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Drinking Water and Health,: Volume 6 (1986)

Chapter: 6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits

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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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Suggested Citation:"6. Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits." National Research Council. 1986. Drinking Water and Health,: Volume 6. Washington, DC: The National Academies Press. doi: 10.17226/921.
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6 Dose-Route Extrapolations: Using Inhalation Toxicity Data to Set Drinking Water Limits This chapter presents a pharmacokinetic model for the disposition of volatile organic compounds (VOCs) and their metabolites in biological systems. It is intended to allow extrapolation from the inhalation dose route in animals to the ingestion route in humans and may play a useful role in the overall risk assessment for such compounds. VOCs are present in many drinking water supplies throughout the United States (Brass et al., 1977; Symons et al., 19751. Chloroform and certain other trihalomethanes are believed to form during chlorination processes, in which chlorine reacts with humic acids and other organic materials in water supplies. Other VOCs are emitted into drinking water during man- ufacturing and other activities involving the use of chemicals. Waterborne VOC concentrations typically vary from several micrograms per liter (ppb) to a few milligrams per liter (ppm). Higher concentrations are found in river water downstream from chemical spills and in well water near pol- lutant point sources such as hazardous-waste disposal sites. Drinking water standards have been established to protect people from potentially adverse health effects associated with ingestion of contaminated waters. Such effects must often be determined by conducting toxicity studies in laboratory animals and in some way extrapolating these results to predict toxic effects in exposed humans. The best toxicity data base from animal studies for predicting risk to humans would be experiments in which VOCs were provided to laboratory animals in their drinking water. Two constraints make this difficult, however. First, achievable concentrations are small because the water solubility of most VOCs is 16~3

Dose-Route extrapolations 169 TABLE 6-1 Physiological Constantsa Used in Kinetic Modeling of Rats and Humans Parameter Rat Human (bw) Body weight (kg) 0.3 70 (Qp) Alveolar ventilation (liters/hr)b 5.74 325 (Qc) Cardiac output (liters/hr)b 5.74 325 (ka) Absorption rate constants 5.0 5.0 (Vw) Water intake (liters/day) 0.030 2.0 Blood flow rates (portion of total) (Q') Liver 0.25 0.25 (Of) Fat 0.09 0.09 (Qm) Muscle 0.19 0.19 (Qr) Richly perfused tissue (viscera) 0.47 0.47 Tissue group volumes (portion of body weight) (Vl) Liver 0.041 0.025 (Vf) Fat 0~090 0.200 (Vm) Muscle 0.720 0.610 (Vr) Richly perfused tissue (viscera) 0.059 0.075 aValues similar to those used by Ramsey and Andersen (1984), with minor changes in alveolar ventilation (increased from 4.5 to 5.7 liters/hr) and percentage of cardiac output that perfuses the liver (decreased from 37% to 25%). bCalculated from an allometric relationship: y = 14(bw)074. CAbsorption rate was assumed to be invariant with body weight. Uptake can be modeled as arising from a blood perfusion rate into a given tissue volume, in which case the human rate constant would be: k ka (bw2) limited (i.e., their wafer: air partition coefficients are small), and second, rats consume only about 30 ml of water daily. In industry and commerce, humans are exposed to VOCs usually by inhalation and to a lesser extent by skin contact. Over the years, the toxicity of many VOCs has been examined through subchronic or chronic inhalation studies in laboratory animals. These are often conducted at high vapor concentrations and can generally be designed to ensure overt toxicity in animals exposed at the highest concentration. In addition to high air- borne vapor concentrations, the inhalation dose rate is much higher than that achieved in a drinking water study, because the daily alveolar ven- tilation (approximately 140,000 ml; see Table 6-1) is much greater than daily water consumption. For many VOCs, inhalation studies provide the only data from which drinking water standards can be derived. Using such results to predict the risks associated with human consumption of contam-

~ 70 DRINKING WATER AND HEALTH inated drinking water therefore requires both a dose-route and interspecies extrapolation of toxicity data. Even in a prospective sense, inhalation may be a very good surrogate route, and perhaps the only one available for estimating expected toxicity of contaminants in drinking water. Inhalation studies, in which a chemical is absorbed at a fairly uniform rate over a specified exposure period, are not very different from drinking water studies, in which a chemical is absorbed at a variable, moderate rate throughout a day. Well-designed, properly conducted inhalation toxicity studies may, in fact, provide an excellent experimental model for deriving drinking water standards for a variety of volatile chemicals. The differences between these two exposure routes are not insignificant, however, as discussed in this chapter and further developed in the two appendixes to this chapter. BACKGROU ND There has been no consensus on how or even whether inhalation studies could be used to establish drinking water standards. Various groups re- sponsible for assessing chemical hazards in drinking water have handled the problem differently. Some have declined to use inhalation data, rea- soning that the target organ, disposition, and ensuing toxic effects of inhaled chemicals may differ markedly from that which occurs when the agents are ingested. Others have argued that whereas results of inhalation studies may be of value from a qualitative standpoint, inhalation data are likely to be of limited utility quantitatively in predicting consequences of the ingestion of chemicals. Stokinger and Woodward (1958) advocated use of threshold limit values (TLVs) to set water standards. They proposed a direct conversion from uptake at the TLV concentration to an acceptable waterborne concentra- tion, assuming that humans ingest 2 liters of drinking water per day. Their approach also attempted to take into account the proportion of chemical absorbed by either route of administration. The general calculation for a water standard based on a TLV would be: TEV (in mg/m3) x 10 m3/day x proportion absorbed in inhalation _ proportion absorbed orally x 2 liters/day x safety factor - (1) where 10 m3/day represents an estimate of a moderately active employee's total ventilation during an 8-hour work shift. Since TLVs apply to human occupational exposure, no additional safety factor was proposed for gen- eral use. Stokinger and Woodward (1958) did provide estimates for both inhalation and ingestion absorption factors. A variation of this general approach has been used in most attempts to establish drinking water standards for humans from inhalation data based

Dose-Route extrapolations 171 on animals. When toxicity data from animal studies are used, the TLV is replaced by the highest no-adverse-effect concentration in the inhalation experiments in animals. The safety factor, varying from 10 to 1,000, depends on the nature of the inhalation study, the seventy of the response, and the presence or absence of a history of human exposure to the test chemical. This approach has recently been used to derive adjusted ac- ceptable daily intake (AADI) values for trichloroethylene, perchloroeth- ylene, and methylchloroform (EPA, 19841. These calculations represent an attempt to conduct a direct route-to-route extrapolation based on de- livered dose, given the most fundamental assumption that the relationship between delivered dose (milligrams absorbed per day) and the dose re- ceived by target organs is independent of the route of exposure and the species. There is no obvious toxicological or pharmacokinetic basis for such an assumption. Routes of Exposure The route of exposure significantly influences the quantity of a chemical that reaches a particular target tissue, the length of time it takes to get there, and the degree and duration of effect. Volatile organic compounds are readily absorbed by the lung because of its large surface area, intimate alveolar-capillary interfaces, and high rate of blood perfusion. VOCs are small, uncharged, lipophilic molecules that are quickly absorbed from the alveolus into the systemic circulation (Astrand, 19751. Although the uptake of inhaled volatile organics varies with the exposure concentration and the chemical, in humans it typically ranges from 25% to 75% (Astrand, 19751. The uptake in the first few breaths is related to blood solubility and ventilation:perfusion ratios. The proportion retained was derived by Haggard (1924a,b,c) and, in the terminology used in this chapter (see Appendix B), is: Fraction retained = Qc b ~ QcPb + Qp I · (2) This relationship also applies throughout exposures to soluble chemicals (i.e., Pb of about 5 or larger) that are well metabolized at the exposure concentrations used. Compounds absorbed into the pulmonary circulation are transported via the arterial blood directly to potential target organs. The gastrointestinal (GI) tract is also well suited for the absorption of volatile organic compounds, although its total surface area is less than that of the lung and it receives only about 4% of the cardiac output that perfuses the lung. The presence of food in the GI tract delays absorption and reduces the availability of orally administered halocarbons (Counts et

|72 DRINKING WATER AND H"LTH al., 1982; D'Souza et al., 19851. Compounds absorbed from the GI tract into the bloodstream are also subject to first-pass elimination by the liver and lungs (i.e., reduction of blood concentrations before the chemical reaches the systemic circulation). Although Andersen (1981a) and his coworkers (Andersen et al., 1980) evaluated hepatic metabolism of inhaled halocarbons and estimated the metabolism of inhaled styrene during a single pass through the systemic circulation (Andersen et al., 1984), no one has yet directly measured first-pass hepatic elimination of orally ad- ministered volatile organics. Nevertheless, there is no reason why the liver should not metabolize ingested VOCs as efficiently as it does those that are inhaled (i.e., the liver removes virtually all the VOC presented to it by the blood when inhaled concentrations are not high enough to saturate metabolism). For most VOCs encountered in the environment, a substan- tial proportion of a low oral dose will be removed by the liver before it reaches the arterial circulation. Andersen (1981a) compiled a list of VOCs that would be expected to demonstrate substantial first-pass extraction by the liver when present in portal blood at concentrations below saturating levels for enzymes. For organics that do undergo extensive first-pass hepatic extraction, the liver will receive a higher dose and may therefore be injured more by an oral dose than it would by a comparable inhalation exposure. This process is expected with well-metabolized halocarbons that are metabolically activated to cytotoxicants. Ingested VOCs will also be subject to a first-pass pulmonary exhalation. The extent of this effect is determined by the blood:gas partition coefficient of the volatile chemical. It is relatively easy to use an analysis similar to that of Haggard (1924 a,b,c) or Andersen (1981a) to derive a relationship for the proportion of circulating volatile compound that will be eliminated in a single pass through the lung when inhaled air contains none of the test chemical: Fraction exhaled = Q A+ Q. (3) which is about 1/~1 + Pb), since cardiac output (QC) and alveolar venti- lation Espy are approximately equal. Acting sequentially, presystemic elimination by the liver and lungs could significantly diminish the amount of VOC that reaches the bloodstream after low-dose oral ingestion. Dose-Response Curves For all routes of administration, VOCs are absorbed by complex pro- cesses involving passage from an exterior compartment through a series of cells, tissues, and organs until some portion of the original dose reaches

Dose-Route Extrapolations 173 the systemic arterial circulation. The toxic chemical can then be distributed to various organs remote from the site of entry. Frequently, toxicologists Know what organs are affected but lack detailed knowledge of the mo- lecular mechanisms of toxicity. Despite the inadequacy of the data base on such mechanisms, toxicologists and regulators must make prudent, well-documented decisions about expected risks to humans based on data derived largely from studies in animals. The linchpin of any risk assessment is determination of a dose- (or concentration-) response curve under specified experimental conditions. The dose-response curve can either assess a virtual no-effect or minimal- effect level (for nongenotoxic or noncarcinogenic effects) or support ex- trapolation to expected low-level incidence (for genotoxic or carcinogenic effects). These curves should, of course, be based on an estimate of target- tissue dose, which, however, may not be related in any simple manner to applied dose (Andersen, 1981b; Gehring et al., 19781. Applied dose for inhalation or drinking water studies is normally expressed as ppm or mg/m3 (in air) or as mg/liter (in drinking water). On the other hand, target- tissue dose, also called internal dose (O'Flaherty, 1985), might be the area under the blood or tissue concentration-time curve, the peak tissue concentration, the total amount metabolized, the area under a tissue me- tabolite concentration curve, or some other appropriate measure of target- tissue exposure. The proper measure of target-tissue dose must be selected carefully. Consideration must be given to whether the toxic chemical is presumed to be the parent compound or a metabolite (i.e., whether the dose-response curve is consistent with parent chemical toxicity or more complex), and to the relative reactivity of the toxic metabolites (i.e., whether they are stable or very short-lived). The pharmacokinetic models developed for risk assessment extrapolations must have sufficient biological and bio- chemical detail to describe these various measures of target-tissue dose. In addition, the toxicologist and regulator should be aware of the critical distinction between target-tissue dose and applied dose and should un- derstand how a proper measure of the former can influence risk assessment computations. Measurement of Tissue Dose Selection of the appropriate measure of target-tissue dose depends on the nature of the chemical component that is associated with the toxic effects. Toxic chemicals can interfere with normal physiological and cel- lular function through a variety of biochemical mechanisms. For the VOCs of interest, toxic effects are most frequently associated with stable me- tabolites or with reactive metabolites that bind covalently and essentially

~ 74 OR ~ N K! NG WATER AN D H "LTH irreversibly with important cellular macromolecules. Their toxicity is not usually simply associated with concentrations of a parent chemical but, rather, with amounts or concentrations of the toxic metabolites. Some toxicants, such as curare, reversibly bind to endogenous receptors. Their toxicity depends on their tissue concentration, receptor binding affinity, and receptor concentration. This is more of a classical pharmacological interaction. Among environmentally important chemicals, the prime ex- ample of a pharmacologically acting toxicant that has very marked inter- species differences in toxic potency is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (Murphy, 1980, p. 3891. For chemicals with pharmacological activity, the proper measure of target-tissue dose would be the tissue concentration divided by some mea- sure of the receptor binding constant for the toxic chemical. High receptor binding affinity arises from strong noncovalent interactions between spe- cific portions of the protein binding surface and the chemical structure of the toxicant. Interspecies differences arise when portions of the binding site are not conserved from species to species, in which case both affinity and toxicity can become markedly species-dependent. Fortunately, a pharmacological mechanism of action, with its potential for significant species differences, is not frequently observed with the chemicals usually found in drinking water. Much more commonly, toxicity is caused by the intrinsic chemical reactivity of foreign compounds or their metabolites. In these cases, there is a direct reaction between critical cellular constituents and reactive chem- icals, and the reactions leading to toxicity are expected to proceed ac- cording to second-order rate equations. Extreme differences in toxicity among species are not expected under these conditions, since tissues and organs are very similar in content and function among species, and the reactivity of their cellular constituents should be similar when exposed to any particularly reactive toxic chemical. The second-order rate equation for loss of a critical cellular component, CCri'' as it reacts with a toxic chemical, T. is: dlCcrit = _ kccritT. (4) In a simplistic way, the rate equation can be rewritten to express the dependence of the reduction in cellular components on the time integral of the toxic chemical, i.e., the area under the tissue-concentration curve (AUTC): t4Ccrit = —kiTdt = —k(AUTC); thus, J ccrit Ash

