Establishing a quantitative framework for regulatory interpretation of genetic toxicity dose–response data: Margin of exposure case study of 48 compounds with both in vivo mutagenicity and carcinogenicity dose–response data

• Published in: Environmental and molecular mutagenesis Vol. 64; no. 1; pp. 4 - 15
• Main Authors: Chepelev, Nikolai, Long, Alexandra S., Beal, Marc, Barton‐Maclaren, Tara, Johnson, George, Dearfield, Kerry L., Roberts, Daniel J., Benthem, Jan, White, Paul
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2023; Wiley Subscription Services, Inc
• Subjects: Animals; benchmark dose modeling; Benchmarks; Biocompatibility; Carcinogenicity; Carcinogens; Carcinogens - toxicity; Decisions; DNA Damage; dose–response; Exposure; Genotoxicity; Humans; In vivo methods and tests; Mathematical analysis; Mutagenesis; Mutagenicity; Mutagenicity Tests - methods; Mutagens - toxicity; risk assessment; Risk management; Rodentia; Toxicity; genotoxicity; dose-response; mutagenicity; risk assessment; benchmark dose modeling
• ISSN: 0893-6692; 1098-2280
• DOI: 10.1002/em.22517

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.