Drinking Water and Health, Volume 8 Pharmacokinetics in Risk Assessment (1987) / Chapter Skim
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V. Poster Session
V. Poster Session
Pages 251-350
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PART V Poster Session
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Introduction Robert L Dedrick During organization of the workshop on which this volume is based, it became clear that additional relevant work and points of view existed that could not be well represented in a limited number of primarily didactic presentations.
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Pritchard BACKGROU N D Physiologically based mathematical models are useful devices for exploring the information contained in pharmacokinetic data. Most of the physiological pharmacokinetic models that have appeared in the literature have been developed for pharmacological research.
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The model was verified for oral administrations by using data that were collected at the Huntingdon Research Centre in England (Angelo et al., 19861. As an example of this, Figure 2 shows the actual blood concentrations of DCM and model simulations following single oral doses of 50 and 200 mg/kg to rats on days 1 and 14 of a daily gavage dosing regimen.
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Inhalation simulations at 50, 500, and 1,500 ppm for 6 h produced curves that were virtually identical to those obtained with continuous oral input, indicating that steady infusions of DCM into the lung and gut compartments produced the same blood distribution profiles. To obtain the inhalation-to-oral dose correlations, we first evaluated delivered doses to target tissues as areas under concentration-time curves (AUCs)
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Data represent the mean + standard error of the mean for six animals; lines are the predictions from the pharmacokinetic model.
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A numerical search was then used to find the oral doses that produced the equivalent delivered or metabolized doses to those obtained in the inhalation simulations. Figure 4 shows the correlations between inhalation exposures and equivalent oral doses of DCM in rats for the lung, liver, and blood compartments.
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by MFO and GSH pathways between oral doses (infusions for 6 h) and 6-h inhalation exposures In rats.
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Therefore, quantitative relationships that are based upon 100% retention of the inhaled dose and/ or complete absorption of an applied oral dose are misleading if internal challenges to the target tissues or to the metabolic systems are not used as the basis for the correlations. By their very nature, physiologically based models are designed to describe the physiological phenomena that control the disposition of absorbed chemicals.
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1983. Physiologically based pharmacokinetic modeling: Pnnciples and applications.
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Compartment symbols Cat Concentration in compartment 1 (nmol/ml) Qua Blood flow rate compartment 1 (ml/min)
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absorption rate constant (min-l) Metabolism rate constant for reaction r2 (min-l)
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The pharmacokinetic model was applied to estimate internal concentrations of toxiphors in the lungs and livers of mice, rats, hamsters, and humans, because these were the two sites of response in the National Toxicology Program's (NTP) inhalation bioassay of methylene chloride in mice (NTP, 19861.
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With all other physiological and biological parameters kept constant, the values of Km' V,~, and Kf were varied in an intricate computer optimization procedure, and the pharmacokinetic model was used to get the best approximations of actual experimental chamber data in which disappearance of methylene chloride over time was monitored. The chamber data were obtained for rats, mice, and hamsters by using five initial concentrations and recording the decrease in the chamber concentration of methylene chloride over a period of up to 6 h.
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choices for the three kinetic constants leads to curves which approximate the actual chamber data about as well as the ReitzAndersen optimized choices, with the exception of the 3,200-ppm initial concentration chamber experiment (Figure 4~. This is not disturbing, however, because there was a transient increase in methylene chloride concentration between 2.5 and 4.5 h, and before this transient increase occurred, the chamber data followed the variant curve.
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1000- . 800 Cal cY 53 600 c~ oo 200 \O "of "'me "I\ "en` "mu` 0 "if: O Chamber Variant — RA %~0W -- ~-~b _ 1 1 i 1 1 1 0 0.5 1 1.5 2 2.5 HOURS FIGURE 2 Modeled versus experimental concentrations (960 ppm at time zero)
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. 3s00 ~ O Chamber ~ ~ ~ Variant 3000- l RA ,,` 2500- ~ ~ A ~ 2000- Dow ~= ~0~ -""it BOO O- 1 1 _ Coo 0 1 2 3 ~ 5 6 7 HOURS FIGURE 4 Modeled versus experimental concentrations (3,200 ppm at time zero)
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CONCLUSIONS It is concluded that although the Reitz-Andersen optimization procedure may lead to kinetic constants that give an optimal fit, by use of the pharmacokinetic model, to the experimental chamber data, there are alternative combinations (one of which was determined in this analysis)
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In press. Physiologically-based pharmacokinetics and the risk assessment process for methylene chloride.
