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G × E interactions as a basis for toxicological uncertainty
To transfer toxicological findings from model systems, e.g. animals, to humans, standardized safety factors are applied to account for intra-species and inter-species variabilities. An alternative approach would be to measure and model the actual compound-specific uncertainties. This biological conc...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256652/ https://www.ncbi.nlm.nih.gov/pubmed/37258688 http://dx.doi.org/10.1007/s00204-023-03500-9 |
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author | Suciu, Ilinca Pamies, David Peruzzo, Roberta Wirtz, Petra H. Smirnova, Lena Pallocca, Giorgia Hauck, Christof Cronin, Mark T. D. Hengstler, Jan G. Brunner, Thomas Hartung, Thomas Amelio, Ivano Leist, Marcel |
author_facet | Suciu, Ilinca Pamies, David Peruzzo, Roberta Wirtz, Petra H. Smirnova, Lena Pallocca, Giorgia Hauck, Christof Cronin, Mark T. D. Hengstler, Jan G. Brunner, Thomas Hartung, Thomas Amelio, Ivano Leist, Marcel |
author_sort | Suciu, Ilinca |
collection | PubMed |
description | To transfer toxicological findings from model systems, e.g. animals, to humans, standardized safety factors are applied to account for intra-species and inter-species variabilities. An alternative approach would be to measure and model the actual compound-specific uncertainties. This biological concept assumes that all observed toxicities depend not only on the exposure situation (environment = E), but also on the genetic (G) background of the model (G × E). As a quantitative discipline, toxicology needs to move beyond merely qualitative G × E concepts. Research programs are required that determine the major biological variabilities affecting toxicity and categorize their relative weights and contributions. In a complementary approach, detailed case studies need to explore the role of genetic backgrounds in the adverse effects of defined chemicals. In addition, current understanding of the selection and propagation of adverse outcome pathways (AOP) in different biological environments is very limited. To improve understanding, a particular focus is required on modulatory and counter-regulatory steps. For quantitative approaches to address uncertainties, the concept of “genetic” influence needs a more precise definition. What is usually meant by this term in the context of G × E are the protein functions encoded by the genes. Besides the gene sequence, the regulation of the gene expression and function should also be accounted for. The widened concept of past and present “gene expression” influences is summarized here as G(e). Also, the concept of “environment” needs some re-consideration in situations where exposure timing (E(t)) is pivotal: prolonged or repeated exposure to the insult (chemical, physical, life style) affects G(e). This implies that it changes the model system. The interaction of G(e) with E(t) might be denoted as G(e) × E(t). We provide here general explanations and specific examples for this concept and show how it could be applied in the context of New Approach Methodologies (NAM). |
format | Online Article Text |
id | pubmed-10256652 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-102566522023-06-11 G × E interactions as a basis for toxicological uncertainty Suciu, Ilinca Pamies, David Peruzzo, Roberta Wirtz, Petra H. Smirnova, Lena Pallocca, Giorgia Hauck, Christof Cronin, Mark T. D. Hengstler, Jan G. Brunner, Thomas Hartung, Thomas Amelio, Ivano Leist, Marcel Arch Toxicol Guest Editorial To transfer toxicological findings from model systems, e.g. animals, to humans, standardized safety factors are applied to account for intra-species and inter-species variabilities. An alternative approach would be to measure and model the actual compound-specific uncertainties. This biological concept assumes that all observed toxicities depend not only on the exposure situation (environment = E), but also on the genetic (G) background of the model (G × E). As a quantitative discipline, toxicology needs to move beyond merely qualitative G × E concepts. Research programs are required that determine the major biological variabilities affecting toxicity and categorize their relative weights and contributions. In a complementary approach, detailed case studies need to explore the role of genetic backgrounds in the adverse effects of defined chemicals. In addition, current understanding of the selection and propagation of adverse outcome pathways (AOP) in different biological environments is very limited. To improve understanding, a particular focus is required on modulatory and counter-regulatory steps. For quantitative approaches to address uncertainties, the concept of “genetic” influence needs a more precise definition. What is usually meant by this term in the context of G × E are the protein functions encoded by the genes. Besides the gene sequence, the regulation of the gene expression and function should also be accounted for. The widened concept of past and present “gene expression” influences is summarized here as G(e). Also, the concept of “environment” needs some re-consideration in situations where exposure timing (E(t)) is pivotal: prolonged or repeated exposure to the insult (chemical, physical, life style) affects G(e). This implies that it changes the model system. The interaction of G(e) with E(t) might be denoted as G(e) × E(t). We provide here general explanations and specific examples for this concept and show how it could be applied in the context of New Approach Methodologies (NAM). Springer Berlin Heidelberg 2023-06-01 2023 /pmc/articles/PMC10256652/ /pubmed/37258688 http://dx.doi.org/10.1007/s00204-023-03500-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Guest Editorial Suciu, Ilinca Pamies, David Peruzzo, Roberta Wirtz, Petra H. Smirnova, Lena Pallocca, Giorgia Hauck, Christof Cronin, Mark T. D. Hengstler, Jan G. Brunner, Thomas Hartung, Thomas Amelio, Ivano Leist, Marcel G × E interactions as a basis for toxicological uncertainty |
title | G × E interactions as a basis for toxicological uncertainty |
title_full | G × E interactions as a basis for toxicological uncertainty |
title_fullStr | G × E interactions as a basis for toxicological uncertainty |
title_full_unstemmed | G × E interactions as a basis for toxicological uncertainty |
title_short | G × E interactions as a basis for toxicological uncertainty |
title_sort | g × e interactions as a basis for toxicological uncertainty |
topic | Guest Editorial |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256652/ https://www.ncbi.nlm.nih.gov/pubmed/37258688 http://dx.doi.org/10.1007/s00204-023-03500-9 |
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