United States Office of Research EPA/600/P-01/002A 

Environmental Protection and Development August 2001 

Agency Washington, DC 20460 External Review Draft 

EPA Trichloroethylene 

Health Risk 

Assessment: 

Synthesis and 

Characterization 

NOTICE 

THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not 

been formally released by the U.S. Environmental Protection 

Agency and should not at this stage be construed to represent 

Agency policy. It is being circulated for comment on its 

technical merit and policy implications. 
DRAFT–DO NOT EPA/600/P-01/002A 

External Review Draft 

CITE OR QUOTE August 2001 

Trichloroethylene Health Risk Assessment: 

Synthesis and Characterization 

NOTICE 

THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been 

formally released by the U.S. Environmental Protection Agency and 

should not at this stage be construed to represent Agency policy. It 

is being circulated for comment on its technical merit and policy 

implications. 

National Center for Environmental Assessment–Washington Office

Office of Research and Development

U.S. Environmental Protection Agency

Washington, DC
DISCLAIMER 

This document is a draft for review purposes only and does not constitute Agency policy. 

Mention of trade names or commercial products does not constitute endorsement or 

recommendation for use. 

ABSTRACT 

This assessment presents EPA’s most current evaluation of the potential health risks from 

exposure to TCE (trichloroethylene). TCE exposure is associated with several adverse health 

effects, including neurotoxicity, immunotoxicity, developmental toxicity, liver toxicity, kidney 

toxicity, endocrine effects, and several forms of cancer. Mechanistic research indicates that 

TCE-induced carcinogenesis is complex, involving multiple carcinogenic metabolites acting 

through multiple modes of action. Under EPA’s proposed (1996) cancer guidelines, TCE can be 

characterized as “highly likely to produce cancer in humans.” 

For effects other than cancer, an oral RfD of 3×10–4 mg/kg-d was based on critical effects 

in the liver, kidney, and developing fetus. An inhalation RfC of 4×10–2 mg/m3

 was based on 

critical effects in the central nervous system, liver, and endocrine system. Several cancer slope 

factors were developed, with most between 2×10–2 and 4×10–1 per mg/kg-d. Several sources of 

uncertainty have been identified and quantified. 

The mechanistic information suggests some risk factors that may make some populations 

more sensitive. There are suggestions that TCE could affect children and adults differently. In 

addition, several chemicals have the potential to alter TCE’s metabolism and clearance and 

subsequent toxicity; conversely, TCE exposure can augment the toxicity of other chemicals. 

Widespread environmental exposure to some of TCE’s metabolites makes it important to 

consider the cumulative effect of TCE along with other environmental contaminants. 

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CONTENTS

LIST OF TABLES ............................................................ vi

LIST OF FIGURES ........................................................... vi

LIST OF ABBREVIATIONS .................................................. vii

PREFACE .................................................................. ix

AUTHORS AND CONTRIBUTORS ............................................. xi

1. SUMMARY AND CONCLUSIONS ......................................... 1-1

1.1. OVERVIEW OF TCE HEALTH HAZARDS .............................. 1-1

1.2. MODE OF ACTION .................................................. 1-2

1.3. WEIGHT OF EVIDENCE UNDER THE PROPOSED AND CURRENT 

CANCER GUIDELINES .............................................. 1-4

1.4. QUANTITATIVE HEALTH REFERENCE VALUES ....................... 1-5


1.5. UNCERTAINTY .................................................... 1-8


1.5.1. Model Uncertainty ............................................. 1-9

1.5.2. Parameter Uncertainty.......................................... 1-10

1.5.3. Consideration of Multiple Areas of Uncertainty...................... 1-10


1.6. HUMAN VARIATION AND SENSITIVE POPULATIONS ................. 1-11


1.6.1. Metabolic Differences .......................................... 1-11


1.6.2. Disease ..................................................... 1-12


1.6.3. Gender Differences ............................................ 1-13

1.6.4. Some Implications ............................................ 1-13


1.7. DIFFERENTIAL RISKS TO CHILDREN ................................ 1-14

1.7.1. Exposure Differences .......................................... 1-14

1.7.2. Pharmacokinetic Differences .................................... 1-14


1.7.3. Pharmacodynamic Differences ................................... 1-15

1.7.4. Some Implications ............................................ 1-15


1.8. CUMULATIVE RISKS INVOLVING TCE .............................. 1-15


2. EXPOSURE CHARACTERIZATION ........................................ 2-1

2.1. ENVIRONMENTAL SOURCES ........................................ 2-1

2.2. ENVIRONMENTAL FATE ............................................ 2-1

2.3. EXPOSURE CONCENTRATIONS ...................................... 2-2

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CONTENTS (continued) 

2.4. EXPOSURE PATHWAYS AND LEVELS ................................ 2-3

3. HAZARD ASSESSMENT AND CHARACTERIZATION ........................ 3-1

3.1. GENERAL APPROACH .............................................. 3-1

3.2. METABOLISM ..................................................... 3-2

3.3. SUSCEPTIBILITY ................................................... 3-4

3.3.1. Intrinsic Factors Affecting Susceptibility ............................ 3-5

3.3.2. Extrinsic Factors Affecting Susceptibility ............................ 3-8

3.4. TOXICITY ......................................................... 3-9

3.4.1. Neurotoxicity ............................................... . . 3-9

3.4.2. Liver ....................................................... 3-10

3.4.3. Kidney ...................................................... 3-12

3.4.4. Immune and Lympho-Hematopoietic Systems ....................... 3-15

3.4.5. Developmental and Reproductive Toxicity, Endocrine Effects .......... 3-17

3.4.6. Lung ....................................................... 3-21

3.4.7. Genetic Toxicity .............................................. 3-22

3.5. MODES OF ACTION OF TCE TOXICITY .............................. 3-23

3.5.1. Liver Cancer ................................................. 3-24

3.5.2. Kidney Cancer ................................................ 3-36

3.5.3. Lung Cancer ................................................. 3-40

3.5.4. Toxicity in Other Organs ....................................... 3-41

3.6. HAZARD CHARACTERIZATION ..................................... 3-44

3.6.1. Consideration of Causality in the Epidemiologic Evidence ............. 3-44

3.6.2. Weight of Evidence under the Current and Proposed Cancer

Guidelines ................................................... 3-47

4. DOSE-RESPONSE ASSESSMENT AND CHARACTERIZATION ................ 4-1

4.1. GENERAL CONSIDERATIONS AND APPROACH ....................... 4-1

4.2. DOSIMETRY MODELING ............................................ 4-2

4.2.1. Pharmacokinetic Modeling ....................................... 4-3

4.2.2. Uncertainty Analyses ........................................... 4-4

4.2.3. Route Extrapolation ............................................ 4-5

4.3. ORAL REFERENCE DOSE FOR EFFECTS OTHER THAN CANCER ........ 4-5

4.3.1. Critical Effects ................................................ 4-6

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CONTENTS (continued) 

4.3.2. Point of Departure .............................................. 4-6

4.3.3. Uncertainty Factors ............................................. 4-7

4.3.4. Calculation and Characterization of the Oral Reference Dose ............ 4-9

4.4. INHALATION REFERENCE CONCENTRATION FOR EFFECTS 

OTHER THAN CANCER ............................................. 4-11

4.4.1. Critical Effects ............................................... 4-11

4.4.2. Point of Departure ............................................. 4-11

4.4.3. Uncertainty Factors ............................................ 4-12

4.4.4. Calculation and Characterization of the Inhalation Reference 

Concentration ................................................ 4-13

4.4.5. Comparison of Reference Dose and Reference Concentration ........... 4-13

4.5. SLOPE FACTOR AND INHALATION UNIT RISK FOR CANCER .......... 4-15

4.5.1. Risk Estimates From Cancer Epidemiology Studies .................. 4-15

4.5.2. Risk Estimates From Liver Tumors in Mice ......................... 4-18

4.5.3. Risk Estimates From Lung Tumors in Mice ......................... 4-23

4.5.4. Risk Estimates From Kidney Tumors in Rats ........................ 4-25

4.5.5. Risk Estimates from Testicular Tumors in Rats ...................... 4-26

4.5.6. Discussion of Alternative Cancer Risk Estimates .................... 4-27

4.5.7. Sources of Uncertainty in the Dose-response Assessment .............. 4-30

5. RESEARCH NEEDS ...................................................... 5-1

5.1. RESEARCH NEEDS FOR HAZARD ASSESSMENT ....................... 5-1

5.2. RESEARCH NEEDS FOR DOSE-RESPONSE ASSESSMENT ............... 5-2

5.3. RESEARCH NEEDS FOR EXPOSURE ASSESSMENT ..................... 5-3

REFERENCES .............................................................R-1

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LIST OF TABLES

Table 2-1. Preliminary dose estimates of TCE and TCE-related chemicals ............ 2-5

Table 3-1. Liver tumors in mice exposed to TCE ............................... 3-50

Table 3-2. Kidney tumors in rats, adjusted for reduced survival ................... 3-51

Table 3-3. Malignant lymphomas in mice exposed to TCE ....................... 3-52

Table 3-4. Testicular tumors in male rats exposed to TCE, adjusted for reduced survival 3-53

Table 3-5. Lung tumors in mice exposed to TCE ............................... 3-54

Table 4-1. Distributions of potential dose metrics from the uncertainty analysis of the

pharmacokinetic models ......................................... 4-32

Table 4-2. Oral studies supporting development of an RfD ....................... 4-33

Table 4-3. Inhalation studies supporting development of an RfC ................... 4-34

Table 4-4. Human-equivalent LED10s (mg/kg-d) derived from liver tumor incidences in four

mouse studies (NTP males or females, NCI males or females), using two dose

metrics (TCA area-under-the-curve, DCA area-under-the-curve) and four

pharmacokinetic models ......................................... 4-35

Table 4-5. Extending the observed range with a study of less-than-lifetime dosing 

of TCE ....................................................... 4-36

Table 4-6. Extending the observed range with studies of TCE’s metabolites .......... 4-37

Table 4-7. Human-equivalent LEC10s (μg/m3

) derived from lung tumor incidences in four

mouse studies, using RfC dosimetry ................................ 4-38

Table 4-8. Human-equivalent LED01s (mg/kg-d) derived from kidney tumor incidences

in rat studies, using 3/4-power scaling ............................... 4-39

Table 4-9. Compilation of cancer estimates ................................... 4-40

LIST OF FIGURES 

Figure 3-1. Metabolism of TCE ............................................... 3-55

Figure 4-1. Extending the observed range with less-than-lifetime dosing ............... 4-41

Figure 4-2. Extending the observed range with studies of TCE’s metabolites ............ 4-42

Figure 4-3. TCE health risk values ............................................. 4-43

8/1/01 vi DRAFT–DO NOT CITE OR QUOTE 
LIST OF ABBREVIATIONS

ADH alcohol dehydrogenase

AIRS Aerometric Information Retrieval System

ALDH aldehyde dehydrogenase

ATSDR Agency for Toxic Substances and Disease Registry

AUC area under the curve

Cal/EPA California Environmental Protection Agency

CEP Cumulative Exposure Project

CH chloral hydrate

confidence interval 

CYP2E1 cytochrome P450 2E1 

CYP450 cytochrome P450 

DCA dichloroacetic acid, dichloroacetate 

DCVC dichlorovinyl cysteine 

DCVG dichlorovinyl glutathione 

DEHP diethylhexyl phthalate 

DHEA dehydroepiandrosterone 

EPA United States Environmental Protection Agency 

GST glutathione S-transferase 

HPT hypothalo-pituitary-testis (axis) 

LOAEL lowest-observed-adverse-effect level 

mg/kg milligrams per kilogram body weight 

mg/kg-d milligrams per kilogram body weight per day 

mg/L milligrams per liter 

mg/m3 milligrams per cubic meter 

mM millimolar (concentration) 

mmol millimoles 

mRNA messenger RNA 

N number (of cases) 

NAT N-acetyl transferase 

NCI National Cancer Institute 

NHANES III Third National Health and Nutrition Examination Survey 

NOAEL no-observed-adverse-effect level 

NTP National Toxicology Program 

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CI 
LIST OF ABBREVIATIONS (continued) 

OR odds ratio

PDH pyruvate dehydrogenase

PPAR peroxisome proliferator activated receptor

ppm parts per million

RfC reference concentration

RfD reference dose

RR relative risk

RXR retinoid-X receptor

SHBG sex hormone binding globulin

TCA trichloroacetic acid, trichloroacetate

TCE trichloroethylene

TCOH trichloroethanol

TEAM Total Exposure Assessment Methodology

TLV threshold limit value

TRI Toxics Release Inventory

ug/L micrograms per liter

ug/m3 micrograms per cubic meter

VHL von Hippel-Lindau tumor suppressor gene

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PREFACE 

This assessment presents EPA’s most current evaluation of the potential health risks from 

exposure to trichloroethylene (TCE). TCE is a chemical solvent used for vapor degreasing of 

metals and as an intermediate in the manufacture of other chemicals. One of the chemicals most 

released into the environment, TCE is highly volatile, with most TCE released into the 

environment finding its way into the air. An important exception occurs when TCE is released 

into groundwater, where TCE can persist for years because of the limited contact between 

groundwater and air. When people are exposed to TCE, it is readily absorbed by all exposure 

routes and is widely distributed throughout the body. 

The potential health risks of TCE in the environment have caused it to be listed as a 

chemical of concern across several environmental programs. TCE is listed as a hazardous air 

pollutant under the Clean Air Act, a toxic pollutant under the Clean Water Act, a contaminant 

under the Safe Drinking Water Act, a hazardous waste under the Resource Conservation and 

Recovery Act, and a hazardous substance under the Comprehensive Environmental Response, 

Compensation, and Liability Act (Superfund). It is a toxic chemical with reporting requirements 

under the Emergency Planning and Community Right-to-Know Act, and under the Toxic 

Substances Control Act certain releases must be reported to the Toxics Release Inventory. 

This assessment draws on 16 state-of-the-science papers published as a supplemental 

issue of Environmental Health Perspectives (Wartenberg et al., 2000; Lash et al., 2000a; Pastino 

et al., 2000; Moore and Harrington-Brock, 2000; Bull, 2000; Green, 2000; Fisher, 2000; Bois, 

2000a,b; Clewell et al., 2000; Boyes et al., 2000; Barton and Clewell, 2000; Chen, 2000; 

Rhomberg, 2000; Wu and Schaum, 2000), plus some other key references. Accordingly, this 

assessment focuses on analysis and interpretation rather than a compilation of study results. 

More detailed information on the epidemiologic and experimental studies of TCE can be found 

in the state-of-the-science papers and in comprehensive reviews compiled by ATSDR (1997) and 

EPA (1985, 1987). 

As this assessment is written, it is being shaped by several new developments in risk 

assessment. The practice of risk assessment is evolving from a focus on one toxic effect of one 

pollutant in one environmental medium toward integrated assessments covering multiple effects 

and multiple media that incorporate information about mode of action, uncertainty, human 

variation, and cumulative effects of multiple pollutants in different media. This evolution 

responds to recommendations of the National Research Council (NRC, 1994), which have been 

embraced in EPA’s proposed cancer guidelines (U.S. EPA, 1996). 

Mode-of-action information is used throughout this assessment, both to understand the 

cancer hazard and to suggest common mechanisms with some of the effects other than cancer. 

8/1/01 ix DRAFT–DO NOT CITE OR QUOTE 
Mode-of-action information is also used to assess the relevance of the animal results and to 

identify risk factors for sensitive human populations. 

Uncertainty analyses are used to quantitatively characterize the range of plausible risk 

estimates and to guide the choices made during the assessment. Uncertainty analyses can be 

particularly useful for explicitly characterizing the ability of the data and the models to provide 

stable, credible risk estimates. 

Humans vary in their response to environmental contaminants, and risk assessors have 

been charged with using mode-of-action information to identify susceptible populations. Of 

special concern has been the potential for disproportionate risks to children. Executive Order 

13045 (1997) requires “each Federal Agency shall make it a high priority to identify and assess 

environmental health and safety risks that may disproportionately affect children, and shall 

ensure that their policies, programs, and standards address disproportionate risks that result from 

environmental health risks or safety risks.” 

Human response to an environmental contaminant can be affected by other exposures and 

conditions that occur in a population. EPA (1997) guidance directs “each office to take into 

account cumulative risk issues in scoping and planning major risk assessments and to consider a 

broader scope that integrates multiple sources, effects, pathways, stressors, and populations for 

cumulative risk analyses in all cases for which relevant data are available.” 

This assessment evaluates the potential health risks of TCE in light of the available 

information and analyses pertinent to mode of action, uncertainty, human variation, and 

cumulative risks. 

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AUTHORS AND CONTRIBUTORS 

Authors 

The final draft of this assessment was developed and written by V. James Cogliano, 

Cheryl Siegel Scott, and Jane C. Caldwell of EPA’s National Center for Environmental 

Assessment (NCEA), a part of the Office of Research and Development. This work, however, 

would not have been possible without the significant contributions of many others, for which the 

authors are most appreciative. 

Contributors 

Quantitative analyses were contributed by Chao W. Chen (NCEA), Jerry Blancato 

(EPA’s National Exposure Research Laboratory), and Gina M. Pastino (formerly with NCEA as 

a fellow of the American Association for the Advancement of Science). Earlier drafts of this 

assessment included contributions by Jean C. Parker (NCEA), Robert P. Beliles (NCEA), and 

Kim-Chi Hoang (formerly with NCEA, now with EPA Region 9 in San Francisco). 

The authors would like to thank William Farland, Vanessa Vu, and Michel Stevens of 

NCEA for management support and scientific comments through all phases of this assessment. 

Special thanks are also due to John C. Lipscomb (NCEA), Robert E. McGaughy (NCEA), 

Gregory M. Blumenthal (NCEA), and Hugh A. Barton (EPA’s National Health and Ecological 

Effects Research Laboratory) for constructive comments and suggestions on earlier drafts. 

Antoinette Johnson (NCEA) deserves appreciation for her work in compiling and formatting 

some of the detailed tables. In addition, the authors would like to thank David Kelly (NCEA) 

and Melissa Revely-Brown (NCEA) for effectively managing contracts to facilitate workshops 

for peer involvement and peer review. 

This assessment has benefited from extensive peer involvement. Its conclusions draw 

from 16 state-of-the-science papers written by experts who have conducted research related to the 

assessment of TCE’s health risks. These papers have been published as a supplemental issue of 

Environmental Health Perspectives (volume 108, supplement 2, May 2000). Cheryl Siegel 

Scott served as editor of this special issue and managed the overall development of the state-ofthe-science papers. Funding was provided by EPA, the U.S. Air Force, the Occupational Safety 

and Health Administration, and private industry. The lead authors, affiliations listed in the stateof-the-science papers, and topics of the state-of-the-science papers include: 

Daniel Wartenberg, Robert Wood Johnson Medical School, Cancer epidemiology. 

Lawrence H. Lash, Wayne State University School of Medicine, Metabolism. 

8/1/01 xi DRAFT–DO NOT CITE OR QUOTE 
AUTHORS AND CONTRIBUTORS (continued) 

Gina M. Pastino, U.S. Environmental Protection Agency (Fellow of the American

Association for the Advancement of Science), Human variation and susceptibility.

Martha M. Moore, U.S. Environmental Protection Agency, Mutagenicity.

Lawrence H. Lash, Wayne State University School of Medicine, Modes of action for

kidney tumorigenesis.

Richard J. Bull, Pacific Northwest National Laboratory, Modes of action for liver

tumorigenesis.

Trevor Green, Zeneca Central Toxicology Laboratory (United Kingdom), Modes of

action for lung tumorigenesis.

Jeffrey W. Fisher, Air Force Research Laboratory, Pharmacokinetic modeling.

Frédéric Yves Bois, Institut National de l’Environnement Industriel et des Risques

(France), Statistical analysis of Fisher model.

Harvey J. Clewell III, ICF Consulting, Pharmacokinetic modeling.

Frédéric Yves Bois, Institut National de l’Environnement Industriel et des Risques

(France), Statistical analysis of Clewell model.

William K. Boyes, U.S. Environmental Protection Agency, Neurotoxicity and

pharmacokinetics.

Hugh A. Barton, ICF Consulting, Dosimetry and mode of action for effects other than

cancer.

Chao W. Chen, U.S. Environmental Protection Agency, Mechanism-based modeling for

liver tumorigenesis.

Lorenz R. Rhomberg, Harvard School of Public Health, Dose-response analyses for

liver, lung, and kidney tumorigenesis.

Chieh Wu, U.S. Environmental Protection Agency, Exposure assessment.

These authors have individually provided many constructive comments and suggestions for this 

synthesis and characterization, but were not asked for consensus on its overall conclusions. 

Peer involvement was also facilitated by an External Involvement Group, composed of 

nine representatives from private industrial organizations, public interest groups, academic 

research institutions, and State and Federal agencies. These representatives were asked to (1) 

propose additional topics for state-of-the-science papers and secure expert scientists as authors, 

(2) review these additional papers for balance and completeness, (3) propose topics for synthesis 

and characterization, (4) suggest peer reviewers, and (5) keep scientists from their sector 

informed. The External Involvement Group includes: 

8/1/01 xii DRAFT–DO NOT CITE OR QUOTE 
AUTHORS AND CONTRIBUTORS (continued) 

Melvin Andersen, Colorado State University.

Paul Cammer, Trichloroethylene Issues Group.

Christopher DeRosa, Agency for Toxic Substances and Disease Registry.

Paul Dugard, Halogenated Solvents Industry Alliance.

Jodi Flaws, Environmental Defense.

Philip Landrigan, Mt. Sinai School of Medicine.

Elizabeth Maull, United States Air Force.

Margaret Round, Melinda Treadwell, Northeast States for Coordinated Air Use

Management.

Lauren Zeise, California Environmental Protection Agency.

These representatives have also provided many constructive comments and suggestions for this 

synthesis and characterization, but were not asked for consensus on its overall conclusions. 

Elizabeth Maull, Paul Cammer, and Paul Dugard were instrumental in identifying authors and 

providing funding for some state-of-the-science papers. 

