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Physiologically based pharmacokinetic (PBPK) models have increasingly been employed in chemical health risk assessments. By incorporating individual variability conferred by genetic polymorphisms, health conditions, and physiological changes during development and aging, PBPK models are ideal for predicting chemical disposition in various subpopulations of interest. In order to improve the parameterization of PBPK models for healthy and health-impaired elderly (herein defined as those aged 65 yr and older), physiological parameter values were obtained from the peer-reviewed literature, evaluated, and entered into a Microsoft ACCESS database. Database records include values for key age-specific model inputs such as ventilation rates, organ volumes and blood flows, glomerular filtration rates, and other clearance-related processes. In total, 528 publications were screened for relevant data, resulting in the inclusion of 155 publications comprising 1051 data records for healthy elderly adults and 115 data records for elderly with conditions such as diabetes, chronic obstructive pulmonary disease (COPD), obesity, heart disease, and renal disease. There are no consistent trends across parameters or their associated variance with age; the gross variance in body weight decreased with advancing age, whereas there was no change in variance for brain weight. The database contains some information to inform ethnic and gender differences in parameters; however, the majority of the published data pertain to Asian (mostly Japanese) and Caucasian males. As expected, the number of records tends to decrease with advancing age. In addition to a general lack of data for parameters in the elderly with various health conditions, there is also a dearth of information on blood and tissue composition in all elderly groups. Importantly, there are relatively few records for alveolar ventilation rate; therefore, the relationship between this parameter and cardiac output (usually assumed to be 1:1) in the elderly is not well informed by the database. Despite these limitations, the database represents a potentially useful resource for parameterizing PBPK models for the elderly to facilitate the prediction of dose metrics in older populations for application in risk assessment.

The available data on binary interactions are yet to be considered within the context of mixture risk assessment because of our inability to predict the effect of a third or a fourth chemical in the mixture on the interacting binary pairs. Physiologically based pharmacokinetic (PBPK) models represent a potentially useful framework for predicting the consequences of interactions in mixtures of increasing complexity. This article highlights the conceptual basis and validity of PBPK models for extrapolating the occurrence and magnitude of interactions from binary to more complex chemical mixtures. The methodology involves the development of PBPK models for all mixture components and interconnecting them at the level of the tissue where the interaction is occurring. Once all component models are interconnected at the binary level, the PBPK framework simulates the kinetics of all mixture components, accounting for the interactions occurring at various levels in more complex mixtures. This aspect was validated by comparing the simulations of a binary interaction-based PBPK model with experimental data on the inhalation kinetics of m-xylene, toluene, ethyl benzene, dichloromethane, and benzene in mixtures of varying composition and complexity. The ability to predict the kinetics of chemicals in complex mixtures by accounting for binary interactions alone within a PBPK model is a significant step toward the development of interaction-based risk assessment for chemical mixtures.

Urinary biomarkers of exposure are used widely in biomonitoring studies. The commonly used urinary biomarkers for the aromatic solvents toluene (T), ethylbenzene (E), and m-xylene (X) are o-cresol, mandelic acid, and m-methylhippuric acid. The toxicokinetics of these biomarkers following inhalation exposure have yet to be described by physiologically based pharmacokinetic (PBPK) modeling. Five male volunteers were exposed for 6 h in an inhalation chamber to 1/8 or 1/4 of the time-weighted average exposure value (TWAEV) for each solvent: toluene, ethylbenzene, and m-xylene were quantified in blood and exhaled air and their corresponding urine biomarkers were measured in urine. Published PBPK model for parent compounds was used and simulations were compared with experimental blood and exhaled air concentration data. If discrepancies existed, Vmax and Km were optimized. Urinary excretion was modeled using parameters found in literature assuming simply stoichiometric yields from parent compound metabolism and first-order urinary excretion rate. Alternative models were also tested for (1) the possibility that CYP1A2 is the only enzyme implicated in o-cresol and (2) a 2-step model for describing serial metabolic steps for mandelic acid. Models adapted in this study for urinary excretion will be further used to interpret urinary biomarker kinetic data from mixed exposures of these solvents. [ABSTRACT FROM AUTHOR]

