No abstract

Physiologically based pharmacokinetic modeling provides important capabilities for improving the reliability of the extrapolations across dose, species, and exposure route that are generally required in chemical risk assessment regardless of the toxic end point being considered. Recently, there has been an increasing focus on harmonization of the cancer and noncancer risk assessment approaches used by regulatory agencies. Although the specific details of applying pharmacokinetic modeling within these two paradigms may differ, it is possible to identify important elements common to both. These elements expand on a four-part framework for describing the development of toxicity: a) exposure, b) tissue dosimetry/pharmacokinetics, c) toxicity process/pharmacodynamics, and d) response. The middle two components constitute the mode of action. In particular, the approach described in this paper provides a common template for incorporating pharmacokinetic modeling to estimate tissue dosimetry into chemical risk assessment, whether for cancer or noncancer end points. Chemical risk assessments typically depend upon comparisons across species that often simplify to ratios reflecting the differences. In this paper we describe the uses of this ratio concept and discuss the advantages of a pharmacokinetic-based approach as compared to the use of default dosimetry.

Organophosphate (OP) exposure can be lethal at high doses while lower doses may impair performance of critical tasks. The ability to predict such effects for realistic exposure scenarios would greatly improve OP risk assessment. To this end, a physiologically based model for diisopropylfluorophosphate (DFP) pharmacokinetics and acetylcholinesterase (AChE) inhibition was developed. DFP tissue/blood partition coefficients, rates of DFP hydrolysis by esterases, and DFP-esterase bimolecular inhibition rate constants were determined in rat tissue homogenates. Other model parameters were scaled for rats and mice using standard allometric relationships. These DFP-specific parameter values were used with the model to simulate pharmacokinetic data from mice and rats. Literature data were used for model validation. DFP concentrations in mouse plasma and brain, as well as AChE inhibition and AChE resynthesis data, were successfully simulated for a single iv injection. Effects of repeated, subcutaneous DFP dosing on AChE activity in rat plasma and brain were also well simulated except for an apparent decrease in basal AChE activity in the brain which persisted 35 days after the last dose. The psychologically based pharmacokinetic (PBPK) model parameter values specific for DFP in humans, for example, tissue/blood partition coefficients, enzymatic and nonenzymatic DFP hydrolysis rates, and bimolecular inhibition rate constants for target enzymes were scaled from rodent data or obtained from the literature. Good agreement was obtained between model predictions and human exposure data on the inhibition of red blood cell AChE and plasma butyrylcholinesterase after an intramuscular injection of 33 pg/kg DFP and at 24 hr after acute doses of DFP (10-54 pg/kg), as well as for repeated DFP exposures. The PBPK model for DFP was also adapted for the purpose of modeling parathion, including its metabolism to the toxic daughter product paraoxon. The development and validation of this PBPK model for two OPs provides a basis for studying the kinetics and in vivo metabolism of other bioactivated organophosphate pesticides and their pharmacodynamic effect in humans. -Environ Health Perspect 102(Suppl 11):51-60 (1994)

No abstract

Gasoline consists of a few toxicologically significant components and a large number of other hydrocarbons in a complex mixture. By using an integrated, physiologically based pharmacokinetic (PBPK) modeling and lumping approach, we have developed a method for characterizing the pharmacokinetics (PKs) of gasoline in rats. The PBPK model tracks selected target components (benzene, toluene, ethylbenzene, o-xylene [BTEX], and n-hexane) and a lumped chemical group representing all nontarget components, with competitive metabolic inhibition between all target compounds and the lumped chemical. PK data was acquired by performing gas uptake PK studies with male F344 rats in a closed chamber. Chamber air samples were analyzed every 10-20 min by gas chromatography/flame ionization detection and all nontarget chemicals were co-integrated. A four-compartment PBPK model with metabolic interactions was constructed using the BTEX, n-hexane, and lumped chemical data. Target chemical kinetic parameters were refined by studies with either the single chemical alone or with all five chemicals together. o-Xylene, at high concentrations, decreased alveolar ventilation, consistent with respiratory irritation. A six-chemical interaction model with the lumped chemical group was used to estimate lumped chemical partitioning and metabolic parameters for a winter blend of gasoline with methyl t-butyl ether and a summer blend without any oxygenate. Computer simulation results from this model matched well with experimental data from single chemical, five-chemical mixture, and the two blends of gasoline. The PBPK model analysis indicated that metabolism of individual components was inhibited up to 27% during the 6-h gas uptake experiments of gasoline exposures.

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales.

The use of physiologically based pharmacokinetic (PBPK) models has been proposed as a means of estimating the dose of the reactive metabolites of carcinogenic xenobiotics reaching target tissues, thereby affording an opportunity to base estimates of potential cancer risk on tissue dose rather than external levels of exposure. In this article, we demonstrate how a PBPK model can be constructed by specifying mass-balance equations for each physiological compartment included in the model. In general, this leads to a system of nonlinear partial differential equations with which to characterize the compartmental system. These equations then can be solved numerically to determine the concentration of metabolites in each compartment as functions of time. In the special case of a linear pharmacokinetic system, we present simple closed-form expressions for the area under the concentration-time curves (AUC) in individual tissue compartments. A general relationship between the AUC in blood and other tissue compartments is also established. These results are of use in identifying those parameters in the models that characterize the integrated tissue dose, and which should therefore be the primary focus of sensitivity analyses. Applications of PBPK modeling for purposes of tissue dosimetry are reviewed, including models developed for methylene chloride, ethylene oxide, 1,4-dioxane, 1-nitropyrene, as well as polychlorinated biphenyls, dioxins, and furans. Special considerations in PBPK modeling related to aging, topical absorption, pregnancy, and mixed exposures are discussed. The linkage between pharmacokinetic models used for tissue dosimetry and pharmacodynamic models for neoplastic transformation of stem cells in the target tissue is explored. Environ Health Perspect 1 02(Suppl 1 1): 37-50 (1994)

SummaryThe Evidence-based Toxicology Collaboration (EBTC)