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1. SUMMARY AND CONCLUSIONS 

1.1. OVERVIEW OF TCE HEALTH HAZARDS 

An extensive database—comprising epidemiologic studies, animal bioassays, metabolism 

studies, and mechanistic studies—shows that trichloroethylene (TCE) exposure is associated 

with a wide array of adverse health effects (see Section 3.4). TCE has the potential to induce 

neurotoxicity, immunotoxicity, developmental toxicity, liver toxicity, kidney toxicity, endocrine 

effects, and several forms of cancer. Overall, similar effects are seen by oral and inhalation 

exposures, suggesting that TCE or its active metabolites reach their target sites after absorption 

from either exposure route. Special attention has been given to investigating TCE’s potential to 

cause cancer. Epidemiologic studies, considered as a whole, have associated TCE exposure with 

excess risks of kidney cancer, liver cancer, lympho-hematopoietic cancer, cervical cancer, and 

prostate cancer. TCE has been extensively tested in animals, with mice developing liver tumors, 

lung tumors, and lymphomas, and rats developing kidney tumors and testicular tumors. The 

epidemiologic evidence is strongest at sites where the animals develop cancer, with site 

concordance for kidney cancer (in rats and humans), liver cancer (in mice and humans), and 

lympho-hematopoietic cancer (in mice and humans). TCE is also associated with cervical cancer 

and prostate cancer in humans, sites for which there are no corresponding animal models. 

Extensive metabolic studies show that exposure to TCE results in internal exposure to a 

complex mixture of TCE’s metabolites (see Section 3.2). Much of TCE-induced toxicity may be 

attributable to its metabolites, as toxicity tests show that some metabolites cause effects similar 

to those of TCE and, in some assays, TCE is inactive in the absence of its metabolites. 

Depending on the effect, the pattern of TCE-induced toxicity does not match any single 

metabolite, but reflects a mixture. The metabolic studies also show that the pattern of metabolite 

formation is different at high and low doses of TCE, and that metabolism can be altered by levels 

of metabolites already in the body. 

This last point takes on added significance in view of widespread environmental exposure 

to some of TCE’s metabolites (see Section 2). These exposures can be either direct (for example, 

TCE’s metabolites trichloroacetic acid [TCA] and dichloroacetic acid [DCA] are also major 

byproducts of drinking water disinfection) or indirect (for example, the dry cleaning solvent 

perchloroethylene can be metabolized to TCA). These common exposures give rise to a 

background level of some metabolites through which TCE may act. Accordingly, these other 

exposures should not be regarded as confounding exposures in the traditional sense. Rather, they 

may help explain why TCE is associated with toxic effects in one study but not in another. 

Determining background exposure to TCE and its metabolites is important to understanding 

TCE-induced toxicity (see Section 1.8). 

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Many of TCE’s toxic metabolites are formed through the enzyme system that also 

metabolizes ethanol, acetaminophen, and many other drugs and environmental pollutants (see 

Section 3.2). Exposure to these chemicals (for example, recent alcohol consumption) can affect 

enzyme levels, temporarily altering or enhancing TCE’s metabolism and toxicity in a manner 

analogous to drug interactions known for many pharmaceuticals (see Sections 1.6 and 3.3). In 

addition, enzyme activity can vary significantly between individuals, through both intrinsic 

factors (for example, genetics) and acquired factors (for example, disease) (see Sections 1.6 and 

3.3). Thus, the effects of TCE can vary, depending on exposures and conditions specific to each 

person. 

1.2. MODE OF ACTION 

Mechanistic research on a few TCE metabolites has yielded insights into TCE’s possible 

modes of action.1 The research to date indicates that TCE-induced carcinogenesis is complex, 

involving multiple carcinogenic metabolites acting through multiple modes of action. Past 

explanations, such as the hypothesis linking mouse liver tumors to peroxisome proliferation, are 

not consistent with the whole of the data, and more complex hypotheses have been formulated 

(see Section 3.5). 

TCE-induced mouse liver tumors may arise through multiple metabolites and multiple 

modes of action (see Section 3.5.1). The preponderance of evidence suggests that formation of 

TCE’s CYP450 metabolites2

 is sufficient to explain the liver tumors caused by TCE (Bull, 2000; 

Chen, 2000). Exposure to the CYP450 metabolites chloral hydrate (CH), TCA, or DCA can 

cause liver tumors in mice, and DCA can also cause liver tumors in rats. A plausible mode of 

action is that TCE induces liver tumors through TCA and DCA modifying cell signaling systems 

1

EPA’s proposed cancer guidelines define mode of action as “a series of key events and processes starting with 

interaction of an agent with a cell, and proceeding through operational and anatomical changes resulting in cancer 

formation.” A key event is “an empirically observed precursor step consistent with a mode of action.” “Mode of 

action” is contrasted with “mechanism of action,” which implies a more detailed understanding and description of 

events, often at the molecular level, than is meant by mode of action. An agent may work by more than one mode of 

action, both at different sites and at the same tumor site (U.S. EPA, 1996, 1999). Mode-of-action data can be used to 

(1) assess the relevance of laboratory animal results to human environmental exposures, (2) identify risk factors and 

sensitive populations, (3) provide insight into the shape of the dose-response curve, and (4) quantify the relative 

sensitivity of laboratory animals and human populations. 

2

Cytochrome P450 (CYP450) is a major system of enzymes that metabolize chemicals through oxidation. TCE 

metabolism is most strongly dependent on activity of one particular enzyme, CYP2E1. CYP2E1 activity has been 

found to vary among humans through both genetic and acquired factors, leading to differences in metabolism and 

sensitivity to TCE-induced toxicity. Involvement of CYP2E1 in TCE metabolism also raises the possibility that 

TCE metabolism and toxicity can be affected by exposure to other chemicals, as CYP2E1 activity can be induced by 

its role in metabolizing many drugs and environmental pollutants, including ethanol, acetaminophen, benzene, 

1,3-butadiene, styrene, methylene chloride, chloroform, carbon tetrachloride, vinyl chloride, vinylidene chloride, and 

perchloroethylene (Pastino et al., 2000). 

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that control rates of cell division and cell death (Bull, 2000). Tumors from the TCA and DCA 

bioassays apparently arise from different mechanisms that can be distinguished from each other 

(see Section 3.5.1). Characterizing the tumors from the TCE bioassays as either TCA-mediated 

or DCA-mediated is a critical research need that limits understanding of the respective roles and 

interaction of TCA and DCA in the TCE-induced tumors (see Section 5). 

There has been much less investigation of the mode of action of TCE-induced mouse 

lung tumors. It has been suggested that the lung tumors arise from rapid CYP2E1 metabolism of 

TCE in the Clara cells of the mouse lung that leads to an accumulation of CH and causes cell 

damage and compensatory cell replication that in turn leads to tumor formation (Green, 2000). 

Although this hypothesis is consistent with the higher level of CYP2E1 in mouse lung, it has not 

yet been determined whether accumulation of CH is a key event causing tumors or a coincidental 

event unrelated to the tumors (see Section 3.5.3). Moreover, several questions about this 

hypothesis remain unresolved. In addition, CH is clearly clastogenic and mutagenic at high 

doses (Moore and Harrington-Brock, 2000), raising the possibility of multiple modes of action. 

TCE-induced rat kidney tumors may arise through multiple modes of action (see Section 

3.5.2). The preponderance of evidence suggests that TCE’s glutathione-S-transferase (GST) 

metabolites

3

 are responsible for kidney toxicity and kidney tumors (Lash et al., 2000b). 

Although these metabolites have not been tested in cancer bioassays, the GST metabolite 

dichlorovinyl cysteine (DCVC) is mutagenic (Moore and Harrington-Brock, 2000). Cytotoxicity 

may also be involved, as cytotoxicity is observed in both rats and humans exposed to high levels 

of TCE (Lash et al., 2000b). Information is lacking on whether the CYP450 metabolites also 

have a role in kidney tumor development. 

Although these possible modes of action are consistent with much of the mechanistic 

research that has been conducted, the explanations so far fall short of identifying the active 

metabolites and sequence of key events involved at each cancer site (see Section 3.5). Ambiguity 

about active metabolites and key events leads to uncertainty in extrapolating the proposed modes 

of action across species. For some suggested modes of action, it is possible that most humans 

may be less sensitive than rats or mice, and this differential sensitivity has been addressed in the 

dose-response assessment (see Sections 4.5.2, 4.5.3). Species-dependent differences in the 

kinetics of TCE’s metabolism can bring about a different proportion of metabolites that depends 

on dose. This, in turn, can lead to different apparent modes of action across species and across 

dose ranges. For example, rat kidney tumors at high experimental doses may involve high-dose 

cytotoxicity, while rat and human kidney tumors at lower doses may arise through a mutagenic, 

3

Glutathione–S–transferase (GST) is an enzyme system that produces a minor quantity of TCE’s metabolites. The 

potentially higher toxicity of these metabolites, however, may make them important agents of TCE’s toxicity. 

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not cytotoxic, mode of action. Accordingly, it may not be appropriate to assume concordance of 

tumor site or mode of action across species or across dose ranges. 

Recent mechanistic research has begun to investigate a more fundamental level of cellular 

activity (see Section 3.5), for example, TCE’s effects on cell signaling and carbohydrate 

metabolism (Bull, 2000). Effects at this level could indirectly accelerate ongoing tumor 

development specific to each species. In this way, TCE could be an “opportunistic carcinogen” 

that can contribute to ongoing processes or conditions that induce different forms of cancer in 

different species or populations. This would imply that TCE poses a cancer risk to humans, 

although mechanism-specific animal models might not predict human cancer risks with much 

confidence. 

1.3. WEIGHT OF EVIDENCE UNDER THE PROPOSED AND CURRENT CANCER 

GUIDELINES 

Under EPA’s proposed (1996, 1999) cancer guidelines, TCE can be characterized as 

“highly likely to produce cancer in humans” (see Section 3.6.2). Support for this characterization 

is strong, including (1) association of TCE exposure with increased risks of human kidney 

cancer, liver cancer, lympho-hematopoietic cancer, cervical cancer, and prostate cancer; (2) 

induction of multiple von Hippel-Lindau (VHL) gene mutations in renal cell carcinoma patients 

who had been exposed to TCE; (3) induction of kidney tumors and testicular tumors in rats and 

liver tumors, lung tumors, and lymphomas in mice; and (4) mechanistic information suggesting 

that the animal tumors arise through processes that may be relevant to humans. Under EPA’s 

current (1986) cancer guidelines, TCE would be classified as a “probable human carcinogen” 

(group B1), with “limited” human evidence and “sufficient” animal evidence of carcinogenicity. 

Two areas of much past discussion are the weight of the human evidence and the 

relevance of the animal tumors. Compared with previous TCE assessments, the weight of the 

epidemiologic evidence has become stronger with the state-of-the-science analysis by 

Wartenberg et al. (2000) (see Sections 3.4, 3.6.1). The epidemiologic studies have recently been 

augmented by molecular information, in which multiple mutations of the VHL tumor suppressor 

gene,4

 primarily C-to-T changes including nucleotide 454, were found in renal cell carcinoma 

patients with high, prolonged TCE exposure (Brüning et al., 1997b) (see Section 3.5.2). The 

4

The von Hippel-Lindau gene normally suppresses renal cell carcinomas. Mutations to this gene have been noted in 

kidney cancers and may be an important risk factor and mode of action for chemically induced renal cell cancer 

(Lash et al., 2000b). To illustrate the difference between “mode” of action and “mechanism” of action, knowledge 

that VHL gene mutations are involved may be enough to identify a plausible “mode” of action, whereas knowing 

how such modifications induce subsequent events leading to kidney cancer would be needed to identify the 

“mechanism” of action. 

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mechanistic research into the mode of action for each animal tumor site has begun to link TCE 

with disturbances in cell signaling and carbohydrate metabolism, which can lead to human cancer 

and other diseases (see Section 3.5). Subject to dose-response adjustments for relative human-toanimal sensitivity (see Sections 4.5.2, 4.5.3), this research makes it plausible that TCE acts 

through mechanisms that can cause cancer in humans. 

These characterizations are consistent with recent assessments by other authoritative 

health agencies, including IARC, which classifies TCE as a “probable human carcinogen” (IARC 

1995) and the National Toxicology Program (NTP), which classifies TCE as “reasonably 

anticipated to be a human carcinogen” (NTP, 2000a).5 The NTP recently considered upgrading 

TCE to “known to be a human carcinogen,” making a case for a stronger classification in the 

future (NTP, 2000b). Key research to support this classification includes further investigation of 

VHL gene mutations in other cohorts exposed to TCE (see Section 5). On the other hand, a 

sizable faction of scientists is not convinced of the relevance of the animal tumors or the strength 

of the epidemiologic studies, which only recently have accumulated enough power to detect 

associations between moderate occupational or residential TCE exposures and some relatively 

common cancers. After considering this alternative view, these health agencies have now 

converged in their assessment that TCE is “highly likely,” “probable,” or “reasonably anticipated 

to be” a human carcinogen. 

1.4. QUANTITATIVE HEALTH REFERENCE VALUES 

For effects other than cancer, an oral reference dose (RfD)6 of 3×10–4 mg/kg-d was based 

on critical effects in the liver, kidney, and developing fetus (see Section 4.3.1). These effects 

were observed in subchronic studies in mice and rats at doses as low as 1 mg/kg-d (see Section 

4.3.2). Because laboratory conditions do not necessarily represent lifetime human environmental 

exposure, including sensitive populations, this dose was adjusted from subchronic to lifetime 

exposure, from animals to humans, and from a general to a sensitive population (see Section 

4.3.3). In addition, humans start higher on the dose-response curve than do animals, as humans 

are exposed to TCE’s active metabolites from sources other than TCE, and this cumulative 

5

IARC’s classification of TCE as a “probable human carcinogen” (their group 2A) is based on “limited evidence” of 

carcinogenicity in human studies and “sufficient evidence” in animal studies (IARC, 1995). The NTP’s listing of 

TCE as “reasonably anticipated to be a human carcinogen” is based on “limited evidence of carcinogenicity from 

studies in humans, sufficient evidence of malignant tumor formation in experimental animals, and convincing 

relevant information that trichloroethylene acts through mechanisms indicating it would likely cause cancer in 

humans” (NTP, 2000a). 

6

A reference dose (RfD) is an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure 

to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious 

noncancer effects during a lifetime (Barnes and Dourson, 1988). 

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1 exposure can alter TCE’s metabolism and toxicity (see Sections 1.8, 3.3, 4.3.3). Differences

2 between average and sensitive humans were estimated in an uncertainty analysis to be about 50-

3 fold, and a further 100-fold overall adjustment was considered appropriate for the other

4 differences (see Section 4.3.3). This latter 100-fold uncertainty indicates the potential for future

5 research to reduce uncertainty and improve this assessment’s accuracy (see Section 5). In the

6 end, however, the RfD was calculated by reducing the 1 mg/kg-d dose by a factor of 3,000,

7 representing the largest divisor used by EPA in the presence of substantial uncertainty (see

8 Section 4.3.4).

9 An inhalation reference concentration (RfC)7 of 4×10–2 mg/m3 was based on critical

10 effects in the central nervous system, liver, and endocrine system (see Section 4.4.1). 

11 Occupational studies showed adverse central nervous system effects in workers at a subchronic

12 exposure to 7 ppm (38 mg/m3

) (see Section 4.4.2). To convert this to an RfC for lifetime

13 exposure, this concentration was divided by a composite uncertainty factor of 1,000, representing

14 a default of 10 for human variation and 100 for uncertainty in extrapolating from subchronic to

15 lifetime exposure and effect levels to NOAELs (see Section 4.4.3).

16 The 3,000-fold and 1,000-fold uncertainty factors used in deriving the RfD and RfC,

17 respectively, are not large, considering that the Agency for Toxic Substances and Disease

18 Registry (ATSDR) (1997) has characterized the chronic studies as inadequate for supporting

19 chronic health reference values.8 The key uncertainty about the RfD and RfC is the relative lack

20 of chronic studies, necessitating reliance on subchronic studies and the numerous attendant

21 uncertainties in using subchronic studies to estimate health reference values for lifetime

22 exposure. This assessment’s use of pharmacokinetic modeling and route extrapolation allows

23 comparison of a wider range of oral and inhalation studies, revealing that critical effects for both

7

A reference concentration (RfC) is an estimate (with uncertainty spanning perhaps an order of magnitude) of a 

continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without 

an appreciable risk of deleterious noncancer effects during a lifetime (U.S. EPA, 1994). 

8

ATSDR (1997) concluded that the available chronic-duration studies had inadequate characterization of exposure, 

inadequate quantification of results, and lack of endpoints suitable for deriving chronic levels. ATSDR did calculate 

an intermediate-duration inhalation level of 0.1 ppm, based on neurologic effects (decreased wakefulness and 

decreased slow-wave sleep) in rats observed at a LOAEL of 50 ppm for 6 weeks (Arito, 1994). The corresponding 

human-equivalent concentration (44 ppm) was divided by a composite uncertainty factor of 300 (10 for human 

variability, 3 for extrapolation from animals to humans, and 10 for use of a LOAEL). ATSDR also calculated an 

acute-duration inhalation level of 2 ppm, based on neurologic effects (fatigue and drowsiness) in six human 

volunteers observed at a LOAEL of 200 ppm for 5 days (Stewart, 1970). The corresponding daylong concentration 

(58 ppm) was divided by a composite uncertainty factor of 30 (10 for human variability and 3 for use of a minimal 

LOAEL). ATSDR calculated an acute-duration oral level of 0.2 mg/kg-d, based on developmental effects 

(decreased rearing) in mice observed at a LOAEL of 50 mg/kg-d (Fredriksson, 1993). This was divided by a 

composite uncertainty factor of 300 (3 for human variability in TCE metabolism, 10 for extrapolation from animals 

to humans, and 10 for use of a LOAEL). 

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exposure routes fall within a narrow dose range and providing strength to both the RfD and RfC 

determinations. 

Several cancer slope factors9 were developed, with most slope factors falling between 

2×10–2 and 4×10–1 per mg/kg-d. This range is supported by estimates derived for kidney cancer 

and liver cancer from occupational exposure to TCE, non-Hodgkin’s lymphoma from exposure 

to drinking water contaminated with an average of 23 μg/L TCE, and liver cancer in laboratory 

mice exposed to TCE (see Sections 4.5.1, 4.5.2). Because this range is supported by diverse 

studies and does not include the highest or lowest estimates that were calculated, it can be 

considered rather robust and independent of the results or methods used to analyze any single 

study (see Section 4.5.6). 

The range of cancer slope factors has not been reduced to a single number. A range is 

reasonable in view of the risk factors that can modify the effects of TCE in different populations 

(see Sections 1.6, 3.3). (Ranges were not developed for the RfD and RfC, because they include 

sensitive individuals by applying a factor to cover human variation.) For most cancer risk 

factors, however, data that would allow differential risks to be quantified are lacking. Only for 

the GST polymorphism that modifies the kidney cancer risk are data available, indicating a 

fourfold increased risk for the more sensitive population. Because the modifying effect of most 

risk factors cannot be quantified at this time, this assessment proposes instead that risk assessors 

use the upper end of the slope factor range for susceptible populations having risk factors for 

TCE-induced cancer. Although the extremes of the slope factor range are not based on data from 

more- or less-susceptible populations, this approach emphasizes the possibility of different risks 

in different circumstances, identifies risk factors that may increase susceptibility to TCE’s 

effects, and provides a practical way to adjust risk estimates to reflect differential susceptibility. 

The purpose of a risk assessment can also affect the choice of a value from the slope 

factor range. An assessment of maximum individual risk would use the upper end of the slope 

factor range, while an assessment of the number of cancer cases in a general population could use 

the midpoint of the range. This implies that the high end of the slope factor range is appropriate 

9

A slope factor is an estimate of a carcinogen’s potency, characterized as a plausible upper bound on the increased 

human cancer risk from lifetime exposure to an average dose of 1 mg/kg-d. That is, the slope factor estimates a 

bound on the risk per mg/kg-d, accordingly, the slope factor is expressed in units of inverse lifetime-average dose, or 

(mg/kg-d)

–1. Multiplying a slope factor by a lifetime-average dose (in mg/kg-d) yields a plausible upper bound on 

the increased probability of developing cancer from exposure to the carcinogen. A unit risk is analogous to a slope 

factor, but expressed in units of inverse lifetime-average ambient air concentration (μg/m3

)

–1 or inverse lifetimeaverage drinking water concentration (μg/L)

–1 instead of inverse lifetime-average dose (mg/kg-d)–1. Unit risks are 

convenient when exposure is expressed in terms of environmental concentrations (:g/m3

 or :g/L). Unit risk 

estimates are based on particular exposure assumptions, specifically, a 70-kg adult drinking 2 L/d and breathing 20 

m3

/d. When applied to other populations with different exposure factors—for example, children—unit risk estimates 

should be adjusted accordingly. 

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for susceptible individuals but is less descriptive of the rest of the population. The 20-fold span 

of the slope factor range is smaller than the uncertainty in dose estimates from the 

pharmacokinetic models (see Table 4-1), suggesting that the range may, in some cases, 

underestimate high-end risk. 

For less-than-lifetime exposure, current risk assessment practice typically assumes that 

cancer risk is proportional to exposure duration, for example, exposure for 7 years of a 70-year 

human lifespan would carry one-tenth the risk of lifetime exposure.

10 There is some evidence for 

TCE to suggest that this practice may underestimate the risk from less-than-lifetime exposures 

(see Section 4.5.2). Accordingly, tempering the assumption of duration-proportionality may be 

warranted in applications involving less-than-lifetime exposure. 

The continuing development of revised cancer guidelines (U.S. EPA, 1996, 1999) has 

raised expectations that nonlinear approaches11 may replace the linear extrapolation used to 

estimate cancer slope factors for TCE. This assessment has pursued nonlinear approaches for 

several tumor sites. The key limitation to the nonlinear analyses is the uncertain identity of the 

active metabolites and key events involved in TCE-induced cancer. This assessment’s 

consideration of potential nonlinear approaches indicates that concern for cancer would extend 

down to doses of 3×10–4 and 8×10–4 mg/kg-d for liver tumors and testicular tumors, respectively 

(see Sections 4.5.2, 4.5.5). These doses are comparable to the RfD for effects other than cancer, 

indicating the importance of considering all health effects in any risk assessment of TCE. 

1.5. UNCERTAINTY 

Several sources of uncertainty have been identified and quantified for TCE.12 

Consideration of these uncertainties has shaped the choices made in this assessment and 

10Cancer risk is calculated by multiplying the slope factor by the lifetime average daily dose (LADD), defined as 

LADD = (C × IR × ED) / (BW × LT), where C is the concentration, IR the intake rate, ED the exposure duration, BW 

the body weight, and LT the lifetime (U.S. EPA, 1992a). 

11Because nonlinear dose-response curves can have substantial model uncertainty below the experimental doses, 

these curves are not extrapolated below the observed data. Instead, the risk assessment discusses current 

understanding of the phenomena that may be occurring at lower doses. Factors to be considered include (1) nature 

of the observed response, (2) shape of the observed dose-response curve, (3) human sensitivity compared with 

experimental animals, (4) human variation in sensitivity, and (5) human exposure (U.S. EPA, 1996, 1999). 