Dose-Route Extrapolations 175 in (ccrit~t = _k(AUTC). ~ crit)O (6) In this case, with a directly reactive species, the loss of critical sites is related to integrated tissue dose. Frequently, metabolites are responsible for toxicity. Metabolite for- mation `'nay occur through first-order processes or capacity-limited, enzyme-catalyzed ones; the latter are more usual. When the reactive toxic chemical is short-lived, the appropriate measure of tissue dose is the ratio of the amount of toxicant produced divided by the volume of tissue in which the reaction takes place (Vr). For an enzyme-mediated formation of reactive metabolite, TM*, d(TM*) (Vm X T) 1 dt \~Km + T} Vr (7) By analogy with Equation 5, the burden of metabolite per unit volume of tissue becomes: id(TM*) = V J 1 r Vm X T ~ AURMC --m V' AMEFF. (8) where the AURMC (area under the rate-of-metabolism curve) could also have been developed for first-order production of metabolites. This equa- tion does not describe a true concentration; it describes an effective con- centration (AMEFF; see Appendix B of this chapter) that would have been achieved by the production of a specified amount of reactive metabolite, TM*, in a particular volume, Vr. The concept of correlating toxicity with the area under the rate-of-metabolism curve was previously developed by Andersen (1981a). Further elaboration of the concept to include the ef- fective volume of reaction is necessary to extend its use to interspecies comparisons (see the section entitled Interspecies Extrapolation later in this chapter). The toxicity of reactive intermediates is related to the portion of the intermediate that ultimately reacts with the critical cellular targets for both carcinogenic and noncarcinogenic end points. In chemical terms, this is illustrated by a scheme in which there are several pathways for con- sumption of the reactive metabolite. Each pathway has a first-order rate constant, the sum of which is iki. There is also a pathway that leads to reaction with critical cellular components; its rate constant is he. Therefore, the relative concentration of the reactive intermediate, TM*, involved in . . . tox~c ~nteract~ons is: CTM* = :$k (AMEFF) (9)

~ 76 DRINKING WATER AND H EALTH This relationship is more important for interspecies extrapolation, i.e., when krl5,ki might vary between species, than for a dose-route extrapo- lation in a given species, where it is assumed that the ratio does not change very much with concentration of the parent VOC. In addition to situations in which the parent chemical or short-lived metabolites are the reactive toxic chemical, other, more complex patterns may exist when stable metabolites are the reactive chemicals associated with toxicity. An example-is the neurotoxicity associated with inhalation of n-hexane or methyl n-butyl ketone, which is due ultimately to circulating concentrations of the metabolite 2,5-hexanedione (DiVincenzo et al., 19781. Although the relationship between concentration of precursor and for- mation of metabolite may be complicated (Clewell et al., 1984), the appropriate target-tissue dose on which risk assessment would be based would be similar to that expressed in Equation 7, except that AUTC would be replaced by area under the target-tissue metabolite-concentration curve (i.e., AUTMC). In a simple system involving a stable toxic metabolite TM formed from a precursor T at a concentration CT, in which a constant fraction of the metabolic pathway is converted to the metabolite of interest, the metabolite is retained in a specified volume of distribution VTM and eliminated by a first-order process with rate constant kTM. The mass-balance equation for the metabolite in its volume of distribution is: VTM4CTM kr (Vm X CT) k V C (10) The AUTMC then is related to the metabolism rate of the parent chemical, but the dependence cannot be expressed as a simple analytical expression. The expected steady-state concentration of the metabolite can be written in a simple form: k /sk (Vm X CT) (CTM)S S kTM X VTM (1 1) The denominator in Equation 11 is identical to the compartmental clearance CITM of the stable metabolite. PHARMACOKINETIC MODELS An attractive and potentially economical approach to risk-assessment extrapolations is the development of predictive physiologically based phar- macokinetic (PB-PK) models for the disposition of volatile organic com- pounds and their metabolites in biological systems. These models are based

Dose-Route Extrapolations ~ 77 INTERSPECIES EXTRAPOLATION RAT No-Effect I nhaled (1 ) Concentration (rat ) No-Effect Drinking Water Concentration (rat ) H UMAN ~ 1 No-Effect I No-Effect (3 No-Effect ~ . ) Drinking Water Target Tissue ~ I Target Tissue - ~ Dose (rat) | Dose (human) Concentration (human) Dose Equivalence FIGURE 6-1 Physiologically based pharmacokinetic extrapolations involved when results of inhalation studies in laboratory animals are used to establish drinking water standards for humans. / on the known physiology of the experimental animals and on known or easily measurable physical, chemical, and biochemical properties of the VOCs. PB-PK models predict general behavior of toxicants, and models successful with one chemical are usually easily adapted to describe the kinetic behavior of many others. This chapter demonstrates a possible PB-PK approach for conducting dose-route and interspecies extrapolation. The underlying premise in a pharmacokinetic risk assessment is that there is a coherent dose-effect curve relating the incidence or severity of response to an appropriate measure of target-tissue dose. Pharmacokinetic risk-assessment extrapo- lations are conducted by assuming that a particular target-tissue dose achieved by one route of exposure in a particular species will have the same biological effect as an equivalent target-tissue dose achieved by another route of exposure or in some other species. The dose-route ex- trapolation between inhalation and oral ingestion is based on an under- standing of the physiological differences in the processes of chemical absorption by these two exposure routes (see Appendix A of this chapter). Interspecies extrapolations are conducted by deterring the drinking water concentration that would lead to the same target-tissue dose in humans achieved at the no-effect (Figure 6-1) or minimal-effect concentration in

]78 DRINKING WATER AND HEALTH the inhalation studies in animals. In Step 1 of Figure 6-1, the target-tissue dose is estimated in a test species based on the no-effect inhalation ex- posure concentration. Step 2 determines the drinking water concentration associated with an equivalent target-tissue dose in the test species. Step 3 determines the drinking water concentration that produces a target-tissue dose in humans equal to the no-effect target-tissue dose from the inhalation study in the test species. In this chapter, the two extrapolation processes are handled separately, but in practice they would be combined and the dose-route extrapolation for test animals would not be conducted as an independent step. To be useful in risk-assessment extrapolations, the pharmacokinetic models used to estimate tissue doses must have sufficient biological detail to describe the differences in absorption by the two exposure routes and to account for structural and physiological differences between various mammalian species. Some PB-PK models do contain the anatomical and biochemical detail for such extrapolations (see, for example, Andersen, 1981a; Fiserova-Bergerova, 1976; Gerlowski and Jain, 1983; Himmelstein and Lutz, 1979; Ramsey and Andersen, 19841. These models, which are particularly easy to develop for a variety of VOCs, are nothing more than a series of mass-balance differential equations describing the movement of a chemical through a number of tissue compartments within the body. In the past, solving such equations was difficult and time-consuming, and there was substantial reluctance to use these descriptions unless the system of equations had a formal, analytical solution (Gibaldi and Perrier, 1975; Teorell, 1937a,b). With modern digital computers and improved techniques for numerical integration, however, the solution of even rel- atively large sets of differential equations is easy. Furthermore, many physiologically based models that are useful for risk assessment with VOCs require only 4 to 10 ordinary differential equations and can be solved rapidly with microcomputer-based procedures. Data-Based Inhalation Models A typical data-based compartmental model attempts to relate the blood or tissue concentration profile of a parent chemical or metabolite to the administered dose of parent chemical using a set of mathematical equa- tions. The parameters for these equations are determined from experiments that follow the time course of the chemical in body fluids, usually blood, and occasionally in specific tissues. A simple data-based inhalation model (Figure 6-2) was developed to examine the pharmacokinetics of inhaled styrene (Ramsey and Young, 19781. This model was used by Young et al. (1979) to describe kinetic behavior in rats and by Ramsey et al. (1980) to describe kinetic behavior in humans. It consisted of a central com-

Dose-Route Extrapolations 179 | PERIPHERAL kn k12 | DISTRIBUTION '| CENTRAL k21 k1c '~ UPTAKE L I CLEARANCE FIGURE 6-2 A conventional, data-based compartmental model used for describing inhalation of styrene in rats and humans. partment in equilibrium with the blood and a peripheral compartment with a concentration related to the central compartment by rate constants in each direction. The time course of a chemical in each compartment is described by a single, mass-balance differential equation. In applying these data-based compartmental models, time-course concentration curves are first determined experimentally. Then compartment volumes and rate con- stants are adjusted to give curves that fit the experimental data. These models can be used for interpolation and limited extrapolation in the same . , . amma1 species. The two equations for the rate at which the amount of chemical in each compartment changes are: A~i _ id ~ = ko—kiCV~C~ —k,2V~C, + k2~V2C2, and (12) dAmt2 = V24C2 = k~2v~c~ —k2~V2C2 (13) In these mass-balance equations, kit and kin are intercompartmental trans- fer rate constants, Vat and V2 are volumes of the two compartments, ko is a zero-order input rate, and k~c is a first-order elimination rate constant. Reitz et al. (1982) used a similar model to describe ethylene dichloride inhalation kinetics but replaced k~c with a term for saturable metabolic clearance when extending this model to examine the kinetic behavior of oral doses. Historically, these data-based descriptions have been very popular because their simple form permits their exact solution as a sum of exponential terms. The existence of a direct solution avoids using more recently developed numerical integration techniques. Physiologically Based Models PB-PK models differ from conventional data-based compartmental ap- proaches in that they are based largely on the actual physiology of the

|80 DRINKING WATER AND HEALTH test species. Instead of compartments whose characteristics are defined by experimental time-course data, these models are based on compartments representing organs and tissue groups with realistic weights and blood flows derived from the literature (Bischoff and Brown, 1966; Himmelstein and Lutz, 19791. Because an accurate anatomical description is used along with measured tissue solubilities, the intercompartmental rate constants are now defined by blood flows, tissue partition coefficients, and tissue volumes. A model derived from this approach can predict the qualitative behavior of the experimental time course without being based directly on the time-course data themselves. Refinement of physiological models to incorporate additional insights gained from direct comparison with ex- perimental data yields validated physiological descriptions that can be used for quantitative extrapolation well beyond the range of experimental conditions. In a physiologically based model, each tissue (or tissue group) is still described by a mass-balance differential equation, but the individual rate constants are related to blood flow, tissue solubility, and volumes of the particular organs. The physiological model used by Ramsey and Andersen (1984) to examine styrene kinetics is shown in Figure 6-3. In this phys- iological description, the mass-balance equation for the liver was: dt i = id—= QiCa—Q~Cv ~—K in+ Ci (14) This mathematical shorthand indicates that the amount of chemical in the liver is increased by a chemical entering in the arterial blood (pica) and diminished by that leaving via the liver venous blood (Q~Cv') and by metabolism (VmaxCv i)I(Km + Cv ~) The physiological model used to examine styrene was based on the assumption that diffusion of the chemical from blood into tissues is much faster than tissue blood flow and therefore was called flow-limited. In addition, in the flow-limited situation, the concentration in effluent tissue blood is in equilibrium with the tissue concentration as dictated by the tissue:blood partition coefficient Pi. The mass balance differential equation for the liver (1) then becomes: Am ~ Vie C C — C _ Vm (CAP ~ dt dt Qua a 0i pit Km + COP (15) The rate constants for tissue uptake and elimination are now defined by physiological parameters and partition coefficients. These are Q~/V~ and Q~/(V~ x Pig, respectively. Kinetic constants of the metabolizing enzyme are Vmax (mg/hr) and Km (mg/liter). In this model, metabolism only occurs in the liver. Mass-balance equations for other tissue groups only have the

Dose-Route extrapolations 18 ~ Qt INHALED (C h) ~ In DEAD SPACE ALV EO LA R SPAC E ARTE R IAL (Ca t) _~ r BLOOD ~ _ ~ ~ METABOLISM LIVER Q r _ I _ I ~ . tVISCERA, BRAIN, ETC. r _ ~ 1 MUSCLE AND SKIN FAT EXHALED (Cexh) VENOUS (Oven) . FIGURE 6-3 Schematic diagram of a physiologically based pharmacoki- netic model for VOC inhalation, used to describe styrene kinetics. Organs or groups of organs are defined with respect to their blood flows (see Ap- pendix B of this chapter for variables). Adapted from Ramsey and Andersen, 1984. equivalent of the first two terms in Equation 15. Diffusion-limited models are used when chemical diffusion from blood into tissue is slower than tissue blood flow (Lutz et al., 19801. Diffusion-limited models have been used with Kepone (Bungay et al., 1981), polybrominated biphenyls (Tuey and Matthews, 1980), and tetrachlorodibenzofuran (King et al., 1983), but they are unnecessary for describing the biological behavior of small lipophilic chemicals that readily diffuse through biological membranes. The chief advantage of a physiologically based model is its much greater predictive power than that of conventional data-based models. Because fundamental metabolic parameters are used in this approach, dose ex- trapolation over a known range of doses, including those where saturation of metabolism occurs, is possible. And because the physiological descrip- tion of the test species is used in the simulation, the behavior of the chemical can be predicted in a different species simply by replacing the appropriate physiological constants (Dedrick, 19731. Similarly, the be- havior for a different route of administration can be determined by adding equations that describe the nature of the new input function. Use of the kinetic model for extrapolation from one exposure scenario to another is

]82 DRINKING WATER AND HEALTH relatively easy and only requires a little ingenuity in writing the equations for this dosing regimen. Finally, because measured physical-chemical and biochemical parameters are used, the behavior of a different chemical can quickly be estimated by the same physiological model after determining the appropriate biochemical constants. The trade-off is the need for an increased number of parameters and equations and for a numerical technique to solve the resulting equations. Values for many parameters, especially for physiological constants, are provided in the literature, however, and several techniques have been developed for the rapid determination of such compound-specific param- eters as tissue solubilities and kinetic constants for metabolism. The de- velopment of physiological models may even be based entirely on data obtained from in vitro or very simple in viva studies. For many VOCs, tissue partition coefficients can be determined by a simple in vitro tech- nique called vial equilibration. The chemical is added to a vial containing a liquid, and then the concentration of the chemical in the head space above the liquid is determined and used to calculate the partition coefficient of the chemical (Sato and Nakajima, 1979a). A variation of this method has also been used to estimate metabolic coefficients (Sato and Nakajima, 1979b). Alternatively, there are rapid in vivo approaches for determining metabolic constants based either on steady-state conditions (Andersen et al., 1984) or on gas uptake experiments (Andersen et al., 1980; Filser and Bolt, 1979; Gargas et al., 19861. These experimentally accessible constants for tissue solubility and VOC metabolism, together with the general physiological parameters, provide a model of parent chemical behavior and rate of metabolism that can predict parent chemical kinetic behavior at various concentrations, for various dose routes, in a variety of species, and with any number of exposure scenarios. AN INHALATION MODEL The absorption, distribution, and elimination of gases and vapors have been extensively studied to understand the time course of action of volatile anesthetics. In the early part of this century, Haggard (1924a,b,c) de- scribed the role that blood flow, pulmonary ventilation, and solubility coefficients play in determining the kinetics of the uptake of diethyl ether. Over the intervening years, there have been other important contributions, which have provided general models for the physiological pharmacoki- netics of volatile anesthetics (Fiserova-Bergerova et al., 1980; Kety, 195 1; Mapleson, 1963; Riggs, 19631. More recently, the importance of under- standing the role of metabolism in order to elucidate the mechanism of a chemical's toxicity has become more widely recognized. Physiological models for inhaled agents can now describe both the formation and con-

