Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice.

Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of "Nano-Tumor Database", which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded "Nano-Tumor Database" and several statistical models that may help nanomedicine design in the future.

An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice.

The critical barrier for clinical translation of cancer nanomedicine stems from the inefficient delivery of nanoparticles (NPs) to target solid tumors. Rapid growth of computational power, new machine learning and artificial intelligence (AI) approaches provide new tools to address this challenge. In this study, we established an AI-assisted physiologically based pharmacokinetic (PBPK) model by integrating an AI-based quantitative structure-activity relationship (QSAR) model with a PBPK model to simulate tumor-targeted delivery efficiency (DE) and biodistribution of various NPs. The AI-based QSAR model was developed using machine learning and deep neural network algorithms that were trained with datasets from a published "Nano-Tumor Database" to predict critical input parameters of the PBPK model. The PBPK model with optimized NP cellular uptake kinetic parameters was used to predict the maximum delivery efficiency (DEmax) and DE at 24 (DE24) and 168 h (DE168) of different NPs in the tumor after intravenous injection and achieved a determination coefficient of R2 = 0.83 [root mean squared error (RMSE) = 3.01] for DE24, R2 = 0.56 (RMSE = 2.27) for DE168, and R2 = 0.82 (RMSE = 3.51) for DEmax. The AI-PBPK model predictions correlated well with available experimentally-measured pharmacokinetic profiles of different NPs in tumors after intravenous injection (R2 ≥ 0.70 for 133 out of 288 datasets). This AI-based PBPK model provides an efficient screening tool to rapidly predict delivery efficiency of a NP based on its physicochemical properties without relying on an animal training dataset.

Biocorona modulates the inflammatory response induced by gold nanoparticles in human epidermal keratinocytes.

The functional activities of gold nanoparticles (AuNPs) on biological systems depend on their physical-chemical properties and their surface functionalizations. Within a biological environment and depending on their surface characteristics, NPs can adsorb biomolecules (mostly proteins) present in the microenvironment, thereby forming a dynamic biomolecular corona on the surface. The presence of this biocorona changes the physical-chemical and functional properties of the NPs and how it interacts with cells. Here, we show that primary human epidermal keratinocytes (HEK) exposed in culture to branched polyethyleneimine (BPEI)-AuNPs, but not to lipoic acid (LA)-AuNPs, show potent particle uptake, decreased cell viability and enhanced production of inflammatory factors, while the presence of a human plasma-derived biocorona decreased NPs uptake and rescued cells from BPEI-AuNP-induced cell death. The mechanistic study revealed that the intracellular oxidative level greatly increased after the BPEI-AuNPs treatment, and the transcriptomic analysis showed that the dominant modulated pathways were related to oxidative stress and an antioxidant response. The stress level measured by flow cytometry also showed a significant decrease in the presence of a biocorona. Further anaylsis discovered that nuclear factor erythroid-2 related factor (Nrf2), a major regulator of anti-oxidant and anti-inflammatory genes, as the key factor related to the AuNPs induced oxidative stress and inflammation. This study provides futher understanding into the mechanisms on how NPs-induced cellular stress and reveals the protective effects of a biocorona on inflammatory responses in HEK at the molecular level, which provides important insights into the biological responses of AuNPs and their biocorona.

Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats.

BACKGROUND: Physiologically based pharmacokinetic (PBPK) modeling is an important tool in predicting target organ dosimetry and risk assessment of nanoparticles (NPs). The methodology of building a multi-route PBPK model for NPs has not been established, nor systematically evaluated. In this study, we hypothesized that the traditional route-to-route extrapolation approach of PBPK modeling that is typically used for small molecules may not be appropriate for NPs. To test this hypothesis, the objective of this study was to develop a multi-route PBPK model for different sizes (1.4-200 nm) of gold nanoparticles (AuNPs) in adult rats following different routes of administration (i.e., intravenous (IV), oral gavage, intratracheal instillation, and endotracheal inhalation) using two approaches: a traditional route-to-route extrapolation approach for small molecules and a new approach that is based on route-specific data that we propose to be applied generally to NPs. RESULTS: We found that the PBPK model using this new approach had superior performance than the traditional approach. The final PBPK model was optimized rigorously using a Bayesian hierarchical approach with Markov chain Monte Carlo simulations, and then converted to a web-based interface using R Shiny. In addition, quantitative structure-activity relationships (QSAR) based multivariate linear regressions were established to predict the route-specific key biodistribution parameters (e.g., maximum uptake rate) based on the physicochemical properties of AuNPs (e.g., size, surface area, dose, Zeta potential, and NP numbers). These results showed the size and surface area of AuNPs were the main determinants for endocytic/phagocytic uptake rates regardless of the route of administration, while Zeta potential was an important parameter for the estimation of the exocytic release rates following IV administration. CONCLUSIONS: This study suggests that traditional route-to-route extrapolation approaches for PBPK modeling of small molecules are not applicable to NPs. Therefore, multi-route PBPK models for NPs should be developed using route-specific data. This novel PBPK-based web interface serves as a foundation for extrapolating to other NPs and to humans to facilitate biodistribution estimation, safety, and risk assessment of NPs.