12Uncertainty arises because study conditions differ from conditions of human environmental exposure. 

Consequently, risk assessments typically involve several extrapolations: from laboratory animals to humans, from 

experimental doses to environmental levels, from one exposure route to another, from one exposure pattern to 

another, and from small samples to large, more heterogeneous populations. 

One type of uncertainty stems from different interpretations of the available data (known as model 

uncertainty), another from estimation errors due to incomplete or imprecise data (parameter uncertainty). 

An understanding of uncertainty can be used (1) to make more informed choices during the conduct of a 

risk assessment, and (2) to characterize the range of plausible risk estimates for different populations. 

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described the range of estimates that are consistent with existing data and current scientific 

understanding. 

1.5.1. Model Uncertainty13 

In this assessment, the two major areas of model uncertainty are pharmacokinetics and 

low-dose extrapolation. 

The two pharmacokinetic models (Fisher, 2000; Clewell et al., 2000) reveal the existence 

of substantial model uncertainty, with risk estimates that differed by 15-fold (see Table 4-4). 

Fitting these models to additional data sets (Bois, 2000a, 2000b) produced calibrated models that 

yielded more compatible results and reduced this source of model uncertainty (see Section 4.2.1). 

The choice between linear and nonlinear extrapolation14 to low doses creates two distinct 

classes of estimates (see Sections 4.5.2, 4.5.3). This dichotomous uncertainty stems from 

different strongly held and widely held interpretations of the information on TCE’s mode of 

action (see Section 3.5). Accordingly, this assessment used both linear and nonlinear 

approaches, the former to bound the risks and the latter to demonstrate the extent of uncertainty 

and value of further research into mode of action and human variation.15 In addition, this 

assessment made efforts to develop risk estimates from human studies as an alternative to 

extrapolating from animal models. Average exposure in one human study was close to the 

occupational standard, reducing the uncertainty of high- to low-dose extrapolation. 

13Model uncertainty refers to a lack of knowledge needed to determine which scientific theory on which a model is 

based is correct. In risk assessment, model uncertainty is reflected in alternative choices for model structure, dose 

metrics, and extrapolation approaches. Other sources of model uncertainty concern whether surrogate data are 

appropriate, e.g., using data on adults to make inferences about children. The full extent of model uncertainty cannot 

be quantified, only models that have been analyzed. Model uncertainty is expressed through separate analyses 

yielding alternative estimates, not as a single estimate or distribution made by combining estimates from 

incompatible models. Combining results of incompatible models to create hybrid risk estimates can undermine the 

value of having preferred models (NRC, 1994, p. 174). 

14Linear extrapolation implies that risk decreases proportionally with dose below the experimental data. Sublinear 

extrapolation (often called by the less specific term nonlinear extrapolation) implies that risk decreases more than 

proportionally. 

Toxicologists often speak of a threshold dose below which an individual does not respond. Risk 

assessments generally do not use this term, as experimental data are not able to estimate thresholds with much 

confidence. For example, a response that is not statistically significant does not indicate a threshold, rather it can be 

consistent with a small risk that falls below the experiment’s power of detection. 

15Mode of action is not the only determinant of the shape of a dose-response curve. Human variation also affects the 

shape by spreading the responses in a heterogeneous population over a wider dose range (see footnotes 17, 21). 

Another factor is background exposure, which determines where to place an incremental environmental exposure on 

the overall dose-response curve (see footnote 26). 

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1.5.2. Parameter Uncertainty16 

Uncertainty analyses and confidence intervals were developed for some of this 

assessment’s key pharmacokinetic and dose-response parameters. Each description of parameter 

uncertainty assumes that the underlying model is valid. 

Uncertainty in the pharmacokinetic data was analyzed and used to gauge which 

applications could be supported by stable models. Confidence intervals spanned approximately 

100-, 5,000-, and 14,000-fold for doses in the liver, kidney, and lung, respectively (see Table 4-1, 

Section 4.2.2). The unstable model estimates for kidney and lung resulted in use of default 

scaling methods for these sites. For all sites, median dose estimates were used in subsequent 

calculations. 

Uncertainty in the animal dose-response data is reflected by the ratio of ED10s to LED10s. 

These generally do not exceed a factor of 2 (Rhomberg, 2000). 

1.5.3. Consideration of Multiple Areas of Uncertainty 

How to combine these individual aspects of uncertainty is an unresolved question in risk 

assessment. Multiplying the sizes of the individual confidence intervals is not recommended, as 

this would yield an overall confidence interval whose extremes are too improbable and whose 

range is too wide to be of practical use. There are more defensible statistical approaches for 

combining multiple uncertain quantities, but these depend on understanding the interrelationships 

between the uncertain quantities and on addressing model uncertainty by assigning numerical 

weights to the different models. Because this question is unresolved, this assessment has adopted 

EPA’s 3,000-fold limit on uncertainty factors for RfDs and RfCs and has developed a cancer 

slope factor range that reflects a middle range of estimates that are supported by several sources 

of data and several lines of reasoning. This implies, however, that the range may, in some cases, 

underestimate high-end risk. 

It has been suggested that to facilitate probabilistic risk assessment, slope factors be 

replaced by slope factor distributions. The quantifiable uncertainties (for example, the twofold 

LED10-to-ED10 range) are less important than others that have not been quantified (for example, 

pharmacokinetic model uncertainty and parameter uncertainties). To avoid misleadingly narrow 

uncertainty distributions that ignore the most important sources of uncertainty, this assessment 

does not recommend distributions based on uncertainties of secondary importance. 

16Parameter uncertainty refers to lack of knowledge about the values of a model’s parameters. This leads to a 

distribution of values for each parameter. Common sources of parameter uncertainty include random measurement 

errors, systematic measurement errors, use of surrogate data instead of direct measurements, misclassification of 

exposure status, random sampling errors, and use of an unrepresentative sample. Most types of parameter 

uncertainty can be quantified by statistical analysis. 

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Consideration of nonlinear dose-response models draws attention to the uncertainty in 

estimating the point where the dose-response curve begins to turn sharply upward. 

Overestimating the dose where the curve turns sharply upward could lead to the error of 

declaring a dose “safe” when the risk actually could be quite high. Another consequence of using 

nonlinear dose-response curves is the need to determine background exposure. In the case of 

TCE, there is substantial background exposure to some of its toxic metabolites. If their doseresponse curves are steep (as for DCA-induced liver tumors in mice), the incremental risk can be 

greater in a population with high background exposure (see Section 1.8). 

The uncertainties quantified for TCE may be similar to what would be found for other 

chemicals if pertinent analyses were performed. Thus, the practical importance of describing 

TCE uncertainty is not to decrease confidence in the TCE estimates, but to understand that all 

risk estimates are uncertain, and that the true risks can be either higher or lower. This assessment 

uses central estimates, not extreme values, for several major sources of uncertainty. The 

resulting risk estimates are supported by several sources of data and several lines of reasoning, 

providing some measure of confidence that the risk estimates are robust and not likely to be 

substantially changed by a single new study or analysis. 

1.6. HUMAN VARIATION AND SENSITIVE POPULATIONS 

The mechanistic information on TCE indicates a potential for considerable human 

variation17 and suggests some risk factors18 that would make a population more sensitive. Further 

research is needed to estimate the magnitude of the increased risk (see Section 5). 

1.6.1. Metabolic Differences 

Because TCE’s metabolites contribute to its toxicity, differences in metabolism can lead 

to differences in response (see Sections 3.2, 3.3). TCE’s metabolism is extremely complex, and 

variation in one metabolic reaction can shift activity from one metabolic pathway to another, 

17Human variation refers to person-to-person differences in susceptibility due to differences in exposure or 

differences in biological sensitivity. Differences in biological sensitivity can be linked to inherited factors (e.g., 

genetics) or acquired factors (e.g., disease). Although human variation and uncertainty both can be characterized as 

ranges or distributions, they are fundamentally different concepts. Uncertainty can be reduced by further research 

that supports a model or improves a parameter estimate, but human variation is a fact that can be better 

characterized, but not reduced, by further research. Fields other than risk assessment use “variation” or “variability” 

to mean dispersion about a central value, including measurement errors and other random errors that risk assessors 

address as uncertainty. 

18A risk factor is a condition (e.g., genetics or disease) or exposure (e.g., alcohol or chlorinated solvents) associated 

with a particular risk. For example, in the context of traffic accidents, known risk factors include alcohol 

consumption, excessive speed, and bad weather. In a particular case, causation is often attributed to a combination 

of risk factors. 

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yielding different proportions of metabolites that, in turn, yield different toxicity. Because of this 

complexity, metabolic differences may not lead to proportionate differences in risk, but do 

indicate a potential for differential risks. 

For GST metabolism, genetic polymorphisms19 have been identified and linked to an 

increased risk of TCE-induced kidney cancer (see Sections 3.3, 3.4.3). The California 

Environmental Protection Agency (Cal/EPA) (1999) has estimated that these may lead to 

fourfold differences in kidney cancer risk (see Section 4.5.1). For CYP2E1 metabolism, several 

investigators have found up to 50-fold variations in small groups of humans (see Section 3.3). 

This suggests a potential for considerable differences in response. 

It has been asserted that increases in metabolic rate due to enzyme induction have little 

effect on the amount of metabolite formed in the liver (Kedderis, 1997).20 This assertion was 

derived from physiologically based pharmacokinetic modeling exercises reported in a poster 

abstract (Kedderis, 1996). A consequence of this assertion is that first-pass metabolism from oral 

dosing would be virtually 100%, leaving no TCE to circulate throughout the body. More 

complex multiple-pathway pharmacokinetic models (Fisher, 2000; Clewell et al., 2000) show 

that this is not the case with concentrations of TCE circulating throughout the body being stored 

as fatty tissue. Moreover, even if the total amount of metabolites formed is unchanged, the 

balance between the rates of formation of GST metabolites and CYP450 metabolites would 

clearly be altered by a relative induction of one enzyme as opposed to another. These 

considerations suggest that enzyme induction and inhibition may be involved in susceptibility to 

the effects of TCE. 

1.6.2. Disease 

Some of TCE’s effects may result from its ability to disturb carbohydrate metabolism and 

cell signaling (Bull, 2000). Diabetes is a disease caused by such disturbances, and diabetics are 

at greater risk for liver cancer, kidney cancer, and many other effects. Further, uncontrolled 

diabetes causes induction of CYP2E1, favoring increased formation of toxic metabolites. This 

suggests that diabetics may be sensitive to effects from TCE (see Section 3.3). 

19A polymorphism is the presence of two or more distinct types due to genetic variation in a population. A familiar 

example is the polymorphism of blood types O, A, B, and AB. A polymorphism of metabolic enzymes indicates 

variation in metabolic activity across groups of people. The genetic basis of polymorphisms implies that they can be 

differently distributed across racial or ethnic groups. 

20Under this hypothesis, known as blood flow limitation, the metabolic rate without enzyme induction is so rapid that 

all TCE would be metabolized, hence metabolite production would be limited only by the amount of TCE present in 

the blood flowing into the liver. Increasing the metabolic rate would have no effect, as there would be no TCE left to 

metabolize. 

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Alcohol, like TCE, is metabolized by CYP2E1, alcohol dehydrogenase (ADH), and 

aldehyde dehydrogenase (ALDH). Concurrent exposure to TCE and alcohol can exacerbate the 

metabolism and acute effects of both in a manner similar to acute drug-alcohol interactions 

known for many pharmaceuticals. More importantly, chronic alcohol consumption can induce 

long-term 20-fold increases in CYP2E1 levels, favoring increased formation of toxic metabolites 

(see Section 3.3). 

1.6.3. Gender Differences 

TCE exposure has been linked to autoimmune diseases that occur mostly in women. In 

addition, recent epidemiologic studies have linked TCE exposure to cervical cancer. These 

differences in risk may be related to either pharmacokinetic or pharmacodynamic differences (see 

Section 3.3). 

1.6.4. Some Implications 

Identifying risk factors that vary across the human population allows us to begin 

identifying who is likely to be more sensitive. Human variation is a determinant of the shape of a 

population dose-response curve, which can be different from the dose-response curve for an 

individual.21 Understanding the functional relationship between a risk factor and the associated 

disease is a critical research need that presently prevents estimating the differential risk faced by 

sensitive populations (see Section 5). 

21To illustrate, consider two sensitivity distributions, centered 

at the same dose, for a homogeneous population (see thin line 

in upper figure) and a heterogeneous population (see thick 

line in upper figure). The corresponding population doseresponse curves have different shapes, with the curve for the 

heterogeneous population spread over a wider range (see 

lower figure). This occurs even when the mode of action 

suggests a threshold, because the threshold would vary across 

a heterogeneous population and the population dose-response 

curve would be indistinguishable from those associated with 

nonthreshold models. In the human population, genetic and 

lifestyle factors contribute to variation in sensitivity that 

spreads the dose-response curve over a wider range (Lutz, 

1990). 

Dose 

Variation in sensitivity

Response


Dose


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1 1.7. DIFFERENTIAL RISKS TO CHILDREN

2 There are several reasons to suspect that TCE could affect children and adults

3 differently.

22

4

5 1.7.1. Exposure Differences

6 Nursing is an exposure pathway unique to children. From its lipophilic nature, TCE

7 would be expected to be present in milk. Limited data indicate that TCE has been detected in

8 each of eight samples of human milk from four U.S. urban areas, but levels were not reported

9 (ATSDR, 1997). Quantification of levels of TCE and its metabolites in milk, in both highly

10 exposed and relatively unexposed populations, is a critical research need that presently prevents

11 comparing levels in milk with levels allowed in drinking water (see Section 5). The widely

12 accepted benefits of nursing highlight the value of minimizing the exposure of nursing mothers

13 to TCE.

14 The cancer unit risk estimates (see Section 4.5) assume that mg/kg-d is an appropriate

15 measure of dose and incorporate exposure factors that are based on adults (specifically, 2 L/d

16 drinking water intake, 20 m3

/d air intake, and 70 kg body weight). Relative to body weight,

17 however, children drink more water, eat more food, and breathe more air than do adults (U.S.

18 EPA, 1999). When assessing risks from less-than-lifetime exposure that occurs during

19 childhood, good exposure assessment practice would replace these adult exposure factors with

20 childhood exposure factors (U.S. EPA, 1999).23

21

22 1.7.2. Pharmacokinetic Differences

23 Children—especially infants—and adults can have different levels of metabolizing

24 enzymes (see Sections 3.2, 3.3). CYP2E1 is present in children, although levels are lower than in

25 adults. The same is true for some enzymes that clear TCE and its metabolites from the body. 

26 Such differences may be responsible for early-life persistence in the body of trichloroethanol

27 (TCOH), the only TCE metabolite studied in infants, children, and adults. Half-life is 8 days in

28 adults, 10 days in children, 28 days in full-term infants, and 40 days in preterm infants (Renwick,

29 1998). This trend runs counter to the general expectation that smaller organisms metabolize and

22Children are not only a subpopulation, rather childhood is a sequence of life stages that affect the whole population. 

EPA has long been concerned about environmental risks to children and has identified areas where risks can be 

different in children and adults (Farland, 1992: (1) different exposures, (2) different remaining life expectancies 

(early exposures leave more time for latent effects to develop), (3) different internal doses from the same external 

dose (pharmacokinetic differences), and (4) different responses to the same internal dose (pharmacodynamic 

differences). 

23For example, to adjust the drinking water unit risk for a 9-kg infant who drinks 1 L/d (instead of a 70-kg adult who 

drinks 2 L/d), multiply the unit risk by (1 L/d / 2 L/d) / (9 kg / 70 kg) = 3.9. 

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clear chemicals more rapidly; instead, TCOH persists longer in children than in adults, longer in 

infants than in children, and longer in preterm than in full-term infants. These differences do not 

necessarily imply greater sensitivity, as we do not know which metabolites are responsible for 

each adverse effect, but it does indicate a need for caution, as limited evidence suggests that TCE 

metabolism and clearance are different in infants and children compared to adults. 

1.7.3. Pharmacodynamic Differences 

TCE’s neurotoxic potential suggests an increased risk during childhood, a period of rapid 

brain development (see Sections 3.4.1, 3.3). TCE’s potential for developmental and endocrine 

effects also supports a concern for childhood exposure (see Section 3.4.5). Cancer, too, is an 

effect where children may be at higher risk. An accepted concept in carcinogenesis is that young 

animals are usually more susceptible to the carcinogenic activity of a chemical than are adults 

(McConnell et al., 1992).24 Because TCE’s metabolites can cross the placenta, the period of 

concern would extend to prenatal exposure. This concern is supported by a larger risk of 

childhood leukemia observed after maternal exposure to TCE-contaminated drinking water 

during the prenatal period (MA-DOH, 1997). 

1.7.4. Some Implications 

The health reference values developed in this assessment (see Sections 4.3–4.5) do not 

address the exposure, pharmacokinetic, and pharmacodynamic considerations discussed here. 

For example, the uncertainty factor for human variation is based on data collected in healthy 

adults. Some assurance that this assessment does not understate risks to children comes from 

consideration of sensitive test systems such as mouse liver. This sensitivity, though, is not 

related to the reasons that children and adults may differ. Consequently, it may be appropriate to 

consider a further factor to address the potential for differential risks to children (see Section 5). 

1.8. CUMULATIVE RISKS INVOLVING TCE 

Several chemicals have the potential to alter TCE’s metabolism and clearance and 

subsequent toxicity. Much TCE-induced toxicity is attributed to its metabolites, and risks from 

TCE exposure can depend on background exposure to its metabolites. Some metabolites are 

highly present in the environment (see Section 2), from both direct sources (for example, 

24Although much has been made of the qualitative finding that perinatal exposure rarely induces tumors not found 

with adult exposures, quantitatively, perinatal exposure in conjunction with adult exposure usually increases the 

incidence and reduces the latent period of a given tumor. For example, in a survey of 22 chemicals tested for 

perinatal exposure (TCE and its metabolites have not been so tested), a higher incidence of neoplasms was found 

when animals were exposed during the last third of pregnancy (McConnell et al., 1992). 

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ingestion of TCA and DCA as byproducts of drinking water disinfection) and indirect sources 

(for example, metabolism of other chlorinated solvents to TCA). Hence, these exposures and 

conditions can modify the effects of TCE on a population.25 

At the same time, TCE exposure can augment toxicity induced by other chemicals. 

Among these, support is strongest for TCE to exacerbate the effects of concurrent exposure to 

alcohol or chlorinated solvents by inducing shared metabolic enzymes (see Section 3.3). 

25Known as cumulative risk assessment, this area of inquiry focuses on a population and considers all chemicals and 

stressors that can affect the population. A cumulative risk assessment goes beyond the effects of each chemical 

individually to consider (1) how exposure to other chemicals and stressors can alter the toxicity of a chemical, (2) 

how exposure to a chemical can alter the toxicity of other chemicals and stressors, and (3) how genetics, life stage, 

lifestyle, diseases, and other conditions in an individual—factors associated with sensitivity—can affect expression 

of a chemical’s toxicity. In this way, cumulative risk assessments consider human-chemical interactions as well as 

chemical-chemical interactions. 

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1 When a nonlinear dose-response curve is assumed, high background exposure can imply

2 greater susceptibility to incremental exposures.

26 Consequently, an estimate of background

3 exposure is needed to determine where to place an incremental exposure to TCE on the dose4 response curve, as the size of the increased risk is different in different regions of the dose5 response curve. Finding a safe dose in an otherwise unexposed population does not mean that

6 that dose is safe when background exposures are considered. This aspect of susceptibility is a

7 new feature that has not been addressed in past risk assessments, but will become important in

8 future assessments that consider nonlinear dose-response curves (see Section 5).

9 This assessment has begun to address cumulative risk in several ways:

10 < Developing a pharmacokinetic model (at EPA’s National Exposure Research Laboratory)

11 for concurrent exposure to TCE, TCA, and DCA. This will enable risk assessors at

12 different geographic sites to tailor the model to the cumulative exposures specific to their

13 sites (see Section 4.2.1).

14 < Identifying diseases and other risk factors that may make some individuals more sensitive

15 to TCE’s effects (see Sections 1.6, 3.3).

16 < Including a modifying factor in the RfD to reflect that background exposures to TCE and

17 its metabolites can be higher in humans than in laboratory animals (see Sections 1.4,

18 4.3.3). An analogous modifying factor was not used in the RfC because the exposed

19 workers on which the RfC was based, unlike the laboratory animals on which the RfD

20 was based, are likely to have had background exposures comparable to those of the

21 general human population.

22 < Developing a range of cancer slope factors to permit risk assessors to choose a slope

23 factor based on a population’s risk factors and background exposures (see Sections 1.4,

4.5.6). 

26The correlation between background exposure and 

susceptibility is a property of sublinear dose-response curves 

in general. A familiar analogy is how federal income tax 

rates increase with income. For example, the increased tax 

on $10,000 additional income can be (see figure at right): 

$0 if there is no other income. 

$1500 if added to $20,000 income. 

$2800 if added to $40,000 income. 

In this analogy, the same incremental income (“dose”) results 

in a tax (“effect”) that depends on background income. 

Tax


$8,000


$4,000


$0


$0 $20,000 $40,000


$1500 

$2800 

Income


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2. EXPOSURE CHARACTERIZATION 

This exposure characterization covers TCE, several metabolites of TCE, and other parent 

compounds that produce a similar profile of metabolites (see Figure 2-1). Direct exposure to 

TCE’s metabolites or to parent compounds that produce these metabolites can alter or enhance 

TCE’s metabolism and toxicity by generating higher internal metabolite concentrations than 

would result from TCE exposure by itself. 

This characterization draws from the state-of-the-science paper on exposure (Wu and 

Schaum, 2000). More detailed exposure information can also be found in a supplemental report 

(Wu and Schaum, 2001). 

2.1. ENVIRONMENTAL SOURCES 

The major use of TCE is as a degreaser for metal cleaning operations. It is also used as a 

paint stripper, adhesive solvent, ingredient in paints and varnishes, and in the manufacture of 

organic chemicals. Releases from nonanthropogenic activities are negligible. TCE is on the list 

for reporting to the Toxics Release Inventory (TRI). Reported releases into air predominate over 

other types. Reported releases decreased by a third between 1987 and 1990 and have been stable 

since then. 

