Using Collaborative Cross Mouse Population to Fill Data Gaps in Risk Assessment: A Case Study of Population-Based Analysis of Toxicokinetics and Kidney Toxicodynamics of Tetrachloroethylene

Genetic and microbial determinants of azoxymethane-induced colorectal tumor susceptibility in Collaborative Cross mice and their implication in human cancer

The insights into interactions between host genetics and gut microbiome (GM) in colorectal tumor susceptibility (CTS) remains lacking. We used Collaborative Cross mouse population model to identify genetic and microbial determinants of Azoxymethane-induced CTS. We identified 4417 CTS-associated single nucleotide polymorphisms (SNPs) containing 334 genes that were transcriptionally altered in human colorectal cancers (CRCs) and consistently clustered independent human CRC cohorts into two subgroups with different prognosis. We discovered a set of genera in early-life associated with CTS and defined a 16-genus signature that accurately predicted CTS, the majority of which were correlated with human CRCs. We identified 547 SNPs associated with abundances of these genera. Mediation analysis revealed GM as mediators partially exerting the effect of SNP UNC3869242 within Duox2 on CTS. Intestine cell-specific depletion of Duox2 altered GM composition and contribution of Duox2 depletion to CTS was significantly influenced by GM. Our findings provide potential novel targets for personalized CRC prevention and treatment.

Research Recommendations for Selected IARC-Classified Agents: Impact and Lessons Learned

BACKGROUND

The International Agency for Research on Cancer (IARC) Monographs program assembles expert working groups who publish a critical review and evaluation of data on agents of interest. These comprehensive reviews provide a unique opportunity to identify research needs to address classification uncertainties. A multidisciplinary expert review and workshop held in 2009 identified research gaps and needs for 20 priority occupational chemicals, metals, dusts, and physical agents, with the goal of stimulating advances in epidemiological studies of cancer and carcinogen mechanisms. Overarching issues were also described.

OBJECTIVES

In this commentary we review the current status of the evidence for the 20 priority agents identified in 2009. We examine whether identified Research Recommendations for each agent were addressed and their potential impact on resolving classification uncertainties.

METHODS

We reviewed the IARC classifications of each of the 20 priority agents and identified major new epidemiological and human mechanistic studies published since the last evaluation. Information sources were either the published Monograph for agents that have been reevaluated or, for agents not yet reevaluated, Advisory Group reports and literature searches. Findings are described in view of recent methodological developments in Monographs evidence evaluation processes.

DISCUSSION

The majority of the 20 priority agents were reevaluated by IARC since 2009. The overall carcinogen classifications of 9 agents advanced, and new cancer sites with either "sufficient" or "limited" evidence of carcinogenicity were also identified for 9 agents. Examination of published findings revealed whether evidence gaps and Research Recommendations have been addressed and highlighted remaining uncertainties. During the past decade, new research addressed a range of the 2009 recommendations and supported updated classifications for priority agents. This supports future efforts to systematically apply findings of Monograph reviews to identify research gaps and priorities relevant to evaluation criteria established in the updated IARC Monograph Preamble. https://doi.org/10.1289/EHP12547.

Application of probabilistic methods to address variability and uncertainty in estimating risks for non-cancer health effects

Human health risk assessment currently uses the reference dose or reference concentration (RfD, RfC) approach to describe the level of exposure to chemical hazards without appreciable risk for non-cancer health effects in people. However, this "bright line" approach assumes that there is minimal risk below the RfD/RfC with some undefined level of increased risk at exposures above the RfD/RfC and has limited utility for decision-making. Rather than this dichotomous approach, non-cancer risk assessment can benefit from incorporating probabilistic methods to estimate the amount of risk across a wide range of exposures and define a risk-specific dose. We identify and review existing approaches for conducting probabilistic non-cancer risk assessments. Using perchloroethylene (PCE), a priority chemical for the U.S. Environmental Protection Agency under the Toxic Substances Control Act, we calculate risk-specific doses for the effects on cognitive deficits using probabilistic risk assessment approaches. Our probabilistic risk assessment shows that chronic exposure to 0.004 ppm PCE is associated with approximately 1-in-1,000 risk for a 5% reduced performance on the Wechsler Memory Scale Visual Reproduction subtest with 95% confidence. This exposure level associated with a 1-in-1000 risk for non-cancer neurocognitive deficits is lower than the current RfC for PCE of 0.0059 ppm, which is based on standard point of departure and uncertainty factor approaches for the same neurotoxic effects in occupationally exposed adults. We found that the population-level risk of cognitive deficit (indicating central nervous system dysfunction) is estimated to be greater than the cancer risk level of 1-in-100,000 at a similar chronic exposure level. The extension of toxicological endpoints to more clinically relevant endpoints, along with consideration of magnitude and severity of effect, will help in the selection of acceptable risk targets for non-cancer effects. We find that probabilistic approaches can 1) provide greater context to existing RfDs and RfCs by describing the probability of effect across a range of exposure levels including the RfD/RfC in a diverse population for a given magnitude of effect and confidence level, 2) relate effects of chemical exposures to clinical disease risk so that the resulting risk assessments can better inform decision-makers and benefit-cost analysis, and 3) better reflect the underlying biology and uncertainties of population risks.