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2. Tissue dose and cell death.
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The MM is subsequently resynthesized by a feedback-controlled enzymatic system. Depletion of MM leads to cell death, which is followed by regenerative hyperplasia.
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.. FIGURE 1 Schematic of the biologically based pharmacokinetic model of Ramsey and Andersen ( 1984)
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and those with one mutation (N2 cells) were modeled as having the same basal death and birth rates, as being equally susceptible to cytotoxicant and as responding to unscheduled cell death, i.e., cytotoxicity, in the same manner.
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We therefore defined a generic MM and estimated reasonable quantitative relationships among the amount of parent compound metabolized, depletion of MM, cell death and birth, and mutation accumulation. Because the definition of MM is not linked to a
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In (c) N1 death as MM is depleted and consequent regenerative hyperplasia are illustrated.
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(For the curious, the model cytotoxicant used has the pharmacokinetic behavior of 1,2-dichloroethane.) RESU LTS Figure 4 illustrates the qualitative relationships among cytotoxicant exposure, depletion of MM, cell death, and regenerative hyperplasia.
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DISCUSSION The model described here incorporates some straightforward assumptions about relationships among the tissue dose of a toxicant, depletion of a critical cellular macromolecule, the likelihood of cell death, and the probability of mutations occurring during regenerative hyperplasia. Depletion of hepatic GSH is known to be linked to increased probability of cell death (Docks and Krishna, 1976; Mitchell et al., 1973; Wells et al., 19801.
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The toxicant-exposed group suffered about 20~o cell death/day, which was completely replaced within 24 h by regenerative hyperplasia. This regenerative hyperplasia leads to the difference between the control and toxicant-exposed groups.
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Modeling of cytotoxicity and other target organ consequences of in viva exposure has little meaning if the tissue dose of the ultimate toxicant is not well-characterized. Limitations of the Mode' What Is Modeled and What Is Not Several processes that would affect cytotoxicity and mutation accumulation have not been specifically described in this model.
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can be used to examine aspects of the quantitative relationships among depletion of critical cellular macromolecules, cytotoxicity, cell death and replication, and the accumulation of mutations. It should be noted that the model does not describe the accumulation of genetic mutations caused by direct interactions of parent compound or metabolites with DNA.
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1986. A physiological pharmacokinetic model for hepatic glutathione (GSH)
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Cytotoxicity and Mutation Accumulation 285 Wells, P
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Unfortunately, the compartmental modeling employed in these studies could not quantitate target organ exposures with different doses and routes of exposure, nor extrapolate the relevance of the observations to human exposure situations for assessing human risk. The objectives of this study were to develop a physiologically based pharmacokinetic (PB-PK)
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~ _ Metabolism Kit/ K\~` Richly-Perfused I Slowly-Perfused _ Tissues ~ - Fat ~ Qc Qs Qf ~ GUT FIGURE 1 Schematic representation of the pharmacokinetic model developed for EDC. The biodistribution of EDC was modeled by dividing body tissues into three compartments based on their blood flow and relative ability to accumulate EDC.
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The partition coefficients of many tissues were similar, and for modeling purposes these tissues were grouped together. The richly perfused group included such tissues as the kidney and spleen, the slowly perfused comprised muscle and skin, and the fat compartment represented body fat.
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The gas uptake studies provided rate constants for total metabolism. Metabolism rates between the liver and lung were split by using data from the literature on relative enzymatic TABLE 1B Metabolism Rate Constants Obtained for EDC EDC Van,` = 3.25 mg/h/kg Km = 0.25 mg/liter Kf = 9.0 h-t kg-' GSH model Kgs = 0.0014 h-i kg- ' Kfee = 4,500 h-t kg-t Kgsm = 0.14 h-t kg- '
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activities between these two organs, as was accomplished for the methylene chloride model (Andersen et al., 1987~. Scaleup of metabolism rates for different species was performed by using allometric scaling (Adolph, 1949; Dedrick, 1973; Lindstedt and Calder, 1981~.
