Effects of urban-induced mutations on ecology, evolution and health.

Increasing evidence suggests that urbanization is associated with higher mutation rates, which can affect the health and evolution of organisms that inhabit cities. Elevated pollution levels in urban areas can induce DNA damage, leading to de novo mutations. Studies on mutations induced by urban pollution are most prevalent in humans and microorganisms, whereas studies of non-human eukaryotes are rare, even though increased mutation rates have the potential to affect organisms and their populations in contemporary time. Our Perspective explores how higher mutation rates in urban environments could impact the fitness, ecology and evolution of populations. Most mutations will be neutral or deleterious, and higher mutation rates associated with elevated pollution in urban populations can increase the risk of cancer in humans and potentially other species. We highlight the potential for urban-driven increased deleterious mutational loads in some organisms, which could lead to a decline in population growth of a wide diversity of organisms. Although beneficial mutations are expected to be rare, we argue that higher mutation rates in urban areas could influence adaptive evolution, especially in organisms with short generation times. Finally, we explore avenues for future research to better understand the effects of urban-induced mutations on the fitness, ecology and evolution of city-dwelling organisms.

AOP report: Development of an adverse outcome pathway for deposition of energy leading to cataracts.

Cataracts are one of the leading causes of blindness, with an estimated 95 million people affected worldwide. A hallmark of cataract development is lens opacification, typically associated not only with aging but also radiation exposure as encountered by interventional radiologists and astronauts during the long-term space mission. To better understand radiation-induced cataracts, the adverse outcome pathway (AOP) framework was used to structure and evaluate knowledge across biological levels of organization (e.g., macromolecular, cell, tissue, organ, organism and population). AOPs identify a sequence of key events (KEs) causally connected by key event relationships (KERs) beginning with a molecular initiating event to an adverse outcome (AO) of relevance to regulatory decision-making. To construct the cataract AO and retrieve evidence to support it, a scoping review methodology was used to filter, screen, and review studies based on the modified Bradford Hill criteria. Eight KEs were identified that were moderately supported by empirical evidence (e.g., dose-, time-, incidence-concordance) across the adjacent (directly linked) relationships using well-established endpoints. Over half of the evidence to justify the KER linkages was derived from the evidence stream of biological plausibility. Early KEs of oxidative stress and protein modifications had strong linkages to downstream KEs and could be the focus of countermeasure development. Several identified knowledge gaps and inconsistencies related to the quantitative understanding of KERs which could be the basis of future research, most notably directed to experiments in the range of low or moderate doses and dose-rates, relevant to radiation workers and other occupational exposures.

A 50-gene biomarker identifies estrogen receptor-modulating chemicals in a microarray compendium.

High throughput transcriptomics (HTTr) profiling has the potential to rapidly and comprehensively identify molecular targets of environmental chemicals that can be linked to adverse outcomes. We describe here the construction and characterization of a 50-gene expression biomarker designed to identify estrogen receptor (ER) active chemicals in HTTr datasets. Using microarray comparisons, the genes in the biomarker were identified as those that exhibited consistent directional changes when ER was activated (4 ER agonists; 4 ESR1 gene constitutively active mutants) and opposite directional changes when ER was suppressed (4 antagonist treatments; 4 ESR1 knockdown experiments). The biomarker was evaluated as a predictive tool using the Running Fisher algorithm by comparison to annotated gene expression microarray datasets including those evaluating the transcriptional effects of hormones and chemicals in MCF-7 cells. Depending on the reference dataset used, the biomarker had a predictive accuracy for activation of up to 96%. To demonstrate applicability for HTTr data analysis, the biomarker was used to identify ER activators in a set of 15 chemicals that are considered potential bisphenol A (BPA) alternatives examined at up to 10 concentrations in MCF-7 cells and analyzed by full-genome TempO-Seq. Using benchmark dose (BMD) modeling, the biomarker genes stratified the ER potency of BPA alternatives consistent with previous studies. These results demonstrate that the ER biomarker can be used to accurately identify ER activators in transcript profile data derived from MCF-7 cells.

House dust-derived mixtures of organophosphate esters alter the phenotype, function, transcriptome, and lipidome of KGN human ovarian granulosa cells.

