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.

Germ cell mutations can be inherited and lead to genetic disorders in the offspring. Thus, it is critical to identify environmental exposures that impact the germline. Polycyclic aromatic hydrocarbons (PAHs) are widespread combustion by-products found in food, tobacco smoke, and urban air. Benzo[b]fluoranthene (BbF) is a PAH that is classified as a possible human carcinogen. Studies using the transgenic mouse lacZ assay and duplex sequencing (DS), an error-corrected sequencing technology, have demonstrated that BbF robustly induces mutations in somatic tissues. However, the mutagenic effects of BbF on germ cells are unknown. We investigated whether long-term exposure to BbF induces mutations in male germ cells. Adult MutaMouse males were orally exposed to BbF in olive oil at doses of 0, 3.25, 6.25, 12.5, 25, or 50 mg/kg body weight per day (BW/day) for 90 days, or to 0, 1.56, 3.125, 6.25, 12.5, or 25 mg/kg BW/day for 180 days. Mutant frequencies were determined using the lacZ assay (n = 8 per group). Control and high dose groups at each time point were then sequenced to detect mutations using DS (n = 6 per group). Neither method detected significant increases in mutations following BbF exposure. Similarly, there was no increase in C:G > A:T transversions at either time point, the main mutation subtype induced by BbF in somatic tissues. These results suggest that BbF or its active metabolites are not present in germ cells at amounts sufficient to cause mutations, and/or DNA damage repair mechanisms in germ cells effectively repair BbF-induced damage.

The use of new approach methods (NAMs), including high-throughput, in vitro bioactivity data, in setting a point-of-departure (POD) will accelerate the pace of human health hazard assessments. Combining hazard and exposure predictions into a bioactivity:exposure ratio (BER) for use in risk-based prioritization and utilizing NAM-based bioactivity flags to indicate potential hazards of interest for further prediction or mechanism-based screening together comprise a prospective approach for management of substances with limited traditional toxicity testing data. In this work, we demonstrate a NAM-based assessment case study conducted via the Accelerating the Pace of Chemical Risk Assessment initiative, a consortium of international research and regulatory scientists. The primary objective was to develop a reusable and adaptable approach for addressing chemicals with limited traditional toxicity data using a NAM-based POD, BER, and bioactivity-based flags for indication of putative endocrine, developmental, neurological, and immunosuppressive effects via data generation and interpretation for 200 substances. Multiple data streams, including in silico and in vitro NAMs, were used. High-throughput transcriptomics and phenotypic profiling data, as well as targeted biochemical and cell-based assays, were combined with generic high-throughput toxicokinetic models parameterized with chemical-specific data to estimate dose for comparison to exposure predictions. This case study further enables regulatory scientists from different international purviews to utilize efficient approaches for prospective chemical management, addressing hazard and risk-based data needs, while reducing the need for animal studies. This work demonstrates the feasibility of using a battery of toxicodynamic and toxicokinetic NAMs to provide a NAM-based POD for screening-level assessment.

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.

Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (quantitative) structure–activity relationship ([Q]SAR) models for the prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for high-throughput screening of a large chemical inventory as it necessitates several models to cover diverse chemistries but will then generate data conflicts. To address this challenge, we developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value while also expanding chemical space coverage. This study developed consensus models for nine toxicological endpoints relating to estrogen receptor (ER) and androgen receptor (AR) interactions (i.e., binding, agonism, and antagonism) and genotoxicity (i.e., bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus). Consensus models were created by combining different (Q)SAR models using various weighting schemes. As a multi-objective optimization problem, there is no single best consensus model, and therefore, Pareto fronts were determined for each endpoint to identify the consensus models that optimize the multiple-criterion decisions simultaneously. Accordingly, this work presents sets of solutions for each endpoint that contain the optimal combination, regardless of the trade-off, with the results demonstrating that the consensus models improved both the predictive power and chemical space coverage. These solutions were further analyzed to find trends between the best consensus models and their components. Here, we demonstrate the development of a flexible and adaptable approach for in silico consensus modeling and its application across nine toxicological endpoints related to ER activity, AR activity, and genotoxicity. These consensus models are developed to be integrated into a larger multi-tier NAM-based framework to prioritize chemicals for further investigation and support the transition to a non-animal approach to risk assessment in Canada.

Polycyclic aromatic hydrocarbons (PAHs) are common environmental pollutants that originate from the incomplete combustion of organic materials. We investigated the clastogenicity and mutagenicity of benzo[b]fluoranthene (BbF), one of 16 priority PAHs, in MutaMouse males after a 28 day oral exposure. BbF causes robust dose-dependent increases in micronucleus frequency in peripheral blood, indicative of chromosome damage. Duplex sequencing (DS), an error-corrected sequencing technology, reveals that BbF induces dose-dependent increases in mutation frequencies in bone marrow (BM) and liver. Mutagenicity is increased in intergenic relative to genic regions, suggesting a role for transcription-coupled repair of BbF-induced DNA damage. At higher doses, the maximum mutagenic response to BbF is higher in liver, which has a lower mitotic index but higher metabolic capacity than BM; however, mutagenic potency is comparable between the two tissues. BbF induces primarily C:G > A:T mutations, followed by C:G > T:A and C:G > G:C, indicating that BbF metabolites mainly target guanines and cytosines. The mutation spectrum of BbF correlates with cancer mutational signatures associated with tobacco exposure, supporting its contribution to the carcinogenicity of combustion-derived PAHs in humans. Overall, BbF's mutagenic effects are similar to benzo[a]pyrene, a well-studied mutagenic PAH. Our work showcases the utility of DS for effective mutagenicity assessment of environmental pollutants.

