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2 Pharmacokinetics and the Risk Assessment of Drinking Water Contaminants This chapter discusses the uses of physiologically based pharmacokinetics in risk assessment. It considers the extrapolation of data from inhalation studies for assessing the risk associated with ingesting drinking water con- taminants. Finally, it discusses the pharmacokinetics related to interactions of multiple chemicals found in drinking water. Some of the more uncertain aspects of risk assessment are related to the extrapolation of data from animals to humans, from one route of exposure to another, from high doses to low doses, and, for carcinogens, from one target organ to another. The reason that data must be extrapolated is that some important kinds of experimental work are impossible, impractical, or unethical. For example, human experiments involving carcinogens are uneth- ical, and animal experiments to study infrequent or very small responses are impractical. To extrapolate among species, doses, routes, and exposure times, one must make assumptions. The assumptions are usually based on scientific facts, informed guesses, or intuition. The use of pharmacokinetics in the risk assessment of single-chemical exposure has been promoted by some scientists for many years (Andersen et al., 1987a; Clewell and Andersen, 1985; Dedrick, 1985; Gehring et al., 1978; Hoel, 1985; Hoel et al., 1983; Lutz and Dedrick, 1985; NRC, 1986, 19871. Until recently, however, the examples available in the literature were based on classical or conventional compartmental pharmacokinetic studies (Curry, 1980; Gibaldi and Perrier, 1982; O'Flaherty, 1981; Renwick, 1982; Wagner, 1975; WHO, 19861. For applications to toxicology, the classical pharmacokinetic studies were intrinsically weak in interspecies extrapolation, because they were largely mathematical manipulations of experimental data 108

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Pharmacokinetics of Drinking Water Contaminants 109 with limited incorporation of physiologic responses or anatomic entities into the model. The current approach in pharmacokinetics includes both physi- ologically based pharmacokinetics and computer modeling. The concepts of physiologically based pharmacokinetics and animal "scaleup" (a term adapted from chemical engineering to express the "al- lometric" extrapolation from one animal species to another or from laboratory animals to humans) originated in the 1920s. They were expanded in the late 1960s and early 1970s with the development of cancer chemotherapy in laboratory animals by investigators experienced in chemical engineering pro- cess design and control (Bischoff and Brown, 1966; Bischoff et al., 1970, 1971; Dedrick, 1973a,b; Dedrick et al., 19701. The scaleup from a mouse to a human, like the scaleup from a chemical engineering process in the laboratory to a full-scale chemical plant, is governed by both physical and chemical processes. In mammals, the physical processes (i.e., mass balances, thermodynamics, transport, and flow) often vary predictably among species, whereas chemical processes, such as metabolic reactions, can vary unpre- dictably. The physical and chemical processes interact in such a way that the pharmacokinetics of a given compound in one species might be predicted from observations of its pharmacokinetics in another species, given the ap- propriate background information (Dedrick, 1973a,b), but potential problems are numerous, and direct validation of a pharmacokinetic model is generally not possible. PHYSIOLOGICALLY BASED PHARMACOKINETICS A physiologically based pharmacokinetic model uses basic physiologic and biochemical data to describe the distribution and disposition of xenobiotic compounds in the body at any given time (NRC, 1987). MacNaughton et al. (1983) and Andersen (1987) summarized the approach in a flowchart (Figure 2-11. Information is categorized into three types: (1) physiologic constants, including body size, organ and tissue volumes, blood flow, and ventilation rates; (2) biochemical constants, including metabolic rates and partition coefficients for blood, tissues, and air; and (3) mechanistic factors, such as target tissues and metabolic pathways. For the most-studied compounds, the biochemical constants, such as Km (the affinity constant of an enzyme for a substrate) and Vm`~ (the maximal velocity of a chemical reaction), are often available from the literature. Physiologic constants, such as organ volumes and blood flow rates for com- mon laboratory animals, are also available. Therefore, for well-studied chem- icals, a dynamic model can be formulated to describe distribution and disposition with little or no further laboratory work. A model can be graphically illus- trated, as shown in Figure 2-2, and mathematically represented by many (sometimes 20 or more) simultaneous differential equations to express mass

