Hepatotoxicity of silver nanoparticles: Benchmark concentration modeling of an in vitro transcriptomics study in human iPSC-derived hepatocytes.

Despite two decades of research on silver nanoparticle (AgNP) toxicity, a safe threshold for exposure has not yet been established, albeit being critically needed for risk assessment and regulatory decision-making. Traditionally, a point-of-departure (PoD) value is derived from dose response of apical endpoints in animal studies using either the no-observed-adverse-effect level (NOAEL) approach, or benchmark dose (BMD) modeling. To develop new approach methodologies (NAMs) to inform human risk assessment of AgNPs, we conducted a concentration response modeling of the transcriptomic changes in hepatocytes derived from human induced pluripotent stem cells (iPSCs) after being exposed to a wide range concentration (0.01-25 µg/ml) of AgNPs for 24 h. A plausible transcriptomic PoD of 0.21 µg/ml was derived for a pathway related to the mode-of-action (MOA) of AgNPs, and a more conservative PoD of 0.10 µg/ml for a gene ontology (GO) term not apparently associated with the MOA of AgNPs. A reference dose (RfD) could be calculated from either of the PoDs as a safe threshold for AgNP exposure. The current study illustrates the usefulness of in vitro transcriptomic concentration response study using human cells as a NAM for toxicity study of chemicals that lack adequate toxicity data to inform human risk assessment.

Multiomics Point of Departure (moPOD) Modeling Supports an Adverse Outcome Pathway Network for Ionizing Radiation.

While adverse biological effects of acute high-dose ionizing radiation have been extensively investigated, knowledge on chronic low-dose effects is scarce. The aims of the present study were to identify hazards of low-dose ionizing radiation to Daphnia magna using multiomics dose-response modeling and to demonstrate the use of omics data to support an adverse outcome pathway (AOP) network development for ionizing radiation. Neonatal D. magna were exposed to γ radiation for 8 days. Transcriptomic analysis was performed after 4 and 8 days of exposure, whereas metabolomics and confirmative bioassays to support the omics analyses were conducted after 8 days of exposure. Benchmark doses (BMDs, 10% benchmark response) as points of departure (PODs) were estimated for both dose-responsive genes/metabolites and the enriched KEGG pathways. Relevant pathways derived using the BMD modeling and additional functional end points measured by the bioassays were overlaid with a previously published AOP network. The results showed that several molecular pathways were highly relevant to the known modes of action of γ radiation, including oxidative stress, DNA damage, mitochondrial dysfunction, protein degradation, and apoptosis. The functional assays showed increased oxidative stress and decreased mitochondrial membrane potential and ATP pool. Ranking of PODs at the pathway and functional levels showed that oxidative damage related functions had relatively low PODs, followed by DNA damage, energy metabolism, and apoptosis. These were supportive of causal events in the proposed AOP network. This approach yielded promising results and can potentially provide additional empirical evidence to support further AOP development for ionizing radiation.

Potency ranking of pyrrolizidine alkaloids in metabolically competent human liver cancer cells and primary human hepatocytes using a genotoxicity test battery.

Pyrrolizidine alkaloids (PAs) occur as contaminants in plant-based foods and herbal medicines. Following metabolic activation by cytochrome P450 (CYP) enzymes, PAs induce DNA damage, hepatotoxicity and can cause liver cancer in rodents. There is ample evidence that the chemical structure of PAs determines their toxicity. However, more quantitative genotoxicity data are required, particularly in primary human hepatocytes (PHH). Here, the genotoxicity of eleven structurally different PAs was investigated in human HepG2 liver cells with CYP3A4 overexpression and PHH using an in vitro test battery. Furthermore, the data were subject to benchmark dose (BMD) modeling to derive the genotoxic potency of individual PAs. The cytotoxicity was initially determined in HepG2-CYP3A4 cells, revealing a clear structure-toxicity relationship for the PAs. Importantly, experiments in PHH confirmed the structure-dependent toxicity and cytotoxic potency ranking of the tested PAs. The genotoxicity markers γH2AX and p53 as well as the alkaline Comet assay consistently demonstrated a structure-dependent genotoxicity of PAs in HepG2-CYP3A4 cells, correlating well with their cytotoxic potency. BMD modeling yielded BMD values in the range of 0.1-10 µM for most cyclic and open diesters, followed by the monoesters. While retrorsine showed the highest genotoxic potency, monocrotaline and lycopsamine displayed the lowest genotoxicity. Finally, experiments in PHH corroborated the genotoxic potency ranking, and revealed genotoxic effects even in the absence of detectable cytotoxicity. In conclusion, our findings strongly support the concept of grouping PAs into potency classes and help to pave the way for a broader acceptance of relative potency factors in risk assessment.