Organophosphate esters (OPEs), used as flame retardants and plasticizers, are present ubiquitously in the environment. Previous studies suggest that exposure to OPEs is detrimental to female fertility in humans. However, no experimental information is available on the effects of OPE mixtures on ovarian granulosa cells, which play essential roles in female reproduction. We used high-content imaging to investigate the effects of environmentally relevant OPE mixtures on KGN human granulosa cell phenotypes. Perturbations to steroidogenesis were assessed using ELISA and qRT-PCR. A high-throughput transcriptomic approach, TempO-Seq, was used to identify transcriptional changes in a targeted panel of genes. Effects on lipid homeostasis were explored using a cholesterol assay and global lipidomic profiling. OPE mixtures altered multiple phenotypic features of KGN cells, with triaryl OPEs in the mixture showing higher potencies than other mixture components. The mixtures increased basal production of steroid hormones; this was mediated by significant changes in the expression of critical transcripts involved in steroidogenesis. Further, the total-OPE mixture disrupted cholesterol homeostasis and the composition of intracellular lipid droplets. Exposure to complex mixtures of OPEs, similar to those found in house dust, may adversely affect female reproductive health by altering a multitude of phenotypic and functional endpoints in granulosa cells. This study provides novel insights into the mechanisms of actions underlying the toxicity induced by OPEs and highlights the need to examine the effects of human relevant chemical mixtures.

Empirical Characterization of False Discovery Rates of Differentially Expressed Genes and Transcriptomic Benchmark Concentrations in Zebrafish Embryos.

High-throughput transcriptomics (HTTr) is increasingly applied to zebrafish embryos to survey the toxicological effects of environmental chemicals. Before the adoption of this approach in regulatory testing, it is essential to characterize background noise in order to guide experimental designs. We thus empirically quantified the HTTr false discovery rate (FDR) across different embryo pool sizes, sample sizes, and concentration groups for toxicology studies. We exposed zebrafish embryos to 0.1% dimethyl sulfoxide (DMSO) for 5 days. Pools of 1, 5, 10, and 20 embryos were created (n = 24 samples for each pool size). Samples were sequenced on the TempO-Seq platform and then randomly assigned to mock treatment groups before differentially expressed gene (DEG), pathway, and benchmark concentration (BMC) analyses. Given that all samples were treated with DMSO, any significant DEGs, pathways, or BMCs are false positives. As expected, we found decreasing FDRs for DEG and pathway analyses with increasing pool and sample sizes. Similarly, FDRs for BMC analyses decreased with increasing pool size and concentration groups, with more stringent BMC premodel filtering reducing BMC FDRs. Our study provides foundational data for determining appropriate experiment designs for regulatory toxicity testing with HTTr in zebrafish embryos.

Transcriptome analysis in mouse skin after exposure to ultraviolet radiation from a canopy sunbed.

Exposure to ultraviolet radiation (UV-R), from both natural and artificial tanning, heightens the risk of skin cancer by inducing molecular changes in cells and tissues. Despite established transcriptional alterations at a molecular level due to UV-R exposure, uncertainties persist regarding UV radiation characterization and subsequent genomic changes. Our study aimed to mechanistically explore dose- and time-dependent gene expression changes, that may drive short-term (e.g., sunburn) and long-term actinic (e.g., skin cancer) consequences. Using C57BL/6N mouse skin, we analyzed transcriptomic expression following exposure to five erythemally weighted UV-R doses (0, 5, 10, 20, and 40 mJ/cm2 ) emitted by a UV-R tanning device. At 96 h post-exposure, 5 mJ/cm2 induced 116 statistically significant differentially expressed genes (DEGs) associated with structural changes from UV-R damage. The highest number of significant gene expression changes occurred at 6 and 48 h post-exposure in the 20 and 40 mJ/cm2 dose groups. Notably, at 40 mJ/cm2 , 13 DEGs related to skin barrier homeostasis were consistently perturbed across all timepoints. UV-R exposure activated pathways involving oxidative stress, P53 signaling, inflammation, biotransformation, skin barrier maintenance, and innate immunity. This in vivo study's transcriptional data offers mechanistic insights into both short-term and potential non-threshold-dependent long-term health effects of UV-R tanning.

Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals.