Characterization of primary glutathione conjugates with acrylamide and glycidamide: Toxicokinetic studies in Sprague Dawley rats treated with acrylamide

Acrylamide (AA) is classified as a probable human carcinogen and is ubiquitous in foods processed at high temperatures. The carcinogenicity of AA has been attributed to its active metabolite, glycidamide (GA). Both AA and GA can spontaneously or enzymatically conjugate with glutathione (GSH) to form their corresponding GSH conjugates. Profiling AA-glutathione conjugate (AA-GSH) and GA-glutathione conjugates (2 isomers: GA2-GSH and GA3-GSH) in serum would better illustrate AA detoxification compared with urinary metabolite analysis. However, the lack of AA-, GA2, and GA3-GSH study remains a critical data gap. Our study aimed to investigate the toxicokinetics of AA-, GA2-and GA3-GSH in Sprague Dawley rats treated with 0.1 mg/kg, 1.0 mg/kg, or 5.0 mg/kg AA. Blood samples were collected for LC-MS/MS analysis of the GSH conjugate products. Within 24 h of treatment, we observed rapid formation, elimination, and linear kinetics of AA-, GA2-and GA3-GSH. The ∑GA-GSH AUC/AA-GSH AUC ratios were 0.14-0.29, similar to ∑GA/AA AUC in serum but different from ∑GA/AA-derived urinary mercapturic acids in rodents. Our analysis of AA- and GA-GSHs values represents direct detoxification of AA and GA in vivo. This study advances our understanding of sex and inter-species differences in AA detoxification and may refine the existing kinetic models for a more relevant risk extrapolation.

Intra- and Inter-Species Variability in Urinary N7-(1-Hydroxy-3-buten-2-yl)guanine Adducts Following Inhalation Exposure to 1,3-Butadiene

1,3-Butadiene is a known carcinogen primarily targeting lymphoid tissues, lung, and liver. Cytochrome P450 activates butadiene to epoxides which form covalent DNA adducts that are thought to be a key mechanistic event in cancer. Previous studies suggested that inter-species, -tissue, and -individual susceptibility to adverse health effects of butadiene exposure may be due to differences in metabolism and other mechanisms. In this study, we aimed to examine the extent of inter-individual and inter-species variability in the urinary N7-(1-hydroxy-3-buten-2-yl)guanine (EB-GII) DNA adduct, a well-known biomarker of exposure to butadiene. For a population variability study in mice, we used the collaborative cross model. Female and male mice from five strains were exposed to filtered air or butadiene (590 ppm, 6 h/day, 5 days/week for 2 weeks) by inhalation. Urine samples were collected, and the metabolic activation of butadiene by DNA-reactive species was quantified as urinary EB-GII adducts. We quantified the degree of EB-GII variation across mouse strains and sexes; then, we compared this variation with the data from rats (exposed to 62.5 or 200 ppm butadiene) and humans (0.004-2.2 ppm butadiene). We show that sex and strain are significant contributors to the variability in urinary EB-GII levels in mice. In addition, we find that the degree of variability in urinary EB-GII in collaborative cross mice, when expressed as an uncertainty factor for the inter-individual variability (UF_H), is relatively modest (≤threefold) possibly due to metabolic saturation. By contrast, the variability in urinary EB-GII (adjusted for exposure) observed in humans, while larger than the default value of 10-fold, is largely consistent with UF_H estimates for other chemicals based on human data for non-cancer endpoints. Overall, these data demonstrate that urinary EB-GII levels, particularly from human studies, may be useful for quantitative characterization of human variability in cancer risks to butadiene.

Utilizing preclinical models of genetic diversity to improve translation of phytochemical activities from rodents to humans and inform personalized nutrition

Mouse models are an essential tool in different areas of research, including nutrition and phytochemical research. Traditional inbred mouse models have allowed the discovery of therapeutical targets and mechanisms of action and expanded our knowledge of health and disease. However, these models lack the genetic variability typically found in human populations, which hinders the translatability of the results found in mice to humans. The development of genetically diverse mouse models, such as the collaborative cross (CC) or the diversity outbred (DO) models, has been a useful tool to overcome this obstacle in many fields, such as cancer, immunology and toxicology. However, these tools have not yet been widely adopted in the field of phytochemical research. As demonstrated in other disciplines, use of CC and DO models has the potential to provide invaluable insights for translation of phytochemicals from rodents to humans, which are desperately needed given the challenges and numerous failed clinical trials in this field. These models may prove informative for personalized use of phytochemicals in humans, including: predicting interindividual variability in phytochemical bioavailability and efficacy, identifying genetic loci or genes governing response to phytochemicals, identifying phytochemical mechanisms of action and therapeutic targets, and understanding the impact of genetic variability on individual response to phytochemicals. Such insights would prove invaluable for personalized implementation of phytochemicals in humans. This review will focus on the current work performed with genetically diverse mouse populations, and the research opportunities and advantages that these models can offer to phytochemical research.

