Genotoxicity plays an important role in chemical safety assessment, as genetic alterations can lead to severe and irreversible health consequences. To date, the assessment of genotoxicity has mostly been limited to hazard identification, followed by rigorous risk mitigation measures if a substance is found to be mutagenic, regardless of potency, the underlying mechanism, and cellular biology. While this regulatory hazard-based approach is straightforward, it is unsatisfactory when exposure to genotoxic substances cannot be completely avoided and/or regulatory measures lead to misperceptions of risk and undesirable socioeconomic side effects. The latter becomes particularly obvious in light of natural genotoxicants, e.g. occurring in plant-based food, and for substances that are difficult to replace but come with a high socioeconomic value but little potency and exposure. Hence, there is an increasing demand for a paradigm shift towards a quantitative interpretation of genotoxicity data in regulatory risk assessment. However, moving away from the traditional hazard-based assessment and doing so safely requires a collective effort of all relevant stakeholders. To this end, the German Federal Institute for Risk Assessment (BfR) organised an international symposium, at which experts from regulatory authorities, academia and industry discussed the opportunities and challenges involved. Here, we present key issues to be considered for a successful implementation of quantitative approaches. In situations where exposure to genotoxic substances cannot be completely avoided, e.g. occurrence of contaminants, quantitative approaches offer the opportunity to better characterise the associated risks and thus enable risk managers to make more informed decisions.

Nitrosamines (NAs) are a diverse class of mutagenic impurities encompassing both small molecules and structurally complex drug-related NAs, referred to as nitrosamine drug substance-related impurities (NDSRIs). NAs display a broad range of carcinogenic potential, from high carcinogenic potency to being weak or even non-carcinogenic. In vitro Ames tests, conducted with both rat and hamster liver-induced S9, and in vivo transgenic rodent (TGR) mutation assays have been used by pharmaceutical sponsors for hazard identification of NDSRIs. A comparative analysis of Ames tests and TGR results for 33 NDSRIs was performed and revealed an accuracy of 79 % between the overall mutagenic calls in the two assays. For NDSRIs with positive TGR results, mutagenic potency estimates were calculated and compared to NAs with robust carcinogenicity and TGR dose-response data. Results from these NAs demonstrated a strong correlation between carcinogenic potency (TD50) and TGR mutagenic potency (BMDL50) (r2 = 0.95), which supports the use of TGR data for both hazard identification and acceptable intake (AI) determination. By integrating quantitative risk assessment tools with TGR assays, this work contributes to a more robust framework for evaluating NA-associated risks.

Micronuclei (MN) are critical biomarkers for pathological conditions, yet their manual scoring is inherently laborious and prone to significant interobserver variability, limiting the reliability and scalability of genotoxicity assessments. Recent advancements in deep learning and computer vision have revolutionized automated MN detection in various assay samples, enhancing accuracy and efficiency and reducing human bias. While these artificial intelligence (AI)-powered techniques have been demonstrated in in vitro genotoxicity testing, their application to the minimally invasive buccal micronucleus cytome (BMCyt) assay for human biomonitoring remains largely unexplored. The BMCyt assay, invaluable for assessing genotoxic damage in environmentally exposed populations, presents unique challenges, including sample variability, confounding factors, and the complexity of scoring multiple cytogenetic endpoints. This review covers the evolution of AI-based MN detection, analysing key methodologies and advancements. It highlights the untapped potential of integrating AI into the BMCyt assay to overcome current analytical limitations, improve reproducibility, increase throughput, and eliminate observer bias. By facilitating more robust and scalable genomic damage monitoring, AI integration will significantly enhance the utility of the BMCyt assay in large-scale epidemiological studies and human biomonitoring.

The proceedings of the 36th annual meeting of the Industrial Genotoxicology Group (IGG) are shared here. The meeting held at Lhasa Limited, Leeds, UK on 28 November 2023, focussed on two aspects; new approach methodologies (NAMs), including those for the assessment of non-standard modalities such as gas-vapour assessments and nanomaterials, and addressing the regulatory challenges associated with understanding the genotoxic and carcinogenic potential of N-nitrosamines and N-nitrosamine impurities. New approach methodologies, such as error-corrected sequencing and enhanced Ames tests that may help address these challenges were also discussed.

