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We present a hypothetical case study to examine the use of a next-generation framework developed by the Genetic Toxicology Technical Committee of the Health and Environmental Sciences Institute for assessing the potential risk of genetic damage from a pharmaceutical perspective. We used etoposide, a genotoxic carcinogen, as a representative pharmaceutical for the purposes of this case study. Using the framework as guidance, we formulated a hypothetical scenario for the use of etoposide to illustrate the application of the framework to pharmaceuticals. We collected available data on etoposide considered relevant for assessment of genetic toxicity risk. From the data collected, we conducted a quantitative analysis to estimate margins of exposure (MOEs) to characterize the risk of genetic damage that could be used for decision-making regarding the predefined hypothetical use. We found the framework useful for guiding the selection of appropriate tests and selecting relevant endpoints that reflected the potential for genetic damage in patients. The risk characterization, presented as MOEs, allows decision makers to discern how much benefit is critical to balance any adverse effect(s) that may be induced by the pharmaceutical. Interestingly, pharmaceutical development already incorporates several aspects of the framework per regulations and health authority expectations. Moreover, we observed that quality dose response data can be obtained with carefully planned but routinely conducted genetic toxicity testing. This case study demonstrates the utility of the next-generation framework to quantitatively model human risk based on genetic damage, as applicable to pharmaceuticals.

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25–5.0 μg/mL) and/or carbendazim (0.8–1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the “DeepFlow” neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for ‘mononucleates’, ‘binucleates’, ‘mononucleates with MN’ and ‘binucleates with MN’, respectively. Successful classifications of ‘trinucleates’ (90%) and ‘tetranucleates’ (88%) in addition to ‘other or unscorable’ phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks. Supplementary Information The online version contains supplementary material available at 10.1007/s00204-021-03113-0.

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.

For decades, regulators have grappled with different approaches to address the issue of control of impurities. Safety-based limits, such as permissible daily exposure (PDE), acceptable intake (AI), threshold of toxicological concern (TTC) and less than lifetime limits (LTL) have all been used. For many years these safety-based limits have been recognized as virtually safe doses (VSDs). Recently, however, many regulatory agencies are seeking to impose limits for N-nitrosamine impurities, which are significantly below the VSD. This commentary will discuss the evolution of safety-based limits for impurities, provide an overview of the valsartan N-nitrosamine contamination issue and review the toxicology of N-nitrosamines. The outcome of a lessons-learned exercise on sartan medications undertaken by the European Medicines Agency (EMA) will also be discussed. The review will also highlight the many analytical challenges inherent with controlling impurities to ppb-based limits. The use of highly sensitive, low ppb limits, methods may lead to future issues of batch rejection, based on false positives. Regulators initially viewed the N-nitrosamine risk as being insufficient to prompt immediate product discontinuation and patients were specifically advised to continue using their affected medication. Patients were also informed that exposure to N-nitrosamines is extremely common via food and drinking water.

In vitro genotoxicity studies are a quick and high throughput approach to assess the genotoxic potential of chemicals; however, the reliability of these tests and their relevance to in vivo effects depends on the choice of representative cell line and optimisation of assay conditions. For chemicals like urethane that require specific metabolic activation to cause genotoxicity, it is important that in vitro tests are conducted using cell lines exhibiting the activity and induction of CYP450 enzymes, including CYP2E1 enzyme that is important in the metabolism of urethane, at a concentration representing actual or perceived chemical exposure. We compared 2D MCL-5 cells and HepG2 cells with 3D HepG2 hanging drop spheroids to determine the genotoxicity of urethane using the micronucleus assay. Our 2D studies with MCL-5 did not show any statistically significant genotoxicity [99% relative population doubling (RPD)] compared to controls for concentrations and time point tested in vitro. HepG2 cells grown as 2D indicated that exposure to urethane of up to 30 mM for 23 h did not cause any genotoxic effect (102% RPD) but, at higher concentrations, genotoxicity was produced with only 89–85% RPD. Furthermore, an exposure of 20–50 mM for 23 h using 3D hanging drop spheroid assays revealed a higher MN frequency, thus exhibiting in vitro genotoxicity of urethane in metabolically active cell models. In comparison with previous studies, this study indicated that urethane genotoxicity is dose, sensitivity of cell model (2D vs. 3D) and exposure dependent.

We compute the effects on the temperature and precise spectrum of Hawking radiation from a Schwarzschild black hole when the emitted object is taken to be spatially extended. We find that in the low-momentum regime, the power emitted is exponentially suppressed for sufficiently large radiated objects, or sufficiently small black holes, though the temperature of emission is unchanged. We numerically determine the magnitude of this suppression as a function of the size and mass of the object and the black hole, and discuss the implications for various extended objects in nature., Comment: 16 pages, 4 figures, 1 table

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.

Mutations induced in somatic cells and germ cells are responsible for a variety of human diseases, and mutation per se has been considered an adverse health concern since the early part of the 20th Century. Although in vitro and in vivo somatic cell mutation data are most commonly used by regulatory agencies for hazard identification, that is, determining whether or not a substance is a potential mutagen and carcinogen, quantitative mutagenicity dose-response data are being used increasingly for risk assessments. Efforts are currently underway to both improve the measurement of mutations and to refine the computational methods used for evaluating mutation data. We recommend continuing the development of these approaches with the objective of establishing consensus regarding the value of including the quantitative analysis of mutation per se as a required endpoint for comprehensive assessments of toxicological risk. Environ. Mol. Mutagen. 61:34-41, 2020. © 2019 Wiley Periodicals, Inc.

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.

Thein vitromicronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25 – 5.0 µg/mL) and/or carbendazim (0.8 – 1.6 µg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the “DeepFlow” neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for ‘mononucleates’, ‘binucleates’, ‘mononucleates with MN’ and ‘binucleates with MN’, respectively. Successful classifications of ‘trinucleates’ (90%) and ‘tetranucleates’ (88%) in addition to ‘other or unscorable’ phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent dose regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.

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 modelling 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 modelling in comparative potency analyses between test conditions.

Genetic toxicology is an essential component of compound safety assessment. In the face of a barrage of new compounds, higher throughput, less ethically divisive in vitro approaches capable of effective, human-relevant hazard identification and prioritisation are increasingly important. One such approach is the ToxTracker assay, which utilises murine stem cell lines equipped with green fluorescent protein (GFP)-reporter gene constructs that each inform on distinct aspects of cellular perturbation. Encouragingly, ToxTracker has shown improved sensitivity and specificity for the detection of known in vivo genotoxicants when compared to existing ‘standard battery’ in vitro tests. At the current time however, quantitative genotoxic potency correlations between ToxTracker and well-recognised in vivo tests are not yet available. Here we use dose–response data from the three DNA-damage-focused ToxTracker endpoints and from the in vivo micronucleus assay to carry out quantitative, genotoxic potency estimations for a range of aromatic amine and alkylating agents using the benchmark dose (BMD) approach. This strategy, using both the exponential and the Hill BMD model families, was found to produce robust, visually intuitive and similarly ordered genotoxic potency rankings for 17 compounds across the BSCL2-GFP, RTKN-GFP and BTG2-GFP ToxTracker endpoints. Eleven compounds were similarly assessed using data from the in vivo micronucleus assay. Cross-systems genotoxic potency correlations for the eight matched compounds demonstrated in vitro–in vivo correlation, albeit with marked scatter across compounds. No evidence for distinct differences in the sensitivity of the three ToxTracker endpoints was found. The presented analyses show that quantitative potency determinations from in vitro data enable more than just qualitative screening and hazard identification in genetic toxicology.