Estimation of Tolerable Daily Intake (TDI) for Immunological Effects of Aflatoxin.

Aflatoxins are toxic chemicals produced by the fungi Aspergillus flavus and Aspergillus parasiticus. In warm climates, these fungi frequently contaminate crops such as maize, peanuts, tree nuts, and sunflower seeds. In many tropical and subtropical regions of the world, populations are coexposed to dietary aflatoxin and multiple infectious pathogens in food, water, and the environment. There is increasing evidence that aflatoxin compromises the immune system, which could increase infectious disease risk in vulnerable populations. Our aim was to conduct a dose-response assessment on a noncarcinogenic endpoint of aflatoxin: immunotoxicological effects. We sought to determine a noncarcinogenic tolerable daily intake (TDI) of aflatoxin, based on the existing data surrounding aflatoxin and biomarkers of immune suppression. To conduct the dose response assessment, mammalian studies were assessed for appropriateness of doses (relevant to potential human exposures) as well as goodness of data, and two appropriate mouse studies that examined decreases in leukocyte counts were selected to generate dose response curves. From these, we determined benchmark dose lower confidence limits (BMDL) as points of departure to estimate a range of TDIs for aflatoxin-related immune impairment: 0.017-0.082 µg/kg bw/day. As aflatoxin is a genotoxic carcinogen, and regulations concerning its presence in food have largely focused on its carcinogenic effects, international risk assessment bodies such as the Joint Expert Committee on Food Additives (JECFA) have never established a TDI for aflatoxin. Our work highlights the importance of the noncarcinogenic effects of aflatoxin that may have broader public health impacts, to inform regulatory standard-setting.

Benchmark dose-response analyses for multiple endpoints in drug safety evaluation.

Hazard characterization during pharmaceutical development identifies the candidate drug's potential hazards and dose-response relationships. To date, the no-observed-adverse-effect-level (NOAEL) approach has been employed to identify the highest dose which results in no observed adverse effects. The benchmark dose (BMD) modeling approach describes potential dose-response relationships and has been used in diverse regulatory domains, but its applicability for pharmaceutical development has not previously been examined. Thus, we applied BMD-modeling to all endpoints in three sequential in vivo studies in a drug development setting, including biochemistry, hematology, organ pathology and clinical observations. In order to compare the results across such a broad range of effects, we needed to standardize the choice of the critical effect size (CES) for the different endpoints. A CES of 5%, previously suggested by the European Food Safety Authority, was compared with the study NOAEL and with the General Theory of Effect Size, which takes natural variability into account. Compared to the NOAEL approach, the BMD-modeling approach resulted in more informative estimates of the doses leading to effects. The BMD-modeling approach handled well situations where effects occurred below the lowest tested dose and the study's NOAEL, and seems advantageous to characterize the potential toxicity during safety assessment. The results imply a considerable step forward from the perspective of reducing and refining animal experiments, as more information is yielded from the same number of animals and at lower doses. Taken together, employing BMD-modeling as a substitute, or as a complement, to the NOAEL approach seems appropriate.

Use of benchmark dose models in risk assessment for occupational handlers of eight pesticides used in pome fruit production.

The benchmark dose has been frequently recommended for the creation of points of departure for regulatory dose limits, but many regulations, including pesticide risk assessment and registration in the United States, continues to rely on NOAEL methods as the OECD toxicological standard methods recommend. This study used data from studies in support of pesticide registration for eight different compounds to build dose-response models and calculate benchmark doses and confidence limits. The results were compared to the NOAEL of the same study. A probabilistic estimate of dose was compared with all points of departure to demonstrate differences in the protective ability of each different selected limit. While neither the BMD/BMDL nor the NOAEL was consistently more protective, the advantage of using the BMD in quantifying the uncertainty of the point of departure is highlighted, and the feasibility of using current OECD-guideline studies for derivation of a BMD is demonstrated in these cases.