Uncertainty exists regarding the validity of a previously developed physiologically-based pharmacokinetic model (PBPK) for inhaled ethanol in humans to predict the blood levels of ethanol (BLE) at low level exposures (<1000 ppm). Thus, the objective of this study is to document the BLE resulting from low levels exposures in order to refine/validate this PBPK model. Human volunteers were exposed to ethanol vapors during 4 h at 5 different concentrations (125-1000 ppm), at rest, in an inhalation chamber. Blood and exhaled air were sampled. Also, the impact of light exercise (50 W) on the BLE was investigated. There is a linear relationship between the ethanol concentrations in inhaled air and (i) BLE (women: r2 = 0.98/men: r2 = 0.99), as well as (ii) ethanol concentrations in the exhaled air at end of exposure period (men: r2 = 0.99/women: r2 = 0.99). Furthermore, the exercise resulted in a net and significant increase of BLE (2-3 fold). Overall, the original model predictions overestimated the BLE for all low exposures performed in this study. To properly simulate the toxicokinetic data, the model was refined by adding a description of an extra-hepatic biotransformation of high affinity and low capacity in the richly perfused tissues compartment. This is based on the observation that total clearance observed at low exposure levels was much greater than liver blood flow. The results of this study will facilitate the refinement of the risk assessment associated with chronic inhalation of low levels of ethanol in the general population and especially among workers. [ABSTRACT FROM AUTHOR]

Chloroacetic acids (monochloroacetic acid [MCA], dichloroacetic acid [DCA], and trichloroacetic acid [TCA]) and trihalomethanes (THMs: chloroform [CHCl 3 ], bromodichloromethane [BDCM], dibromochloromethane [DBCM], and bromoform [TBM]) are common by-products of the chlorination of drinking water. The purpose of this study was to evaluate the influence of chloroacetic acids on the pharmacokinetics of trihalomethanes in the male Sprague-Dawley rat. In the first series of studies, groups of 5 animals were given, by intravenous injections, a single dose of 0.125 mmol/kg of one of the four THMs. Additional groups received a binary mixture containing 0.125 mmol/kg of a THM plus 0.125 mmol/kg of a chloroacetic acid. The venous blood concentrations of unchanged THMs were measured by headspace gas chromatography from 5 min to 6 h postadministration. The areas under the blood concentration versus time curves (AUCs) of CHCl 3 , BDCM, and DBCM were increased by a factor of 3.5, 1.6, and 2, respectively, by coadministration of TCA. DCA coadministration resulted in an increase in the AUC of DBCM (×2.5) and TBM (×1.3), whereas MCA modified the Cmax (×1.5) and AUC (×1.8) of BDCM and the AUC of DBCM (×2.5). In the second series of experiments, animals received either a single dose of 0.03125 mmol/kg of one of the four THMs, a mixture containing 0.03125 mmol/kg of each of the four THMs (total dose = 0.125 mmol/kg), or a mixture containing 0.03125 mmol/kg of each of the four THMs plus 0.125 mmol/kg of either TCA or DCA. Results indicated that the AUCs of CHCl 3 , BDCM, DBCM, and TBM were increased during coadministration compared to single administrations (+2.5-fold). Combined administration of the four THMs with TCA, and not DCA, resulted in an increase of the AUCs of THMs (CHCl 3 : ×11.7; BDCM, DBCM, and TBM: ×3.9) and an increase in the Cmax of CHCl 3 (×1.9). Overall, these results indicate that, at the dose levels tested in this study, TCA alters the blood concentration profiles of THMs. [ABSTRACT FROM AUTHOR]