TCE’s metabolites and their other parent compounds are also generally used in a wide 

variety of manufacturing industries. Exceptions to this pattern are TCA and DCA, which are 

formed as byproducts of drinking water chlorination; 1,1-dichloroethane, which can be formed 

by biodegradation of 1,1,1-trichloroethane in landfills and other anaerobic environments; and 

perchloroethylene, whose releases have come primarily from the large number of local dry 

cleaning operations. 

The TRI is the primary source of this information and can be considered recent and well 

documented. The major uncertainty is the number of minor releases that occur, which are 

exempt from reporting but, collectively, may be important. 

2.2. ENVIRONMENTAL FATE 

The chemical properties of TCE are well documented and widely accepted. Because of 

its high vapor pressure, TCE in the atmosphere is expected to be present primarily in the vapor 

phase rather than adsorbed to particles. Some removal from the atmosphere during wet 

precipitation is expected because of the moderate solubility of TCE in water (1.1 g/L). The 

major degradation process affecting vapor-phase TCE is photooxidation by hydroxyl radicals 

(half-life of 1–11 days). 

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The dominant fate of TCE released to surface soils or surface waters is volatilization to 

the air. Because of its moderate water solubility, TCE in soil (for example, landfills) has the 

potential to migrate into groundwater. The relatively frequent detection of TCE in groundwater 

confirms its mobility in soils. Biodegradation in soil and groundwater is thought to be slow 

(half-life on the order of months to years). For releases to surface waters, bioconcentration, 

biodegradation, and adsorption to sediments and suspended solids are thought to be insignificant. 

2.3. EXPOSURE CONCENTRATIONS 

No statistically based national sampling programs have been conducted that would allow 

estimates of true national means for any environmental medium. A substantial amount of air 

data, however, has been collected and summarized. TCE has been detected in the air throughout 

the United States. Ambient air measurement data can be obtained from the Aerometric 

Information Retrieval System (AIRS)27, which contains about 1,200 measurements from 25 

States during 1985–1998. Air levels in 1998 can be summarized as follows: range=0.01–3.9 

μg/m3, mean=0.88, median=0.32, 90th percentile=1.76. 

TCE ambient air concentrations in 1990 were modeled for all census tracts of the 

continental United States as part of EPA’s Cumulative Exposure Project (CEP)28. The modeling 

suggests that 97% of the census tracts have TCE concentrations ranging from 0 to 1.5 μg/m3

. 

The average level was estimated as 0.37 μg/m3

 and the maximum as 32 μg/m3. The averages and 

percentiles can be interpreted as population-weighted values because all census tracts have 

roughly equal populations (but vary in geographic size). The modeling uses population 

weighting for its estimates of concentration within each census tract. CEP data suggest a pattern 

of generally low TCE levels in rural areas, higher levels in urban areas, and highest levels in 

small commercial or industrial sectors across most States. The pattern is consistent with the 

monitoring data. 

These modeled values should be interpreted with caution. They are not as reliable as 

measured values for specific locations. AIRS data show an average for 1990 across 59 

monitoring stations of 1.84 μg/m3. This is much higher than the national average from CEP of 

0.37 μg/m3. An important difference, though, is that the CEP estimate represents all areas of the 

continental United States, whereas the 1990 AIRS data for TCE represent only 59 monitors 

located in eight States. 

TCE has been reported in rainwater, surface water, groundwater, drinking water, and 

seawater. ATSDR reports that TCE is the most frequently reported organic contaminant in 

27

www.epa.gov/airsdata 

28

www.epa.gov/CumulativeExposure/air/air.htm 

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groundwater and has estimated that between 9% and 34% of drinking water supply sources have 

some TCE contamination. Most municipal water supplies that do not use contaminated 

groundwater, however, are in compliance with the maximum contaminant level of 5 μg/L. 

TCE has been reported in marine sediments, marine invertebrates, marine mammals, 

foods, human milk, and human urine and blood. The food with the highest reported mean 

concentration is butter and margarine (73.6 ppb). 

Biological monitoring is an important supplement to environmental monitoring for 

characterizing human exposure, as environmental monitoring data (especially for air) may not be 

representative of actual exposures. TCE has been most frequently detected in persons exposed 

through occupational degreasing operations. In 1982, TCE was detected in each of eight human 

milk samples from four U.S. urban areas. NHANES III reported that, from 1988 to 1994, TCE 

levels in whole blood were below the detection limit of 0.01 mg/L for about 90% of the people 

sampled. 

2.4. EXPOSURE PATHWAYS AND LEVELS 

Most people are exposed to TCE through drinking water, air, or food. TCE has been 

detected in human milk samples from the general population; consequently, nursing infants may 

be exposed. Also, because TCE can be present in soil, children may be exposed through 

activities such as playing in or ingesting soil. 

AIRS monitoring data for 1998 indicate a mean outdoor air level of about 0.8 μg/m3. 

Based on an inhalation rate of 20 m3

/d, intake by this pathway is about 18 μg/d. This is 

consistent with ATSDR, which reported an average daily air intake for the general population of 

11–33 μg/d. Total inhalation exposures are likely to be higher, because limited studies suggest 

that indoor air may contribute more to overall exposure than outdoor air. 

A survey of large water utilities in California for 1984 found a median concentration of 3 

μg/L. Based on a water consumption rate of 2 L/d, intake by this pathway is about 6 μg/d. This 

is consistent with ATSDR, which reported an average daily water intake for the general 

population of 2–20 :g/d. 

TCE can be found in groundwater. It is not known how often TCE reaches levels of 

concern, but the highest potential for this to happen would be at wells located near TCE disposal 

or contamination sites where leaching occurs. TCE is a common contaminant at Superfund sites. 

It has been identified in at least 852 of the 1,416 hazardous waste sites proposed for inclusion on 

the EPA National Priorities List. About 41 million people live less than 4 miles from a 

Superfund site. 

TCE exposures may be elevated for people living near waste sites, residents of some 

urban or industrialized areas, people exposed at work, and people using products containing TCE 

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with poor ventilation. In addition, TCE is transferred from shower water to air (mean efficiency, 

61%); ATSDR reported that a 10-minute shower in TCE-contaminated water could result in 

inhalation exposure comparable to that from drinking TCE-contaminated tap water. 

Table 2-1 presents adult exposure estimates for TCE and several of its metabolites and 

other parent compounds that also produce those metabolites. Of special note is that generalpopulation exposures to TCA and DCA are each approximately 10-fold higher than exposure to 

TCE. This indicates that there is a high background level of these metabolites that not only can 

alter and enhance the metabolism of TCE, but also have toxic effects themselves. This topic is 

further discussed in Section 3. 

The general population exposure estimates are derived directly from environmental media 

data and, therefore, have the same uncertainties as described above. In addition, inhalation 

exposures are probably low, because they are based on ambient air measurements, which do not 

reflect indoor levels. It is likely that indoor levels are generally higher and contribute more to 

inhalation exposure than does outdoor air. For example, the 1987 EPA TEAM (Total Exposure 

Assessment Methodology) study shows that the ratio of indoor to outdoor TCE concentrations 

for Greensboro, NC, was about 5:1. The prevalence of TCE exposure on the basis of blood 

levels is based on NHANES III, which was recent (1988 to 1994) and fairly extensive (644 

individuals). 

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Table 2-1. Preliminary dose estimates of TCE and TCE-related chemicals 

Chemical Population Medium 

Range of 

estimated adult 

exposures 

(:g/day) 

Range of estimated 

adult doses 

(mg/kg/day) Data sources 

Trichloroethylene General Air 11 – 33 1.57E-04 – 4.71E-04 (3) 

General Water 2 – 20 2.86E-05 – 2.86E-04 (3) 

Worker Air 2,232 – 9,489 3.19E-02 – 1.36E-01 (3) 

Tetrachloroethylene General Air 80 – 200 1.14E-03 – 2.86E-03 (13) 

General Water 0.1 – 0.2 1.43E-06 – 2.86E-06 (13) 

Worker Air 5,897 – 219,685 8.43E-02 – 3.14 (13) 

1,1,1-Trichloroethane General Air 10.8 – 108 1.54E-04 – 1.54E-03 (16) 

General Water 0.38 – 4.2 5.5E-06 – 6.00E-05 (16) 

1,2-Dichloroethylene General Air 1 – 6 1.43E-05 – 8.57E-05 (18) 

General Water 2.2 3.14E-05 (18) 

cis-1,2-Dichloroethylene General Air 5.4 7.71E-05 (6) 

General Water 0.5 – 5.4 7.14E-06 – 7.71E-05 (6) 

1,1,1,2-Tetrachloroethane General Air 142 2.03E-03 (6) 

1,1-Dichloroethane General Air 4 5.71E-05 (7) 

General Water 2.47 – 469.38 3.53E-05 – 6.71E-03 (7) 

Chloral General Water 0.02 – 36.4 2.86E-07 – 5.20E-04 (6) 

Monochloroacetic acid General Water 2 – 2.4 2.86E-05 – 3.43E-05 (19) 

Dichloroacetic acid General Water 10 – 266 1.43E-04 – 3.80E-03 (19) 

Trichloroacetic acid General Water 8.56 – 322 1.22E-03 – 4.60E-03 (19) 

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3. HAZARD ASSESSMENT AND CHARACTERIZATION 

3.1. GENERAL APPROACH 

The database for TCE is relatively rich in toxicity information that includes 

epidemiologic studies, animal bioassays, metabolism studies, genetic toxicity studies, and 

mechanistic studies. Interpretation of this information, especially the cancer studies, has sparked 

controversies concerning the strength of the human evidence and the human relevance of the 

animal responses. These controversies have prompted some new epidemiologic studies and 

reviews, plus a wealth of mechanistic research committed to developing and supporting 

hypotheses about TCE’s potential modes of action in animals. 

To help provide a sound scientific basis for addressing these and other issues, this 

assessment commissioned a set of state-of-the-science papers that appears in a May 2000 

supplement of Environmental Health Perspectives. One paper addresses the controversy over the 

weight of the human evidence. Wartenberg et al. (2000) conducted a joint analysis of the cancer 

epidemiology studies, using a statistically based weight-of-evidence approach that stratified the 

available studies into tiers according to how well each study’s results can be associated with TCE 

exposure specifically. Such an analysis affords an opportunity to put differing results in 

perspective by evaluating whether the positive or the nonpositive results overall have greater 

weight, and whether the overall result is statistically significant. 

Other state-of-the-science papers focus on describing the effects of TCE and its 

metabolites as well as the human relevance of observed animal responses. Lash et al. (2000a) 

describe the metabolism of TCE, providing a foundation for the consideration of various 

metabolites and target sites throughout the body. Pastino et al. (2000) discuss factors that can 

cause humans to vary in their susceptibility to adverse health effects from TCE exposure and 

identify populations that are likely to be more susceptible. Moore and Harrington-Brock (2000) 

discuss the genetic toxicity of TCE and its metabolites, a critical step in understanding TCE’s 

mode of action at different sites. Bull (2000), Green (2000), and Lash et al. (2000b) discuss 

potential modes of action for tumorigenesis in the liver, lung, and kidney, respectively. These 

latter papers reflect the considerable research effort that has gone into developing the modes of 

action for cancer for their respective tumor sites, described as targets in past TCE assessments 

(U.S. EPA, 1985, 1987). The paper of Barton and Clewell (2000) reviews the evidence in the 

rodent for effects other than cancer (discussed in this section), and also provides support for the 

development of an inhalation reference concentration and oral reference dose (see Section 4). 

Bull’s state-of-the-science paper (Bull, 2000) has turned attention toward cell signaling to 

give insight into possible modes of action for TCE. This in turn, has helped provide focus for 

common modes and targets for cancer and other forms of toxicity. Many potential targets of 

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TCE toxicity (e.g., heart, brain, testes, and the developing fetus) may be affected by disturbances 

in carbohydrate metabolism and alterations in cell signaling, thus, common themes may 

ultimately emerge for different targets and effects. Particularly relevant is the work of Bannasch 

(Bannasch et al., 1984, 1986, 1997; Bannasch, 1986, 1996) demonstrating that disturbance of 

carbohydrate metabolism and cell signaling may be an early marker and common feature of 

carcinogenesis. Discussions with the state-of-the-science authors have also directed attention to 

the potential role in the toxicity of TCE of activation of the peroxisomal proliferator-activated 

receptor by TCE or its metabolites. They also suggested a focus on the rationale supporting 

linear or nonlinear carcinogenic responses in the liver. Accordingly, an expanded mode-ofaction section includes a detailed discussion of potentially relevant epigenetic mechanisms of 

carcinogenesis. 

This hazard assessment focuses on analysis and interpretation rather than a compilation of 

study results. More detailed information on the epidemiologic and experimental studies on TCE 

can be found in the state-of-the-science papers and in comprehensive reviews compiled by 

ATSDR (1997), IARC (1995), and EPA (1985, 1987). Considerable new literature has become 

available on TCE’s potential toxicity and modes of action, including several effects other than 

cancer. Some of the new information postdates the publication of the state-of-the-science papers 

and is integrated into this hazard assessment. Thus, the discussion of toxicity and mode of action 

is considered more current and the recent references expand the scope beyond cancer. Section 

3.2 examines TCE’s metabolism and clearance, because TCE-induced toxicity is often attributed 

to TCE’s metabolites. This leads to a discussion in Section 3.3 of susceptibility, because several 

features of TCE metabolism and clearance have been found to be differentially distributed across 

the general population, suggesting some potentially susceptible populations. Section 3.4 

summarizes the toxic effects reported from TCE exposure. These effects are grouped by organ 

system, and for each organ system, cancer and other effects are discussed together. Each 

summary of TCE-induced effects is followed by a summary of available information about 

similar effects reported in studies of TCE’s metabolites. Section 3.5 discusses some potential 

modes of action through which TCE may induce these effects. A mode of action can involve 

several systems, and conversely, effects in one system may arise from several distinct modes of 

action. Much of the research supporting the hypothesized modes of action has concentrated on a 

few TCE metabolites rather than on TCE itself. 

3.2. METABOLISM 

The state-of-the-science paper by Lash et al. (2000a) discusses the absorption, 

distribution, metabolism, and excretion of TCE and its metabolites. TCE is rapidly and 

extensively absorbed by all routes of environmental exposure—ingestion, inhalation, and skin 

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contact. Once absorbed, TCE is distributed via the circulatory system throughout the body, 

where it can accumulate in fat and other tissues. Storage of TCE in fat represents an internal 

source of exposure that can later release TCE again into the circulation. The pathways of 

biotransformation observed in humans and animals are thought to be similar qualitatively 

(Pastino et al., 2000). 

TCE is metabolized primarily in the liver, but metabolism can also occur in other tissues. 

Metabolism in these other tissues may be important to TCE-induced toxicity. The kidney is the 

only other tissue that has been extensively studied to any extent. Metabolism occurs through two 

main pathways (oxidation or conjugation with glutathione) (see Figure 3-1). Although several 

metabolic products derived from these pathways have been identified for TCE, only a few have 

been characterized pharmacokinetically (Lash et al., 2000a). The first major pathway begins with 

oxidation of TCE by at least four isozymes of the cytochrome P450 (CYP450) system to CH, 

which is then acted upon by ADH and ALDH to form trichloroethanol (TCOH) and TCA, 

respectively. CYP450 is also most likely involved in the reduction of CH to TCOH with TCA as 

a product. TCOH undergoes glucuronidation and is either excreted in the urine or persists in the 

body through enterohepatic recirculation.29 TCOH can then be excreted from the liver to the 

small intestines, where it is reabsorbed into the circulation. In general, enterohepatic 

recirculation is more extensive in humans than rodents (Barton and Clewell, 2000), with the 

result of a larger area-under-the-curve (AUC) in humans than rodents for an equivalent oral dose. 

Overall, the rate-limiting step for this pathway is oxidation of TCE to CH (Lash et al., 2000a). 

DCA is another metabolite detected in mice and humans, but the source of DCA 

formation is not clear. DCA may result from the oxidative pathway by further oxidation of both 

TCA and TCOH or by rearrangement directly from dichloroacetyl chloride, from glutathione 

conjugation, or, likely, from both pathways (Lash et al., 2000a; Cai and Guengerich, 2000; 

Völkel et al., 1998). The estimation of the amount of DCA formed in humans contains 

uncertainty because there are problems associated with the analytical methodology, particularly 

in the presence of large amounts of TCA, and because of DCA’s relatively rapid metabolism to 

oxalic acid, monochloroacetic acid, glycolic acid, and glyoxylic acid. Conversion to glyoxylic 

acid is thought to be by a newly described isoform of glutathione S-transferase (GST), identified 

as GST zeta (GSTZ) (Lash et al., 2000a). GSTZ enzyme activity in vitro differs between rats and 

humans and is relatively more sensitive to inhibition by DCA in rats (Cornett et al., 1999). In 

general, GSTs are present in multiple forms in mammals and have a large degree of overlapping 

29Enterohepatic recirculation is the process by which metabolites formed from the biotransformation of the parent 

compound in the liver are excreted directly into the bile without first entering systemic circulation. From the bile, 

the metabolites pass into the small intestine and are available for reabsorption (Klaussen, 1980). 

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substrate specificity among the differing forms. They also exhibit polymorphisms and species 

differences in expression and catalytic activities. Hence, the further characterization of this 

pathway as it relates to TCE is important. 

The second major pathway of TCE metabolism also involves GST and begins with 

glutathione conjugation, primarily in the liver, to S-(1,2-dichlorovinyl)glutathione (DCVG). 

DCVG formed in the liver is excreted in the bile and converted in the biliary tract and intestines 

to S-(1,2-dichlorovinyl)-L-cysteine (DCVC). DCVC is translocated to the kidneys, where its 

concentration modulates the amount of toxic metabolites generated in the kidney. DCVC can be 

detoxified by N-acetyltransferase (NAT) in the kidney and excreted in the urine, or it can be 

metabolically activated to a thioacetylating agent by $-lyase. Although the capacity of this 

pathway is small compared with the CYP450 pathway, the chemically unstable and highly 

reactive nature of the metabolites produced by $-lyase metabolism make it likely that relatively 

small amounts can induce significant responses (Lash et al., 2000a). 

Quantitatively, the comparative rate of TCE metabolic reactions between species or 

between potential target organs is a complex question. The interspecies relationship of TCE 

metabolic pathways has been investigated using pharmacokinetic models (Clewell et al., 2000; 

Fisher, 2000) and analyses that look at uncertainty in those models (Bois, 2000a,b). Peak blood 

levels of TCE’s oxidative metabolites (e.g., TCA) are higher in mice and rats than in humans 

administered equivalent doses, whereas blood levels of TCE in humans generally remain for a 

longer time period, i.e., longer half-life. Differences in enterohepatic recirculation and 

metabolism could be factors in interspecies differences. These factors result in greater 

persistence in the body and a larger AUC for some TCE metabolites in humans compared to 

rodents. For the GST conjugation pathways in the kidney, in vitro work suggests that DCVG 

formation in human liver and kidney was only slightly lower than in the rat, with DCVG 

formation in mice shown to be markedly faster than in either humans or rats (Lash et al., 2000a). 

However, DCVG formation is only the initial step in the generation of nephrotoxic species, with 

additional studies necessary to characterize this metabolic pathway (Lash et al., 2000a). A 

gender difference also is apparent, with an overall higher GSH conjugation in male rats than in 

female rats (Lash et al., 2000a). 

3.3. SUSCEPTIBILITY 

The state-of-the-science paper by Pastino et al. (2000) discusses physiologic factors (e.g., 

genetics, gender, and age) and acquired factors (e.g., disease, alcohol consumption, and exposure 

to other solvents) that may cause humans to vary in their susceptibility to adverse health effects 

from TCE exposure. Another way to view the factors that can influence susceptibility to 

potential TCE-related toxicity is to categorize them as either intrinsic or extrinsic. Inherent 

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differences in an individual’s genetic makeup, gender, age, or disease state may affect 

susceptibility intrinsically, whereas coexposure to a pollutant that alters their metabolism or 

clearance, or that adds on to background levels of metabolites, may affect susceptibility 

extrinsically. Through either mechanism, increased susceptibility to potential TCE-related 

toxicity may occur by altering the body burden of TCE or its metabolites, sensitizing the 

individual to other agents that act in common metabolic pathways, or augmenting underlying 

conditions that share common effects with TCE (e.g., effects on cell signaling). 

The variation observed for specific metabolic reactions or responses within individual 

populations suggests potential for considerable overall differences in response between 

individuals in the whole population. It is, however, difficult to describe quantitatively the overall 

extent of human variability, as the range of variation in a single process or metabolic reaction, as 

well as variation within a specific ethnic group, may not encompass the span of variation across 

the entire population or take into account the multiplicity of factors that can determine the 

toxicity of TCE. Nonetheless, the variation observed for specific metabolic reactions or groups 

not only suggests a potential for considerable overall differences in response between 

individuals, but can be used to identify populations and conditions that may be associated with a 

higher risk from TCE exposure. 

Human variation is also a determinant of the shape of a population dose-response curve. 

The dose-response curve for a susceptible population can be different and changed when 

compared to the dose-response curve for the heterogeneous population. Additionally, direct or 

background exposure to some of TCE’s metabolites can be considerable and, for individuals with 

these exposures, can aggravate the effect of small incremental exposures of TCE. That is, 

background and cumulative exposures place the susceptible population on a different part of the 

dose-response curve. For example, given a metabolite with an “S”-shaped dose-response curve, 

a small dose would induce a minimal response in the absence of background exposure, yet the 

same dose could induce a greater response in the presence of background exposure that places an 

individual higher on the “S”-shaped curve, in the range where the risk increases rapidly with 

dose. 

3.3.1. Intrinsic Factors Affecting Susceptibility 

3.3.1.1. Differences in Metabolism and Clearance 

Adverse effects resulting from TCE exposure are believed to be attributed mainly to its 

metabolites. Hence, individual differences in metabolism can lead to individual differences in 

response. The influence of physiologic and acquired factors on metabolism, particularly on 

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CYP2E1,30 which may be responsible for more than 60% of TCE metabolism, is important 

(Lipscomb et al., 1997). Rates for TCE metabolism by the oxidative pathway are believed to 

vary across the population. In vitro, an eightfold variation in metabolic activity of TCE via 

CYP2E1 was seen in 23 human liver samples (Lipscomb et al., 1997); several investigators have 

found activity of CYP2E1, in general, to vary up to 50-fold in humans (Stevens et al., 1994; 

Peters et al., 1990; Yoo et al., 1998; Raucy, 1995; Lieber 1997; Pastino et al., 2000). 

