Regulations of cosmetic ingredients and products have been the most advanced in embracing new approach methodologies (NAMs). Consequently, the cosmetic industry has assumed a forerunner role in the development and implementation of animal-free next-generation risk assessment (NGRA) that incorporates defined approaches (DAs) to assess the skin sensitization potency of ingredients. A Bayesian network DA predicting four potency categories (SkinSens-BN) was constructed against reference Local Lymph Node Assay data for a total of 297 substances, achieving a predictive performance similar to that of other DAs. With the aim of optimally informing risk assessment with a continuous point of departure (PoD), a weighted sum of the SkinSens-BN probabilities for four potency classes (non-, weak, moderate, and strong/extreme sensitizer) was calculated, using fixed weights based on associated LLNA EC3-values. The approach was promising, e.g., the derived PoDs for substances classified as non-sensitizers did not overlap with any others and 77% of PoDs were similar or more conservative than LLNA EC3. In addition, the predictions were assigned a level of confidence based on the probabilities to inform the evaluation of uncertainty in an NGRA context. In conclusion, the PoD derivation approach can substantially contribute to reliable skin sensitization NGRAs.

A key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene set-based method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3-1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets.

Fluoride is a naturally occurring substance that is also added to drinking water, dental hygiene products, and food supplements for preventing dental caries. Concerns have been raised about several other potential health risks of fluoride.
To conduct a robust synthesis of evidence regarding human health risks due to exposure to fluoride in drinking water, and to develop a point of departure (POD) for setting a health-based value (HBV) for fluoride in drinking water.
A systematic review of evidence published since recent reviews of human, animal, and in vitro data was carried out. Bradford Hill considerations were used to weigh the evidence for causality. Several key studies were considered for deriving PODs.
The current review identified 89 human studies, 199 animal studies, and 10 major in vitro reviews. The weight of evidence on 39 health endpoints was presented. In addition to dental fluorosis, evidence was considered strong for reduction in IQ scores in children, moderate for thyroid dysfunction, weak for kidney dysfunction, and limited for sex hormone disruptions.
The current review identified moderate dental fluorosis and reduction in IQ scores in children as the most relevant endpoints for establishing an HBV for fluoride in drinking water. PODs were derived for these two endpoints, although there is still some uncertainty in the causal weight of evidence for causality for reducing IQ scores in children and considerable uncertainty in the derivation of its POD. Given our evaluation of the overall weight of evidence, moderate dental fluorosis is suggested as the key endpoint until more evidence is accumulated on possible reduction of IQ scores effects. A POD of 1.56 mg fluoride/L for moderate dental fluorosis may be preferred as a starting point for setting an HBV for fluoride in drinking water to protect against moderate and severe dental fluorosis. Although outside the scope of the current review, precautionary concerns for potential neurodevelopmental cognitive effects may warrant special consideration in the derivation of the HBV for fluoride in drinking water.

The N-nitrosamine, N-nitrosodimethylamine (NDMA), is an environmental mutagen and rodent carcinogen. Small levels of NDMA have been identified as an impurity in some commonly used drugs, resulting in several product recalls. In this study, NDMA was evaluated in an OECD TG-488 compliant Muta™Mouse gene mutation assay (28-day oral dosing across seven daily doses of 0.02-4 mg/kg/day) using an integrated design that assessed mutation at the transgenic lacZ locus in various tissues and at the endogenous Pig-a gene-locus, along with micronucleus frequencies in peripheral blood. Liver pathology was determined together with NDMA exposure in blood and liver. The additivity of mutation induction was assessed by including two acute single-dose treatment groups (i.e. 5 and 10 mg/kg dose on Day 1), which represented the same total dose as two of the repeat dose treatment groups. NDMA did not induce statistically significant increases in mean lacZ mutant frequency (MF) in bone marrow, spleen, bladder, or stomach, nor in peripheral blood (Pig-a mutation or micronucleus induction) when tested up to 4 mg/kg/day. There were dose-dependent increases in mean lacZ MF in the liver, lung, and kidney following 28-day repeat dosing or in the liver and kidney after a single dose (10 mg/kg). No observed genotoxic effect levels (NOGEL) were determined for the positive repeat dose-response relationships. Mutagenicity did not exhibit simple additivity in the liver since there was a reduction in MF following NDMA repeat dosing compared with acute dosing for the same total dose. Benchmark dose modelling was used to estimate point of departure doses for NDMA mutagenicity in Muta™Mouse and rank order target organ tissue sensitivity (liver > kidney or lung). The BMD50 value for liver was 0.32 mg/kg/day following repeat dosing (confidence interval 0.21-0.46 mg/kg/day). In addition, liver toxicity was observed at doses of ≥ 1.1 mg/kg/day NDMA and correlated with systemic and target organ exposure. The integration of these results and their implications for risk assessment are discussed.

Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.

Studies on the comparison of developmental (neuro) toxicity of parabens are currently limited, and unharmonized concentrations between phenotypic observations and transcriptome analysis hamper the understanding of their differential molecular mechanisms. Thus, developmental toxicity testing was conducted herein using the commonly used methyl- (MtP), ethyl- (EtP), and propyl-parabens (PrP) in zebrafish embryos. With a benchmark dose of 5%, embryonic-mortality-based point-of-departure (M-POD) values of the three parabens were determined, and changes in locomotor behavior were evaluated at concentrations of 0, M-POD/50, M-POD/10, and M-POD, where transcriptome analysis was conducted to explore the underlying neurotoxicity mechanism. Higher long-chained parabens were more toxic than short-chained parabens, as determined by the M-POD values of 154.1, 72.6, and 24.2 µM for MtP, EtP, and PrP, respectively. Meanwhile, exposure to EtP resulted in hyperactivity, whereas no behavioral effect was observed with MtP and PrP. Transcriptome analysis revealed that abnormal behaviors in the EtP-exposed group were associated with distinctly enriched pathways in signaling, transport, calcium ion binding, and metal binding. In contrast, exposure to MtP and PrP mainly disrupted membranes and transmembranes, which are closely linked to abnormal embryonic development rather than neurobehavioral changes. According to the changes in the expressions of signature mRNAs, tentative transcriptome-based POD values for each paraben were determined as MtP (2.68 µM), EtP (3.85 µM), and PrP (1.4 µM). This suggests that different molecular perturbations initiated at similar concentrations determined the extent and toxicity outcome differently. Our findings provide insight into better understanding the differential developmental neurotoxicity mechanisms of parabens.

The purpose of this study was to update the existing Cancer Potency Database (CPDB) in order to support the development of a dataset of compounds, with associated points of departure (PoDs), to enable a review and update of currently applied values for the Threshold of Toxicological Concern (TTC) for cancer endpoints. This update of the current CPDB, last reviewed in 2012, includes the addition of new data (44 compounds and 158 studies leading to additional 359 dose-response curves). Strict inclusion criteria were established and applied to select compounds and studies with relevant cancer potency data. PoDs were calculated from dose-response modeling, including the benchmark dose (BMD) and the lower 90% confidence limits (BMDL) at a specified benchmark response (BMR) of 10%. The updated full CPDB database resulted in a total of 421 chemicals which had dose-response data that could be used to calculate PoDs. This candidate dataset for cancer TTC is provided in a transparent and adaptable format for further analysis of TTC to derive cancer potency thresholds.

