remain central in studies of eco-coronas because of the ease of monitoring and structurally characterizing proteins, as well as their crucial role in receptor engagement and signalling. The proteins within the eco-corona are optimal targets to establish the biophysicochemical principles of corona formation and transformation, as well as downstream impacts on nanomaterial uptake, distribution and impacts on the environment. Moreover, proteins appear to impart a biological identity, leading to cellular or organismal recognition of nanomaterials, a unique characteristic compared with natural organic matter. We contrast insights into protein corona formation from clinical samples with those in environmentally relevant systems. Principles specific to the environment are also explored to gain insights into the dynamics of interaction with or replacement by other biomolecules, including changes during trophic transfer and ecotoxicity. With many challenges remaining, we also highlight key opportunities for method development and impactful systems on which to focus the next phase of eco-corona studies. By interrogating these environmental dimensions of the protein corona, we offer a perspective on how mechanistic insights into protein coronas in the environment can lead to more sustainable, environmentally safe nanomaterials, as well as enhancing the efficacy of nanomaterials used in remediation and in the agri-food sector.

In silico nanosafety assessment tools and their ecosystem-level integration prospect

Engineered nanomaterials (ENMs) have tremendous potential in many fields, but their applications and commercialization are difficult to widely implement due to their safety concerns. Recently, in silico nanosafety assessment has become an important and necessary tool to realize the safer-by-design strategy of ENMs and at the same time to reduce animal tests and exposure experiments. Here, in silico nanosafety assessment tools are classified into three categories according to their methodologies and objectives, including (i) data-driven prediction for acute toxicity, (ii) fate modeling for environmental pollution, and (iii) nano-biological interaction modeling for long-term biological effects. Released ENMs may cross environmental boundaries and undergo a variety of transformations in biological and environmental media. Therefore, the potential impacts of ENMs must be assessed from a multimedia perspective and with integrated approaches considering environmental and biological effects. Ecosystems with biodiversity and an abiotic environment may be used as an excellent integration platform to assess the community- and ecosystem-level nanosafety. In this review, the advances and challenges of in silico nanosafety assessment tools are carefully discussed. Furthermore, their integration at the ecosystem level may provide more comprehensive and reliable nanosafety assessment by establishing a site-specific interactive system among ENMs, abiotic environment, and biological communities.

Machine learning-integrated omics for the risk and safety assessment of nanomaterials

With the advancement in nanotechnology, we are experiencing transformation in world order with deep insemination of nanoproducts from basic necessities to advanced electronics, health care products and medicines. Therefore, nanoproducts, however, can have negative side effects and must be strictly monitored to avoid negative outcomes. Future toxicity and safety challenges regarding nanomaterial incorporation into consumer products, including rapid addition of nanomaterials with diverse functionalities and attributes, highlight the limitations of traditional safety evaluation tools. Currently, artificial intelligence and machine learning algorithms are envisioned for enhancing and improving the nano-bio-interaction simulation and modeling, and they extend to the post-marketing surveillance of nanomaterials in the real world. Thus, hyphenation of machine learning with biology and nanomaterials could provide exclusive insights into the perturbations of delicate biological functions after integration with nanomaterials. In this review, we discuss the potential of combining integrative omics with machine learning in profiling nanomaterial safety and risk assessment and provide guidance for regulatory authorities as well.

In vivo Protein Corona Formation: Characterizations, Effects on Engineered Nanoparticles' Biobehaviors, and Applications

Understanding the basic interactions between engineered nanoparticles (ENPs) and biological systems is essential for evaluating ENPs’ safety and developing better nanomedicine. Profound interactions between ENPs and biomolecules such as proteins are inevitable to occur when ENPs are administered or exposed to biological systems, for example, through intravenous injection, oral, or respiration. As a key component of these interactions, protein corona (PC) is immediately formed surrounding the outlayer of ENPs. PC formation is crucial because it gives ENPs a new biological identity by altering not only the physiochemical properties, but also the biobehaviors of ENPs. In the past two decades, most investigations about PC formation were carried out with in vitro systems which could not represent the true events occurring within in vivo systems. Most recently, studies of in vivo PC formation were reported, and it was found that the protein compositions and structures were very different from those formed in vitro. Herein, we provide an in-time review of the recent investigations of this in vivo PC formation of ENPs. In this review, commonly used characterization methods and compositions of in vivo PC are summarized firstly. Next, we highlight the impacts of the in vivo PC formation on absorption, blood circulation, biodistribution, metabolism, and toxicity of administered ENPs. We also introduce the applications of modulating in vivo PC formation in nanomedicine. We further discuss the challenges and future perspectives.