For the GST pathway,

31 genetic polymorphisms may not only influence metabolism of 

TCE and its target of toxicity. One epidemiologic study has linked polymorphisms of the GST 

pathway (GSTT and GSTM) to increased risk of kidney cancer from TCE exposure (Brüning et 

al., 1997a). Additionally, one isozyme, GSTZ, is important to the further metabolism of DCA 

(Tong et al., 1998), and in vitro has been shown to be irreversibly inhibited in mice, rats, and 

humans with chronic DCA exposure (Tzeng et al., 2000). In vitro studies of cytosolic GST 

conjugation of TCE in humans have revealed considerable (3-8 fold) variation in relatively small 

samples (Lash et al., 2000a). In general, regulation of GST expression differs among tissues, 

such that not all GST isoforms are expressed in every tissue. Partial deletions are also found to 

varying degrees in specific ethnic populations (15%–20% of Caucasians and 60% of Asians 

having partial deletions). Therefore, it is difficult to accurately predict expression of GST genes 

in a given tissue (Eaton and Bammler, 1999). 

3.3.1.2. Disease States 

Certain diseases may increase susceptibility to TCE’s adverse effects through induction 

of metabolic enzymes, favoring increased formation of toxic metabolites of TCE, or by adding to 

ongoing physiological processes. For example, uncontrolled diabetes causes induction of 

CYP2E1 (Wang, 2000; Nakajima, 2000). Furthermore, diabetics may be a population 

susceptible to TCE’s adverse effects on the basis of recent mechanistic understanding about the 

30CYP2E1 is one of the CYP450 enzymes and is the primary catalyst of hydrocarbon bioactivation in animals and, 

most likely, in humans as well (Raucy et al., 1993). CYP2E1 also metabolizes many drugs and environmental 

pollutants, including ethanol, acetaminophen, benzene, 1,3-butadiene, styrene, methylene chloride, chloroform, 

carbon tetrachloride, vinyl chloride, vinylidene chloride, and perchloroethylene. Exposure to these chemicals can 

affect enzyme levels or activity and, in so doing, alter or enhance TCE’s toxicity. CYP2E1 is found in greatest 

concentration in liver, but is also expressed in parenchymal and non-parenchymal cells of the liver (e.g., Kupffer 

cells) and in many extrahepatic tissues (e.g., kidney, brain, testes, lung, and bone marrow). 

31GST enzymes are involved in metabolizing many drugs and environmental pollutants, including polycyclic 

aromatic hydrocarbons, phenolic antioxidants, reactive oxygen species, isothiocyanate, trivalent arsenicals, 

barbiturates, and synthetic glucocorticoids (Hayes and Pulford, 1995). GST metabolism aids in the clearance of 

xenobiotics and is generally considered a detoxification process. This is not the case for trichloroethylene, where 

highly reactive metabolites can be produced. 

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relationship between TCE and its metabolites and carbohydrate handling and cell signaling (Bull, 

2000) (see Section 3.5.1). 

3.3.1.3. Gender 

Gender-related differences in metabolism may influence susceptibility by shifting 

production of metabolites from one toxic pathway to another, or by providing differing targets for 

TCE’s toxicity. Males have been reported to have higher rates of DCVG formation from acute 

exposure (Lash et al., 2000a). There may also be gender-related differences in target organs. 

One recent case-control study (Dosemeci et al., 1999) observed an elevated risk among females, 

but not males, between kidney cancer and occupational TCE exposure. This study was not able 

to address potential gender differences in level of exposure, however. Females have also shown 

a greater susceptibility than males to some of TCE’s immune system effects (Sanders et al., 

1992; Barton and Clewell, 2000). Moreover, gender-specific organs may be targets of TCE’s 

effects. TCE exposure has been associated with excess risks of cervical cancer in occupationally 

or environmentally exposed women (Blair et al., 1998; Anttila et al., 1995; Burg, 1997), and in 

male rats, has been shown to concentrate in the testes (Zenick et al., 1984), decrease serum 

testosterone, and alter testicular steroid precursors (Kumar et al., 2000). 

3.3.1.4. Age 

Several inherent aspects of uptake, distribution, and clearance of TCE in children can 

predispose children to its toxicity. In general, children are believed to metabolize and clear 

xenobiotics faster than adults, although rate differences vary across chemicals and the patterns of 

metabolism may be different from those of adults. In contrast to this general expectation, the 

half-life of TCOH in the body has been reported to be 20% higher in children, 3.5-fold higher in 

full-term infants, and fivefold higher in preterm infants compared with healthy adults (Renwick, 

1998). This finding, which could extend to other TCE metabolites, may be due to differing 

enzyme activities in children. For example, glucuronidation does not reach adult levels until 3 to 

6 months of age. Individual children may also have low levels of a particular isoform, as shown 

by their inability to efficiently carry out glucuronidation and their increased risk for developing 

hyperbilirubenemia. Such children may also be less able to glucuronidate TCOH. Furthermore, 

ADH activity has been shown to be decreased in the neonate (Pastino et al., 2000). Other life 

stage differences, such as the amount of body fat in an organ, can also lead to differences in TCE 

distribution and accumulation. The brain of the newborn contains more lipid than that of adults, 

thus affecting the distribution of TCE. 

The prenatal and postnatal periods of development may be critical for TCE’s potential 

carcinogenic and noncarcinogenic toxicity. Although the modes of action of the carcinogenicity 

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of TCE are unknown, if TCE behaves similarly to the limited number of chemicals tested during 

perinatal exposure as well as adult exposure, the carcinogenic potential of TCE may also be 

greater in the young (McConnell, 1992). Moreover, TCE exposure to mice during the perinatal 

period has been reported to be associated with long-lasting alterations in spontaneous motor 

activity (Fredriksson et al., 1993), although this report is only suggestive because of its lack of 

statistical analysis on the litter. Exposure during the prenatal period has been shown to induce 

toxicity. For example, animal studies report associations between maternal exposure to TCE in 

drinking water and cardiac malformations in offspring (Johnson et al., 1998a,b; Dawson et al., 

1993; Epstein et al., 1993). Finally, the longer life expectancy of children compared with adults 

may lead to a longer period for expression of toxicity. 

3.3.2. Extrinsic Factors Affecting Susceptibility

3.3.2.1. Alteration of Metabolism and Clearance Through Coexposure to Other Chemicals

Exogenous exposures that induce CYP450 enzymes can enhance TCE metabolism and 

toxicity. Cytochrome P450, particularly CYP2E1, is important to the metabolism of many 

exogenous chemicals, including solvents and ethanol; CYP2E1 is also readily induced by a large 

number of these substrates. For example, ethanol is metabolized by CYP2E1, alcohol 

dehydrogenase, and aldehyde dehydrogenase. Although interactions have been demonstrated in 

the toxicity of both ethanol and TCE given simultaneously at relatively high levels for short 

periods, more profound effects on TCE toxicity are liable to occur from chronic ethanol 

exposure. Chronic ethanol consumption can result in 20-fold induction of CYP2E1 (Pastino et 

al., 2000; Fisher, 2000), significant because large numbers of people consume ethanol. In 

addition, ethanol is a weak inducer of peroxisomes, with ethanol consumption linked with 

increased risk of liver cancer (Falk, 1982; NIH, 1985). Thus, exposure to both ethanol and TCE 

may create conditions that increase risk through shared pathways of metabolism, induction of 

metabolic enzymes, and potential targets of toxicity. Methanol is often a concurrent exposure for 

TCE and shows affinity for many of the same enzymes. Furthermore, TCE exposure can 

increase the toxicity associated with methanol and ethanol exposure by altering not only 

metabolism to aldehydes but also their detoxification. For example, TCE exposure has been 

reported to induce an “alcohol-flush,” similar to those who possess an inactive ALDH allele (as 

seen in half of individuals of Asian heritage), which may be due to TCE’s ability to decrease 

ALDH activities at relatively low concentrations (Wang et al., 1999). In addition, the induction 

of P450 metabolism or aldehyde clearance by TCE may increase aldehyde production as well 

(Wang et al., 1999). 

3.3.2.2. Additive Effects from Coexposure to Other Chemicals 

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The addition of TCE exposure to other background exposure has the potential to increase 

susceptibility to TCE’s adverse effects. Exposure to perchloroethylene, the dry cleaning solvent, 

can be considerable in some populations, and TCA is a primary metabolite of perchloroethylene. 

Moreover, TCA and DCA may be found in drinking water, with background exposures to TCA 

and DCA each about 10-fold higher than background exposure to TCE (Wu and Schaum, 2000). 

3.3.2.3. Age 

Children and infants may be more susceptible to TCE’s toxicity because they may receive 

higher exposures via the mother either through systemic circulation during pregnancy or 

mother’s milk, as well as from pharmacokinetic differences between a developing child and 

adult. Limited data collected from residents in U.S. urban areas have indicated that TCE is 

present in human milk (ATSDR, 1997); nursing infants may receive a larger exposure to TCE 

owing to its presence in mother’s milk (Fisher et al., 1989). The fetus may also serve as a 

reservoir for maternally derived metabolites; CYP2E1 has been reported to be expressed in fullterm placentas (Vieira et al., 1998). TCA has been reported to accumulate in mouse fetuses and 

amniotic fluid (Danielsson, 1990; Ghantous et al., 1986). DCA also readily crosses the placenta 

to the fetus (Smith et al., 1992). 

3.4. TOXICITY 

TCE affects many organs and systems of the body, consistent with its lipophilic nature 

and ability to distribute widely throughout the body. This section summarizes effects reported 

for exposure to TCE and its metabolites, presented by organ system. A discussion of 

mechanisms and modes of action are discussed in Section 3.5 because they are, in several cases, 

applicable to more than one system. 

3.4.1. Neurotoxicity 

The ability of TCE to cause neurotoxic effects is well established. In general, TCE 

produces a “solvent narcosis” that may be related to effects on membrane fluidity and may 

include anesthetic effects. TCE was formerly used as a general anesthetic and induces this effect 

at about 2,000 ppm (ASTDR, 1997). The array of symptoms observed with TCE exposure is 

similar to other solvents, such as alcohol, ethers, petroleum distillates, and other halogenated 

solvents. 

Acute or short-term inhalation of TCE has been associated with dizziness, headache, 

sleepiness, nausea, confusion, blurred vision, and weakness in several human studies cited by 

Barton and Das (1996). TCE is distributed to the brain, and brain concentrations of TCE in the 

rat tend to mimic blood concentrations (Boyes et al., 2000). Acute neurologic effects of TCE in 

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both humans and rats are more closely associated with peak concentrations rather than AUC 

(Boyes et al., 2000), but rats require a higher peak concentration to elicit these effects (Bushnell, 

1997). Hence, humans appear to be a sensitive species for the neurotoxic effects of TCE. 

Longer term or chronic inhalation exposure is associated in a number of human studies 

with a similar array of neurological symptoms as those observed with acute exposure (ATSDR, 

1997). One study (Vandervort et al., 1973), used by Cal/EPA (1999) to derive a reference 

exposure level (similar to an RfC), reported eye irritation, drowsiness, heart palpitations, cough, 

weakness, and dizziness among workers exposed to a wide range of TCE concentrations. Other 

studies of occupational exposure to TCE have reported similar LOAELs (see Table 4-3). 

Tremors have been reported in several inhalation studies (Grandjean et al., 1955; Liu et al., 1988; 

Bardodej and Vyskocil, 1956) and in one study of a population exposed to a mixture of TCE and 

perchloroethylene in drinking water (White et al., 1997). Long-term inhalation exposure at an 

average concentration of 44 ppm TCE has also been associated with early nerve function 

impairment (Ruitjen et al., 1991). 

It is not clear whether some of TCE’s neurotoxic effects may be attributed to TCE as the 

parent or to one of its metabolites such as DCA, which has shown longer term effects on the 

central nervous system. A recent study (Moser et al., 1999) in two strains of rats exposed to 

DCA for up to 2 years showed neuromuscular toxicity and mild tremors with neurotoxicity being 

route, duration, and strain dependent. Drinking water exposure was shown to be more toxic than 

gavage, with effects seen at the lowest drinking water dose (16 mg/kg-d). In addition, 

neurotoxicity from DCA was described as more pronounced and persistent, and occurring at 

lower doses than previously reported (Moser et al., 1999). Subchronic exposures to DCA in 

drinking water are associated with brain lesions in dogs at doses of 12.5 mg/kg-d or higher 

(Cicmanec et al., 1991) and rats at doses of 125 mg/kg-d or higher (Bhat et al., 1991; Katz et al., 

1981). At much higher doses, focal reactive gliosis—proliferation of reactive astrocytes after 

dead neurons have been cleared away—and vacuolization were observed in the brains of DCAtreated rats (Bhat et al., 1991). DCA given orally is also reported to decrease nerve conduction 

velocity in tibial nerves in terms of areas, perimeters, and diameter in rats (Yount et al., 1982) 

and, in a human case report, to induce a peripheral neuropathy (Moore et al., 1979). 

3.4.2. Liver 

3.4.2.1. General Hepatotoxicity 

TCE can induce hepatotoxicity in humans, as reported in several studies cited by ATSDR 

(1997). This is not unexpected, as most metabolism of TCE occurs in the liver. TCE can affect 

liver functions not reflected in a standard battery of liver function tests. Workplace exposure to 

TCE has been reported to change serum cholesterol and increase bile acids (Nagaya et al., 1993; 

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Driscoll et al., 1992). Alterations of plasma bile acids have been noted in workers at inhalation 

exposures of less than 5 ppm (Driscoll et al., 1992; Neghab et al., 1997). Elevations of serum 

bile acids, possibly an early sign of liver dysfunction, have also been seen in exposed rats (Wang 

and Stacey, 1990; Hamdan and Stacey, 1993). Alterations of cholesterol metabolism have been 

observed in workers with low-level chronic TCE exposure, effects that persisted after 2 years 

(Nagaya et al., 1993). TCE exposure also can alter insulin and endocrine profiles in 

occupationally exposed individuals (mean, 30 ppm) (Chia et al., 1997; Goh et al., 1998), effects 

that may be important to responses of the liver. 

Increased liver weight, primarily resulting from cytomegaly, is one of the most frequently 

reported effects in animals dosed chronically with TCE by either the inhalation or oral route. 

Barton and Clewell (2000) summarize several studies in which orally administered TCE changed 

the liver-weight-to-body-weight ratio in rats and mice. These effects have been observed at 

gavage doses as low as 50 mg/kg-d for 14 days (Berman et al., 1995). TCE was reported to 

increase cell replication but not cytotoxicity or reparative hyperplasia. TCE metabolites can also 

stimulate cell replication; TCA, DCA, and CH have all been associated with increased liver size 

(Bull, 2000). 

Although the state-of-the-science papers have focused on the effects of TCE on 

parenchymal cells of the liver, nonparenchymal cells may also have a role in TCE hepatotoxicity. 

TCE and DCA have been reported to alter Kupffer cell morphology, indicating activation, in 

mice and dogs, respectively (Kjellstrand et al., 1983; Katz et al., 1981). Kupffer cells32 contain 

CYP2E1, which is highly inducible, and have been shown to be critical in the toxicity of ethanol, 

acetaminophen, phenobarbital, galactosamine, and carbon tetrachloride (Laskin et al., 1988, 

1986, 1995; Knecht et al., 1995; Laskin, 1996; Lieber, 1997). 

3.4.2.2. Liver Cancer 

The review of the TCE cancer epidemiology finds an overall excess incidence of liver 

cancer in the tier-I studies (RR=1.9, 95% CI=1.0–3.4, N=12) and the tier-II studies (RR=2.0, 

95% CI=1.3–3.3, N=15), with null results in the tier-III studies (Wartenberg et al., 2000). 

Although none of the tier-I studies achieves statistical significance individually, the relative risks 

exceed 1.0 in all three incidence studies, and in the only study that reported mortality for this 

32Kupffer cells, which remove particulate material from the blood and drain the digestive system before it enters 

systemic circulation, can release potent mediators such as eicosinoids, toxic cytokines, proteolytic enzymes, 

superoxide anion, and tumor necrosis factor and be involved in hepatotoxicity (Caldwell-Kenkel et al., 1991). 

Biologically active mediators released by activated macrophages have been implicated in tumor promotion, as well 

as regulation of CYP450-mediated drug biotransformation (Laskin et al., 1988). Kupffer cells may also have 

implications for increased toxicity or increased likelihood of tumor progression if their role in immune and endocrine 

function is altered. 

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specific site. There is a biological gradient seen in the Anttila study (Anttila et al., 1995), as liver 

cancer incidence increased with increasing exposure and with increasing time since first 

exposure. Among those with the longest time since first exposure, relative risks are large 

(RR=6.1, 95% CI=2.8–17.7). A more recent study (Ritz, 1999) supports the observations in the 

Anttila study. Although the study was based on few deaths, risks increased with increasing 

potential TCE exposure and with increasing time since first exposure. Case-control studies are 

supportive, generally, of statistically significant associations with organic solvents as a class. 

These findings are supported by the well-established and widely acknowledged result that 

TCE causes liver tumors in male and female mice (see Table 3-1). The NTP conducted two 

gavage studies in B6C3F1 mice (NCI, 1976; NTP, 1990), and Maltoni conducted inhalation 

studies in Swiss mice and B6C3F1 mice (Maltoni et al., 1986, 1988). In each study, TCE 

exposure caused a statistically significant increased incidence of liver tumors in male and female 

mice. Neither series of studies, however, observed liver tumors in rats. 

Previously, it had been suggested that the liver tumors might be caused by 

epichlorohydrin, 1,2-epoxybutane, or other stabilizers present with the TCE (Henschler et al., 

1980). Subsequent testing, however, has ruled this out (Henschler et al., 1984). The state-of-thescience papers (Lash et al., 2000a; Bull, 2000) suggest that TCE induces liver tumors through its 

metabolites. TCA and DCA, as well as CH, have all been shown to cause liver tumors in mice 

(Bull, 2000), with DCA also causing liver tumors in rats (DeAngelo et al., 1996). 

3.4.3. Kidney 

3.4.3.1. General Nephrotoxicity 

A range of kidney toxicity following human exposure to TCE has been reported in several 

studies cited by ATSDR (1997) and Lash et al. (2000b). Tubular damage, as assessed by altered 

excretion of urinary proteins, has been noted in occupationally exposed individuals (ATSDR, 

1997), as well as in renal cell carcinoma patients with high-level occupational exposure to 

trichloroethylene (Brüning et al., 1999a,b). TCE causes dose-related nephrotoxicity in male and 

female rats and mice (NTP, 1988, 1990; Maltoni et al., 1986, 1988). The lesions are not the 

chronic interstitial nephrosis usually observed in aged rats, but consist of cytomegaly, 

kayomegaly, and toxic nephrosis of tubular epithelial cells in the inner renal cortex (Lash et al., 

2000b). As in the liver, TCE can change the kidney-weight-to-body-weight ratio in rats, mice, 

and gerbils (Barton and Clewell, 2000; Maltoni et al., 1986, 1988; Kjellstrand et al., 1981, 1983). 

3.4.3.2. Kidney Cancer 

Consistency across epidemiological studies is strongest for an association between TCE 

exposure and kidney cancer (Wartenberg et al., 2000). There is an overall excess of kidney 

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cancer incidence in the tier-I studies (RR=1.7, 95% CI=1.1–2.7, N=21) and tier-II studies 

(RR=3.7, 95% CI=1.7-8.1, N=6). Mortality was also elevated in the tier-III studies (RR=2.3, 

95% CI=1.5–3.50, N=20), but not incidence. The relative risk exceeds 1.0 in three of four 

studies in tier 1 that reported incidence, one of which was a follow-up cluster report and achieved 

statistical significance individually. It is not obvious that index cases were excluded from these 

studies. Supporting the tier-I findings are the tier-II studies and case-control studies of kidney 

cancer and TCE (Vamvakas et al., 1998; Dosemeci et al., 1999), degreasers (Schlehofer et al., 

1995), and solvents (Mellemgaard et al., 1994). Dosemeci (Dosemeci et al., 1999) and Blair 

(Blair et al., 1998) observed differences in risk between men and women, with higher risk in 

women. Dosemeci (Dosemeci et al., 1999) puts forth several hypotheses, including a possible 

gender-related difference in the distribution of polymorphisms of the GST metabolic pathway or 

differences in level of exposure. The report of Brüning (Brüning et al., 1997a) showed an 

increased risk for kidney cancer among individuals with GST isozymes M1 and T1, suggesting 

the GST pathway and polymorphisms of this pathway as important contributors to kidney cancer 

risk (Lash et al., 2000b). 

Recently, occupational exposure to TCE has been linked to somatic mutations of the 

VHL gene33 (Brüning, 1997b; Brauch et al., 1999). Thirty-three of the 44 renal cell carcinomas 

had one or more mutations in the VHL gene, predominately characterized as changes in cytosine 

to thymidine (Brauch et al., 1999). An interesting observation was a mutation in cytosine at a 

specific point in the VHL gene, at nucleotide (nt) 454, in 13 of these tumors; none of the 107 

renal cell carcinomas from individuals not exposed to TCE displayed this mutation. High levels 

of exposure were associated with a higher frequency of tumors with multiple mutations and with 

the mutation at nucleotide 454 than lower exposure. This mutation was, moreover, found in 

normal kidney tumor tissue that was adjacent to neoplastic tissue in four patients. This 

observation suggests that a mutation at nucleotide 454 may precede tumor development. Both 

the studies of Brüning (1997b) and Brauch et al. (1999) point to an association of increased 

occurrence of mutated sequence in the entire gene for TCE-exposed renal cancer patients, with 

the work of Brauch indicating multiple mutations and a specificity in where and how mutations 

occurred within the nucleotide sequence of this gene. 

Another study (Schraml, 1999) assessed the entire genome of 12 renal cell tumors from 

individuals with suspected exposure to TCE and other organic solvents (exposure was not 

described in this report, nor was an exposure score assigned to individual cases) and observed 

33The VHL gene, consisting of hundreds of nucleotides, normally suppresses renal cell carcinoma. Somatic 

alterations to the gene are noted in sporadic kidney cancers, and mutation of the VHL gene is considered a risk factor 

for kidney cancer (Lash et al., 2000b). 

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mutations to genes on several chromosomes besides chromosome 3, the location of the VHL 

gene. Mutations found on these other chromosomes included both DNA losses and DNA gains. 

There were no differences between TCE and non-TCE exposed renal cell tumors regarding 

which chromosomes were affected. Schraml (1999), however, observed a much lower frequency 

of mutation of the VHL gene (25%, 4 tumors) among the 12 tumors from TCE-exposed 

individuals compared to either the report of Brauch et al. (1999) or to background rates (around 

60% of all renal cell carcinomas) (Brauch et al., 1999). This study provides only limited 

information because of its absence of complete information on exposure and on tumor stage or 

grade, its smaller sample size, and its lower than expected number of tumors with VHL 

mutations. 