A critical review of the literature to conduct a toxicity assessment for oral exposure to methyl salicylate.

Methyl salicylate is the predominant constituent of oil of wintergreen and is used as a pesticide, a denaturant, an external analgesic, a fragrance ingredient, and a flavoring agent in products such as chewing gum, baked goods, syrups, candy, beverages, ice cream, and tobacco products; and it occurs naturally in some vegetables and berries. Methyl salicylate is of interest to the tobacco industry as oil of wintergreen is used as a flavorant in tobacco products. The purpose of this investigation was to conduct a critical review of the available literature for oral exposure to methyl salicylate, incorporating an analysis of the quality of the studies available and the current understanding of the mode of action. Following a review of all of the available literature, the most appropriate data sets for dose-response modeling were reported by Gulati et al. in which significant changes in reproductive/development endpoints were reported to occur after exposure to 500 mg/kg/d of methyl salicylate in male and female mice. Benchmark dose modeling was performed and the most sensitive endpoint, the number of litters per mating pair, was associated with a BMDL of 220 mg/kg/d. This BMDL was chosen as the point of departure and adjusted by a body weight scaling factor to derive a human equivalent dose. Based on the uncertainty factor analysis, the POD for methyl salicylate was adjusted by a UF of 3 for interspecies uncertainty to derive an allowable daily intake of 11 mg/kg/d.

A quantitative framework to group nanoscale and microscale particles by hazard potency to derive occupational exposure limits: Proof of concept evaluation.

The large and rapidly growing number of engineered nanomaterials (ENMs) presents a challenge to assessing the potential occupational health risks. An initial database of 25 rodent studies including 1929 animals across various experimental designs and material types was constructed to identify materials that are similar with respect to their potency in eliciting neutrophilic pulmonary inflammation, a response relevant to workers. Doses were normalized across rodent species, strain, and sex as the estimated deposited particle mass dose per gram of lung. Doses associated with specific measures of pulmonary inflammation were estimated by modeling the continuous dose-response relationships using benchmark dose modeling. Hierarchical clustering was used to identify similar materials. The 18 nanoscale and microscale particles were classified into four potency groups, which varied by factors of approximately two to 100. Benchmark particles microscale TiO2 and crystalline silica were in the lowest and highest potency groups, respectively. Random forest methods were used to identify the important physicochemical predictors of pulmonary toxicity, and group assignments were correctly predicted for five of six new ENMs. Proof-of-concept was demonstrated for this framework. More comprehensive data are needed for further development and validation for use in deriving categorical occupational exposure limits.

Derivation of a No-Significant-Risk-Level (NSRL) for diethanolamine (DEA).

Diethanolamine (DEA) has been listed on the State of California's Proposition 65 List. This listing is based in part on tumors reported in a National Toxicology Program (NTP) 2-year dermal carcinogenicity study in mice which found clear evidence of carcinogenic activity in B6C3F1 mice based on increased incidences of liver neoplasms in both sexes, and increased incidences of renal tubule neoplasms in males. Although considerable controversy exists on the relevance of the NTP study to humans, industries are obligated to comply with the Proposition 65 labeling requirement and drinking water discharge prohibition, unless they are able to demonstrate that DEA levels in their products are below a specific No Significant Risk Level (NSRL). The State of California has not published an NSRL for DEA. In this article, a NSRL of 5.6 µg/day and a life-stage-adjusted NSRL(adj) of 1.4 µg/day are derived from the NTP carcinogenicity study using a benchmark dose modeling method based on the incidence of hepatocellular carcinomas in female mice, in accordance with the guidelines of California EPA.

Analyzing high-throughput assay data to advance the rapid screening of environmental chemicals for human reproductive toxicity.

