Analysis of in vivo mutation data can inform cancer risk assessment
| Autor: | Annette M. Shipp, Martha M. Moore, Robert H. Heflich, Ralph L. Kodell, Bruce C. Allen, Lynne T. Haber |
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| Rok vydání: | 2008 |
| Předmět: |
Oncology
medicine.medical_specialty Concordance Guidelines as Topic Gene mutation Toxicology medicine.disease_cause Risk Assessment Neoplasms Internal medicine Animals Humans Medicine United States Environmental Protection Agency Carcinogen Dose-Response Relationship Drug business.industry Mechanism (biology) Cancer DNA General Medicine medicine.disease United States Mutation Mutation (genetic algorithm) Carcinogens Risk assessment business Carcinogenesis Mutagens |
| Zdroj: | Regulatory Toxicology and Pharmacology. 51:151-161 |
| ISSN: | 0273-2300 |
| DOI: | 10.1016/j.yrtph.2008.01.015 |
| Popis: | Under the new U.S. Environmental Protection Agency (EPA) Cancer Risk Assessment Guidelines [U.S. EPA, 2005. Guidelines for Carcinogen Risk Assessment. EPA/630/P-03/001B, March 2005], the quantitative model chosen for cancer risk assessment is based on the mode-of-action (MOA) of the chemical under consideration. In particular, the risk assessment model depends on whether or not the chemical causes tumors through a direct DNA-reactive mechanism. It is assumed that direct DNA-reactive carcinogens initiate carcinogenesis by inducing mutations and have low-dose linear dose-response curves, whereas carcinogens that operate through a nonmutagenic MOA may have nonlinear dose-responses. We are currently evaluating whether the analysis of in vivo gene mutation data can inform the risk assessment process by better defining the MOA for cancer and thus influencing the choice of the low-dose extrapolation model. This assessment includes both a temporal analysis of mutation induction and a dose-response concordance analysis of mutation with tumor incidence. Our analysis of published data on riddelliine in rats and dichloroacetic acid in mice indicates that our approach has merit. We propose an experimental design and graphical analysis that allow for assessing time-to-mutation and dose-response concordance, thereby optimizing the potential for in vivo mutation data to inform the choice of the quantitative model used in cancer risk assessment. |
| Databáze: | OpenAIRE |
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