Omics techniques have been increasingly recognized as promising tools for Next Generation Risk Assessment. Targeted metabolomics offer the advantage of providing readily interpretable mechanistic information about perturbed biological pathways. In this study, a high-throughput LC-MS/MS-based broad targeted metabolomics system was applied to study nitrofurantoin metabolic dynamics over time and concentration and to provide a mechanistic-anchored approach for point of departure (PoD) derivation. Upon nitrofurantoin exposure at five concentrations (7.5 µM, 15 µM, 20 µM, 30 µM and 120 µM) and four time points (3, 6, 24 and 48 h), the intracellular metabolome of HepG2 cells was evaluated. In total, 256 uniquely identified metabolites were measured, annotated, and allocated in 13 different metabolite classes. Principal component analysis (PCA) and univariate statistical analysis showed clear metabolome-based time and concentration effects. Mechanistic information evidenced the differential activation of cellular pathways indicative of early adaptive and hepatotoxic response. At low concentrations, effects were seen mainly in the energy and lipid metabolism, in the mid concentration range, the activation of the antioxidant cellular response was evidenced by increased levels of glutathione (GSH) and metabolites from the de novo GSH synthesis pathway. At the highest concentrations, the depletion of GSH, together with alternations reflective of mitochondrial impairments, were indicative of a hepatotoxic response. Finally, a metabolomics-based PoD was derived by multivariate PCA using the whole set of measured metabolites. This approach allows using the entire dataset and derive PoD that can be mechanistically anchored to established key events. Our results show the suitability of high throughput targeted metabolomics to investigate mechanisms of hepatoxicity and derive point of departures that can be linked to existing adverse outcome pathways and contribute to the development of new ones.

Soman, a warfare nerve agent, poses a significant threat by inducing severe brain damage that often results in death. Nonetheless, our understanding of the biological changes underlying persistent neurocognitive dysfunction caused by low dosage of soman remains limited. This study used mice to examine the effects of different doses of soman over time. Phosphoproteomic analysis of the mouse brain is the first time to be used to detect toxic effects of soman at such low or ultra-low doses, which were undetectable based on measuring the activity of acetylcholinesterase at the whole-animal level. We also found that phosphoproteome alterations could accurately track the soman dose, irrespective of the sampling time. Moreover, phosphoproteome revealed a rapid and adaptive cellular response to soman exposure, with the points of departure 8-38 times lower than that of acetylcholinesterase activity. Impaired long-term potentiation was identified in phosphoproteomic studies, which was further validated by targeted quantitative proteomics, immunohistochemistry, and immunofluorescence analyses, with significantly increased levels of phosphorylation of protein phosphatase 1 in the hippocampus following soman exposure. This increase in phosphorylation inhibits long-term potentiation, ultimately leading to long-term memory dysfunction in mice.

Genotoxicity data are mainly interpreted in a qualitative way, which typically results in a binary classification of chemical entities. For more than a decade, there has been a discussion about the need for a paradigm shift in this regard. Here, we review current opportunities, challenges and perspectives for a more quantitative approach to genotoxicity assessment. Currently discussed opportunities mainly include the determination of a reference point (e.g., a benchmark dose) from genetic toxicity dose-response data, followed by calculation of a margin of exposure (MOE) or derivation of a health-based guidance value (HBGV). In addition to new opportunities, major challenges emerge with the quantitative interpretation of genotoxicity data. These are mainly rooted in the limited capability of standard in vivo genotoxicity testing methods to detect different types of genetic damage in multiple target tissues and the unknown quantitative relationships between measurable genotoxic effects and the probability of experiencing an adverse health outcome. In addition, with respect to DNA-reactive mutagens, the question arises whether the widely accepted assumption of a non-threshold dose-response relationship is at all compatible with the derivation of a HBGV. Therefore, at present, any quantitative genotoxicity assessment approach remains to be evaluated case-by-case. The quantitative interpretation of in vivo genotoxicity data for prioritization purposes, e.g., in connection with the MOE approach, could be seen as a promising opportunity for routine application. However, additional research is needed to assess whether it is possible to define a genotoxicity-derived MOE that can be considered indicative of a low level of concern. To further advance quantitative genotoxicity assessment, priority should be given to the development of new experimental methods to provide a deeper mechanistic understanding and a more comprehensive basis for the analysis of dose-response relationships.