Dose-Route extrapolations Il33 gumption of the metabolites formed during and after exposure to volatile chemicals (Fiserova-Bergerova, 1984; Gargas and Andersen, 1985; Ram- sey and Andersen, 19841. Several basic assumptions are made in PB-PK models. For example, in the physiological models for pulmonary uptake, end alveolar air and arterial blood are assumed to be in equilibrium and arterial blood con- centrations are equal to the end alveolar air concentrations times the blood:air partition coefficient (Pb). The total uptake of an inhaled chemical depends in a complex way on the blood:air partition coefficient, pulmonary alveolar ventilation (Qp), cardiac output (Qc), and the difference between arterial and venous blood concentrations (Andersen, 1981a). This difference in concentrations is related to the extent that the vapor undergoes systemic metabolism. The equation used to calculate the arterial blood concentration (Car~) is: Ca — b(QpCinh + QcCven) PbQc + Qp . (16) This equation represents We steady-state solution of a mass-balance equation of the total lung compartment. Mixed venous blood concentration (Cven) is the weighted average of the concentration from all the tissue groups: CVen = (~;QiCv' i)/Qc (17) These equations and four simultaneous, mass-balance differential equa- tions for the four tissue groups constitute the basic physiological model for inhalation (Figure 6-31. The use of this inhalation model to analyze the pharmacokinetics of inhaled styrene in rats at concentrations from 80 to 1,200 ppm (344 to 5,160 mg/m3) has been described in detail (Ramsey and Andersen, 19841. For styrene, the use of realistic metabolic constants for V,,~ and Km (Andersen et al., 1984) enabled investigators to make both accurate predictions of kinetic nonlinearities in this concentration range and determinations of the general physiological factors that govern kinetic behavior of lipophilic, well-metabolized vapors. AN ORAL MODEL The physiological kinetic model used to study inhaled styrene was modified to examine styrene absorption after intubation of a single bolus dose in a saline vehicle. Withey (1976) studied the intragastric kinetics of styrene. Ramsey and Andersen (1984) fit these data with the inhalation model by varying a first-order uptake rate constant until the best repre- sentation of the data was obtained (Figure 6-41. In this description, the chemical was absorbed by a h~rst-order process directly into the liver. The

|84 DRINKING WATER AND HEALTH 10.0 a, - E - z o <t 1.0 z US z o Cal 01 Z Us 0.01 ~ ORAL · · ~ 1 1 1 1 1 1 0 0.4 0.8 1.2 1.6 2.0 2.4 2.8 HOURS FIGURE 6-4 Styrene concentrations over time in mixed venous blood from a rat after a 9.3- mg/kg oral dose of styrene in aqueous solution, as determined by Withey (1976). Solid line is the model prediction with a first-order rate constant for absorption of 5.5/hr. Reproduced with permission from Ramsey and Andersen (1984). best-fit absorption rate constant was 5.5/hr. The uptake of other chemi- cals methylene chloride, trichloroethylene, 1,2-dichloroethane, 1,1- dichloroethylene, and vinyl chloride monomer (Withey, 1976; Withey et al., 1983)—also occurs very rapidly when they are instilled in the stomach in aqueous solution. When the published curves for the first four chemicals were analyzed with the styrene model, the best-fit uptake rate constant was found to be about 7/fur (Gargas and Andersen, Air Force Aerospace Medical Research Laboratory, Wright-Patterson Air Force Base, Ohio, personal communication, 19851. To describe drinking water exposures accurately, the temporal patterns of water ingestion in the test species must be approximated by the input equations. Figure 6-5 shows the results of an analysis based on a 12-hour water intake by four different drinking patterns in the rat a series of sips (equal amounts of water every 10 minutes, shown in the insert for the first 4 hours of the dosing period), two episodic consumption patterns

Dose-Route Extrapolations IS5 24 - I o z - z o a: 4 2 1.00 n 1N o, E 0.80 o - z o 0.60 0.40 0.20 0.00 I I I I 0.00 0.68 1.33 2.00 2.67 3.33 4.00 HOURS He's ~1 ' 1 12 HOU RS 24 FIGURE 6-5 Multiple dosing. Computer-generated drawing. VOC presence in the stomach of rats based on four drinking water consumption patterns: A = Approximately equal amounts every 10 minutes (sipping); B = Equal amounts every hour; C = Equal amounts every 2 hours; D = Single bolus intubation. (equal amounts every 1 or 2 hours), and bolus intubation of the entire daily intake. The curves show the amount in the stomach compartment during a single 24-hour period. The VOC concentration in ingested water was 800 mg/liter, and the total water consumption was assumed to be 30 ml/day for a 300-g rat, as compared with 2 liters/day for a 70-kg human (see Table 6-1~. This is equivalent to an allometric relationship of the following form: y (ml/day) = 76<bwy0 77 (18) In this equation, bw represents body weight in kilograms and y represents intake per day. Adolph (1949) published a similar equation for water intake based on an adult intake of 4.4 liters per day. When expressed in the same units, his equation is: y (ml/day) = 104. 8(bw)° 88 . (19)

|86 DRINKING WATER AND HEATH TABLE 6-2 Partition Coefficientsa and Metabolic Constants for Tnchloroethylene (ICE) and Benzene (BEN) in Rats Parameter TCE BEN Saline:air partition 1.3 2.8 Blood:a~r partition 22.0 18.0 Fat:blood partition 26.0 28.0 Liver:blood partition 1.3 1.0 Muscle:blood partition 0.5 0.6 V,,~cb (mg/kg) 12.0 3.3 Km (mg/liter) 0.25 0.25 Intrinsic clearance (liter/hr) 48.0 13.0 Human blood:a~r partition 9.5 8.0 aDeterrnined by vial-equilibration (Sato and Nakajima, 1979b). by = V,,~C(bW) Equation 18 was a recalculation of Equation 19 based on a daily water intake of 2 liters by a 70-kg human, an assumption used in most risk- assessment procedures, rather than the original 4.4 liters. In the model developed here for drinking water consumption, a small dose of chemical appears in the stomach at equally spaced intervals. The chemical is com- pletely absorbed into the liver compartment with a first-order absorption rate constant of S/hr. The same rate constant was used with both rats and humans (see Table 6-11. TWO EXAMPLES OF DOSE-ROUTE EXTRAPOLATIONS Two extensively studied chemicals trichloroethylene and benzene- are used in this section to illustrate how the results of inhalation studies can be applied to estimate the equivalent dose in a drinking water study. These two chemicals were selected because subchronic inhalation toxicity studies have defined no-effect or minimal-effect levels for the noncarcin- ogenic responses to these VOCs in rats and because the target organs for toxicity are different. Trichloroethylene causes hepatic toxicity, and ben- zene is toxic to the blood-forming tissues in bone marrow. For both chemicals, the kinetic constants for oxidative metabolism, determined by gas uptake experiments (Table 6-2), indicate that the parent chemical has a high affinity for the metabolizing enzymes (i.e., low Km) and that first- pass effects in the liver (i.e., the ability of metabolism to reduce blood concentrations before the chemical reaches the systemic circulation) will be significant at low oral doses. The PB-PK extrapolations developed here focus on determining the equivalent no-effect levels for inhalation and drinking water exposures,

Dose-Route Extrapolations IB7 but not on risk assessments of the carcinogenic activity of these chemicals. Yet this approach could also be applied to assessing carcinogenic risk, a process that is essentially a high-dose to low-dose extrapolation. Regard- less of whether the effect investigated is chronic toxicity or carcinogen- icity, risk assessment should still be based on the amount of toxic chemical at the appropriate target tissue. The development of PB-PK models for tri- chloroethylene and benzene requires knowledge of their partition coeffi- cients. metabolic constants, and physiological indices in the rat. TrichIoroethylene In a subchronic inhalation study, Kimmerle and Eben (1973) found increased liver weights in rats exposed to 56-ppm (302.4-mg/m3) con- centrations of trichloroethylene (the lowest-observed-effect concentration) for 8 hours/day, 5 days/week for 98 days. Essentially continuous exposure (23.5 hours/day) to 35 ppm (189 mg/m3) for 90 days caused no untoward effects in exposed rats (Prendergast et al., 19671. Considered together, these two studies suggest that 56 ppm (302.4 mg/m3) is the minimal effect concentration and that the liver should be used as the target tissue in the pharmacokinetic tissue-dose extrapolation. The toxicity of trichloroethylene appears to be associated with its me- tabolites not with the compound itself. Trichloroethylene is metabolized by microsomal oxidation to an unstable, reactive epoxide intermediate, the majority of which is rearranged to yield trichloroacetaldehyde. The aldehyde is oxidized to trichloroacetic acid or reduced to trichloroethanol, which is then conjugated with glucuronic acid and excreted in the urine. The choice of the measure of target-tissue dose depends on the presumed mechanism of toxicity. If toxicity is due to the concentration profile of one of these stable metabolites, the model would have to include infor- mation on the disposition and elimination of these metabolites (see section on PB-PK Models in Risk Assessment toward the end of this chapter). If toxicity results from the presence of the short-lived reactive epoxide, the target-tissue dose can be estimated with Equation 8 and should be related to the total amount metabolized per unit liver volume. This latter rela- tionship was chosen as the measure of target-tissue dose for the trichlo- roethylene extrapolations. Buben and O'Flaherty (1985) have shown that liver enlargement in mice orally dosed with trichloroethylene dissolved in corn oil correlates well with the amount metabolized. The next task is determination of the target-tissue dose in the rat fol- lowing an 8-hour inhalation exposure to 56 ppm (302.4 mg/m3) (Figure 6-6A). This amount was the lowest-observed-effect concentration reported by Kimmerle and Eben (1973), which is used in place of the no-effect tissue dose indicated in Figure 6-1. To do this, the inhalation PB-PK

|~3 DRINKING WATER AND HEALTH 6,000 a) - C53 it 3,000 llJ 940 o A INHALATION—TRICHLOROETHYLENE ( Rat) - - 56 ppm - 0 500 1,000 INHALED CONCENTRATION (ppm) (1 ppm = 5.4 mg/m3 ) 2,500 ~ B DRINKING WATER—TRICHLOROETHYLENE 72 ( Rat) /12 /< - a) ._ - 1,250 o 380 mg/liter 940 384 mg/liter /G~ ,.03~92 it, ~ ~ 1 ~ - - _— _~ ~ 594 mg/liter _ ~ 1 __ , _~ 1 1 1 o 500 D R I NK I NG WATE R CONCENTRATION (mg/liter) 1 ,000 FIGURE 6-6 Dose-route extrapolation for inhalation and drinking water exposures of rats to trichloroethylene. AMEFF is the effective concentration of reactive metabolite formed in a compartment of specified volume. The numbers at the ends of the drinking water curves indicate the number of equal doses given to the test animal.

Dose-Route Extrapolations 189 model was run at a variety of exposure concentrations to calculate the effective concentration of metabolite formed in the liver (the AMEFF, in mg/liter). This integrated measure of metabolite dose was taken over a 24-hour period, which included the 8 hours of exposure and the 16 hours before the next exposure. At each inhaled concentration, the computer program calculated the total amount of metabolite produced in the liver for the entire 24-hour period, i.e., the AURMC. The curve at the high inhaled concentrations shows the nonlinear behavior introduced by met- abolic saturation. All values of AMEFF from the multiple simulations were then plotted to yield a smooth curve. The value of AMEFF for rats following an 8-hour exposure to 56 ppm (302.4 mg/m3) was calculated to be 940 mg/liter. The physiological model was next used by the committee to estimate the drinking water concentration that would produce a dose of toxic me- tabolite in rat liver equivalent to that produced when rats were exposed to 56 ppm (302.4 mg/m3) by inhalation (Figure 6-6B). The model was applied at a variety of drinking water concentrations to estimate target- tissue dose for the reactive trichloroethylene metabolite. With this model, tissue dose was determined for the 24 hours beginning with ingestion of the first aliquot of water. Curves are depicted for water ingestion for four different exposure patterns: repetitive sipping, two episodic drinking pat- terns, and bolus ingestion. The goal of these simulations was to determine the drinking water concentration that produced a target-tissue dose of 940 mg/liter in the liver. The concentrations were 380, 384, 403, and 594 ma/ liter for the models in which the dose was divided into 72, 12, or 6 equal parts, or given as 1 dose only, respectively. For this particular measure of target-tissue dose, the three (i.e., more realistic divided dose) scenarios provided very similar estimates of the minimal-effect drinking water con- centration in rats. Benzene Deichmann et al. (1963) exposed Sprague-Dawley rats to inhalation concentrations of benzene ranging from 15 to 831 ppm (45 to 2,493 ma/ may. The animals were exposed 7 hours/day, 4 days/week for periods ranging from 4 weeks to 8 months. Leukopenia was observed in rats exposed to 47 ppm (141 mg/m3) during 180 days within an 8-month period and in those exposed to 44 ppm (132 mg/m3) for 45 to 54 days within a 3- to 4-month period, whereas no toxicity was observed at exposure con- centrations of 31, 29, or 15 ppm (93, 87, or 45 mg/m31. The exposures to 29 ppm (87 mg/m3) were administered for 62 days within an 88-day period. For the physiologically based extrapolation with benzene, the no- observed-effect concentration of 31 ppm (93 mg/m3) given 7 hours/day

|90 DRINKING WATER AND H"LTH for 90 days within a 126-day period represented the benchmark exposure. The target-tissue dose of benzene must in some way be related to the length of time that the benzene remains in the blood-forming tissues. Rickert et al. (1979) found that benzene concentrations in bone marrow closely follow blood benzene concentrations, indicating that the marrow responds kinetically as if it were part of the well-perfused visceral tissue compartment. Although the myelotoxicity of benzene is believed to be due to further metabolism of some unidentified benzene metabolites in the marrow tissue itself, there are no estimates of the kinetic constants for benzene metabolism in marrow tissue. In the absence of more complete information, the best measure of target-tissue dose for benzene will be estimates of the integrated visceral tissue dose of some unidentified stable metabolite (see Equation 101. The model parameters for benzene do not include estimates of either kTl~ki or of CITM. Since these terms are linear, an estimate of each was used in the present calculations. It was assumed that kTliki was 1.0 and CITM for the rat was 250 Bohr, which is equivalent to clearance of a chemical with a 1/2-hour half-life from the body water of a rat. These values were used to derive a surrogate for the AUTMC in the simulations of the benzene model. A second measure of tissue dose, AUBC (area under the blood concentration-time curve)—Equation 4— was also taken. The procedure for setting the no-effect benzene tissue dose is the same as that used for trichloroethylene, except that the units of surrogate tissue metabolite dose are mg/liter x hours instead of mg/liter. The no-effect tissue metabolite dose in the 7-hour, 31-ppm (93-mg/m3) exposure with 17 hours between the end of exposure and the next exposure period is calculated to be 10.13 mg/liter x hours. The overall behavior of the AUTMC for exposures to a variety of concentrations of benzene again is nonlinear at high concentrations (Figure 6-7A). The benzene drinking water model predicted equivalent no-effect target-tissue doses for drinking water concentrations of 109, 111, 118, and 183 mg/liter for the sipping, 1-hour, and 2-hour episodic scenarios and for the bolus dosing regimen of water consumption, respectively (Figure 6-7B). For visceral tissue doses of benzene itself, the 7-hour, 31-ppm (93-mg/ m3) exposure produced an AUBC of 2.87 mg/liter x hours, whereas the four drinking water regimens provided equivalent tissue doses of 310, 219, 167, and 89 mg/liter x hours (Figure 6-81. With benzene, the pat- tern of water ingestion by the rat has a marked effect on tissue dose. This physiologically based approach could obviously be refined by includ- ing a more realistic pattern of water intake. In the drinking water model, ingestion was divided into equal increments at evenly spaced intervals over 12 hours. More complicated patterns of water consumption

Dose-Route extrapolations 19 ~ Coo a) 4 - ._ C53 50 Cal 10.13 o 100 4 - ._ - 50 6 10.13 o A =1. 1 1 1 ~ I I INHALATION—BENZENE (Rat) - - 0 500 1,000 INHALED CONCENTRATION (ppm) (1 ppm = 3 mg/m3 ) _ B 111 mg/liter ~ 109 1 18 mg/liter mg/liter ~_~' ~ ._ _ DRINKING WATER—BENZENE (Rat) __ 183 mg/liter 0 500 1,000 D R I NK I NO WATE R CONCENTRATION (mg/liter) FIGURE 6-7 Dose-route extrapolation for inhalation and drinking water exposures of rats to benzene. AUTMC is area under the target-tissue metabolite-concentration curve. The numbers at the ends of the drinking water curves indicate the number of equal doses given to the test animals.

