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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. To enrich the program, a poster session was added in which the presenters were also asked to introduce themselves in the plenary session, give 1- or 2-minute summaries of their work, and then invite general discussion of their topic by workshop participants. 253

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Route-to-Route Extrapolation of DichIoromethane Exposure Using a Physiological Pharmacokinetic Mode! Michael ]. Angelo and Alan B. Pritchard BACKGROU N D Physiologically based mathematical models are useful devices for ex- ploring the information contained in pharmacokinetic data. Most of the physiological pharmacokinetic models that have appeared in the literature have been developed for pharmacological research. A number of these applications have been reviewed by Gerlowski and Jain (1983), who also present some of the fundamental principles that are used to derive this type of mechanistic model. There has been increasing interest within the field of toxicology in the use of models to process pharmacokinetic information, especially with regard to exposure assessment. Their value has been recognized for a number of reasons, including their ability to increase a scientist's under- standing of physiological factors that control chemical disposition, quan- tification of in vivo metabolism rates, and the extrapolation of pha~m- acokinetic predictions across dose levels and mammalian species. This paper illustrates how we have used a previously developed phys- iological model for the disposition of dichloromethane (DCM; methylene chloride) to compare the pharmacokinetic patterns that result from inhal- ation and oral administration of DCM in rats. With information generated by computer simulations, we determined the correlations between inhal- ation doses and the corresponding oral doses of DCM that produce the same level of pharmacokinetic impact. By examining the pharmacokinetic information in this way, we were able to infer how dosing route depen- dencies can influence the calculation and interpretation of DCM exposure. 254

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Route Extrapolation Using a PK Model 255 RESULTS AND DISCUSSION The schematic diagram in Figure 1 shows the structure of the physio- logically based model that was used to describe the pharmacokinetics of DCM following inhalation and oral administrations. The mathematical details of the model, including parameter values, are given in the Appendix to this paper. Table 1 contains the physiological constants for a 250-g rat that are available in the literature, and Table 2 contains the pharmaco- kinetic parameters that were determined by using numerical optimization techniques. The kinetics of DCM biotransformation via pathways depen- dent on the mixed-function oxidase (MFO) system and glutathione (GSH) has been presented recently by Gargas et al. (1986), and it has been incorporated into the mass balance equations. 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 concen- trations 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. In developing the correlation between inhalation and oral doses, we first determined that a continuous oral infusion of DCM into the gut lumen DCM ~ ~ ALVE~R ~ =2 [~ ~~Q CO CO, ~ ~ . ~ . ·~_ · LIVER ~ GUT ~L: 1. ~14 1 ~ -| CARCASS | ~ FIGURE 1 Physiological pharmacokinetic model for dichloromethane (DCM).

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256 MICHAEL J. ANGELO AND AWN B. PRITCHARD TABLE 1 Physiological Parameters for a 250-g Rat _ _ _ Parameter Abbreviation Value Compartment flow rates (ml/min) Lunga QL 32.3 Liver I 16.0 Kidney QK 10.0 Gut QG 9 1 Carcassb Qc 6.3 Compartment volumes (ml) Whole animal (g) BW 250 Blood VB 22.5 Lung V'g 2.9 Liver Via 10.0 Kidney VK 2.3 Gut VG 10.1 Gut lumen VG! 13.6 CarcassC Vc 183 Alveolar space VE 6.25 Alveolar ventilation (ml air/min) I 90 Blood/air distribution ratio A 12.8 Red blood cell/plasma distribution ratio Rrbc 5.4 aSum of venous flows from other compartments. Volumetric average of muscle, skin, and fat. CSum of muscle, skin, and fat. compartment of the model would produce pharmacokinetic profiles similar to those observed during an inhalation exposure. This was done to dem- onstrate that a constant oral input mimicked the distribution patterns that were achieved during a continuous input to the pulmonary system. There- fore, the correlations between the two methods of administration depended upon the dosing route and not upon the disposition factors that were influenced by the rate of administration. Using the data of McKenna and coworkers (1982), Figure 3 shows that the model simulations of DCM blood concentration during a 6-h oral infusion agree well with the inhal- ation data. Inhalation simulations at 50, 500, and 1,500 ppm for 6 h produced curves that were virtually identical to those obtained with con- tinuous 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) for a series of inhalation exposures. Metabolized doses were also