Quantitative Characterization of Population-Wide Tissue- and Metabolite-Specific Variability in Perchloroethylene Toxicokinetics in Male Mice

Quantification of interindividual variability is a continuing challenge in risk assessment, particularly for compounds with complex metabolism and multi-organ toxicity. Toxicokinetic variability for perchloroethylene (perc) was previously characterized across 3 mouse strains and in 1 mouse strain with various degrees of liver steatosis. To further characterize the role of genetic variability in toxicokinetics of perc, we applied Bayesian population physiologically based pharmacokinetic (PBPK) modeling to the data on perc and metabolites in blood/plasma and tissues of male mice from 45 inbred strains from the Collaborative Cross (CC) mouse population. After identifying the most influential PBPK parameters based on global sensitivity analysis, we fit the model with a hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation. We found that the data from 3 commonly used strains were not representative of the full range of variability in perc and metabolite blood/plasma and tissue concentrations across the CC population. Using interstrain variability as a surrogate for human interindividual variability, we calculated dose-dependent, chemical-, and tissue-specific toxicokinetic variability factors (TKVFs) as candidate science-based replacements for the default uncertainty factor for human toxicokinetic variability of 100.5. We found that toxicokinetic variability factors for glutathione conjugation metabolites of perc showed the greatest variability, often exceeding the default, whereas those for oxidative metabolites and perc itself were generally less than the default. Overall, we demonstrate how a combination of a population-based mouse model such as the CC with Bayesian population PBPK modeling can reduce uncertainty in human toxicokinetic variability and increase accuracy and precision in quantitative risk assessment.

Host genetics and gut microbiota cooperatively contribute to azoxymethane-induced acute toxicity in Collaborative Cross mice

Azoxymethane (AOM) is a widely used carcinogen to study chemical-induced colorectal carcinogenesis and is an agent for studying fulminant hepatic failure. The inter-strain susceptibility to acute toxicity by AOM has been reported, but its association with host genetics or gut microbiota remains largely unexplored. Here a cohort of genetically diverse Collaborative Cross (CC) mice was used to assess the contribution of host genetics and the gut microbiome to AOM-induced acute toxicity. We observed variation in AOM-induced acute liver failure across CC strains. Quantitative trait loci (QTL) analysis revealed three chromosome regions significantly associated with AOM toxicity. Genes located within these QTL, including peroxisome proliferator-activated receptor alpha (Ppara), were enriched for enzyme activator and nucleoside-triphosphatase regulator activity. We further demonstrated that the protein level of PPARα in liver tissues from sensitive strains was remarkably lower compared to levels in resistant strains, consistent with protective role of PPAR family in liver injury. We discovered that the abundance levels of gut microbial families Anaeroplasmataceae, Ruminococcaceae, Lactobacillaceae, Akkermansiaceae and Clostridiaceae were significantly higher in the sensitive strains compared to the resistant strains. Using a random forest classifier method, we determined that the relative abundance levels of these microbial families predicted AOM toxicity with the area under the receiver-operating curve (AUC) of 0.75. Combining the three genetic loci and five microbial families increased the predictive accuracy of AOM toxicity (AUC of 0.99). Moreover, we found that Ruminococcaceae and Lactobacillaceae acted as mediators between host genetics and AOM toxicity. In conclusion, this study shows that host genetics and specific microbiome members play a critical role in AOM-induced acute toxicity, which provides a framework for analysis of the health effects from environmental toxicants.

PBPK modeling of impact of nonalcoholic fatty liver disease on toxicokinetics of perchloroethylene in mice

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD), a major cause of chronic liver disease in the Western countries with increasing prevalence worldwide, may substantially affect chemical toxicokinetics and thereby modulate chemical toxicity.

OBJECTIVES

This study aims to use physiologically-based pharmacokinetic (PBPK) modeling to characterize the impact of NAFLD on toxicokinetics of perchloroethylene (perc).

METHODS

Quantitative measures of physiological and biochemical changes associated with the presence of NAFLD induced by high-fat or methionine/choline-deficient diets in C57B1/6 J mice are incorporated into a previously developed PBPK model for perc and its oxidative and conjugative metabolites. Impacts on liver fat and volume, as well as blood:air and liver:air partition coefficients, are incorporated into the model. Hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation is conducted to characterize uncertainty, as well as disease-induced variability in toxicokinetics.

RESULTS

NAFLD has a major effect on toxicokinetics of perc, with greater oxidative and lower conjugative metabolism as compared to healthy mice. The NAFLD-updated PBPK model accurately predicts in vivo metabolism of perc through oxidative and conjugative pathways in all tissues across disease states and strains, but underestimated parent compound concentrations in blood and liver of NAFLD mice.

CONCLUSIONS

We demonstrate the application of PBPK modeling to predict the effects of pre-existing disease conditions as a variability factor in perc metabolism. These results suggest that non-genetic factors such as diet and pre-existing disease can be as influential as genetic factors in altering toxicokinetics of perc, and thus are likely contribute substantially to population variation in its adverse effects.