Permitted daily exposure limits for noteworthy N-nitrosamines.

A genotoxic carcinogen, N-nitrosodimethylamine (NDMA), was detected as a synthesis impurity in some valsartan drugs in 2018, and other N-nitrosamines, such as N-nitrosodiethylamine (NDEA), were later detected in other sartan products. N-nitrosamines are pro-mutagens that can react with DNA following metabolism to produce DNA adducts, such as O6 -alkyl-guanine. The adducts can result in DNA replication miscoding errors leading to GC>AT mutations and increased risk of genomic instability and carcinogenesis. Both NDMA and NDEA are known rodent carcinogens in male and female rats. The DNA repair enzyme, methylguanine DNA-methyltransferase can restore DNA integrity via the removal of alkyl groups from guanine in an error-free fashion and this can result in nonlinear dose responses and a point of departure or "practical threshold" for mutation at low doses of exposure. Following International recommendations (ICHM7; ICHQ3C and ICHQ3D), we calculated permissible daily exposures (PDE) for NDMA and NDEA using published rodent cancer bioassay and in vivo mutagenicity data to determine benchmark dose values and define points of departure and adjusted with appropriate uncertainty factors (UFs). PDEs for NDMA were 6.2 and 0.6 µg/person/day for cancer and mutation, respectively, and for NDEA, 2.2 and 0.04 µg/person/day. Both PDEs are higher than the acceptable daily intake values (96 ng for NDMA and 26.5 ng for NDEA) calculated by regulatory authorities using simple linear extrapolation from carcinogenicity data. These PDE calculations using a bench-mark approach provide a more robust assessment of exposure limits compared with simple linear extrapolations and can better inform risk to patients exposed to the contaminated sartans.

The use of benchmark dose uncertainty measurements for robust comparative potency analyses.

The Benchmark Dose (BMD) method is the favored approach for quantitative dose-response analysis where uncertainty measurements are delineated between the upper (BMDU) and lower (BMDL) confidence bounds, or confidence intervals (CIs). Little has been published on the accurate interpretation of uncertainty measurements for potency comparative analyses between different test conditions. We highlight this by revisiting a previously published comparative in vitro genotoxicity dataset for human lymphoblastoid TK6 cells that were exposed to each of 10 clastogens in the presence and absence (+/-) of low concentration (0.25%) S9, and scored for p53, γH2AX and Relative Nuclei Count (RNC) responses at two timepoints (Tian et al., 2020). The researchers utilized BMD point estimates in potency comparative analysis between S9 treatment conditions. Here we highlight a shortcoming that the use of BMD point estimates can mischaracterize potency differences between systems. We reanalyzed the dose responses by BMD modeling using PROAST v69.1. We used the resulting BMDL and BMDU metrics to calculate "S9 potency ratio confidence intervals" that compare the relative potency of compounds +/- S9 as more statistically robust metrics for comparative potency measurements compared to BMD point estimate ratios. We performed unsupervised hierarchical clustering that identified four S9-dependent groupings: high and low-level potentiation, no effect, and diminution. This work demonstrates the importance of using BMD uncertainty measurements in potency comparative analyses between test conditions. Irrespective of the source of the data, we propose a stepwise approach when performing BMD modeling in comparative potency analyses between test conditions.

Benchmark Dose Analysis of DNA Damage Biomarker Responses Provides Compound Potency and Adverse Outcome Pathway Information for the Topoisomerase II Inhibitor Class of Compounds.

Genetic toxicology data have traditionally been utilized for hazard identification to provide a binary call for a compound's risk. Recent advances in the scientific field, especially with the development of high-throughput methods to quantify DNA damage, have influenced a change of approach in genotoxicity assessment. The in vitro MultiFlow® DNA Damage Assay is one such method which multiplexes γH2AX, p53, phospho-histone H3 biomarkers into a single-flow cytometric analysis (Bryce et al., [2016]: Environ Mol Mutagen 57:546-558). This assay was used to study human TK6 cells exposed to each of eight topoisomerase II poisons for 4 and 24 hr. Using PROAST v65.5, the Benchmark Dose approach was applied to the resulting flow cytometric datasets. With "compound" serving as covariate, all eight compounds were combined into a single analysis, per time point and endpoint. The resulting 90% confidence intervals, plotted in Log scale, were considered as the potency rank for the eight compounds. The in vitro MultiFlow data showed a maximum confidence interval span of 1Log, which indicates data of good quality. Patterns observed in the compound potency rank were scrutinized by using the expert rule-based software program Derek Nexus, developed by Lhasa Limited. Compound sub-classification and structural alerts were considered contributory to the potencies observed for the topoisomerase II poisons studied herein. The Topo II poison Adverse Outcome Pathway was evaluated with MultiFlow endpoints serving as Key Events. The step-wise approach described herein can be considered as a foundation for risk assessment of compounds within a specific mode of action of interest. Environ. Mol. Mutagen. 2020. © 2020 Wiley Periodicals, Inc.

Predictions of genotoxic potential, mode of action, molecular targets, and potency via a tiered multiflow® assay data analysis strategy.

The in vitro MultiFlow® DNA Damage Assay multiplexes γH2AX, p53, phospho-histone H3, and polyploidization biomarkers into a single flow cytometric analysis. The current report describes a tiered sequential data analysis strategy based on data generated from exposure of human TK6 cells to a previously described 85 chemical training set and a new pharmaceutical-centric test set (n = 40). In each case, exposure was continuous over a range of closely spaced concentrations, and cell aliquots were removed for analysis following 4 and 24 hr of treatment. The first data analysis step focused on chemicals' genotoxic potential, and for this purpose, we evaluated the performance of a machine learning (ML) ensemble, a rubric that considered fold increases in biomarkers against global evaluation factors (GEFs), and a hybrid strategy that considered ML and GEFs. This first tier further used ML output and/or GEFs to classify genotoxic activity as clastogenic and/or aneugenic. Test set results demonstrated the generalizability of the first tier, with particularly good performance from the ML ensemble: 35/40 (88%) concordance with a priori genotoxicity expectations and 21/24 (88%) agreement with expected mode of action (MoA). A second tier applied unsupervised hierarchical clustering to the biomarker response data, and these analyses were found to group certain chemicals, especially aneugens, according to their molecular targets. Finally, a third tier utilized benchmark dose analyses and MultiFlow biomarker responses to rank genotoxic potency. The relevance of these rankings is supported by the strong agreement found between benchmark dose values derived from MultiFlow biomarkers compared to those generated from parallel in vitro micronucleus analyses. Collectively, the results suggest that a tiered MultiFlow data analysis pipeline is capable of rapidly and effectively identifying genotoxic hazards while providing additional information that is useful for modern risk assessments-MoA, molecular targets, and potency. Environ. Mol. Mutagen. 60:513-533, 2019. © 2019 Wiley Periodicals, Inc.