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Route Extrapolation Using a PK Model 257 TABLE 2 Pharmacokinetic Parameters for a 250-g Rat _ . Parameter Abbreviation Value Tissue/plasma distribution ratio Blood RB 2.8 Lung REg 1 Liver R! 1 Kidney RK 1 Gut RG 4 5 Carcass RC 2.4 Gut contents/water distribution ratio Metabolism parameters Vm,~,` (nmol/ml/min) Km (nmol/ml) k2 (min ~ ) A a 1 A2a fco Alveolar permeability x area product (PA; ml/min) Carbon monoxide clearance (CLCo; ml/min) Binding constants for 14Co to Hb kot (nmol/ml) kp (nmol/ml) Gut absorption (ka; min - i) RGI~ 40 535 0.016 0.120 0.056 0.60 0.26 600 40 0.029 aReitz et al. (1986). E 5000- 0' E 1000 l c . 0- . 100- . z z 8 10- o o m c, \ \200 mg/kg 50 mg/kg \ DAY 1 ~ \ I _. ’\ DAY 14 \ \ ~ 50 mg/kg\ ~ \ 200 mglkg \ ~ ~ 60 120 180 240 300 360 ~ __ 0 60 120 180 240 300 360 0 TIME (min) FIGURE 2 Blood concentrations of dichloromethane (DCM) on days 1 and 14 of a repeated oral dosing schedule. Daily gavage doses were administered to rats at SO (~) and 200 mg/kg (~) in a water vehicle. Data represent the mean + standard error of the mean for six animals; lines are the predictions from the pharmacokinetic model.

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258 MICHAEL].ANGELO AND ALAN B.PRITCHARD 10,000 E - E 1,000_ - z in al in o o 0 1- m o 100- tO- 0.1 - - _ 1 ~ 50ppm _ 500 ppm ~1500ppm ~1 :4 0 60 120 180 240 300 360 TIME(min) FIGURE 3 Physiological model predictions of dichloromethane (DCM) in blood of rats during 6-h oral infusions. Simulations are compared to data from 6-h inhalation exposures of DCM at 50, 500, and 1,500 ppm. SOURCE: McKenna et al. (1982). determined in terms of the amount of DCM that was metabolized by each of two biotransformation pathways. 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 equiv- alent oral doses of DCM in rats for the lung, liver, and blood compart- ments. The correlations indicate that at 1,500 ppm, the equivalent oral dose of DCM is between 200 and 300 mg/kg for each tissue. Figure 5 shows the correlations that were based on the equivalent amounts of metabolized DCM by the two biotransformation pathways. The figure indicates that the correlation for the GSH-dependent pathway is a linear relationship between the oral and inhalation exposures, whereas the MFO pathway exhibited a nonlinear pattern. At 1,500 ppm, the equivalent oral dose was approximately 200 mg/kg for the glutathione pathway and about 100 mg/kg for the MFO pathway, although the latter reached this level between 500 and 750 ppm. The retention of DCM during steady-state inhalation was less than 15% of the exposure concentration in the 50- to 1,500-ppm treatment range. This was determined by using Equation 1 lb from the Appendix. Values for the alveolar concentration of DCM (Ca) that were used in the calcu-

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Route Extrapolation Using a PK Model 259 ~ 300- x E 200- ~n o o V 100 - O ~ LUND BLOOD - LIVER ~ I ~ . I 0 250 500 750 ~ 000 1250 1500 DCM INHALATION EXPOSURE (ppm for oh) FIGURE 4 Correlations based on equivalent tissue AUC in rats between oral doses (infusions for 6 h) and 6-h inhalation exposures of dichloromethane (DCM). ~ 300- ~D v, A Cal ~ 200- In o ~ 100- ~r o o 0 250 500 750 1000 1250 1500 DCM INHALAT1014 EXPOSURE (ppm for Oh) FIGURE S Correlations based on equivalent amounts of metabolized dichloromethane (DCM) by MFO and GSH pathways between oral doses (infusions for 6 h) and 6-h inhalation exposures In rats. rations were obtained as a numerical output from the simulations of in- halation exposures. Previous studies have shown that the retention of DCM during steady- state inhalation is less than 100% (Fiserova-Bergerova, 19831. In these situations, calculations that are based upon complete absorption of an

