* The Sapphire Group, 4027 Colonel Glenn Highway, Fourth Floor, Dayton, Ohio 45431;
BP, Arlington, Virginia 22209;
1803 Jones Ferry Road, Chapel Hill, North Carolina 27516
Received June 21, 2002; accepted October 2, 2002
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ABSTRACT |
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Key Words: acrylonitrile; cyanoethylene oxide; physiologically based pharmacokinetic modeling; interspecies extrapolation; sensitivity analysis; variability analysis.
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INTRODUCTION |
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The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) model to compare dosimetry of ACN and CEO in rats and humans for application in risk assessment. Sensitivity and variability analyses of the model were performed to identify key parameters and sources of interindividual variability. Because no in vivo pharmacokinetic data were available for human model validation, human in vitro data were scaled using a "parallelogram approach" (Reitz et al., 1989). Rat in vivo metabolic parameters were determined by model optimization (Gargas et al., 1995
; Kedderis et al., 1996
) and in vitro studies (Kedderis et al., 1993
; Kedderis and Batra, 1993
). A correction factor for in vitro to in vivo scaling in the rat was calculated and applied to scaling the human in vitro data. A sensitivity analysis of the human model indicated parameter values that are key in determining the predicted dose metrics of interest for the ACN-CEO model and permitted a more focused critique of these parameter values and hence the reliability of the model predictions. Model sensitivity combined with known parameter variability permitted a preliminary evaluation of how much interindividual variability in relevant dose metrics would be expected. Based on a normal distribution of parameter values, it was calculated that the 95th-percentile individual would have a blood CEO concentration roughly twice the value of an average individual, a moderate degree of interindividual variability.
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MATERIALS AND METHODS |
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The alveolar ventilation rate, cardiac output, and percent blood flow to liver, brain, and fat are taken directly from human values recommended by Brown et al.(1997). The alveolar ventilation rate and cardiac output are consistent with the
1:1 ventilation/perfusion ratio expected in a healthy individual (West and Wagner, 1991
). The percent blood flow to the stomach is the percentage measured for the rat by Delp et al.(1991)
. Similar to the tissue weights, the percent flows to tissues presented in Brown et al.(1997)
do not add up to 100; rather, they add up to only 91.5. The remaining 8.5% was divided between the richly and poorly perfused tissues based on their relative contributions to the percent of cardiac output.
Partition coefficients for ACN and CEO in rat tissue were reported (Teo et al., 1994). The human blood:air partition coefficient for ACN was determined in freshly drawn heparinized blood samples (n = 5) containing diethyl maleate using vial equilibration techniques (Teo et al., 1994
). Tissue:blood partition coefficients for the human model were calculated as rat tissue:air partition coefficient (determined by Teo et al., 1994
) divided by the measured human blood:air partition coefficient. The rate of reaction of ACN with human blood sulfhydryls was determined from the time course of ACN disappearance using gas chromatography. A literature review was conducted to identify GSH concentrations in human tissues. Reported values (Table 1
) are based on tissues retrieved from healthy individuals via biopsy from three separate studies, with the exception of brain, for which data from one in vivo study (by nuclear magnetic resonance measurements) and one study using surgically removed tissues (lobectomy) were used.
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Hydrolysis of CEO was scaled from human in vitro microsomal rates by assuming that the in vivo:in vitro ratio for P450 mediated metabolism was the same as the in vivo:in vitro ratio for hydrolysis, since these reactions both occur in the endoplasmic reticulum. Kms for all reactions were assumed to be equal to the average of the human in vitro values.
GSH conjugation rates for ACN and CEO were also scaled from the in vivo rates for the rat, adjusted for differences in metabolism rates in vitro, liver size and protein content, and differing tissue concentrations of the cofactor GSH. Details of the scaling procedures are provided in Appendix 2.
Simulation.
Simulations were conducted using ACSL Sim 11.8 (AEgis Technologies, Austin, TX).
Internal dosimetry comparison.