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DRINKING WATER AND HEALTH r MECHANISMS l | OF TOXICITY l REFINE MODEL PROBLEM IDENTIFICATION | LITERATURE EVALUATION BIOCHEMICAL _ CONSTANTS _ 1 PHYSIOLOGICAL | CONSTANTS | r r MO1 DEL | | FORMULATION | 1 SIMU;;~ I COMPARE TO I _ I VALIDATE | __ | KINETI ~)EL . 1 DESIGN/' CONDUCT E)(TRAPt MOTION CRITICAL EXPERIMENTS TO HUMANS COMPARE TO KINETIC DATA l l FIGURE 2-1 Flowchart illustrating processes involved in physiologically based pharmacokinetics. From Andersen, 1987. balance. These cannot in general be solved explicitly, but computer simu- lations can estimate changes in end points over time, as well as steady states (such as blood concentrations of the parent compound and liver concentrations of a reactive metabolite), and similar information can be extrapolated for different species at lower or higher doses, via different routes of exposure, or both. The simulated data can then be compared with the experimental kinetic data found in the literature. As Andersen et al. (1987a) emphasized, the validation of a physiologically based pharmacokinetic model is not an ex- ercise in curve-fitting, and experimental data for validation should be obtained after the a priori prediction. A completely validated model is not easily obtainable, but agreement indicates that simulation results are appropriate, compared with experimental reality. If the model is adequately validated, it can be used to extrapolate, directly or by computer simulation, to other animal species (for further validation) or to humans. Lack of agreement means that the model is deficient and that the investigator needs more scientific information, which can be obtained from focused experiments designed to help to refine the model. The refinement process can be repeated for further improvement. Physiologically based pharmacokinetic models use a large body of phys- iologic and physicochemical data that are not chemical-specific; they allow

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Pharmacokinetics of Drinking Water Contaminants 111 interspecies extrapolation with more confidence; they can be used to predict a priors the pharmacokinetic behavior of some chemicals from sparse data; their compartments correspond to anatomic entities, so organ- or tissue- specific biochemical interactions can be incorporated (bedrock, 1973a,b); and they are more complex and versatile than compartmental pharmacokinetic models. In the past, the application of physiologically based pharmacokinetics was limited to a few investigators because of the complexity of the mathe- matics involved, the large numbers of parameters in the models, and the requirement for simultaneous solution of many differential equations. In recent years, advances in computer science and readily available software for personal computers have overcome most of the computational limitations. The model illustrated in Figure 2-2 reflects basic mammalian physiology and anatomy with compartmental entities, such as the liver and kidney, connected by the circulatory system. In this specific model, the exposure route of interest is inhalation, with intake and exhalation vapor concentrations indicated. However, oral or cutaneous exposures can be added to the gas- trointestinal tract compartment or general venous circulation. Some tissues (e.g., viscera and brain in Figure 2-2) can be lumped together, when there is no reason to believe that they are kinetically or mechanistically distinct EXHALED (CEXH) QT INHALED (CINH) DEAD SPACE ALVEOLAR ARTERIAL (CART) SPACE BLOOD QK J ~I I I KIDNEY ~ EXCRETION | ~ Gl TRACT L| ~1 ~ its T ~, QL r LIVER ~METABOLISM Ql ~ 1 ~ VISCERA BRAIN I I Qll ~ 61 MUSCLE AND SKIN I | Qlil ~FAT I I VENOUS (CVEN) FIGURE 2-2 Graphic representation of a physiologically based pharmaeokinetic model. C, con- eentration of chemical of interest; Q. flow rate; direction of arrow indicates direction of movement of chemical of interest. From Clewell and Andersen, 1985, with permission.