The human results are supported by the animal studies, where TCE causes kidney cancer 

in rats by both ingestion and inhalation exposure (see Table 3-2). Maltoni observed a 

significantly increased incidence of kidney adenocarcinomas in male Sprague-Dawley rats 

inhaling TCE (Maltoni et al., 1986, 1988). The NTP conducted parallel corn-oil gavage studies 

in ACI, August, Marshall, and Osborne-Mendel rats (NTP, 1988), and a similar study in F344/N 

rats (NTP, 1990). The NTP considered the former to be inadequate studies of carcinogenic 

activity because of chemically induced toxicity, reduced survival, and deficiencies in the conduct 

of the studies. Despite these limitations, however, the NTP concluded that there were tubular 

cell neoplasms of the kidney in rats exposed to TCE. Consistent with these results, an earlier 

NCI study also reported a tubular adenocarcinoma in one male Osborne-Mendel rat exposed to 

TCE for 78 weeks (NCI, 1976). 

As noted by the NTP (1988), the survival of exposed rats was substantially reduced 

compared with controls. Consequently, the tumor incidences tabulated in the NTP report may be 

misleading, because animals dying early may not be at full risk of developing tumors. To 

ascertain the effect of this reduced survival on the tumor incidences, the individual animal tumor 

pathology in the NTP report was used to adjust the tumor incidences to reflect the number of 

animals at risk. Animals were discounted if they died before the first kidney tumor was 

observed, at 57 weeks. Counting only animals alive at 57 weeks leads to the corrected incidences 

reported in Table 3-2. 

Rhomberg (2000) analyzed the findings of TCE bioassays in rats and suggests that 

females are as sensitive as males to TCE-induced renal carcinogenicity. Previously, the kidney 

tumors in male rats had received more attention than those in females (ATSDR, 1997). 

Consideration of all strains together in Table 3-2, however, reveals that there is virtually no 

difference between males and females in the overall incidence of these tumors. 

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3.4.4. Immune and Lympho-Hematopoietic Systems

3.4.4.1. General Effects on the Immune and Lympho-Hematopoietic System

Immune system34 changes have been noted in individuals presumed to have been exposed 

to TCE and other chlorinated solvents in drinking water (ATSDR, 1997). The ability of TCE to 

alter immune responses has been observed in mice exposed to TCE via drinking water or 

inhalation (Khan et al., 1995; Aranyi et al., 1986; Hobara et al., 1984; Parks et al., 1993; Sanders 

et al., 1982; Tucker et al., 1982). Mice exposed to TCE in drinking water at 0.1 mg/mL and 

higher showed inhibition of humoral and cell-mediated immunity, as well as effects on 

macrophage function and monocyte-granulocyte progenitor cells (Sanders et al., 1982). Females 

showed a greater susceptibility than males to these immunotoxic effects of TCE (Sanders et al., 

1982; Barton and Clewell, 2000). 

TCE and one of its metabolites have also been shown to affect bone marrow function or 

components. Sanders et al. (1982) observed an inhibition of stem cell colonization in both male 

and female mice. Lock et al. (1996) reported that DCVC causes renal toxicity and fatal aplastic 

anemia in calves at a single dose of 4 mg/kg, a dose much larger than that expected from TCE 

metabolism, and that toxicity may be related to the $-lyase metabolic pathway (Anderson and 

Schultze, 1965; Bhattacharya and Schultze, 1967). However, aplastic anemia has not been 

reported from similar exposures in rat, guinea pig, dog, or cat (Lock et al., 1996). 

TCE exposure may also accelerate an autoimmune response. Several studies of 

occupational exposure have associated TCE and organic solvent exposure with several 

autoimmune diseases:35 systemic sclerosis (scleroderma), fascitis, and systemic lupus 

erythematosus (SLE) (Lockey et al., 1987; Saihian et al., 1978; Niertert et al., 1998; Bovenzi et 

al., 1995; Goldman, 1996; Schaeverbeke et al., 1995; Waller et al., 1994). Furthermore, two 

studies of populations exposed to TCE and other solvents in drinking water report changes in Tlymphocytes and increased autoantibodies (Byers et al., 1988; Kilburn and Warshaw, 1992). 

Autoimmune-disease-prone mice exposed to 2.5 or 5.0 mg/mL TCE in drinking water show an 

increase in autoimmune antinuclear antibodies, elevated serum levels of several 

immunoglobulins, activation of CD+4, and a cytokine profile consistent with a TH1 type 

34Immune cells are derived from stem cells in the bone marrow or fetal liver and are widely distributed throughout 

the body (Selgrade, 1995). They are also controlled by cytokines. The immune system is highly conserved across 

species such that organs and cells of the immune system in humans, mice, and rats are similar. In instances where 

controlled human studies have been possible, results of immune function studies in mice have been accurate 

predictors of effects in humans. 

35Autoimmunity can be defined as a loss of self-tolerance that results in an immune reaction against oneself or selfantigens. Autoimmunity appears to have a multifactorial etiology that includes genetic and environmental factors 

(chemicals and microbes) (D’Cruz, 2000). 

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response36 (Gilbert, 1999; Griffin et al., 2000a,b). In another recent drinking water study (Griffin 

et al., 2000c), these investigators noted many of these same observations, but these effects 

occurred at much lower exposures to TCE than previously reported; doses in this latter study 

were 0.1 mg/mL, 0.5 mg/mL, and 2.5 mg/mL. An early response to TCE exposure was a doserelated and significant increase in serum antinuclear antibodies at the lowest exposure (0.1 

mg/mL, or 4 mg/kg/d). Furthermore, histopathologic changes that included portal infiltration by 

mononuclear cells were seen at the termination of the study, 32 weeks, and were consistent with 

the induction of autoimmune disease in the liver (Griffin et al., 2000c). Additionally, hepatocyte 

reactive changes were observed at all exposures including the lowest tested dose (0.1 mg/mL, or 

4 mg/kg/d). 

3.4.4.2. Lymphoid Cancer 

Overall, an increased incidence of lympho-hematopoietic cancer (RR=1.4, 95% 

CI=1.0–2.0, N=40) was noted in tier-I by Wartenberg et al. (2000), primarily a reflection of the 

elevated, but not statistically significant, incidence of non-Hodgkin’s lymphoma (RR=1.5, 95% 

CI=0.9–2.3, N=22). Null results were seen in the tier-II and tier-III studies (Wartenberg et al., 

2000). Two drinking water studies of ecologic design provide some evidence of an association 

between non-Hodgkin’s lymphoma and drinking water that includes TCE and other chemically 

similar solvents (Fagliano et al., 1990; Vartianen et al., 1993). Both studies were of ecological 

design without individual exposure or residential histories. The case-control studies that 

examined occupational exposures were not informative, primarily because of few cases and, 

consequently, lower statistical power. However, case-control studies that evaluated lymphoid 

cancer (leukemia and non-Hodgkin’s lymphoma) and exposure to drinking water contaminated 

with TCE report elevated risks, although not all risks were statistically significant (Cohn et al., 

1994; MA-DOH, 1997). One study of childhood leukemia (MA-DOH, 1997) observed a very 

strong association with exposure during pregnancy (OR=13.2, 95% CI=0.9-205.2). For both 

leukemia and non-Hodgkin’s lymphoma, these studies report exposure-response gradients 

supporting drinking water contamination as the etiologic agent (Cohn et al., 1994; MA-DOH, 

1997). Exposures are not sufficiently specific to a single chemical, although TCE is often the 

chemical found in highest concentration and the others have similar metabolism and targets. 

TCE induces malignant lymphomas in mice in a number of inhalation or gavage 

bioassays. Mice have been shown to be an excellent immunopathologic model for human 

36The profile of cytokines secreted by activated CD4+ T cells is often measured as an indicator of the type of 

immune response induced by a toxicant. A T-helper type 1 (TH1) type response reflects macrophage activation and 

inflammation and is marked by an increased secretion of IFN-( with a simultaneous decrease in the secretion of the 

cytokine IL-4 (Griffin et al., 2000a). 

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lymphoid cancers (Pattengale, 1986, 1994). Henschler found a statistically significant increased 

incidence of malignant lymphomas in female Han:NMRI mice exposed by inhalation (Henschler 

et al., 1980). The NTP found a significant increase in malignant lymphomas in female B6C3F1 

mice exposed by gavage (p<0.05 by life-table test), although this result was not mentioned in the 

abstract or summary of peer review comments (NTP, 1990). Consistent results were found in the 

NCI study (NCI, 1976). Lymphosarcomas and malignant lymphomas were just beginning to 

appear in exposed mice when the NCI study was terminated at 90 weeks, although the numbers 

were too few for statistical significance. For comparison, in the 104-week NTP study only 1 of 

the 13 malignant lymphomas in exposed female mice was observed before week 90, at week 85. 

3.4.5. Developmental and Reproductive Toxicity, Endocrine Effects 

3.4.5.1. Developmental Effects 

Epidemiologic studies of women exposed to degreasing solvents, including TCE, have 

reported elevated risks for cardiac anomalies in their offspring (Wilson et al., 1998; Goldberg et 

al., 1990; Ferencz et al., 1997). Large, statistically significant excesses were observed for 

specific cardiac defects: left-sided obstructive defects (OR=6.0, 95% CI=1.7-21.3) and 

hypoplastic left heart (OR=3.4, 95% CI=1.6-6.9) with an attributable risk37 of 4.6% (Wilson et 

al., 1998; Ferencz et al., 1997). Neural tube defects have also been noted with either 

occupational or drinking water exposure to solvents including TCE (Bove et al., 1992; 

Holmberg, 1980a,b, 1982). 

TCE or its metabolites, TCA and DCA, cause cardiac malformations and eye 

malformations (anopthalmia, micropthalmia) in rat pups38 (Dawson et al., 1993; Smith et al., 

1989, 1992; Johnson et al., 1998a,b; Epstein et al., 1993) given drinking water exposure; 

increases for both malformations were dose related (Dawson et al., 1993; Smith et al., 1989, 

1992). These malformations have not been reported in inhalation studies, though there is a 

question as to whether the ability of the methods or the number of pups in the studies were 

adequate to detect these malformations (Barton and Clewell, 2000). The study of Dawson et al. 

(1993) raises issues for quantitative analysis because of the wide spacing between low and high 

dose (exposure to TCE was at 1.5 ppm and 1,100 ppm) and whether TCE or one of its 

metabolites was responsible for the effect reported (Barton and Clewell, 2000). 

37Attributable risk is the risk or rate difference that may be attributable to the exposure (Rothman, 1986). 

38Although no battery of animal tests can provide complete assurance in predicting human developmental effects, the 

similarity in timing and sequencing of events during embryogenesis and organogenesis, particularly cardiogenesis, 

between humans and rats suggests that the rat is a suitable nonprimate choice for studying developmental effects. 

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Graeter et al. (2000) reported that gavage exposure to TCE, DCA, or TCA did not 

produce a statistically significant difference in cardiac malformations in an abstract lacking 

information on number of animals examined and exposure levels. DCA exposure, however, 

produced a statistically significantly increased number of external malformations, and both DCA 

and TCA individually produced lower fetal body weights in Sprague-Dawley rats. TCA also 

produced a statistically significant lower mean uterine weight in dams compared with vehicle 

controls. 

Recently Boyer (2000) reported TCE exposure in vitro produces a dose-dependent 

inhibition of mesenchymal cell transformation (a critical event in development of the heart) in 

progenitors of the valves and septa of the heart, and briefly mentions the findings of a concurrent 

study that showed altered gene expression in embryo hearts with maternal exposure to drinking 

water with 110 ppm TCE. The concentration in the in vitro study was in the millimolar (mM) 

range, comparable to doses of TCE studied in vivo and, because of factors inherent with using an 

in vitro paradigm, likely to be much less than those administered to the culture medium (Runyan, 

2000). However, although direct quantitative extrapolation of in vitro doses to those studied in 

vivo is problematic, these observations support a TCE effect on cardiac development. Moreover, 

the finding of cardiac anomalies in rat pups exposed to two TCE metabolites, TCA and DCA, 

supports a hazard for this effect with TCE (Smith et al., 1989, 1992; Johnson et al., 1998a,b; 

Epstein et al., 1993). 

Both TCA and DCA induce other effects besides cardiac and eye anomalies in offspring. 

Both TCA and DCA exposure were associated with a substantial increase in implantation and 

resorption sites

39 and decreased fetal weight (Smith et al., 1989, 1992). These effects were 

observed in the absence of maternal toxicity. 

Exposures to TCE during the prenatal period have been reported to induce 

neurobehavioral alterations in rat pups, including changes in long-term exploratory and 

locomotor behavior, altered glucose uptake and metabolism in the neonatal brain, and decreases 

in myelin (Taylor et al., 1985; Isaacson et al., 1990). In addition, changes in adult behavior are 

suggested with perinatal exposure to 50 mg/kg TCE (Fredricksson et al., 1993). Statistical 

analysis of the data used individual pups, possibly resulting in an overestimate of the statistical 

significance of observed results. 

Exposure to TCE can potentially modify responses to other chemical exposures, which is 

an important consideration because TCE environmental exposure is usually not isolated but also 

concurrent with other chemicals. TCE and DEHP (a peroxisome proliferator) together were 

39Implantation sites are sites of initial attachment of a fertilized ovum, but with no further growth. Resorption sites 

are sites of implantation with embryonic growth, but arrest and necrosis during development. 

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reported to have synergistic effects with regard to prenatal loss, pup weight changes, and 

anopthalmia (Narotsky et al., 1995a,b). 

3.4.5.2. Endocrine System Effects and Reproductive Toxicity 

Reports of TCE-induced effects on human sex hormones and their binding proteins are 

just beginning to emerge in the published literature. For the most part, workplace exposure to 

TCE has not been reported to change hormones, spermatogenesis, or semen quality in men (Goh 

et al., 1998). Recently, however, low-level subchronic TCE exposure (mean 30 ppm) was 

associated with changes in insulin and steroid level, decreased serum testosterone, and decreased 

sex hormone binding globulin (SHBG40) in male workers (Goh et al., 1998). Exposure gradients 

were observed for several parameters. In analyses adjusted for age, serum testosterone and 

SHBG were observed to decrease whereas free androgen levels increased with increasing 

duration of TCE exposure.41 Moreover, dehydroepiandrosterone (DHEA)42 increased with years 

of TCE exposure (Chia et al., 1997), contradicting expectations as DHEA levels normally 

decrease with age (Metzger et al., 1997; Chiu et al., 1999). A more complicated picture was 

observed for insulin. Insulin levels were elevated for exposures of less than 2 years, but 

decreased to below-normal levels for increasing durations of exposure. The normal 

SHBG/insulin relationship appeared to be disturbed with longer durations of exposure (Goh et 

al., 1998). 

TCE exposure has been shown to affect the reproductive system of males; studies in 

females are few. In another report of the same population of workers studied above, increase in 

urinary levels of TCA was correlated with hyperzoospermia, which has been implicated in 

infertility (Chia et al., 1996). A lower percentage of normal sperm, compared to referent values 

identified by the World Health Organization, was also observed in this population (Chia et al., 

1996). TCE has been shown to accumulate in the male reproductive tract of rats and to decrease 

testosterone levels (Zenick et al., 1984; Kumar et al., 2000). Kumar (2000) recently reported 

significant decreases in total epididymal sperm count and motility, with the magnitude of loss 

correlated with increased duration of exposure. A concurrent increase in total testicular 

cholesterol and decreases in enzyme activities, which are rate limiting in the biosynthesis of 

testosterone, was also reported. Of note is the use of young rats (starting weights of 50 g) in this 

40SHBG has high affinity but low capacity for testosterone and estradiol 17p, and its level may be related to liver 

toxicity, as it is produced in the liver. 

41Free androgen is calculated by the division of testosterone levels by SHBG and is that portion free to be 

biologically active. A decreased index was achieved by differential rates of reduction of SHBG and testosterone. 

42DHEA is an adrenal steroid associated with insulin resistance, androgenic effects, peroxisome proliferation, and 

induction of CYP4A (Mayer, 1998, 1999). 

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subchronic (12 and 24 weeks) study. This report describes results from a preliminary study that 

indicated TCE-induced decreases in fertility as well. DCA, a metabolite of TCE, is also a 

testicular toxin to rats and to dogs at exposures as low as 12.5 mg/kg-d (Katz et al., 1981, 

Cicmanec et al., 1991). 

One report exists of increased menstrual disorders in female workers exposed to TCE 

(Zielinski, 1973). 

3.4.5.3. Cervical, Prostate, and Testicular Cancer 

The human studies provide some limited evidence for increased cancer risk for certain 

reproductive organs. Cervical cancer, though sparsely reported, is elevated in tier-I incidence 

studies (RR=2.4, 95% CI=1.2–4.8, N=8) and in tier-III (dry cleaner) mortality studies (RR=1.7, 

95% CI=1.5–2.0, N=43) (Wartenberg et al., 2000). The incidence study by Anttila et al. (1995) 

provided the greatest weight for an association and reported a statistically significant elevation of 

cervical cancer among those with greatest urinary TCA levels. The comparison with internal 

controls in Blair et al. (1998) also is suggestive that TCE may be the etiologic agent, and not 

some other confounder such as socioeconomic class. Prostate cancer incidence and mortality are 

also elevated in tier-I studies (RR=1.3, 95% CI=1.0-1.6, N=95; RR=1.2, 95% CI=1.0-1.4, 

N=131). 

In an NTP study (NTP, 1988), interstitial cell neoplasms of the testis were reported in 

Marshall rats exposed to TCE by gavage. Increased incidences are apparent in the other rat 

strains when counting the animals alive when the first testicular tumor was observed. In another 

study, (Maltoni et al., 1986, 1988) reported significantly increased incidences of Leydig cell 

tumors in male rats exposed to TCE by inhalation. Although testicular cancer is often thought of 

as occurring spontaneously in some strains of rats late in life, the testicular cancers reported for 

TCE were shown in multiple strains with lower background rates and at earlier times of 

appearance (see Table 3-4). 

DCA at levels of 50 mg/kg-d and higher for 13 weeks produced prostate gland atrophy 

and testicular changes that were considered dose dependent (Katz et al., 1981). 

3.4.5.4. Pancreatic Cancer 

Pancreatic cancer incidence mortality is statistically significantly elevated in the dry 

cleaner (tier-III) studies (RR=1.7, 95% CI=1.2–2.6, N=22; RR=1.3, 95% CI=1.0–1.7, N=42), 

with mixed results in tiers I and II (Wartenberg et al., 2000). Disturbances in insulin levels as 

noted by Goh et al. (1998) and Kato-Weinstein et al. (1998) and effects on carbohydrate handling 

may have a role in the induction of effects on the pancreas by TCE in humans. For example, 

diabetics have an increased risk of pancreatic cancer (Calle, 1998). In experimental animals, 

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cancer in this organ has not been well studied in the carcinogenic assays carried out to date. Katz 

et al. (1981) reported that DCA causes pancreatitis in dogs. 

3.4.6. Lung

3.4.6.1. Pulmonary Effects

There are few human data reporting pulmonary toxicity for TCE (ATSDR, 1997; Barton 

and Clewell, 2000). Mice appear to be more sensitive than rats to histopathological changes in 

the lung via inhalation; pulmonary effects are also seen in rats with gavage exposure. Lungs of 

mice exposed via inhalation to 500 ppm TCE for 30 minutes displayed vacuole formation and 

endoplasmic reticulum dilation, specifically in Clara cells of the bronchial tree (Villaschi, 1991). 

Additionally, a reduction in pulmonary CYP450 enzyme activity was observed after a 6-hour 

exposure to 100 ppm TCE (Odum et al., 1992). TCE administered to rats via gavage at doses 

1,000 mg/kg-d and higher caused rales and dyspnea (Narotsky et al., 1995) and pulmonary 

vasculitis (NTP, 1990). 

At relatively high doses (drinking water exposures of 80.5 mM DCA or 45.8 mM TCA), 

lung inflammation (perivascular) is seen in rats exposed to both these TCE metabolites, but not 

in controls (Bhat et al., 1991). At lower doses, a secondary effect of DCA exposure in dogs has 

been reported to be pulmonary inflammation (Katz et al., 1981). 

3.4.6.2. Lung Cancer 

The epidemiologic studies are not generally supportive of lung cancer; lung cancer 

mortality was statistically significantly elevated only in dry cleaners (tier-III) (RR=1.3, 95% CI: 

1.1-1.5, N=137) (Wartenberg et al., 2000). TCE has been reported to significantly increase the 

incidence of lung tumors in male Swiss mice and female B6C3F1 mice by inhalation (see Table 

3-5) (Maltoni et al., 1986, 1988). Another study extended this result to female ICR mice (males 

were not tested) (Fukuda et al., 1983). These studies did not observe lung tumors in rats. 

Ingested TCE has not been observed to cause lung tumors in either rats or mice. This is the only 

tumor site reported to have a positive carcinogenic response by one route but not another. 

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3.4.7. Genetic Toxicity 

TCE and a number of its metabolites (CH, DCA, and TCA) have been evaluated in a 

large number of genotoxic assays.

43 On the basis of the available data, definitive conclusions 

cannot be drawn as to whether TCE is or is not a genotoxic carcinogen. Taken together, TCE 

and its metabolites CH, DCA, and TCA are, at most, very poor genotoxicants. There is evidence 

that the metabolite DCVC is more potent as a potential genotoxic agent. Evidence from a 

number of different analyses and a number of different laboratories using a fairly complete array 

of genetic endpoints indicates that TCE may have some potential to be genotoxic. However, the 

evidence is not convincing that it is genotoxic, and is confounded by the fact that TCE is often 

stabilized with a very low concentration of epichlorohydrin or 1,2-epoxybutane, both known to 

be potent mutagens. A series of carefully controlled studies evaluating TCE (without mutagenic 

stabilizers) found TCE incapable of inducing point mutations in standard Salmonella assays. 

Furthermore, in vitro gross chromosome aberration assays were negative. In vivo, there is some 

evidence that TCE or its metabolites bind to DNA and can induce single-strand DNA breaks in 

both hepatic and kidney cells. However, the dose required to cause these DNA breaks was very 

high (4-10 mM of TCE/kg body weight) and the response was very low. More recent data show 

TCE to affect DNA methylation in whole-liver preparations at lower exposures with increased 

expression of the two proto-oncogenes c-jun and c-myc (Tao et al., 1999). Moore and 

Harrington-Brock’s review of the potential genotoxicity of TCE (Moore and Harrington-Brock, 

2000) notes that TCE is negative in a number of standard tests, and furthermore, in those tests 

where positive responses were observed, the dose was very high and response low. Accordingly, 

although it is not possible to totally eliminate the possibility that TCE would be a genotoxic 

carcinogen, it is clear from these evaluations that TCE is not a potent genotoxicant (Moore and 

Harrington-Brock, 2000). 