]92 DRINKING WATER AND HEALTH 200 ._ - 1 00 m 75 4 - - E - c~ m o A INHALATION—BENZENE (Rat) no-effect cone.: 31 ppm integrated dose (AUBC): 2.87 1 / o B 167 mg/liter 219 mg/liter 89 mg/liter | 2 37 ~ r _J 253 INHALED CONCENTRATION (ppm) (1 ppm = 3 mg/m3) DRINKING WATER—BENZENE ( Rat) integrated dose (AUBC) = 2.87 500 1 / 311 ~ mg/liter ~ ~C=~-—~ 1 1 1 1 1 0 ~ 500 1,000 DRINKING WATER CONCENTRATION (mg/liter) FIGURE 6-8 Dose-route extrapolation for inhalation and drinking water exposures of rats to benzene, based on area under the blood benzene concentration curve. The numbers at the ends of the drinking water curves indicate the number of equal doses given to the test animals. Panel A: Same as in Figure 6-7 except AUBC was evaluated instead of AUTMC Panel B: Same as in Figure 6-7, but the extrapolation corresponds to a no-effect AUBC value of 2.87 (mg/liter x hours).

Dose-Route Extrapolations 193 could be readily modeled if there were more detailed data on the daily water consumption patterns of rats. I NTERSPECI ES EXTRAPOLATION Allometric Relationships In applying Stokinger and Woodward's (1958) approach to the calcu- lation of acceptable water concentrations from TLVs, an exact corre- spondence is assumed between an acceptable absorbed dose from inhalation and the acceptable absorbed dose from oral ingestion. When this type of calculation also includes interspecies scaling, some assumptions are then made to estimate the relative sensitivity of humans compared with the test species. Presently, the no-effect level in humans is estimated from the rodent no-effect level by applying a surface area correction, i.e., Human dose (mg) = rat dose (lug) ( b ) Human dose (mg/kg) = rat (lose (mg/kg) (h b J (21) This adjustment uniformly reduces the acceptable weight-adjusted dos- age for humans compared with that determined in smaller laboratory test animals. This correction factor is based largely on limited experience with chemotherapeutic drugs (Freireich et al., 1966) and appears applicable to some chemicals. However, it does not have either a toxicological or a pharmacokinetic basis and should not be applied to every chemical. Since a number of kinetic factors may be important in influencing tissue doses in various species, the appropriateness of a rigid approach to interspecies scaling for all chemicals must be more closely examined. The cornerstone of a scientific approach to interspecies extrapolation of physiological pharmacokinetic behavior is the predictable relationship observed between the values of various physiological parameters and spe- cies body weight. The allometric relationship for water intake from Adolph (1949), given in Equation 19, describes the dependence of one particular physiological process on body weight. This allometric relation has the following form: , and (20) ~ I/3 y = a~bw)X. (22) Dependencies of this type have been developed for a variety of physio- logical processes (Adolph, 1949; Schmidt-Nielsen, 1970, 1984~. As a general rule, organ volumes tend to increase in direct proportion to body weight (x = 1.0), whereas surface area, blood flows, ventilation rates,

|94 DRINKING WATER AND H"LTH and metabolic rates are related to a fractional power of body weight (x < 1.0) that is closer to 0.7. For instance, the exponents proposed in the equations for ventilation and surface area are 0.74 (Guyton, 1947) and 0.67 (Spiers and Candas, 1984), respectively. These general allometric relationships can be used as the basis of interspecies scaling strategies. Tissue Dose Scale-Up The basis for choosing an appropriate tissue dose scale-up strategy is the kinetic behavior of the particular chemical and its mechanisms of toxicity. In this first example, the chemical has a well-defined volume of distribution; it is eliminated by processes dependent on organ perfusion in the liver, kidney, or lung, and its toxicity is related to the area under its blood or tissue concentration curves. A dose of this chemical is dis- tributed into a volume (V~) and eliminated with a rate constant determined by its clearance (Cl) and Vat. Clearance is some proportion (P) of perfusion to the organs of elimination, i.e., Cl = PQi. By conventional compart- mental kinetics, the AUBC in any species (i) is: AUBC dose (Vd dose = PQi (23) If P (the proportion of flow cleared) is species-invariant, the dose in a second species that has the same integrated target-tissue dose AUBC becomes: (ma of dose)2 = (ma of dosed (Q2) Since organ blood flows are proportional to (bw)0 7, 0.7 (dose)2= (dose)~ bw2 bow (24) (25) This equation is consistent with the correction factor used in Equation 20. Species sensitivity could also be calculated as a direct function of the exposure concentration instead of absorbed dose. For example, in a simple compartmental model, consider a chemical metabolized to a stable me- tabolite that is eliminated by flow-dependent processes and whose toxicity is related to the AUBC of the metabolite. During metabolism of brominated hydrocarbons such as halothane or bromochloromethane, this kinetic be- havior is expected to occur following the release of the bromide ion (Gargas and Andersen, 19821. Oxidative metabolism yields inorganic bromide that is eliminated by renal filtration. In the kidney, most of the bromide is resorbed by the same processes that conserve chloride; only small amounts

Dose-Route Extrapolations 195 escape resorption. In the animal, therefore, bromide concentrations are increased by its metabolism and subsequent distribution into extracellular water. Metabolic rates are proportional to and limited to (bw)0 7, as clearance. Steady-state concentrations (Cs s) are reached after exposure certain airborne concentrations of a chemical: C rate of formation k~(bw~07 kit s-s 1 k2(bw ~ )0 7 clearance (26) k2 The rate of approach to or recession from a steady state depends on the clearance and the volume of distribution. Thus, the rate constant for the approach will decrease as body weight increases (see Table 6-11. However, the extra area under the uptake curve for the smaller species is exactly offset by the decreased area under the elimination curve, and the net area under the bromide curve, for inhalation of equivalent concentrations of the brominated hydrocarbons, should be invariant with body weight for the bromide produced during their metabolism. The same analysis used for the bromide ion applies to inhalation of a VOC by various species. If metabolism is not extensive, the steady-state blood concentrations are determined by the blood:air partition coefficients. To the extent that blood solubility is a thermodynamic parameter (Dedrick, 1973), it is not expected to be widely variable from species to species, and the achieved blood concentrations should be quite similar regardless of species body weight. Rate constants for the approach to a steady state will decrease with increasing body weight, but the area under the blood concentration-time curve during inhalation of an equivalent VOC con- centration will be nearly independent of the body weight of the species. An examination of drinking water ingestion can also be based on the water concentration required to give a particular integrated tissue dose of parent chemical. Ingested dose (Equation 23) is the water concentration (Cw) times the volume of water consumed (Vw). The latter is determined by the allometric relationship shown in Equation 18. Thus, ingested dose is used in Equation 23 to give: AUBC = pw Qw, and (27) AUBC = Cw x a~(bw) ~ Cw x ai (28) where AUBC is independent of body weight and essentially only a function of water concentration. An examination of the AUTMC relationship reveals that steady-state blood concentrations are proportional to the ratio of Vma,` to the clearance

]96 DRINKING WATER AND HEALTH of the metabolite from its volume of distribution (see Equation 111. Both V,~ and CITM should be proportional to about the same fractional power of body weight. CS S and AUTMC should also be essentially independent of body weight when expressed as a function of drinking water concen- tration. Whether toxicity is correlated with integrated tissue dose of a parent or a stable metabolite, equivalent water or inhaled concentrations are expected to produce similar tissue doses regardless of body weight. Even with a conservative approach to standard setting, therefore, no-effect concentrations in test animals should provide a good estimate for the expected no-effect concentrations in humans. This very useful rule of thumb was derived from an examination of the pharmacokinetic bases of interspecies extrapolation strategies. Short-Lived Reactive Metabolizes Some toxicity is mediated by chemically reactive intermediates that are not sufficiently stable to be isolated. For these chemicals, the rate of metabolism is assumed to be proportional to (bw)0 7 and the intermediate is formed in a target tissue whose volume is directly related to body weight. The tissue load of reactive metabolite becomes: (AURMC)~ doseTM* = V Tissue a~<bwy0 7 allay (29) a2(bw)~ The relationship that expresses the ratio of maximum target-tissue dose of an unstable reactive metabolite in one species to that in another species with different body weight is: (dose)2 _ allay (buy )0 3 (dose)~ (bw21° 3 (allay) \ 0.3 bwl ~ bw2J (30) The maximum effective tissue dose in the smaller species will be signif- icantly larger than the maximum effective dose in the larger species. An argument very similar to this was proposed by Gehring et al. (1978) for assessing risk to humans from vinyl chloride inhalation on the basis of data from toxicity and metabolism studies in rats. The appropriate measure of target-tissue dose is likely to be a subject of lively controversy for many chemicals. Yet, decisions on how to con- duct interspecies scaling must be based on some presumed measure of the toxic moiety. The equations for interspecies scaling are not intended as hard-and-fast rules. Rather, they point out the variety of behaviors that can be expected and emphasize that it would be shortsighted to depend on a standard equation for conducting interspecies extrapolation. A de- fensible interspecies extrapolation strategy can only be developed once an

Dose-Route Extrapolations 197 argument has been made for a particular measure of target-tissue dose and once a realistic description of the exposure scenario has been developed for use in an appropriate PB-PK description. Interspecies PB-PK Mode' Simulations Ramsey and Andersen (1984) successfully modeled human styrene ki- netics by scaling all volumes in direct proportion to body weight and all flows by body weight raised to the 0.7 power. The same partition coef- ficients were used in each species, except that the brood: air partition coefficient was independently determined for the blood of rats and humans. The values were 40.2 + 3.7 (Ramsey and Andersen, 1984) and 51.9 + 2.0 (Sato and Nakajima, 1979a), respectively. Following the line of rea- soning developed by Gehring et al. (1978) for vinyl chloride metabolism, V,~ was scaled as (bw)0 7 and the same Km was used for both humans and rats. As pointed out earlier in this chapter for dioxin toxicity, binding constants for molecules much more complex in structure than styrene may vary unpredictably among animal species. With these simple VOCs, there are no intricate functional groups to dictate highly selective binding that could give rise to significant species variability in metabolism. More work is clearly needed to evaluate species dependencies of xenobiotic affinity constants for protein binding in general and for binding to metabolizing enzymes in particular. Even with the most successful descriptions of phar- macokinetic behavior in a test species, there is still some element of uncertainty and art involved in restructuring the model to describe kinetics in humans. Part of this uncertainty would quickly evaporate with consci- entious efforts to validate some of the assumptions, such as the scaling of V,,~ and the body weight independence of Km, presently made when scaling up animal kinetic models to predict human kinetic behavior. In the interspecies extrapolation strategy based on a PB-PK model, the measure of target-tissue dose in the human is the same as that used in the rodent model. A drinking water model for humans is then used to determine which concentration leads to the same value for target-tissue dose as that associated with the no-effect (or minimal-effect) level in rodents (Figure 6-1~. Again, the model for humans requires a description of the pattern of water consumption by humans and an estimate of the total volume consumed per day. For this report, water intake was calculated from Equation 18 and calculations were based on all four scenarios used in the drinking water model for rats. Another change in scaling the physiological model for rats was an adjustment for differing amounts of fat in adult humans and in young adult rats. The human fat compartment volume was set at 20% of body weight versus 9% in a 300-g rat (Table 6-21. Blood:air partition coefficients for humans were determined from fresh samples of

]98 DRINKING WATER AND HEALTH blood taken from volunteers at Wright-Patterson Air Force Base; they were found to be significantly lower than those for rats (M. L. Gargas, Air Force Aerospace Medical Research Laboratory, Wright-Patterson Air Force Base, Ohio, personal communication, 19851. The blood:air partition coefficients for humans are in close agreement with those published by Sato and Nakajima ( 1979a,b). The PB-PK drinking water models for humans were run at a variety of concentrations to predict target-tissue dose for various ingested concen- trations and the four drinking patterns for both trichloroethylene and ben- zene (Figure 6-91. The no-effect or minimal-effect drinking water concentrations are determined as follows: the no-effect target-tissue dose from Figure 6-6A or Figure 6-8A is located on the y-axis, and a line parallel to the x-axis is drawn through this point. To estimate the human no-effect drinking water concentration associated with each pattern of water consumption, lines are dropped down perpendicular to the x-axis from the intersection points on the curves. For trichloroethylene, the target- tissue, minimal-effect 940-mg/liter dose of reactive metabolite is expected at a drinking water concentration of 1,528 mg/liter, assuming ingestion of 2 liter/day in six equal portions. In agreement with the expectations from Equation 30, the scaling for trichloroethylene predicts that humans can be exposed to a much higher drinking water concentration before attaining a similar target-tissue dose of metabolite (1,528 mg/liter, com- pared with 403 mg/liter for rats). For benzene, the target-tissue, no-effect dose for the area under the surrogate tissue metabolite curve (10.13 ma/ liter x hours) is expected at a drinking water concentration of 98 ma/ liter (not shown). As expected from Equation 28, similar drinking water concentrations (118 compared with 98 mg/liter) yield equivalent tissue doses, expressed as the area under the benzene tissue metabolite curve, regardless of species. Alternatively, because AUBC and AUTMC are expected to have similar interspecies relationships, the extrapolation for benzene could be based on AUBC. In this case, the target-tissue, no- effect dose for AUBC in the rat (2.87 mg/liter x hours) is expected in humans at a drinking water concentration of 183 mg/liter (Figure 6-91. The equivalent value for the rat was 167 mg/liter (Figure 6-~. Both these measures of tissue dose of benzene are surrogate estimates: AUBC because it examines parent compound behavior; AUTMC because it has to assume the fraction metabolized to active metabolite and clearance of this unknown metabolite. These estimates of no-effect or minimal-effect drinking water concen- trations are based on strict equivalence of target-tissue dose, as determined from a PB-PK model representative of a standard, healthy adult male. Safety factors have not been incorporated in the models for either rats or humans to account for any uncertainties in the data base. Nonetheless,

Dose-Route Extrapolations 199 2,000 - a, 4 - ._ - ~ 1,000 11 tll A DRIN KING WATER—TRICHLOROETHYLENE (Human) 12 72 _ .' . 940 .' ~ 1 ~ I 0 /~ I t I I I I o 1 ! 1,528 ms/liter ~ ~_ 6 __ - 0.66 1.33 2.00 2.67 3.33 4.00 D R I N K I NG WATE ~ CONCE NT RATI ON (mg/1 iter X 103 ) 20 C: m 10 _ o B DRINKING WATE R—BENZENE (Human) 1 2.87 ~ k'.~' _— ~~ t 183 mg/liter I ' / // / '. 1 / 6 ,/ // - 0 100 200 300 400 500 DRINKING WATER CONCENTRATION (mg/liter) FIGURE 6-9 Interspecies extrapolations for trichloroethylene and benzene, based on PB-PK estimates of equivalent target-tissue doses. The numbers at the ends of the drinking water curves indicate the number of equal doses given to the test animals.