Current in vitro genotoxicity tests can produce misleading positive results, indicating an inability to effectively predict a compound’s subsequent carcinogenic potential in vivo. Such oversensitivity can incur unnecessary in vivo tests to further investigate positive in vitro results, supporting the need to improve in vitro tests to better inform risk assessment. It is increasingly acknowledged that more informative in vitro tests using multiple endpoints may support the correct identification of carcinogenic potential. The present study, therefore, employed a holistic, multiple-endpoint approach using low doses of selected carcinogens and non-carcinogens (0.001–770 µM) to assess whether these chemicals caused perturbations in molecular and cellular endpoints relating to the Hallmarks of Cancer. Endpoints included micronucleus induction, alterations in gene expression, cell cycle dynamics, cell morphology and bioenergetics in the human lymphoblastoid cell line TK6. Carcinogens ochratoxin A and oestradiol produced greater Integrated Signature of Carcinogenicity scores for the combined endpoints than the “misleading” in vitro positive compounds, quercetin, 2,4-dichlorophenol and quinacrine dihydrochloride and toxic non-carcinogens, caffeine, cycloheximide and phenformin HCl. This study provides compelling evidence that carcinogens can successfully be distinguished from non-carcinogens using a holistic in vitro test system. Avoidance of misleading in vitro outcomes could lead to the reduction and replacement of animals in carcinogenicity testing. Electronic supplementary material The online version of this article (10.1007/s00204-020-02902-3) contains supplementary material, which is available to authorized users.

Schistosomiasis is a neglected disease of poverty that is caused by infection with blood fluke species contained within the genus Schistosoma. For the last 40 years, control of schistosomiasis in endemic regions has predominantly been facilitated by administration of a single drug, praziquantel. Due to limitations in this mono-chemotherapeutic approach for sustaining schistosomiasis control into the future, alternative anti-schistosomal compounds are increasingly being sought by the drug discovery community. Herein, we describe a multi-pronged, integrated strategy that led to the identification and further exploration of the quinoxaline core as a promising anti-schistosomal scaffold. Firstly, phenotypic screening of commercially available small molecules resulted in the identification of a moderately active hit compound against Schistosoma mansoni (1, EC50 = 4.59 μM on schistosomula). Secondary exploration of the chemical space around compound 1 led to the identification of a quinoxaline-core containing, non-genotoxic lead (compound 22). Compound 22 demonstrated substantially improved activities on both intra-mammalian (EC50 = 0.44 μM, 0.20 μM and 84.7 nM, on schistosomula, juvenile and adult worms, respectively) and intra-molluscan (sporocyst) S. mansoni lifecycle stages. Further medicinal chemistry optimisation of compound 22, resulting in the generation of 20 additional analogues, improved our understanding of the structure-activity relationship and resulted in considerable improvements in both anti-schistosome potency and selectivity (e.g. compound 30; EC50 = 2.59 nM on adult worms; selectivity index compared to the HepG2 cell line = 348). Some derivatives of compound 22 (e.g. 31 and 33) also demonstrated significant activity against the two other medically important species, Schistosoma haematobium and Schistosoma japonicum. Further optimisation of this class of anti-schistosomal is ongoing and could lead to the development of an urgently needed alternative to praziquantel for assisting in schistosomiasis elimination strategies., Graphical abstract Image 1, Highlights • Lead compound 22 was identified with EC50 of 0.44 µM and 84.7 nM for schistosomula and adult worms. • 20 analogues of the lead compound 22 were synthesised. • Compounds 25, 30 and 32 showed the best selectivity profile. • Compounds 31 and 33 are the most active on three medically important schistosome species.

Lidocaine has not been associated with cancer in humans despite 8 decades of therapeutic use. Its metabolite, 2,6-xylidine, is a rat carcinogen, believed to induce genotoxicity via N-hydroxylation and DNA adduct formation, a non-threshold mechanism of action. To better understand this dichotomy, we review literature pertaining to metabolic activation and genotoxicity of 2,6-xylidine, identifying that it appears resistant to N-hydroxylation and instead metabolises almost exclusively to DMAP (an aminophenol). At high exposures (sufficient to saturate phase 2 metabolism), this may undergo metabolic threshold-dependent activation to a quinone-imine with potential to redox cycle producing ROS, inducing cytotoxicity and genotoxicity. A new rat study found no evidence of genotoxicity in vivo based on micronuclei in bone marrow, comets in nasal tissue or female liver, despite high level exposure to 2,6-xylidine (including metabolites). In male liver, weak dose-related comet increases, within the historical control range, were associated with metabolic overload and acute systemic toxicity. Benchmark dose analysis confirmed a non-linear dose response. The weight of evidence indicates 2,6-xylidine is a non-direct acting (metabolic threshold-dependent) genotoxin, and is not genotoxic in vivo in rats in the absence of acute systemic toxic effects, which occur at levels 35 × beyond lidocaine-related exposure in humans.

We study how the constraints on the primordial black hole density arising from the extragalactic photon background are modified in the scenario that there exist extra large spatial dimensions. We find that though the overall magnitude of the constraints is not substantially different, the mass ranges to which they apply are, and for some choices of mass it is possible for the black holes to constitute the entirety of the dark matter., 17 pages, 4 figures

The purpose of the present investigation is to analyze the in vivo genotoxicity dose–response data of ethylene oxide (EO) and the applicability of the derived point‐of‐departure (PoD) values when estimating permitted daily exposure (PDE) values. A total of 40 data sets were identified from the literature, and benchmark dose analyses were conducted using PROAST software to identify a PoD value. Studies employing the inhalation route of exposure and assessing gene or chromosomal mutations and chromosomal damage in various tissues were considered the most relevant for assessing risk from EO, since these effects are likely to contribute to adverse health consequences in exposed individuals. The PoD estimates were screened for precision and the values were divided by data‐derived adjustment factors. For gene mutations, the lowest PDE was 285 parts per trillion (ppt) based on the induction of lacI mutations in the testes of mice following 48 weeks of exposure to EO. The corresponding lowest PDE value for chromosomal mutations was 1,175 ppt for heritable translocations in mice following 8.5 weeks of EO exposure. The lowest PDE for chromosomal aberrations was 238 ppt in the mouse peripheral blood lymphocytes following 48 weeks of inhalation exposure. The diverse dose–response data for EO‐induced genotoxicity enabled the derivation of PoDs for various endpoints, tissues, and species and identified 238 ppt as the lowest PDE in this retrospective analysis.

The screen-and-bin approach for interpretation of genotoxicity data is predicated on three false assumptions: that genotoxicants are rare, that genotoxicity dose-response functions do not contain a low-dose region mechanistically characterized by zero-order kinetics, and that genotoxicity is not a bona fide toxicological endpoint. Consequently, there is a need to develop and implement quantitative methods to interpret genotoxicity dose-response data for risk assessment and regulatory decision-making. Standardized methods to analyze dose-response data, and determine point-of-departure (PoD) metrics, have been established; the most robust PoD is the benchmark dose (BMD). However, there are no standards for regulatory interpretation of mutagenicity BMDs. Although 5-10% is often used as a critical effect size (CES) for BMD determination, values for genotoxicity endpoints have not been established. The use of BMDs to determine health-based guidance values (HBGVs) requires assessment factors (AFs) to account for interspecies differences and variability in human sensitivity. Default AFs used for other endpoints may not be appropriate for interpretation of in vivo mutagenicity BMDs. Analyses of published dose-response data showing the effects of compensatory pathway deficiency indicate that AFs for sensitivity differences should be in the range of 2-20. Additional analyses indicate that the AF to compensate for short treatment durations should be in the range of 5-15. Future work should use available data to empirically determine endpoint-specific CES values; similarly, to determine AF values for BMD adjustment. Future work should also evaluate the ability to use in vitro dose-response data for risk assessment, and the utility of probabilistic methods for determination of mutagenicity HBGVs. Environ. Mol. Mutagen. 61:66-83, 2020. © 2019 Her Majesty the Queen in Right of Canada.