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches.

Background: Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined. Methods: This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model. Results: The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R2) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DEmax), delivery efficiency at 24 h (DE24), at 168 h (DE168), and at the last sampling time (DETlast). The corresponding R2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19-29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy. Conclusion: This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.

The synergistic strategies for the immuno-oncotherapy with photothermal nanoagents.

Immuno-oncotherapy has shown great promise for the cure of late-stage and metastatic cancer. Great efforts have tried to improve the overall response rate (ORR) and to reduce the immune-related adverse events (irAEs). Antigen presentation, T cell activation and killing are interlocking and distinct steps to initiate effective anti-tumor immune responses. Aiming to overcome the tumor immune evasion whose mechanisms include limited release of neoantigen, suppressed infiltration of antigen-presenting cells (APCs) and T cells, and the expression of immune checkpoints (ICPs), combinational therapeutic strategies have shown great potential by activating the anti-tumor immune responses together with deactivating immunosuppressive conditions simultaneously. In this direction, photothermal therapy (PTT) has attracted attention due to the efficient ablation of tumor cells, of which the released immunogenic tumor debris can activate host immune responses. The combination of immunoadjuvants and/or ICP inhibitors can boost the anti-tumor immune responses, realizing PTT-synergized immuno-oncotherapy. In this regard, numerous multifunctional nanomaterials have been designed with integration of photothermal and immuno-oncotherapeutic agents into one package via well-designed surface modification and functionalization. This review summarizes the recent studies on the synergistic strategies for the immuno-oncotherapy based on photothermal nanoagents and the mechanisms that trigger the systemic anti-tumor immune responses and PTT-synergized immuno-oncotherapy. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Meta-Analysis of Nanoparticle Delivery to Tumors Using a Physiologically Based Pharmacokinetic Modeling and Simulation Approach.

Numerous studies have engineered nanoparticles with different physicochemical properties to enhance the delivery efficiency to solid tumors, yet the mean and median delivery efficiencies are only 1.48% and 0.70% of the injected dose (%ID), respectively, according to a study using a nonphysiologically based modeling approach based on published data from 2005 to 2015. In this study, we used physiologically based pharmacokinetic (PBPK) models to analyze 376 data sets covering a wide range of nanomedicines published from 2005 to 2018 and found mean and median delivery efficiencies at the last sampling time point of 2.23% and 0.76%ID, respectively. Also, the mean and median delivery efficiencies were 2.24% and 0.76%ID at 24 h and were decreased to 1.23% and 0.35%ID at 168 h, respectively, after intravenous administration. While these delivery efficiencies appear to be higher than previous findings, they are still quite low and represent a critical barrier in the clinical translation of nanomedicines. We explored the potential causes of this poor delivery efficiency using the more mechanistic PBPK perspective applied to a subset of gold nanoparticles and found that low delivery efficiency was associated with low distribution and permeability coefficients at the tumor site (P < 0.01). We also demonstrate how PBPK modeling and simulation can be used as an effective tool to investigate tumor delivery efficiency of nanomedicines.

Amino/Amido Conjugates Form to Nanoscale Cobalt Physiometacomposite (PMC) Materials Functionally Delivering Nucleic Acid Therapeutic to Nucleus Enhancing Anticancer Activity via Ras-Targeted Protein Interference.

Aberrant splicing and protein interaction of Ras binding domain (RBD) are associated with melanoma drug resistance. Here, cobalt or nickel doped zinc oxide (ZnO) physiometacomposite (PMC) materials bind to RNA and peptide shown by Ninhydrin staining, UV-vis, Fourier transform infrared, and circular dichroism spectroscopy. PMCs deliver splice switching oligomer (SSO) into melanoma cells or 3-D tumor spheroids shown by flow cytometry, fluorescence microscopy, and bioluminescence. Stability in serum, liver, or tumor homogenate up to 48 h and B16F10 melanoma inhibition ≥98-99% is shown. These data suggest preclinical potential of PMC for delivery of SSO, RBD, or other nucleic acid therapeutic and anticancer peptides.