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112 DRINKING WATER AND HEALTH enough to warrant separate compartments. The arrows in each compartment depict the partition of the chemical between blood and organ tissues. Some models might have as few as two compartments; most would have more. They can be flow-limited, like the example given here, or membrane-limited, as suggested by Himmelstein and Lutz (1979~. The kinetic constants and model parameters used in pharmacokinetic modeling can be illustrated best with an actual example, such as methylene chloride, as in Table 2-1 (Andersen et al., 1987a). The kinetic constants and model parameters listed in Table 2-1 for humans and three laboratory species were mainly the results of direct use, estimation or deduction based on scientific reasoning, or extrapolation from published information; the investigators had relatively few new laboratory data before pharmacokinetic modeling. However, the agreement is excellent between the model predictions (a priori) and the experimental kinetic data, which were obtained from three laboratories (Andersen et al., 1984; Angelo et al., 1984; R. H. Reitz, Toxicology Research Laboratory, Dow Chemical Co., Midland, Michigan, personal communication, 1988) through two routes of adminis- tration (intravenous and inhalation) in four species (B6C3F1 mice, Syrian golden hamsters, Fischer 344 rats, and humans). The computer modeling of physiologically based pharmacokinetics is evolving. It is a powerful tool, and the modeling needs to incorporate some form of uncertainty analysis, which is not usually done now. With so many parameters involved, there is no clear relationship between the effects of parameter errors and predicted errors; nor are there clear tests of adequacy of the fit of the model when the parameter estimates have multiple sources. Sensitivity analyses of parameters involved will be important for the improved understanding of physiologically based pharmacokinetic modeling, the design of research to improve models, and the interpretation and application of the results. Cohn (1987) has published a critical discussion on this issue with a specific example. EXTRAPOLATION BETWEEN INHALATION AND DRINKING WATER ROUTES Pharmacokinetic studies of chemicals in drinking water or feed are often difficult to conduct. Water and food intakes in rodents, the most commonly used laboratory animals, are episodic and erratic. In addition, rodents are nocturnal, and most of their drinking and eating occur at night. Those factors make sampling of body fluids and tissues difficult. It is not only a problem of time of day (e.g., multiple sampling in the middle of the night, when animal facilities are in the dark part of the cycle), but also a problem of informed guesses about the time of peak blood concentrations (sampling

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Pharmacokinetics of Drinking Water Contaminants 113 TABLE 2-1 Kinetic Constants and Model Parameters Used in the Physiologically Based Pharmacokinetic Model for Methylene Chloridea B6C3F1 Miceb F344 RatsC HamstersC Humans Weights Body (kg) 0.0345 0.233 0.140 70.0 Lung (a) 0.410 2.72 1.64 772.0 Percentage of body weight Liver 4.0 4.0 4.0 3.14 Rapidly perfused tissue 5.0 5.0 5.0 3.71 Slowly perfused tissue 78.0 75.0 75.0 62.1 Fat 4.0 7.0 7.0 23.1 Flows (liters/hour) Alveolar ventilation 2.32 5.10 3.50 348.0 Cardiac output 2.32 5.10 3.50 348.0 Percentage of cardiac output Liver 0.24 0.24 0.20 0.24 Rapidly perfused tissue 0.52 0.52 0.56 0.52 Slowly perfused tissue 0.19 0.19 0.19 0.19 Fat 0.05 0.05 0.05 0.05 Partition coefficients Blood/air 8.29 19.4 22.5 9.7 Liver/blood 1.71 0.732 0.840 1.46 Lung/blood 1.71 0.732 0.840 1.46 Rapidly perfused tissue 1.71 0.732 0.840 1.46 blood Slowly perfused tissue 0.960 0.408 1.196 0.82 blood Fat/blood 14.5 6.19 6.00 12.4 Metabolic constants Vma,` (mg/hour) 1.054 1.50 2.047 118.9 Km (mg/liter) 0.396 0.771 0.649 0.580 KF (hour~') 4.017 2.21 1.513 0.53 Aid 0.416 0.136 0.0638 0.00143 A2d 0.137 0.0558 0.0774 0.0473 aFrom Andersen et al., 1987a, with permission. Copyright 1987 by Academic Press. bParameters correspond to average body weight of B6C3F1 mice in NTP bioassay (NTP, 1985). CParameters correspond to average body weight in gas-uptake studies. dA1 = ratio of MFO (mixed-function oxidase) activity in lung to MFO activity in liver. A2 ratio of GST (glutathione S-transferase) activity in lung to GST activity in liver. points at critical stages can be missed). In addition, many drinking water contaminants are volatile, lipophilic, organic compounds and are likely to be unstable in drinking water or feed formulations. If radioactive compounds are to be used in the study, the potential contamination problems with respect to the animals, equipment, and facility are difficult to handle. Even in the