To modernize genotoxicity assessment and reduce reliance on experimental animals, new approach methodologies (NAMs) that provide human-relevant dose-response data are needed. Two transcriptomic biomarkers, GENOMARK and TGx-DDI, have shown a high classification accuracy for genotoxicity. As these biomarkers were extracted from different training sets, we investigated whether combining the two biomarkers in a human-derived metabolically competent cell line (i.e., HepaRG) provides complementary information for the classification of genotoxic hazard identification and potency ranking. First, the applicability of GENOMARK to TempO-Seq, a high-throughput transcriptomic technology, was evaluated. HepaRG cells were exposed for 72 h to increasing concentrations of 10 chemicals (i.e., eight known in vivo genotoxicants and two in vivo nongenotoxicants). Gene expression data were generated using the TempO-Seq technology. We found a prediction performance of 100%, confirming the applicability of GENOMARK to TempO-Seq. Classification using TGx-DDI was then compared to GENOMARK. For the chemicals identified as genotoxic, benchmark concentration modeling was conducted to perform potency ranking. The high concordance observed for both hazard classification and potency ranking by GENOMARK and TGx-DDI highlights the value of integrating these NAMs in a weight of evidence evaluation of genotoxicity.

The functional mutational landscape of the lacZ gene.

The lacZ gene of Escherichia coli encodes ß-galactosidase (ß-gal), a lactose metabolism enzyme of the lactose operon. Previous chemical modification or site-directed mutagenesis experiments have identified 21 amino acids that are essential for ß-gal catalytic activity. We have assembled over 10,000 lacZ mutations from published studies that were collected using a positive selection assay to identify mutations in lacZ that disrupted ß-gal function. We analyzed 6,465 independent lacZ mutations that resulted in 2,732 missense mutations that impaired ß-gal function. Those mutations affected 492 of the 1,023 lacZ codons, including most of the 21 previously known residues critical for catalytic activity. Most missense mutations occurred near the catalytic site and in regions important for subunit tetramerization. Overall, our work provides a comprehensive and detailed map of the amino acid residues affecting the structure and catalytic activity of the ß-gal enzyme.

Toxicological Mechanisms and Potencies of Organophosphate Esters in KGN Human Ovarian Granulosa Cells as Revealed by High-throughput Transcriptomics.

Despite the growing number of studies reporting potential risks associated with exposure to organophosphate esters (OPEs), their molecular mechanisms of action remain poorly defined. We used the high-throughput TempO-Seq™ platform to investigate the effects of frequently detected OPEs on the expression of ∼3000 environmentally responsive genes in KGN human ovarian granulosa cells. Cells were exposed for 48 h to one of five OPEs (0.1 to 50 µM): tris(methylphenyl) phosphate (TMPP), isopropylated triphenyl phosphate (IPPP), tert-butylphenyl diphenyl phosphate (BPDP), triphenyl phosphate (TPHP), or tris(2-butoxyethyl) phosphate (TBOEP). The sequencing data indicate that four OPEs induced transcriptional changes, whereas TBOEP had no effect within the concentration range tested. Multiple pathway databases were used to predict alterations in biological processes based on differentially expressed genes. At lower concentrations, inhibition of the cholesterol biosynthetic pathway was the predominant effect of OPEs; this was likely a consequence of intracellular cholesterol accumulation. At higher concentrations, BPDP and TPHP had distinct effects, primarily affecting pathways involved in cell cycle progression and other stress responses. Benchmark concentration (BMC) modelling revealed that BPDP had the lowest transcriptomic point of departure. However, in vitro to in vivo extrapolation modeling indicated that TMPP was bioactive at lower concentrations than the other OPEs. We conclude that these new approach methodologies provide information on the mechanism(s) underlying the effects of data-poor compounds and assist in the derivation of protective points of departure for use in chemical read-across and decision-making.

Error-corrected next generation sequencing - Promises and challenges for genotoxicity and cancer risk assessment.

Error-corrected Next Generation Sequencing (ecNGS) is rapidly emerging as a valuable, highly sensitive and accurate method for detecting and characterizing mutations in any cell type, tissue or organism from which DNA can be isolated. Recent mutagenicity and carcinogenicity studies have used ecNGS to quantify drug-/chemical-induced mutations and mutational spectra associated with cancer risk. ecNGS has potential applications in genotoxicity assessment as a new readout for traditional models, for mutagenesis studies in 3D organotypic cultures, and for detecting off-target effects of gene editing tools. Additionally, early data suggest that ecNGS can measure clonal expansion of mutations as a mechanism-agnostic early marker of carcinogenic potential and can evaluate mutational load directly in human biomonitoring studies. In this review, we discuss promising applications, challenges, limitations, and key data initiatives needed to enable regulatory testing and adoption of ecNGS - including for advancing safety assessment, augmenting weight-of-evidence for mutagenicity and carcinogenicity mechanisms, identifying early biomarkers of cancer risk, and managing human health risk from chemical exposures.

Early microRNA responses in rodent liver mediated by furan exposure establish dose thresholds for later adverse outcomes.