Toxicity assessment of six titanium dioxide nanoparticles in human epidermal keratinocytes.

PURPOSE: The aim of this study was to evaluate and compare the toxicity of six different types of titanium dioxide (TiO2) nanoparticles (NP) on human epidermal keratinocytes (HEK). MATERIALS AND METHODS: Six TiO2 NP (A (10 nm), A*(32 nm), B (27.5 nm), C (200 nm), C*(30-40 nm), and D*(200-400 nm)) were suspended in water or culture medium and characterized by transmission electron microscopy (TEM) and dynamic light scattering (DLS). In addition, these NP were assayed with cell viability, cytokine release and cellular uptake in HEK. RESULTS: TiO2NP did not change in shape in the culture medium when visualized by TEM. There was an increase in agglomeration with all TiO2NP in the medium when measured by DLS. Since TiO2NP interfered with the CellTiter 96®AQueous One and MTT assays but had a minimal effect on alamar Blue (aB). The aB viability assay was selected to assess all six types of TiO2NP and sample B had a statistically significant decrease in viability at 0.4 mg/ml. A slight increase in TNF-α was noted in sample A*, C, and D* at as low as 0.05 mg/ml. Sample A* and B at certain concentrations showed an increase in Interleukin (IL)-6. IL-10 and IL-1ß release for all TiO2NP were noted around the detection limit with no significant changes compared to control. A statistically significant decrease in IL-8 was noted for all TiO2NP at the highest concentrations due to the adsorption of IL-8 by TiO2. All TiO2NP were localized within cytoplasmic vacuoles of HEK and the element Ti was detected by energy-dispersive x-ray spectroscopy analysis. CONCLUSIONS: Based on cell viability, only sample B was slightly cytotoxic to HEK and samples B and A* have the potential to cause inflammation indicated by an increase in IL-6.

Modeling gold nanoparticle biodistribution after arterial infusion into perfused tissue: effects of surface coating, size and protein corona.

A detailed understanding of the factors governing nanomaterial biodistribution is needed to rationally design safe nanomedicines. This research details the pharmacokinetics of gold nanoparticle (AuNP) biodistribution after arterial infusion of 40 or 80 nm AuNP (1 µg/ml) into the isolated perfused porcine skin flap (IPPSF). AuNP had surface coatings consisting of neutral polyethylene glycol (PEG), anionic lipoic acid (LA), or cationic branched polyethylenimine (BPEI). Effect of a porcine plasma corona (PPC) on 40 nm BPEI and PEG-AuNP were assessed in the IPPSF. Au concentrations were determined by ICP/MS and arterial to venous concentration-time profiles were analyzed over 8 hr (4 hr infusion, 4 hr washout) using a two-compartment pharmacokinetic model. IPPSF viability and vascular function were assessed by change in glucose utilization, vascular resistance, or weight gain after perfusion. All AuNP demonstrated some degree of AuNP arterial extraction and skin flap retention, as well as enhanced kinetic parameters of tissue uptake; with BPEI-AuNP consistently having the greatest biodistribution even with a PPC. Toxicological effects were not detected. Transmission electron microscopy confirmed intracellular uptake of AuNP. These studies paralleled previous in vitro cell culture studies using the same AuNP in human endothelial and renal proximal tubule cells, hepatocytes, keratinocytes, showing BPEI-AuNP having the greatest uptake, although the presence of a PPC did not reduce IPPSF biodistribution as in the cell culture studies. These findings clearly indicate arterial to the venous extraction of AuNP after infusion with the magnitude of extraction being greatest with the BPEI surface coating and provide data and model structure necessary to construct the whole body physiologically based pharmacokinetic models capable of utilizing available in vitro data.

Probabilistic risk assessment of gold nanoparticles after intravenous administration by integrating in vitro and in vivo toxicity with physiologically based pharmacokinetic modeling.