The ICH M7(R1) guideline describes a framework to assess the carcinogenic risk of mutagenic and carcinogenic pharmaceutical impurities following less-than-lifetime (LTL) exposures. This LTL framework is important as many pharmaceuticals are not administered for a patient's lifetime and as clinical trials typically involve LTL exposures. While there has been regulatory caution about applying LTL concepts to cohort of concern (COC) impurities such as N-nitrosamines, ICH M7 does not preclude this and indeed literature data suggests that the LTL framework will be protective of patient safety for N-nitrosamines. The goal was to investigate if applying the LTL framework in ICH M7 would control exposure to an acceptable excess cancer risk in humans. Using N-nitrosodiethylamine as a case study, empirical data correlating exposure duration (as a percentage of lifespan) and cancer incidence in rodent bioassays indicate that the LTL acceptable intake (AI) as derived using the ICH M7 framework would not exceed a negligible additional risk of cancer. Therefore, controlling N-nitrosamines to an LTL AI based on the ICH M7 framework is thus demonstrated to be protective for potential carcinogenic risk to patients over the exposure durations typical of clinical trials and many prescribed medicines.

Human exposure to carcinogens occurs via a plethora of environmental sources, with 70–90% of cancers caused by extrinsic factors. Aberrant phenotypes induced by such carcinogenic agents may provide universal biomarkers for cancer causation. Both current in vitro genotoxicity tests and the animal-testing paradigm in human cancer risk assessment fail to accurately represent and predict whether a chemical causes human carcinogenesis. The study aimed to establish whether the integrated analysis of multiple cellular endpoints related to the Hallmarks of Cancer could advance in vitro carcinogenicity assessment. Human lymphoblastoid cells (TK6, MCL-5) were treated for either 4 or 23 h with 8 known in vivo carcinogens, with doses up to 50% Relative Population Doubling (maximum 66.6 mM). The adverse effects of carcinogens on wide-ranging aspects of cellular health were quantified using several approaches; these included chromosome damage, cell signalling, cell morphology, cell-cycle dynamics and bioenergetic perturbations. Cell morphology and gene expression alterations proved particularly sensitive for environmental carcinogen identification. Composite scores for the carcinogens’ adverse effects revealed that this approach could identify both DNA-reactive and non-DNA reactive carcinogens in vitro. The richer datasets generated proved that the holistic evaluation of integrated phenotypic alterations is valuable for effective in vitro risk assessment, while also supporting animal test replacement. Crucially, the study offers valuable insights into the mechanisms of human carcinogenesis resulting from exposure to chemicals that humans are likely to encounter in their environment. Such an understanding of cancer induction via environmental agents is essential for cancer prevention. Electronic supplementary material The online version of this article (10.1007/s00204-017-2102-y) contains supplementary material, which is available to authorized users.

There is growing interest in quantitative analysis of in vivo genetic toxicity dose-response data, and use of point-of-departure (PoD) metrics such as the benchmark dose (BMD) for human health risk assessment (HHRA). Currently, multiple transgenic rodent (TGR) assay variants, employing different rodent strains and reporter transgenes, are used for the assessment of chemically-induced genotoxic effects in vivo. However, regulatory issues arise when different PoD values (e.g., lower BMD confidence intervals or BMDLs) are obtained for the same compound across different TGR assay variants. This study therefore employed the BMD approach to examine the ability of different TGR variants to yield comparable genotoxic potency estimates. Review of over 2000 dose-response datasets identified suitably-matched dose-response data for three compounds (ethyl methanesulfonate or EMS, N-ethyl-N-nitrosourea or ENU, and dimethylnitrosamine or DMN) across four commonly-used murine TGR variants (Muta™Mouse lacZ, Muta™Mouse cII, gpt delta and BigBlue® lacI). Dose-response analyses provided no conclusive evidence that TGR variant choice significantly influences the derived genotoxic potency estimate. This conclusion was reliant upon taking into account the importance of comparing BMD confidence intervals as opposed to directly comparing PoD values (e.g., comparing BMDLs). Comparisons with earlier works suggested that with respect to potency determination, tissue choice is potentially more important than choice of TGR assay variant. Scoring multiple tissues selected on the basis of supporting toxicokinetic information is therefore recommended. Finally, we used typical within-group variances to estimate preliminary endpoint-specific benchmark response (BMR) values across several TGR variants/tissues. We discuss why such values are required for routine use of genetic toxicity PoDs for HHRA. Environ. Mol. Mutagen. 58:632-643, 2017. © 2017 Her Majesty the Queen in Right of Canada. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc.

The tests used and the general principles behind test strategies are now often over 30 years old. It may be time by now, given that our knowledge of genetic toxicology has improved and that we also technically are better able to investigate DNA damage making use of modern molecular biological techniques, to start thinking on a new test strategy. In the present paper, it is discussed that the time is there to consider a new approach for genotoxicity assessment of substances. A fit for all test strategy was discussed making use of the most recent technological methods and techniques. It was also indicated that in silico tools should be more accepted by regulatory institutes/bodies as supporting information to better conclude which tests should be required for each separate substance to demonstrate its genotoxic potency. Next to that there should be a good rationale for performing in vivo studies. Finally, the need for germ cell genotoxicity testing, essential when classification and labeling of substances is mandatory, was discussed. It was suggested to change the GHS for genotoxicity classification and labelling from in vivo tests in germ cells into in vivo tests in somatic cells. Quantitative genotoxicology was also discussed. It appeared that we are currently at a transition, where the science developing to justify carrying out human health risk assessments based on genetic toxicology data sets supported by mechanistic data and exposure data. However, implementation will take time, and acceptance will be supported through the development of numerous case studies. Major remaining questions are: is genetic damage a relevant endpoint in itself, or should the risk assessment be carried out on the apical endpoint of cancer and which genotoxic endpoint should be used to derive the point of departure (PoD) for the human exposure limit?

A database of 91 chemicals with published data from both transgenic rodent mutation (TGR) and rodent comet assays has been compiled. The objective was to compare the sensitivity of the two assays for detecting genotoxicity. Critical aspects of study design and results were tabulated for each dataset. There were fewer datasets from rats than mice, particularly for the TGR assay, and therefore, results from both species were combined for further analysis. TGR and comet responses were compared in liver and bone marrow (the most commonly studied tissues), and in stomach and colon evaluated either separately or in combination with other GI tract segments. Overall positive, negative, or equivocal test results were assessed for each chemical across the tissues examined in the TGR and comet assays using two approaches: 1) overall calls based on weight of evidence (WoE) and expert judgement, and 2) curation of the data based on a priori acceptability criteria prior to deriving final tissue specific calls. Since the database contains a high prevalence of positive results, overall agreement between the assays was determined using statistics adjusted for prevalence (using AC1 and PABAK). These coefficients showed fair or moderate to good agreement for liver and the GI tract (predominantly stomach and colon data) using WoE, reduced agreement for stomach and colon evaluated separately using data curation, and poor or no agreement for bone marrow using both the WoE and data curation approaches. Confidence in these results is higher for liver than for the other tissues, for which there were less data. Our analysis finds that comet and TGR generally identify the same compounds (mainly potent mutagens) as genotoxic in liver, stomach and colon, but not in bone marrow. However, the current database content precluded drawing assay concordance conclusions for weak mutagens and non-DNA reactive chemicals.

The reproducibility of the in vivo Pig-a gene mutation test system was assessed across 13 different Japanese laboratories. In each laboratory rats were exposed to the same dosing regimen of N-nitroso-N-ethylurea (ENU), and red blood cells (RBCs) and reticulocytes (RETs) were collected for mutant phenotypic analysis using flow cytometry. Mutant frequency dose response data were analysed using the PROAST benchmark dose (BMD) statistical package. Laboratory was used as a covariate during the analysis to allow all dose responses to be analysed at the same time, with conserved shape parameters. This approach has recently been shown to increase the precision of the BMD analysis, as well as providing a measure of equipotency. This measure of equipotency was used here to demonstrate a reasonable level of interlaboratory reproducibility. Increased reproducibility could have been achieved by increasing the number of cells scored, as this would reduce the number of zero values within the mutant frequency data. Overall, the interlaboratory trial was successful, and these findings support the transferability of the in vivo Pig-a gene mutation assay.

