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Updated: Jun 18, 2026

Assessment of the Acute Inhalation Toxicity of Airborne Particles by Exposing Cultivated Human Lung Cells at the Air-Liquid Interface
Published on: February 23, 2020
Chemical risk assessment and uncertainty associated with extrapolation across exposure duration
H R Pohl1, C-H S J Chou, P Ruiz
1Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, USA. hpohl@cdc.gov
Abstract:
The Agency for Toxic Substances and Disease Registry (ATSDR) prepares toxicological profiles on priority substances in which available epidemiologic and toxicologic data are reviewed, summarized, and interpreted. When adequate data are available, ATSDR derives health guidance values called minimal risk levels (MRLs) for acute, intermediate, and chronic durations of exposure for oral and inhalation routes of exposure. The MRLs are generally derived by use of the no-observed-adverse-effect level (NOAEL) or the lowest-observed-adverse-effect level/uncertainty factor (LOAEL/UF) approach. The UF usually employed are for LOAEL-to-NOAEL extrapolation, animal to -human extrapolation, and inter-human variability. These health guidance values are intended to serve as screening tools for health assessors and other responders to identify contaminants of concern and potential health effects in the community at hazardous waste sites and during unplanned releases. When guidance values are not available for a specific exposure scenario because of a lack of chronic data, extrapolation across exposure durations may be made. For example, chronic guidance values may be derived from subchronic data by applying an additional uncertainty factor of 10 for extrapolation to chronic exposure duration. In this paper, we analyzed the ratio of chemical-specific LOAELs from acute to intermediate and from intermediate to chronic durations for oral and inhalation exposure routes. In addition, we investigated the impact of chemical structure and chemical structure activity relationship on validation of predictions across exposure durations.
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