James E. Dennison

Characterization of the Pharmacokinetics of Gasoline Using PBPK Modeling with a Complex Mixtures Chemical Lumping Approach

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.

Mode of action and tissue dosimetry in current and future risk assessments

Two fundamental concepts have emerged to organize contemporary approaches to chemical risk assessment - mode of action and tissue dosimetry. Mode of action specifies the nature of the interactions between the chemical and the body that lead to toxic responses and should, under optimal circumstances, also specify the form of the tissue dose that leads to these effects. This paper highlights recent development of biologically based dose response (BBDR) models for specific toxic endpoints that use knowledge on mode of action to specify measures of dose. These dose measures then are used to support low dose and interspecies extrapolations. We first focus on a series of dose response models developed for several compounds that produce nasal toxicity. These examples demonstrate a range of model structures from simple dosimetry models (methylmethacrylate) to linkage of dosimetry with specific biological processes involved in carcinogenesis (formaldehyde). Two BBDR models with dioxin illustrate the organization of biological and dosimetry information into specific testable hypotheses that could distinguish these different models and lead to a more uniform approach to risk assessment for this compound. A final section discusses the impact of molecular biology and the genomic revolution in relation to development of BBDR models for specific toxic endpoints.

Evaluation of potential toxicity from co-exposure to three CNS depressants (toluene, ethylbenzene, and xylene) under resting and working conditions using PBPK modeling.

Under OSHA and American Conference of Governmental Industrial Hygienists (ACGIH®) guidelines, the mixture formula (unity calculation) provides a method for evaluating exposures to mixtures of chemicals that cause similar toxicities. According to the formula, if exposures are reduced in proportion to the number of chemicals and their respective exposure limits, the overall exposure is acceptable. This approach assumes that responses are additive, which is not the case when pharmacokinetic interactions occur. To determine the validity of the additivity assumption, we performed unity calculations for a variety of exposures to toluene, ethylbenzene, and/or xylene using the concentration of each chemical in blood in the calculation instead of the inhaled concentration. The blood concentrations were predicted using a validated physiologically based pharmacokinetic (PBPK) model to allow exploration of a variety of exposure scenarios. In addition, the Occupational Safety and Health Administration and ACGIH® occupational exposure limits were largely based on studies of humans or animals that were resting during exposure. The PBPK model was also used to determine the increased concentration of chemicals in the blood when employees were exercising or performing manual work. At rest, a modest overexposure occurs due to pharmacokinetic interactions when exposure is equal to levels where a unity calculation is 1.0 based on threshold limit values (TLVs®). Under work load, however, internal exposure was 87% higher than provided by the TLVs. When exposures were controlled by a unity calculation based on permissible exposure limits (PELs), internal exposure was 2.9 and 4.6 times the exposures at the TLVs at rest and workload, respectively. If exposure was equal to PELs outright, internal exposure was 12.5 and 16 times the exposure at the TLVs at rest and workload, respectively. These analyses indicate the importance of (1) selecting appropriate exposure limits, (2) performing unity calculations, and (3) considering the effect of work load on internal doses, and they illustrate the utility of PBPK modeling in occupational health risk assessment.

Mechanistic approaches for mixture risk assessments-present capabilities with simple mixtures and future directions.

Mechanistic studies with simple mixtures have provided insights into the nature of interactions among chemicals that lead to non-additive effects and have elucidated the exposure conditions under which interactions are likely to occur. This paper discusses studies on four mixtures: (1) 1,1-dichloroethylene and trichloroethylene, (2) carbon tetrachloride and Kepone, (3) hexane and methyl-n-butylketone, and (4) coplanar and non-coplanar polychlorinated biphenyls. These mechanistic studies show that interactions should be described at the level of target tissue dose and are best categorized as either pharmacokinetic (PK) or pharmacodynamic (PD) interactions. In PK interactions the presence of a second chemical alters the kinetics such that a unit of administered dose no longer produces a unit of dose at the target tissue. In PD interactions, the presence of other compounds alters the PDs such that a unit tissue dose no longer produces a unit of response. Physiologically based pharmacokinetic (PBPK) models for mixtures have become important tools for predicting conditions under which interactions are likely to alter the assumption of additivity and have permitted calculation of interaction thresholds with more confidence. New cumulative risk assessment approaches have provided opportunities to classify compounds on the basis of similar chemistry-based modes of action (cholinesterase inhibitors) or similar physiological modes of action (diverse chemicals that alter a common biological outcome, such as defeminization of the developing nervous system). The latter examples present challenges for expanding our risk assessment paradigm to focus on the biology of responses more than on the kinetics of the xenobiotics. Some of the future advances in mixture research will depend on progress in systems biology, a discipline that integrates information across multiple level of biological organization producing PD models of normal function and assessing conditions under which exposures to chemicals lead to the perturbations sufficiently great to produce toxicity and disease. We describe briefly the elements of a systems biology approach for assessing the interactions between various PCB congeners.

Application of PBPK modeling in support of the derivation of toxicity reference values for 1,1,1-trichloroethane.