High-Throughput Screening Platform for Nanoparticle-Mediated Alterations of DNA Repair Capacity

The potential genotoxic effects of engineered nanomaterials (ENMs) may occur through the induction of DNA damage or the disruption of DNA repair processes. Inefficient DNA repair may lead to the accumulation of DNA lesions and has been linked to various diseases, including cancer. Most studies so far have focused on understanding the nanogenotoxicity of ENM-induced damages to DNA, whereas the effects on DNA repair have been widely overlooked. The recently developed fluorescence multiplex-host-cell reactivation (FM-HCR) assay allows for the direct quantification of multiple DNA repair pathways in living cells and offers a great opportunity to address this methodological gap. Herein an FM-HCR-based method is developed to screen the impact of ENMs on six major DNA repair pathways using suspended or adherent cells. The sensitivity and efficiency of this DNA repair screening method were demonstrated in case studies using primary human small airway epithelial cells and TK6 cells exposed to various model ENMs (CuO, ZnO, and Ga2O3) at subcytotoxic doses. It was shown that ENMs may inhibit nucleotide-excision repair, base-excision repair, and the repair of oxidative damage by DNA glycosylases in TK6 cells, even in the absence of significant genomic DNA damage. It is of note that the DNA repair capacity was increased by some ENMs, whereas it was suppressed by others. Overall, this method can be part of a multitier, in vitro hazard assessment of ENMs as a functional, high-throughput platform that provides insights into the interplay of the properties of ENMs, the DNA repair efficiency, and the genomic stability.

In vivo protein corona on nanoparticles: does the control of all material parameters orient the biological behavior?

Nanomaterials have a huge potential in research fields from nanomedicine to medical devices. However, surface modifications of nanoparticles (NPs) and thus of their physicochemical properties failed to predict their biological behavior. This requires investigating the "missing link" at the nano-bio interface. The protein corona (PC), the set of proteins binding to the NPs surface, plays a critical role in particle recognition by the innate immune system. Still, in vitro incubation offers a limited understanding of biological interactions and fails to explain the in vivo fate. To date, several reports explained the impact of PC in vitro but its applications in the clinical field have been very limited. Furthermore, PC is often considered as a biological barrier reducing the targeting efficiency of nano vehicles. But the protein binding can actually be controlled by altering PC both in vitro and in vivo. Analyzing PC in vivo could accordingly provide a deep understanding of its biological effect and speed up the transfer to clinical applications. This review demonstrates the need for clarifications on the effect of PC in vivo and the control of its behavior by changing its physicochemical properties. It unfolds the recent in vivo developments to understand mechanisms and challenges at the nano-bio interface. Finally, it reports recent advances in the in vivo PC to overcome and control the limitations of the in vitro PC by employing PC as a boosting resource to prolong the NPs half-life, to improve their formulations and thereby to increase its use for biomedical applications.

Membrane engineering of cell membrane biomimetic nanoparticles for nanoscale therapeutics

In recent years, cell membrane camouflaging technology has emerged as an important strategy of nanomedicine, and the modification on the membranes is also a promising approach to enhance the properties of the nanoparticles, such as cancer targeting, immune evasion, and phototherapy sensitivity. Indeed, diversified approaches have been exploited to re-engineer the membranes of nanoparticles in several studies. In this review, first we discuss direct modification strategy of cell membrane camouflaged nanoparticles (CM-NP) via noncovalent, covalent, and enzyme-involved methods. Second, we explore how the membranes of CM-NPs can be re-engineered at the cellular level using strategies such as genetic engineering and membranes fusion. Due to the innate biological properties and excellent biocompatibility, the functionalized cell membrane-camouflaged nanoparticles have been widely applied in the fields of drug delivery, imaging, detoxification, detection, and photoactivatable therapy.