Peak and AUC of blood and brain ACN and CEO were considered to be dose metrics potentially relevant to the carcinogenicity of ACN in rats (Kirman et al., 2000). These dose metrics were compared in rats and humans for different exposure scenarios. The first scenario was an 8-h inhalation exposure to 2 ppm, the current threshold limit value for ACN (ACGIH, 2001
). The second scenario was one week of continuous inhalation exposure to 868 µg/m3 ACN (0.4 ppm). The third exposure scenario was one week of periodic exposure to 0.12 mg/l ACN in drinking water. The periodic exposure consists of multiple po doses (described as a bolus input to the stomach) throughout the day on a schedule that approximately mimics drinking water patterns of rats and humans (Kirman et al., 2000
; Reitz et al., 1997
). The exposure levels in the second and third scenarios (continuous inhalation and intermittent drinking water consumption) were selected based on conditions estimated to produce a lifetime excess cancer risk of 1 x 106 in rats, using a threshold assumption (Kirman et al., 2000
).
Sensitivity analysis.
Sensitivity analysis was performed to identify which parameters must be known with the greatest precision in order to most accurately calculate human risk. Each parameter value was increased by 1% and dose metrics of interest computed. The percent increase in the prediction of interest divided by 1% (the parameter increase) was defined as the normalized sensitivity coefficient (normalized SC). The two continuous exposure scenarios described above were used as the baseline for the sensitivity analysis. The dose metrics to be examined for this exposure were peak blood and brain concentrations of ACN and CEO and the area under the blood concentration versus time curves for ACN and CEO. The precise values of the exposure concentrations were not expected to be important, as they were low enough that all blood and tissue concentrations of ACN and CEO had a linear relationship with exposure concentration. In the inhalation scenario, only the sensitivity of the blood and brain ACN and CEO AUC predictions were determined, because with continuous exposure the steady-state blood or tissue concentration was essentially equal to the peak concentration.
To identify parameters of importance in population variation in the dose metrics, a variability analysis was conducted. The variability analysis accounts for both the model sensitivity (i.e., does the model prediction change when a parameter value changes) and the known or estimated variability in the input parameter (i.e., what is the range of reasonable input values for this parameter). The approximate CV for the model output (CVm) is predicted using a propagation of error formula (Vardeman, 1994, cited in Licata et al., 2001
, with formulas substituted to be in terms of normalized SC) shown below.
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Where i indicates the ith parameter, normalized SCi is the normalized sensitivity coefficient relative to changes in parameter i, and CVi is the coefficient of variation in parameter i.
CVi values were calculated from experimental data or estimates taken from the published literature for all model parameters. CVm was calculated for the same dose metrics for which the sensitivity analyses were conducted. The fractional contribution of each parameter to the predicted variance was calculated, and those with the greatest contribution to the total model variance were identified.
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RESULTS |
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Internal Dosimetry
The results are presented in Table 2. Blood and brain concentrations of ACN and CEO are generally predicted to be similar in rats and humans. An exception is that under the periodic drinking water scenario, peak concentrations of ACN in the blood and brain of rats are expected to be higher. It should be noted that the comparisons are based on equal concentrations in drinking water. Volume of drinking water consumed is scaled based on BW0.7, so the daily dose in mg/kg/day is higher in rats than in humans.
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Some rate terms (VmaxCs for ACN oxidation and CEO hydrolysis and first order rates for ACN and CEO conjugation with glutathione) are composites of variability in the various factors used in scaling (e.g., microsomal protein content of the liver). "Composite" CVs for rate terms were calculated as the square root of the sum of the squares of the CVs of the parameters that went into scaling the term. For cytochrome P450 and epoxide hydrolase-mediated reactions, variability in microsomal protein content of human liver was taken from Lipscomb et al. (in press a). In the absence of any information on variability of cytosolic protein content, this same value was used for first order reactions with GSH. The variability of human Vmax and Km in subcellular fractions was calculated from studies of Kedderis and coworkers.
The CV of binding rates to hemoglobin and reaction with blood RSH was estimated as 0.3, based on Allen et al.(1996). The CV for the oral uptake rate, 0.2, was also taken from Allen et al.(1996)
.
CEO-related dose metrics.
Normalized sensitivity coefficients (normalized SC) were calculated for 46 model parameters for four CEO-related dose metricsAUC for CEO in the blood and brain and peak concentration of CEO in the blood and brain. Normalized SC ranged from 0.75 to 1.00. For 23 parameters, |normalized SC| was < 0.1 for each of the four dose metrics, i.e., the model predictions were essentially insensitive to changes in these parameters. For the sake of brevity, only those parameters with |normalized SC| > 0.20 for at least one dose metric are shown in Figure 2.