200 DRINKING WATER AND HEALTH there is a need to recognize the potential uncertainties in any PB-PK description when determining the chemical nature of the toxic component and when extrapolating from a homogeneous rodent population to the heterogeneous human population. These uncertainties could be addressed by using the PB-PK models for changes in various physiological or bio- chemical parameters to predict, for example, the kinetics in young children drinking large amounts of water or in elderly persons with impaired me- tabolism. Another approach would be to assume a particular range~of sensitivities in the human population and to base the lower acceptable exposure limit on the tissue concentration associated with the lower limit of susceptibility in the population. Incorporating Time-Dependent Physiological and Metabolic Changes In rats, chronic exposure and aging can alter kinetic behavior. Repeated exposure might induce xenobiotic metabolism, as it does with chloroben- zene (Sullivan et al., 1983) or styrene (Andersen et al., 19841. Stable metabolites might accumulate and either inhibit or induce metabolism of the parent chemical, as observed with metabolites of hexane (Clewell et al., 19841. Basal enzyme activities will change with age. Volumes of distribution may also change as rats mature and gain a larger amount of body fat (Yang et al., 19841. In much older rats, there is degeneration of physiological function, which can influence kinetic behavior. The potential for such factors to confound kinetic modeling becomes more serious in long-term studies. The value of long-term chronic studies could be enhanced considerably if small groups of test animals were maintained exclusively for evaluation of important biochemical and phys- iological parameters toward the end of the test animals' life span. The results of such studies could form the basis for introducing time-dependent physiological changes into the general PB-PK model developed for risk assessment. Not only does the modeling use a description of a standard rat, the scale-up approach simulates a standard, healthy, young adult male weigh- ing 70 kg. Given the extensive heterogeneity in the human population, it is vital to either evaluate or simulate the effect of altered metabolism or altered physiological states on kinetic behavior. Physiological models can be readily used for this, since metabolic constants and physiological pa- rameters are explicitly defined for organs of elimination and for target tissues. One possibility for predicting the variability in expected target- tissue dose is to simulate kinetic behavior for different conditions of organ function and metabolic capacity and for both extremely young and very old members of an exposed population. This would indicate the variability

Dose-Route Ectrapolations 201 expected in target-tissue doses for extremes of physiological status, age, and patterns of water consumption. Although more research is needed to demonstrate the general utility and limits of PB-PK approaches in assessing risk based on extrapolation of data on other classes of environmental chemicals, techniques already exist for collecting the necessary data to develop PB-PK models for many VOCs. Implementation of this general approach could improve the sci- entific basis of risk assessment extrapolation procedures and could usefully focus toxicologists' attention on acquiring experimental data more relevant for quantitative physiological models that describe the kinetic behavior of important toxicants and their metabolites. PB-PK MODELS IN RISK ASSESSM ENT A commonly voiced concern about introducing pharmacokinetic con- siderations in general risk-assessment decision making is that it is too difficult to determine the time-course cubes necessary to model parent chemicals and to undertake the analytical studies needed to quantify and identify the metabolites of the test chemical. Although this is Sue, PB- PK models can still be useful without complete pharmacokinetic data. The kinetic models for trichloroethylene and benzene described in this chapter are not complete models. They can be used to keep track of the parent chemical and the total amount metabolized, but they do not identify the metabolites that are formed, the concentrations these metabolites reach, or the routes by which they are eliminated. Yet, even these simple PB- PK parent chemical models account for metabolic nonlinearities, exhal- ation, and f~rst-pass liver and pulmonary effects. And they are known to provide a good approximation of kinetic behavior in the rat (Murphy et al., 1984~. Furthermore, such PB-PK models do not require an extensive data base on blood or tissue concentrations as related to time at various dose levels. Time-course studies are essential to the development of data-based kinetic models, but they are only secondary in the design of PB-PK models, where the data serve more for model validation and fine tuning than for model articulation. For many volatile chemicals, the necessary biochem- ical, physiological, and solubility constants needed for a PB-PK model can easily be obtained in short-term experiments requiring a limited num- ber of animals. Extending Basic Models to Accommodate Reality These basic PB-PK descriptions of VOC disposition can play a role in risk assessment, especially in providing a framework for analyzing the

202 DRINKING WATER AND HEALTH effect of various factors on target-tissue dose. The simulations presented here show the complex influence of drinking water patterns on various measures of target-tissue dose in rats and humans. Not every physiological or biochemical factor that can alter kinetic behavior is accounted for by these simple descriptions. For instance, does repeated exposure to trichlo- roethylene induce metabolism of the compound in the same way that styrene induces its own metabolism (Andersen et al., 19841? These factors are not included initially, because their importance is unclear until they have been demonstrated by laboratory experimentation. However, the absence of such information in these basic models reflects less the accuracy or utility of the model than its completeness. Other important factors can be incorporated as further information and new insights are gathered during toxicity testing or through epidemio- logical investigations. In this respect, PB-PK descriptions are much like toxicity data bases. They contain the best available data, yet they are never truly complete and may require restructuring or rethinking based on new observations. Nonetheless, even provisional data bases or kinetic models can be useful. In fact, refinements may not substantially alter the conclu- sions reached during their original use in risk assessment. For instance, enzyme induction may occur at high concentrations but may be of no practical importance at environmental concentrations. The goal of devel- oping these rudimentary PB-PK models is to explore the possibility of introducing a quantitative, scientifically sound pharmacokinetic strategy into risk-assessment extrapolations. When more elaborate models are required, formulation of the kinetic model at an early stage of chemical toxicity evaluation can guide exper- imental strategies for collecting the biochemical and kinetic constants necessary for successful model development. For example, a metabolite of trichloroethylene, trichloroacetic acid (TCA), may cause peroxisomal proliferation in rats and mice, and this proliferative response may be involved in the carcinogenicity of trichloroethylene in Savage bioassay experiments (Green and Prout, 1985; Prout et al., 19851. To assess relative risks from an integrated tissue dose of this metabolite, the kinetic model would have to include TCA production and its elimination and predict liver concentrations of TCA at different times. A model then would be articulated to include major pathways for this metabolite (Figure 6-101. Experiments or literature searches should focus on obtaining kinetic constants and describing localization of the enzymatic steps shown in Figure 6-10 for aldehyde reduction (3), aldehyde oxidation (1), and alcohol glucuronidation (41. Other required information would include plasma protein-binding characteristics of TCA itself (5), urinary excretion rates for the glucuronide (7) and the acid (6), and the equilibrium constant in biological media for the fornication of the hemiacetal from the aldehyde

Dose-Route Extrapolations 203 Urinary Excretion (6) C13( ~CO2H ( ) _ Protein ~ Binding C1 C1 ~ / C =C C1 H (1) O2 ~ C1 O H ~ /\ / C—C ~ Cl3CCHO C1 C1 ~1 (3) C13 CCH2 OH /1 (4) a- 1f W°~ C13CCH2—O ~ (7) Urinary Excretion CH2 OH FIGURE 6-10 Major metabolic pathways for mchloroacetic acid. /OH C13 C—C—OH H (2~. Experiments should be designed to provide quantitative values for the biochemical factors as well as a physiological basis for tissue localization, volumes of distribution, and filtration rates. This coordinated approach to data acquisition would facilitate ultimate interspecies extrapolation with the kinetic model, a process that should be a primary goal of any phar- macokinetic model intended for risk-assessment extrapolations. Similarly, a more extensive kinetic model for benzene might include biochemical constants for metabolism in marrow, identification of the stable toxic metabolite, and determination of its clearance mechanisms. The model then could examine AUTMC for visceral tissues like marrow, the con- centration dependence of metabolism in these tissues, and the target-tissue dose of metabolite either as total amount metabolized or total amount metabolized per target-tissue volume. Other factors concerning xenobiotic metabolism might also be incor- porated into more complex PB-PK models. It appears paradoxical that short-lived toxic metabolites are often presumed to be responsible for toxicity, although they do not need to be explicitly identified in the kinetic models. In general, this approach is justified by assuming that the amount

204 DRINKING WATER AND H"LTH of active toxic chemical formed will be linearly related to the total amount metabolized at all doses or exposure concentrations. This may not be true for chemicals when cofactor depletion by reactive metabolite shifts the distribution of chemical reactions by various pathways. The toxicity of a reactive metabolite is partially determined by the ratio of the amount reacting with target sites divided by the total amount reacting by all pathways, i.e., the krliki term in Equation 9. The interspecies extrapo- lations assume that this distribution of reactive intermediate would be the same in humans as in rats. If evidence indicated a species variation in this ratio, it should be incorporated into the calculations of AMEFF and in the risk-assessment extrapolations. For chemicals with large intrinsic hepatic clearances, first-pass hepatic elimination is important at low concentrations or low dose rates in de- creasing the amounts of chemical reaching the systemic circulation. This behavior is readily apparent when examining the AUBC for benzene ad- ministered in drinking water (Figure 6-~. Other tissues could be involved to a lesser extent in presystemic elimination, including the gut and intes- tinal microflora (for oral ingestion) and the nasal mucosa and upper airway pulmonary tissues (for inhalation). If these tissues are not themselves targets for toxicity, their metabolic capacity would reduce delivery of VOCs to blood at low concentrations. Thus, even the PB-PK risk as- sessment procedure could overestimate risk at very low administered con- centrations. Much work must be done to understand the metabolic capabilities of nonhepatic tissues and their role in presystemic elimination of these VOCs. i

Dose-Route Extrapolations 205 APPENDIX A: METABOLIC PROCESSES IN THE RESPIRATORY TRACT, THE GASTROINTESTINAL TRACT, AND THE LIVER To understand the principles described in the body of Chapter 6, the committee prepared this appendix, which describes in greater detail the metabolic processes that occur in the respiratory tract, the gastrointestinal tract, and the liver. All these processes must be considered when assessing risk from exposure to chemicals. TH E R ESPI RATORY TRACT: ABSORPTION, M ETABOLISM, AN D PRESYSTEMIC ELIMINATION Pulmonary Structure and Cell Types One of the striking features of the lung is the diversity of cell types within it. At least 40 have been identified, each with a characteristic location and function (Gil, 19821. Lung cells provide an epithelial lining, an immunological defense, an endocrine function, and xenobiotic metab- olism. Cell types differ widely in the various regions of the lung. The conducting airways are lined with ciliated cells that move mucous secre- tions and goblet cells that secrete mucus. At least five other cell types are present in the airways; the exact number depends on the species (Kuhn, 19761. The bronchioles contain Clara cells and are frequent targets of injury by toxicants (Boyd, 1977; Reznik-Schuller and Lijinsky, 1979~. As distance from the trachea increases, the goblet cell number decreases and the number of Clara cells increases. The gas-exchanging regions of the lung, the alveolar sacs, also have many cell types. The alveoli have continuous linings by two cell types the Type I cell, which is squamous and covers 95% of the alveolar surface, and the Type II cell, which is cuboidal and is purported to be the progenitor of the Type I cell (Gil, 19821. Type II cells are responsible for the pro- duction of pulmonary surfactant. Macrophages, also present in the alveolar spaces, are important because of their immunological role (Brain et al., 1977). The metabolic activities of the cells differ widely (Gil, 19821. Ciliated cells, basal cells, epithelial serous cells, and many other cells in the upper airways have little capacity for xenobiotic metabolism. Clara cells have considerable cytochrome P450 activity, because they have large amounts of smooth endoplasmic reticulum. Alveolar Type I cells, although they generally have few organelles, have relatively large amounts of smooth

206 DRINKING WATER AND HEALTH endoplasmic reticulum, which seems to proliferate under the effects of some toxic compounds. Type II cells generally have a large supply of organelles, including smooth endoplasmic reticulum, and therefore are often used in pharmacokinetic studies for examining mechanisms of action (Sahebjami et al., 1978; Young and Silbajoris, 19851. Thus, in addition to its function in gas exchange, the lung has varying regional capacities for the metabolism of xenobiotic compounds. Metabolic Properties of Lung Cells CLARA CELLS Clara cells are conciliated bronchiolar cells that increase in number as distance from the bronchioles increases. Although their physiological func- tion is unknown, Clara cells differentiate to replenish injured ciliated bronchiolar cells. The potential for the metabolism of xenobiotic compounds by Clara cells was studied extensively by Boyd (1977, 1980~. Clara cells contain mixed-function oxidase activity capable of metabolizing xenobiotic com- pounds by several pathways. Boyd used 4-ipomeanol to demonstrate se- lective damage to Clara cells after intraperitoneal and oral administration. Toxicity was apparently related to a reactive metabolite formed by cy- tochrome P450. Liver microsomes also metabolized 4-ipomeanol, but the Km for lung microsomal metabolism was more than 10 times lower than that of hepatic microsomes. The selective toxicity for the lung may be explained by these kinetic factors. Carbon tetrachloride is also a Clara-cell toxicant (Boyd, 1980; Longo et al., 1978~. Severe damage, including lipid accumulation, dilation of endoplasmic reticulum, and frank necrosis, was observed in rats and mice given oral doses of the compound. These changes were selective within the bronchiolar region for Clara cells. Clara cells are also a target for reactive intermediates formed during the metabolism of other toxicants, e.g., 3-methylfuran, other furans, and bromobenzene (Boyd, 19801. Reznik-Schuller and Lijinsky (1979) sug- gested that CIara cells might be responsible for pulmonary tumors induced by some nitrosamines. Paraquat, although primarily a toxicant to alveolar pneumocytes, has been shown to damage bronchioles in mice (Etherton and Gresham, 19791. Studies by Devereux et al. (1981) on rabbit Clara cells showed the presence of two major forms of cytochrome P450 with molecular weights of 52,000 and 58,000 daltons.