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260 MICHAEL3.ANGELO AND ALAN B.PRITCHARD inhaled dose overestimate the amount of material that enters the systemic portion of the body. Similarly, a sizable hepato-pulmonary first-pass effect during oral absorption of DCM reduces the systemic bioavailability of the compound, even though the retention of an oral dose by the gastrointestinal compartment is usually considered to be 100%. 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 ab- sorbed chemicals. With respect to DCM, this means that the first-pass effects and incomplete pulmonary retention of an inhaled dose are ac- counted for in the mass-balance equations that comprise the model. Be- cause of this, the model establishes a theoretical basis for quantifying internal doses of DCM that should be used in the development of route- to-route pharrnacokinetic comparisons. CONCLUSIONS 1. We have established that a physiologically based pharmacokinetic model can be used to compare organ-specific doses between inhalation and oral administrations of dichloromethane. 2. We demonstrated that internal exposures, as measured by the deliv- ered doses to target tissues and by metabolic challenges, are different for a given administered dose. 3. We determined that the retention of DCM during steady-state in- halation was less than 15% over the concentration range tested. 4. The dose of DCM delivered to target organs is overestimated if total absorption of an inhaled dose is assumed and/or the hepato-pulmonary first-pass effect is ignored. 5. The model provides an effective tool for exposure assessment by quantifying the internal doses of DCM that are the appropriate measures to use in route-to-route pharrnacokinetic comparisons. REFERENCES Angelo, M. J., and A. B. Pritchard. 1984. Simulations of methylene chloride pharmaco- kinetics using a physiologically based model. Reg. Toxicol. Pharmacol. 4:329-339. Angelo, M. J., A. B. Pritchard, D. R. Hawkins, E. R. Walter, and A. Roberts. 1986. The pharmacokinetics of dichloromethane. II. Disposition in Fischer 344 rats following intravenous and oral administration. Food Chem. Toxicol. 24:975-980. Fiserova-Bergerova, V. 1983. Pp. 101-132 in Modeling of Inhalation Exposure to Vapors: Uptake, Distribution and Elimination, Vol. I. Boca Raton, Fla.: CRC Press.

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Route Extrapolation Using a PK Model 261 Gargas, M. L., H. J. Clewell III, and M. E. Andersen. 1986. Metabolism of inhaled dihalomethanes ~n vivo: Differentiation of kinetic constants for two independent pathways. Toxicol. Appl. Pharmacol. 82:21 1-223. Gerlowski, L. E., and R. K. Jain. 1983. Physiologically based pharmacokinetic modeling: Pnnciples and applications. J. Pharm. Sci. 72:1103-1127. McKenna, M. J., J. A. Zempel, and W. H. Braun. 1982. The pharmacokinetics of inhaled methylene chlonde in rats. Toxicol. Appl. Pharmacol. 65:1-10. Reitz, R. H., F. A. Smith, M. L. Gargas, H. J. Clewell, and M. E. Andersen. 1986. Physiological modeling and nsk assessment: Example, methylene chlonde. Toxicologist 6:7. APPENDIX The following are the mass-balance equations for each compartment in the physiological pharmacokinetic model for dichloromethane. ·iver Gut Lung VL d = QL (CB,art RL ) QG(RGCG CB,artJ VLL(1—A~)rlL + r2L], where Vmax R L K + CL RL ,~, rlL = ~, and r2L = k2 RL (1) VG = Q G (CB,art — —CG) + D ~ where dt RG (2) D = a exp(— at) RGI RGI V Lg dt = QLg ( CB,ven R CLg )