For several decades, regulatory testing schemes for genetic damage have been standardized where the tests being utilized examined mutations and structural and numerical chromosomal damage. This has served the genetic toxicity community well when most of the substances being tested were amenable to such assays. The outcome from this testing is usually a dichotomous (yes/no) evaluation of test results, and in many instances, the information is only used to determine whether a substance has carcinogenic potential or not. Over the same time period, mechanisms and modes of action (MOAs) that elucidate a wider range of genomic damage involved in many adverse health outcomes have been recognized. In addition, a paradigm shift in applied genetic toxicology is moving the field toward a more quantitative dose-response analysis and point-of-departure (PoD) determination with a focus on risks to exposed humans. This is directing emphasis on genomic damage that is likely to induce changes associated with a variety of adverse health outcomes. This paradigm shift is moving the testing emphasis for genetic damage from a hazard identification only evaluation to a more comprehensive risk assessment approach that provides more insightful information for decision makers regarding the potential risk of genetic damage to exposed humans. To enable this broader context for examining genetic damage, a next generation testing strategy needs to take into account a broader, more flexible approach to testing, and ultimately modeling, of genomic damage as it relates to human exposure. This is consistent with the larger risk assessment context being used in regulatory decision making. As presented here, this flexible approach for examining genomic damage focuses on testing for relevant genomic effects that can be, as best as possible, associated with an adverse health effect. The most desired linkage for risk to humans would be changes in loci associated with human diseases, whether in somatic or germ cells. The outline of a flexible approach and associated considerations are presented in a series of nine steps, some of which can occur in parallel, which was developed through a collaborative effort by leading genetic toxicologists from academia, government, and industry through the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) Genetic Toxicology Technical Committee (GTTC). The ultimate goal is to provide quantitative data to model the potential risk levels of substances, which induce genomic damage contributing to human adverse health outcomes. Any good risk assessment begins with asking the appropriate risk management questions in a planning and scoping effort. This step sets up the problem to be addressed (e.g., broadly, does genomic damage need to be addressed, and if so, how to proceed). The next two steps assemble what is known about the problem by building a knowledge base about the substance of concern and developing a rational biological argument for why testing for genomic damage is needed or not. By focusing on the risk management problem and potential genomic damage of concern, the next step of assay(s) selection takes place. The work-up of the problem during the earlier steps provides the insight to which assays would most likely produce the most meaningful data. This discussion does not detail the wide range of genomic damage tests available, but points to types of testing systems that can be very useful. Once the assays are performed and analyzed, the relevant data sets are selected for modeling potential risk. From this point on, the data are evaluated and modeled as they are for any other toxicology endpoint. Any observed genomic damage/effects (or genetic event(s)) can be modeled via a dose-response analysis and determination of an estimated PoD. When a quantitative risk analysis is needed for decision making, a parallel exposure assessment effort is performed (exposure assessment is not detailed here as this is not the focus of this discussion; guidelines for this assessment exist elsewhere). Then the PoD for genomic damage is used with the exposure information to develop risk estimations (e.g., using reference dose (RfD), margin of exposure (MOE) approaches) in a risk characterization and presented to risk managers for informing decision making. This approach is applicable now for incorporating genomic damage results into the decision-making process for assessing potential adverse outcomes in chemically exposed humans and is consistent with the ILSI HESI Risk Assessment in the 21st Century (RISK21) roadmap. This applies to any substance to which humans are exposed, including pharmaceuticals, agricultural products, food additives, and other chemicals. It is time for regulatory bodies to incorporate the broader knowledge and insights provided by genomic damage results into the assessments of risk to more fully understand the potential of adverse outcomes in chemically exposed humans, thus improving the assessment of risk due to genomic damage. The historical use of genomic damage data as a yes/no gateway for possible cancer risk has been too narrowly focused in risk assessment. The recent advances in assaying for and understanding genomic damage, including eventually epigenetic alterations, obviously add a greater wealth of information for determining potential risk to humans. Regulatory bodies need to embrace this paradigm shift from hazard identification to quantitative analysis and to incorporate the wider range of genomic damage in their assessments of risk to humans. The quantitative analyses and methodologies discussed here can be readily applied to genomic damage testing results now. Indeed, with the passage of the recent update to the Toxic Substances Control Act (TSCA) in the US, the new generation testing strategy for genomic damage described here provides a regulatory agency (here the US Environmental Protection Agency (EPA), but suitable for others) a golden opportunity to reexamine the way it addresses risk-based genomic damage testing (including hazard identification and exposure). Environ. Mol. Mutagen. 58:264-283, 2017. © 2016 The Authors. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc.

From a risk assessment perspective, DNA-reactive agents are conventionally assumed to have genotoxic risks at all exposure levels, thus applying a linear extrapolation for low-dose responses. New approaches discussed here, including more diverse and sensitive methods for assessing DNA damage and DNA repair, strongly support the existence of measurable regions where genotoxic responses with increasing doses are insignificant relative to control. Model monofunctional alkylating agents have in vitro and in vivo datasets amenable to determination of points of departure (PoDs) for genotoxic effects. A session at the 2013 Society of Toxicology meeting provided an opportunity to survey the progress in understanding the biological basis of empirically-observed PoDs for DNA alkylating agents. Together with the literature published since, this review discusses cellular pathways activated by endogenous and exogenous alkylation DNA damage. Cells have evolved conserved processes that monitor and counteract a spontaneous steady-state level of DNA damage. The ubiquitous network of DNA repair pathways serves as the first line of defense for clearing of the DNA damage and preventing mutation. Other biological pathways discussed here that are activated by genotoxic stress include post-translational activation of cell cycle networks and transcriptional networks for apoptosis/cell death. The interactions of various DNA repair and DNA damage response pathways provide biological bases for the observed PoD behaviors seen with genotoxic compounds. Thus, after formation of DNA adducts, the activation of cellular pathways can lead to the avoidance of a mutagenic outcome. The understanding of the cellular mechanisms acting within the low-dose region will serve to better characterize risks from exposures to DNA-reactive agents at environmentally-relevant concentrations.

Motivated by recent constructions of TeV-scale strongly-coupled dynamics, either associated with the Higgs sector itself as in pseudo-Nambu-Goldstone boson (pNGB) Higgs models or in theories of asymmetric dark matter, we show that stable solitonic Q- balls can be formed from light pion-like pNGB fields carrying a conserved global quantum number in the presence of the Higgs field. We focus on the case of thick-wall Q-balls, where solutions satisfying all constraints are shown to exist over a range of parameter values. In the limit that our approximations hold, the Q-balls are weakly bound and parametrically large, and the form of the interactions of the light physical Higgs with the Q-ball is determined by the breaking of scale symmetry., Comment: 24 pages, 6 figures, 1 table