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Figure 4 depicts PB-PK model predictions and experimentally determined EDC blood concentrations in the rat. Model predictions were in good agreement with observed data at the dose range tested.
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(1982) after both oral and inhalation exposures in the rat were also in close agreement with model predictions.
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From the close agreement between model predictions and observed concentrations both from our studies and data reported in the literature, it is clear that the PB-PK model developed has strong predictive powers. This has been seen with different dose levels, routes of exposure, and two species.
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studied hepatic DNA damage by EDC after pretreating mice with either piperonyl butoxide to block microsomal oxidation or diethyl maleate to deplete GSH, and found that the GSH pathway was responsible for DNA damage and not the oxidative pathway. With methylene chloride, Andersen et al.
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FIGURE 8 Relationship between EDC administered dose and the dose surrogate (the amount of liver GC metabolite) in the mouse.
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Administered dose versus GC exposure plots were also constructed for inhalation exposure for the mouse and for oral and inhalation exposures for the rat, with similar findings (plots not shown here)
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tumor site. That is, instead of correlating tumors with the administered dose, or the external dose, the dose surrogate at the target site, or the internal dose, was employed.
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It is interesting to note that lower amounts of the dose surrogates are predicted at the maximum tolerated inhalation dose of 150 ppm, compared with the other two oral doses. This observation may be a possible reason that treatment-related tumors were not seen in the inhalation bioassay, but were observed both at 75 and 150 mg/kg for the oral bioassay.
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The model does not provide insight into the mechanism of cancer, nor does it predict sensitivity of one target organ over another or one species over another. The model is simply a tool to quantitate target organ exposure to the relevant chemical species.
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Although PBPK models do not predict sensitivity of a biological response of one species over another, they can reliably quantitate target organ doses between species. Differences in the physiology of different species, like organ blood flow or pulmonary ventilation, are taken into account in these models.
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Cardiac output Fat blood flow Liver blood flow Lung blood flow Alveolar ventilation Blood flow to richly perfused tissues Blood flow to slowly perfused tissues Maximum capacity of oxidation pathway (mg/h)
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of humans and laboratory animals (Miller et al., 19851. Originally, the dosimetry model was developed to simulate the local absorption of O3 only in the lower respiratory tract (LRT)
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, the model simulates, during one or more breathing periods, the transport and absorption of O3 in airways and alveolar air spaces of each generation or segment of a respiratory tract anatomical model. Species lung dimensions are taken into account by making use of anatomical or airway models, as illustrated in Figure 1, a stylized diagram.
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~ 1-—-— - - — t rT I T1 |~ TRACH EA ~1 · URT ~ TRACHEOBRONCHIAL PULMONARY REGION /~* REGION - LRT FIGURE 1 A stylized diagram of the type of respiratory tract anatomical models used with the dosimetry model.
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t ;~> ~~, V axial axial wall expansion convection dispersion loss contraction VC 1 "X FIGURE 2 Equations used to describe the transport and chemical reactions of O3 in the compartments of the respiratory tract. Definition of terms includes: t = time; x = distance along airway path; z = distance within compartments; C = average cross-sectional O3 concentration; Cc = pointwise O3 concentration in compartment; D = effective dispersion coefficient of O3 in lumen; DC—molecular diffusion coefficient of O3 in a compartment; u = average air velocity in lumen; S = surface area; V = volume of airway; V = time rate of change of airway volume; Jw = radial flux to wall; kg = gas phase mass transfer coefficient; k2C = effective second-order chemical rate constants; [HC]
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The two anatomical models were found to result in significantly different LET total and pulmonary percent uptakes: 20%~0% (depending on breathing frequency) higher uptakes for the Kliment rat model than for the Yeh et al.