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340 JOHN F. YOUNG and FRED F. KADLUBAR 220 - 2 1 0 - 200 ~ 190 ~ 180 ~ 170 ~ 160 ~ 150 ~ 140 ~ 130 - 120 ~ 110 - 100 - 90 - 80 - 70 - 60 - 50 — 40 — So 1 20 ~ 10 - 0 2 4 O pH~4 ~ pHs5 O pH=5.5 VOIDING INTERVAL (hours) ~/~ '' - _ Human * A * Dog ~~ * Monkey 8 10 pH=6 X pH =7 FIGURE 5 Plot of integral H versus voiding interval as a function of pH. The asterisk represents the positions on the graph for each species as determined by average urine pH and voiding interval taken from the literature. Symbols: By, pH 4; +, pH 5; O. pH 5.5; L\, pH 6; x, pH 7. 60 50 x ~ 30 v' 20 o 0 2 4 O pH = 4 4- pH ~ 5 O pH = S.S VOIDING INTERVAL (hours) 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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PK Stimulation to Predict Bladder Exposure 341 Another relationship to estimate bladder exposure was obtained from the plot of integral U (bladder exposure to the N-OH arylamine conjugate) versus voiding interval as a function of pH (Figure 81. The data from Figures 5 and 8 were then combined to generate Figure 9, which is a plot of integral H percentages Integral H x 100/integral H + integral U)] as a function of pH and voiding interval. The data in Figure 9 can then be used to predict bladder exposure (integral H) based on total urinary recovery of the arylamine and its matabolites. To test our hypothesis that N-OH arylamine bladder exposure can be used as a biological marker for assessing carcinogenic potential, t3H]4- A-BP has thus far been given orally (5 mg/kg) to three dogs. The only differences experimentally between the three dogs is the manner in which urine was collected: dog 2, natural voiding intervals; dog 3, 2-h voiding intervals for the first 12 h via bladder catheterization and natural voiding thereafter; dog 4, continuous voiding via indwelling bladder catheter~za- tion for the first 12 h and natural voiding thereafter. To Coo 90 80 70 C5 50 UJ by 60 40 30 20 10 O ~ O pH r 4 ~ pH ~ 5 1 A /: /' ,~ r I I I I I . I I . 0 2 4 6 8 10 VOIDING INTERVAL (hours! O pH · 5.5 ~ pH r 6 X pH s7 FIGURE 7 Plot of integral H/sum X versus voiding interval as a function of pH. By knowing the amount of.N-OH arylamine excreted in the urine, the urinary pH, and the voiding interval, an estimate of the bladder exposure to N-OH arylamine can be determined (I\ symbols in Figure 13). For symbol definitions, see Figure 5 legend.

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342 JOHN F. YOUNG and FRED F. KADLUBAR 2.4 - 2.2 - 2 — 1.8 - 1.6 - 1.4 - 1.2 - 0.8 0.4 - 0.2 - O pH = 4 ~ pH - 5 0 2 4 6 VOIDING INTERVAL (hours) O pH ~ 5.5 8 10 pH = 6 X pH =7 FIGURE 8 Plot of interval U versus voiding integral as a function of pH. For symbol definitions, see Figure 5 legend. 90 - . 80 70 g x c ~ 50 c 40 - by 60 30 - 10 O- 1 art 0 2 4 6 8 10 O pH = 4 ~ pH 5 5 O pH = 5.5 VOIDING INTERVAL (hours) ~ 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). For symbol definitions, see Figure 5 legend.

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PK Stimulation to Predict Bladder Exposure 343 The carcinogenic potential of 4-ABP is assumed to be directly related to the amount of DNA adducts formed in the bladder from N-OH-4-ABP, which is assumed to be proportional to the residence time of the N-OH metabolite in the bladder lumen. The DNA adduct measurements involve the use of highly sensitive and specific immunoassays and are currently In progress. 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 aryl- amines. Hemoglobin adducts (Hb-ABP) offer a good possibility because they can be readily measured from a blood sample and may reflect ac- cumulative exposure. Figures 10-12 are the percentage of dose versus time plots for whole blood, plasma, and hemoglobin-4-ABP adducts (Hb-ABP). The data for the three dogs were very similar, regardless of the manner in which the urine was collected. This was somewhat unexpected because we had anticipated that the Hb-ABP adducts might be formed as a consequence of reabsorption of the N-OH metabolite across the bladder wall rather than hepatic metabolism/transfer or direct formation in the blood. 19 — LU o C, 6 — o O ~ 1:: Hb-ABP ~ Whole Blood ,l,~0 I I I - I I ~ I I 0 4 8 12 16 20 24 TIME (hours) O Plasma FIGURE 10 Plot of percentage of dose (5 mg/kg) versus time for dog 2. Urine samples were collected as they occurred naturally. Symbols: By, Hb-ABP; +, whole blood; O. plasma.