Use of imaging flow cytometry to assess induced DNA damage via the cytokinesis block micronucleus (CBMN) assay has thus far been limited to radiation dosimetry in human lymphocytes using high end, 'ImageStream X' series imaging cytometers. Its potential to enumerate chemically induced DNA damage using in vitro cell lines remains unexplored. In the present manuscript, we investigate the more affordable FlowSight® imaging cytometry platform to assess in vitro micronucleus (MN) induction in the human lymphoblastoid TK6 and metabolically competent MCL-5 cells treated with Methyl Methane Sulfonate (MMS) (0-5 µg/ml), Carbendazim (0-1.6 µg/ml), and Benzo[a]Pyrene (B[a]P) (0-6.3 µg/ml) for a period of 1.5-2 cell-cycles. Cells were fixed, and nuclei and MN were stained using the fluorescent nuclear dye DRAQ5™. Image acquisition was carried out using a 20X objective on a FlowSight® imaging cytometer (Amnis, part of Merck Millipore) equipped with a 488 nm laser. Populations of ∼20000 brightfield cell images, alongside DRAQ5™ stained nuclei/MN were rapidly collected (≤10 min). Single, in-focus cells suitable for scoring were then isolated using the IDEAS® software. An overlay of the brightfield cell outlines and the DRAQ5 nuclear fluorescence was used to facilitate scoring of mono-, bi-, tri-, and tetra-nucleated cells with or without MN events and in context of the cytoplasmic boundary of the parent cell.To establish the potential of the FlowSight® platform, and to establish 'ground truth' cell classification for the supervised machine learning based scoring algorithm that represents the next stage of our project, the captured images were scored manually. Alongside, MN frequencies were also derived using the 'gold standard' light microscopy and manual scoring. A minimum of 3000 bi-nucleated cells were assessed using both approaches. Using the benchmark dose approach, the comparability of genotoxic potency estimations for the different compounds and cell lines was assessed across the two scoring platforms as highly similar. This study therefore provides essential proof-of-concept that FlowSight® imaging cytometry is capable of reproducing the results of 'gold standard' manual scoring by light microscopy. We conclude that, with the right automated scoring algorithm, imaging flow cytometry could revolutionise the reportability and scoring throughput of the CBMN assay.

The interpretation and significance of DNA adduct data, their causal relationship to mutations, and their role in risk assessment have been debated for many years. An extended effort to identify key questions and collect relevant data to address them was focused on the ubiquitous low MW N7-alkyl/hydroxyalkylguanine adducts. Several academic, governmental, and industrial laboratories collaborated to gather new data aimed at better understanding the role and potential impact of these adducts in quantifiable genotoxic events (gene mutations/micronucleus). This review summarizes and evaluates the status of dose-response data for DNA adducts and mutations from recent experimental work with standard mutagenic agents and ethylene oxide and propylene oxide, and the importance for risk assessment. This body of evidence demonstrates that small N7-alkyl/hydroxyalkylguanine adducts are not pro-mutagenic and, therefore, adduct formation alone is not adequate evidence to support a mutagenic mode of action. Quantitative methods for dose-response analysis and derivation of thresholds, benchmark dose (BMD), or other points-of-departure (POD) for genotoxic events are now available. Integration of such analyses of genetox data is necessary to properly assess any role for DNA adducts in risk assessment. Regulatory acceptance and application of these insights remain key challenges that only the regulatory community can address by applying the many learnings from recent research. The necessary tools, such as BMDs and PODs, and the example datasets, are now available and sufficiently mature for use by the regulatory community. Environ. Mol. Mutagen. 60: 100-121, 2019. © 2018 The Authors. Environmental and Molecular Mutagenesis published by Wiley Periodicals, Inc. on behalf of Environmental Mutagen Society.

South Wales has been nurturing a nascent Life Sciences cluster through initiatives including the Institute of Life Science (ILS) at Swansea University Medical School. ILS aims to provide an entrepreneurial learning environment transcending industry, health care, academia and further education. This chapter describes how efforts to develop the sector have been undertaken through structured efforts of Understanding, Acting and Measuring, resulting in new ventures formed through spin-outs to commercialise research output and collaboration with other enterprises. Building upon concepts of clustering and regional innovation systems, the approach demonstrates the harnessing of a long-term strategy involving smart specialisation resulting in emerging and meaningful economic impact. Networking and knowledge exchange are shown as core components of a system reaching across wider sectors involving a diversity of skills. The conclusions demonstrate how entrepreneurial learning has also helped develop further actions including Talent Bank in support of the region’s broader ambition of A Regional Collaboration for Health.

The publisher would like to apologize for the failed cross-linking to the following Letter to the Editor by Paul A.

The anti-parasitic benzimidazole flubendazole has been used for many years to treat intestinal infections in humans and animals. Previous genotoxicity studies have shown that the compound is not a bacterial mutagen and a bone marrow micronucleus test, using a formulation that limited systemic absorption, was negative. The purpose of this study is to explore the genotoxicity of flubendazole and its main metabolites in in vitro micronucleus studies and to test a new oral formulation that improves systemic absorption in an in vivo micronucleus test. The isolated metabolites were also screened using the Ames test for bacterial mutagenicity. It was found that flubendazole, like other chemically related benzimidazoles used in anti-parasitic therapies, is a potent aneugen in vitro. The hydrolysed metabolite of flubendazole is negative in these tests, but the reduced metabolite (R- and S-forms) shows both aneugenic and clastogenic activity. However, in vitro micronucleus tests of flubendazole in the presence of rat liver S9 gave almost identical signals for aneugenicity as they did in the absence of S9, suggesting that any clastogenicity from the reduced metabolite is not sufficient to change the overall profile. Like flubendazole itself, both metabolites are negative in the Ames test. Analysis of dose–response curves from the in vitro tests, using recently developed point of departure approaches, demonstrate that the aneugenic potency of flubendazole is very similar to related anti-parasitic benzimidazoles, including albendazole, which is used in mass drug administration programmes to combat endemic filarial diseases. The in vivo micronucleus test of the new formulation of flubendazole also showed evidence of induced aneugenicity. Analysis of the in vivo data allowed a reference dose for aneugenicity to be established which can be compared with therapeutic exposures of flubendazole when this has been established. Analysis of the plasma from the animals used in the in vivo micronucleus test showed that there is increased exposure to flubendazole compared with previously tested formulations, as well as significant formation of the non-genotoxic hydrolysed metabolite of flubendazole and small levels of the reduced metabolite. In conclusion, this study shows that flubendazole is a potent aneugen in vitro with similar potency to chemically related benzimidazoles currently used as anti-parasitic therapies. The reduced metabolite also has aneugenic properties as well as clastogenic properties. Treatment with a new formulation of flubendazole that allows increased systemic exposure, compared with previously used formulations, also results in detectable aneugenicity in vivo. Based on the lack of carcinogenicity of this class of benzimidazoles and the intended short-term dosing, it is unlikely that flubendazole treatment will pose a carcinogenic risk to patients.

Assessment of genetic toxicity and/or carcinogenic activity is an essential element of chemical screening programs employed to protect human health. Dose-response and gene mutation data are frequently analysed by industry, academia and governmental agencies for regulatory evaluations and decision making. Over the years, a number of efforts at different institutions have led to the creation and curation of databases to house genetic toxicology data, largely, with the aim of providing public access to facilitate research and regulatory assessments. This article provides a brief introduction to a new genetic toxicology portal called Mutation Analysis Informatics Tools (MutAIT) (www.mutait.org) that provides easy access to two of the largest genetic toxicology databases, the Mammalian Gene Mutation Database (MGMD) and TransgenicDB. TransgenicDB is a comprehensive collection of transgenic rodent mutation data initially compiled and collated by Health Canada. The updated MGMD contains approximately 50 000 individual mutation spectral records from the published literature. The portal not only gives access to an enormous quantity of genetic toxicology data, but also provides statistical tools for dose-response analysis and calculation of benchmark dose. Two important R packages for dose-response analysis are provided as web-distributed applications with user-friendly graphical interfaces. The 'drsmooth' package performs dose-response shape analysis and determines various points of departure (PoD) metrics and the 'PROAST' package provides algorithms for dose-response modelling. The MutAIT statistical tools, which are currently being enhanced, provide users with an efficient and comprehensive platform to conduct quantitative dose-response analyses and determine PoD values that can then be used to calculate human exposure limits or margins of exposure.

