Celia Schacht is a postdoctoral researcher and mathematician within the Center for Public Health and Environmental Assessment (CPHEA) in the Office of Research and Development (ORD) of the U.S. Environmental Protection Agency (EPA) and works on projects related to physiologically based pharmacokinetic (PBPK) models and chemical risk assessment. Celia earned an M.S. in applied mathematics and a Ph.D. in biomathematics from North Carolina State University, where she focused on characterizing uncertainty and variability in aggregate mathematical models and PBPK models. After completing her Ph.D. in 2023, Celia began an ORISE appointment at the U.S. EPA’s Center for Computational Toxicology and Exposure (CCTE), where she focused on developing PBPK models for inhalation and mixtures modeling as part of the high throughput toxicokinetics (HTTK) effort. Some of Celia’s other research interests include code development and programming, uncertainty quantification, and nonlinear dynamics.
Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models
Celia M. Schacht1 , Annabel E. Meade2 , Amanda S. Bernstein 1,3 , Bidya Prasad4 , Paul M. Schlosser 1 , Hien T. Tran5 , Dustin F. Kapraun1
1 Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Durham, North Carolina 27711, USA
2 Applied Research Associates, Inc. Raleigh, North Carolina 27615, USA
3 Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37830, USA
4 NJ DEP, Trenton, New Jersey 08608, USA
5 Center for Research in Scientific Computation, NC State University, Raleigh, North Carolina 27607, USA
Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.
The views expressed in this presentation are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.