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344 jOHN ~ YOUNG and fRED ~ K^DLUB^R 12 - 11 - 10 - e- 8 - ~ e- o / . ' ~ I I 1 i ~ : i 1 ~ 1 0 4 8 12 16 20 24 ARE Rout O Hb-^BP + WhoIe BI_ ~ PIesm" FIGURE 11 Plot of percentage of dose (5 mg/kg) versus time far dog 3. Uhne samples wed collected eve~ 2 h via a catbeter. ~r symbol de~nitions, see Figure 10 legend. o 0 ~ ^ . ' I 1 # 1 1 1 1 1 0 4 8 12 16 1 1 1 1 20 24 O Hb-^- + WhoIe BIo~ ~E ~ou~ PIasma FIGURE 12 Plot of percentage of dose (3 mg/kg) versus dme ~r dog 4. Uhne samples wem collec~d condnuously via a c~he~r. ~r symbol deAnidons, see Figum 10 k~end.

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PK Stimulation to Predict Bladder Exposure 345 Through pharrnacokinetic modeling, three other potential biological markers are being examined. Plasma AUC determination requires multiple blood samples and therefore may not be useful in population studies. Unnary excretion patterns either for total excretion or specifically for the N-OH arylamine levels may be easier to obtain but do not reflect multiple exposures over extended times. Table 1 presents the experimental data and calculated exposure data from the hybrid computer simulation for dog 2. Four biological markers are presented that are potential measures of bladder exposure to the N- OH arylamine. The Hb-ABP adduct level was measured directly from the blood sample (column D). The area under the plasma concentration-time curve was calculated by use of the trapezoid rule (column H). Two separate cumulative integral H values were calculated based on total recovery of radioactivity (columns E and I-K) or on excretion of N-OH-ABP (columns F and L-N). These latter two values differed by about a factor of 2 but had the same shape as the Hb-ABP curve (Figure 131. Figures 13-15 are plots of these various exposure measurements versus time for the three dogs. The most complete set of data is for dog 2. The analysis of the rest of the data for dogs 3 and 4 is ongoing. 20 9 ~ 18 - 17 - _ 16 ~ c ,5 `~' 44 - `~: 13 ~ [r 12 - c,0 1 1 - lo: 1 0 - 9 - ~: 8 - a 7 ~ `~O 6 - OX 5 ~ 113 4 - 3 ~ 2 - 1 - 0 1 ·, . I it' I ~ -—~ T 0 4 8 - 1 1 - 1 -1 1 ~ 1 12 16 20 24 TIME (hours) O HB-ABP ~ P-AUC O Sum H (a) ~ Sum H (b) FIGURE 13 Plot of four measures of N-OH arylamine bladder exposure as a function of time for dog 2. The dose of 4-ABP was 5 mg/kg. Symbols: G. Hb-ABP; +, p-AUC; O. sum H (a); A, sum H (b). (a) Sum of integral H based on total urinary recovery. (b) Sum of integral H based on urinary recovery of N-OH arylamine.

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346 Ct - Ct By Hi_ o so OCR for page 251
PK Stimulation to Predict Bladder Exposure 347 20 — 19 — 18 — 17 — 16 — 15 - 14 — 13 - 12 — 11 - 10 - 9 _ 8 — 7 — 6 — 5 — 4 — 3 — 2 — 1 - O ~ ~ 1 _ 0 4 8 12 TIME (hours) 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. The dose of 4-ABP was 5 mg/kg. Symbols: 2, Hb-ABP; +, P-AUC; O. sum - H (a). (a) Sum of integral H based on total urinary recovery. 20 - 19 - 18 — 17 - - - a' co MU a: LU tar En to x 16 - 15 - 14 - 13 - 12 - 11 - 10 - 9 _ 8 — 7 — 6 — 4 — 3 — 2 — 1 - O ~ i/ I/ I /' ~ - - I - 1 - I - I r 7 1 ~ ~ ~ 1 4 8 12 16 20 24 TIME (hours) to O HB-ABP ~ P-AUC O Sum H (a) FIGURE 15 Plot of three measures of N-OH arylamine bladder exposure as a function of time for dog 4. The dose of 4-ABP was 5 mg/kg. For symbol definitions, see Figure 14 legend. (a) Sum of integral H based on total urinary recovery.

