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.

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.

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.

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.

Comprehensive interpretation of in vitro micronucleus test results for 292 chemicals: from hazard identification to risk assessment application.

Risk assessments are increasingly reliant on information from in vitro assays. The in vitro micronucleus test (MNvit) is a genotoxicity test that detects chromosomal abnormalities, including chromosome breakage (clastogenicity) and/or whole chromosome loss (aneugenicity). In this study, MNvit datasets for 292 chemicals, generated by the US EPA's ToxCast program, were evaluated using a decision tree-based pipeline for hazard identification. Chemicals were tested with 19 concentrations (n = 1) up to 200 µM, in the presence and absence of Aroclor 1254-induced rat liver S9. To identify clastogenic chemicals, %MN values at each concentration were compared to a distribution of batch-specific solvent controls; this was followed by cytotoxicity assessment and benchmark concentration (BMC) analyses. The approach classified 157 substances as positives, 25 as negatives, and 110 as inconclusive. Using the approach described in Bryce et al. (Environ Mol Mutagen 52:280-286, 2011), we identified 15 (5%) aneugens. IVIVE (in vitro to in vivo extrapolation) was employed to convert BMCs into administered equivalent doses (AEDs). Where possible, AEDs were compared to points of departure (PODs) for traditional genotoxicity endpoints; AEDs were generally lower than PODs based on in vivo endpoints. To facilitate interpretation of in vitro MN assay concentration-response data for risk assessment, exposure estimates were utilized to calculate bioactivity exposure ratio (BER) values. BERs for 50 clastogens and two aneugens had AEDs that approached exposure estimates (i.e., BER < 100); these chemicals might be considered priorities for additional testing. This work provides a framework for the use of high-throughput in vitro genotoxicity testing for priority setting and chemical risk assessment.

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology.

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment.

PURPOSE: Benchmark dose (BMD) modeling is used to determine the dose of a stressor at which a predefined increase in any biological effect above background occurs (e.g. 10% increase from control values). BMD analytical tools have the capacity to model transcriptional dose-response data to derive BMDs for genes, pathways and gene ontologies. We recently demonstrated the value of this approach to support various areas of radiation research using predominately 'in-house' generated datasets. MATERIALS AND METHODS: As a continuation of this work, transcriptomic studies of relevance to ionizing radiation were retrieved through the Gene Expression Omnibus (GEO). The datasets were compiled and filtered, then analyzed using BMDExpress. The objective was to determine the reproducibility of BMD values in relation to pathways and genes across different exposure scenarios and compare to those derived using cytogenetic endpoints. A number of graphic visualization approaches were used to determine if BMD outputs could be correlated to parameters such as dose-rate, radiation quality and cell type. RESULTS: Curated studies were diverse and derived from experiments with varied design and intent. Despite this, common genes and pathways were identified with low and high dose thresholds. The higher BMD values were associated with immune response and cell death, while transcripts with lower BMD values were generally related to the classic DNA damage response/repair processes, centered on TP53 signaling. Analysis of datasets with relatively similar dose-ranges under comparable experimental conditions showed a bi-modal distribution with a high degree of consistency in BMD values across shared genes and pathways, particularly for those below the 25th percentile of total distribution by dose. The median BMD values were noted to be approximately 0.5 Gy for genes/pathways that comprised mode 1. Furthermore, transcriptional BMD values derived from a subset of genes using in vivo and in vitro datasets were in accord to those using cytogenetic endpoints. CONCLUSION: Overall, the results from this work highlight the value of the BMD methodology to derive meaningful outputs that are consistent across different models, provided the studies are conducted using a similar dose-range.

A cross-sector call to improve carcinogenicity risk assessment through use of genomic methodologies.

Robust genomic approaches are now available to realize improvements in efficiencies and translational relevance of cancer risk assessments for drugs and chemicals. Mechanistic and pathway data generated via genomics provide opportunities to advance beyond historical reliance on apical endpoints of uncertain human relevance. Published research and regulatory evaluations include many examples for which genomic data have been applied to address cancer risk assessment as a health protection endpoint. The alignment of mature, robust, reproducible, and affordable technologies with increasing demands for reduced animal testing sets the stage for this important transition. We present our shared vision for change from leading scientists from academic, government, nonprofit, and industrial sectors and chemical and pharmaceutical safety applications. This call to action builds upon a 2017 workshop on "Advances and Roadblocks for Use of Genomics in Cancer Risk Assessment." The authors propose a path for implementation of innovative cancer risk assessment including incorporating genomic signatures to assess mechanistic relevance of carcinogenicity and enhanced use of genomics in benchmark dose and point of departure evaluations. Novel opportunities for the chemical and pharmaceutical sectors to combine expertise, resources, and objectives to achieve a common goal of improved human health protection are identified.

