We just had a wonderful talk from Dr. Dan Hoer. Dr Hoer is a physical scientist with the US Environmental Protection Agency, Office of Pesticide Programs. His talk will focus on non-linear PBPK modeling, and the advantages offered by this approach.
What you’ll learn:
Important advantages of PBPK modeling over statistical methods that are often restricted to bimodal distinctions of ‘proportional’ versus ‘non-proportional’ dose groups.
How a mechanistically credible PBPK model can be used as a tool for integrating available ADME data from existing in vivo studies or in vitro assays to simulate a potential IEC relationship.
How you can reduce animal use by avoiding study repetition, minimize animal distress, and maximize the value of these studies.
Dr. Daniel Hoer
Dr. Daniel (Dan) Hoer is a physical scientist in the Environmental Protection Agency’s Office of Pesticide Programs’ Health Effects Division where his work focuses on the evaluation and development of physiologically based pharmacokinetic (PBPK) models in support of human health risk assessment. He earned a B.S. in Environmental Sciences and a Ph.D. in Marine Sciences from the University of North Carolina at Chapel Hill. Following his Ph.D., he completed a postdoctoral fellowship in the Department of Organismic and Evolutionary Biology at Harvard University and worked in the same department as a research associate prior to joining EPA. Dan’s past research investigated the interdependencies of aquatic chemistry and ecosystem ecology, and his work frequently benefited from conducting in situ investigations of unmanipulated systems. As such, he developed prototype or purpose-built sensors and other tools to improve the sensitivity, precision, or frequency of data collection as required to effectively answer his research questions. Through his over 10 years of research work, he developed an extensive modeling, data science, and engineering skillset which he is leveraging in his current role with EPA.
Dose-response analysis is an essential part of risk assessment. External concentrations are more readily quantified, and thus are subject to regulatory or experimental control. However, it is the corresponding internal concentrations, or the concentration of a chemical in blood, plasma, and tissue, that are more directly tied to the toxicological response in the exposed organism. The relationship between internal and external concentrations is quantitatively dictated by pharmacokinetic processes, including absorption, distribution, metabolism, and excretion (ADME). Given that many ADME processes are mediated by enzymes or transporters, they are correspondingly saturable at high substrate concentrations, which can lead to changes in internal concentration that deviate from being proportional to external concentration. Nonlinearities in dose response relationships can complicate their interpretation and can impact the design of animal studies (e.g., to avoid ethically tenuous or low-value data collection in in vivo studies). Analyzing dose response data using a physiologically based pharmacokinetic (PBPK) model can be an alternative to common statistical methods for analyzing dose proportionality in the internal to external concentration (IEC) relationship. By generating a continuous simulation of the IEC relationship across a wide range of doses, a PBPK modeling approach has important advantages over statistical methods that are often restricted to bimodal distinctions of ‘proportional’ versus ‘non-proportional’ dose groups. Case study discussions of saturable clearance and absorption will demonstrate that a mechanistically credible PBPK model can be used as a tool for integrating available ADME data from existing in vivo studies or in vitro assays to simulate a potential IEC relationship. These simulations can be integrated into a weight of evidence approach to interpret dose-response data, or to inform the selection of dose magnitude, dose spacing, or sample collection intervals in whole animal studies. This can potentially reduce animal use by avoiding study repetition, minimize animal distress, and maximize the value of these studies. Disclaimer: the views expressed in this abstract are those of the author and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
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