This report summarizes the discussion, conclusions, and points of consensus of the IWGT Working Group on Quantitative Approaches to Genetic Toxicology Risk Assessment (QWG) based on a meeting in Foz do Iguacu, Brazil October 31–November 2, 2013. Topics addressed included (1) the need for quantitative dose–response analysis, (2) methods to analyze exposure–response relationships & derive point of departure (PoD) metrics, (3) points of departure (PoD) and mechanistic threshold considerations, (4) approaches to define exposure-related risks, (5) empirical relationships between genetic damage (mutation) and cancer, and (6) extrapolations across test systems and species. This report discusses the first three of these topics and a companion report discusses the latter three. The working group critically examined methods for determining point of departure metrics (PoDs) that could be used to estimate low-dose risk of genetic damage and from which extrapolation to acceptable exposure levels could be made using appropriate mode of action information and uncertainty factors. These included benchmark doses (BMDs) derived from fitting families of exponential models, the N o O bserved G enotoxic E ffect L evel (NOGEL), and “threshold” or breakpoint dose (BPD) levels derived from bilinear models when mechanistic data supported this approach. The QWG recognizes that scientific evidence suggests that thresholds below which genotoxic effects do not occur likely exist for both DNA-reactive and DNA-nonreactive substances, but notes that small increments of the spontaneous level cannot be unequivocally excluded either by experimental measurement or by mathematical modeling. Therefore, rather than debating the theoretical possibility of such low-dose effects, emphasis should be placed on determination of PoDs from which acceptable exposure levels can be determined by extrapolation using available mechanistic information and appropriate uncertainty factors. This approach places the focus on minimization of the genotoxic risk, which protects against the risk of the development of diseases resulting from the genetic damage. Based on analysis of the strengths and weaknesses of each method, the QWG concluded that the order of preference of PoD metrics is the statistical lower bound on the BMD > the NOGEL > a statistical lower bound on the BPD. A companion report discusses the use of these metrics in genotoxicity risk assessment, including scaling and uncertainty factors to be considered when extrapolating below the PoD and/or across test systems and to the human.

Genetic toxicology testing has a crucial role in the safety assessment of new and existing substances of societal value by reducing/eliminating human exposure to potential somatic and germ cell mutagens. Genetic toxicology assays have historically been used in a qualitative manner to arrive at the binary decision of ‘yes’ or ‘no’ with regards to the mutagenic potential. However, the field is currently at a crossroads, with new methods being developed and new proposals being made to use genetic toxicity data in a more quantitative manner. Technological advances have made it possible to perform high-content, high-throughput and high-precision analysis to increase the number of “scored” events leading to increased statistical precision of the endpoint under evaluation. Automated flow cytometry and image analysis are providing significant advantages for the evaluation of gene mutations as well as cytogenetic damage both in vitro and in vivo. In addition, statistical methods such as the benchmark dose (BMD) approach can be used to identify point of departure (PoD) metrics for use in human health risk assessments, including estimation of reference dose (RfD) and margins of exposure (MOE) from in vivo data. Here we provide new data to compare different in vitro micronucleus approaches, observing that the flow based assay performs very well in defining a PoD for methyl methanesulfonate. We also present reanalysis of published in vivo Pig-a gene mutation data, to show how covariate analysis increases precision and reduces the effects of outliers when defining BMD values. Furthermore, we show how in vivo BMD metrics can be used to define RfD values, and then provide comparisons to other human exposure limit values such as permitted daily exposure (PDE). Finally, the principles of empirical correlation using BMD metrics are presented, with methods for derivation of BMD values for endpoint B, when using data from only endpoint A. These developments are opening the possibility of genetic toxicity data being used as an apical endpoint to define negligible risk in human health risk assessments. Expert groups consisting of stakeholders representing academia, industry and the government are now developing guidance on transforming genetic toxicology testing from a qualitative to a quantitative science, keeping in mind the 3R principles of animal welfare.

Mutagens can be carcinogens, and traditionally, they have been identified in vitro using the Salmonella ‘Ames’ reverse mutation assay. However, prokaryotic DNA packaging, replication and repair systems are mechanistically very different to those in the humans we inevitably seek to protect. Therefore, for many years, mammalian cell line genotoxicity assays that can detect eukaryotic mutagens as well as clastogens and aneugens have been used. The apparent lack of specificity in these largely rodent systems, due partly to their mutant p53 status, has contributed to the use of animal studies to resolve data conflicts. Recently, silencing mutations at the PIG-A locus have been demonstrated to prevent glycophosphatidylinositol (GPI) anchor synthesis and consequentially result in loss of GPI-anchored proteins from the cell’s extracellular surface. The successful exploitation of this mutant phenotype in animal studies has triggered interest in the development of an analogous in vitro PIG-A mutation screening assay. This article describes the development of a robust assay design using metabolically active human cells. The assay includes viability and cell membrane integrity assessment and conforms to the future ideas of the 21st-century toxicology testing.

Temozolomide (TMZ), a monofunctional alkylating agent, was selected as a model compound to determine its quantitative genotoxic dose–response relationship in different tissues (blood, liver, and jejunum) and endpoints [Pig-a-, comet-, and micronucleus assay (MNT)] in male rats. TMZ was administered p.o. over 5 consecutive days (day 1–5), followed by a treatment-free period of 50 days (day 6–56) and a final administration prior to necropsy (day 57–59). TMZ showed a dose-dependent increase in DNA damage in all interrogated endpoints. A statistically significant increase in Pig-a mutant phenotypes was observed on day 44 starting at 7.5 mg/kg/day for mutant reticulocytes (for RETCD59−) and at 3.75 mg/kg/day for mutant red blood cells (RBCCD59−), respectively. In addition, a statistically significant increase in cytogenetic damage, as measured by micronucleated reticulocytes, was observed starting at 3.75 mg/kg/day on day 3 and 1.5 mg/kg/day on day 59. DNA strand breaks, as detected by the comet assay, showed a dose-dependent and statistically significant increase in liver, blood, and jejunum starting at doses of 3.75, 3.75, and 7.5 mg/kg/day, respectively. The dose–response relationships of the Pig-a, MNT, and comet data were analyzed for possible points of departure (PoD) using the benchmark-dose (BMD) software PROAST with different critical effect sizes (CES) (BMD0.1, BMD0.5, BMD1, and BMD1SD). Overall, PoD values show a high concordance between different tissues and endpoints, underlining the suitability of this experimental design to explore quantitative dose–response relationships in a variety of different tissues and endpoints, while minimizing animal use.

Genetic toxicology data have traditionally been employed for qualitative, rather than quantitative evaluations of hazard. As a continuation of our earlier report that analyzed ethyl methanesulfonate (EMS) and methyl methanesulfonate (MMS) dose-response data (Gollapudi et al., 2013), here we present analyses of 1-ethyl-1-nitrosourea (ENU) and 1-methyl-1-nitrosourea (MNU) dose-response data and additional approaches for the determination of genetic toxicity point-of-departure (PoD) metrics. We previously described methods to determine the no-observed-genotoxic-effect-level (NOGEL), the breakpoint-dose (BPD; previously named Td), and the benchmark dose (BMD10 ) for genetic toxicity endpoints. In this study we employed those methods, along with a new approach, to determine the non-linear slope-transition-dose (STD), and alternative methods to determine the BPD and BMD, for the analyses of nine ENU and 22 MNU datasets across a range of in vitro and in vivo endpoints. The NOGEL, BMDL10 and BMDL1SD PoD metrics could be readily calculated for most gene mutation and chromosomal damage studies; however, BPDs and STDs could not always be derived due to data limitations and constraints of the underlying statistical methods. The BMDL10 values were often lower than the other PoDs, and the distribution of BMDL10 values produced the lowest median PoD. Our observations indicate that, among the methods investigated in this study, the BMD approach is the preferred PoD for quantitatively describing genetic toxicology data. Once genetic toxicology PoDs are calculated via this approach, they can be used to derive reference doses and margin of exposure values that may be useful for evaluating human risk and regulatory decision making.

