Partition tissue-blood

Blood cell partitioning Red blood cell binding is representative of tissue binding Human blood cell/buffer partition ratio [26]... 

The process by which an absorbed xenobiotic and/or its metabolites partition between blood and various tissues and organs in the body. 

Blood-tissue uptake rates (l< ) can often be approximated from data at early (t < 10 minutes) time points in IV studies, provided the blood has been washed from the organ (e.g., liver) or the contribution from blood to the tissue residue is subtracted (fat). High accuracy is not usually required since these parameters can be optimized to fit the data when they are used in more complex models. Tissue-blood recycling rates (A y) and residence times can be computed from partition coefficients if estimates of uptake rates are available. 

Tissue blood partition coefficients (R ) should be determined when steady-state has been achieved. Estimates based on samples obtained during the elimination phase following a single dose of the test substance may lead to underestimates of this ratio in both eliminating and noneliminating tissues unless its half-life is very long. Correction of these values for elimination has been described by several authors (Yacobi et al., 1989 Shargel and Yu, 1999 Renwick, 2000). 

Chloroform is lipid soluble and readily passes through cell membranes, causing narcosis at high concentrations. Blood chloroform concentrations during anesthesia (presumed concentrations 8,000-10,000 ppm) were 7-16.2 mg/mL in 10 patients (Smith et al. 1973). An arterial chloroform concentration of 0.24 mg/mL during anesthesia corresponded to the following partition coefficients blood/gas, 8 blood/vessel rich compartment, 1.9 blood/muscle compartment, 1.9 blood/fat compartment, 31 blood/vessel poor compartment, 1 and blood/liver, 2 (Feingold and Holaday 1977). Recently, partition coefficients were calculated for humans based on results in mice and rats, and in human tissues in vitro blood/air, 7.4 liver/air, 17 kidney/air, 11 and fat/air, 280 (Corley et al. 1990). 

Physiological toxicokinetic models have been presented describing the behaviour of inhaled butadiene in the human body. Partition coefficients for tissue air and tissue blood, respectively, had been measured directly using human tissue samples or were calculated based on theoretical considerations. Parameters of butadiene metabolism were obtained from in-vitro studies in human liver and lung cell constituents and by extrapolation of parameters from experiments with rats and mice in vivo (see above). In these models, metabolism of butadiene is assumed to follow Michaelis-Menten kinetics. 

Internal burdens of epoxybutene in humans resulting from exposure to butadiene were predicted from models by Kohn and Melnick (1993), Johanson and Filser (1996) and Csanady et al. (1996) and were compared with simulations for rats and mice. In the model of Kohn and Melnick (1993), metabolic parameters were incorporated which had been obtained by Csanady et al. (1992) by measuring butadiene and epoxybutene oxidation and epoxybutene hydrolysis in human liver and lung microsomes in vitro, and conjugation of epoxybutene with glutathione in human liver and lung cytosol. Tissue blood partition coefficients were theoretically derived. The body burden of epoxy butene following exposure to 100 ppm butadiene for 6 h was predicted to be 0.056 pmol/kg in humans. 

In the model of Csanady et al. (1996), the biochemical parameters for butadiene in rats and mice were obtained by fitting model simulations to in-vivo data of Bolt et al. (1984) and Kreiling et al. (1986). The biochemical parameters for epoxybutene were identical to those of Johanson and Filser (1993, 1996). This model accurately predicted experimental data on epoxybutene. The most advanced models are those of Csanady etal. (1996) and Sweeney et al. (1997), since they can simulate both epoxybutene and diepoxybutane as metabolites of butadiene. The tissue blood partition coefficients for diepoxybutane were estimated by Csanady et al. (1996) to have a value of 1 for all tissues. Sweeney et al. (1997) obtained tissue blood partition coefficients from in-vitro measurements (Table 23). Both models yielded good predictions for mice and rats for both metabolites. For humans, no measured data have been reported against which the predictions could be validated. In addition, the model of Csanady et al. (1996) predicted accurately the measured haemoglobin adduct levels (Osterman-Golkar etal., 1993 Albrecht et al., 1993) of epoxybutene in rodents following exposure to butadiene. None of the models published has included the fonnation and elimination of epoxybutanediol. 

Tissue Blood Partition Coefficients Used in the Keys et al. (1999) Model 3-6. Physiological Parameter Values Used in the Keys et al. (1999) Model... 

Tissue blood partition coefficients for DEHP and nonionized MEHP were estimated from their n-octanol water partition coefficients (Kow), using the approach reported by Poulin and Krishnan (1993). 

Tissue blood partition coefficients for total MEHP (ionized and nonionized) were determined experimentally using a vial-equilibration method with correction for pH (Table 3-5). 

