The finding of N-nitrosodiethylamine (NDEA) and N-nitrosodimethylamine (NDMA) in marketed drugs has led to implementation of risk assessment processes intended to limit exposures to the entire class of N-nitrosamines. A critical component of the risk assessment process is establishing exposure limits that are protective of human health. One approach to establishing exposure limits for novel N-nitrosamines is to conduct an in vivo transgenic rodent (TGR) mutation study. Existing regulatory guidance on N-nitrosamines provides decision making criteria based on interpreting in vivo TGR mutation studies as an overall positive or negative. However, point of departure metrics, such as benchmark dose (BMD), can be used to define potency and provide an opportunity to establish relevant exposure limits. This can be achieved through relative potency comparison of novel N-nitrosamines with model N-nitrosamines possessing robust in vivo mutagenicity and carcinogenicity data. The current work adds to the dataset of model N-nitrosamines by providing in vivo TGR mutation data for N-nitrosopiperidine (NPIP). In vivo TGR mutation data was also generated for a novel N-nitrosamine impurity identified in sitagliptin-containing products, 7-nitroso-3-(trifluoromethyl)-5,6,7,8-tetrahydro-[1,2,4]triazolo-[4,3-a]pyrazine (NTTP). Using the relative potency comparison approach, we have demonstrated the safety of NTTP exposures at or above levels of 1500 ng/day.

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.

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.

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.

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.
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.

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.

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.
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).
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.
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.

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.

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.

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.