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Implementing In Vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories

Internationally, there are thousands of existing and newly introduced chemicals in commerce, highlighting the ongoing importance of innovative approaches to identify emerging chemicals of concern. For many chemicals, there is a paucity of hazard and exposure data. Thus, there is a crucial need for e...

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Autores principales: Beal, Marc A., Gagne, Matthew, Kulkarni, Sunil A., Patlewicz, Grace, Thomas, Russell S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8973434/
https://www.ncbi.nlm.nih.gov/pubmed/34818430
http://dx.doi.org/10.14573/altex.2106171
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author Beal, Marc A.
Gagne, Matthew
Kulkarni, Sunil A.
Patlewicz, Grace
Thomas, Russell S.
author_facet Beal, Marc A.
Gagne, Matthew
Kulkarni, Sunil A.
Patlewicz, Grace
Thomas, Russell S.
author_sort Beal, Marc A.
collection PubMed
description Internationally, there are thousands of existing and newly introduced chemicals in commerce, highlighting the ongoing importance of innovative approaches to identify emerging chemicals of concern. For many chemicals, there is a paucity of hazard and exposure data. Thus, there is a crucial need for efficient and robust approaches to address data gaps and support risk-based prioritization. Several studies have demonstrated the utility of in vitro bioactivity data from the ToxCast program in deriving points of departure (PODs). ToxCast contains data for nearly 1,400 endpoints per chemical, and the bioactivity concentrations, indicative of potential adverse outcomes, can be converted to human-equivalent PODs using high-throughput toxicokinetics (HTTK) modeling. However, data gaps need to be addressed for broader application: the limited chemical space of HTTK and quantitative high-throughput screening data. Here we explore the applicability of in silico models to address these data needs. Specifically, we used ADMET predictor for HTTK predictions and a generalized read-across approach to predict ToxCast bioactivity potency. We applied these models to profile 5,801 chemicals on Canada’s Domestic Substances List (DSL). To evaluate the approach’s performance, bioactivity PODs were compared with in vivo results from the EPA Toxicity Values database for 1,042 DSL chemicals. Comparisons demonstrated that the bioactivity PODs, based on ToxCast data or read-across, were conservative for 95% of the chemicals. Comparing bioactivity PODs to human exposure estimates supports the identification of chemicals of potential interest for further work. The bioactivity workflow shows promise as a powerful screening tool to support effective triaging of chemical inventories.
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spelling pubmed-89734342023-01-01 Implementing In Vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories Beal, Marc A. Gagne, Matthew Kulkarni, Sunil A. Patlewicz, Grace Thomas, Russell S. ALTEX Article Internationally, there are thousands of existing and newly introduced chemicals in commerce, highlighting the ongoing importance of innovative approaches to identify emerging chemicals of concern. For many chemicals, there is a paucity of hazard and exposure data. Thus, there is a crucial need for efficient and robust approaches to address data gaps and support risk-based prioritization. Several studies have demonstrated the utility of in vitro bioactivity data from the ToxCast program in deriving points of departure (PODs). ToxCast contains data for nearly 1,400 endpoints per chemical, and the bioactivity concentrations, indicative of potential adverse outcomes, can be converted to human-equivalent PODs using high-throughput toxicokinetics (HTTK) modeling. However, data gaps need to be addressed for broader application: the limited chemical space of HTTK and quantitative high-throughput screening data. Here we explore the applicability of in silico models to address these data needs. Specifically, we used ADMET predictor for HTTK predictions and a generalized read-across approach to predict ToxCast bioactivity potency. We applied these models to profile 5,801 chemicals on Canada’s Domestic Substances List (DSL). To evaluate the approach’s performance, bioactivity PODs were compared with in vivo results from the EPA Toxicity Values database for 1,042 DSL chemicals. Comparisons demonstrated that the bioactivity PODs, based on ToxCast data or read-across, were conservative for 95% of the chemicals. Comparing bioactivity PODs to human exposure estimates supports the identification of chemicals of potential interest for further work. The bioactivity workflow shows promise as a powerful screening tool to support effective triaging of chemical inventories. 2022 2021-11-23 /pmc/articles/PMC8973434/ /pubmed/34818430 http://dx.doi.org/10.14573/altex.2106171 Text en https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is appropriately cited.
spellingShingle Article
Beal, Marc A.
Gagne, Matthew
Kulkarni, Sunil A.
Patlewicz, Grace
Thomas, Russell S.
Implementing In Vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories
title Implementing In Vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories
title_full Implementing In Vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories
title_fullStr Implementing In Vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories
title_full_unstemmed Implementing In Vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories
title_short Implementing In Vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories
title_sort implementing in vitro bioactivity data to modernize priority setting of chemical inventories
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8973434/
https://www.ncbi.nlm.nih.gov/pubmed/34818430
http://dx.doi.org/10.14573/altex.2106171
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