Characterization and prediction of chemical functions and weight fractions in consumer products

Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via...

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Autores principales: Isaacs, Kristin K., Goldsmith, Michael-Rock, Egeghy, Peter, Phillips, Katherine, Brooks, Raina, Hong, Tao, Wambaugh, John F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5616074/
https://www.ncbi.nlm.nih.gov/pubmed/28959598
http://dx.doi.org/10.1016/j.toxrep.2016.08.011
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author Isaacs, Kristin K.
Goldsmith, Michael-Rock
Egeghy, Peter
Phillips, Katherine
Brooks, Raina
Hong, Tao
Wambaugh, John F.
author_facet Isaacs, Kristin K.
Goldsmith, Michael-Rock
Egeghy, Peter
Phillips, Katherine
Brooks, Raina
Hong, Tao
Wambaugh, John F.
author_sort Isaacs, Kristin K.
collection PubMed
description Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via consideration of chemical functional role. We obtained function information for thousands of chemicals from public sources and used a clustering algorithm to assign chemicals into 35 harmonized function categories (e.g., plasticizers, antimicrobials, solvents). We combined these functions with weight fraction data for 4115 personal care products (PCPs) to characterize the composition of 66 different product categories (e.g., shampoos). We analyzed the combined weight fraction/function dataset using machine learning techniques to develop quantitative structure property relationship (QSPR) classifier models for 22 functions and for weight fraction, based on chemical-specific descriptors (including chemical properties). We applied these classifier models to a library of 10196 data-poor chemicals. Our predictions of chemical function and composition will inform exposure-based screening of chemicals in PCPs for combination with hazard data in risk-based evaluation frameworks. As new information becomes available, this approach can be applied to other classes of products and the chemicals they contain in order to provide essential consumer product data for use in exposure-based chemical prioritization.
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spelling pubmed-56160742017-09-28 Characterization and prediction of chemical functions and weight fractions in consumer products Isaacs, Kristin K. Goldsmith, Michael-Rock Egeghy, Peter Phillips, Katherine Brooks, Raina Hong, Tao Wambaugh, John F. Toxicol Rep Article Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via consideration of chemical functional role. We obtained function information for thousands of chemicals from public sources and used a clustering algorithm to assign chemicals into 35 harmonized function categories (e.g., plasticizers, antimicrobials, solvents). We combined these functions with weight fraction data for 4115 personal care products (PCPs) to characterize the composition of 66 different product categories (e.g., shampoos). We analyzed the combined weight fraction/function dataset using machine learning techniques to develop quantitative structure property relationship (QSPR) classifier models for 22 functions and for weight fraction, based on chemical-specific descriptors (including chemical properties). We applied these classifier models to a library of 10196 data-poor chemicals. Our predictions of chemical function and composition will inform exposure-based screening of chemicals in PCPs for combination with hazard data in risk-based evaluation frameworks. As new information becomes available, this approach can be applied to other classes of products and the chemicals they contain in order to provide essential consumer product data for use in exposure-based chemical prioritization. Elsevier 2016-09-01 /pmc/articles/PMC5616074/ /pubmed/28959598 http://dx.doi.org/10.1016/j.toxrep.2016.08.011 Text en http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Isaacs, Kristin K.
Goldsmith, Michael-Rock
Egeghy, Peter
Phillips, Katherine
Brooks, Raina
Hong, Tao
Wambaugh, John F.
Characterization and prediction of chemical functions and weight fractions in consumer products
title Characterization and prediction of chemical functions and weight fractions in consumer products
title_full Characterization and prediction of chemical functions and weight fractions in consumer products
title_fullStr Characterization and prediction of chemical functions and weight fractions in consumer products
title_full_unstemmed Characterization and prediction of chemical functions and weight fractions in consumer products
title_short Characterization and prediction of chemical functions and weight fractions in consumer products
title_sort characterization and prediction of chemical functions and weight fractions in consumer products
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5616074/
https://www.ncbi.nlm.nih.gov/pubmed/28959598
http://dx.doi.org/10.1016/j.toxrep.2016.08.011
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