Abstract
There is growing interest in quantitative analysis of in vivo genetic toxicity dose‐response data, and use of point‐of‐departure (PoD) metrics such as the benchmark dose (BMD) for human health risk assessment (HHRA). Currently, multiple transgenic rodent (TGR) assay variants, employing different rodent strains and reporter transgenes, are used for the assessment of chemically‐induced genotoxic effects in vivo. However, regulatory issues arise when different PoD values (e.g., lower BMD confidence intervals or BMDLs) are obtained for the same compound across different TGR assay variants. This study therefore employed the BMD approach to examine the ability of different TGR variants to yield comparable genotoxic potency estimates. Review of over 2000 dose‐response datasets identified suitably‐matched dose‐response data for three compounds (ethyl methanesulfonate or EMS, N‐ethyl‐N‐nitrosourea or ENU, and dimethylnitrosamine or DMN) across four commonly‐used murine TGR variants (Muta™Mouse lacZ, Muta™Mouse cII, gpt delta and BigBlue® lacI). Dose‐response analyses provided no conclusive evidence that TGR variant choice significantly influences the derived genotoxic potency estimate. This conclusion was reliant upon taking into account the importance of comparing BMD confidence intervals as opposed to directly comparing PoD values (e.g., comparing BMDLs). Comparisons with earlier works suggested that with respect to potency determination, tissue choice is potentially more important than choice of TGR assay variant. Scoring multiple tissues selected on the basis of supporting toxicokinetic information is therefore recommended. Finally, we used typical within‐group variances to estimate preliminary endpoint‐specific benchmark response (BMR) values across several TGR variants/tissues. We discuss why such values are required for routine use of genetic toxicity PoDs for HHRA. Environ. Mol. Mutagen. 58:632–643, 2017. © 2017 Her Majesty the Queen in Right of Canada. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc.
Highlights
Genetic toxicity testing is an essential component of safety assessments for new and existing substances
In order to compare genotoxic potency estimates derived from dose-response data generated using different variants of the transgenic rodent (TGR) assay, a meta-analysis filtration of the Transgenic Rodent Assay Information Database (TRAID) database was performed to identify suitablymatched datasets that are well-suited for potency estimate comparisons
Consistent dose-response data are better-suited to support human health risk assessments, whereby findings are often compared across studies during the process of weight-of-evidence based regulatory decision-making [Dearfield et al, 2017; MacGregor et al, 2015a]
Summary
Genetic toxicity testing is an essential component of safety assessments for new and existing substances (e.g., food additives, therapeutic products, pesticides, industrial chemicals). Its goal is to identify genotoxic substances and/or assess genotoxic potency, permitting regulatory decisions that minimize the risk of adverse human health effects (e.g., cancer and heritable genetic disorders) mediated by genetic damage. There is growing interest in moving beyond binary categorizations that fail to acknowledge and appreciate variations in the genotoxic potency of tested agents (i.e., variations in the magnitude of the effect associated with a given dose). The alternative quantitative methods employ statistical analyses of genotoxicity dose-response data to determine a point-of-departure (PoD) that provides quantitative information regarding genotoxic potency. Employment of quantitative methods, which acknowledge the relevance of genetic toxicity as a bona fide regulatory endpoint, permit the determination of values that can be used for human health risk assessment (HHRA) and regulatory decision-making. Recent evaluations by several expert working groups have acknowledged the regulatory utility of quantitative doseresponse analyses of genetic toxicity data [Johnson et al, 2015; MacGregor et al, 2015a,b; White and Johnson, 2016]
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