Management of N-Nitrosamine impurity levels in pharmaceutical drug substances and products is guided by ICH M7 where N-nitrosamines are defined as Cohorts of Concern. Regulatory agencies have suggested using read-across of rodent carcinogenicity TD50 values for structurally similar compounds to assess the potency of various data-poor N-nitrosamines. The TD50 for N-Methyl-N-nitrosophenethylamine (NMPEA) as reported in the CPDB with a harmonic mean TD50 value 7.88 μg/kg/day (or an Acceptable Intake (AI) level of 8 ng/day) did not follow the recommendations of ICH M7. Mixed tissues (oesophagus, forestomach, tongue, and nasal cavity) were combined into a single group termed "upper gastro-intestinal tract". Upon examination of the original data, the oesophagus was considered the most sensitive organ of effect. The TD50 value for the oesophagus was recalculated to 40.1 μg/kg/day (or an AI of 40.1 ng/day). Subsequently, Benchmark Dose (BMD) analysis was performed on the same data set yielding a BMD10 of 3.06-17.6 μg/kg/day in rat (or Permitted Daily Exposure range of 306-1760 ng/day). Theses updated values are 5 times (or higher than) the current AI level of 8 ng/day and could result in significantly higher AI limits for marketed drug impurities that use NMPEA as a suitable analog (e.g., N-nitroso- nortriptyline) to derive an AI.

Chemical risk assessment has historically focused on single compounds, neglecting the implications of combined exposures. To bridge this gap, several methodologies, such as concentration addition (CA) and independent action (IA), have been developed. However, a systematic, consistent, and integrated approach across various legislative frameworks is still lacking. The assessment of combined effects of genotoxicants is even more challenging, as genotoxicity data are typically evaluated qualitatively, without considering the effect size. This study aimed to develop a quantitative approach for evaluating the combined effects of genotoxic compounds with both similar and dissimilar modes of action (MoA), based on the benchmark concentration (BMC) principle. A proof-of-concept study was conducted using the in vitro micronucleus (MNvit) test to examine two types of binary mixtures: ethyl methanesulfonate (EMS) and methyl methanesulfonate (MMS), which share similar MoA, and MMS and etoposide (ETP), which have dissimilar MoA. The methodology involved collecting data for individual compounds, calculating BMC values, composing mixtures with different ratios and inducing various effect levels, testing these mixtures, and comparing the experimental results with the modelled data to verify additivity. The findings indicated that for both mixtures, the experimental responses aligned with the predicted additive effects, supporting the validity of the additivity principle. This study highlights the potential of an optimized BMC-based approach as a robust framework for testing chemical mixtures. It should be adopted in future studies to evaluate a wider range of genotoxic compounds, offering a more comprehensive and quantitative strategy for assessing combined chemical exposures.

Acceptable intake (AI) limits for nitrosamine drug substance related impurities (NDSRIs) that lack carcinogenicity data could be estimated from mutagenic potency relative to anchor nitrosamines with carcinogenicity data. This approach integrates points of departure (PoDs) derived from in vivo mutagenicity studies with in silico predictions generated by a validated quantum-mechanical (QM) model. N-nitrosodiethanolamine (NDELA) and N-nitrosopiperidine (NPIP), with AIs derived from robust carcinogenicity data, were tested in the transgenic rodent (TGR) gene mutation assay. Liver mutant frequency and benchmark dose (BMD) modeling provided a suitable, robust, and precise PoD metric. BMD confidence intervals (CIs) calculated from mutant frequency expanded the potency range of previously reported BMD CIs for other anchor nitrosamines. Cancer-protective AIs for mutagenic NDSRIs can be pragmatically calculated on a potency basis by comparing their lower bound TGR BMD CIs with the BMD CIs and AIs derived from model/anchor nitrosamines that have results for in vivo gene mutation and cancer bioassays. In vivo modeling was supported by the Computer-Aided Discovery and RE-design (CADRE) program, a validated QM model for predicting NDSRI carcinogenic potency based on the underlying mechanism of mutagenicity. CADRE distinguished between anchor nitrosamines N-nitrosodiethylamine (NDEA) and N-nitrosodimethylamine (NDMA) and the less potent NDELA and NPIP. Scrutiny of underlying reactivity indices and relevant physicochemical properties rationalized the observed trend in metabolic activity and thus predicted carcinogenic potency. Leveraging the in vivo-in silico approach is valuable in gaining confidence in the proposed AIs, whereby the QM model serves as mechanistic validation of in vivo results.