This study aimed to conduct an integrated and probabilistic risk assessment of gold nanoparticles (AuNPs) based on recently published in vitro and in vivo toxicity studies coupled to a physiologically based pharmacokinetic (PBPK) model. Dose-response relationships were characterized based on cell viability assays in various human cell types. A previously well-validated human PBPK model for AuNPs was applied to quantify internal concentrations in liver, kidney, skin, and venous plasma. By applying a Bayesian-based probabilistic risk assessment approach incorporating Monte Carlo simulation, probable human cell death fractions were characterized. Additionally, we implemented in vitro to in vivo and animal-to-human extrapolation approaches to independently estimate external exposure levels of AuNPs that cause minimal toxicity. Our results suggest that under the highest dosing level employed in existing animal studies (worst-case scenario), AuNPs coated with branched polyethylenimine (BPEI) would likely induce ∼90-100% cellular death, implying high cytotoxicity compared to <10% cell death induced by low-to-medium animal dosing levels, which are commonly used in animal studies. The estimated human equivalent doses associated with 5% cell death in liver and kidney were around 1 and 3 mg/kg, respectively. Based on points of departure reported in animal studies, the human equivalent dose estimates associated with gene expression changes and tissue cell apoptosis in liver were 0.005 and 0.5 mg/kg, respectively. Our analyzes provide insights into safety evaluation, risk prediction, and point of departure estimation of AuNP exposure for humans and illustrate an approach that could be applied to other NPs when sufficient data are available.

Bacterial endotoxin (lipopolysaccharide) binds to the surface of gold nanoparticles, interferes with biocorona formation and induces human monocyte inflammatory activation.

Nanoparticles (NPs) are easily contaminated by bacterial endotoxin (lipopolysaccharide [LPS]). The presence of LPS can be responsible for many immune/inflammatory effects attributed to NPs. In this study, we examined the effects of LPS adsorption on the NP surface on the formation of a biocorona in biological fluids and on the subsequent inflammation-inducing activity of NPs. Different gold (Au) NPs with sizes ranging from 10 to 80 nm and with different surface functionalization (sodium citrate, lipoic acid, and branched polyethyleneimine (BPEI), or polyethylene glycol (PEG)) were exposed to E. coli LPS under different conditions. The binding capacity of LPS to the surface of AuNPs was dose- and time-dependent. LPS attached to sodium citrate and lipoic acid coatings, but did not adhere to BPEI- or PEG-coated NPs. By computational simulation, the binding of LPS to AuNPs seems to follow the Langmuir absorption isotherm. The presence of LPS on AuNP surface interfered and caused a decrease in the formation of the expected biomolecular corona upon incubation in human plasma. LPS-coated AuNPs, but not the LPS-free NPs, induced significant inflammatory responses in vitro. Notably, while free LPS did also induce an anti-inflammatory response, LPS bound to NPs appeared unable to do so. In conclusion, the unintentional adsorption of LPS onto the NP surface can affect the biocorona formation and the inflammatory properties of NPs. Thus, for an accurate interpretation of NP interactions with cells, it is extremely important to be able to distinguish the intrinsic NP biological effects from those caused by biologically active contaminants such as endotoxin.

Surface chemistry of gold nanoparticles determines the biocorona composition impacting cellular uptake, toxicity and gene expression profiles in human endothelial cells.

This study investigated the role of nanoparticle size and surface chemistry on biocorona composition and its effect on uptake, toxicity and cellular responses in human umbilical vein endothelial cells (HUVEC), employing 40 and 80 nm gold nanoparticles (AuNP) with branched polyethyleneimine (BPEI), lipoic acid (LA) and polyethylene glycol (PEG) coatings. Proteomic analysis identified 59 hard corona proteins among the various AuNP, revealing largely surface chemistry-dependent signature adsorbomes exhibiting human serum albumin (HSA) abundance. Size distribution analysis revealed the relative instability and aggregation inducing potential of bare and corona-bound BPEI-AuNP, over LA- and PEG-AuNP. Circular dichroism analysis showed surface chemistry-dependent conformational changes of proteins binding to AuNP. Time-dependent uptake of bare, plasma corona (PC) and HSA corona-bound AuNP (HSA-AuNP) showed significant reduction in uptake with PC formation. Cell viability studies demonstrated dose-dependent toxicity of BPEI-AuNP. Transcriptional profiling studies revealed 126 genes, from 13 biological pathways, to be differentially regulated by 40 nm bare and PC-bound BPEI-AuNP (PC-BPEI-AuNP). Furthermore, PC formation relieved the toxicity of cationic BPEI-AuNP by modulating expression of genes involved in DNA damage and repair, heat shock response, mitochondrial energy metabolism, oxidative stress and antioxidant response, and ER stress and unfolded protein response cascades, which were aberrantly expressed in bare BPEI-AuNP-treated cells. NP surface chemistry is shown to play the dominant role over size in determining the biocorona composition, which in turn modulates cell uptake, and biological responses, consequently defining the potential safety and efficacy of nanoformulations.

