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Lowe K, Dawson J, Phillips K, Minucci J, Wambaugh JF, Qian H, Ramanarayanan T, Egeghy P, Ingle B, Brunner R, Mendez E, Embry M, Tan YM. Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies. Regul Toxicol Pharmacol 2021; 127:105073. [PMID: 34743952 DOI: 10.1016/j.yrtph.2021.105073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/23/2021] [Accepted: 10/27/2021] [Indexed: 10/20/2022]
Abstract
Human health risks from chronic exposures to environmental chemicals are typically estimated from potential human exposure estimates and dose-response data obtained from repeated-dose animal toxicity studies. Various criteria are available for selecting the top (highest) dose used in these animal studies. For example, toxicokinetic (TK) and toxicological data provided by shorter-term or dose range finding studies can be evaluated in a weight of evidence approach to provide insight into the dose range that would provide dose-response data that are relevant to human exposures. However, there are concerns that a top dose resulting from the consideration of TK data may be too low compared to other criteria, such as the limit dose or the maximum tolerated dose. In this paper, we address several concerns related to human exposures by discussing 1) the resources and methods available to predict human exposure levels and the associated uncertainty and variability, and 2) the margin between predicted human exposure levels and the dose levels used in repeated-dose animal studies. A series of case studies, ranging from data-rich to data-poor chemicals, are presented to demonstrate that expected human exposures to environmental chemicals are typically orders of magnitude lower than no-observed-adverse-effect levels/lowest-observed-adverse-effect levels (NOAELs/LOAELs) when available (used as conservative surrogates for top doses). The results of these case studies support that a top dose based, in part, on TK data is typically orders of magnitude higher than expected human exposure levels.
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Affiliation(s)
- Kelly Lowe
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Jeffrey Dawson
- U.S. Environmental Protection Agency, Office of Chemical Safety and Pollution Prevention, Washington, DC, USA
| | - Katherine Phillips
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Jeffrey Minucci
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Hua Qian
- ExxonMobil Biomedical Sciences, Inc., Annandale, NJ, USA
| | | | - Peter Egeghy
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Brandall Ingle
- U.S. Environmental Protection Agency, Office of Pesticide Program, Durham, NC, USA
| | - Rachel Brunner
- U.S. Environmental Protection Agency, Office of Pesticide Program, Durham, NC, USA
| | - Elizabeth Mendez
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Michelle Embry
- Health and Environmental Sciences Institute, Washington, DC, USA.
| | - Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Program, Durham, NC, USA
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Tan YM, Barton HA, Boobis A, Brunner R, Clewell H, Cope R, Dawson J, Domoradzki J, Egeghy P, Gulati P, Ingle B, Kleinstreuer N, Lowe K, Lowit A, Mendez E, Miller D, Minucci J, Nguyen J, Paini A, Perron M, Phillips K, Qian H, Ramanarayanan T, Sewell F, Villanueva P, Wambaugh J, Embry M. Opportunities and challenges related to saturation of toxicokinetic processes: Implications for risk assessment. Regul Toxicol Pharmacol 2021; 127:105070. [PMID: 34718074 DOI: 10.1016/j.yrtph.2021.105070] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/18/2021] [Accepted: 10/25/2021] [Indexed: 02/08/2023]
Abstract
Top dose selection for repeated dose animal studies has generally focused on identification of apical endpoints, use of the limit dose, or determination of a maximum tolerated dose (MTD). The intent is to optimize the ability of toxicity tests performed in a small number of animals to detect effects for hazard identification. An alternative approach, the kinetically derived maximum dose (KMD), has been proposed as a mechanism to integrate toxicokinetic (TK) data into the dose selection process. The approach refers to the dose above which the systemic exposures depart from being proportional to external doses. This non-linear external-internal dose relationship arises from saturation or limitation of TK process(es), such as absorption or metabolism. The importance of TK information is widely acknowledged when assessing human health risks arising from exposures to environmental chemicals, as TK determines the amount of chemical at potential sites of toxicological responses. However, there have been differing opinions and interpretations within the scientific and regulatory communities related to the validity and application of the KMD concept. A multi-stakeholder working group, led by the Health and Environmental Sciences Institute (HESI), was formed to provide an opportunity for impacted stakeholders to address commonly raised scientific and technical issues related to this topic and, more specifically, a weight of evidence approach is recommended to inform design and dose selection for repeated dose animal studies. Commonly raised challenges related to the use of TK data for dose selection are discussed, recommendations are provided, and illustrative case examples are provided to address these challenges or refute misconceptions.