The benchmark dose (BMD) approach constitutes the most effective and pragmatic strategy for the derivation of a point of departure (PoD) for comparative potency analysis, risk assessment, and regulatory decision-making. There is considerable controversy regarding the most appropriate benchmark response (BMR) for genotoxicity endpoints. This work employed the Slob (2017) Effect Size (ES) theory to define robust BMR values for the in vivo transgenic rodent (TGR) and Pig-a mutagenicity endpoints. An extensive database of dose-response data was prepared and curated; BMD analyses were used to determine endpoint-specific maxima (i.e., parameter c) and within-group variance (i.e., var). Detailed analyses investigated the dependence of var on experimental factors such as tissue, administration route, treatment duration, and post-exposure tissue sampling time. The overall lack of influence of these experimental factors on var permitted the determination of typical values for the endpoints investigated. Typical var for the TGR endpoint is 0.19; the value for the Pig-a endpoint is 0.29. Endpoint-specific var values were used to calculate endpoint-specific BMR values; the values are 47% for TGR and 60% for Pig-a. Endpoint-specific BMR values were also calculated using the trimmed distribution of study-specific standard deviation (SD) values for concurrent controls. Those analyses yielded endpoint-specific BMR values for the TGR and Pig-a endpoints of 33% and 58%, respectively. Considering the results obtained, and the in vivo genetic toxicity BMR values noted in the literature, we recommend a BMR of 50% for in vivo mutagenicity endpoints. The value can be employed to interpret mutagenicity dose-response data in a risk assessment context.

The tumorigenic dose 50 (TD50) is a widely used measure of carcinogenic potency which has historically been used to determine acceptable intake limits for carcinogenic compounds. Although broadly used, the TD50 model was not designed to account for important biological factors such as DNA repair and cell compensatory mechanisms, changes in absorption, etc., leading to the development of benchmark dose (BMD) approaches, which use more flexible dose-response models that are better able to account for these processes. Using a nitrosamine dataset as a case study, we compare the impact of moving to a BMD-based limit as opposed to a TD50-based limit. Although there are differences in individual potency estimates between the two approaches for some compounds, we show that the key metrics such as the 5th percentile of the respective potency distributions, used when calculating class-specific default acceptable intakes, are not greatly affected. Furthermore, potency estimates for nitrosamine compounds relevant to read-across do not vary by more than a factor of 3, which is little in the context of the inherent variability in a biological response, in an overall landscape wherein potencies can vary by four orders of magnitude. This suggests a move to BMD-based limits is achievable without significant disruption to existing limits while utilising a more robust methodology.

In vitro genotoxicity has historically served a hazard identification role, with simple binary outcomes provided for each of several single endpoint assays. This will need to change, given: (i) efforts to curtail animal testing, (ii) the increased use of multiplexed in vitro assays and the ongoing development of NAMS, and (iii) the desire to holistically consider quantitative results from multiple biomarkers/endpoints that take potency into consideration. To help facilitate more quantitative analyses of multiple biomarkers and/or assay streams, we explored the combined use of PROAST and Toxicological Prioritization Index (ToxPi) software. As a proofofconcept, this investigation employed the MultiFlow DNA damage assay, focusing on γH2AX and p53 biomarkers at two time points, whereby 10 genotoxicants were evaluated in the presence and absence of rat liver S9 metabolic activation. Whereas PROAST was used to calculate BMD point estimates and confidence intervals (CIs), ToxPi synthesized the BMD results into visual, quantitative summaries conveying genotoxicity and metabolic properties. Our analyses suggest that ToxPi's data synthesis and visualization modules provide useful insights into compound response, chemical grouping, and genotoxic mechanisms. By integrating multiple data sources, we find that ToxPi offers a powerful complementary approach to traditional BMD CI graphs, particularly for the simultaneous analysis of multiple biomarkers, enhancing chemical potency analysis of complex datasets.