Integrated in silico and in vitro genotoxicity assessment of thirteen data-poor substances.

The Canadian Domestic Substances List (DSL) contains chemicals that have not been tested for genotoxicity as their use pre-dates regulatory requirements. In the present study, (quantitative) structure-activity relationships ((Q)SAR) model predictions and in vitro tests were conducted for genotoxicity assessment of 13 data-poor chemicals from the DSL (i.e. CAS numbers 19286-75-0, 13676-91-0, 2478-20-8, 6408-20-8, 74499-36-8, 26694-69-9, 29036-02-0, 120-24-1, 84696-48-9, 4051-63-2, 5718-26-3, 632-51-9, and 600-14-6). First, chemicals were screened by (Q)SAR models in Leadscope® and OASIS TIMES; two chemicals were excluded from (Q)SAR as they are complex mixtures. Six were flagged by (Q)SAR as potentially mutagenic and were subsequently confirmed as mutagens using the Ames assay. Of nine chemicals with clastogenic (Q)SAR flags, eight induced micronuclei in TK6 cells. Benchmark dose analysis was used to evaluate the potency of the chemicals. Four chemicals were bacterial mutagens with similar potencies. Three chemicals were more potent in micronuclei induction than the prototype alkylating agent methyl methanesulfonate and three were equipotent to the mutagenic carcinogen benzo[a]pyrene in the presence of rat liver S9. Overall, 11 of the 13 DSL chemicals demonstrated at least one type of genotoxicity in vitro. This study demonstrates the application of genotoxic potency analysis for prioritizing further investigations.

Hexabromocyclododecane (HBCD): A case study applying tiered testing for human health risk assessment.

Current global efforts are aiming to increase use of mechanistic information in regulatory testing. In tiered testing paradigms, in vitro, in silico, and in vivo studies are employed progressively to identify and classify health hazards, which are then compared against human equivalent doses. We used data from three companion papers on the brominated flame retardant hexabromocyclododecane (HBCD) to conduct a case study on tiered testing. We included ToxCast™ and in vitro-in vivo extrapolation (Tier 1), rat liver transcriptomic (Tier 2), and conventional rat (Tier 3) data. Bioactivity-exposure ratios (BERs) were derived by comparing human administered dose equivalents of the measured effects to Canadian exposure levels. Biological perturbations were highly aligned between Tiers 1/2, and consistent with apical effects in Tier 3. Tier 1 had the smallest BERs, and Tiers 2/3 were similar. The study demonstrates the promise of using physiologically-based pharmacokinetic modeling and mechanistic analyses in a tiered framework to identify pathways through which chemicals exert toxicological effects; however, they also point to some shortcomings associated with in vitro and in silico approaches. Additional case studies of chemicals from multiple classes are required to define optimal tiered screening procedures to reduce future in vivo requirements in health hazard assessments.

Assessment of the performance of the TGx-DDI biomarker to detect DNA damage-inducing agents using quantitative RT-PCR in TK6 cells.

Gene expression biomarkers are now available for application in the identification of genotoxic hazards. The TGx-DDI transcriptomic biomarker can accurately distinguish DNA damage-inducing (DDI) from non-DDI exposures based on changes in the expression of 64 biomarker genes. The 64 genes were previously derived from whole transcriptome DNA microarray profiles of 28 reference agents (14 DDI and 14 non-DDI) after 4 h treatments of TK6 human lymphoblastoid cells. To broaden the applicability of TGx-DDI, we tested the biomarker using quantitative RT-PCR (qPCR), which is accessible to most molecular biology laboratories. First, we selectively profiled the expression of the 64 biomarker genes using TaqMan qPCR assays in 96-well arrays after exposing TK6 cells to the 28 reference agents for 4 h. To evaluate the classification capability of the qPCR profiles, we used the reference qPCR signature to classify 24 external validation chemicals using two different methods-a combination of three statistical analyses and an alternative, the Running Fisher test. The qPCR results for the reference set were comparable to the original microarray biomarker; 27 of the 28 reference agents (96%) were accurately classified. Moreover, the two classification approaches supported the conservation of TGx-DDI classification capability using qPCR; the combination of the two approaches accurately classified 21 of the 24 external validation chemicals, demonstrating 100% sensitivity, 81% specificity, and 91% balanced accuracy. This study demonstrates that qPCR can be used when applying the TGx-DDI biomarker and will improve the accessibility of TGx-DDI for genotoxicity screening. Environ. Mol. Mutagen. 60: 122-133, 2019. © 2018 Her Majesty the Queen in Right of Canada Environmental and Molecular Mutagenesis.