To reduce reliance on long-term in vivo studies, short-term data linking early molecular-based measurements to later adverse health effects is needed. Although transcriptional-based benchmark dose (BMDT) modeling has been used to estimate potencies and stratify chemicals based on potential to induce later-life effects, dose-responsive epigenetic alterations have not been routinely considered. Here, we evaluated the utility of microRNA (miRNA) profiling in mouse liver and blood, as well as in mouse primary hepatocytes in vitro, to indicate mechanisms of liver perturbation due to short-term exposure of the known rodent liver hepatotoxicant and carcinogen, furan. Benchmark dose modeling of miRNA measurements (BMDmiR) were compared to the referent transcriptional (BMDT) and apical (BMDA) estimates. These analyses indicate a robust dose response for 34 miRNAs to furan and involvement of p53-linked pathways in furan-mediated hepatotoxicity, supporting mRNA and apical measurements. Liver-sourced miRNAs were also altered in the blood and primary hepatocytes. Overall, these results indicate mechanistic involvement of miRNA in furan carcinogenicity and provide evidence of their potential utility as accessible biomarkers of exposure and disease.

Error-corrected duplex sequencing enables direct detection and quantification of mutations in human TK6 cells with strong inter-laboratory consistency.

Error-corrected duplex sequencing (DS) enables direct quantification of low-frequency mutations and offers tremendous potential for chemical mutagenicity assessment. We investigated the utility of DS to quantify induced mutation frequency (MF) and spectrum in human lymphoblastoid TK6 cells exposed to a prototypical DNA alkylating agent, N-ethyl-N-nitrosourea (ENU). Furthermore, we explored appropriate experimental parameters for this application, and assessed inter-laboratory reproducibility. In two independent experiments in two laboratories, TK6 cells were exposed to ENU (25-200 µM) and DNA was sequenced 48, 72, and 96 h post-exposure. A DS mutagenicity panel targeting twenty 2.4-kb regions distributed across the genome was used to sample diverse, genome-representative sequence contexts. A significant increase in MF that was unaffected by time was observed in both laboratories. Concentration-response in the MF from the two laboratories was strongly positively correlated (r = 0.97). C:G>T:A, T:A>C:G, T:A>A:T, and T:A>G:C mutations increased in consistent, concentration-dependent manners in both laboratories, with high proportions of C:G>T:A at all time points. The consistent results across the three time points suggest that 48 h may be sufficient for mutation analysis post-exposure. The target sites responded similarly between the two laboratories and revealed a higher average MF in intergenic regions. These results, demonstrating remarkable reproducibility across time and laboratory for both MF and spectrum, support the high value of DS for characterizing chemical mutagenicity in both research and regulatory evaluation.

Duplex sequencing provides detailed characterization of mutation frequencies and spectra in the bone marrow of MutaMouse males exposed to procarbazine hydrochloride.

Mutagenicity testing is an essential component of health safety assessment. Duplex Sequencing (DS), an emerging high-accuracy DNA sequencing technology, may provide substantial advantages over conventional mutagenicity assays. DS could be used to eliminate reliance on standalone reporter assays and provide mechanistic information alongside mutation frequency (MF) data. However, the performance of DS must be thoroughly assessed before it can be routinely implemented for standard testing. We used DS to study spontaneous and procarbazine (PRC)-induced mutations in the bone marrow (BM) of MutaMouse males across a panel of 20 diverse genomic targets. Mice were exposed to 0, 6.25, 12.5, or 25 mg/kg-bw/day for 28 days by oral gavage and BM sampled 42 days post-exposure. Results were compared with those obtained using the conventional lacZ viral plaque assay on the same samples. DS detected significant increases in mutation frequencies and changes to mutation spectra at all PRC doses. Low intra-group variability within DS samples allowed for detection of increases at lower doses than the lacZ assay. While the lacZ assay initially yielded a higher fold-change in mutant frequency than DS, inclusion of clonal mutations in DS mutation frequencies reduced this discrepancy. Power analyses suggested that three animals per dose group and 500 million duplex base pairs per sample is sufficient to detect a 1.5-fold increase in mutations with > 80% power. Overall, we demonstrate several advantages of DS over classical mutagenicity assays and provide data to support efforts to identify optimal study designs for the application of DS as a regulatory test.

From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow.

The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications.

Next Generation Sequencing Workshop at the Royal Society of Medicine (London, May 2022): how genomics is on the path to modernizing genetic toxicology.