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Affiliation(s)
- Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Durham, NC, USA
| | | | | | - Rachel Brunner
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Durham, NC, USA
| | | | - Rhian Cope
- Australian Pesticides and Veterinary Medicines Authority, Sydney, NSW, Australia
| | - Jeffrey Dawson
- U.S. Environmental Protection Agency, Office of Chemical Safety and Pollution Prevention, Washington, DC, USA
| | | | - Peter Egeghy
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Pankaj Gulati
- Australian Pesticides and Veterinary Medicines Authority, Sydney, NSW, Australia
| | - Brandall Ingle
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Durham, NC, USA
| | - Nicole Kleinstreuer
- National Toxicology Program, Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, NC, USA
| | - Kelly Lowe
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Anna Lowit
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Elizabeth Mendez
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - David Miller
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Jeffrey Minucci
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - James Nguyen
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Alicia Paini
- European Commission, Joint Research Centre, Ispra, Italy
| | - Monique Perron
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Katherine Phillips
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Hua Qian
- ExxonMobil Biomedical Sciences, Inc., Annandale, NJ, USA
| | | | - Fiona Sewell
- National Centre for the Replacement, Refinement, and Reduction of Animals in Research, London, UK
| | - Philip Villanueva
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - John Wambaugh
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Michelle Embry
- Health and Environmental Sciences Institute, Washington DC, USA.
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Aylward L, Vilone G, Cowan-Ellsberry C, Arnot JA, Westgate JN, O'Mahony C, Hays SM. Exposure to selected preservatives in personal care products: case study comparison of exposure models and observational biomonitoring data. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2020; 30:28-41. [PMID: 30518793 PMCID: PMC6914665 DOI: 10.1038/s41370-018-0104-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 11/12/2018] [Accepted: 11/14/2018] [Indexed: 05/03/2023]
Abstract
Exposure models provide critical information for risk assessment of personal care product ingredients, but there have been limited opportunities to compare exposure model predictions to observational exposure data. Urinary excretion data from a biomonitoring study in eight individuals were used to estimate minimum absorbed doses for triclosan and methyl-, ethyl-, and n-propyl- parabens (TCS, MP, EP, PP). Three screening exposure models (European Commission Scientific Commission on Consumer Safety [SCCS] algorithms, ConsExpo in deterministic mode, and RAIDAR-ICE) and two higher-tier probabilistic models (SHEDS-HT, and Creme Care & Cosmetics) were used to model participant exposures. Average urinary excretion rates of TCS, MP, EP, and PP for participants using products with those ingredients were 16.9, 3.32, 1.9, and 0.91 μg/kg-d, respectively. The SCCS default aggregate and RAIDAR-ICE screening models generally resulted in the highest predictions compared to other models. Approximately 60-90% of the model predictions for most of the models were within a factor of 10 of the observed exposures; ~30-40% of the predictions were within a factor of 3. Estimated exposures from urinary data tended to fall in the upper range of predictions from the probabilistic models. This analysis indicates that currently available exposure models provide estimates that are generally realistic. Uncertainties in preservative product concentrations and dermal absorption parameters as well as degree of metabolism following dermal absorption influence interpretation of the modeled vs. measured exposures. Use of multiple models may help characterize potential exposures more fully than reliance on a single model.