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Blood and brain ACN concentrations are predicted to be less variable than the CEO concentrations (model CVs of 0.290.51 for ACN-related endpoints). Unlike CEO, where contributors to variability were similar for inhalation and drinking water routes, contributors to predicted variability of ACN concentrations are route-specific. For the drinking water exposure scenario, variability in Vmax for biotransformation of ACN to CEO accounts for more than half the expected variability. Additional contributors to variability are the Km for ACN metabolism to CEO and (for peak concentrations) the blood flow to the liver. Predicted variability in blood and brain ACN AUC for inhalation exposure is controlled mostly by the fraction of cardiac output flowing to the liver and alveolar ventilation rate. The brain:blood partition coefficient also contributes significantly to predicted variability in the brain ACN AUC.
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DISCUSSION |
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Since the parameters contributing most to variability in predicted blood CEO concentrations are parameters describing the metabolism of CEO, confidence in these values is critical to confidence in use of the human model in risk assessment. The Km for CEO hydrolysis is based on six samples, with values ranging from 0.6 to 3.2 mM (Kedderis and Batra, 1993). If Km could be measured in a greater number of samples, the extreme values may be found to be outliers, and the CV for this parameter (currently 0.454) might be reduced. The first order reaction rate between CEO and GSH and the Vmax for hydrolysis of CEO are both "composite" parameters, in that they are calculated, by scaling, from other parameters. The largest contributor to variability of both of these parameters is the protein content of the subcellular fractions. The information on the variability of microsomal protein content (Lipscomb et al., in press a,b) is based on a sample size of 20. Additional analyses would not be expected to substantially increase the calculated SD. There was minimal information available on the actual cytosolic protein content of human liver and its variability, so the variability of microsomal protein content was used as surrogates. Thus the actual amount and the interindividual variability of cytosolic protein content of human liver are probably the areas in which further research may impact human PBPK model predictions of CEO dosimetry. In general, the biochemical parameters are more variable and/or known with less certainty than most of the physiological parameters. This uncertainty extends not only to the metabolic parameters, but to the GSH measurements. Reliable data (e.g., using fresh tissue with a reasonable number of individual samples) are not readily available for many important tissues.
Toxicity related to ACN exposure may also potentially be considered to be related to the ACN concentrations, rather than metabolite concentrations. Important parameters for predictions of ACN-related dose metrics vary based on the route of exposure. For humans exposed by inhalation, internal dosimetry is controlled primarily by physiological factors (flow to liver) and physicochemical factors (tissue solubility). For an oral exposure, metabolic parameters (Vmax and Km for ACN epoxidation to CEO) are more important.
Assessments of the risk to human health from ACN exposure are likely to be based on toxicity studies in laboratory animals, extrapolated to estimated human risks. Postulated modes of action for ACN generally consider the epoxide metabolite CEO to be a greater concern than ACN itself. Based on this hypothesis, risk evaluations should be based on concentrations of CEO in blood or target tissue. According to the PBPK model, blood and brain concentrations of CEO in rats and humans are generally expected to be similar for a given exposure scenario. Because of the lack of human data for model validation, the sensitivity of the model to measured and estimated parameter values was evaluated. Because parameter values are known to be variable among individuals, a variability analysis for the model predictions was also conducted. While the model structure has many parameters (> 40), the outputs of interest were sensitive (|normalized SC| > 0.2) to fewer than half of these parameters (Figs. 2, 4, 6, and 8). When variability or uncertainty was taken into account, the number of parameters of concern in the risk assessment was reduced even further, with seven or fewer parameters accounting for > 85% of the variability (Figs. 3, 5, 7, and 9
).