The use of error-corrected Next Generation Sequencing (ecNG) to determine mutagenicity has been a subject of growing interest and potentially a disruptive technology that could supplement, and in time, replace current testing paradigms in preclinical safety assessment. Considering this, a Next Generation Sequencing Workshop was held at the Royal Society of Medicine in London in May 2022, supported by the United Kingdom Environmental Mutagen Society (UKEMS) and TwinStrand Biosciences (WA, USA), to discuss progress and future applications of this technology. In this meeting report, the invited speakers provide an overview of the Workshop topics covered and identify future directions for research. In the area of somatic mutagenesis, several speakers reviewed recent progress made with correlating ecNGS to classic in vivo transgenic rodent mutation assays as well as exploring the use of this technology directly in humans and animals, and in complex organoid models. Additionally, ecNGS has been used for detecting off-target effects of gene editing tools and emerging data suggest ecNGS potential to measure clonal expansion of cells carrying mutations in cancer driver genes as an early marker of carcinogenic potential and for direct human biomonitoring. As such, the workshop demonstrated the importance of raising awareness and support for advancing the science of ecNGS for mutagenesis, gene editing, and carcinogenesis research. Furthermore, the potential of this new technology to contribute to advances in drug and product development and improve safety assessment was extensively explored.

Applying genomics in regulatory toxicology: a report of the ECETOC workshop on omics threshold on non-adversity.

In a joint effort involving scientists from academia, industry and regulatory agencies, ECETOC's activities in Omics have led to conceptual proposals for: (1) A framework that assures data quality for reporting and inclusion of Omics data in regulatory assessments; and (2) an approach to robustly quantify these data, prior to interpretation for regulatory use. In continuation of these activities this workshop explored and identified areas of need to facilitate robust interpretation of such data in the context of deriving points of departure (POD) for risk assessment and determining an adverse change from normal variation. ECETOC was amongst the first to systematically explore the application of Omics methods, now incorporated into the group of methods known as New Approach Methodologies (NAMs), to regulatory toxicology. This support has been in the form of both projects (primarily with CEFIC/LRI) and workshops. Outputs have led to projects included in the workplan of the Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) group of the Organisation for Economic Co-operation and Development (OECD) and to the drafting of OECD Guidance Documents for Omics data reporting, with potentially more to follow on data transformation and interpretation. The current workshop was the last in a series of technical methods development workshops, with a sub-focus on the derivation of a POD from Omics data. Workshop presentations demonstrated that Omics data developed within robust frameworks for both scientific data generation and analysis can be used to derive a POD. The issue of noise in the data was discussed as an important consideration for identifying robust Omics changes and deriving a POD. Such variability or "noise" can comprise technical or biological variation within a dataset and should clearly be distinguished from homeostatic responses. Adverse outcome pathways (AOPs) were considered a useful framework on which to assemble Omics methods, and a number of case examples were presented in illustration of this point. What is apparent is that high dimension data will always be subject to varying processing pipelines and hence interpretation, depending on the context they are used in. Yet, they can provide valuable input for regulatory toxicology, with the pre-condition being robust methods for the collection and processing of data together with a comprehensive description how the data were interpreted, and conclusions reached.

Per- and polyfluoroalkyl substances (PFAS) in mixtures show additive effects on transcriptomic points of departure in human liver spheroids.

Per- and polyfluoroalkyl substances (PFAS) are a wide range of chemicals that are used in a variety of consumer and industrial products leading to direct human exposure. Many PFAS are chemically nonreactive and persistent in the environment, resulting in additional exposure from water, soil, and dietary intake. While some PFAS have documented negative health effects, data on simultaneous exposures to multiple PFAS (PFAS mixtures) are inadequate for making informed decisions for risk assessment. The current study leverages data from previous work in our group using Templated Oligo-Sequencing (TempO-Seq) for high-throughput transcriptomic analysis of PFAS-exposed primary human liver cell spheroids; herein, we determine the transcriptomic potency of PFAS in mixtures. Gene expression data from single PFAS and mixture exposures of liver cell spheroids were subject to benchmark concentration (BMC) analysis. We used the 25th lowest gene BMC as the point of departure to compare the potencies of single PFAS to PFAS mixtures of varying complexity and composition. Specifically, the empirical potency of 8 PFAS mixtures were compared to predicted mixture potencies calculated using the principal of concentration addition (ie, dose addition) in which mixture component potencies are summed by proportion to predict mixture potency. In this study, for most mixtures, empirical mixture potencies were comparable to potencies calculated through concentration addition. This work supports that the effects of PFAS mixtures on gene expression largely follow the concentration addition predicted response and suggests that effects of these individual PFAS in mixtures are not strongly synergistic or antagonistic.

