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 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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Julia Pletz [verfasserIn] Samantha Blakeman [verfasserIn] Alicia Paini [verfasserIn] Nikolaos Parissis [verfasserIn] Andrew Worth [verfasserIn] Anna-Maria Andersson [verfasserIn] Hanne Frederiksen [verfasserIn] Amrit K. Sakhi [verfasserIn] Cathrine Thomsen [verfasserIn] Stephanie K. Bopp [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Physiologically based kinetic modelling Human biomonitoring Chemical mixtures Biomonitoring equivalents Human biomonitoring guidance values

Übergeordnetes Werk:	In: Environment International - Elsevier, 2019, 143(2020), Seite 105978-
Übergeordnetes Werk:	volume:143 ; year:2020 ; pages:105978-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.envint.2020.105978

Katalog-ID:	DOAJ050701053

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spelling	10.1016/j.envint.2020.105978 doi (DE-627)DOAJ050701053 (DE-599)DOAJ0f925ea555cf44ee95c9910495857d45 DE-627 ger DE-627 rakwb eng GE1-350 Julia Pletz verfasserin aut Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Physiologically based kinetic modelling Human biomonitoring Chemical mixtures Biomonitoring equivalents Human biomonitoring guidance values Environmental sciences Samantha Blakeman verfasserin aut Alicia Paini verfasserin aut Nikolaos Parissis verfasserin aut Andrew Worth verfasserin aut Anna-Maria Andersson verfasserin aut Hanne Frederiksen verfasserin aut Amrit K. Sakhi verfasserin aut Cathrine Thomsen verfasserin aut Stephanie K. Bopp verfasserin aut In Environment International Elsevier, 2019 143(2020), Seite 105978- (DE-627)306580829 (DE-600)1497569-5 01604120 nnns volume:143 year:2020 pages:105978- https://doi.org/10.1016/j.envint.2020.105978 kostenfrei https://doaj.org/article/0f925ea555cf44ee95c9910495857d45 kostenfrei http://www.sciencedirect.com/science/article/pii/S0160412020319334 kostenfrei https://doaj.org/toc/0160-4120 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 143 2020 105978-
allfields_unstemmed	10.1016/j.envint.2020.105978 doi (DE-627)DOAJ050701053 (DE-599)DOAJ0f925ea555cf44ee95c9910495857d45 DE-627 ger DE-627 rakwb eng GE1-350 Julia Pletz verfasserin aut Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Physiologically based kinetic modelling Human biomonitoring Chemical mixtures Biomonitoring equivalents Human biomonitoring guidance values Environmental sciences Samantha Blakeman verfasserin aut Alicia Paini verfasserin aut Nikolaos Parissis verfasserin aut Andrew Worth verfasserin aut Anna-Maria Andersson verfasserin aut Hanne Frederiksen verfasserin aut Amrit K. Sakhi verfasserin aut Cathrine Thomsen verfasserin aut Stephanie K. Bopp verfasserin aut In Environment International Elsevier, 2019 143(2020), Seite 105978- (DE-627)306580829 (DE-600)1497569-5 01604120 nnns volume:143 year:2020 pages:105978- https://doi.org/10.1016/j.envint.2020.105978 kostenfrei https://doaj.org/article/0f925ea555cf44ee95c9910495857d45 kostenfrei http://www.sciencedirect.com/science/article/pii/S0160412020319334 kostenfrei https://doaj.org/toc/0160-4120 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 143 2020 105978-
allfieldsGer	10.1016/j.envint.2020.105978 doi (DE-627)DOAJ050701053 (DE-599)DOAJ0f925ea555cf44ee95c9910495857d45 DE-627 ger DE-627 rakwb eng GE1-350 Julia Pletz verfasserin aut Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Physiologically based kinetic modelling Human biomonitoring Chemical mixtures Biomonitoring equivalents Human biomonitoring guidance values Environmental sciences Samantha Blakeman verfasserin aut Alicia Paini verfasserin aut Nikolaos Parissis verfasserin aut Andrew Worth verfasserin aut Anna-Maria Andersson verfasserin aut Hanne Frederiksen verfasserin aut Amrit K. Sakhi verfasserin aut Cathrine Thomsen verfasserin aut Stephanie K. Bopp verfasserin aut In Environment International Elsevier, 2019 143(2020), Seite 105978- (DE-627)306580829 (DE-600)1497569-5 01604120 nnns volume:143 year:2020 pages:105978- https://doi.org/10.1016/j.envint.2020.105978 kostenfrei https://doaj.org/article/0f925ea555cf44ee95c9910495857d45 kostenfrei http://www.sciencedirect.com/science/article/pii/S0160412020319334 kostenfrei https://doaj.org/toc/0160-4120 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 143 2020 105978-