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114 DRINKING WATER AND HEALTH absence of those problems, it is hard to interpret, for example, a blood concentration-time curve with peaks of different heights and shapes at irreg- ular intervals. A National Research Council report (NRC, 1986) suggested an approach to overcome the above problems. It used pharmacokinetic data on volatile organic chemicals (VOCs) from inhalation studies for the risk assessment of exposure to these compounds through the ingestion of drinking water. A brief summary of an example using trichloroethylene (TCE) is given in Appendix A; a more detailed discussion appears in the report just mentioned (NRC, 19861. PHARMACOKINETICS INVOLVING INTERACTIONS Physiologically based pharmacokinetic modeling of toxic interactions is a new field, and the only published studies are limited to binary mixtures (Andersen et al., 1987b; Clewell and Andersen, 19851. Andersen et al. (1987b) illustrated the use of physiologically based pharmacokinetic mod- eling of the metabolic interactions between TCE and 1,1-dichloroethylene (1,1-DCE). A physiologic model was constructed for each of the two com- pounds individually, and the two models were linked via the mass-balance equation for the liver compartment that had been generalized to account for various mechanisms of interaction between the two compounds. The gen- eralized scheme was used to account for inhibitory interactions including provisions for competitive, noncompetitive, and uncompetitive mecha- nisms as well as for substrate inhibition. The correspondence between predicted and observed kinetics was excellent, if it could be assumed that the inhibition was purely competitive and if 1,1-DCE was considered to be a slightly better substrate for microsomal oxidation than TCE in the model. Figure 3-3 shows two uptake curves for 1,1-DCE in gas-uptake experiments; one is for exposure to 1,1-DCE alone at 500 ppm, and the other is for exposure to a vapor mixture of 1,1-DCE at 500 ppm and TCE at 2,000 ppm. The disappearance of 1,1-DCE (as a result of metabolism) was markedly retarded when coexposure with TCE was carried out. When the scientific hypothesis was based on known biology, the a priori prediction and the experimental kinetic data agreed very well (Figure 2-31. PHARMACOKI N ETICS AN D TOXIC M ECHAN ISMS OF M U LTI PLE CHEMICAL EXPOSURE Recent discussion of the role of pharmacokinetics in the study of complex mixtures (NRC, 1988) has emphasized that little is known about the joint pharmacokinetics of two or more chemicals. Generation and examination of such data have been suggested (Yang, 1987a,b), but the application of phar

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Pharmacokinetics of Drinking Water Contaminants 115 jo4 jo3 boo 10 1 10 ~/~AAAA 1~1-DCE + TOE BAA A _~ 1, 1 -DOE ALONE -in 0.00 1.00 2.00 3.00 4.00 5.00 6.00 TIME (hrs) FIGURE 2-3 Two uptake curves for 1, 1 -dichloroethylene ( 1 ,1 -DCE) from experimental gas-uptake studies (circles and triangles) and from physiologically based pharmacokinetic models (smooth curves), assuming strictly competitive interactions between two chloroethylenes. Lower curve, ex- posure to 1,1-DCE alone at 500 ppm. Upper curve, exposure to 1 ,1-DCE at 500 ppm and trichlo- roethylene (TCE) at 2,000 ppm. From Andersen et al., 1987b, with permission. Copyright 1987 by Academic Press. macokinetics to the risk assessment of multiple chemical exposures through contaminated drinking water remains difficult and subject to large uncer- tainties. Several toxicologic studies (Chu et al., 1981; Cote et al., 1985; Webster et al., 1985) have dealt with the health effects of exposures to multiple chemicals at low doses, including a carcinogenicity study. Thus, some toxicologic information can be used in the risk assessment of multiple chemicals, although the mixtures in those studies are of only selected classes of chemicals (e.g., halogenated volatile organic chemicals, inorganic chem- icals, and pesticides). A mixture of 25 groundwater contaminants (Table 2- 2), selected on the basis of EPA surveys of groundwater in and around hazardous-waste disposal sites, is being evaluated toxicologically by the National Toxicology Program (Yang and Rauckman, 1987), but the results of relatively long-term studies, are not yet available. Methods for risk as- sessment of mixtures of chemicals in drinking water are still based largely on speculation, and no quick relief is in sight. Although a small fraction of the U. S. population living close to hazardous