This analysis indicates that human variability in blood predictions is modesta model CV of 0.48 to 0.56 for peak or average blood concentrations of CEO for ACN exposure in drinking water or by inhalation. Based on a normal distribution, this suggests that the 95th percentile individual (1.6 SDs above the mean) would have blood CEO concentrations approximately 1.8 times the concentration in the average individual. For the 99th-percentile individual (2.3 SDs above the mean) the ratio would be 2.2. This is low, relative to the default pharmacokinetic uncertainty factor for intraspecies human variability of 3.2 (Renwick and Lazarus, 1998). Overall, the PBPK modeling analysis suggests no heightened sensitivity of humans in general to toxic effects of ACN based on pharmacokinetic differences.
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APPENDIX 1 |
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Alveolar ventilation
QP = QPC x BW0.74 (l/h)
Cardiac output.
QC = QCC x BW0.74 (l/h)
Tissue blood flow
Qj = QjC x QC (l/h) where j = L (liver), F (fat), S (slowly perfused tissue), ST (stomach), BR (brain), R (richly perfused tissues)
Tissue volumes
Vj = VjC x BW (l) where j = same tissues as for blood flows
Venous blood volume
VVB = BVC x BW x VVBC (l) where BVC = fraction of body weight as blood and VVBC is the fraction of blood that is venous blood
Arterial blood volume
VAB = BVC x BW x (1 VVBC) (l)
Maximal oxidative metabolism of ACN
VMAX = VMAXC x BW0.7 (mg/h)
Rate of oxidative metabolism of ACN
RAM1 = (VMAX x CVL)/(KM + CVL) (mg/h)
Maximal enzymatic hydrolysis of CEO
VMAX2 = VMAXC2 x BW0.7 (mg/h)
Rate of hydrolysis of CEO
RAM2 = (VMAX2 x CVL)/(KM2 + CVL) (mg/h)
Reaction rate for GSH conjugation with ACN in liver
Kf = KfC/BW0.3 (h1)
Rate of GSH conjugation with ACN in the liver
RAMGL1 = Kf x CVL x VL (mg/h)
Reaction rate for GSH conjugation with CEO in liver
Kf2 = KfC2/BW0.3 (h1)
Rate of GSH conjugation with CEO in liver
RAMGL2 = Kf2 x CVL2 x VL (mg/h)
Tissue:blood partition coefficients for ACN
Pj = PjA/PB, where j = same tissues as listed for blood flows
Tissue:blood partition coefficients for CEO
Pj2 = PjA2/PB2, where j = same tissues as listed for blood flows
CI = inhaled concentration of ACN
CI = CONC x molecular weight/24450 or 0 (mg/l)
CAL = Concentration of ACN in arterial lung blood (mg/l)
CAL= (QC x CV + QP x CI)/(QC + (QP/PB)) (mg/l)
Concentration of ACN in systemic arterial blood after binding
dAAB/dt = (QC x CAL) (QC x CA) (KB x CA x VAB) (KFB x CA x VAB) (mg/h) where KB = binding rate to hemoglobin and KFB = binding rate to blood sulfhydryls
CA = AAB/VAB (mg/l)
Aj = Amount of ACN in tissue "j" with nonenzymatic GSH conjugation but no oxidative metabolism (slowly perfused, rapidly perfused, and brain tissue)
d Aj/dt = Qj x (CA CVj) KSO x Cj x GSHj x Vj (mg/h) where KSO is the second-order spontaneous GSH conjugation rate with ACN
CVj = Aj/(Vj x Pj) (mg/l)
Cj = Aj/Vj (mg/l)
Amount of ACN in gut (AG) and amount absorbed from the stomach (AAG)
Daily dose = 0.102 x BW0.7 x concentration in drinking water (mg/day)
Total dose = Fraction of daily dose ingested x daily dose (mg)
AG = Total dose AAG (mg)
d AAG/dt = KA x AG (mg/h)
Stomach mass balance for ACN
d AST/dt = QST x (CA CVST) + KA x AG KSO x CST x GSHST x VST (mg/h)
CVST = AST/(VST x PST) (mg/l)
CST = AST/VST (mg/l)
Liver mass balance, and venous blood and tissue concentrations for ACN
d AL/dt = QL x CA + QST x CVST (QL + QST) x CVL) RAM1 RAMGL1 (mg/h)
CVL = AL/(VL x PL) (mg/l)
CL = AL/VL (mg/l)
Fat mass balance and venous blood and tissue concentrations for ACN
d AF/dt = QF x (CA CVF) (mg/h)
CVF = AF/(VF x PF) (mg/l)
CF = AF/VF (mg/l)
