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Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment

Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure to multiple chemicals from various sources and over time. Therefore, such data are particularly valuable for assessing potential risks from combined exposure to multiple chemicals. One way to interpr...

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Autores principales: Pletz, Julia, Blakeman, Samantha, Paini, Alicia, Parissis, Nikolaos, Worth, Andrew, Andersson, Anna-Maria, Frederiksen, Hanne, Sakhi, Amrit K., Thomsen, Cathrine, Bopp, Stephanie K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7684529/
https://www.ncbi.nlm.nih.gov/pubmed/32763630
http://dx.doi.org/10.1016/j.envint.2020.105978
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author Pletz, Julia
Blakeman, Samantha
Paini, Alicia
Parissis, Nikolaos
Worth, Andrew
Andersson, Anna-Maria
Frederiksen, Hanne
Sakhi, Amrit K.
Thomsen, Cathrine
Bopp, Stephanie K.
author_facet Pletz, Julia
Blakeman, Samantha
Paini, Alicia
Parissis, Nikolaos
Worth, Andrew
Andersson, Anna-Maria
Frederiksen, Hanne
Sakhi, Amrit K.
Thomsen, Cathrine
Bopp, Stephanie K.
author_sort Pletz, Julia
collection PubMed
description Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure to multiple chemicals from various sources and over time. Therefore, such data are particularly valuable for assessing potential risks from combined exposure to multiple chemicals. One way to interpret HBM data is establishing safe levels in blood or urine, called Biomonitoring Equivalents (BE) or HBM health based guidance values (HBM-HBGV). These can be derived by converting established external reference values, such as tolerable daily intake (TDI) values. HBM-HBGV or BE values are so far agreed only for a very limited number of chemicals. These values can be established using physiologically based kinetic (PBK) modelling, usually requiring substance specific models and the collection of many input parameters which are often not available or difficult to find in the literature. The aim of this study was to investigate the suitability and limitations of generic PBK models in deriving BE values for several compounds with a view to facilitating the use of HBM data in the assessment of chemical mixtures at a screening level. The focus was on testing the methodology with two generic models, the IndusChemFate tool and High-Throughput Toxicokinetics package, for two different classes of compounds, phenols and phthalates. HBM data on Danish children and on Norwegian mothers and children were used to evaluate the quality of the predictions and to illustrate, by means of a case study, the overall approach of applying PBK models to chemical classes with HBM data in the context of chemical mixture risk assessment. Application of PBK models provides a better understanding and interpretation of HBM data. However, the study shows that establishing safety threshold levels in urine is a difficult and complex task. The approach might be more straightforward for more persistent chemicals that are analysed as parent compounds in blood but high uncertainties have to be considered around simulated metabolite concentrations in urine. Refining the models may reduce these uncertainties and improve predictions. Based on the experience gained with this study, the performance of the models for other chemicals could be investigated, to improve the accuracy of the simulations.
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spelling pubmed-76845292020-12-07 Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment Pletz, Julia Blakeman, Samantha Paini, Alicia Parissis, Nikolaos Worth, Andrew Andersson, Anna-Maria Frederiksen, Hanne Sakhi, Amrit K. Thomsen, Cathrine Bopp, Stephanie K. Environ Int Article Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure to multiple chemicals from various sources and over time. Therefore, such data are particularly valuable for assessing potential risks from combined exposure to multiple chemicals. One way to interpret HBM data is establishing safe levels in blood or urine, called Biomonitoring Equivalents (BE) or HBM health based guidance values (HBM-HBGV). These can be derived by converting established external reference values, such as tolerable daily intake (TDI) values. HBM-HBGV or BE values are so far agreed only for a very limited number of chemicals. These values can be established using physiologically based kinetic (PBK) modelling, usually requiring substance specific models and the collection of many input parameters which are often not available or difficult to find in the literature. The aim of this study was to investigate the suitability and limitations of generic PBK models in deriving BE values for several compounds with a view to facilitating the use of HBM data in the assessment of chemical mixtures at a screening level. The focus was on testing the methodology with two generic models, the IndusChemFate tool and High-Throughput Toxicokinetics package, for two different classes of compounds, phenols and phthalates. HBM data on Danish children and on Norwegian mothers and children were used to evaluate the quality of the predictions and to illustrate, by means of a case study, the overall approach of applying PBK models to chemical classes with HBM data in the context of chemical mixture risk assessment. Application of PBK models provides a better understanding and interpretation of HBM data. However, the study shows that establishing safety threshold levels in urine is a difficult and complex task. The approach might be more straightforward for more persistent chemicals that are analysed as parent compounds in blood but high uncertainties have to be considered around simulated metabolite concentrations in urine. Refining the models may reduce these uncertainties and improve predictions. Based on the experience gained with this study, the performance of the models for other chemicals could be investigated, to improve the accuracy of the simulations. Elsevier Science 2020-10 /pmc/articles/PMC7684529/ /pubmed/32763630 http://dx.doi.org/10.1016/j.envint.2020.105978 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pletz, Julia
Blakeman, Samantha
Paini, Alicia
Parissis, Nikolaos
Worth, Andrew
Andersson, Anna-Maria
Frederiksen, Hanne
Sakhi, Amrit K.
Thomsen, Cathrine
Bopp, Stephanie K.
Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
title Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
title_full Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
title_fullStr Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
title_full_unstemmed Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
title_short Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
title_sort physiologically based kinetic (pbk) modelling and human biomonitoring data for mixture risk assessment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7684529/
https://www.ncbi.nlm.nih.gov/pubmed/32763630
http://dx.doi.org/10.1016/j.envint.2020.105978
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