A model-based dependence of human tissue-blood partition coefficients of chemicals on lipophilidty and tissue composition was recently described [78], For 36 neutral chemicals, the partitioning between seven different tissues and blood in humans was modeled, considering accumulation in the membrane, protein binding, and dis-... 

Tissue blood PCs indicate the relative affinity of compounds for the various tissues of the body compared to blood. The values are determined by the relative lipophilic/hydrophilic nature of the compound and relative affinity for the macromolecules found in tissue and blood. Each individual tissue will make up a specific balance of water, neutral lipid, phospholipid, and protein. Partitioning therefore is determined by the relative affinity of the compound for the specific tissue constituents. 

Balaz and Lukacova (1999) attempted to model the partitioning of 36 non-ionizable compounds in 7 tissues. Amphiphilic compounds, or those possessing extreme log Kow values, tended to show complex distribution kinetics because of their slow membrane transport. However for the non-amphiphilic, non-ionizable compounds with non-extreme log Kow values studied it should be possible to characterize their distribution characteristics based on tissue blood PCs. Distribution is dependent on membrane accumulation, protein binding, and distribution in the aqueous phase. As these features are global rather than dependent on specific 3D structure, distribution is not expected to be structure-specific. In this study, tissue compositions in terms of their protein, lipid, and water content were taken from published data. This information was used to generate models indicating that partitioning was a non-linear function of the compound s lipophilicity and the specific tissue composition. 

Poulin and Krishnan (1995) developed a method to predict tissue blood PCs for incorporation into physiologically based pharmacokinetic (PBPK) models. Tissue blood partitioning was calculated as an additive function of partitioning into the water, neutral lipids and phospholipids constituent of individual tissues. These were calculated using published values for lipid and water content of tissues and the octanol-water PC of the compounds. Poulin and Krishnan (1998 1999) used this method to predict tissue blood PCs that were subsequently incorporated into a quantitative structure-toxicokinetic model. The prediction of tissue plasma PCs to describe distribution processes and as input parameters for PBPK models has been extensively researched by Poulin and coworkers a great deal of further information can be obtained from their references (Poulin and Theil, 2000 Poulin et al., 2001 Poulin and Theil, 2002a Poulin and Theil, 2002b). 

Of all the tissue blood PCs, none has been more intensively studied than the partitioning between the blood and the brain. This is a reflection of the importance of the brain as a site of action of drugs and as a site of potentially serious toxic side-effects of drugs, designed to act elsewhere, or for environmental pollutants. 

Balaz, S. and Lukacova, V., A model-based dependence of the human tissue/blood partition coefficients of chemicals on lipophilicity and tissue composition, Quant. Struct.-Act. Relat., 18, 361-368, 1999. 

DeJongh, J., Verhaar, H.J.M., and Hermens, J.L.M., A quantitative property-property (QPPR) approach to estimate in vitro tissue-blood partition coefficients of organic chemicals in rats and humans, Arch. Toxicol., 72, 17-25, 1997. 

Poulin, P. and Krishnan, K., An algorithm for predicting tissue blood partition coefficients of organic chemicals from n-octanol water partition coefficient data, J. Toxicol. Environ. Health, 46, 117-129, 1995. 

Haddad S, Poulin P, Krishnan K. 2000a. Relative lipid content as the sole mechanistic determinant of the adipose tissue blood partition coefficients of highly lipophilic organic chemicals. Chemosphere 40 839-843. 

A physiologically based pharmacokinetics (PBPK) model based on the ventilation rate, cardiac output, tissue blood flow rates, and volumes as well as measured tissue/air and blood/air partition coefficients has been developed (Medinsky et al. 1989a Travis et al. 1990). Experimentally determined data and model simulations indicated that during and after 6 hours of inhalation exposure to benzene, mice metabolized benzene more efficiently than rats (Medinsky et al. 1989a). After oral exposure, mice and rats appeared to metabolize benzene similarly up to oral doses of 50 mg/kg, above which rats metabolized more benzene than did mice on a per kg body weight basis (Medinsky et al. 1989b). This model may be able to predict the human response based on animal data. Benzene metabolism followed Michaelis-Menton kinetics in vivo primarily in the liver, and to a lesser extent in the bone marrow. Additional information on PBPK modeling is presented in Section 2.3.5. 

Ruark, C.D., Hack, C.E. Sterner, T.R., Robinson, P.J., Gearhart, J.M. (2008). Quantitative structure activity relationship (QSAR) for extrapolation of tissue/blood partition coefficients of nerve agents across species. DTRA 16th Biennial Medical Chemical Defense Bioscience Review June 1-6, 2008, Hunt Valley, MD. 

Zhang, H., Zhang, Y. (2006). Convenient nonlinear model for predicting the tissue/blood partition coefficients of seven human tissues of neutral, acidic, and basic structurally diverse compounds. J. Med. Chem. 49 5815-29. 

---