The assumption that mutagens have linear dose-responses recently has been challenged. In particular, ethyl methanesulfonate (EMS), a DNA-reactive mutagen and carcinogen, exhibited sublinear or thresholded dose-responses for LacZ mutation in transgenic Muta™Mouse and for micronucleus (MN) frequency in CD1 mice (Gocke E and Muller L [2009]: Mutat Res 678:101-107). In order to explore variables in establishing genotoxicity dose-responses, we characterized the genotoxicity of EMS using gene mutation assays anticipated to have lower spontaneous mutant frequencies (MFs) than Muta™Mouse. Male gpt-delta transgenic mice were treated daily for 28 days with 5 to 100 mg/kg EMS, and measurements were made on: (i) gpt MFs in liver, lung, bone marrow, kidney, small intestine, and spleen; and (ii) Pig-a MFs in peripheral blood reticulocytes (RETs) and total red blood cells. MN induction also was measured in peripheral blood RETs. These data were used to calculate Points of Departure (PoDs) for the dose responses, i.e., no-observed-genotoxic-effect-levels (NOGELs), lower confidence limits of threshold effect levels (Td-LCIs), and lower confidence limits of 10% benchmark response rates (BMDL10 s). Similar PoDs were calculated from the published EMS dose-responses for LacZ mutation and CD1 MN induction. Vehicle control gpt and Pig-a MFs were 13-40-fold lower than published vehicle control LacZ MFs. In general, the EMS genotoxicity dose-responses in gpt-delta mice had lower PoDs than those calculated from the Muta™Mouse and CD1 mouse data. Our results indicate that the magnitude and possibly the shape of mutagenicity dose responses differ between in vivo models, with lower PoDs generally detected by gene mutation assays with lower backgrounds.

Recent evidence has challenged the default assumption that all DNA-reactive alkylating agents exhibit a linear dose-response. Emerging evidence suggests that the model alkylating agents methyl- and ethylmethanesulfonate and methylnitrosourea (MNU) and ethylnitrosourea observe a nonlinear dose-response with a no observed genotoxic effect level (NOGEL). Follow-up mechanistic studies are essential to understand the mechanism of cellular tolerance and biological relevance of such NOGELs. MNU is one of the most mutagenic simple alkylators. Therefore, understanding the mechanism of mutation induction, following low-dose MNU treatment, sets precedence for weaker mutagenic alkylating agents. Here, we tested MNU at 10-fold lower concentrations than a previous study and report a NOGEL of 0.0075 µg/ml (72.8nM) in human lymphoblastoid cells, quantified through the hypoxanthine (guanine) phosphoribosyltransferase assay (OECD 476). Mechanistic studies reveal that the NOGEL is dependent upon repair of O(6)-methylguanine (O(6)MeG) by the suicide enzyme O(6)MeG-DNA methyltransferase (MGMT). Inactivation of MGMT sensitizes cells to MNU-induced mutagenesis and shifts the NOGEL to the left on the dose axis.

The use of manual microscopy for the scoring of chromosome damage in the in vitro micronucleus assay is often associated with user subjectivity. This level of subjectivity can be reduced by using automated platforms, which have added value of faster with high-throughput and multi-endpoint capabilities. However, there is a need to assess the reproducibility and sensitivity of these automated platforms compared with the gold standard of the manual scoring. The automated flow cytometry-based MicroFlow® and image analysis-based Metafer™ were used for dose response analyses in human lymphoblastoid TK6 cells exposed to the model clastogen, methyl methanesulfonate (MMS), aneugen, carbendazim, and the weak genotoxic carcinogen, ochratoxin A (OTA). Cells were treated for 4 or 30 h, with a 26- or 0-h recovery. Flow cytometry scoring parameters and the Metafer™ image classifier were investigated, to assess any potential differences in the micronucleus (MN) dose responses. Dose response data were assessed using the benchmark dose approach with chemical and scoring system set as covariate to assess reproducibility between endpoints. A clear increase in MN frequency was observed using the MicroFlow® approach on TK6 cells treated for 30 h with MMS, carbendazim and OTA. The MicroFlow®-based MN frequencies were comparable to those derived by using the Metafer™ and manual scoring platforms. However, there was a potential overscoring of MN with the MicroFlow® due to the cell lysis step and an underscoring with the Metafer™ system based on current image classifier settings. The findings clearly demonstrate that the MicroFlow® and Metafer™ MN scoring platforms are powerful tools for automated high-throughput MN scoring and dose response analysis. Electronic supplementary material The online version of this article (doi:10.1007/s00204-016-1903-8) contains supplementary material, which is available to authorized users.

For several decades, regulatory testing schemes for genetic damage have been standardized where the tests being utilized examined mutations and structural and numerical chromosomal damage. This has served the genetic toxicity community well when most of the substances being tested were amenable to such assays. The outcome from this testing is usually a dichotomous (yes/no) evaluation of test results, and in many instances, the information is only used to determine whether a substance has carcinogenic potential or not. Over the same time period, mechanisms and modes of action (MOAs) that elucidate a wider range of genomic damage involved in many adverse health outcomes have been recognized. In addition, a paradigm shift in applied genetic toxicology is moving the field toward a more quantitative dose-response analysis and point-of-departure (PoD) determination with a focus on risks to exposed humans. This is directing emphasis on genomic damage that is likely to induce changes associated with a variety of adverse health outcomes. This paradigm shift is moving the testing emphasis for genetic damage from a hazard identification only evaluation to a more comprehensive risk assessment approach that provides more insightful information for decision makers regarding the potential risk of genetic damage to exposed humans. To enable this broader context for examining genetic damage, a next generation testing strategy needs to take into account a broader, more flexible approach to testing, and ultimately modeling, of genomic damage as it relates to human exposure. This is consistent with the larger risk assessment context being used in regulatory decision making. As presented here, this flexible approach for examining genomic damage focuses on testing for relevant genomic effects that can be, as best as possible, associated with an adverse health effect. The most desired linkage for risk to humans would be changes in loci associated with human diseases, whether in somatic or germ cells. The outline of a flexible approach and associated considerations are presented in a series of nine steps, some of which can occur in parallel, which was developed through a collaborative effort by leading genetic toxicologists from academia, government, and industry through the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) Genetic Toxicology Technical Committee (GTTC). The ultimate goal is to provide quantitative data to model the potential risk levels of substances, which induce genomic damage contributing to human adverse health outcomes. Any good risk assessment begins with asking the appropriate risk management questions in a planning and scoping effort. This step sets up the problem to be addressed (e.g., broadly, does genomic damage need to be addressed, and if so, how to proceed). The next two steps assemble what is known about the problem by building a knowledge base about the substance of concern and developing a rational biological argument for why testing for genomic damage is needed or not. By focusing on the risk management problem and potential genomic damage of concern, the next step of assay(s) selection takes place. The work-up of the problem during the earlier steps provides the insight to which assays would most likely produce the most meaningful data. This discussion does not detail the wide range of genomic damage tests available, but points to types of testing systems that can be very useful. Once the assays are performed and analyzed, the relevant data sets are selected for modeling potential risk. From this point on, the data are evaluated and modeled as they are for any other toxicology endpoint. Any observed genomic damage/effects (or genetic event(s)) can be modeled via a dose-response analysis and determination of an estimated PoD. When a quantitative risk analysis is needed for decision making, a parallel exposure assessment effort is performed (exposure assessment is not detailed here as this is not the focus of this discussion; guidelines for this assessment exist elsewhere). Then the PoD for genomic damage is used with the exposure information to develop risk estimations (e.g., using reference dose (RfD), margin of exposure (MOE) approaches) in a risk characterization and presented to risk managers for informing decision making. This approach is applicable now for incorporating genomic damage results into the decision-making process for assessing potential adverse outcomes in chemically exposed humans and is consistent with the ILSI HESI Risk Assessment in the 21st Century (RISK21) roadmap. This applies to any substance to which humans are exposed, including pharmaceuticals, agricultural products, food additives, and other chemicals. It is time for regulatory bodies to incorporate the broader knowledge and insights provided by genomic damage results into the assessments of risk to more fully understand the potential of adverse outcomes in chemically exposed humans, thus improving the assessment of risk due to genomic damage. The historical use of genomic damage data as a yes/no gateway for possible cancer risk has been too narrowly focused in risk assessment. The recent advances in assaying for and understanding genomic damage, including eventually epigenetic alterations, obviously add a greater wealth of information for determining potential risk to humans. Regulatory bodies need to embrace this paradigm shift from hazard identification to quantitative analysis and to incorporate the wider range of genomic damage in their assessments of risk to humans. The quantitative analyses and methodologies discussed here can be readily applied to genomic damage testing results now. Indeed, with the passage of the recent update to the Toxic Substances Control Act (TSCA) in the US, the new generation testing strategy for genomic damage described here provides a regulatory agency (here the US Environmental Protection Agency (EPA), but suitable for others) a golden opportunity to reexamine the way it addresses risk-based genomic damage testing (including hazard identification and exposure). Environ. Mol. Mutagen. 58:264-283, 2017. © 2016 The Authors. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc.