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1 16 DRINKING WATER AND HEALTH TABLE 2-2 Groundwater Contaminants Selected for Study as a Mixture by the National Toxicology Programa Concentrations in Groundwater Samples, ppm Chemical AverageHighest Acetone 6.9250 Arochlor 1260 0.212.9 Arsenic 30.63,670 Benzene 5.01,200 Cadmium 0.85225 Carbon tetrachlor~de 0.5420 Chlorobenzene 0.113 Chlorofo ~1.46220 Chromium 0.69188 1,1 -Dichloroethane 0.3156.1 1,2-Dichloroethane 6.33440 1,1 -Dichloroethylene (1,1 -DCE) 0.2438.0 1,2-trans-Dichloroethylene 0.7375.2 Di-(2-ethylhexyl)phthalate (DEHP) 0.135.8 Ethylbenzene 0.6525 Lead 37.031,000 Mercury 0.3450 Methylene chloride 11.27,800 Nickel 0.595.2 Phenol 34.07,713 Tetrachloroethylene 9.6821,570 Toluene 5.181,100 1,1,1 -Tr~chloroethane 1.25618 Trichloroethylene (ICE) 3.82790 Xylenes 4.07150 aCondensed from Yang and Rauckrnan, 1987, with permission; analytic survey of groundwater samples in and around 180 hazardous-waste sites covering all 10 EPA regions. Survey conducted for EPA by Lockheed Engineering and Management Co. waste disposal sites might be consuming groundwater containing one or more of the chemicals listed at near the average concentrations shown, the con- centrations of contaminants in public drinking water supplies used by most Americans (see Table 4-1) are much lower than the averages listed in Table 2-2. Consideration of the hypothetical mixture of 25 chemicals (Table 2-2- a worst case) can yield insight into the possible pharmacokinetic and toxic consequences of consuming drinking water that contains multiple contami- nants. On the basis of the toxicity of the individual chemicals, it is probably safe to suggest that none of the 25 (Table 2-2) taken singly (for example, in an 8-ounce glass of water) at the average concentration found in drinking water

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Pharmacokinetics of Drinking Water Contaminants 117 surveys would approach the saturation kinetic level unless a genetic variation has deprived a person of a pathway. However, under the conditions of acute exposure at very high concentrations (e.g., the highest listed in Table 2-2, or even higher) or repeated or chronic exposure at lower concentrations (e. g., the average in Table 2-2), the situation could be quite different. Given the usual dose-response relationships, each organic chemical in a sample of contaminated drinking water probably has little toxic consequence at low concentrations. Metals, however, tend to accumulate in the body and might therefore pose a long-term health threat. What about toxic interactions under those circumstances? For a mixture containing chemicals in the average amounts found in the published surveys, like the one represented in Table 2-2, it is not clear what toxicity to expect or how to predict it. We know too little for informed speculation about the synergistic effects of the com- ponents of such a mixture on toxic end points, such as immunotoxicity, or on such mechanisms as the promotion stage of carcinogenesis. Recent pre- liminary findings of the National Toxicology Program (Germolec et al., in press) suggested that a mixture of 25 groundwater contaminants, at concen- trations close to the averages listed in Table 2-2, is associated with mild but definite immunosuppression in B6C3F1 mice. Those findings merit further examination and suggest that there might be exceptions to the concept of simple response additivity in mixtures of chemicals, or even that the concept is quite broadly wrong. In the absence of adequate information, and to anticipate possible synergism, it might be prudent to incorporate an uncer- tainty factor in the risk assessment of mixtures of chemicals in drinking water. The