N-nitroso-bisoprolol (NBP) is a nitrosamine drug substance-related impurity (NDSRI) of bisoprolol, which is used to treat cardiac diseases since decades. To investigate the mutagenic potential of NBP, in vitro methods such as Enhanced Ames Test (EAT) and a mammalian cell gene mutation (HPRT) assay were used. To assess the in vivo mutagenicity, a 28-day repeat-dose study was conducted in wild-type NMRI mice, and liver and bone marrow samples were subjected to error-corrected next-generation sequencing (i.e., duplex sequencing) followed by benchmark dose analysis (BMD). NBP did not show mutagenic effects in Ames tests using 10

LebensmittelchemieVolume 78, Issue S1 p. S1-059-S1-059 Abstract Bewertung des genotoxischen und zytotoxischen Potenzials strukturell unterschiedlicher Nitrosamine in primären Rattenhepatozyten Christina Felske, Christina Felske Abteilung für Lebensmittelchemie und Toxikologie, Fachbereich Chemie, RPTU Kaiserslautern-Landau, 67663 Kaiserslautern, DeutschlandSearch for more papers by this author Melanie-Abel-Beckmann, Melanie-Abel-Beckmann Abteilung für Lebensmittelchemie und Toxikologie, Fachbereich Chemie, RPTU Kaiserslautern-Landau, 67663 Kaiserslautern, DeutschlandSearch for more papers by this authorAnke Londenberg, Anke Londenberg Fraunhofer-Institut für Toxikologie und Experimentelle Medizin ITEM, 30625 Hannover, DeutschlandSearch for more papers by this authorMichelle Djuari, Michelle Djuari Fraunhofer-Institut für Toxikologie und Experimentelle Medizin ITEM, 30625 Hannover, DeutschlandSearch for more papers by this authorChristina Ziemann, Christina Ziemann Fraunhofer-Institut für Toxikologie und Experimentelle Medizin ITEM, 30625 Hannover, DeutschlandSearch for more papers by this authorGeorge Johnson, George Johnson University of Swansea, Institute of Life Science, SA1 Swansea, Wales, GroßbritannienSearch for more papers by this authorMatthias Vogel, Matthias Vogel Bundesinstitut für Arzneimittel und Arzneimittel, 53175 Bonn, DeutschlandSearch for more papers by this authorJörg Fahrer, Jörg Fahrer Abteilung für Lebensmittelchemie und Toxikologie, Fachbereich Chemie, RPTU Kaiserslautern-Landau, 67663 Kaiserslautern, DeutschlandSearch for more papers by this author Christina Felske, Christina Felske Abteilung für Lebensmittelchemie und Toxikologie, Fachbereich Chemie, RPTU Kaiserslautern-Landau, 67663 Kaiserslautern, DeutschlandSearch for more papers by this author Melanie-Abel-Beckmann, Melanie-Abel-Beckmann Abteilung für Lebensmittelchemie und Toxikologie, Fachbereich Chemie, RPTU Kaiserslautern-Landau, 67663 Kaiserslautern, DeutschlandSearch for more papers by this authorAnke Londenberg, Anke Londenberg Fraunhofer-Institut für Toxikologie und Experimentelle Medizin ITEM, 30625 Hannover, DeutschlandSearch for more papers by this authorMichelle Djuari, Michelle Djuari Fraunhofer-Institut für Toxikologie und Experimentelle Medizin ITEM, 30625 Hannover, DeutschlandSearch for more papers by this authorChristina Ziemann, Christina Ziemann Fraunhofer-Institut für Toxikologie und Experimentelle Medizin ITEM, 30625 Hannover, DeutschlandSearch for more papers by this authorGeorge Johnson, George Johnson University of Swansea, Institute of Life Science, SA1 Swansea, Wales, GroßbritannienSearch for more papers by this authorMatthias Vogel, Matthias Vogel Bundesinstitut für Arzneimittel und Arzneimittel, 53175 Bonn, DeutschlandSearch for more papers by this authorJörg Fahrer, Jörg Fahrer Abteilung für Lebensmittelchemie und Toxikologie, Fachbereich Chemie, RPTU Kaiserslautern-Landau, 67663 Kaiserslautern, DeutschlandSearch for more papers by this author First published: 01 March 2024 https://doi.org/10.1002/lemi.202452042AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume78, IssueS1Supplement: Abstracts der Vorträge der Regionalverbände und die der Posterflashtalks der AG JLCMarch/April 2024Pages S1-059-S1-059 RelatedInformation