Genotoxicity tests have traditionally been used only for hazard identification, with qualitative dichotomous groupings being used to identify compounds that have the capacity to induce mutations and/or cytogenetic alterations. However, there is an increasing interest in employing quantitative analysis of in vivo dose-response data to derive point of departure (PoD) metrics that can be used to establish human exposure limits or margins of exposure (MOEs), thereby supporting human health risk assessments and regulatory decisions. This work is an extension of our companion article on in vitro dose-response analyses and outlines how the combined benchmark dose (BMD) approach across included covariates can be used to improve the analyses and interpretation of in vivo genetic toxicity dose-response data. Using the BMD-covariate approach, we show that empirical comparisons of micronucleus frequency dose-response data across multiple studies justifies dataset merging, with subsequent analyses improving the precision of BMD estimates and permitting attendant potency ranking of seven clastogens. Similarly, empirical comparisons of Pig-a mutant phenotype frequency data collected in males and females justified dataset merging across sex. This permitted more effective scrutiny regarding the effect of post-exposure sampling time on the mutagenicity of N-ethyl-N-nitrosourea observed in reticulocytes and erythrocytes in the Pig-a assay. The BMD-covariate approach revealed tissue-specific differences in the induction of lacZ transgene mutations in Muta™Mouse specimens exposed to benzo[a]pyrene (BaP), with the results permitting the formulation of mechanistic hypotheses regarding the observed potency ranking. Lastly, we illustrate how historical dose-response data for assessments that examined numerous doses (i.e. induced lacZ mutant frequency (MF) across 10 doses of BaP) can be used to improve the precision of BMDs derived from datasets with far fewer doses (i.e. lacZ MF for 3 doses of dibenz[a,h]anthracene). Collectively, the presented examples illustrate how innovative use of the BMD approach can permit refinement of the use of in vivo data; improving the efficacy of experimental animal use in genetic toxicology without sacrificing PoD precision.

Genetic toxicity testing has traditionally been used for hazard identification, with dichotomous classification of test results serving to identify genotoxic agents. However, the utility of genotoxicity data can be augmented by employing dose-response analysis and point of departure determination. Via interpolation from a fitted dose-response model, the benchmark dose (BMD) approach estimates the dose that elicits a specified (small) effect size. BMD metrics and their confidence intervals can be used for compound potency ranking within an endpoint, as well as potency comparisons across other factors such as cell line or exposure duration. A recently developed computational method, the BMD covariate approach, permits combined analysis of multiple dose-response data sets that are differentiated by covariates such as compound, cell type or exposure regime. The approach provides increased BMD precision for effective potency rankings across compounds and other covariates that pertain to a hypothesised mode of action (MOA). To illustrate these applications, the covariate approach was applied to the analysis of published in vitro micronucleus frequency dose-response data for ionising radiations, a set of aneugens, two mutagenic azo compounds and a topoisomerase II inhibitor. The ionising radiation results show that the precision of BMD estimates can be improved by employing the covariate method. The aneugen analysis provided potency groupings based on the BMD confidence intervals, and analyses of azo compound data from cells lines with differing metabolic capacity confirmed the influence of endogenous metabolism on genotoxic potency. This work, which is the first of a two-part series, shows that BMD-derived potency rankings can be employed to support MOA evaluations as well as facilitate read across to expedite chemical evaluations and regulatory decision-making. The follow-up (Part II) employs the combined covariate approach to analyse in vivo genetic toxicity dose-response data focussing on how improvements in BMD precision can impact the reduction and refinement of animal use in toxicological research.

Applied genetic toxicology is undergoing a transition from qualitative hazard identification to quantitative dose-response analysis and risk assessment. To facilitate this change, the Health and Environmental Sciences Institute (HESI) Genetic Toxicology Technical Committee (GTTC) sponsored a workshop held in Lancaster, UK on July 10-11, 2014. The event included invited speakers from several institutions and the contents was divided into three themes-1: Point-of-departure Metrics for Quantitative Dose-Response Analysis in Genetic Toxicology; 2: Measurement and Estimation of Exposures for Better Extrapolation to Humans and 3: The Use of Quantitative Approaches in Genetic Toxicology for human health risk assessment (HHRA). A host of pertinent issues were discussed relating to the use of in vitro and in vivo dose-response data, the development of methods for in vitro to in vivo extrapolation and approaches to use in vivo dose-response data to determine human exposure limits for regulatory evaluations and decision-making. This Special Issue, which was inspired by the workshop, contains a series of papers that collectively address topics related to the aforementioned themes. The Issue includes contributions that collectively evaluate, describe and discuss in silico, in vitro, in vivo and statistical approaches that are facilitating the shift from qualitative hazard evaluation to quantitative risk assessment. The use and application of the benchmark dose approach was a central theme in many of the workshop presentations and discussions, and the Special Issue includes several contributions that outline novel applications for the analysis and interpretation of genetic toxicity data. Although the contents of the Special Issue constitutes an important step towards the adoption of quantitative methods for regulatory assessment of genetic toxicity, formal acceptance of quantitative methods for HHRA and regulatory decision-making will require consensus regarding the relationships between genetic damage and disease, and the concomitant ability to use genetic toxicity results per se.

In this study, we investigated the applicability of using in vivo mouse micronucleus (MN) data to derive cancer potency information. We also present a new statistical methodology for correlating estimated potencies between in vivo MN tests and cancer studies, which could similarly be used for other systems (e.g. in vitro vs. in vivo genotoxicity tests). The dose-response modelling program PROAST was used to calculate benchmark doses (BMDs) for estimating the genotoxic and carcinogenic potency for 48 compounds in mice; most of the data were retrieved from the National Toxicology Program (NTP) database, while some additional data were retrieved from the Carcinogenic Potency Database and published studies. BMD05s (doses with 5% increase in MN frequency) were derived from MN data, and BMD10s (doses with 10% extra cancer risk) were derived from carcinogenicity data, along with their respective lower (BMDL) and upper (BMDU) confidence bounds. A clear correlation between the in vivo MN BMD05s and the cancer BMD10s was observed when the lowest BMD05 from the in vivo MN was plotted against the lowest BMD10 from the carcinogenicity data for each individual compound. By making a further selection of BMDs related to more or less equally severe cancer lesions, the correlation was considerably improved. Getting a general scientific consensus on how we can quantitatively compare different tumour lesion types and investigating the impact of MN study duration are needed to refine this correlation analysis. Nevertheless, our results suggest that a BMD derived from genotoxicity data might provide a prediction of the tumour potency (BMD10) with an uncertainty range spanning roughly a factor of 100.