allfieldsSound	10.1016/j.envint.2020.105978 doi (DE-627)DOAJ050701053 (DE-599)DOAJ0f925ea555cf44ee95c9910495857d45 DE-627 ger DE-627 rakwb eng GE1-350 Julia Pletz verfasserin aut Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Physiologically based kinetic modelling Human biomonitoring Chemical mixtures Biomonitoring equivalents Human biomonitoring guidance values Environmental sciences Samantha Blakeman verfasserin aut Alicia Paini verfasserin aut Nikolaos Parissis verfasserin aut Andrew Worth verfasserin aut Anna-Maria Andersson verfasserin aut Hanne Frederiksen verfasserin aut Amrit K. Sakhi verfasserin aut Cathrine Thomsen verfasserin aut Stephanie K. Bopp verfasserin aut In Environment International Elsevier, 2019 143(2020), Seite 105978- (DE-627)306580829 (DE-600)1497569-5 01604120 nnns volume:143 year:2020 pages:105978- https://doi.org/10.1016/j.envint.2020.105978 kostenfrei https://doaj.org/article/0f925ea555cf44ee95c9910495857d45 kostenfrei http://www.sciencedirect.com/science/article/pii/S0160412020319334 kostenfrei https://doaj.org/toc/0160-4120 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 143 2020 105978-
language	English
source	In Environment International 143(2020), Seite 105978- volume:143 year:2020 pages:105978-
sourceStr	In Environment International 143(2020), Seite 105978- volume:143 year:2020 pages:105978-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Physiologically based kinetic modelling Human biomonitoring Chemical mixtures Biomonitoring equivalents Human biomonitoring guidance values Environmental sciences
isfreeaccess_bool	true
container_title	Environment International
authorswithroles_txt_mv	Julia Pletz @@aut@@ Samantha Blakeman @@aut@@ Alicia Paini @@aut@@ Nikolaos Parissis @@aut@@ Andrew Worth @@aut@@ Anna-Maria Andersson @@aut@@ Hanne Frederiksen @@aut@@ Amrit K. Sakhi @@aut@@ Cathrine Thomsen @@aut@@ Stephanie K. Bopp @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	306580829
id	DOAJ050701053
language_de	englisch
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callnumber-first	G - Geography, Anthropology, Recreation
author	Julia Pletz
spellingShingle	Julia Pletz misc GE1-350 misc Physiologically based kinetic modelling misc Human biomonitoring misc Chemical mixtures misc Biomonitoring equivalents misc Human biomonitoring guidance values misc Environmental sciences Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
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topic_title	GE1-350 Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment Physiologically based kinetic modelling Human biomonitoring Chemical mixtures Biomonitoring equivalents Human biomonitoring guidance values
topic	misc GE1-350 misc Physiologically based kinetic modelling misc Human biomonitoring misc Chemical mixtures misc Biomonitoring equivalents misc Human biomonitoring guidance values misc Environmental sciences
topic_unstemmed	misc GE1-350 misc Physiologically based kinetic modelling misc Human biomonitoring misc Chemical mixtures misc Biomonitoring equivalents misc Human biomonitoring guidance values misc Environmental sciences
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title	Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
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title_full	Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
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author_browse	Julia Pletz Samantha Blakeman Alicia Paini Nikolaos Parissis Andrew Worth Anna-Maria Andersson Hanne Frederiksen Amrit K. Sakhi Cathrine Thomsen Stephanie K. Bopp
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title_sort	physiologically based kinetic (pbk) modelling and human biomonitoring data for mixture risk assessment
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title_auth	Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
abstract	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.
abstractGer	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.
abstract_unstemmed	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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title_short	Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment
url	https://doi.org/10.1016/j.envint.2020.105978 https://doaj.org/article/0f925ea555cf44ee95c9910495857d45 http://www.sciencedirect.com/science/article/pii/S0160412020319334 https://doaj.org/toc/0160-4120
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Physiologically based kinetic (PBK) modelling and human biomonitoring data for mixture risk assessment

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