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A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model

Current efforts to assess human health response to chemicals based on high-throughput in vitro assay data on intra-cellular changes have been hindered for some illnesses by lack of information on higher-level extracellular, inter-organ, and organism-level interactions. However, a dose-response funct...

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Autores principales: Englehardt, James D., Chiu, Weihsueh A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6377108/
https://www.ncbi.nlm.nih.gov/pubmed/30768598
http://dx.doi.org/10.1371/journal.pone.0211780
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author Englehardt, James D.
Chiu, Weihsueh A.
author_facet Englehardt, James D.
Chiu, Weihsueh A.
author_sort Englehardt, James D.
collection PubMed
description Current efforts to assess human health response to chemicals based on high-throughput in vitro assay data on intra-cellular changes have been hindered for some illnesses by lack of information on higher-level extracellular, inter-organ, and organism-level interactions. However, a dose-response function (DRF), informed by various levels of information including apical health response, can represent a template for convergent top-down, bottom-up analysis. In this paper, a general DRF for chronic chemical and other health stressors and mixtures is derived based on a general first-order model previously derived and demonstrated for illness progression. The derivation accounts for essential autocorrelation among initiating event magnitudes along a toxicological mode of action, typical of complex processes in general, and reveals the inverse relationship between the minimum illness-inducing dose, and the illness severity per unit dose (both variable across a population). The resulting emergent DRF is theoretically scale-inclusive and amenable to low-dose extrapolation. The two-parameter single-toxicant version can be monotonic or sigmoidal, and is demonstrated preferable to traditional models (multistage, lognormal, generalized linear) for the published cancer and non-cancer datasets analyzed: chloroform (induced liver necrosis in female mice); bromate (induced dysplastic focia in male inbred rats); and 2-acetylaminofluorene (induced liver neoplasms and bladder carcinomas in 20,328 female mice). Common- and dissimilar-mode mixture models are demonstrated versus orthogonal data on toluene/benzene mixtures (mortality in Japanese medaka, Oryzias latipes, following embryonic exposure). Findings support previous empirical demonstration, and also reveal how a chemical with a typical monotonically-increasing DRF can display a J-shaped DRF when a second, antagonistic common-mode chemical is present. Overall, the general DRF derived here based on an autocorrelated first-order model appears to provide both a strong theoretical/biological basis for, as well as an accurate statistical description of, a diverse, albeit small, sample of observed dose-response data. The further generalizability of this conclusion can be tested in future analyses comparing with traditional modeling approaches across a broader range of datasets.
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spelling pubmed-63771082019-03-01 A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model Englehardt, James D. Chiu, Weihsueh A. PLoS One Research Article Current efforts to assess human health response to chemicals based on high-throughput in vitro assay data on intra-cellular changes have been hindered for some illnesses by lack of information on higher-level extracellular, inter-organ, and organism-level interactions. However, a dose-response function (DRF), informed by various levels of information including apical health response, can represent a template for convergent top-down, bottom-up analysis. In this paper, a general DRF for chronic chemical and other health stressors and mixtures is derived based on a general first-order model previously derived and demonstrated for illness progression. The derivation accounts for essential autocorrelation among initiating event magnitudes along a toxicological mode of action, typical of complex processes in general, and reveals the inverse relationship between the minimum illness-inducing dose, and the illness severity per unit dose (both variable across a population). The resulting emergent DRF is theoretically scale-inclusive and amenable to low-dose extrapolation. The two-parameter single-toxicant version can be monotonic or sigmoidal, and is demonstrated preferable to traditional models (multistage, lognormal, generalized linear) for the published cancer and non-cancer datasets analyzed: chloroform (induced liver necrosis in female mice); bromate (induced dysplastic focia in male inbred rats); and 2-acetylaminofluorene (induced liver neoplasms and bladder carcinomas in 20,328 female mice). Common- and dissimilar-mode mixture models are demonstrated versus orthogonal data on toluene/benzene mixtures (mortality in Japanese medaka, Oryzias latipes, following embryonic exposure). Findings support previous empirical demonstration, and also reveal how a chemical with a typical monotonically-increasing DRF can display a J-shaped DRF when a second, antagonistic common-mode chemical is present. Overall, the general DRF derived here based on an autocorrelated first-order model appears to provide both a strong theoretical/biological basis for, as well as an accurate statistical description of, a diverse, albeit small, sample of observed dose-response data. The further generalizability of this conclusion can be tested in future analyses comparing with traditional modeling approaches across a broader range of datasets. Public Library of Science 2019-02-15 /pmc/articles/PMC6377108/ /pubmed/30768598 http://dx.doi.org/10.1371/journal.pone.0211780 Text en © 2019 Englehardt, Chiu http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Englehardt, James D.
Chiu, Weihsueh A.
A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model
title A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model
title_full A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model
title_fullStr A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model
title_full_unstemmed A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model
title_short A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model
title_sort general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6377108/
https://www.ncbi.nlm.nih.gov/pubmed/30768598
http://dx.doi.org/10.1371/journal.pone.0211780
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