Exposure levels without appreciable human health risk may be determined by dividing a point of departure on a dose-response curve (e.g., benchmark dose) by a composite adjustment factor (AF). An "effect severity" AF (ESAF) is employed in some regulatory contexts. An ESAF of 10 may be incorporated in the derivation of a health-based guidance value (HBGV) when a "severe" toxicological endpoint, such as teratogenicity, irreversible reproductive effects, neurotoxicity, or cancer was observed in the reference study. Although mutation data have been used historically for hazard identification, this endpoint is suitable for quantitative dose-response modeling and risk assessment. As part of the 8th International Workshops on Genotoxicity Testing, a sub-group of the Quantitative Analysis Work Group (WG) explored how the concept of effect severity could be applied to mutation. To approach this question, the WG reviewed the prevailing regulatory guidance on how an ESAF is incorporated into risk assessments, evaluated current knowledge of associations between germline or somatic mutation and severe disease risk, and mined available data on the fraction of human germline mutations expected to cause severe disease. Based on this review and given that mutations are irreversible and some cause severe human disease, in regulatory settings where an ESAF is used, a majority of the WG recommends applying an ESAF value between 2 and 10 when deriving a HBGV from mutation data. This recommendation may need to be revisited in the future if direct measurement of disease-causing mutations by error-corrected next generation sequencing clarifies selection of ESAF values.

Genetic toxicity testing assesses the potential of compounds to cause DNA damage. There are many genetic toxicology screening assays designed to assess the DNA damaging potential of chemicals in early drug development aiding the identification of promising drugs that have low-risk potential for causing genetic damage contributing to cancer risk in humans. Despite this, in vitro tests generate a high number of misleading positives, the consequences of which can lead to unnecessary animal testing and/or the abandonment of promising drug candidates. Understanding chemical Mode of Action (MoA) is vital to identifying the true genotoxic potential of substances and, therefore, the risk translation into the clinic. Here we demonstrate a simple, robust protocol for staining fixed, human-lymphoblast p53 proficient TK6 cells with antibodies against ɣH2AX, p53 and pH3S28 along with DRAQ5™ DNA staining that enables analysis of un-lysed cells via microscopy approaches such as imaging flow cytometry. Here, we used the Cytek® Amnis® ImageStream®X Mk II which provides a high-throughput acquisition platform with the sensitivity of flow cytometry and spatial morphological information associated with microscopy. Using the ImageStream manufacturer’s software (IDEAS® 6.2), a masking strategy was developed to automatically detect and quantify micronucleus events (MN) and characterise biomarker populations. The gating strategy developed enables the generation of a template capable of automatically batch processing data files quantifying cell-cycle, MN, ɣH2AX, p53 and pH3 populations simultaneously. In this way, we demonstrate how a multiplex system enables DNA damage assessment alongside MN identification using un-lysed cells on the imaging flow cytometry platform. As a proof-of-concept, we use the tool chemicals carbendazim and methyl methanesulphonate (MMS) to demonstrate the assay’s ability to correctly identify clastogenic or aneugenic MoAs using the biomarker profiles established.

This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high-dimensional data.