Photooxidation products resulting from volatile organic compounds (VOCs) reacting with sunlight are important contributors to gas-phase air pollution. We characterized the product-weighted mutagenic potencies (rev m(3) mgC(-1) h(-1)) in Salmonella TA100 of atmospheres resulting from the hydroxyl radical (OH)-initiated photochemical oxidation of 11 C-4 or C-5 alkenes or dienes in the presence of nitric oxide (NO) and from the ozonolysis of four VOCs without NO (isoprene; 1,3-pentadiene; 1,4-pentadiene; and 1,3-butadiene). Irradiated atmospheres from precursors with a single C=C bond (3-methyl-1-butene, 2-methyl-1-butene, cis/trans-2-pentene, 2-methyl-2-butene, 1-butene, and 1-pentene) had low potencies (<5), whereas linear dienes with terminal C=C bonds had high potencies (50-65). Dienes with a branched structure (isoprene) or internal C=C bonds (1,3-pentadiene) had intermediate potencies (15-20). No VOCs were mutagenic without photochemical oxidation. VOCs+O-3 in the dark produced less mutagenic atmospheres than photochemistry in the presence of NO. Atmospheres induced primarily C to T and C to A mutations, the main base substitutions in nonsmoker lung cancer. Atmospheres from the photooxidation of isoprene and 1,3-pentadiene also induced GG to TT, the signature mutation of peroxyacetyl nitrate. Five molecular compositions identified by Chemical Ionization Mass Spectrometry (CIMS), most containing nitrogen, correlated (r = 0.76-0.85) with the mutagenic potencies of irradiated atmospheres; most had a likely nitrate functional group. Assessment of the mutagenicity of emitted VOCs should consider VOC photooxidation products, especially dienes with terminal C=C bonds, as these products likely contribute to overall health effects from ambient air pollution.

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.

Genotoxicity assessment is a critical component in the development and evaluation of chemicals. Traditional genotoxicity assays (i.e., mutagenicity, clastogenicity, and aneugenicity) have been limited to dichotomous hazard classification, while other toxicity endpoints are assessed through quantitative determination of points-of-departures (PODs) for setting exposure limits. The more recent higher-throughput in vitro genotoxicity assays, many of which also provide mechanistic information, offer a powerful approach for determining defined PODs for potency ranking and risk assessment. In order to obtain relevant human dose context from the in vitro assays, in vitro to in vivo extrapolation (IVIVE) models are required to determine what dose would elicit a concentration in the body demonstrated to be genotoxic using in vitro assays. Previous work has demonstrated that application of IVIVE models to in vitro bioactivity data can provide PODs that are protective of human health, but there has been no evaluation of how these models perform with in vitro genotoxicity data. Thus, the Genetic Toxicology Technical Committee, under the Health and Environmental Sciences Institute, conducted a case study on 31 reference chemicals to evaluate the performance of IVIVE application to genotoxicity data. The results demonstrate that for most chemicals considered here (20/31), the PODs derived from in vitro data and IVIVE are health protective relative to in vivo PODs from animal studies. PODs were also protective by assay target: mutations (8/13 chemicals), micronuclei (9/12), and aneugenicity markers (4/4). It is envisioned that this novel testing strategy could enhance prioritization, rapid screening, and risk assessment of genotoxic chemicals.

Charmaz, K. (2014). Constructing grounded theory (2nd ed.). SAGE. (p. 344) suggests that 'a reflexive stance informs how the researcher conducts his or her research, relates to the research participants and represents them in written reports'. As playworkers, we position ourselves outside of children's play, as observers looking in, often, though not always, trying to make sense of what we observe. How we interpret children's play, our interventions and those of our peers and colleagues, is influenced by our own lived experiences, our biographies and the history we bring to the play setting. This article explores the place of reflexivity in playwork and proposes that when researching children's play from a playwork perspective a Constructivist Grounded Theory approach responds to some of the unique characteristics of playwork and the ambiguity of children's play.

Quantitative relationships between carcinogenic potency and mutagenic potency have been previously examined using a benchmark dose (BMD)-based approach. We extended those analyses by using human exposure data for 48 compounds to calculate carcinogenicity-derived and genotoxicity-derived margin of exposure values (MOEs) that can be used to prioritize substances for risk management. MOEs for 16 of the 48 compounds were below 10,000, and consequently highlighted for regulatory concern. Of these, 15 were highlighted using genotoxicity-derived (micronucleus [MN] dose-response data) MOEs. A total of 13 compounds were highlighted using carcinogenicity-derived MOEs; 12 compounds were overlapping. MOEs were also calculated using transgenic rodent (TGR) mutagenicity data. For 10 of the 12 compounds examined using TGR data, the results similarly revealed that mutagenicity-derived MOEs yield regulatory decisions that correspond with those based on carcinogenicity-derived MOEs. The effect of benchmark response (BMR) on MOE determination was also examined. Reinterpretation of the analyses using a BMR of 50% indicated that four out of 15 compounds prioritized using MN-derived MOEs based on a default BMR of 5% would have been missed. The results indicate that regulatory decisions based on in vivo genotoxicity dose-response data would be consistent with those based on carcinogenicity dose-response data; in some cases, genotoxicity-based decisions would be more conservative. Going forward, and in the absence of carcinogenicity data, in vivo genotoxicity assays (MN and TGR) can be used to effectively prioritize substances for regulatory action. Routine use of the MOE approach necessitates the availability of reliable human exposure estimates, and consensus regarding appropriate BMRs for genotoxicity endpoints.

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.