Physiologically Based Pharmacokinetic PBPK Modeling

The application of PBPK modeling to better understand the disposition of potentially toxic compounds soon followed. Styrene and dichloromethane (DCM, or methylene chloride) are notable examples (Angelo et al. 1984 Angelo and Pritchard 1984 Ramsey and Andersen 1984 Andasen et al. 1987) and applications continue well into the current era (Blancato et al. 2007). 

While there are many uses for computational methods such as PBPK models in risk assessment, some knowledge of the mode of action is cmcial for using them to enhance the characterization and quantification of the dose-response function. All 

This was a great step forward for risk assessment. First, a reasonable dose metric was chosen for which there was evidence of a close mechanistic relationship with the potential adverse outcome. Second, because of this understanding and the knowledge of the metabolism and pharmacokinetics, a PBPK model was constructed. This model enabled a quantification of the dose metric in different species and for different dosing and exposure regimens and routes. Thus interspecies and interdose extrapolations could be done more rationally and with less uncertainty. Third, because of the first two developments the risk assessment was based on mode of action hence the relevance of dose, exposure levels, and test species relevance was better understood and considered. As a result, the cancer potency for DCM was changed by close to an order of magnitude from the previous value. 

In contrast, the second case illustrates use under different conditions. In this case a PBPK model was developed for methyl tertiary butyl ether (MTBE) (Blancato et al. 2007). There was more uncertainty on the exact mode of action and even on the details of metabolism beyond two pathways. Several models had been published, 

Illustrated here was a different use of a computational model. It should be noted that even a model using somewhat more limited data (no adequate time course concentration disposition in humans, especially for metabolic species) was stUl a valuable tool for predicting a dose metric of choice for which there wctc more data (parent compound) and for predicting across different routes of exposure and between different species. Furthermore, the model was used to assess potential impact on that risk from variability in a hiunan population of inteest. Thus it sawed as an in silica laboratory. 

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Conceptual Representation of a Physiologically Based Pharmacokinetic (PBPK) Model for a Hypothetical Chemical Substance... 

Note This is a conceptual representation of a physiologically based pharmacokinetic (PBPK) model for a hypothetical chemical substance. The chemical substance is shown to be absorbed via the skin, by inhalation, or by ingestion, metabolized in the liver, and excreted in the urine or by exhalation. 

Physiologically Based Pharmacokinetic (PBPK) Model—is comprised of a series of compartments representing organs or tissue groups with realistic weights and blood flows. These models require a variety of physiological information tissue volumes, blood flow rates to tissues, cardiac output, alveolar ventilation rates and, possibly membrane permeabilities. The models also utilize biochemical information such as air/blood partition coefficients, and metabolic parameters. PBPK models are also called biologically based tissue dosimetry models. 

Notice Approaches for the Application of Physiologically-Based Pharmacokinetic (PBPK) Models and Supporting Data in Risk Assessment E-Docket ID No. ORD-2005-0022. Fed Reg July 28, 2005 70 (144) 43692-43693. 

Geary RS, Wall CM, Miller MA, et al. 1994. Partition coefficient measurements of diisopropyl methylphosphonate (DIMP) and trichloroethylene in rats using microdialysis and incorporated in physiologically-based pharmacokinetic (PBPK) modelling [Abstract], Society of Toxicology 33rd Annual Meeting, Dallas, TX 13-17 March, 1994. Paper No. 82. 

FUN tool is a new integrated software based on a multimedia model, physiologically based pharmacokinetic (PBPK) models and associated databases. The tool is a dynamic integrated model and is capable of assessing the human exposure to chemical substances via multiple exposure pathways and the potential health risks (Fig. 9) [70]. 2-FUN tool has been developed in the framework of the European project called 2-FUN (Full-chain and UNcertainty Approaches for Assessing Health Risks in FUture ENvironmental Scenarios www.2-fun.org). 

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