Dose-Route Extrapolations 207 ALVEOLAR TYPE II CELLS Type II pneumocytes are present along the alveolar wall and are prob- ably the progenitors of the Type I cells. The capability of Type II cells for metabolic activation of toxicants is considerable. This is consistent with the fact that they share a common embryologic origin with Clara cells (O'Hare and Sheridan, 19701. Studies by Devereux et al. (1981) supported this conjecture by showing that the cytochrome P450 isozymes present in rabbit Type II cells were identical to Clara cell P450 isozymes, both immunologically and with respect to molecular weight. Carbon tetrachloride produced alterations in Type II cells in guinea pigs after a single intraperitoneal injection and in rats after oral administration or inhalation (Chen et al., 1977; Gould and Smuckler, 1971; Valdivia and Sonnad, 19661. The damage was similar to that (i.e., fatty changes) pro- duced by carbon tetrachloride in the liver. These data are consistent with metabolic reduction of carbon tetrachloride by cytochrome P450 in the Type II cells. Other Pulmonary Cells There are few data on the capabilities of other pulmonary cell types to metabolize xenobiotic compounds. Alveolar macrophages have been in- vestigated by Devereux et al. (1981) and Hook et al. (1972), who con- cluded that their xenobiotic-compound metabolizing activity is very low or nonexistent. The vascular endothelium is exposed to large quantities of xenobiotic compounds in the bloodstream, but there have been no reports that reactive intermediates have been formed at this site as the result of the metabolic processes. Ciliated bronchiolar cells bind 4- ipomeanol to a much lower extent than do Clara cells, suggesting that their metabolic activity is rather low (Boyd, 19801. There is evidence that butylated hydroxytoluene (BHT) damages al- veolar Type I cells in mice (Boyd, 19801. BHT is metabolized to a reactive intermediate in liver, but identification of this metabolite and its site of formation in the lung is incomplete. The lack of xenobiotic compound-metabolizing enzymes in the tracheo- bronchial region presents an apparent paradox, since this region is a com- mon site of tumor formation in humans. This apparent inconsistency may be resolved by the development of deposition and metabolism models that account for true regional deposition. Xenobiotic compounds may be me- tabolized in Clara cells, and the products of that metabolism may be translocated to the tracheobronchial region via mucociliary transport. Some reactive intermediates, such as bromobenzene-3,4-oxide, readily trans-

208 DRINKING WATER AND H"LTH locate from intracellular metabolic sites into the extracellular milieu (Monks et al., 19841. Elimination of Compounds by the Lung In addition to its functions as an organ of absorption and metabolism, the lung serves as a site of elimination of volatile xenobiotic compounds. The most extensively studied compounds eliminated by the lung are the volatile anesthetics. In general, these compounds have a low capacity for metabolism (i.e., small V,~), and at the concentrations generally used in humans, their elimination rate is controlled by the same factors that are important to uptake: pulmonary ventilation, blood flow, and solubility in blood and tissues (Cowles et al., 19681. The respiratory loss of xenobiotic compounds may be an important means of lowering toxicant concentrations in the blood. Volatile com- pounds, such as the halogenated hydrocarbons, are eliminated in part by diffusion across the pulmonary epithelium into the air spaces of the lung. The epithelial lining of the alveolus is only 5- to 10-~m thick in some regions covered by Type I cells, thus forming an efficient surface for gas exchange (Gil, 19821. Compounds with low blood:air solubilities (Pb) diffuse quickly out of the blood if the air in the lung is relatively free of that compound. Since the entire cardiac output perfuses the lung, this concentration gradient could allow rapid elimination by gas exchange. There have been several published examples of the elimination of halo- genated organic compounds via the lung. Elimination of carbon tetra- chloride has been investigated in humans, monkeys, and rats. Humans given 80-ppm (480-mg/m3) concentrations of carbon tetrachloride in a single breath expired 33% of the dose unchanged within 1 hour (Morgan et al., 19701. Monkeys expired 51% of a dose unchanged 1,800 hours after inhalation exposure (McCollister et al., 1951), and within 18 hours, rats expired 75% of an intraduodenally administered dose (Paul and Ru- binstein, 19631. A small amount of chloroform formed as a metabolite of carbon tetrachloride was also exhaled by the rats, but this accounted for less than 1% of the total metabolism (Paul and Rubinstein, 19631. Chloroform was excreted unchanged in the breath of several test species given chloroform orally (Brown et al., 19741. The expired levels ranged from 6% of the dose in mice to 78% in squirrel monkeys after 48 hours. Similar results were found by Paul and Rubinstein (19631. Humans given chloroform by ingestion exhaled 68.3% of the dose unchanged (Fry et al., 19724. In this study, obese subjects were found to exhale less chlo- roform than did subjects of normal weight. Brown et al. (1974) and Fry

Dose-Route Extrapolations 209 et al. (1972) noted that most of the carbon in the chloroform that was not eliminated unchanged was exhaled as carbon dioxide. The chloroethylenes and chloroethanes are also eliminated unchanged by the lung. In mice, 10% to 42% of an intraperitoneally administered dose of 1,2-dichloroethane was expired unchanged (Yllner, 19711. Hake et al. (1960) showed that 98.7% of an intraperitoneal dose of 1,1,1- trichloroethane was exhaled unchanged by rats within 25 hours. Rapid pulmonary excretion of 1, 1,1-trichloroethane by humans has been reported after a single inhalation exposure (Morgan et al., 1970~. In 1 hour, 44% of the dose was exhaled unchanged. Reichert and Werner (1978) reported that an oral dose of vinylidene chloride given to rats was eliminated unchanged by the lung in a dose- dependent manner. At 0.5 mg/kg bw, 0.9% was expired unchanged. At 50 mg/kg bw, 20% of the dose was expired unchanged. Jones and Hathway (1978) reported that 28% of a vinylidene chloride dose was exhaled un- changed by rats after oral administration of a 50-mg/kg bw dose. Tetra- chloroethylene was eliminated by the lung to an even greater extent. Rats given the compound orally expired 97.9% unchanged within 48 hours (Daniel, 19631. The amount of benzene in the breath of both occupationally exposed and nonexposed volunteers has been measured (EPA, 19791. At work, the breath of filling station attendants exposed to airborne benzene con- centrations ranging from 190 to 260 ,ug/m3 contained levels of 17 to 432 ~g/m3. Only nonsmoking, nonoccupationally exposed subjects had lower levels while at work. The breath of residents of Love Canal in Niagara Falls, New York, contained benzene levels ranging from 0.7 to 6.9 lung/ m3, the highest levels being exhaled by smokers. Carbon tetrachloride, chloroform, 1, 1,1-trichloroethane, tetrachloroethylene, dichlorobenzene, chlorotoluene, and tetrachlorobenzene were also measured in the breath of subjects not occupationally exposed. At first glance there seems to be unexplained variation in the percentage of chemical eliminated by exhalation when different dose routes, dosages, and species are compared for any particular VOC. This is not at all unexpected, however, and is nothing more than an expression of the differences in kinetic character of metabolic clearance and clearance by exhalation. The former is capacity-limited and displays zero-order kinetics at high blood concentrations, whereas the latter displays first-order kinetics at all concentrations. For most volatile, metabolized substrates, total clearance (CIT) is the sum of hepatic, metabolic, and pulmonary clearance, i.e., CIT = Clmetabo~ism + Clexhalation

2~0 DRINKING WATER AND HEALTH The two organ-specific clearance terms written more explicitly become: QiVml(Km + Ci) p C~T — Ql + Vml (Km + Cl ~ QP + QCPb The hepatic term (see Pang and Gillette, 1978) is saturable, since it has the liver concentration (C~) in the denominator. The pulmonary term was developed earlier in Equation 3. The percentage exhaled is the ratio of CleXhala~ion to CIT. As circulating concentrations become lower (C~ < < Km), the proportion exhaled decreases. Then as concentrations increase, hepatic clearance diminishes and the percent exhaled becomes greater. This be- havior is readily explainable kinetically but greatly complicates the inter- pretation of limited data on the relative amounts of VOCs exhaled after exposure by various routes at different dosages. These differences in elimination would be much more easily interpreted if investigators esti- mated such basic constants as the maximum rate of metabolism (Vmax), the substrate binding affinity (Km), and the blood:air partition coefficients (Pb) of the test substances. TH E CASTRO! NTEST! NAL TRACT: ABSORPTION, METABOLISM, AND PRESYSTEMIC ELIMINATION The Absorption Process The major function of the GI tract is the ingestion, digestion, and absorption of nutrients as well as the excretion of nonabsorbable material and certain waste products. Anatomically, the GI tract may be regarded as layers of muscle cells supporting a single layer of mucosal cells. This apparent simplicity is deceiving, however, in that the various regions of the tract are in fact highly complex and specialized for their different functions. For this brief review, focus is placed on the aspects that are important in the absorption of toxicants. The pioneering studies of Brodie and colleagues in the 1950s clearly established that the major mechanism by which nonionizable toxicants are absorbed from the GI tract is their passive diffusion through the lipoidal membranes of the mucosal cells (Hogben, 1971; Hogben et al., 1957, 1959; Schanker et al., 1957, 1958~. The solubility of toxicants in the aqueous milieu of the lumen of the tract is also of obvious importance in that to be absorbed, a molecule must be kinetically free to reach the absorbing surface. Thus, particulate matter or toxicants strongly absorbed onto macromolecules at the absorbing surface may completely pass ~rough the gastrointestinal tract without significant absorption.

Dose-Route Extrapolations 21~ The area of the absorbing surface is also a major factor in the uptake of xenobiotic compounds. The extent of involution of the mucosal surface varies widely in different regions of the tract and is greatest in the small intestine. As discussed here, the small intestine is the major site at which toxicants are absorbed into the body. Anatomical Regions of the Gastrointestinal Tract BUCCAL CAVITY AND ESOPHAGUS The buccal cavity and the esophagus are the sites of the initial processing of food and liquids. These tissues represent about 12% of the length of the tract in humans and have a relatively high blood supply. As a general rule, absorption of toxicants from the buccal cavity is considered to be minimal, largely because of the short time most materials remain in this initial section of the tract (Beckett and Hossie, 19711. One important feature of this site, however, is that the venous blood supply drains from this top portion of the GI tract directly into the systemic venous system. That is, it does not enter the portal vein and hence does not first pass through the liver before reaching the general circulation. STOMACH This region, which accounts for approximately 7% of the total length of the GI tract in humans, is specialized for the processing and digestion of food (Davenport, 19821. The inner surface of the stomach is involuted, thereby increasing the absorbing surface. However, the lumen is large relative to other areas of the tract; the overall ratio of lumen volume to mucosal surface area is lower than in the small intestine. The length of time material stays in the stomach is highly variable. The gastric emptying time (between ingestion and elimination from the stom- ach) depends on the food content, the nature of the food, and the general muscular tone of the stomach. With a few exceptions, the most notable being ethanol, the absorption of toxicants from the stomach is usually minimal. However, it is likely that appreciable amounts of VOCs would be absorbed from the stomach when administered by intubation in an aqueous~solution. Factors influencing gastric absorption include residence time, which may be short if the stomach is empty; the diluting effect of food; the availability of macromolecules for absorption and lipid as a solvent if the xenobiotic compound is ingested with or immediately after a meal; and, as noted above, the relatively low ratio of mucosal surface to lumen volume (Schanker, 19711.

2~2 DRINKING WATER AND H"LTH SMALL INTESTINE The small intestine has three distinct areas: the duodenum, the jejunum, and the ileum, which together account for approximately 30% of the length of the GI tract in humans. Anatomically, these areas are characterized by their highly involuted surfaces. These involutions (termed villi) provide the highest ratio of absorbing-surface-to-lumen volume found in the GI tract. The pH of the lumen contents is significantly higher (about 7.5) than that of the gastric juice (Davenport, 1982, p. 119~. Of particular importance is the entry of bile into the small intestine, where it emulsifies and otherwise promotes the dissolution of lipoidal toxicants. Collectively these factors the high surface-area-to-lumen-volume ratio, the moderate pH, and the presence of bile act together to provide the most favorable environment for absorption. Blood draining the stomach, small intestine, and colon passes through the hepatic portal vein. As noted below, absorbed nutrients and toxicants in the portal vein must traverse the liver before reaching the systemic circulation. COLON AND RECTUM Although the colon and rectum together are almost half the length of the gastrointestinal tract in humans, they are generally considered to con- tribute only minimally to the absorption of toxicants. Factors minimizing absorption in this region include a less favorable surface-area-to-lumen- volume ratio and the decrease in fluidity of the lumen contents as water is withdrawn. Part of the blood supply draining the lower rectum and the anal canal passes directly into the systemic venous circulation and hence bypasses the portal circulation. Presystemic Elimination The amount of an orally ingested VOC that eventually reaches systemic target tissues depends largely on the physiochemical characteristics of the compound, which determine its rate of absorption across the membranes of stomach and small intestine, and on the extent to which it is degraded within the GI tract, the liver, and the lung on its way to the systemic arterial circulation. Elimination before reaching the systemic arterial cir- culation is referred to as presystemic elimination.

Dose-Route Extrapolations 213 METABOLISM WITHIN THE INTESTINAL LUMEN within the gut lumen, metabolic elimination may be catalyzed by the digestive enzymes of the host, by the enzyme content of crypt cells that have been shed from the villi, or by the enzymatic activities of the intestinal flora. Except for a few toxicants that structurally resemble normal nutrients such as peptides, lipids, and carbohydrates, the host digestive enzymes are gen- erally considered to be unimportant in presystemic elimination. Mucosal cells, shed in humans at an estimated rate of 250 g/day (Davenport, 1982), are known to have significant xenobiotic-metabolizing activity (see below) and probably contribute to metabolism of xenobiotic compounds in the lumen. Little attention has been paid to this source of lumen activity, however, and its quantitative importance is largely unknown. A greater role is played by the enzymes produced by the flora of the gut. Microorganisms within the lumen of the intestine represent a dynamic and diverse population, the nature of which varies widely among host species and within a species, depending on its location within the GI tract. For example, the stomach and small intestine of the rat support relatively high populations of enterobacteria, lactobacteria, enterococci, and bifido- bacteria, whereas these regions of the fasted healthy human intestine may approach sterility. Generally speaking, bacterial counts increase with dis- tance down the intestinal tract. In contrast with the metabolic activity of the host tissues, the major enzyme activities of the flora are hydrolytic and reductive in nature. Oxidative reactions occur, but they tend to be minor in extent and are often restricted to specific types of compounds. It is these oxidative pro- cesses that initiate the metabolism of most VOCs. The reductive enzymes catalyze the reduction of a variety of alcohols, aldehydes, ketones, azo compounds, hydroxylamines, and nitro deriva- tives. Reduction of nitro groups in this group of enzymes is of particular interest, since this activity appears to be absent from host tissue. For example, the conversion of the nitro-containing antibiotic chloramphenicol to its amine derivative appears to be entirely due to the action of enzymes of the intestinal flora. METABOLISM BY GASTROINTESTINAL MUCOSA The ability of the GI mucosa to metabolize a variety of toxicants is now well established. As with the flora of the lumen, however, the quan- titative importance of this metabolism is less well defined. The metabolic reactions in the intestinal mucosa are generally similar to those of the liver. The major differences are quantitative with respect to both the specific activities of the individual enzymes in the two tissues

2 14 DRINKING WATER AND HEALTH and, of course, the weight of the two organs. Metabolic reactions found in the mucosal cells include typical Phase I activities, such as oxidation of aliphatic and aromatic carbons, N- and O-dealkylation, S- and N-oxi- dation, and desulfurization. Phase II reactions are equally well represented and include glucuronide, sulfate, glutathione, and glycine conjugation and N-acetylation. Epoxide hydrolase and a variety of hydrolytic activities are also present. Cytochrome P450 and associated Phase I oxidative activities in the intestine are only approximately 2% of that found in the liver (Vainio and Hietanen, 19801. Phase II activities, such as uridine diphosphate (UDP)- glucuronyltransferase and 3'-phosphoadenosine-5'-phosphosulfate (PAPS)- sulfotransferase, may occur in relatively greater amounts (up to approx- imately 10% to 15% of liver levels), whereas others, such as reduced glutathione (GSH)-transferase and epoxide hydrolase, occur to a consid- erably lesser extent (0.5% of liver levels). However, these relative per- centages may be misleading when comparing the contributions to xenobiotic clearance made by the two organs. During absorption, virtually all the toxicant must traverse the mucosal cell barrier and, hence, be exposed to the metabolic activity within these cells. In contrast, during passage through the liver, some part of the xenobiotic dose may remain associated with plasma proteins and be excluded from the parenchymal cells of the liver. Relatively speaking, the metabolic machinery of the mucosal cells may have greater access to the toxicant than the liver does, and clearance in the mucosal cells may be disproportionally greater. VOCs, however, do not seem to be restrictively bound to plasma proteins. This concept should be extended further. Since the concentration of toxicant in the mucosal cells may transiently become very high during absorption, the enzymes of the mucosal cells may become transiently saturated or, and perhaps more likely, become capacity-limited due to the cell's inability to generate essential cosubstrates such as PAPS and acetyl coenzyme A at a sufficiently high rate. These two possibilities—a greater- than-expected organ clearance and a greater likelihood of capacity limi- tation point to the complexity of assessing the role of intestinal mucosa in the metabolism and toxicity of xenobiotic compounds. Major change in the relative importance of these two factors may be expected as the dose of toxicant is increased. At low doses or concentrations, first-pass clearance by the mucosal cells may be a major component of body clear- ance, comparatively small amounts of toxicant penetrating to the systemic arterial circulation. At higher doses or concentrations, however, nonlinear behavior is to be expected, and much greater proportions of the toxicant could reach the general circulation and, hence, target tissues. These in- testinal first-pass effects are very likely to have a substantial influence on the uptake of well-metabolized VOCs ingested at low concentrations.