Integrated In Vivo Genotoxicity Assessment of Procarbazine Hydrochloride Demonstrates Induction of Pig-a and LacZ Mutations, and Micronuclei, in MutaMouse Hematopoietic Cells.

Procarbazine hydrochloride (PCH) is a DNA-reactive hematopoietic carcinogen with potent and well-characterized clastogenic activity. However, there is a paucity of in vivo mutagenesis data for PCH, and in vitro assays often fail to detect the genotoxic effects of PCH due to the complexity of its metabolic activation. We comprehensively evaluated the in vivo genotoxicity of PCH on hematopoietic cells of male MutaMouse transgenic rodents using a study design that facilitated assessments of micronuclei and Pig-a mutation in circulating erythrocytes, and lacZ mutant frequencies in bone marrow. Mice were orally exposed to PCH (0, 6.25, 12.5, and 25 mg/kg/day) for 28 consecutive days. Blood samples collected 2 days after cessation of treatment exhibited significant dose-related induction of micronuclei in both immature and mature erythrocytes. Bone marrow and blood collected 3 and 70 days after cessation of treatment also showed significantly elevated mutant frequencies in both the lacZ and Pig-a assays even at the lowest dose tested. PCH-induced lacZ and Pig-a (immature and mature erythrocytes) mutant frequencies were highly correlated, with R2 values ≥0.956, with the exception of lacZ vs. Pig-a mutants in mature erythrocytes at the 70-day time point (R2 = 0.902). These results show that PCH is genotoxic in vivo and demonstrate that the complex metabolism and resulting genotoxicity of PCH is best evaluated in intact animal models. Our results further support the concept that multiple biomarkers of genotoxicity, especially hematopoietic cell genotoxicity, can be readily combined into one study provided that adequate attention is given to manifestation times. Environ. Mol. Mutagen. 60:505-512, 2019. © 2018 Her Majesty the Queen in Right of Canada.

TGx-DDI, a Transcriptomic Biomarker for Genotoxicity Hazard Assessment of Pharmaceuticals and Environmental Chemicals.

Genotoxicity testing is an essential component of the safety assessment paradigm required by regulatory agencies world-wide for analysis of drug candidates, and environmental and industrial chemicals. Current genotoxicity testing batteries feature a high incidence of irrelevant positive findings-particularly for in vitro chromosomal damage (CD) assays. The risk management of compounds with positive in vitro findings is a major challenge and requires complex, time consuming, and costly follow-up strategies including animal testing. Thus, regulators are urgently in need of new testing approaches to meet legislated mandates. Using machine learning, we identified a set of transcripts that responds predictably to DNA-damage in human cells that we refer to as the TGx-DDI biomarker, which was originally referred to as TGx-28.65. We proposed to use this biomarker in conjunction with current genotoxicity testing batteries to differentiate compounds with irrelevant "false" positive findings in the in vitro CD assays from true DNA damaging agents (i.e., for de-risking agents that are clastogenic in vitro but not in vivo). We validated the performance of the TGx-DDI biomarker to identify true DNA damaging agents, assessed intra- and inter- laboratory reproducibility, and cross-platform performance. Recently, to augment the application of this biomarker, we developed a high-throughput cell-based genotoxicity testing system using the NanoString nCounter® technology. Here, we review the status of TGx-DDI development, its integration in the genotoxicity testing paradigm, and progress to date in its qualification at the US Food and Drug Administration (FDA) as a drug development tool. If successfully validated and implemented, the TGx-DDI biomarker assay is expected to significantly augment the current strategy for the assessment of genotoxic hazards for drugs and chemicals.

Integration of sperm DNA damage assessment into OECD test guidelines for genotoxicity testing using the MutaMouse model.