While high-throughput (HTP) assays have been proposed as platforms to rapidly assess reproductive toxicity, there is currently a lack of established assays that specifically address germline development/function and fertility. We assessed the applicability domains of yeast (S. cerevisiae) and nematode (C. elegans) HTP assays in toxicity screening of 124 environmental chemicals, determining their agreement in identifying toxicants and their concordance with reproductive toxicity in vivo. We integrated data generated in the two models and compared results using a streamlined, semi-automated benchmark dose (BMD) modeling approach. We then extracted and modeled relevant mammalian in vivo data available for the matching chemicals included in the Toxicological Reference Database (ToxRefDB). We ranked potencies of common compounds using the BMD and evaluated correlation between the datasets using Pearson and Spearman correlation coefficients. We found moderate to good correlation across the three data sets, with r = 0.48 (95% CI: 0.28-1.00, p<0.001) and rs = 0.40 (p=0.002) for the parametric and rank order correlations between the HTP BMDs; r = 0.95 (95% CI: 0.76-1.00, p=0.0005) and rs = 0.89 (p=0.006) between the yeast assay and ToxRefDB BMDs; and r = 0.81 (95% CI: 0.28-1.00, p=0.014) and rs = 0.75 (p=0.033) between the worm assay and ToxRefDB BMDs. Our findings underscore the potential of these HTP assays to identify environmental chemicals that exhibit reproductive toxicity. Integrating these HTP datasets into mammalian in vivo prediction models using machine learning methods could further enhance the predictive value of these assays in future rapid screening efforts.

Establishing a quantitative framework for regulatory interpretation of genetic toxicity dose-response data: Margin of exposure case study of 48 compounds with both in vivo mutagenicity and carcinogenicity dose-response data.

Quantitative relationships between carcinogenic potency and mutagenic potency have been previously examined using a benchmark dose (BMD)-based approach. We extended those analyses by using human exposure data for 48 compounds to calculate carcinogenicity-derived and genotoxicity-derived margin of exposure values (MOEs) that can be used to prioritize substances for risk management. MOEs for 16 of the 48 compounds were below 10,000, and consequently highlighted for regulatory concern. Of these, 15 were highlighted using genotoxicity-derived (micronucleus [MN] dose-response data) MOEs. A total of 13 compounds were highlighted using carcinogenicity-derived MOEs; 12 compounds were overlapping. MOEs were also calculated using transgenic rodent (TGR) mutagenicity data. For 10 of the 12 compounds examined using TGR data, the results similarly revealed that mutagenicity-derived MOEs yield regulatory decisions that correspond with those based on carcinogenicity-derived MOEs. The effect of benchmark response (BMR) on MOE determination was also examined. Reinterpretation of the analyses using a BMR of 50% indicated that four out of 15 compounds prioritized using MN-derived MOEs based on a default BMR of 5% would have been missed. The results indicate that regulatory decisions based on in vivo genotoxicity dose-response data would be consistent with those based on carcinogenicity dose-response data; in some cases, genotoxicity-based decisions would be more conservative. Going forward, and in the absence of carcinogenicity data, in vivo genotoxicity assays (MN and TGR) can be used to effectively prioritize substances for regulatory action. Routine use of the MOE approach necessitates the availability of reliable human exposure estimates, and consensus regarding appropriate BMRs for genotoxicity endpoints.

Considerations for application of benchmark dose modeling in radiation research: workshop highlights.

BACKGROUND: Exposure to different forms of ionizing radiation occurs in diverse occupational, medical, and environmental settings. Improving the accuracy of the estimated health risks associated with exposure is therefore, essential for protecting the public, particularly as it relates to chronic low dose exposures. A key aspect to understanding health risks is precise and accurate modeling of the dose-response relationship. Toward this vision, benchmark dose (BMD) modeling may be a suitable approach for consideration in the radiation field. BMD modeling is already extensively used for chemical hazard assessments and is considered statistically preferable to identifying low and no observed adverse effects levels. BMD modeling involves fitting mathematical models to dose-response data for a relevant biological endpoint and identifying a point of departure (the BMD, or its lower bound). Recent examples in chemical toxicology show that when applied to molecular endpoints (e.g. genotoxic and transcriptional endpoints), BMDs correlate to points of departure for more apical endpoints such as phenotypic changes (e.g. adverse effects) of interest to regulatory decisions. This use of BMD modeling may be valuable to explore in the radiation field, specifically in combination with adverse outcome pathways, and may facilitate better interpretation of relevant in vivo and in vitro dose-response data. To advance this application, a workshop was organized on June 3rd, 2022, in Ottawa, Ontario that brought together BMD experts in chemical toxicology and the radiation scientific community of researchers, regulators, and policy-makers. The workshop's objective was to introduce radiation scientists to BMD modeling and its practical application using case examples from the chemical toxicity field and demonstrate the BMDExpress software using a radiation dataset. Discussions focused on the BMD approach, the importance of experimental design, regulatory applications, its use in supporting the development of adverse outcome pathways, and specific radiation-relevant examples. CONCLUSIONS: Although further deliberations are needed to advance the use of BMD modeling in the radiation field, these initial discussions and partnerships highlight some key steps to guide future undertakings related to new experimental work.