Protein corona modulation of hepatocyte uptake and molecular mechanisms of gold nanoparticle toxicity.

Protein corona formation over gold nanoparticles (AuNP) can modulate cellular responses by altering AuNP physicochemical properties. The liver plays an essential role in metabolism, detoxification and elimination of xenobiotics and drugs as well as circulating NP clearance. We investigated human hepatic uptake of 40 and 80 nm AuNP with branched polyethylenimine (BPEI), lipoic acid (LA) and polyethylene glycol (PEG) coatings as well as human plasma protein (HP) and human serum albumin (HSA) coronas. AuNP-mediated cytotoxicity, reactive oxygen/reactive nitrogen species (ROS/RNS), and CYP activity in human hepatocytes as well as molecular mechanisms with 40 nm bare and HP BPEI-AuNP were investigated. Time-dependent increase in uptake occurred for all bare AuNP but HP and HSA decreased uptake except for 40 nm HP PEG-AuNP. BPEI-AuNP showed time-and concentration-dependent increase in ROS/RNS which correlated with cytotoxicity at 24 h. HP corona substantially reduced ROS/RNS. The 40 and 80 nm bare, HP or HSA LA- and PEG-AuNP were not toxic but HP was as cytotoxic as bare BPEI-AuNP. All bare and HP BPEI-AuNP, except for HP 80 nm BPEI-AuNP toward CYP1A2, inhibited CYP1A2, CYP2C9 and CYP3A4 activity. Transcriptional profiling was dose-dependent with 40 nm bare BPEI-AuNP (1.9% genes at IC10 and 18.9% at LC50) and HP (23.5% at LC50). Differentially expressed genes at LC50 were mainly involved in phase I metabolism and phospholipidosis pathways. Cytotoxicity of bare BPEI-AuNP caused an upregulation of antioxidant and pro-apoptotic genes. These studies contribute to a better understanding of the dramatic effect of protein coronas (PC) on AuNP cellular uptake, cytotoxicity and their underlying molecular mechanisms of action.

Intracellular imaging of quantum dots, gold, and iron oxide nanoparticles with associated endocytic pathways.

Metallic nanoparticles (NP) have been used for biomedical applications especially for imaging. Compared to nonmetallic NP, metallic NP provide high contrast images because of their optical light scattering, magnetic resonance, X-ray absorption, or other physicochemical properties. In this review, a series of in vitro imaging techniques for metallic NP will be introduced, meanwhile their strengths and weaknesses will be discussed. By utilizing these imaging methods, the cellular uptake of metallic NP can be easily visualized to better understand the endocytic mechanisms of NP intracellular delivery. Several types of metallic NP that are used for imaging or as contrast agents such as quantum dots, gold, iron oxide, and other metallic NP will be presented. Cellular uptake of metallic NP and associated endocytic mechanisms highly depends upon the NP size, charge, surface coating, shape, or other factors such as cell type, cell differentiation status, cell surface status, external forces, protein binding, temperature, and the biological milieu. Classical endocytic routes such as lipid raft-mediated pathways, clathrin or caveolae-mediated pathways, macropinocytosis, and phagocytosis have been investigated, yet there is still a demand to determine other endocytic pathways. Knowing the different methodologies used to determine the endocytic pathways will increase the understanding of NP toxicity, cancer cell targeting, and imaging, so that surface coatings can be created for efficient cell uptake of metallic NP with minimal cytotoxicity WIREs Nanomed Nanobiotechnol 2017, 9:e1419. doi: 10.1002/wnan.1419 For further resources related to this article, please visit the WIREs website.

Mechanisms of cell uptake, inflammatory potential and protein corona effects with gold nanoparticles.

AIM: To assess inflammation, cellular uptake and endocytic mechanisms of gold nanoparticles (AuNP) in human epidermal keratinocytes with and without a protein corona. MATERIALS & METHODS: Human epidermal keratinocytes were exposed to 40 and 80 nm AuNP with lipoic acid, polyethylene glycol (PEG) and branched polyethyleneimine (BPEI) coatings with and without a protein corona up to 48 h. Inhibitors were selected to characterize endocytosis. RESULTS & CONCLUSION: BPEI-AuNP showed the greatest uptake, while PEG-AuNP had the least. Protein coronas decreased uptake and affected their mechanism. AuNP uptake was energy-dependent, except for 40 nm lipoic-AuNP. Most AuNP were internalized by clathrin and lipid raft-mediated endocytosis, except for 40 nm PEG was by raft/noncaveolae mediated endocytosis. Coronas inhibited caveolae-mediated-endocytosis with lipoic acid and BPEI-AuNP and altered 40 nm PEG-AuNP from raft/noncaveolae to clathrin. Inflammatory responses decreased with a plasma corona. Results suggest protein coronas significantly affect cellular uptake and inflammatory responses of AuNP.