CH has been extensively studied as a potentially genotoxic agent. It has been evaluated 

in the recommended battery44 and several other assays, including genetic alterations in rodent 

germ cells. CH is weakly positive at very high concentrations in in vitro genotoxicity assays that 

detect point mutations, chromosomal mutations, and/or aneuploidy. There is a mixture of 

positive and negative in vivo data, and because there is no reason to weigh any of the studies 

43Genetic toxicity can be defined using mutational endpoints, cytogenetic analysis, and evaluation of primary DNA 

damage with mutagenicity described as the ability to induce heritable mutations (Moore and Harrington-Brock, 

2000). Primary DNA damage may occur via adduct formation, strand or chromosomal breakage, and unscheduled 

DNA synthesis. Mutational damage consists of point mutations, chromosomal rearrangement, deletions, loss or gain 

of chromosomes (aneuploidy), or changes in whole chromosome complements (polyploidy) (Moore and 

Harrington-Brock, 2000). 

44The core battery of recommended tests for genetic toxicity include the Salmonella assay, in vitro mouse lymphoma 

mutation assay, and in vivo cytogenetic assay (Moore and Harrington-Brock, 2000). 

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more than the others, it is not clear whether CH has the potential to be genotoxic in vivo (Moore 

and Harrington-Brock, 2000). 

DCA and TCA have also been extensively studied using a variety of genotoxicity assays, 

including the recommended battery. DCA is genotoxic at very high concentrations in the 

Salmonella assay, the in vitro mouse lymphoma assay, and in vivo cytogenetic and gene mutation 

assays. DCA can cause DNA strand breaks in mouse and rat liver cells following in vivo 

administration by gavage. As with TCE and CH, the concentration of DCA required to induce 

damage is very high and the level of response is generally very low. Therefore, although one 

cannot eliminate the possibility that DCA might induce tumors via a mutagenic mode of action, 

the weight of the evidence argues that TCE-induced tumors would not be mediated by DCAinduced mutation. TCA is the least mutagenic of the TCE metabolites discussed so far. It is 

negative in the Salmonella assay, and although positive in the mouse lymphoma assay, it is 

substantially less potent than DCA or CH. It is unclear whether TCA can induce chromosomal 

damage in vivo; some assays are positive and some negative. TCOH is negative in the 

Salmonella assay but has not been evaluated in the other recommended screening assays. 

Therefore, it is unclear whether TCOH is genotoxic (Moore and Harrington-Brock, 2000). 

Cysteine intermediates (DCVC and DCVG) formed by the GST pathway are capable of 

inducing point mutations, as evidenced by the fact that they are positive in the Salmonella assay. 

DCVC is the most potent of the TCE metabolites as a Salmonella mutagen; DCVG appears to be 

similar in potency to DCA. Although DCVC and DCVG have not been evaluated in the other 

EPA-recommended screening assays, there is some indication that they can induce primary DNA 

damage in mammalian cells in vitro and in vivo. Long-term DCVC exposure can induce dedifferentiation of LLC-PK1 cell clones, supporting evidence of genetic toxicity (Vamavakas et 

al., 1996). DCVC has also been shown to induce the expression to two proto-oncogenes, c-fos 

and c-myc (Lash et al., 2000b). Moreover, chlorothioketenes, which are the intermediates formed 

from the further metabolism of DCVC by $-lyase, have been shown in vitro to react with DNA 

(Müller et al., 1998a,b) and, in an aqueous solution, to bind with cytosine (Volkel et al., 1998). 

3.5. MODES OF ACTION OF TCE TOXICITY 

The previous section identifies TCE and its metabolites as eliciting toxicity in a number 

of organs in both humans and laboratory animals. Possible modes of action for these observed 

effects are discussed in this section, which focuses primarily on events occurring in the rodent so 

as to put into perspective observations in chronic toxicity assays. A discussion of the human 

relevance of these possible modes of action is also presented. Much of the discussion focuses on 

events that may give rise to rodent liver, kidney, and lung tumors. The epidemiologic analysis, 

however, suggests other organ systems may also be targets. Moreover, noncancer toxicity is 

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observed and may arise through a common mode of action with the carcinogenic effects. Modeof-action hypotheses for these observations are presented last. 

An understanding of how TCE may elicit toxicity in a number of organ systems can arise 

through the identification of very specific events such as changes in gene regulation and cell 

signaling, or through a discussion of more generalized events such as common changes to 

physiological processes. Data supporting mode-of-action hypotheses are not uniform across 

potential targets of toxicity. Identifying mode(s) of action for TCE toxicity entails obtaining 

answers to several key questions concerning the relative amount of metabolites formed between 

species, the ability to measure active metabolites in biological samples (e.g., DCA), the 

determination of which metabolites may impart adverse effects, the sequence of biological 

changes that can lead to tumor development, and the extrapolation of these events from rodents 

to humans and from higher level exposure in the bioassay to lower environmental exposure. 

The carcinogenicity data on TCE are complex in that a consistent site-specific response is 

not seen across species. Rather, carcinogenicity is demonstrated in the mouse and the rat, but in 

different organs. At the most basic level, the observation of rodent tumors indicates a potential 

human cancer hazard, and the epidemiologic data are further suggestive of a cancer hazard. 

Mode-of-action data have been generated to explain liver tumor development in the mouse and 

are rich compared with observed effects in other organ systems; however, much uncertainty still 

exists. In all cases, key pieces of information are still missing. The similarity between 

carcinogenic effects induced by parent compound and metabolites supports the conclusion that 

TCE metabolites are mostly responsible for the liver and kidney tumors observed in TCE 

bioassays. Accordingly, much of the mechanistic data on the liver are derived from experiments 

on the oxidative metabolites, TCA and DCA; parallel studies in the parent compound are few, 

making it difficult to confidently assign the mode of action to a particular metabolite. The 

complexity of TCE metabolism and clearance also complicate the identification of a 

metabolite(s) that can be identified as responsible for TCE-induced effects. Not surprisingly, 

more than one mode of action may explain TCE-induced carcinogenicity and several hypotheses 

are discussed. In all likelihood, a number of events will be significant to tumor development in 

the rodent under bioassay conditions. Uncertainty exists, however, as to which events may be 

more relevant to human exposure to TCE at environmental levels. 

3.5.1. Liver Cancer 

Much attention has focused on understanding the development of liver tumors in the TCE 

bioassay and the role of the oxidative metabolites, CH, DCA, and TCA. Each of these 

metabolites has induced liver tumors in mice under bioassay conditions; DCA also induces liver 

tumors in the rat. Mechanistic studies have concentrated on TCA and DCA. One of the largest 

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uncertainties associated with mode-of-action hypotheses based on these metabolites is the 

shortage of parallel evidence with TCE itself. Little experimental evidence is available on CH, 

and its rapid metabolism to the haloacetic acids is believed to minimize a direct role of CH in 

TCE-induced liver cancer (Bull, 2000). Several hypotheses have been put forth regarding the 

development of liver tumors in the mouse. These include the role of the peroxisome proliferatoractivated receptor (PPAR) and peroxisome proliferation, disturbances in cell signaling, and 

effects on DNA (Bull, 2000). A short discussion of how these events are consistent with other 

physiologic conditions associated with hepatocarcinogenicity is presented at the end of the 

section. 

3.5.1.1. Peroxisome Proliferation 

The prevailing view of TCE-induced mouse liver carcinogenesis has been that these 

tumors arise in parallel with peroxisome proliferation in the liver by TCE metabolites (Elcombe, 

1985; Elcombe et al., 1985; Goldsworthy and Popp, 1987; Melnick et al., 1987; DeAngelo et al., 

1989; Cattley et al., 1998). Specifically, TCA is most closely associated with this hypothesized 

mode of action because a sustained response with chronic exposure has been noted, in contrast to 

the brief response reported for DCA (Bull, 2000). How tumors may develop from peroxisome 

proliferation remains unclear (Bull, 2000; Zhou and Waxman, 1998). It has been proposed that 

oxidative damage caused by marked increases in free-radical generating enzymes and 

peroxisomal $-oxidation might initiate carcinogenesis. It was generally believed that as 

peroxisome proliferation has not been observed in humans, agents that produced this result in the 

rodent would not present a carcinogenic hazard to humans. 

This view had merit in the past; however, emerging data on the peroxisome proliferatoractivated receptor (PPAR) implicate nonperoxisomal events such as PPAR-triggered gene 

transcription as having important bearing on liver toxicity and subsequent tumor development. 

This position is consistent with the view taken by an expert panel convened by ILSI, which 

concluded that the ability to act as a peroxisome proliferator does not exclude the possibility that 

other properties independently contribute to the development of cancer (Cattley et al., 1998; Bull, 

2000). Moreover, this expert panel determined that there was insufficient basis for concluding 

that these compounds did not represent a carcinogenic hazard to humans. More recent 

experimental evidence suggests that chemicals that induce peroxisome proliferation also produce 

a carcinogenic response in rodents through the activation of the PPAR by the chemical or its 

metabolite(s). The activated PPAR will interact with the retinoid-X receptor (RXR) and other 

specific response elements, resulting in increased synthesis of peroxisomal enzymes and 

promotion of gene transcription including transcription of enzymes important in lipid 

metabolism. 

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The PPAR belongs to the steroid hormone receptor superfamily and includes several 

distinct types: PPAR(, PPAR*, and PPAR". PPAR" is found in many species including mice, 

rats, and humans. It has been mostly identified in the liver of rodents and more recently in the 

human liver. PPAR" is also found in other human tissues including muscle, liver, kidney, heart, 

and testis (Youseff and Badr, 1999; Schultz et al., 1999). Human liver PPAR" mRNA has been 

reported to be 10-fold lower than that of mouse liver, whereas that of skeletal muscle is higher in 

humans than in rats (Youseff and Badr, 1999). Interindividual variations are also evident 

(Tugwood et al., 1998). 

TCE and its metabolites have been shown to induce peroxisome proliferation in rodents 

and its metabolites (TCA and DCA) to activate PPAR" (Goldsworthy and Popp, 1987; Maloney 

and Waxman, 1999; Zhou and Waxman, 1998). However, TCE, TCA, and DCA are considered 

as weak peroxisome proliferators compared to the model drug, WY-14643. Recent in vitro 

experimental evidence at high concentrations of both TCA and DCA shows activation of human 

PPAR" (Maloney and Waxman, 1999). No species differences were noted in the magnitude of 

activation, which, for both human and mouse PPAR" activation, appeared to increase with 

increasing concentration of either TCA or DCA. High concentrations of TCA, but not DCA, 

were further shown to activate PPAR( from mice; no activity was demonstrated for human 

PPAR( (Maloney and Waxman, 1999). 

PPAR activation is likely more important as a possible mode of action for TCA than for 

DCA, even though both DCA and TCA are shown to activate mice and human PPAR". As 

discussed more thoroughly in the next section, TCA- and DCA-induced tumors are 

characteristically very different. These observations support an inference of differing modes of 

action for tumor induction by each of these metabolites. PPAR" activation is seen in vitro with 

TCA concentrations in mM range (Issemann and Green, 1990; Maloney and Waxman, 1999), 

approximately the same range that would be reached in blood of mice by treatment with TCE at 

the doses employed in the in vivo cancer bioassays (Bull, 2000). Like TCA, mM concentrations 

of DCA are also shown to activate PPAR". However, in contrast to TCA, systemic 

concentrations of DCA associated with experimental doses that induce liver tumors in mice are 

expected to be very small (Barton et al., 1999). Moreover, apoptosis and other effects seen with 

DCA exposure (discussed in the following section) are expected to occur at DCA concentrations 

much lower than the in vitro concentrations producing PPAR activation. Thus, PPAR activation 

and the resulting pleotrophic responses associated with receptor activation are not expected to 

contribute greatly to DCA’s carcinogenic mode of action. 

TCA’s ability to activate PPAR" has cross-species relevance. The observations of 

Maloney and Waxman (1999) support a qualitative similarity between mice and humans; thus, a 

PPAR hypothesized mode of action is relevant to both mice and humans. As identified above, 

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quantitative differences in the expression of PPAR" exist between mice and humans, with 

humans in general having a lower expression of PPAR" compared to mice. These quantitative 

differences have import to the dose-response analysis of the mouse liver tumors and will be more 

rigorously treated in Section 4, Dose-Response Assessment and Characterization. 

3.5.1.2. Cell Signaling and Effect of Negative Selection and Apoptosis 

Bull (2000) suggests that modification of cell signal pathways by TCA and DCA 

resulting in alterations in cell replication, selection, and apoptosis (programmed cell death) are 

likely important contributions to the hepatocarcinogenicity of TCE and its metabolites. TCA’s 

ability to activate PPAR and the subsequent cascade of pleotrophic responses, including effects 

on gene transcription, is a classic example of cell signaling. DCA exposure has additionally been 

shown to influence other cell signaling pathways, and observed perturbations provide insight on 

mode-of-action hypotheses regarding induction of DCA tumors. 

Hepatomegaly, or increase in liver size, is a common feature of TCE, DCA, TCA, and 

CH exposure to rodents. Hepatomegaly associated with TCE is observed at experimental 

exposures well below those shown to induce liver tumors in mice (Bull, 2000). Hepatomegaly is 

influenced by increases in either cell number or cell size. Cell number is affected by cell 

replication rates and by rates of apoptosis. There is also some evidence that hepatomegaly 

associated with PPAR" activation results from suppressed apoptosis (Roberts et al., 1995); 

Kupffer cells have been shown to contribute to this process (Chevalier and Roberts, 1998; Rose 

1997). One of the roles of apoptosis appears to be the prevention of damaged cells from dividing 

and progressing to tumors. 

Hepatomegaly seen with TCA and DCA exposure is engendered by different processes. 

Hepatomegaly associated with DCA is attributable to cytomegaly, increases in cell size, rather 

than increases in cell numbers (Bull, 2000). Hepatocytes stain heavily for glycogen, which 

accumulates in cells within 1 to 2 weeks of treatment and becomes more severe over time (Bull, 

2000). These observations are quite different from the hepatomegaly induced by TCA. For 

TCA, increases in liver weight are observed soon after exposure and are linear with experimental 

dose (Bull, 2000); however, this is primarily a reflection of cell number and not cell size. 

Experimental observations support the inference that DCA and TCA induce tumors that 

vary in growth rate and develop from different clones of cells in mouse liver (Bull, 2000). 

Tumors produced from DCA and TCA exposure display different characteristics. DCA produces 

more benign foci that lead to small adenomas, although larger lesions include carcinomas. TCA 

elicits fewer altered foci but more carcinomas. These two metabolites, moreover, show divergent 

responses once exposure is terminated (Bull, 2000). Termination of DCA treatment halts 

progression of the foci and adenomas to carcinomas. In contrast, carcinomas are still observed 

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after termination of TCA exposure, although fewer in number (Pereira and Phelps, 1996). 

Differences are also reported in the immunoreactivity of tumors induced by TCA and DCA in 

regard to the proto-oncogenes c-jun and c-fos (Bull, 2000). DCA- and TCA-induced tumors also 

appear to have differences as to the shape of their respective dose-response curves, with the 

number of DCA-induced tumors appearing to increase disproportionately with dose while those 

of TCA appear to be linear. 

One possible explanation for these observations is that DCA and TCA may selectively 

modify growth rates of different clones of cells. TCE and its metabolites could alter cell 

replication and apoptosis rates in altered hepatic foci and preneoplastic foci, and provide a 

selective growth advantage for differing clones of cells rather than a generalized proliferative 

response. Cells resistant to the downregulation of mitosis induced by a chemical may have 

selective growth advantage over other cells in that organ (Bull, 2000). 

Effects of DCA and TCA on replication rates in foci and normal hepatocytes vary and 

appear to be dose dependent in some cases. DCA at higher doses can increase replication rates of 

tumor cells (Bull, 2000), whereas only a small stimulatory effect on replication is seen in 

extrafocal (normal) hepatocytes. Further, as treatment progresses, an inhibitory effect is 

observed in the extrafocal hepatocytes. This suppression of cell replication in extrafocal 

hepatocytes is accompanied by a parallel decrease in apoptosis. These observations raise a 

question as to which is the driver of the decreased cell turnover in extrafocal hepatocytes seen 

with DCA. It is unknown if the decrease in cell turnover would increase the probability of 

transformation of hepatocytes to allow clonal expansion of damaged hepatocytes that would 

normally be eliminated (Bull, 2000). This possibility is suggested by the modeling of DCA 

lesion growth rates by Miller et al. (2000). 

TCA depresses replication rates in normal hepatocytes with no indication that replication 

rate within tumors are modified by TCA treatment (Bull, 2000). TCA appears to be selecting 

initiated cells that are more aggressive than those selected by DCA at low doses. The extent to 

which replication rates in extrafocal hepatocytes are decreased with longer term TCE exposure 

has not been investigated, nor those in foci or tumors. 

Exposure to DCA has been shown to perturb a number of normal cellular functions 

including cell signaling pathways, particularly those associated with carbohydrate handling (Bull, 

2000). DCA has more recently been shown to alter serum insulin concentrations in mice 

(Kato-Weinstein et al., 1998). Insulin is recognized to be mitogenic in liver (Bull, 2000) and to 

have differing effects on apoptosis (Hermann et al., 1999). Further, insulin receptor expression 

in extrafocal portions of the liver is suppressed by DCA (Bull, 2000). The principal intracellular 

substrate of the insulin receptor, tyrosine kinase, is linked to enzymes regulating glycogen 

synthesis. In fact, DCA exposure to mice inhibits the glycogen synthesis enzymes and is also 

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associated with increased glycogen deposition in extrafocal hepatocytes, but not in preneoplastic 

foci, which are glycogen poor. DCA can also inhibit pyruvate dehydrogenase kinase, although at 

levels higher than those needed for effects on glycogen storage. The dose-response for glycogen 

deposition in the liver is in the same range as that seen with liver tumors, raising questions of 

whether modification of a same-cell signaling pathway may be responsible for both observations. 

Moreover, the accumulation of glycogen in normal hepatocytes may contribute to the differential 

effects of DCA treatment on insulin receptor expressed in extrafocal hepatocytes and those in 

altered foci (Bull, 2000). DCA thus may alter the insulin signaling pathway and provide a 

growth advantage to initiated cells through a signal transduction pathway necessary for tumor 

growth (Kato-Weinstein et al., 1998). The precise nature of this pathway, however, has yet to be 

fully elucidated. 

Further, a number of metabolic disturbances are associated with exposure to DCA. A 

major site of action of DCA at high doses is pyruvate dehydrogenase (PDH), the rate-limiting 

enzyme of glucose oxidation (Stacpoole et al., 1992). DCA exposure has been shown to inhibit 

hepatic triglyceride and cholesterol biosynthesis, whereas the stimulation of PDH results in 

effects on gluconeogenesis (Stacpoole et al., 1992). Emerging experimental evidence, moreover, 

suggests that DCA may interfere with tyrosine catabolism. DCA’s further metabolism is 

dependent on a glutathione-S-transferase isozyme, GST zeta (GSTZ) (Anderson et al., 1999), 

also identified as maleylacetoacetate isomerase (Fernández-Cañón et al., 1998). 

Maleylacetoacetate isomerase is one step in the catabolism of tyrosine. Chronic exposure to 

DCA causes an irreversible inactivation of GSTZ (Anderson et al., 1999), with the rate of 

inactivation greater for rats than for humans; the rate of inactivation in the mouse falls in 

between those for the rat and human (Tzeng et al., 2000). 

The interrelationship between rates of cell replication with rates of cell deaths make it 

very difficult to determine if the effects of TCA and DCA are exerted on one process or the other 

(Bull, 2000). Although the mechanisms underlying DCA- and TCA-induced alterations in cell 

division and death are not yet known, the experimental observations suggest that each metabolite 

differentially alters these processes. However, the patterns of effects on the processes of cell 

division and death provide a reasonable framework for TCE-induced liver cancer. Moreover, 

although it is not yet clear whether DCA-induced effects on carbohydrate handling and on other 

metabolic processes are directly related to cancer induction, such effects are consistent with a 

hypothesis regarding selection and can be consistent with other physiologic conditions associated 

with hepatocarcinogenicity. 

3.5.1.3. Effects on DNA 

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A key question to risk assessment is whether TCE-induced tumors are mediated through 

an induced mutational mechanism (Moore and Harrington-Brock, 2000). Examination of tumors 

for possible mutation has been carried out for investigation of this potential mode of action. TCE 

and its metabolites CH, DCA, and TCA are, at best, very poor genotoxicants (Moore and 

Harrington-Brock, 2000). TCE and DCA at high concentrations have been shown to bind to 

DNA and proteins, and to induce single-strand breaks (Moore and Harrington-Brock, 2000; Bull, 

2000). The magnitude of response, however, is low compared to other more potent 

genotoxicants such as MMS and DMN (Moore and Harrington-Brock, 2000). DCA is genotoxic 

and has been shown to cause DNA strand breaks in mouse and rat liver cells following high level 

in vivo gavage exposure. TCA is the least mutagenic of the TCE metabolites (Moore and 

Harrington-Brock, 2000). 

Mutation frequency and mutation spectra in the ras oncogene have been examined in 

TCE-, TCA-, and DCA-induced liver tumors from male mice; it is interesting to note that very 

few codon 61 h-ras mutations are seen in liver tumors from female mice treated with DCA. The 

actual amount of mode-of-action information on induced mutational mechanisms from these data 

is quite minimal (Moore and Harrington-Brock, 2000). However, the pattern of mutation seen 

helps provide insight into the relative contribution of different metabolites to TCE-induced liver 

cancer (Bull, 2000). Mutational frequency in DCA and TCE tumors (arising from drinking water 

exposure) does not differ significantly from that observed in spontaneous tumors; TCA tumors 

show both a higher mutation frequency and a pattern atypical of that found with other 

peroxisome proliferators (Bull, 2000; Maronpot et al., 1995). Comparison of nucleotide base 

sequences within codon 61 of h-ras shows some differences between spontaneous tumors and 

DCA and TCE tumors, suggesting a similarity between tumors from DCA and TCE-treated 

animals (Moore and Harrington-Brock, 2000). Route of exposure, however, appears important to 

mutational frequency associated with TCE-induced liver tumors (Bull, 2000). 