Evaluating the influences of confounding variables on benchmark dose using a case study in the field of ionizing radiation.

PURPOSE: A vast amount of data regarding the effects of radiation stressors on transcriptional changes has been produced over the past few decades. These data have shown remarkable consistency across platforms and experimental design, enabling increased understanding of early molecular effects of radiation exposure. However, the value of transcriptomic data in the context of risk assessment is not clear and represents a gap that is worthy of further consideration. Recently, benchmark dose (BMD) modeling has shown promise in correlating a transcriptional point of departure (POD) to that derived using phenotypic outcomes relevant to human health risk assessment. Although frequently applied in chemical toxicity evaluation, our group has recently demonstrated application within the field of radiation research. This approach allows the possibility to quantitatively compare radiation-induced gene and pathway alterations across various datasets using BMD values and derive meaningful biological effects. However, before BMD modeling can confidently be used, an understanding of the impact of confounding variables on BMD outputs is needed. METHODS: To this end, BMD modeling was applied to a publicly available microarray dataset (Gene Expression Omnibus #GSE23515) that used peripheral blood ex-vivo gamma-irradiated at 0.82 Gy/min, at doses of 0, 0.1, 0.5 or 2 Gy, and assessed 6 hours post-exposure. The dataset comprised six female smokers (F-S), six female nonsmokers (F-NS), six male smokers (M-S), and six male nonsmokers (M-NS). RESULTS: A combined total of 412 genes were fit to models and the BMD distribution was noted to be bi-modal across the four groups. A total of 74, 41, 62 and 62 genes were unique to the F-NS, M-NS, F-S and M-S groups. Sixty-two BMD modeled genes and nine pathways were common across all four groups. There were no differential sensitivity of BMD responses in the robust common genes and pathways. CONCLUSION: For radiation-responsive genes and pathways common across the study groups, the BMD distribution of transcriptional activity was unaltered by sex and smoking status. Although further validation of the data is needed, these initial findings suggest BMD values for radiation relevant genes and pathways are robust and could be explored further in future studies.

Harmonization of transcriptomic and methylomic analysis in environmental epidemiology studies for potential application in chemical risk assessment.

Recent efforts have posited the utility of transcriptomic-based approaches to understand chemical-related perturbations in the context of human health risk assessment. Epigenetic modification (e.g., DNA methylation) can influence gene expression changes and is known to occur as a molecular response to some chemical exposures. Characterization of these methylation events is critical to understand the molecular consequences of chemical exposures. In this context, a novel workflow was developed to interrogate publicly available epidemiological transcriptomic and methylomic data to identify relevant pathway level changes in response to chemical exposure, using inorganic arsenic as a case study. Gene Set Enrichment Analysis (GSEA) was used to identify causal methylation events that result in concomitant downstream transcriptional deregulation. This analysis demonstrated an unequal distribution of differentially methylated regions across the human genome. After mapping these events to known genes, significant enrichment of a subset of these pathways suggested that arsenic-mediated methylation may be both specific and non-specific. Parallel GSEA performed on matched transcriptomic samples determined that a substantially reduced subset of these pathways are enriched and that not all chemically-induced methylation results in a downstream alteration in gene expression. The resulting pathways were found to be representative of well-established molecular events known to occur in response to arsenic exposure. The harmonization of enriched transcriptional patterns with those identified from the methylomic platform promoted the characterization of plausibly causal molecular signaling events. The workflow described here enables significant gene and methylation-specific pathways to be identified from whole blood samples of individuals exposed to environmentally relevant chemical levels. As future efforts solidify specific causal relationships between these molecular events and relevant apical endpoints, this novel workflow could aid risk assessments by identifying molecular targets serving as biomarkers of hazard, informing mechanistic understanding, and characterizing dose ranges that promote relevant molecular/epigenetic signaling events occuring in response to chemical exposures.

Determination of potential thresholds for N-ethyl-N-nitrosourea and ethyl methanesulfonate based on a multi-endpoint genotoxicity assessment platform in rats.