The objective of present study was to develop quantitative structure–property relationships (QSPRs) for the chemical-specific input parameters of rat physiologically based pharmacokinetic (PBPK) models (i.e., blood:air partition coefficient (Pb), liver:air partition coefficient (Pl), muscle:air partition coefficient (Pm), fat:air partition coefficient (Pf), and hepatic clearance (CLh)), for simulating the inhalation pharmacokinetics of volatile organic chemicals (VOCs). The literature data on Pb, Pl, Pf, and Pm for 46 low-molecular-weight VOCs as well as CLh for 25 such VOCs primarily metabolized by CYP2E1 (alkanes, haloalkanes, haloethylenes, and aromatic hydrocarbons) were analysed to develop QSPRs. The QSPRs developed in this study were essentially multilinear additive models, which imply that each fragment in the molecular structure has an additive and constant contribution to partition coefficients and hepatic clearance. Most of the values in the calibration set could be reproduced adequately with the QSPR approach, which involved the calculation of the sum of the frequency of occurrence of fragments (CH3, CH2, CH, C, C&z.dbnd6;C, H, Cl, Br, F, benzene ring, and H in benzene ring structure) times the fragment-specific contributions determined in this study. The QSPRs for Pb, Pl, Pm, Pf, and CLh were then included within a PBPK model, which only required the specification of the frequency of occurrence of fragments in a molecule along with exposure concentration and duration as input for conducting pharmacokinetic simulations. This QSPR–PBPK model framework facilitated the prediction of the inhalation pharmacokinetics of four VOCs present in the calibration dataset (toluene, dichloromethane, trichloroethylene, and 1,1,1-trichloroethane) and four VOCs that were not part of the calibration set (1,2,4-trimethyl benzene, ethyl benzene, 1,3-dichloropropene, and 2,2-dichloro-1,1,1-trifluoroethane) but that could be described using the molecular fragments investigated in the present study. The QSPRs developed in this study should be potentially useful for providing a first-cut evaluation of the inhalation pharmacokinetics of VOCs prior to experimentation, as long as the number and nature of the fragments do not exceed the ones in the calibration dataset used in this study. [Copyright &y& Elsevier]

The objectives of the present study were: (1) to develop a risk assessment methodology for chemical mixtures that accounts for pharmacokinetic interactions among components, and (2) to apply this methodology to assess the health risk associated with occupational inhalation exposure to airborne mixtures of dichloromethane, benzene, toluene, ethylbenzene, and m-xylene. The basis of the proposed risk assessment methodology relates to the characterization of the change in tissue dose metrics (e.g., area under the concentration-time curve for parent chemical in tissues [AUCtissue], maximal concentration of parent chemical or metabolite [Cmax], quantity metabolized over a period of time) in humans, during mixed exposures using PBPK models. For systemic toxicants, an interaction-based hazard index was calculated using data on tissue dose of mixture constituents. Initially, the AUCtarget tissue (AUCtt) corresponding to guideline values (e.g., threshold limit value [TLV]) of individual chemicals were obtained. Then, the AUCtt for each chemical during mixed exposure was obtained using a mixture PBPK model that accounted for the binary and higher order interactions occurring within the mixture. An interaction-based hazard index was then calculated for each toxic effect by summing the ratio of AUCtt obtained during mixed exposure (predefined mixture) and single exposure (TLV). For the carcinogenic constituents of the mixture, an interaction-based response additivity approach was applied. This method consisted of adding the cancer risk for each constituent, calculated as the product of q*tissue dose and AUCtt. The AUCtt during mixture exposures was obtained using an interaction-based PBPK model. The approaches developed in the present study permit, for the first time, the consideration of the impact of multichemical pharmacokinetic interactions at a quantitative level in mixture risk assessments. [ABSTRACT FROM PUBLISHER]

A physiologically-based pharmacokinetic (PBPK) model for a mixture of toluene (TOL) and xylene (XYL), developed and validated in the rat, was used to predict the uptake and disposition kinetics of TOL/XYL mixture in humans. This was accomplished by substituting the rat physiological parameters and the blood:air partition coefficient with those of humans, scaling the maximal velocity for hepatic metabolism on the basis of body weight[sup0.75], and keeping all other model parameters species-invariant. The human TOL/XYL mixture PBPK model, developed based on the quantitative biochemical mechanism of interaction elucidated in the rat (i.e., competitive metabolic inhibition), simulated adequately the kinetics of TOL and XYL during combined exposures in humans. The simulations with this PBPK model indicate that an eight hour co-exposure to concentrations that remain within the current threshold limit values of TOL (50 ppm) and XYL (100 ppm) would not result in significant pharmacokinetic interferences, thus implying that data on biological monitoring of worker exposure to these solvents would be unaffected during co-exposures. [ABSTRACT FROM AUTHOR]