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Assessment of carcinogenic hazard of chemical mixtures through analysis of binary chemical interaction data.

Assessment of the potential health hazard of environmental complex chemical mixtures is one of the most difficult and challenging problems in toxicology. In this article, we describe the development of an innovative computerized system for ranking and predicting potential cancer hazard of chemical m...

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Detalles Bibliográficos
Autores principales: Woo, Y T, Di Carlo, F J, Arcos, J C, Argus, M F, Polansky, G, DuBose, J
Formato: Texto
Lenguaje:English
Publicado: 1994
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1566772/
https://www.ncbi.nlm.nih.gov/pubmed/7698072
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author Woo, Y T
Di Carlo, F J
Arcos, J C
Argus, M F
Polansky, G
DuBose, J
author_facet Woo, Y T
Di Carlo, F J
Arcos, J C
Argus, M F
Polansky, G
DuBose, J
author_sort Woo, Y T
collection PubMed
description Assessment of the potential health hazard of environmental complex chemical mixtures is one of the most difficult and challenging problems in toxicology. In this article, we describe the development of an innovative computerized system for ranking and predicting potential cancer hazard of chemical mixtures. We take into consideration both the additive risk of individual carcinogens present and the projected overall interaction effect of the mixture based on analyzing and integrating the possible interaction effects of all binary pairs of individual constituents of the mixture. Using this system, it can be predicted that a number of mixtures of polycyclic aromatic hydrocarbons should have a carcinogenic risk lower than that calculated by the simple additivity model, whereas the reverse is true for a number of other mixtures. The system can be very useful in hazard ranking and priority setting in dealing with mixture problems such as cleanup of hazardous waste.
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spelling pubmed-15667722006-09-19 Assessment of carcinogenic hazard of chemical mixtures through analysis of binary chemical interaction data. Woo, Y T Di Carlo, F J Arcos, J C Argus, M F Polansky, G DuBose, J Environ Health Perspect Research Article Assessment of the potential health hazard of environmental complex chemical mixtures is one of the most difficult and challenging problems in toxicology. In this article, we describe the development of an innovative computerized system for ranking and predicting potential cancer hazard of chemical mixtures. We take into consideration both the additive risk of individual carcinogens present and the projected overall interaction effect of the mixture based on analyzing and integrating the possible interaction effects of all binary pairs of individual constituents of the mixture. Using this system, it can be predicted that a number of mixtures of polycyclic aromatic hydrocarbons should have a carcinogenic risk lower than that calculated by the simple additivity model, whereas the reverse is true for a number of other mixtures. The system can be very useful in hazard ranking and priority setting in dealing with mixture problems such as cleanup of hazardous waste. 1994-11 /pmc/articles/PMC1566772/ /pubmed/7698072 Text en
spellingShingle Research Article
Woo, Y T
Di Carlo, F J
Arcos, J C
Argus, M F
Polansky, G
DuBose, J
Assessment of carcinogenic hazard of chemical mixtures through analysis of binary chemical interaction data.
title Assessment of carcinogenic hazard of chemical mixtures through analysis of binary chemical interaction data.
title_full Assessment of carcinogenic hazard of chemical mixtures through analysis of binary chemical interaction data.
title_fullStr Assessment of carcinogenic hazard of chemical mixtures through analysis of binary chemical interaction data.
title_full_unstemmed Assessment of carcinogenic hazard of chemical mixtures through analysis of binary chemical interaction data.
title_short Assessment of carcinogenic hazard of chemical mixtures through analysis of binary chemical interaction data.
title_sort assessment of carcinogenic hazard of chemical mixtures through analysis of binary chemical interaction data.
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1566772/
https://www.ncbi.nlm.nih.gov/pubmed/7698072
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