Computational Toxicology Communities of Practice:Experimental variability and uncertainty in the context of new approach methodologies for potential use in chemical safety evaluation
Date and TimeThursday 08/27/2020 11:00AM to 12:00PM EDT
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Topic: Experimental variability and uncertainty in the context of new approach methodologies for potential use in chemical safety evaluation
Who: Dr. Prachi Pradeep, ORISE Postdoctoral Fellow in the Center for Computational Toxicology and Exposure
When: August 27, 2020 from 11:00 AM- 12:00 PM EST
Where: Register for webinar using Eventbrite and check the confirmation email for webinar information.
New approach methodologies (NAMs), such as quantitative structure activity relationship (QSAR) models based on chemical structure information, are commonly used to predict hazard in the absence of experimental data. QSAR models are developed and validated using experimental (i.e., in vitro or in vivo) toxicity data. However, variability in experimental toxicity data introduces limitations in the development, evaluation, validation, reliability and regulatory acceptance of computational models. Experimental variability can arise from biological (e.g., test species, environmental conditions) and/or technical (e.g., measurement errors, different experimental protocols) sources. Characterization of various sources of data variability, adequate incorporation of variability in computational model development, and quantification of data-driven uncertainty in model predictivity are critically needed to improve the reliability and acceptance of computational models. This talk will present these ideas using repeat dose toxicity studies and also illustrate the use of variability to quantify uncertainty in developing QSAR models for points of departure and neurotoxicity equivalency values for polychlorinated biphenyls.
This abstract does not necessarily reflect U.S. EPA policy.
For more information visit the EPA's Computational Toxicology Communities of Practice webpage