Quantification of nanoparticle pesticide adsorption: computational approaches based on experimental data.

Quantitative analysis of the interactions between nanomaterials and environmental contamINANts, such as pesticides, in natural water systems and food residuals is crucial for the application of nanomaterials-based tools for the detection of the presence of toxic substances, monitoring pollution levels and environmental remediation. Previously, the Biological Surface Adsorption Index (BSAI) has demonstrated promising capabilities of interaction characterization and prediction based on experimental data from small organic molecules. In this article, the first attempt of the application of such quantitative measures toward environmental endpoints by analyzing the interactions of a selected group of nanomaterials with a variety of pesticides was made. Statistical modeling was conducted on the experimental obtained adsorption data based on polynomial BSAI models, as well as models with the incorporation of artificial neural network methodologies. Finally, clustering analyzes were performed for the categorization of nanomaterials based on surface physicochemical properties using both polynomial indices and physical adsorption modeling parameters. These quantitative computational approaches support the application of BSAI modeling in the area of environmental contamINANt detection and remediation.

Assessing the safety of cosmetic chemicals: Consideration of a flux decision tree to predict dermally delivered systemic dose for comparison with oral TTC (Threshold of Toxicological Concern).

Threshold of Toxicological Concern (TTC) aids assessment of human health risks from exposure to low levels of chemicals when toxicity data are limited. The objective here was to explore the potential refinement of exposure for applying the oral TTC to chemicals found in cosmetic products, for which there are limited dermal absorption data. A decision tree was constructed to estimate the dermally absorbed amount of chemical, based on typical skin exposure scenarios. Dermal absorption was calculated using an established predictive algorithm to derive the maximum skin flux adjusted to the actual 'dose' applied. The predicted systemic availability (assuming no local metabolism), can then be ranked against the oral TTC for the relevant structural class. The predictive approach has been evaluated by deriving the experimental/prediction ratio for systemic availability for 22 cosmetic chemical exposure scenarios. These emphasise that estimation of skin penetration may be challenging for penetration enhancing formulations, short application times with incomplete rinse-off, or significant metabolism. While there were a few exceptions, the experiment-to-prediction ratios mostly fell within a factor of 10 of the ideal value of 1. It can be concluded therefore, that the approach is fit-for-purpose when used as a screening and prioritisation tool.

Oxidative stress response in canine in vitro liver, kidney and intestinal models with seven potential dietary ingredients.

In vitro cell culture systems are a useful tool to rapidly assess the potential safety or toxicity of chemical constituents of food. Here, we investigated oxidative stress and organ-specific antioxidant responses by 7 potential dietary ingredients using canine in vitro culture of hepatocytes, proximal tubule cells (CPTC), bone marrow-derived mesenchymal stem cells (BMSC) and enterocyte-like cells (ELC). Cellular production of free radical species by denatonium benzoate (DB), epigallocatechin gallate (EPI), eucalyptol (EUC), green tea catechin extract (GTE) and sodium copper chlorophyllin (SCC), tetrahydroisohumulone (TRA) as well as xylitol (XYL) were continuously measured for reactive oxygen/nitrogen species (ROS/RNS) and superoxide (SO) for up to 24h. DB and TRA showed strong prooxidant activities in hepatocytes and to a lesser degree in ELC. DB was a weak prooxidant in BMSC. In contrast DB and TRA were antioxidants in CPTC. EPI was prooxidant in hepatocytes and BMSC but showed prooxidant and antioxidant activity in CPTC. SCC in hepatocytes (12.5mg/mL) and CPTC (0.78mg/mL) showed strong prooxidant and antioxidant activity in a concentration-dependent manner. GTE was effective antioxidant only in ELC. EUC and XYL did not induce ROS/RNS in all 4 cell types. SO production by EPI and TRA increased in hepatocytes but decreased by SCC in hepatocytes and ELC. These results suggest that organ-specific responses to oxidative stress by these potential prooxidant compounds may implicate a mechanism of their toxicities.