The main goal of the study was to investigate the genotoxic response of N-ethyl-N-nitrosourea (ENU) and ethyl methanesulfonate (EMS) at low doses in a multi-endpoint genotoxicity assessment platform in rats and to derive potential thresholds and related metrics. Male Sprague-Dawley rats were treated by daily oral gavage for 28 consecutive days with ENU (0.25 ~ 8 mg/kg bw) and EMS (5 ~ 160 mg/kg bw), both with six closely spaced dose levels. Pig-a gene mutation assay, micronucleus test, and comet assay were performed in several timepoints. Then, the dose-response relationships were analyzed for possible points of departure (PoD) using the no observed genotoxic effect level and benchmark dose (BMD) protocols with different critical effect sizes (CES, 0.05, 0.1, 0.5, and 1SD). Overall, dose-dependent increases in all investigated endpoints were found for ENU and EMS. PoDs varied across genetic endpoints, timepoints, and statistical methods, and selecting an appropriate lower 95% confidence limit of BMD needs a comprehensive consideration of the mode of action of chemicals, the characteristics of tests, and the model fitting methods. Under the experimental conditions, the PoDs of ENU and EMS were 0.0036 mg/kg bw and 1.7 mg/kg bw, respectively.

Utilizing literature-based rodent toxicology data to derive potency estimates for quantitative risk assessment.

Evaluating the potential occupational health risk of engineered nanomaterials is an ongoing need. The objective of this meta-analysis, which consisted of 36 studies containing 86 materials, was to assess the availability of published in vivo rodent pulmonary toxicity data for a variety of nanoscale and microscale materials and to derive potency estimates via benchmark dose modeling. Additionally, the potency estimates based on particle mass lung dose associated with acute pulmonary inflammation were used to group materials based on toxicity. The commonalities among the physicochemical properties of the materials in each group were also explored. This exploration found that a material's potency tended to be associated primarily with the material class based on chemical composition and form (e.g. carbon nanotubes, TiO2, ZnO) rather than with particular physicochemical properties. Limitations in the data available precluded a more extensive analysis of these associations. Issues such as data reporting and appropriate experimental design for use in quantitative risk assessment are the main reasons publications were excluded from these analyses and are discussed.

Python BMDS: A Python interface library and web application for the canonical EPA dose-response modeling software.

Several primary sources of publicly available, quantitative dose-response data from traditional toxicology study designs relevant to predictive toxicology applications are now available, including the redeveloped U.S. Environmental Protection Agency's Toxicity Reference Database (ToxRefDB v2.0), the Health Assessment Workspace Collaborative (HAWC), and the National Toxicology Program's Chemical Program's Chemical Effects in Biological Systems (CEBS). These resources provide effect level information but modeling these data to a curve may be more informative for predictive toxicology applications. Benchmark Dose Software (BMDS) has been recognized broadly and used for regulatory applications at multiple agencies. However, the current BMDS software was not amenable to modeling large datasets. Herein we describe development and use of a Python package that implements a wrapper around BMDS, a software that requires manual input in the dose-response modeling process (i.e., best-fitting model-selection, reporting, and dose-dropping). In the Python BMDS, users can select the BMDS version, customize model recommendation logic, and export summaries of the resultant BMDS output. Further, using the Python interface, a web-based application programming interface (API) has been developed for easy integration into other software systems, pipelines, or databases. Software utility was demonstrated via modeling nearly 28,000 datasets in ToxRefDB v2.0, re-creation of an existing, published large-scale analysis, and demonstration of usage in software such as CEBS and HAWC. Python BMDS enables rapid-batch processing of dose-response datasets using a modeling software with broad acceptance in the toxicology community, thereby providing an important tool for leveraging the publicly available quantitative toxicology data in a reproducible manner.

Toxicity of copper oxide and basic copper carbonate nanoparticles after short-term oral exposure in rats.