Knowledge gaps in immune response and immunotherapy involving nanomaterials: Databases and artificial intelligence for material design

Exploring the interactions between the immune system and nanomaterials (NMs) is critical for designing effective and safe NMs, but large knowledge gaps remain to be filled prior to clinical applications (e.g., immunotherapy). The lack of databases on interactions between the immune system and NMs affects the discovery of new NMs for immunotherapy. Complement activation and inhibition by NMs have been widely studied, but the general rules remain unclear. Biomimetic nanocoating to promote the clearance of NMs by the immune system is an alternative strategy for the immune response mediation of the biological corona. Immune response predictions based on NM properties can facilitate the design of NMs for immunotherapy, and artificial intelligences deserve much attention in the field. This review addresses the knowledge gaps regarding immune response and immunotherapy in relation to NMs, effective immunotherapy and material design without adverse immune responses.

Lipid and protein corona of food-grade TiO2 nanoparticles in simulated gastrointestinal digestion

In the presence of biological matrices, engineered nanomaterials, such as TiO₂, develop a biomolecular corona composed of lipids, proteins, etc. In this study, we analyzed the biocorona formed on the food grade TiO₂ (E171) going through an in vitro simulated gastrointestinal digestion system in either a fasting food model (FFM), a standardized food model (SFM), or a high fat food model (HFFM). Lipids and proteins were extracted from the biocorona and underwent untargeted lipidomic and label-free shotgun proteomic analyses. Our results showed that the biocorona composition was different before and after food digestion. After digestion, more diverse lipids were adsorbed compared to proteins, most of which were the enzymes added to the simulated digestion system. The corona lipid profile was distinct from the digested food model they presented in, although similarity in the lipid profiles between the corona and the food matrix increased with the fat content in the food model. The corona formed in the two low-fat environments of FFM and SFM shared a higher degree of similarity while very different from their corresponding matrix, with some lipid species adsorbed with high enrichment factors, indicating specific interaction with the TiO₂ surface outperforming lipid matrix concentration in determination of corona formation. Formation of the biocorona may have contributed to the reduced oxidative stress as well as toxicological impacts observed in cellular studies. The present work is the first to confirm persistent adsorption of biomolecules could occur on ingested nanomaterials in food digestae. More future studies are needed to study the in vivo impacts of the biocorona, and shed lights on how the biocorona affects the biotransformations and fate of the ingested nanomaterials, which may impose impacts on human health.

Asymmetrical Flow Field Flow Fractionation Coupled to Nanoparticle Tracking Analysis for Rapid Online Characterization of Nanomaterials

Increasing applications of nanomaterials in consumer goods, industrial products, medical practices, etc., calls for the development of tools for rapid separation, quantification, and sizing of nanoparticles to ensure their safe and sustainable employment. While many techniques are available for characterization of pure, homogeneous nanomaterial preparations, particle sizing and counting remains difficult for heterogeneous mixtures that resulted from imperfect synthesis conditions, aggregation from product instability, or degradation during storage. Herein, nanoparticle tracking analysis (NTA) was coupled to asymmetrical flow field flow fraction (AF4) using a splitter manifold to enable online particle separation and counting. The high pressure and flow rate in AF4 were reduced to the levels compatible with NTA by the proper flow splitting design, and a syringe pump was employed to withdraw fluid through the exit port of the NTA and maintain consistent flow rates entering NTA for proper particle sizing. Successful AF4-NTA coupling was demonstrated by analyzing a mixture of polystyrene particles with the average diameters of ∼50, 100, and 200 nm. Good correlation was observed between the amount of each type of particle injected to and measured by the hyphenated system. The particle concentrations acquired using online and offline coupling of AF4-NTA also agreed well with each other. The nonspherical nanoparticles like gold nanorods and hexagonal boron nitride nanosheets were also analyzed to demonstrate the versatile applicability of this system. Our work has proved that AF4-NTA can achieve accurate online particle counting on different populations of the nanomaterials in a mixture, which cannot be done by either AF4 or NTA alone. It will be a valuable tool for rapid characterization of heterogeneous nanomaterial solutions without purification to fulfill the regulation requirement on the nanomaterial-containing products.