development of such an uncertainty factor is considered in more detail in Chapter 3. CONCLUSIONS AND RECOMMENDED RESEARCH Physiologically based pharmacokinetic models are useful in the risk as- sessment of contaminants in drinking water when one or possibly two ma- terials are to be considered. Unfortunately, we know little about how pharmacokinetic variables of a single chemical might be affected in multiple- chemical exposures, nor do we understand the pharmocokinetics of multiple chemicals under such exposure scenarios. Improved understanding and mod- eling of the pharmacokinetics of mixtures should lead to more accurate estimation of the risks associated with exposure to multiple chemicals in drinking water. Development of appropriate pharmacokinetic models for mixtures will require considerable theoretical and experimental work. The subcommittee recommends the following research: Potential pharmacokinetic changes of individual model chemicals (those which seem representative of others similar in structure, mode of action, or

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1 is DRINKING WATER AND HEALTH toxic end point) under the influence of long-term, low-concentration intake of a mixture of contaminants in drinking water should be investigated. Several physiologically based pharrnacokinetic models of complex chemical mixtures simulating contaminated drinking water should be devel oped and subjected to rigorous validation testing. The physiologically based pharmacokinetics of pesticides and some other, relatively nonvolatile chemicals should be studied. The frequency of toxic interactions among drinking water contaminants and the threshold concentrations, if any, for such interactions should be investigated. A computerized data base on toxic interactions should be built. REFERENCES Andersen, M. E. 1987. Tissue dosimetry in risk assessment, or what's the problem here anyway? Pp. 8-23 in Drinking Water and Health, Vol. 8. Pharmacokinetics in Risk As- sessment. Washington, D.C.: National Academy Press. Andersen, M. E., R. L. Archer, H. J. Clewell III, and M. G. MacNaughton. 1984. A physiological model of the intravenous and inhalation pharmacokinetics of three dihalo- methanes CH2Cl2, CH2Br~, CH2Br2 in the rat. Toxicologist 4:443. (Abstract) Andersen, M. E., H. J. Clewell III, M. L. Gargas, F. A. Smith, and R. H. Reitz. 1987a. Physiologically based pharmacokinetics and risk assessment process for methylene chloride. Toxicol. Appl. Pharmacol. 87:185-205. Andersen, M. E., M. L. Gargas, H. J. Clewell III, and K. M. Severyn. 1987b. Quantitative evaluation of the metabolic interactions between trichloroethylene and 1,1-dichloroethylene in viva using gas uptake methods. Toxicol. Appl. Pharmacol. 89:149-157. Angelo, M. J., K. B. Bischoff, A. B. Pritchard, and M. A. Presser. 1984. A physiological model for the pharmacokinetics of methylene chloride in B6C3F1 mice following intravenous administration. J. Pharmacokinet. Biopharm. 12:413-436. Bischoff, K. B., and R. G. Brown. 1966. Drug distribution in mammals. Chem. Eng. Prog. Symp. Ser. No. 66 62:32-45. Bischoff, K. B., R. L. Dedrick, and D. S. Zaharko. 1970. Preliminary model for methotrexate pharmacokinetics. J. Pharm. Sci. 59:149-154. Bischoff, K. B., R. L. Dedrick, D. S. Zaharko, and J. A. Longstreth. 1971. Methotrexate pharmacokinetics. J. Pharm. Sci. 60:1128-1133. Chu, I., D. C. Villeneuve, G. C. Becking, and R. Lough. 1981. Subchronic study of a mixture of inorganic substances present in the Great Lakes ecosystem in male and female rats. Bull. Environ. Contam. Toxicol. 26:42-45. Clewell, H. J., III, and M. E. Andersen. 1985. Risk assessment extrapolations and physio- logical modeling. Toxicol. Ind. Health 1(4):111-131. Cohn, M. S. 1987. Sensitivity analysis in pharmacokinetic modeling. Pp. 265-272 in Drinking Water and Health, Vol. 8. Pharmacokinetics in Risk Assessment. Washington, D.C.: Na- tional Academy Press. Cote, M. G., G. L. Plaa, V. E. Valli, and D. C. Villeneuve. 1985. Subchronic effects of a mixture of "persistent" chemicals found in the Great Lakes. Bull. Environ. Contam. Tox- icol. 34:285-290. Curry, S. H. 1980. Drug Disposition and Pharmacokinetics: With a Consideration of Pharm