Considerations for application of benchmark dose modeling in radiation research: workshop highlights.

BACKGROUND: Exposure to different forms of ionizing radiation occurs in diverse occupational, medical, and environmental settings. Improving the accuracy of the estimated health risks associated with exposure is therefore, essential for protecting the public, particularly as it relates to chronic low dose exposures. A key aspect to understanding health risks is precise and accurate modeling of the dose-response relationship. Toward this vision, benchmark dose (BMD) modeling may be a suitable approach for consideration in the radiation field. BMD modeling is already extensively used for chemical hazard assessments and is considered statistically preferable to identifying low and no observed adverse effects levels. BMD modeling involves fitting mathematical models to dose-response data for a relevant biological endpoint and identifying a point of departure (the BMD, or its lower bound). Recent examples in chemical toxicology show that when applied to molecular endpoints (e.g. genotoxic and transcriptional endpoints), BMDs correlate to points of departure for more apical endpoints such as phenotypic changes (e.g. adverse effects) of interest to regulatory decisions. This use of BMD modeling may be valuable to explore in the radiation field, specifically in combination with adverse outcome pathways, and may facilitate better interpretation of relevant in vivo and in vitro dose-response data. To advance this application, a workshop was organized on June 3rd, 2022, in Ottawa, Ontario that brought together BMD experts in chemical toxicology and the radiation scientific community of researchers, regulators, and policy-makers. The workshop's objective was to introduce radiation scientists to BMD modeling and its practical application using case examples from the chemical toxicity field and demonstrate the BMDExpress software using a radiation dataset. Discussions focused on the BMD approach, the importance of experimental design, regulatory applications, its use in supporting the development of adverse outcome pathways, and specific radiation-relevant examples. CONCLUSIONS: Although further deliberations are needed to advance the use of BMD modeling in the radiation field, these initial discussions and partnerships highlight some key steps to guide future undertakings related to new experimental work.

Application of a new approach methodology (NAM)-based strategy for genotoxicity assessment of data-poor compounds.

The conventional battery for genotoxicity testing is not well suited to assessing the large number of chemicals needing evaluation. Traditional in vitro tests lack throughput, provide little mechanistic information, and have poor specificity in predicting in vivo genotoxicity. New Approach Methodologies (NAMs) aim to accelerate the pace of hazard assessment and reduce reliance on in vivo tests that are time-consuming and resource-intensive. As such, high-throughput transcriptomic and flow cytometry-based assays have been developed for modernized in vitro genotoxicity assessment. This includes: the TGx-DDI transcriptomic biomarker (i.e., 64-gene expression signature to identify DNA damage-inducing (DDI) substances), the MicroFlow® assay (i.e., a flow cytometry-based micronucleus (MN) test), and the MultiFlow® assay (i.e., a multiplexed flow cytometry-based reporter assay that yields mode of action (MoA) information). The objective of this study was to investigate the utility of the TGx-DDI transcriptomic biomarker, multiplexed with the MicroFlow® and MultiFlow® assays, as an integrated NAM-based testing strategy for screening data-poor compounds prioritized by Health Canada's New Substances Assessment and Control Bureau. Human lymphoblastoid TK6 cells were exposed to 3 control and 10 data-poor substances, using a 6-point concentration range. Gene expression profiling was conducted using the targeted TempO-Seq™ assay, and the TGx-DDI classifier was applied to the dataset. Classifications were compared with those based on the MicroFlow® and MultiFlow® assays. Benchmark Concentration (BMC) modeling was used for potency ranking. The results of the integrated hazard calls indicate that five of the data-poor compounds were genotoxic in vitro, causing DNA damage via a clastogenic MoA, and one via a pan-genotoxic MoA. Two compounds were likely irrelevant positives in the MN test; two are considered possibly genotoxic causing DNA damage via an ambiguous MoA. BMC modeling revealed nearly identical potency rankings for each assay. This ranking was maintained when all endpoint BMCs were converted into a single score using the Toxicological Prioritization (ToxPi) approach. Overall, this study contributes to the establishment of a modernized approach for effective genotoxicity assessment and chemical prioritization for further regulatory scrutiny. We conclude that the integration of TGx-DDI, MicroFlow®, and MultiFlow® endpoints is an effective NAM-based strategy for genotoxicity assessment of data-poor compounds.