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

Chemical safety testing plays a crucial role in product and pharmacological development, as well as chemoprevention; however, in vitro genotoxicity safety tests do not always accurately predict the chemicals that will be in vivo carcinogens. If chemicals test positive in vitro for genotoxicity but negative in vivo, this can contribute to unnecessary testing in animals used to confirm erroneous in vitro positive results. Current in vitro tests typically evaluate only genotoxicity endpoints, which limits their potential to detect non-genotoxic carcinogens. The frequency of misleading in vitro positive results can be high, leading to a requirement for more informative in vitro tests. It is now recognized that multiple-endpoint genotoxicity testing may aid more accurate detection of carcinogens and non-carcinogens. The objective of this review was to evaluate the utility of our novel, multiple-endpoint in vitro test, which uses multiple cancer-relevant endpoints to predict carcinogenic potential. The tool assessed micronucleus frequency, p53 expression, p21 expression, mitochondrial respiration, cell cycle abnormalities and, uniquely, cell morphology changes in human lymphoblastoid cell lines, TK6 and MCL-5. The endpoints were used to observe cellular responses to 18 chemicals within the following categories: genotoxic carcinogens, non-genotoxic carcinogens, toxic non-carcinogens, and misleading in vitro positive and negative agents. The number of endpoints significantly altered for each chemical was considered, alongside the holistic Integrated Signature of Carcinogenicity score, derived from the sum of fold changes for all endpoints. Following the calculation of an overall score from these measures, carcinogens exhibited greater potency than non-carcinogens. Genotoxic carcinogens were generally more potent than non-genotoxic carcinogens. This novel approach therefore demonstrated potential for correctly predicting whether chemicals with unknown mechanism may be considered carcinogens. Overall, while further validation is recommended, the test demonstrates potential for the identification of carcinogenic compounds. Adoption of the approach could enable reduced animal use in carcinogenicity testing.

Quantitative risk assessments of chemicals are routinely performed using in vivo data from rodents; however, there is growing recognition that non-animal approaches can be human-relevant alternatives. There is an urgent need to build confidence in non-animal alternatives given the international support to reduce the use of animals in toxicity testing where possible. In order for scientists and risk assessors to prepare for this paradigm shift in toxicity assessment, standardization and consensus on in vitro testing strategies and data interpretation will need to be established. To address this issue, an Expert Working Group (EWG) of the 8th International Workshop on Genotoxicity Testing (IWGT) evaluated the utility of quantitative in vitro genotoxicity concentration-response data for risk assessment. The EWG first evaluated available in vitro methodologies and then examined the variability and maximal response of in vitro tests to estimate biologically relevant values for the critical effect sizes considered adverse or unacceptable. Next, the EWG reviewed the approaches and computational models employed to provide human-relevant dose context to in vitro data. Lastly, the EWG evaluated risk assessment applications for which in vitro data are ready for use and applications where further work is required. The EWG concluded that in vitro genotoxicity concentration-response data can be interpreted in a risk assessment context. However, prior to routine use in regulatory settings, further research will be required to address the remaining uncertainties and limitations.

For genotoxic carcinogens, covalent binding to DNA is a critical initiating event in tumorigenesis. The present research investigated dose-effect relationships of three genotoxic carcinogens representing different structural classes, 2-acetylaminofluorene (2-AAF), benzo[a]pyrene (B[a]P) and quinoline (QUI), to assess the existence of no-observed-effect-levels (NOELs) for the formation of DNA adducts. Carcinogens were administered into the air sac of fertilized turkey eggs over wide dose ranges in three daily injections on days 22 to 24 of incubation. DNA adducts were measured in the fetal turkey livers by the 32P-nucleotide postlabeling (NPL) assay. B[a]P and QUI produced DNA adducts in a dosage-related manner and exhibited NOELs at 0.65 and 0.35 mg/kg bw/day, respectively. In contrast, 2-AAF formed DNA adducts at all tested dosages down to 0.005 mg/kg bw/day. Benchmark dose (BMD) analysis identified the potencies of 2-AAF and QUI to be similar, while B[a]P was the least potent compound. Overall, findings in fetal turkey livers demonstrated that exposure levels to genotoxic compounds that do not result in DNA adducts can exist but are not evident with all carcinogens of this type. The use of mechanistic dose-effect studies for genotoxic endpoints can provide critical information for prioritization of concerns for risk assessment.