Mixed venous blood concentration of ACN before binding
CVb = (QF x CVF + (QL + QST) x CVL + QS x CVS + QR x CVR + QBR x CVBR)/QC (mg/l)
Mixed venous blood concentration of ACN after binding
dAVB/dt = (QC x CVB) (QC x CV) (KB x CV x VVB) (KFB x CV x VVB) (mg/h) where KB = binding rate to hemoglobin and KFB = binding rate to blood sulfhydryls
CV = AVB/VVB (mg/l)
Concentration of CEO in arterial lung blood (mg/l)
CAL2= QC x CV2/(QC + (QP/PB2)) (mg/l)
Concentration of CEO in systemic arterial blood after binding
dAAB2/dt = (QC x CAL2) (QC x CA2) (KB2 x CA2 x VAB) (KFB2 x CA2 x VAB) (mg/h) where KB2 = binding rate to hemoglobin and KFB2 = binding rate to blood sulfhydryls
CA2 = AAB2/VAB (mg/l)
Liver mass balance, and venous blood and tissue concentrations for ACN
d AL2/dt = QL x CA2 + QST x CVST2 (QL +QST) x CVL2) +RAM1 x MW2/MW1 RAM2 RAMGL2 (mg/h)
CVL2 = AL2/(VL x PL2) (mg/l)
CL2 = AL2/VL (mg/l)
Fat mass balance and venous blood and tissue concentrations for CEO
d AF2/dt = QF x (CA2 CVF2)
CVF2 = AF2/(VF x PF2) (mg/l)
CF2 = AF2/VF (mg/l)
Aj2 = Amount of CEO in tissue "j" with GSH conjugation but no enzymatic hydrolysis (all tissues except liver and fat)
d Aj2/dt = Qj x (CA2 CVj2) 0.1 x Kf2 x (GSHj/GSHL) x CVj2 x Vj (mg/h)
CVj2 = Aj2/(Vj x Pj2) (mg/l)
Cj2 = Aj2/Vj (mg/l)
Mixed venous blood concentration of CEO before binding
CVb2 = (QF x CVF2 + (QL + QST) x CVL2 + QS x CVS2 + QR x CVR2 + QBR x CVBR2)/QC (mg/l)
Mixed venous blood concentration of CEO after binding
dAVB2/dt = (QC x CVb2) (QC x CV2) (KB2 x CV2 x VVB) (KFB2 x CV2 x VVB) (mg/h) where KB2 = binding rate to hemoglobin and KFB2 = binding rate to blood sulfhydryls
CV2 = AVB2/VVB (mg/l)
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APPENDIX 2 |
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Vmaxin vivo = Vmaxin vitro x [microsomal protein/g liver] x VL x CFin vivo/in vitro
CFin vivo/in vitro can be calculated for rat (where Vmaxin vivo was found by optimization), and it is assumed that this correction factor is constant across species (Corley et al., 1990; Reitz et al., 1989
, 1996a
, b
; Thrall et al., 2000
).
Since Vmaxin vivo = VmaxCin vivo x BW0.7, and VL = VLC x BW
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and
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Substituting,
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The GSH conjugation rates derived for the rat (Kedderis et al., 1996) were scaled to the human by multiplying by the ratio of combined microsomal and cytosol enzymatic conjugation in humans in vitro by the combined rate in rats in vitro, as reported in Table 4
of Kedderis et al.(1995)
. As the rat GSH conjugation rates were based on the tissue content of GSH, they were adjusted to reflect the differences in rat versus human tissue GSH content. The values in Table 4
of Kedderis et al.(1995)
were also adjusted, using average rates rather than the highest human rates, and more recent values for microsomal (Lipscomb et al. [in press a], for human, Ploemen et al. [1997]
, for rat) and cytosolic protein content (Boogaard et al., 2000
).
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Since Kf = KfC x BW-03
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For tissues, an adjustment factor of 0.1 for conjugation with GSH in tissues other than liver was empirically derived for the rat (Gargas et al., 1995), and represents an adjustment for the relative amounts of glutathione-S-transferase activity in the different tissues.
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ACKNOWLEDGMENTS |
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NOTES |
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