The Organisation for Economic Co-operation and Development (OECD) endorses test guidelines (TG) for identifying chemicals that are genotoxic, such as the transgenic rodent gene mutation assay (TG 488). Current OECD TG do not include assays for sperm DNA damage resulting in a critical testing gap. We evaluated the performance of the Sperm Chromatin Structure Assay (SCSA) and the Terminal Deoxynucleotidyl Transferase-Mediated Deoxyuridine Triphosphate Nick end Labeling (TUNEL) assay to detect sperm DNA damage within the recommended TG 488 protocol. MutaMouse males received 0, 0.5, 1, or 2 mg/kg/day triethylenemelamine (TEM), a multifunctional alkylating agent, for 28 days orally and tissues were collected two (blood) and three (sperm and bone marrow) days later. TEM significantly increased the frequency of lacZ mutants in bone marrow, and of micronuclei (MN) in both reticulocytes (%MN-RET) and normochromatic erythrocytes (%MN-NCE) in a dose-dependent manner (P < 0.05). The percentage of DNA fragmentation index (%DFI) and %TUNEL positive cells demonstrated dose-related increases in sperm (P < 0.05), and the two assay results were strongly correlated (R = 0.9298). Within the same animal, a good correlation was observed between %MN-NCE and %DFI (R = 0.7189). Finally, benchmark dose modelling (BMD) showed comparable BMD10 values among the somatic and germ cell assays. Our results suggest that sperm DNA damage assays can be easily integrated into standard OECD designs investigating genotoxicity in somatic tissues to provide key information on whether a chemical is genotoxic in germ cells and impact its risk assessment.

Simulation of mouse and rat spermatogenesis to inform genotoxicity testing using OECD test guideline 488.

The Organisation for Economic Co-operation and Development Test Guideline (TG) 488 for the transgenic rodent (TGR) mutation assay recommends two sampling times for assessing germ cell mutagenicity following the required 28-day exposure period: 28 + > 49 days for mouse sperm and 28 + >70 days for rat sperm from the cauda epididymis, or three days (i.e., 28 + 3d) for germ cells from seminiferous tubules (hereafter, tubule germ cells) plus caudal sperm for mouse and rat. Although the latter protocol is commonly used for mutagenicity testing in somatic tissues, it has several shortcomings for germ cell testing because it provides limited exposure of the proliferating phase of spermatogenesis when mutations are fixed in the transgene. Indeed, analysis of sperm at 28 + 3d has generated negative results with established germ cell mutagens, while the analysis of tubule germ cells has generated both positive and either negative or equivocal results. The Germ Cell workgroup of the Genetic Toxicology Technical Committee of the Health and Environmental Sciences Institute modelled mouse and rat spermatogenesis to better define the exposure history of the cell population collected from seminiferous tubules. The modelling showed that mouse tubule germ cells at 28 + 3d receive, as a whole, 42% of the total exposure during the proliferating phase. This percentage increases to 99% at 28 + 28d and reaches 100% at 28 + 30d. In the rat, these percentages are 22% and 80% at 28 + 3d and 28 + 28d, reaching 100% at 28 + 44d. These results show that analysis of tubule germ cells at 28 + 28d may be an effective protocol for assessing germ cell mutagenicity in mice and rats using TG 488. Since TG 488 recommends the 28 + 28d protocol for slow dividing somatic tissues, this appears to be a better compromise than 28 + 3d when slow dividing somatic tissues or germ cells are the critical tissues of interest.

Identifying germ cell mutagens using OECD test guideline 488 (transgenic rodent somatic and germ cell gene mutation assays) and integration with somatic cell testing.

The Organisation for Economic Co-operation and Development Test Guideline 488 (TG 488) provides recommendations for assessing germ cell and somatic cell mutagenicity using transgenic rodent (TGR) models. However, important data gaps exist for selecting an optimal approach for simultaneously evaluating mutagenicity in both cell types. It is uncertain whether analysis of germ cells from seminiferous tubules (hereafter, tubule germ cells) or caudal sperm within the recommended design for somatic tissues (i.e., 28 days of exposure plus three days of fixation time, 28 + 3d) has enough sensitivity to detect an effect as compared with the analysis of sperm within the recommended design for germ cells (i.e., 28 + 49d and 28 + 70d for mouse and rat, respectively). To address these data gaps, the Germ Cell workgroup of the Genetic Toxicology Technical Committee of the Health and Environmental Sciences Institute reviewed the available TGR mutagenicity data in male germ cells, and, characterized the exposure history of tubule germ cells for different sampling times to evaluate its impact on germ cell mutagenicity testing using TG 488. Our analyses suggest that evaluating mutant frequencies in: i) sperm from the cauda epididymis at 28 + 3d does not provide meaningful mutagenicity data; ii), tubule germ cells at 28 + 3d provides reliable mutagenicity data only if the results are positive; and iii) tubule germ cells at 28 + 28d produces reliable positive and negative results in both mice and rats. Thus, the 28 + 28d regimen may provide an approach for simultaneously assessing mutagenicity in somatic tissues and germ cells from the same animals. Further work is required to support the 28 + 28d protocol for tissues other than slowly proliferating tissues as per current TG 488. Finally, recommendations are provided to guide the experimental design for germ cell mutagenicity data for regulatory submission, as well as other possible approaches to increase the reliability of the TGR assay.

