Statistical Inferences from Formaldehyde DNA–Protein Cross-Link Data: Improving Methods for Characterization of Uncertainty

Abstract

Physiologically based pharmacokinetic (PBPK) modeling has reached considerable sophistication in its application to pharmacological and environmental health problems. Yet, mature methodologies for making statistical inferences have not been routinely incorporated in these applications except in a few data-rich cases. This paper demonstrates how improved statistical inference on estimated model parameters from both frequentist and Bayesian points of view can be routinely carried out. We work with a previously developed PBPK model for the formation and disposition of DNA–protein cross-links formed by inhaled formaldehyde in the nasal lining of rats and rhesus monkeys. We purposefully choose this model because it is based on sparse time-course data.

Key Words:

• DNA–protein cross-links (DPX)
• Formaldehyde
• Nonlinear regression models
• Ordinary differential equations
• PBPK models

ACKNOWLEDGMENTS

Our sincere thanks are due to Paul White and Paul Schlosser of the U.S. Environmental Protection Agency for various discussions and encouragement on this topic. Thanks to two reviewers and an associate editor for their helpful comments which substantially improved the quality of the paper. Bimal Sinha's research was supported in part by an appointment to the Research Participation Program for USEPA/ORD administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the USDOE and USEPA. This report is released to inform interested parties of research and to encourage discussion. The views expressed in this article are solely those of the authors and do not necessarily reflect the views or policies of the U.S. Census Bureau or the U.S. Environmental Protection Agency, or the views of those we acknowledge. This work constitutes a portion of Martin Klein's doctoral dissertation research completed at the University of Maryland, Baltimore County.