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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

Nikolai Chepelev, Alexandra S. Long, Marc Beal Orcid Logo, Tara Barton‐Maclaren, George Johnson Orcid Logo, Kerry L. Dearfield, Daniel J. Roberts, Jan van Benthem, Paul White

Environmental and Molecular Mutagenesis, Volume: 64, Issue: 1

Swansea University Author: George Johnson Orcid Logo

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DOI (Published version): 10.1002/em.22517

Abstract

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 ex...

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Published in: Environmental and Molecular Mutagenesis
ISSN: 0893-6692 1098-2280
Published: Wiley 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62130
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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. 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spelling v2 62130 2022-12-06 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 37d0f121db69fd09f364df89e4405e31 0000-0001-5643-9942 George Johnson George Johnson true false 2022-12-06 BMS 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. Journal Article Environmental and Molecular Mutagenesis 64 1 Wiley 0893-6692 1098-2280 6 1 2023 2023-01-06 10.1002/em.22517 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University Government of Canada's Chemicals Management Plan 2023-06-12T16:22:18.9433184 2022-12-06T15:18:53.9005798 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Nikolai Chepelev 1 Alexandra S. Long 2 Marc Beal 0000-0003-2760-4368 3 Tara Barton‐Maclaren 4 George Johnson 0000-0001-5643-9942 5 Kerry L. Dearfield 6 Daniel J. Roberts 7 Jan van Benthem 8 Paul White 9 62130__26212__b5e6e4c6d8ed456f8c5505a0ef5d6079.pdf 62130.pdf 2023-01-09T10:38:55.7893711 Output 3128355 application/pdf Version of Record true © 2022 His Majesty the King in Right of Canada and The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title 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
spellingShingle 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
George Johnson
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
author_id_str_mv 37d0f121db69fd09f364df89e4405e31
author_id_fullname_str_mv 37d0f121db69fd09f364df89e4405e31_***_George Johnson
author George Johnson
author2 Nikolai Chepelev
Alexandra S. Long
Marc Beal
Tara Barton‐Maclaren
George Johnson
Kerry L. Dearfield
Daniel J. Roberts
Jan van Benthem
Paul White
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institution Swansea University
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hierarchy_top_title Faculty of Medicine, Health and Life Sciences
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department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
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description 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.
published_date 2023-01-06T16:22:17Z
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