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Pharmacokinetics of Drinking Water Contaminants ~19 ecological and Clinical Relationships, 3rd Ed. Oxford: Blackwell Scientific Publications. 330 pp. Dedrick, R. L. 1973a. Animal scale-up. J. Pharmacokinet. Biopharm. 1:435-461. Dedrick, R. L. 1973b. Physiological pharmacokinetics. J. Dynamic Syst. Measurement Cont. (Sept. ):255-258. Dedrick, R. L. 1985. Application of model systems in pharmacokinetics. Pp. 187-198 in Risk Quantitation and Regulatory Policy, D. G. Hoel, E. A. Merrill, and F. P. Perera, eds. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory. Dedrick, R. L., K. B. Bischoff, and D. S. Zaharko. 1970. Interspecies correlation of plasma concentration history of methotrexate (NSC-740). Cancer Chemotherapy Rep. Part I 54:95- 101. Gehring, P. J., P. G. Watanabe, and C. N. Park. 1978. Resolution of dose-response toxicity data for chemicals requiring metabolic activation: Example- vinyl chloride. Toxicol. Appl. Pharmacol. 44:581-591. Germolec, D. R., R. S. H. Yang, M. P. Ackerman, G. S. Rosenthal, G. A. Boorman, M. Thompson, P. Blair, and M. I. Luster. In press. Toxicology studies of chemical mixtures of 25 groundwater contaminants: (II) immunosuppression in B6C3F~ mice. Fundam. Appl. Toxicol. Gibaldi, M., and D. Pemer. 1982. Pharmacokinetics, 2nd Ed. New York: Marcel Dekker. 494 pp. Himmelstein, K. J., and R. J. Lutz. 1979. A review of the applications of physiologically based pharmacokinetic modeling. J. Pharmacokinet. Biopharmacol. 1:127-145. Hoel, D. G. 1985. Incorporation of pharmacokinetics in low-dose risk estimation. Pp. 205- 214 in Biological and Statistical Criteria, D. B. Clayson, D. Krewski, and I. Munro, eds. Toxicologic Risk Assessment, Vol. I. Boca Raton, Fla.: CRC Press. Hoel, D. G., N. L. Kaplan, and M. W. Anderson. 1983. Implication of nonlinear kinetics on risk estimation in carcinogenesis. Science 219:1032-1037. Lutz, R. J., and R. L. Dedrick. 1985. Physiological pharmacokinetics: Relevance to human risk assessment. Pp. 129-149 in New Approaches in Toxicity Testing and Their Application in Human Risk Assessment, A. P. Li, ed. New York: Raven Press. MacNaughton, M. G., M. E. Andersen, and H. J. Clewell III. 1983. Toxicokinetics: An analytical tool for assessing chemical hazards to man. USAF Med. Ser. Digest 39:26-29. NRC (National Research Council). 1986. Dose-route extrapolations: Using inhalation toxicity data to set drinking water limits. Pp. 168-225 in Drinking Water and Health, Vol. 6. Washington D.C.: National Academy Press. NRC (National Research Council). 1987. Drinking Water and Health, Vol. 8. Pharmacokinetics in Risk Assessment. Washington, D.C.: National Academy Press. 488 pp. NRC (National Research Council). 1988. Complex mixtures: Methods for In Vivo Toxicity Testing. Washington, D.C.: National Academy Press. 227 pp. NTP (National Toxicology Program). 1985. NTP Technical Report on the Toxicology and Carcinogenesis Studies of Dichloromethane in F-344/N Rats and B6C3F1 Mice (Inhalation Studies). NTP-TR-306 (board draft). Research Triangle Park, N.C.: U.S. Department of Health and Human Services. O'Flaherty, E. O. 1981. Toxicants and Drugs: Kinetics and Dynamics. New York: John Wiley & Sons. 398 pp. Renwick, A. G. 1982. Pharmacokinetics in Toxicology. Pp. 659-710 in Principles and Methods of Toxicology, A. Wallace Hayes, ed. New York: Raven Press. Wagner, J. G. 1975. Fundamentals of Clinical Pharmacokinetics. Hamilton, Ill.: Drug Intel- ligence Publications. 461 pp.

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120 DRINKING WATER AND HEALTH Webster, P. W., C. A. Van Der Heijden, A. Bisschop, G. J. Van Esch, R. C. C. Wegman, and T. De Vries. 1985. Carcinogenicity study in rats with a mixture of eleven volatile halogenated hydrocarbon drinking water contaminants. Sci. Total Environ. 47:427-432. WHO (World Health Organization). 1986. Principles of Toxicokinetic Studies. Environmental Health Criteria 57. Geneva: World Health Organization. 166 pp. Yang, R. S. H. 1987a. Acute versus chronic toxicity and toxicological interactions involving pesticides. Pp. 20-36 in Pesticides: Minimizing the Risks, N. N. Ragsdale and R. J. Kuhr, eds. ACS Symposium Series Vol. 336. Washington, D.C.: American Chemical Society. Yang, R. S. H. 1987b. A Toxicologic View of Pesticides. Chemtech 17:698-703. Yang, R. S. H., and E. J. Rauckman. 1987. Toxicological studies of chemical mixtures of environmental concern at the National Toxicology Program: Health effects of ground water contaminants. Toxicology 47:15-34.