The merging of physiology and toxicokinetics, or pharmacokinetics, with computational modeling to characterize dosimetry has led to major advances for both the chemical and pharmaceutical research arenas. Driven by the mutual need to estimate internal exposures where in vivo data generation was simply not possible, the application of toxicokinetic modeling has grown exponentially in the past 30 years. In toxicology the need has been the derivation of quantitative estimates of toxicokinetic and toxicodynamic variability to evaluate the suitability of the tenfold uncertainty factor employed in risk assessment decision-making. Consideration of a host of physiologic, ontogenetic, genetic, and exposure factors are all required for comprehensive characterization. Fortunately, the underlying framework of physiologically based toxicokinetic models can accommodate these inputs, in addition to being amenable to capturing time-varying dynamics. Meanwhile, international interest in advancing new approach methodologies has fueled the generation of in vitro toxicity and toxicokinetic data that can be applied in in vitro-in vivo extrapolation approaches to provide human-specific risk-based information for historically data-poor chemicals. This review will provide a brief introduction to the structure and evolution of toxicokinetic and physiologically based toxicokinetic models as they advanced to incorporate variability and a wide range of complex exposure scenarios. This will be followed by a state of the science update describing current and emerging experimental and modeling strategies for population and life-stage variability, including the increasing application of in vitro-in vivo extrapolation with physiologically based toxicokinetic models in pharmaceutical and chemical safety research. The review will conclude with case study examples demonstrating novel applications of physiologically based toxicokinetic modeling and an update on its applications for regulatory decision-making. Physiologically based toxicokinetic modeling provides a sound framework for variability evaluation in chemical risk assessment.

By Anna Kreutz, Xiaoqing Chang, Helena T. Hogberg & Barbara A. Wetmore