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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
OCR for page 109
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
OCR for page 110
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.
OCR for page 112
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
OCR for page 114
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
OCR for page 115
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
OCR for page 116
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
OCR for page 117
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
OCR for page 118
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
OCR for page 119
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.
OCR for page 120
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.
Representative terms from entire chapter:
physiologically based