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The level of exercise increases with increasing curve number, with curve 1 being for normal respiration and curve 4 for heavy exercise. The simulations show that exercise has very little effect on O3 tissue dose in the tracheobronchial region and a very large effect on this dose in the pulmonary region.
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. \ 11 _ CURVE 1 2 4 VT f 0.5 15.0 1.0 1 5.0 1.75 20.3 2.25 30.0 VE 7.5 15.0 35.5 67.5 i_ 0 2 4 6 8 10 12 14 16 18 20 22 24 AIRWAY GENERATION TO _ I _ 1FIGURE 4 Effect of O3 exercise on predicted tissue O3 dose in the lower respiratory tract of man (based on data from Miller et al., 1985)
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The model was developed to simulate the uptake and distribution of irreversible chemically reacting toxic gases, such as O3 and NO2, in the LRT of man and laboratory animals and takes into account species LRT anatomy and ventilatory characteristics, transport in the lumen of the airways and in alveolar air spaces, and transport and chemical reactions in the liquid lining and the underlying tissue and blood compartments. Model predictions were illustrated with the results of two investigations.
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1980. Morphology of the guinea pig respiratory tract.
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. Physiologically based pharmacokinetic (PB-PK)
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The behavior of inhaled CC14 in humans was also predicted, and the results were compared with previously published data describing the elimination of CC14 in human volunteers. Lastly, the model was used to study the potential for day-to-day accumulation of CC14 in the adipose tissue of rats and humans following repeated inhalation exposure to 5 ppm CC14 (the current American Conference of Governmental Industrial Hygienists LACGIH]
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Ramsey and Andersen (1984) described a physiologically based model for examining the kinetic behavior of inhaled gases and vapors that are essentially nonirritating to the respiratory tract.
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Elimination of these various metabolite pools was assumed to follow first-order behavior for all the material in the compartment. The rate constants for elimination of metabolites via urinary, fecal, and carbon dioxide metabolites were K2, K3, and Kit, respectively.
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~ "ON ~ ~ .. 1 i | Urinary14C | 1 > | Exhaled '4CO k3 ~ | Fecal14C 1 delay I I Compartmental Based Model for Metabolites FIGURE 1 Schematic of the physiologically based pharmacokinetic (PB-PK)
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Biochemical Constants Carbon tetrachloride is metabolized via an oxidative pathway involving cytochrome P-450. Lipid peroxidation, presumably initiated by a free radical metabolite of CC14 (Butler, 1961)
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were: liver, Who; fat, 25%; muscle, 7.5%; richly perfused organs, 57%. Blood flows (as percentage of total)
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This plot shows the actual versus model predicted elimination following the day 7 (4 + 3 days) of exposure to the 11.5-h/day schedule.
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FIGURE 3 Comparison of the actual versus predicted concentration of 14CO2 in the expired air of rats exposed to 100 ppm of CC14 for 8 in/day for 10 days (5 of 7, plus 5 of 7 days)
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. The PB-PK model predicted that rats exposed to 5 ppm for 8 in/day will not accumulate CC14 or its metabolites in adipose tissue with repeated exposure.
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We obtained an excellent description of the elimination of parent CC14 in the exhaled breath and were able to predict the concentration of CC14 in the adipose tissue of rats at any time following exposure to either an 8- or 11.5-h/day dosing regimen. By making some basic assumptions, we also predicted the time course of formation and elimination of CC14 metabolites in the urine, the breath (CO2)
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. PB-PK models have also been used to improve the quantitative risk assessment process for methylene chloride (Andersen et al., 1987a)
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1987b. Adjusting exposure limits for long and short exposure periods using a physiological pharmacokinetic model.
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1972. On the mechanism of carbon tetrachloride toxicity coincidence of loss of drug metabolizing activity with perioxidation of microsomal lipid.
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1986a. A comparative study of the pharmacokinetics of carbon tetrachloride in the rat following repeated inhalation exposures of 8 and 11.5 Friday.
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It is also known that optimization of the experimental design of chronic bioassays (in terms of the number of treatment groups, their relative size, and their placement relative to the maximum tolerated dose) so as to minimize the uncertainty in predicted risks at low exposure levels does not significantly improve the situation (Portier and Hoel, 19831.