PBPK modeling has been increasingly applied in chemical risk assessment for dose, route, and species extrapolation. The use of PBPK modeling was explored in deriving toxicity reference values for 1,1,1-trichloroethane (1,1,1-TCE). This effort involved a 5-step process: (i) reconstruction of several published PBPK models for 1,1,1-TCE in the rat and human; (ii) selection of appropriate pharmacokinetic datasets for model comparison; (iii) determination of the most suitable PBPK model for supporting reference value derivation; (iv) PBPK model simulation of two critical studies to estimate internal dose metrics; and (v) calculation of internal dose metrics for human exposure scenarios for reference value derivation. The published model by Reitz et al. [Reitz, R.H., McDougal, J.N., Himmelstein, M.W., Nolan, R.J., Schumann, A.M., 1988. Physiologically based pharmacokinetic modeling with methylchloroform: implications for interspecies, high dose/low dose, and dose route extrapolations. Toxicol. Appl. Pharmacol. 95, 185-199] was judged the most suitable. This model has liver, fat, and rapidly and slowly perfused compartments, contains a saturable process for 1,1,1-TCE hepatic metabolism, and accommodates multiple exposure pathways in three species. Data from a human volunteer study involving acute inhalation exposure [Mackay, C.J., Campbell, L., Samuel, A.M., Alderman, K.J., Idzikowski, C., Wilson, H.K., Gompertz, D., 1987. Behavioral changes during exposure to 1,1,1-trichloroethane: time-course and relationship to blood solvent levels. Am. J. Ind. Med. 11, 223-239] and a chronic rat inhalation study [Quast, J.F., Calhoun, L.L., Frauson, L.E., 1988. 1,1,1-Trichloroethane formulation: a chronic inhalation toxicity and oncogenicity study in Fischer 344 rats and B6C3F1 mice. Fundam. Appl. Toxicol. 11, 611-625] were selected to simulate appropriate internal dosimetry data from which to derive reference value points of departure. Duration, route, and species extrapolations were performed based on internal dose metrics.

PBPK modeling of complex hydrocarbon mixtures: gasoline.

Petroleum hydrocarbon mixtures such as gasoline, diesel fuel, aviation fuel, and asphalt liquids typically contain hundreds of compounds. These compounds include aliphatic and aromatic hydrocarbons within a specific molecular weight range and sometimes lesser amounts of additives, and often exhibit qualitatively similar pharmacokinetic (PK) and pharmacodynamic properties. However, there are some components that exhibit specific biological effects, such as methyl t-butyl ether and benzene in gasoline. One of the potential pharmacokinetic interactions of many components in such mixtures is inhibition of the metabolism of other components. Due to the complexity of the mixtures, a quantitative description of the pharmacokinetics of each component, particularly in the context of differing blends of these mixtures, has not been available. We describe here a physiologically-based pharmacokinetic (PBPK) modeling approach to describe the PKs of whole gasoline. The approach simplifies the problem by isolating specific components for which a description is desired and treating the remaining components as a single lumped chemical. In this manner, the effect of the non-isolated components (i.e. inhibition) can be taken into account. The gasoline model was based on PK data for the single chemicals, for simple mixtures of the isolated chemicals, and for the isolated and lumped chemicals during gas uptake PK experiments in rats exposed to whole gasoline. While some sacrifice in model accuracy must be made when a chemical lumping approach is used, our lumped PK model still permitted a good representation of the PKs of five isolated chemicals (n-hexane, benzene, toluene, ethylbenzene, and o-xylene) during exposure to various levels of two different blends of gasoline. The approach may be applicable to other hydrocarbon mixtures when appropriate PK data are available for model development.

Occupational exposure limits in the context of solvent mixtures, consumption of ethanol, and target tissue dose.

Individuals are exposed to mixtures, and never to single chemicals. Depending on the composition of the elements of mixtures, significant toxicological interactions between the components may occur. These interactions are complex and often difficult to predict, ranging from synergistic to additive and subadditive interactions. The nature of the interactions needs to be evaluated as the target tissue dose of the active form of each chemical. PBPK modeling is an effective tool for determining the target tissue dose and evaluating these interactions when data are available for model development. Some of the interactions are pharmacokinetic in nature, affecting the disposition of other chemicals in the body. Other interactions can be pharmacodynamic in nature, altering the effects that other chemicals have on the organism. For many organic solvents, these interactions occur principally at the level of the metabolizing enzyme, cytochrome P-450 2E1 (CYP2E1). Many solvents are known to induce or inhibit CYP2E1, or both. Mixtures may be comprised of concomitant exposures to chemicals or from components encountered separately on-the-job, off-the-job, through the diet, and otherwise. Examples of mixtures where the exposure to separate components occurs off the job will be discussed, with special emphasis on ethanol consumption as a modifier of solvent pharmacokinetics. The present practice of the linear extrapolation of the toxicity of individual mixture components in the interpretation of occupational exposure limits will also be critiqued.

Toxicokinetic Models: Where We've Been and Where We Need to Go!

Toxicokinetic (TK) models have many uses, some of which are now regarded as almost routine, in areas related to pharmaceutics, toxicology, and chemical risk assessment. These TK models span a range from simple empirical curve-fitting analyses of blood/tissue time courses to physiologically based toxicokinetic (PBTK) models that incorporate anatomical, physiological, and biochemical properties of laboratory animals and humans. While the PBTK models require more effort to develop and validate than do data-based compartmental models, the biological detail in these descriptions permits extrapolation to different doses, different exposure conditions, and different species, including humans. Efforts to develop PBTK models are frequently rewarded with reduced work on subsequent compounds, since the physiologic structure, once developed for a particular life stage and class of compounds, is not expected to change for other compounds in the class. A review of the literature shows that TK models have had many uses in occupational health and industrial hygiene; however, they have not been widely or systematically employed in these disciplines. This overview discusses the history of uses of TK models in occupational health areas and suggests future possibilities for these models. Notably, TK models and especially PBTK models could play much more important roles in establishing occupational exposure limits such as the U.S. Occupational Safety and Health Administrations Permissible Exposure Limits based on either animal or human studies; in assessing the range of susceptibility of diverse human populations based on individual variability; in interpreting epidemiological and biomarker studies for various exposure situations; in developing common methods to assess risks for exposures to both the general population and to worker populations; and in assessing exposures to chemical mixtures.