@article{schacht_meade_bernstein_prasad_schlosser_tran_kapraun_2024, title={Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models}, ISSN={["1096-0929"]}, url={https://doi.org/10.1093/toxsci/kfae067}, DOI={10.1093/toxsci/kfae067}, abstractNote={Abstract 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 (PODs) 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.}, journal={TOXICOLOGICAL SCIENCES}, author={Schacht, Celia M. and Meade, Annabel E. and Bernstein, Amanda S. and Prasad, Bidya and Schlosser, Paul M. and Tran, Hien T. and Kapraun, Dustin F.}, year={2024}, month={May} }
 @article{banks_meade_schacht_catenacci_thompson_abate-daga_enderling_2020, title={Parameter estimation using aggregate data}, volume={100}, ISSN={["0893-9659"]}, DOI={10.1016/j.aml.2019.105999}, abstractNote={In biomedical/physiological/ecological experiments, it is common for measurements in time series data to be collected from multiple subjects. Often it is the case that a subject cannot be measured or identified at multiple time points (often referred to as aggregate population data). Due to a lack of alternative methods, this form of data is typically treated as if it is collected from a single individual. As we show by examples, this assumption leads to an overconfidence in model parameter (means, variances) values and model based predictions. We discuss these issues in the context of a mathematical model to determine T-cell behavior with cancer chimeric antigen receptor (CAR) therapies where during the collection of data cancerous mice are sacrificed at each measurement time.}, journal={APPLIED MATHEMATICS LETTERS}, author={Banks, H. . T. and Meade, Annabel E. and Schacht, Celia and Catenacci, Jared and Thompson, W. Clayton and Abate-Daga, Daniel and Enderling, Heiko}, year={2020}, month={Feb} }
 @article{schacht_meade_banks_enderling_abate-daga_2019, title={Estimation of probability distributions of parameters using aggregate population data: analysis of a CAR T-cell cancer model}, volume={16}, ISSN={["1551-0018"]}, DOI={10.3934/mbe.2019365}, abstractNote={In this effort we explain fundamental formulations for aggregate data inverse problems requiring estimation of probability distribution parameters. We use as a motivating example a class of CAR T-call cancer models in mice. After ascertaining results on model stability and sensitivity with respect to parameters, we carry out first elementary computations on the question how much data is needed for successful estimation of probability distributions.}, number={6}, journal={MATHEMATICAL BIOSCIENCES AND ENGINEERING}, author={Schacht, Celia and Meade, Annabel and Banks, H. T. and Enderling, Heiko and Abate-Daga, Daniel}, year={2019}, pages={7299–7326} }