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Consequently, a critical assumption is made when low-dose risk extrapolation is performed in the absence of data regarding internal measures of exposure. This assumption is that the dose administered in a bioassay is a valid linear proxy for the biologically active dose delivered to specific target tissues (EPA, 1986; Starr and Buck, 19841.
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, all as nonlinear functions of the airborne formaldehyde concentration. Because each of these phenomena appears to be an important controlling factor in the relationship between administered and delivered doses (Starr and Gibson, 1985)
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the cancer risk associated with formaldehyde exposure. QUESTIONS STILL TO BE RESOLVED Related mechanistic studies have also provided strong evidence that dramatic interspecies differences in the inhaled dose are likely to exist even when the species are exposed identically to the same airborne formaldehyde concentration (Chang et al., 19831.
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Clearly, interspecies differences in anatomy and physiology, metabolism, and the rates of cell proliferation and repair of DNA damage may each play a critical role in the accurate assessment of the risk to humans from formaldehyde exposure. If the cancer risk in two species as similar as rats and mice can differ by a factor of 50, even though they are identically exposed to the same airborne concentration, can we have much confidence in the extrapolation of risks from rodents to the very different human species under very different exposure conditions?
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1983. Carcinogenicity of formaldehyde in rats and mice after long-term inhalation exposure.
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1984. The importance of delivered dose in estimating lowdose cancer risk from inhalation exposure to formaldehyde.
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and LOTUS 1-2-3 on an International Business Machines (IBM) -AT personal computer was used to predict the extent of urinary bladder exposure to N-hydroxy (N-OH)
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The spreadsheet software package LOTUS 1-2-3 was used extensively to manipulate the exposure data obtained from the hybrid computer-generated curves. Values for integral H (N-OH arylamine bladder exposure)
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I \'C] ~ ECU 1 1 1 Counter 1 1 Q O Holding Time I ~ _ ~ Voidi 9 Time 1 1 1 Bladder Exposure (Integrated Areas)
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The ability to control voiding interval was also included for consistency because release of the free N-OH metabolite occurs as a linear, time-dependent process. Both of these latter parts of the model were unique to this application; normally, in pharmacokinetic modeling the excretion of a chemical into the urine is considered an end product that is not available for recycling and is dealt with as a continuous, cumulative function.
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Figure 5 is a plot of integral H (urinary bladder exposure to N-OH arylamine) versus voiding interval; each curve represents a different pH value.
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8 10 pH = 6 X pH =7 FIGURE 6 Plot of sum X versus voiding interval as a function of pH. For symbol definitions, see Figure 5 legend.
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To test our hypothesis that N-OH arylamine bladder exposure can be used as a biological marker for assessing carcinogenic potential, t3H] 4A-BP has thus far been given orally (5 mg/kg)
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~ pH = 6 X pH =7 FIGURE 9 Plot of integral H percent versus voiding integral as a function of pH. By knowing the total percentage of dose excreted for a given voiding interval and urinary pH, the bladder exposure to N-OH arylamine can be estimated (O symbols in Figures 13-15)
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At present, however, this is not a convenient or readily convertible measurement to be applied to human populations. Therefore, a biological marker is needed to assess exposure to the potentially carcinogenic arylamines.
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versus time far dog 3. Uhne samples wed collected eve~ 2 h via a catbeter.
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Four biological markers are presented that are potential measures of bladder exposure to the NOH arylamine. The Hb-ABP adduct level was measured directly from the blood sample (column D)
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346 Ct Ct By Hi_ o so |
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O HB-ABP ~ P-AUC O Sum H (a) 16 20 24 FIGURE 14 Plot of three measures of N-OH arylamine bladder exposure as a function of time for dog 3.
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Nevertheless, HbABP appears to be an accurate measure of the external dose and hence a potentially useful biological marker. Prediction of N-OH arylamine bladder exposure based on integral H percent calculations from total recovery in the urine also does not seem predictable across venous conditions.
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A hybrid computer system for pharmacokinetic modeling.
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