Copper oxide (CuO) nanoparticles (NPs) and copper carbonate nanoparticles (Cu2CO3(OH)2 NPs have applications as antimicrobial agents and wood preservatives: an application that may lead to oral ingestion via hand to mouth transfer. Rats were exposed by oral gavage to CuO NPs and Cu2CO3(OH)2 NPs for five consecutive days with doses from 1 to 512 mg/kg and 4 to 128 mg/kg per day, respectively, and toxicity was evaluated at days 6 and 26. Both CuO NPs and Cu2CO3(OH)2 NPs induced changes in hematology parameters, as well as clinical chemistry markers (e.g. increased alanine aminotransferase, ALT) indicative of liver damage For CuO NPs histopathological alterations were observed in bone marrow, stomach and liver mainly consisting of an inflammatory response, ulceration, and degeneration. Cu2CO3(OH)2 NPs induced morphological alterations in the stomach, liver, intestines, spleen, thymus, kidneys, and bone marrow. In spleen and thymus lymphoid, depletion was noted that warrants further immunotoxicological evaluation. The NPs showed partial dissolution in artificial simulated stomach fluids, while in intestinal conditions, the primary particles simultaneously shrank and agglomerated into large structures. This means that both copper ions and the particulate nanoforms should be considered as potential causal agents for the observed toxicity. For risk assessment, the lowest bench mark dose (BMD) was similar for both NPs for the serum liver enzyme AST (an indication of liver toxicity), being 26.2 mg/kg for CuO NPs and 30.8 mg/kg for Cu2CO3(OH)2 NPs. This was surprising since the histopathology evidence demonstrates more severe organ damage for Cu2CO3(OH)2 NPs than for CuO NPs.

Dose-response analysis of epigenetic, metabolic, and apical endpoints after short-term exposure to experimental hepatotoxicants.

Identification of sensitive and novel biomarkers or endpoints associated with toxicity and carcinogenesis is of a high priority. There is increasing interest in the incorporation of epigenetic and metabolic biomarkers to complement apical data; however, a number of questions, including the tissue specificity, dose-response patterns, early detection of those endpoints, and the added value need to be addressed. In this study, we investigated the dose-response relationship between apical, epigenetic, and metabolomics endpoints following short-term exposure to experimental hepatotoxicants, clofibrate (CF) and phenobarbital (PB). Male F344 rats were exposed to PB (0, 5, 25, and 100 mg/kg/day) or CF (0, 10, 50, and 250 mg/kg/day) for seven days. Exposure to PB or CF resulted in dose-dependent increases in relative liver weights, hepatocellular hypertrophy and proliferation, and increases in Cyp2b1 and Cyp4a1 transcripts. These changes were associated with altered histone modifications within the regulatory units of cytochrome genes, LINE-1 DNA hypomethylation, and altered microRNA profiles. Metabolomics data indicated alterations in the metabolism of bile acids. This study provides the first comprehensive analysis of the apical, epigenetic and metabolic alterations, and suggests that the latter two occur within or near the dose response curve of apical endpoint alterations following exposure to experimental hepatotoxicants.

Transcriptional Profiling of Dibenzo[def,p]chrysene-induced Spleen Atrophy Provides Mechanistic Insights into its Immunotoxicity in MutaMouse.

Dibenzo[def,p]chrysene (DBC) is the most carcinogenic polycyclic aromatic hydrocarbon (PAH) examined to date. We investigated the immunotoxicity of DBC, manifested as spleen atrophy, following acute exposure of adult MutaMouse males by oral gavage. Mice were exposed to 0, 2.0, 6.2, or 20.0 mg DBC /kg-bw per day, for 3 days. Genotoxic endpoints (DBC-DNA adducts and lacZ mutant frequency in spleen and bone marrow, and red blood cell micronucleus frequency) and global gene expression changes were measured. All of the genotoxicity measures increased in a dose-dependent manner in spleen and bone marrow. Gene expression analysis showed that DBC activates p53 signaling pathways related to cellular growth and proliferation, which was evident even at the low dose. Strikingly, the expression profiles of DBC exposed mouse spleens were highly inversely correlated with the expression profiles of the only published toxicogenomics dataset of enlarged mouse spleen. This analysis suggested a central role for Bnip3l, a pro-apoptotic protein involved in negative regulation of erythroid maturation. RT-PCR confirmed expression changes in several genes related to apoptosis, iron metabolism, and aryl hydrocarbon receptor signaling that are regulated in the opposite direction during spleen atrophy versus benzo[a]pyrene-mediated splenomegaly. In addition, benchmark dose modeling of toxicogenomics data yielded toxicity estimates that are very close to traditional toxicity endpoints. This work illustrates the power of toxicogenomics to reveal rich mechanistic information for immunotoxic compounds and its ability to provide information that is quantitatively similar to that derived from standard toxicity methods in health risk assessment.