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348 JOHN F. YOUNG and FRED F. KADLUBAR CONCLUSIONS If carcinogenic potential is related to N-OH arylamine bladder exposure and DNA adduct formation, then Hb-ABP measurements may not be indicative of this process because the level of Hb-ABP adduct formation was about the same for all 3 days under three different urinary voiding conditions. We had anticipated that dog 4 (Figure 12) would have had a much lower level of Hb-ABP adducts because the bladder was continu- ously drained via the indwelling catheter, and therefore, the potential for bladder exposure should have been greatly reduced. Nevertheless, Hb- ABP 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. We would anticipate that continuous urinary excretion would allow the least amount of bladder exposure; how- ever, Figure 15 indicates a high level of exposure potential from this calculation. This might be an artifact of the simulation and data manip- ulation, or it could be an indication that the experimental conditions were not exact and that the bladder was not entirely empty at all times. Prediction of N-OH Melamine bladder exposure based on integral H calculations from excretion levels of N-OH arylamine appears to be most promising, but we must wait for the completion of the urine analyses for 4-ABP metabolites. On completion of the arylamine-DNA adduct determinations in the urothelium, the endpoint evaluation of our simulations will have more meaning, and new avenues of analysis can then be explored. ACKNOWLEDGM ENTS The authors wish to thank Mary Ann Butler, Candee Teitel, John Bailey, Ken Dooley, Paul Skipper, and Steven Tannenbaum for the use of their data in this simulation exercise. REFERENCES Deichmann, W. G. 1967. Bladder Cancer, A Symposium, K. F. Lampe, ed. Birmingham, Ala.: Aesculapius, p. 30. Frederick, C. B., J. B. Mays, and F. F. Kadlubar. 1981. A chromatographic technique for the analysis of oxidized metabolites: Application to carcinogenic N-hydroxyarylam- ines in urine. Anal. Biochem. 118:120-125.

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PK Stimulation to Predict Bladder Exposure 349 Green, L. C., P. L. Skipper, R. J. Turesky, M. S. Bryant, and S. R. Tannenbaum. 1984. In vivo dosimetry of 4-aminobiphenyl in rats via a cysteine adduct in hemoglobin. Cancer Res. 44:4254-4259. Kadlubar, F. F., J. F. Anson, K. L. Dooley, and F. A. Beland. 1981. Formation of urothelial and hepatic DNA adducts from the carcinogen 2-naphthylamine. Carcinogen- esis 2:467-470. Oglesby, L. A., T. J. Flammang, D. L. Tullis, and F. F. Kadlubar. 1981. Rapid absorption, distribution, and excretion of carcinogenic N-hydroxy-arylamines after direct urethral instillation into the rat urinary bladder. Carcinogenesis 2:15-20. Pearce, B. A., and J. F. Young. A hybrid computer system for pharmacokinetic modeling. I. Software considerations. Pp. 117-121 in Proceedings of the 1981 Summer Computer Simulation Conference. La Jolla, Calif.: Simulation Council, Inc. Radomski, J. L. 1979. The primary aromatic amines: Their biological properties and structural-activity relationships. Annul Rev. Pharmacol. Toxicol. 19:129. Wynder, E. L., and R. Goldsmith, Jr. 1977. The epidemiology of bladder cancer. Cancer 40: 1 246. Young, J. F., and F. F. Kadlubar. 1982. A pharmacokinetic model to predict exposure of the bladder epithelium to urinary N-hydroxyarylamine carcinogens as a function of urine pH, voiding interval, and resorption. Drug Metab. Dispos. 10(6):641-644. Young, J. F., C. G. White, and B. A. Pearce. 1981. A hybrid computer system for pharmacokinetic modeling. II. Applications. Pp. 122-129 in Proceedings of the 1981 Summer Computer Simulation Conference. La Jolla, Calif.: Simulation Council, Inc.

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