Promise and peril in nanomedicine: the challenges and needs for integrated systems biology approaches to define health risk.

In the 1966s visionary film 'Fantastic Voyage' a submarine crew was shrunk to 100 nm in size and injected into the body of an injured scientist to repair his damaged brain. The movie (written by Harry Kleiner; directed by Richard Fleischer; novel by Isaac Asimov) drew attention to the potential power of engineered nanoscale structures and devices to construct, monitor, control, treat, and repair individual cells. Even more interesting was the fact that the film elegantly noted that the structure had to be miniaturized to a size that is not detected by the body's immune surveillance system, and highlighted the many physiological barriers that are encountered on the submarine's long journey to the target. Although the concept of miniaturizing humans remains an element of science fiction, targeted drug delivery through nanobots to treat diseases such as cancer is now a reality. The ability of nanobots to evade immune surveillance is one of the most attractive features of nanoscale materials that are exploited in the field of medicine for molecular diagnostics, targeted drug delivery, and therapy of diseases. This article will provide a concise opinion on the state-of-the-art, the challenges, and the use of systems biology-another equally revolutionary field of science-to assess the unique health hazards of nanomaterial exposures. WIREs Nanomed Nanobiotechnol 2018, 10:e1465. doi: 10.1002/wnan.1465 This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials.

Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment.

There is increasing interest in the use of quantitative transcriptomic data to determine benchmark dose (BMD) and estimate a point of departure (POD) for human health risk assessment. Although studies have shown that transcriptional PODs correlate with those derived from apical endpoint changes, there is no consensus on the process used to derive a transcriptional POD. Specifically, the subsets of informative genes that produce BMDs that best approximate the doses at which adverse apical effects occur have not been defined. To determine the best way to select predictive groups of genes, we used published microarray data from dose-response studies on six chemicals in rats exposed orally for 5, 14, 28, and 90 days. We evaluated eight approaches for selecting genes for POD derivation and three previously proposed approaches (the lowest pathway BMD, and the mean and median BMD of all genes). The relationship between transcriptional BMDs derived using these 11 approaches and PODs derived from apical data that might be used in chemical risk assessment was examined. Transcriptional BMD values for all 11 approaches were remarkably aligned with corresponding apical PODs, with the vast majority of toxicogenomics PODs being within tenfold of those derived from apical endpoints. We identified at least four approaches that produce BMDs that are effective estimates of apical PODs across multiple sampling time points. Our results support that a variety of approaches can be used to derive reproducible transcriptional PODs that are consistent with PODs produced from traditional methods for chemical risk assessment.

Next generation testing strategy for assessment of genomic damage: A conceptual framework and considerations.

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.

Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field.

Recent advances in "-omics" technologies have simplified capacity to concurrently assess expression profiles of thousands of targets in a cellular system. However, compilation and analysis of "omics" data in support of human health protection remains a challenge. Benchmark dose (BMD) modeling is currently being employed in chemical risk assessment to estimate acceptable levels of exposure. Although typically applied to conventional endpoints, newer software has enabled this application to be extended to transcriptomic datasets. BMD analytical tools now have the capacity to model transcriptional dose-response data to derive meaningful BMD values for genes, pathways and gene ontologies. In this report, radiation data obtained from the Gene Expression Omnibus (GEO) were analyzed to generate BMD values for transcriptional responses. The datasets comprised microarray analyses of human blood gamma-irradiated ex vivo (0-20 Gy) and human-derived cell lines exposed to alpha particle radiation (0.5-1.5 Gy). The distributions of BMDs for statistically significant genes and pathways in response to radiation exposure were examined and compared across studies. BMD modeling could identify pathway/gene sensitivities across wide radiation dose ranges, experimental conditions (time-points, cell types) and radiation qualities. BMD analysis offered a new approach to examine transcriptional data. The results were shown to provide information on transcriptional thresholds of effects to support refined risk assessments for low dose ionizing radiation exposures, derive gene-based values for relative biological effectiveness and identify pathways involved in radiation sensitivities across cell types which may extend to applications a clinical setting. Environ. Mol. Mutagen. 57:589-604, 2016. © 2016 Wiley Periodicals, Inc.