Dose-Route Extrapolations 215 METABOLISM IN THE LIVER The liver constitutes approximately 2% of the body weight of the adult human and about 4% of the adult rodent. It is an unusual organ in that it receives both an arterial (hepatic artery) and venous (portal vein) blood supply. Of the total blood flow (approximately 1 ml/min/g liver), an estimated 70% to 80% arrives via the portal vein. It is particularly noteworthy that the portal vein drains more than 90% of the length of the GI tract, including the bulk of the absorbing surface of the intestine. Anatomically, most orally ingested VOCs must pass through the liver to reach the systemic arterial circulation. Within the liver, blood from both arterial and venous supplies traverses the sinusoids (incompletely lined channels between the parenchymal cells of the liver) before exiting via the hepatic vein. This arrangement ensures that toxicants sufficiently lipophilic to have gained entry across the mu- cosal membrane will rapidly equilibrate into the parenchymal cells and become available to the xenobiotic chemical-metabolizing enzymes there. Metabolism of a toxicant during this first passage through the liver is a major component of the presystemic elimination of toxicants. The metabolic capability of the liver for the elimination of toxicants is well known and has been described extensively elsewhere (for a general review, see Goldstein et al., 1974; Testa and Jenner, 19761. Briefly, two groups of reactions are recognized. The first, termed Phase I metabolism, is usually oxidative in nature and acts to insert or reveal a polar function in the toxicant. The second, or Phase II metabolism, acts to conjugate such polar groups with endogenous, highly water-soluble compounds, such as glucuronic acid and inorganic sulfate. In only two steps, Phase I and II metabolism usually converts highly lipophilic toxicants that cannot be excreted in the kidneys to highly hydrophilic derivatives that cannot be retained in the body. Phase I reactions oxidize toxicants and are usually catalyzed by enzymes dependent on cytochrome P450. Phase II reactions include a wide range of activities. In addition to glucuronide and sulfate conjugation, activities in this category involve addition of the sulfur in glutathione and the oxygen in water to arene oxides, glutathione substitution reactions, conjugation of carboxylic groups with the nitrogen of glycine, and the acetylation of basic groups. Of particular importance, all these reactions require endog- enous cosubstrates that, with the exception of water required for epoxide hydrolase activity, are capable of being depleted during the metabolism of xenobiotic compounds. The fraction of a dose of VOC that penetrates to the systemic arterial circulation (F) may be expressed in terms of the fraction extracted by the liver during the first pass (E). Thus,

2 ~ 6 DRINKING WATER AND H"LTH F= 1 —E. Furthermore, since liver clearance (elk) is the effective volume of blood from which the toxicant is completely removed per unit time, hepatic clearance (Cli) is given by the product of the blood flow to the liver per unit time (I) and the fraction extracted (E). Thus, Clot= ME. The fraction extracted (known as the extraction ratio) depends on several factors, including the ratio of unbound to bound (to plasma proteins) toxicant, mass transfer and permeability terms, and the intrinsic clearance of the toxicant in the liver (CIi,:~. During the latter, clearance occurs under conditions that do not limit the rate at which the toxicant is delivered to the surface of the metabolizing enzymes, i.e., when the tissue substrate concentration is much below the apparent binding constant (see the second equation in this appendix). From a biochemical viewpoint, the free intrinsic clearance under first-order conditions may be estimated by dividing the maximal velocity of the enzyme reaction (V,~) by the apparent Michaelis- Menten-Henri binding constant (Km) Thus, the intrinsic clearance is equiv- alent to the first-order rate constant for the enzymic reaction (see the second equation in this appendix) as Cli approaches zero. Rearrangement and substitution of these and associated mathematical expressions lead to the following relationship: Q V F = Q + Cl ; Clint = K Since intrinsic clearance can be related to the enzyme parameters V,~ and Km' this expression indicates that under first-order conditions, the fraction of the dose reaching the systemic arterial circulation depends on flow of blood to the liver and the metabolic capability of the liver to remove that drug. Several important consequences follow from this relationship. First, the coadministration of an inhibitor or a competitive substrate for a major metabolic elimination pathway of a potentially toxic substance would lead to a decrease in the pathway's intrinsic clearance and a major increase in systemic availability. Thus, a dose of substance normally considered safe could, under appropriate circumstances, become highly toxic. Conversely, induction of the xenobiotic chemical-metabolizing enzyme (that is, an increase in V,na,`) would act to decrease systemic availability. Because both the constituent level and inductive capacity of hepatic metabolism are under genetic control in humans, there may be a great variation in intrinsi clearance, and the activity of specific pathways for specific toxicants in some subsets of the population may be very low. Thus, some persons

Dose-Route Extrapolations 217 may have very low intrinsic clearance rates for specific xenobiotic com- pounds and may exhibit atypical systemic availability and toxic response. When intrinsic clearance is very high relative to liver blood flow, the relationship reduces to: F = QICIin~. Since the intrinsic clearance for a given toxicant and person may be regarded as constant, the systemic availability will depend largely on liver blood flow. Thus, drugs that alter blood flow may influence availability, whereas induction or even partial inhibition may have little effect. Con- versely, for toxicants with intrinsic clearances that are low relative to blood flow, availability may be very sensitive to induction or inhibition effects and resistant to blood-flow alteration. In recent years, it has become increasingly apparent that most VOCs are well metabolized by hepatic oxidation at appropriate concentration ranges. The term well metabolized refers to the condition where Clint is much greater than liver blood flow. Andersen (1981a) has provided a list of VOCs that show this behavior. Although these compounds are well metabolized at low concentrations, the enzymes have only moderate max- imum velocities and are readily saturated. The earlier idea that VOCs were poorly metabolized came from experiments in which high oral doses were administered and large amounts of VOC were eliminated unchanged. As noted earlier, this does not indicate lack of metabolism but, rather, saturation of metabolism and an increased relative clearance by exhalation (see Equation 3~. Even many anesthetics are now known to be well me- tabolized. The V,na,` for the oxidative metabolism of halothane in a 250- g rat is approximately 2.5 mg/hr. At low inhaled concentrations, its in- trinsic clearance is greater than hepatic blood flow (Andersen, 1981a; Gargas and Andersen, 19821. However, its metabolism becomes saturated at an inhaled concentration of 100 ppm, which corresponds to an arterial blood concentration of only 2 mg/liter. Not surprisingly, when humans or animals were exposed to anesthetic concentrations, about 1,500 ppm, most absorbed halothane was eliminated in exhaled breath as the parent chemical. More recent, as-yet-unpublished studies by Gargas and asso- ciates show clearly that diethyl ether is also well metabolized by micro- somal oxidation at inhaled concentrations below 200 ppm (M. E. Andersen, Air Force Aerospace Medical Research Laboratory, Wright-Patterson Air Force Base, Ohio, personal communication, 19851. In fact, most VOCs have large intrinsic clearances and will be subject to very significant f~rst- pass elimination upon oral ingestion.

2 l~ DRINKING WATER AND H"LTH APPENDIX B: DEFINITIONS OF SYMBOLS AND ABBREVIATIONS Symbol or Abbreviation Units Definition AUBC mg/liter x hours Area under the blood concentration-time curve AMEFF mg/liter The effective concentration of reactive metabolite formed in a compartment of specified volume AURMC mg Area under the rate of metabolism curve AUTC mg/liter x hours Area under the tissue concentration curve AUTMC mg/liter x hours Area under the tissue metabolite time curve bw kg Body weight C mg/liter or ppm Concentration Cl liter/hr Clearance Clint liter/hr Intrinsic metabolic clearance Cw mg/liter Water concentration of contaminant E Extraction ratio for an organ of elimination F Fraction of substance passing through an organ of elimination Km mg/liter Apparent Michaelis constant for substrate binding to metabolizing enzyme kr hr- ~ Rate constant for pathway leading to reaction with critical cellular components

Dose-Route Extrapolations 219 Symbol or Abbreviation Units p Definition Proportion of flow cleared by perfusion to organs of elimination Pb liter/liter Blood: air partition coefficient Qc liter/hr Cardiac output Is liter/hr Dead-space ventilation Of liter/hr Blood flow in fat A liter/hr Liver blood flow Up liter/hr Alveolar ventilation Qr liter/hr Blood flow in highly vascularized organs Qs liter/hr Blood flow in muscle and skin Q. liter/hr Total pulmonary ventilation T concentration Toxic substance TLV concentration Threshold limit value TM concentration Toxic metabolite Vat liter Volume of distribution Am percent body weight Muscle volume V,~ mg/hr Maximum rate of enzymatic reaction Vw liter Water consumption in a given time period y ml/day Intake REFERENCES Adolph, E. F. 1949. Quantitative relations in the physiological constitutions of mammals. Science 109:579-585. Andersen, M. E. 1981 a. A physiologically based toxicokinetic description of the metabolism of inhaled gases and vapors: Analysis at steady state. Toxicol. Appl. Pharmacol. 60:509- 526.

220 DRINKING WATER AND H"LTH Andersen, M. E. 1981b. Saturable metabolism and its relationship to toxicity. CRC Crit. Rev. Toxicol. 9:105-150. Andersen, M. E., M. L. Gargas, R. A. Jones, and L. J. Jenkins, Jr. 1980. Determination of the kinetic constants for metabolism of inhaled toxicants in viva using gas uptake measurements. Toxicol. Appl. Pharmacol. 54:100-116. Andersen, M. E., M. L. Gargas, and J. C. Ramsey. 1984. Inhalation pharmacokinetics: Evaluating systemic extraction, total in vivo metabolism, and the time course of enzyme induction for inhaled styrene in rats based on arterial blood:inhaled air concentration ratios. Toxicol. Appl. Pharmacol. 73:176-187. ~strand, I. 1975. Uptake of solvents in the blood and tissues of man. A review. Scand. J. Work Environ. Health 1:199-218. Beckett, A. H., and R. D. Hossie. 1971. Buccal absorption of drugs. Pp. 25-46 in B. B. Brodie, J. R. Gillette, and H. S. Acke~lllan, eds. Concepts in Biochemical Pharmacology, Vol. 28, Part 1. Springer-Verlag, New York. Bischoff, K. B., and R. G. Brown. 1966. Drug distribution in mammals. Chem. Eng. Prog. Symp. Ser. 62(66):33-45. Boyd, M. R. 1977. Evidence for the Clara cell as a site of cytochrome P450-dependent mixed-function oxidase activity in lung. Nature 269:713-715. Boyd, M. R. 1980. Biochemical mechanisms in chemical-induced lung injury: Roles of metabolic activation. CRC Crit. Rev. Toxicol. 7:103-176. Brain, J. D., J. J. Godleski, and S. S. Sorokin. 1977. Quantif~cation, origin, and fate of pulmonary macrophages. Pp. 849-892 in J. D. Brain, D. F. Proctor, and L. M. Reid, eds. Respiratory Defense Mechanisms. Marcel Dekker, New York. Brass, H. J., M. A. Feige, T. Halloran, J. W. Mello, D. Munch, and R. F. Thomas. 1977. The National Organic Monitoring Survey: Samplings and analyses for purgeable organic compounds. Pp. 393-416 in R. B. Pojasek, ed. Drinking Water Quality En- hancement through Source Protection. Ann Arbor Science Publishers, Inc., Ann Arbor, Mich. Brown, D. M., P. F. Langley, D. Smith, and D. C. Taylor. 1974. Metabolism of chlo- roform. I. The metabolism of [~4C]chloroform by different species. Xenobiotica 4:151- 163. Buben, J. A., and E. J. O'Flaherty. 1985. Delineation of the role of metabolism in the hepatotoxicity of trichloroethylene and perchloroethylene: A dose-effect study. Toxicol. Appl. Pharmacol. 78:105-122. Bungay, P. M., R. L. Dedrick, and H. B. Matthews. 1981. Enteric transport of chlordecone (Kepone~) in the rat. J. Pharmacokinet. Biopharm. 9:309-341. Chen, W.-J., E. Y. Chi, and E. A. Smuckler. 1977. Carbon tetrachloride-induced changes in mixed function oxidases and microsomal cytochromes in the rat lung. Lab. Invest. 36:388-394. Clewell, H. J., M. E. Andersen, and M. G. MacNaughton. 1984. Inhalation pharmaco- kinetics: Inhibitory interactions between n-hexane (HX), methyl-n-butylketone, and 2,5- hexanedione (HD). (Abstract 442.) Toxicologist 4(1):111. Counts, C. A., J. V. Bruckner, and S. Feldman. 1982. Role of dose level, food intake and diluent in toxicokinetics of orally administered 1,1-dichloroethylene (1,1-DCE). (Abstract 421.) Pharmacologist 24:171. Cowles, A. L., H. H. Borgstedt, and A. J. Gillies. 1968. Uptake and distribution of inhalation anesthetic agents in clinical practice. Anesth. Analg. 47:404-414. Daniel, J. W. 1963. The metabolism of 36Cl-labelled trichloroethylene and tetrachloro- ethylene in the rat. Biochem. Pharmacol. 12:795-802.