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Affiliation(s)
| | | | | | - Jon A Arnot
- ARC Arnot Research & Consulting, Toronto, ON, Canada
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Isaacs KK, Dionisio K, Phillips K, Bevington C, Egeghy P, Price PS. Establishing a system of consumer product use categories to support rapid modeling of human exposure. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2020; 30:171-183. [PMID: 31712628 PMCID: PMC7745729 DOI: 10.1038/s41370-019-0187-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 08/23/2019] [Accepted: 09/17/2019] [Indexed: 05/22/2023]
Abstract
Consumer product categorizations for use in predicting human chemical exposure provide a bridge between product composition data and consumer product use pattern information. Furthermore, the categories reflect other factors relevant to developing consumer product exposure scenarios, such as microenvironment of use (e.g., indoors or outdoors), method of application/form of release (e.g., spray versus liquid), release to various media, removal processes (e.g., rinse-off or wipe-off), and route-specific exposure factors (dermal surface areas of application, fraction of release in respirable form). While challenging, developing harmonized product categories can generalize the factors described above allowing for rapid parameterization of route-specific exposure scenario algorithms for new chemical/product applications and efficient utilization of new data on product use or composition. This can be accomplished via mapping product categories to likewise categorized release and use patterns or exposure factors. Here, hierarchical product use categories (PUCs) for consumer products that provide such mappings are presented and crosswalked with other internationally harmonized product categories for consumer exposure assessment. The PUCs were defined by applying use and exposure scenario information to the products in EPA's Chemical and Products Database (CPDat). This paper demonstrates how these PUCs are being used to rapidly parameterize algorithms for scenario-specific use, fate, and exposure in a probabilistic aggregate model of human exposure to chemicals used in consumer products. The PUCs provide a generic representation of consumer products for use in exposure assessment and provide an efficient framework for flexible and rapid data reporting and consumer exposure model parameterization.
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Affiliation(s)
- Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA.
| | - Kathie Dionisio
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Katherine Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Charles Bevington
- Office of Pollution Prevention and Toxics, 1200 Pennsylvania Avenue, North West Washington, DC, 20460, USA
| | - Peter Egeghy
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Paul S Price
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
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Leonard JA. Supporting systems science through in silico applications: A focus on informing metabolic mechanisms. CURRENT OPINION IN TOXICOLOGY 2019. [DOI: 10.1016/j.cotox.2019.03.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Environment and Human Health: The Challenge of Uncertainty in Risk Assessment. GEOSCIENCES 2018. [DOI: 10.3390/geosciences8010024] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Moretto A, Bachman A, Boobis A, Solomon KR, Pastoor TP, Wilks MF, Embry MR. A framework for cumulative risk assessment in the 21st century. Crit Rev Toxicol 2016; 47:85-97. [PMID: 27685779 DOI: 10.1080/10408444.2016.1211618] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
The ILSI Health and Environmental Sciences Institute (HESI) has developed a framework to support a transition in the way in which information for chemical risk assessment is obtained and used (RISK21). The approach is based on detailed problem formulation, where exposure drives the data acquisition process in order to enable informed decision-making on human health safety as soon as sufficient evidence is available. Information is evaluated in a transparent and consistent way with the aim of optimizing available resources. In the context of risk assessment, cumulative risk assessment (CRA) poses additional problems and questions that can be addressed using the RISK21 approach. The focus in CRA to date has generally been on chemicals that have common mechanisms of action. Recently, concern has also been expressed about chemicals acting on multiple pathways that lead to a common health outcome, and non-chemical other conditions (non-chemical stressors) that can lead to or modify a common outcome. Acknowledging that CRAs, as described above, are more conceptually, methodologically and computationally complex than traditional single-stressor risk assessments, RISK21 further developed the framework for implementation of workable processes and procedures for conducting assessments of combined effects from exposure to multiple chemicals and non-chemical stressors. As part of the problem formulation process, this evidence-based framework allows the identification of the circumstances in which it is appropriate to conduct a CRA for a group of compounds. A tiered approach is then proposed, where additional chemical stressors and/or non-chemical modulating factors (ModFs) are considered sequentially. Criteria are provided to facilitate the decision on whether or not to include ModFs in the formal quantitative assessment, with the intention to help focus the use of available resources to have the greatest potential to protect public health.
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Affiliation(s)
- Angelo Moretto
- a Dipartimento di Scienze Biomediche e Cliniche, and International Centre for Pesticides and Health Risks Prevention (ICPS), ASST Fatebenefratelli Sacco, Luigi Sacco Hospital , University of Milan , Milan , Italy
| | - Ammie Bachman
- b ExxonMobil Biomedical Sciences, Inc , Annandale , NJ , USA
| | | | - Keith R Solomon
- d Centre for Toxicology, School of Environmental Sciences , University of Guelph , Guelph , ON , Canada
| | | | - Martin F Wilks
- f Swiss Centre for Applied Human Toxicology , University of Basel , Basel , Switzerland
| | - Michelle R Embry
- g ILSI Health and Environmental Sciences Institute , Washington , DC , USA
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