Dose-Route Extrapolations 221 Davenport, H. W. 1982. Physiology of the Digestive Tract. An Introductory Text, 5th ed. Year Book Medical Publishers, Chicago. 245 pp. Dedrick, R. L. 1973. Animal scale-up. J. Pharmacokinet. Biopharm. 1:435-461. Deichmann, W. B., W. E. MacDonald, and E. Bernal. 1963. The hemopoietic tissue toxicity of benzene vapors. Toxicol. Appl. Pharmacol. 5:201-224. Devereux, T. R., C. J. Serabjit-Singh, S. R. Slaughter, C. R. Wolf, R. M. Philpot, and J. R. Fouts. 1981. Identification of cytochrome P-450 isozymes in conciliated bronchiolar epithelial (Clara) and alveolar type II cells isolated from rabbit lung. Exp. Lung Res. 2:221-230. DiVincenzo, G. D., M. L. Hamilton, C. J. Kaplan, W. J. Krasavage, and J. L. O'Don- oghue. 1978. Studies on the respiratory uptake and excretion and the skin absorption of methyl n-butyl ketone in humans and dogs. Toxicol. Appl. Pharmacol. 44:593-604. D'Souza, R. W., J. V. Bruckner, and S. Feldman. 1985. Oral and intravenous trichloro- ethylene pharmacokinetics in the rat. J. Toxicol. Environ. Health 15:587-601. EPA (Environmental Protection Agency). 1979. Analytical protocols for making a prelim- inary assessment of halogenated organic compounds in man and environmental media. Report No. EPA-560/13-79-010. Office of Toxic Substances, Environmental Protection Agency, Washington, D.C. 319 pp. (Available from the National Technical Information Service, Springfield, Va., as Publication No. PB80-109168.) EPA (Environmental Protection Agency). 1984. National primary drinking water regula- tions; volatile synthetic organic chemicals. Fed. Regist. 49:24330-24355. (40 CFR Part 141) Etherton, J. E., and G. A. Gresham. 1979. Early bronchiolar damage following paraquat poisoning in mice. J. Pathol. 128:21 -27. Filser, J. G., and H. M. Bolt. 1979. Pharmacokinetics of halogenated ethylenes in rats. Arch. Toxicol. 42:123-136. Fiserova-Bergerova, V. 1976. Mathematical modeling of inhalation exposure. J. Combust. Toxicol. 3:201-210. Fiserova-Bergerova, V. 1984. Inhibitory effect of isoflurane upon oxidative metabolism of halothane. Anesth. Analg. 63:399-404. Fiserova-Bergerova, V., J. Vlach, and J. C. Cassady. 1980. Predictable "individual dif- ferences" in uptake and excretion of gases and lipid soluble vapours: Simulation study. Br. J. Ind. Med. 37:42-49. Freireich, E. J., E. A. Gehan, D. P. Rall, L. H. Schmidt, and H. E. Skipper. 1966. Quantitative comparison of toxicity of anticancer agents in mouse, rat, hamster, dog, monkey, and man. Cancer Chemother. Rep. 50:219-244. Fry, B. J., T. Taylor, and D. E. Hathway. 1972. Pulmonary elimination of chloroform and its metabolite in man. Arch. Int. Pharmacodyn. Ther. 196:98-111. Gargas, M. L., and M. E. Andersen. 1982. Metabolism of inhaled brominated hydrocar- bons: Validation of gas uptake results by determination of a stable metabolite. Toxicol. Appl. Pharmacol. 66:55-68. Gargas, M. L., and M. E. Andersen. 1985. Physiologically based simulation analysis of gas uptake data. (Abstract 117.) Toxicologist 5(1):30. Gargas, M. L., H. J. Clewell, and M. E. Andersen. 1986. Metabolism of inhaled dihalo- methanes in vivo: Differentiation of kinetic constants for two independent pathways. Toxicol. Appl. Pharmacol. 82:211-223. 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.

222 DRINKING WATER AND HEALTH Gerlowski, L. E., and R. K. Jain. 1983. Physiologically based pharmacokinetic modeling: Principles and applications. J. Pharm. Sci. 72:1103-1127. Gibaldi, M., and D. Perrier. 1975. Pharmacokinetics. Marcel Dekker, New York. 329 pp. Gil, J. 1982. Comparative morphology and ultrastructure of the airways. Pp. 3-25 in H. Witschi and P. Nettesheim, eds. Mechanisms in Respiratory Toxicology, Vol. I. CRC Press, Boca Raton, Fla. Goldstein, A., L. Aronow, and S. M. Kalman. 1974. Principles of Drug Action: The Basis of Pharmacology, 2nd ed. John Wiley, New York. 854 pp. Gould, V. E., and E. A. Smuckler. 1971. Alveolar injury in acute carbon tetrachloride intoxication. Arch. Intern. Med. 128:109-117. Green, T., and M. S. Prout. 1985. Species differences in response to trichloroethylene. II. Biotransformation in rats and mice. Toxicol. Appl. Pharmacol. 79:401-411. Guyton, A. C. 1947. Measurement of the respiratory volumes of laboratory animals. Am. J. Physiol. 150:70-77. Haggard, H. W. 1924a. The absorption, distribution, and elimination of ethyl ether. I. The amount of ether absorbed in relation to the concentration inhaled and its fate in the body. J. Biol. Chem. 59:737-751. Haggard, H. W. 1924b. The absorption, distribution, and elimination of ethyl ether. II. Analysis of the mechanism of absorption and elimination of such a gas or vapor as ethyl ether. J. Biol. Chem. 59:753-770. Haggard, H. W. 1924c. The absorption, distribution, and elimination of ethyl ether. m. The relation of the concentration of ether, or any similar volatile substance, in the central nervous system to the concentration in the arterial blood, and the buffer action of the body. J. Biol. Chem. 59:771-781. Hake, C. L., T. B. Waggoner, D. N. Robertson, and V. K. Rowe. 1960. The metabolism of 1, 1,1-trichloroethane by the rat. Arch. Environ. Health 1:101-105. Himmelstein, K. J., and R. J. Lutz. 1979. A review of the applications of physiologically based pharmacokinetic modeling. J. Pharmacokinet. Biopharm. 7:127-145. Hogben, C. A. M. 1971. Biological membranes and their passage by drugs. Pp. 1-8 in B. B. Brodie, J. R. Gillette, and H. S. Ackerman, eds. Concepts in Biochemical Phar- macology, Vol. 28, Part 1. Springer-Verlag, New York. Hogben, C. A. M., L. S. Schanker, D. J. Tocco, and B. B. Brodie. 1957. Absorption of drugs from the stomach. II. The human. J. Pharmacol. Exp. Ther. 120:540-545. Hogben, C. A. M., D. J. Tocco, B. B. Brodie, and L. S. Schanker. 1959. On the mechanism of intestinal absorption of drugs. J. Pharmacol. Exp. Ther. 125:275-282. Hook, G. E. R., J. R. Bend, and J. R. Fouts. 1972. Mixed-function oxidases and the alveolar macrophage. Biochem. Pharmacol. 21:3267-3277. Jones, B. K., and D. E. Hathway. 1978. Differences in metabolism of vinylidine chloride between mice and rats. Br. J. Cancer 37:411-417. Kety, S. S. 1951. The theory and applications of the exchange of inert gas at the lungs and tissues. Pharmacol. Rev. 3:1-41. Kimmerle, G., and A. Eben. 1973. Metabolism, excretion and toxicology of trichloro- ethylene after inhalation. 1. Experimental exposure on rats. Arch. Toxicol. 30: 115-126. King, F. G., R. L. Dedrick, J. M. Collins, H. B. Mat~ews, and L. S. Birnbaum. 1983. Physiological model for the pharmacokinetics of 2,3,7,8-tetrachlorodibenzofuran in sev- eral species. Toxicol. Appl. Pharmacol. 67:390-400. Kuhn, C., m. 1976. The cells of the lung and their organelles. Pp. 3-48 in R. G. Crystal, ed. The Biochemical Basis of Pulmonary Function. Marcel Dekker, New York.

Dose-Route extrapolations 223 Longo, N., C. Statham, H. Sasame, and M. Boyd. 1978. Pulmonary Clara-cell damage by carbon tetrachloride. (Abstract 1536.) Fed. Proc. 37:505. Lutz, R. J., R. L. Dedrick, and D. S. Zaharko. 1980. Physiological pharmacokinetics: An in viva approach to membrane transport. Pharmacol. Ther. 11:559-592. Mapleson, W. W. 1963. An electric analogue for uptake and exchange of inert gases and other agents. J. Appl. Physiol. 18:197-204. McCollister, D. D., W. H. Beamer, G. J. Atchison, and H. C. Spencer. 1951. The absorption, distribution and elimination of radioactive carbon tetrachloride by monkeys upon exposure to low vapor concentrations. J. Pharmacol. Exp. Ther. 102:112-124. Monks, T. J., S. S. Lau, and J. R. Gillette. 1984. Diffusion of reactive metabolites out of hepatocytes: Studies with bromobenzene. J. Pharmacol. Exp. Ther. 228:393-399. Morgan, A., A. Black, and D. R. Belcher. 1970. The excretion in breath of some aliphatic halogenated hydrocarbons following administration by inhalation. Ann. Occup. Hyg. 13:219-233. Murphy, J. P., M. E. Andersen, M. L. Gargas, and H. J. Clewell. 1984. Predictive pharmacokinetic models for inhaled JP-10, benzene, and toluene from in vitro data. (Abstract 266.) Toxicologist 4:67. Murphy, S. D. 1980. Pesticides. Pp. 357-408 in J. Doull, C. D. Klaassen, and M. O. Amdur, eds. Casarett and Doull's Toxicology: The Basic Science of Poisons, 2nd ed. Macmillan, New York. O'Flaherty, E. J. 1985. Differences in metabolism at different dose levels. Pp. 53-90 in D. B. Clayson, D. Krewski, and I. Munro, eds. Toxicological Risk Assessment. Vol. I. Biological and Statistical Criteria. CRC Press, Boca Raton, Fla. O'Hare, K. H., and M. N. Sheridan. 1970. Electron microscopic observations on the morphogenesis of the albino rat lung, with special reference to pulmonary epithelial cells. Am. J. Anat. 127:181-205. Pang, K. S., and J. R. Gillette. 1978. Complications in the estimation of hepatic blood flow in vivo by pharmacokinetic parameters. The area under the curve after the concom- itant intravenous and intraperitoneal (or intraportal) administration of acetaminophen in the rat. Drug Metab. Dispos. 6:567-576. Paul, B. B., and D. Rubinstein. 1963. Metabolism of carbon tetrachloride and chloroform by the rat. J. Pharmacol. Exp. Ther. 141:141-148. Prendergast, J. A., R. A. Jones, L. J. Jenkins, Jr., and J. Siegel. 1967. Effects on experimental animals of long-term inhalation of trichloroethylene, carbon tetrachloride, 1, 1,1-trichloroethane, dichlorodifluoromethane, and 1,1-dichloroethylene. Toxicol. Appl. Pharmacol. 10:270-289. Prout, M. S., W. M. Provan, and T. Green. 1985. Species differences in response to trichloroethylene. I. Pharmacokinetics in rats and mice. Toxicol. Appl. Pharmacol. 79:389-400. Ramsey, J. C., and M. E. Andersen. 1984. A physiologically based description of the inhalation pharmacokinetics of styrene in rats and humans. Toxicol. Appl. Pharmacol. 73: 159-175. Ramsey, J. C., and J. D. Young. 1978. Pharmacokinetics of inhaled styrene in rats and humans. Scand. J. Work Environ. Health 4(Suppl. 2):84-91. Ramsey, J. C., J. D. Young, R. Karbowski, M. B. Chenoweth, L. P. McCarty, and W. H. Braun. 1980. Pharmacokinetics of inhaled styrene in human volunteers. Toxicol. Appl. Pharmacol. 53:54-63. Reichert, D., and H. W. Werner. 1978. Disposition and metabolism of [~4C]l,l dichloro- ethylene after single oral administration in rats. (Abstract 87.) Naunyn-Schmiedeberg's Arch. Pharmacol. 302(Suppl.):R22.

224 DRINKING WATER AND HEATH Reitz, R. H., T. R. Fox, J. C. Ramsey, J. F. Quast, P. W. Langvardt, and P. G. Watanabe. 1982. Pharmacokinetics and macromolecular interactions of ethylene dichloride in rats after inhalation or Savage. Toxicol. Appl. Pharmacol. 62:190-204. Reznik-Schuller, H., and W. Liiinsky. 1979. In viva autoradiography and nitrosohepta- methyleneimine carcinogenesis in hamsters. Cancer Res. 39:72-74. Rickert, D. E., T. S. Baker, J. S. Bus, C. S. Barrow, and R. D. Irons. 1979. Benzene disposition in the rat after exposure by inhalation. Toxicol. Appl. Pharmacol. 49:417- 423. Riggs, D. S. 1963. The Mathematical Approach to Physiological Problems. A Critical Primer. MIT Press, Cambridge, Mass. 445 pp. Sahebiami, H., C. L. Vassallo, and J. A. Wirman. 1978. Lung mechanics and ultrastructure in prolonged starvation. Am. Rev. Respir. Dis. 117:77-83. Sato, A., and T. Nakajima. 1979a. A vial-equilibration method to evaluate the drug- metabolizing enzyme activity for volatile hydrocarbons. Toxicol. Appl. Pharmacol. 47:41- 46. Sato, A., and T. Nakajima. 1979b. Partition coefficients of some aromatic hydrocarbons and ketones in water, blood and oil. Br. J. Ind. Med. 36:231-234. Schanker, L. S. 1971. Absorption of drugs from the gastrointestinal tract. Pp. 9-24 in B. B. Brodie, J. R. Gillette, and H. S. Ackerman, eds. Concepts in Biochemical Phar- macology, Vol. 28, Part 1. Springer-Verlag, New York. Schanker, L. S., P. A. Shore, B. B. Brodie, and C. A. M. Hogben. 1957. Absorption of drugs from the stomach. I. The rat. J. Pharmacol. Exp. Ther. 120:528-539. Schanker, L. S., D. J. Tocco, B. B. Brodie, and C. A. M. Hogben. 1958. Absorption of drugs from the rat small intestine. J. Pharmacol. Exp. Ther. 123:81-88. Schmidt-Nielsen, K. 1970. Energy metabolism, body size, and problems of scaling. Fed. Proc. 29:1524-1532. Schmidt-Nielsen, K. 1984. Scaling: Why Is Animal Size So Important? Cambridge Uni- versity Press, New York. 241 pp. Spiers, D. E., and V. Candas. 1984. Relationship of skin surface area to body mass in the immature rat: A reexamination. J. Appl. Physiol. Respir. Environ. Exercise Physiol. 56:240-243. Stokinger, H. E., and R. L. Woodward. 1958. Toxicologic methods for establishing drink- ing water standards. J. Am. Water Works Assoc. 50:515-529. Sullivan, T. M., G. S. Born, G. P. Carlson, and W. V. Kessler. 1983. The pharmacokinetics of inhaled chlorobenzene in the rat. Toxicol. Appl. Pharmacol. 71:194-203. Symons, J. M., T. A. Bellar, J. K. Carswell, J. DeMarco, K. L. Kropp, G. G. Robeck, D. R. Seeger, C. J. Slocum, B. L. Smith, and A. A. Stevens. 1975. National organics reconnaissance survey for halogenated organics. J. Am. Water Works Assoc. 67:634- 647. Teorell, T. 1937a. Kinetics of distribution of substances administered to the body. I. The extravascular modes of administration. Arch. Int. Pharmacodyn. Ther. 57:205-225. Teorell, T. 1937b. Kinetics of distribution of substances administered to the body. II. The intravascular modes of administration. Arch. Int. Phannacodyn. Ther. 57:226-240. Testa, B., and P. Jenner. 1976. Drug Metabolism: Chemical and Biochemical Aspects. Marcel Dekker, New York. 500 pp. Tuey, D. B., and H. B. Matthews. 1980. Distribution and excretion of 2,2',4,4',5,5'- hexabromobiphenyl in rats and man: Phannacokinetic model predictions. Toxicol. Appl. Pharmacol. 53:420-431.

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The most recent volume in the Drinking Water and Health series contains the results of a two-part study on the toxicity of drinking water contaminants. The first part examines current practices in risk assessment, identifies new noncancerous toxic responses to chemicals found in drinking water, and discusses the use of pharmacokinetic data to estimate the delivered dose and response. The second part of the book provides risk assessments for 14 specific compounds, 9 presented here for the first time.

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