The similarity between DCA and TCE tumors in terms of frequency of mutation and its 

spectra support an inference that DCA contributes an important role in the hepatocarcinogenicity 

of TCE. The theoretical modeling of TCE, DCA, and TCA liver tumors of Chen (2000) provides 

further support for DCA’s contribution to TCE-induced liver cancer, although a role for TCA 

cannot be excluded either. As additional supportive evidence, lower doses of DCA that 

accompany higher doses of TCA appear to exert a large influence on tumor development. 

Female mice initiated with N-methyl-N-nitrosourea followed by promotion with a mixture 

containing both DCA and TCA developed tumors that appeared more similar to tumors promoted 

by DCA alone than those by TCA alone (Pereira et al., 1997). Moreover, the number of tumors 

produced from the mixture was greater than would be achieved by adding together tumor 

incidences for the individual exposures. 

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The interaction of TCE and its metabolites to bind directly with DNA may not be as 

important at lower exposure levels as effects on gene expression. Specifically, effects on gene 

expression, whether through activation of PPAR or other signal pathways, may be more 

important to their potential carcinogenic properties than induced mutation. Moreover, effects 

such as the mistargeting of covalent modification to the proteins that package DNA (e.g., histone 

acetylation) or to DNA itself (e.g., cytosine methylation) may be involved in cancer induction, 

not by altering gene sequence, but by modifying transcription of the gene (Wolffe, 1999). For 

example, DNA methylation is believed to play a role in the control of gene transcription 

(Goodman and Counts, 1993; Counts and Goodman, 1995). 

TCE and its oxidative metabolites have been shown recently to decrease methylation of 

DNA as 5-methylcytosine (5MeC) in addition to demethylation of the promoter regions of two 

proto-oncogenes, c-jun and c-myc (Tao et al., 1998, 1999, 2000a). Moreover, the decreased 

methylation and increased expression of c-jun and c-myc with TCA and DCA exposures also 

occurred in the presence of increased DNA methyltransferase activity (Tao et al., 2000b). The 

proto-oncogenes, c-jun and c-myc, participate in the control of cell proliferation and apoptosis 

(Tao et al., 2000a). The expression of mRNA for these two genes was shown to be transiently 

increased as well (Tao et al., 1999). This is an early observation, seen after 3 days of exposure to 

TCE. In fact, cell proliferation is enhanced during the first few weeks of exposure to DCA and 

TCA (Bull, 2000). Hypomethylation associated with exposure to DCA appears to be reversed 

once exposure is terminated (Tao et al., 1998), in contrast to the extent of hypomethylation 

associated with exposure to TCA, which remained elevated upon exposure cessation. These 

observations support the hypothesis that DCA, and not some other process or event, is affecting 

methylation in the tumors. Tao (2000a) recently suggested that depletion of Sadenosylmethionine may be responsible for the observed hypomethylation of DNA. The 

prevention of DCA-, TCA-, and TCE-induced DNA hypomethylation by the addition of 

methionine supports this hypothesis. 

The relationship between DNA methylation and cancer induction is not well elucidated, 

and the events and their sequence, which are important to neoplastic cell transformation, are 

lacking. Consequently, at the current time, DNA methylation may be best considered an effect of 

exposure rather than a precursor specific to the development of tumors. 

3.5.1.4. Evaluating Proposed Modes of Action for Liver Carcinogenesis 

There are several important questions that arise from the discussion above: which of the 

hypothesized modes of action can be judged as more plausible; is there evidence to reason any 

one is the mode of action for TCE hepatocarcinogenicity, and if so, is it important to EPA’s 

proposed cancer guidelines (U.S. EPA, 1999); whether an event considered as a precursor to 

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carcinogenesis can be identified. Hence, insight into these questions can support weight-ofevidence determinations and approaches that will be used in the quantitative dose-response 

assessment presented in Section 4. EPA’s proposed cancer guidelines (U.S. EPA, 1996, 1999) 

note the necessity to outline the sequence of events leading to cancer, to identify key events that 

can be measured, and to weight information to determine whether there is a causal relationship 

between events and cancer formation. The validation of a causal relationship between exposure 

and cancer risk includes the consideration of a number of factors, many of which have been 

borrowed from the epidemiologic literature. These criteria include (1) consideration of the 

strength, consistency, and specificity of the association between event and tumor response; (2) 

the presence of a dose-response relationship between the key event and tumor endpoint; (3) the 

temporal relationship between tumor onset and occurrence of the key event (i.e., the key event 

should precede tumor formation); (4) the biological plausibility and coherence of the mode of 

action; and (5) consideration of other modes of action. Experimental evidence that can be used 

to address these criteria is often derived in the species in which tumors were observed. However, 

the ultimate question is the human relevance of the proposed mode of action. Hence, it is 

necessary to demonstrate that the sequence of steps leading to tumorigenesis in the rodents will 

also occur in humans. 

Experimental evidence for support of mode-of-action hypotheses in the liver focuses on 

two of TCE’s oxidative metabolites, TCA and DCA. Few data are available on TCE exposure 

directly, and only in studies involving activation of the PPAR, albeit in vitro, experiments of 

human hepatic cells. Accordingly, it is difficult to determine which of the metabolites of TCE 

may be responsible for its toxicity, what the key events may be, and what is their relevance to 

humans. There is enough evidence, however, to suggest that many of the responses to TCE 

metabolites are relevant to the ability of TCE to induce cancer of the liver in animals, as well as 

to the extrapolation of these observations to humans. 

TCA and DCA have been shown to induce a number of different events in livers of mice 

(discussed in the preceding sections). Both have been shown to activate PPAR" as evidenced by 

peroxisomal proliferation; however, a role of PPAR activation as a possible mode of action is 

more plausible for TCA than for DCA. TCA induces a more sustained proliferative response 

than DCA. Furthermore, DCA induces effects on cell signaling processes and on carbohydrate 

handling at lower concentrations than those associated with peroxisome proliferation. Besides 

PPAR", TCA has been shown to activate PPAR( from mice but not humans. 

The role of PPAR" activation and the sequence of events following activation that are 

important to tumorigenesis are not well defined, nor is the contribution of other forms of PPAR 

to observed TCE carcinogenicity currently known. Furthermore, extraperoxisomal effects of 

activation of transcription by PPAR" may elicit a number of changes in cell signaling that may 

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also be linked to increased cancer risk. Moreover, key events critical to the induction of tumors 

and the cross-species relevance of these key events have yet to be identified. Further 

investigations regarding activation of PPAR through the use of genetically altered experimental 

animals may provide insight regarding the role of PPAR" in the induction of liver tumors by 

TCE and the contribution of TCA formation. 

DCA has the ability to affect other cell signaling processes and to disturb carbohydrate 

handling at lower concentrations than those associated with PPAR activation. Specifically, DCA 

has been shown to alter cell replication, selection, and apoptosis. The observed disturbances to 

insulin signaling and to carbohydrate handling associated with DCA exposure may also be 

connected to these alterations. The events that lead to and result in a selective growth advantage 

or in depressed apoptosis of altered hepatic foci have yet to be clearly defined. As with 

activation of PPAR, critical events important to the induction of liver tumors in mice 

administered DCA have yet to be identified. 

Another potential mode-of-action hypothesis of DCA liver tumor induction recently put 

forth by Cornett et al. (1999) involves disturbance of tyrosine catabolism. DCA’s inhibitory 

effects on GSTZ may also inhibit maleylacetoacetate isomerase. Inhibition of maleylacetoacetate 

isomerase activity could consequently lead to a buildup of intermediates of tyrosine catabolism, 

several of which have are considered alkylating agents (Cornett et al., 1999). This proposed 

mode of action needs further investigation because direct experimental evidence regarding TCEinduced effects on tyrosine intermediates such as maleylacetoacetate and maleylacetone is 

lacking. At the present time, the hypothesis of DCA-induced effects on cell signaling is more 

compelling. 

Another approach for investigating the possible role of TCE metabolites in its toxicity is 

to ascertain whether enough of the metabolite is present after administration of the parent 

compound to induce tumors at the target of concern. A critical issue is the amount of DCA 

produced from chronic exposure to TCE and whether this amount would be sufficient to elicit the 

events that have been observed with experimental administration of DCA. Chronic exposure to 

DCA has been shown to increase the elimination half-life of subsequent exposures in mice, rats, 

and humans, suggesting that chronic exposure to DCA inhibits its own metabolism (GonzalesLeon et al., 1999). Moreover, recent reports of irreversible inactivation of DCA by GSTZ 

suggest larger internal doses of DCA may occur than previously assumed (Tong et al., 1998; 

Tzeng et al., 2000). Thus, factors that may affect the clearance of DCA and the role of a possible 

buildup of toxic metabolites are complex and are another uncertainty associated with this 

assessment. 

Nevertheless, Barton et al. (1999) have examined this question for mice by carrying out a 

theoretical modeling study of both TCE and DCA pharmacokinetics. This study estimated that 

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the maximum levels of DCA in the serum produced from 1,000 and 2,000 mg/kg gavage doses of 

TCE (tumorigenic doses in the bioassay) would be equivalent to internal dose metrics that were 

approximately half of those estimated from exposure to 0.5 mg/L DCA in drinking water. 

Exposure of mice to 0.5 g/L DCA in drinking water produced a 48% to 63% incidence of 

hepatocellular carcinomas and a 75% incidence of combined liver tumors (carcinomas and 

adenomas), with an increase in the number of carcinomas per liver compared with livers from 

control animals (Daniel et al., 1992; DeAngelo et al., 1999; Bull, 2000). The analysis of Chen 

(2000) provides further support that DCA produced by TCE is capable of producing the 

carcinogenic response seen in mice from gavage exposure to TCE. 

Information needed to extrapolate the potential risk of DCA formation from TCE 

exposure in regard to human liver cancer induction is more limited. Unfortunately, Barton et al., 

(1999) do not examine the amount of DCA produced in humans from TCE exposure, and only 

one study (Fisher, 1998) is available that assayed DCA in serum from 17 human volunteers (9 

males and 8 females) exposed via inhalation for 4 hours to 100 ppm TCE. Fisher (1998) noted 

low but detectable levels of DCA in serum in five volunteers (three males and two females) with 

levels ranging between 4 and 12 ppm. The lack of detection of DCA in the other 12 subjects may 

be due to the rapid metabolism of DCA, as inhibition of DCA’s metabolism would not be 

expected to occur from a 4-hour exposure. Alternatively, but less likely, the absence of DCA in 

these human subjects may be from lack of DCA formation from TCE. Only one subject with 

detectable DCA levels in serum was exposed to TCE at the lower level of 50 ppm for 4 hours, 

and DCA was not detected in this experimental protocol (Fisher, 1998, personal communication 

with J. Fisher). Furthermore, DCA has been detected in children and adults who received chloral 

hydrate (Henderson et al., 1997; Yan, 1999). These studies together provide support that 

humans, like mice, have the potential to produce DCA from either TCE when administered as 

parent or from a key TCE metabolite, chloral hydrate. 

The technical problems with detection of DCA, however, are difficult, particularly in the 

presence of large amounts of trichloroacetic acid, and samples are subject to the presence of 

DCA as an artifact (Ketcha et al., 1996). Hence, it is not clear whether the reported findings of 

DCA in humans are accurate or are a reflection of conversion of TCA to DCA under acidic 

conditions. The more recent data of Gonzalez-Leon (1999) and analysis of Barton et al., (1999) 

are not subject to analytical artifact; thus, it can be concluded that mice convert DCA from TCE. 

Given the large uncertainty associated with the human data, by default, it is reasonable to assume 

that humans would similarly produce DCA and observations in mice can be extrapolated across 

species to humans. 

Several lines of reasoning lead to the conclusion that no one metabolite can be identified 

as responsible for TCE’s carcinogenicity. The modeling studies of Barton et al. (1999) and Chen 

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(2000) support an inference that the concentrations of DCA in mice produced from exposures 

experienced in the TCE gavage bioassay are of sufficient quantity to induce hepatic tumors. 

Conclusions of Chen (2000) further suggest ambiguity in identifying one metabolite as 

explaining TCE hepatocarcinogenicity. Both metabolites may each have their separate modes of 

action and may, in fact, work in concert to alter each other’s toxicity. This is suggested from the 

experimental evidence of joint TCA and DCA exposure by Pereira et al. (1997) and from newly 

emerging experimental data of Bull (personal communication). Bull (personal communication) 

has examined the frequency of TCE-induced liver tumors that are c-jun+; the frequency of 

hepatic tumors in which c-jun has been mutated is high in DCA-induced liver tumors. 

Preliminary results from Bull suggest a role for DCA, in that many TCE-induced tumors were 

c-jun+ (Bull, personal communication). Also noteworthy was a finding of a similar number of 

tumors that did not display a mutation to c-jun (Bull, personal communication); c-jun is not 

mutated in liver tumors from TCA exposure. These findings suggest that TCA and DCA both 

contribute to the hepatocarcinogenicity of TCE. 

3.5.1.5. Consistency With Other Physiologic Conditions and Associated 

Hepatocarcinogenicity 

The consistency in events seen with exposure to TCE metabolites, specifically DCA, and 

physiologic conditions associated with several human diseases can provide insight into mode-ofaction hypotheses and may support inferences regarding potential increased human cancer risks. 

Many cellular events observed with exposure to DCA, including changes in carbohydrate 

handling, are also seen with several diseases such as glycogen storage diseases and diabetes. 

Although the pathogenesis of these diseases and what is known about liver tumor induction with 

DCA exposure may have distinct differences, disturbances in cell metabolism and in 

carbohydrate handling are common events. As discussed below, diabetics and individuals with 

glycogen storage diseases are at increased risk for liver cancer. Furthermore, individuals with 

tyrosinemia also have an increased risk of liver cancer (Tanguay et al., 1996; Laberge et al., 

1986). 

Glycogen storage diseases are caused by inherited deficiencies of enzymes that regulate 

the synthesis, transport, or degradation of glycogen. Individuals with glycogen storage diseases 

store excess glycogen in their livers. This storage resembles that seen in the extrafocal 

hepatocytes of DCA-treated livers. The majority of individuals with glycogen storage diseases 

develop liver cancer early in life. In fact, multiple hepatocellular neoplasms are often seen in 

these individuals. In a similar pattern, DCA exposure induces multiple foci in mice livers. 

Diabetes is a disease involving insulin disturbances. Poorly controlled diabetes results in 

excess glycogen storage in livers as well. Hepatomegaly is seen in both Type I and Type II 

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diabetics (Herrman et al., 1999) and diabetics have an increased risk for liver cancer (LaVecchia 

et al., 1994; Adami et al., 1996; Wideroff et al., 1997). It is possible that greater sensitivity of 

tumor cells than normal cells to insulin could mimic such disturbance and contribute to DCA 

liver tumor induction as well (Bull, 2000). 

Moreover, observations on cellular changes that include markers in liver tumors resulting 

from exposure to TCE, DCA, or TCA overall are consistent with a more generalized description 

of hepatocarcinogenesis proposed by Bannasch and others (Bannasch et al., 1986, 1996, 1997; Su 

et al., 1997). A number of findings have been reported that suggest the sequence of cellular 

changes during hepatocarcinogenesis is in principle identical in humans and experimental 

animals (Bannasch et al., 1986, 1996, 1997). For example, Bannasch (1986) notes a correlation 

between changes in cell metabolism, carbohydrate handling, and neoplastic cell transformation. 

3.5.2. Kidney Cancer 

Mode-of-action studies investigating kidney tumors induced in gavage and inhalation 

bioassays have focused on metabolites, particularly those associated with the glutathione 

pathway, where the experimental evidence is more persuasive. There are several lines of 

investigation for potential modes of action for TCE in the kidney as cited by Lash et al. (2000b): 

peroxisome proliferation, alpha-2u globulin nephropathy, mutagenicity or other genetic toxicity, 

formic acid formation, and cytotoxicity-induced damage. The question is which, if any, of these 

mechanisms are operative and likely to lead to the development of cancer at doses that were 

lower than those used in the bioassays of TCE in animals. 

3.5.2.1. Genotoxicity 

Cysteine intermediates (DCVC and DCVG) formed in the GST pathway are capable of 

inducing point mutations, as evidenced from positive findings in the Salmonella assay (Moore 

and Harrington-Brock, 2000). DCVC is the most potent of the TCE metabolites as a Salmonella 

mutagen. DCVG is not as potent as DCVC, but this is consistent with the hypothesis that DCVG 

needs to be converted to DCVC to induce point mutations. Although DCVC and DCVG have 

not been evaluated in other screening assays, there is some indication that they can induce 

primary DNA damage in mammalian cells in vitro and in vivo. Further, formation of covalent 

adducts with proteins and other macromolecules in mitochondria have been documented (Lash et 

al., 2000b). The covalent binding of DCVC is largely dependent on its metabolism by the $­

lyase pathway. DCVC has been shown to induce the express of two proto-oncogenes, c-fos and 

c-myc (Lash et al., 2000b). At noted in the liver mode-of-action discussion, c-myc is involved in 

the control of cell proliferation and apoptosis (Tao et al., 2000a). 

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A key question in risk assessment, like that identified in the liver mode-of-action 

discussion, is whether TCE-induced kidney tumors are mediated through a compound- or 

metabolite-induced mutational mechanism. Two reports provide suggestive evidence for TCEinduced mutations in the VHL gene of renal cell carcinoma patients (Brüning et al., 1997b; 

Brauch et al., 1999). Brauch et al. (1999) expanded the work of Brüning et al. (1997b) and 

observed somatic mutations in 75% of the renal cell carcinomas from TCE-exposed individuals. 

Many (42%) of the carcinomas taken from TCE-exposed cases had multiple mutations, and 

nucleotide base changes were found in five regions. A specific “hot spot” on gene nucleotide 

454 was altered in 13 renal cell carcinoma patients with TCE exposure, but this mutation was not 

detected in renal cell carcinoma patients without TCE exposure or was carried by healthy 

subjects. Brauch et al. (1999) hypothesize that the C>T mutational changes in the VHL gene of 

TCE-exposed renal cell carcinoma patients may be associated with alkylation and DNA adduct 

formation of the opposite DNA strand. It is probable that these mutations play a role in the 

development of these tumors because a function of this gene is to suppress the development of 

kidney cancer. These data identify a profile that seems to be specific to TCE exposure. The 

finding of the mutation in nucleotide 454 in normal kidney parenchymal tissue from four patients 

adds further support that this mutation may precede tumor formation. The halothioketenes, 

products from $-lyase cleavage of DCVC, form DNA adducts in vitro and could conceivably 

contribute to the induction of these mutations (Müller et al., 1994, 1998a,b). Moreover, 

chlorothioketenes have been shown in vitro to form adducts with cytosine in an aqueous solution 

(Volkel et al., 1998). There may, however, be alternative mechanisms such as selection of cells 

within the mutation, rather than producing the mutation, that may account for or at least 

contribute to these observations. Although this is not conclusive proof of a mutagenic 

mechanism, it is prudent to consider this as an important and likely mode of action and, at the 

minimum, a marker for TCE-induced kidney cancer. 

3.5.2.2. Accumulation of Alpha-2u Globulin 

In the past, the production of alpha-2u globulin was considered as contributing to TCEinduced kidney tumors, with the initiation of several studies to examine this hypothesis. 

Alpha-2u globulin accumulation is unique to male rats, and when in excess is associated with 

nephropathy, cytotoxicity, cellular proliferation, and consequent renal tumors. Goldsworthy 

shows that TCE does not cause an accumulation of alpha-2u globulin in the male (Goldsworthy 

et al., 1988; Lash et al., 2000b). Moreover, TCE is a renal toxicant and clearly causes kidney 

damage in both male and female rats and in mice (Barton and Clewell, 2000). With respect to 

kidney tumors, the analysis of Rhomberg (2000) shows that kidney tumors are found in both 

TCE-treated male and female rats. Thus, alpha-2u globulin accumulation does not appear to be a 

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mode of action of TCE-induced kidney toxicity in rats or relevant to human risk (Lash et al., 

2000b). 

3.5.2.3. Peroxisome Proliferation 

A potential role of peroxisome proliferation should be included in any mode-of-action 

discussion for kidney tumor induction because renal enzymes generate chloroacetates that are 

known to induce peroxisome proliferation, and the renal proximal tubular epithelial cells are 

relatively rich in peroxisomes. One study specifically has addressed the issue of TCE-induced 

peroxisome proliferation in the kidney, with other studies examining the influence of TCE 

metabolites on peroxisome proliferation (Lash et al., 2000b). Peroxisome proliferation was 

reported to occur in both rat and mouse kidney following exposure to TCE, with mice showing a 

greater response, as measured by cyanide-insensitive palmitoyl-CoA oxidation, than that seen in 

rats (Goldsworthy and Popp, 1987). TCE and its metabolites have not been shown to induce 

kidney cancer in mice, however. Odum et al. (1988), additionally, suggest that TCA-induced 

peroxisome proliferation does not play a role in male rat kidney carcinogenicity. A role for 

peroxisome proliferation of TCE-induced kidney carcinogenicity is unlikely. 

3.5.2.4. Nephrotoxicity and Cytotoxicity 

TCE has been demonstrated to cause nephrotoxicity and to induce persistent changes to 

the proximal tubules in both rats and humans. It has been proposed that kidney tumor induction 

results from cellular necrosis and activation of repair processes that lead to cellular proliferation. 

Most of the studies aimed at elucidating this possible mode of action have focused on DCVG or 

DCVC, rather than the parent compound, as it is believed that flux through the GSH conjugation 

pathway and the subsequent production of reactive species are responsible for observed 

nephrotoxicity (Lash et al., 2000b). The in vivo formation in both animals and humans of these 

intermediates from TCE further adds significance to these studies in an assessment of TCE. In 

spite of the importance of $-lyase metabolism in cysteine conjugated-induced nephrotoxicity, 

there is evidence in several other studies that suggests additional enzymatic activities may also 

bioactivate DCVC and may be important to nephrotoxicity (Lash et al., 2000b). 

A number of in vivo and in vitro studies support that TCE and its cysteine conjugates are 

associated with nephrotoxicity, specifically proximal tubular damage, as measured by elevated 

excretion of renal enzymes or by histopathologic examination. Nephrotoxicity has also been 

shown in individuals with occupational exposure to high levels of TCE (Lash et al., 2000b; 

Brüning et al., 1999a, 1999b). Nephrotoxicity as well as tumors were observed concurrently in 

the NTP bioassay (NTP, 1988). The incidence of nephrotoxicity in the rat bioassay was high, 

however, and far exceeded the reported incidence of tumors (NTP, 1988). Further, 

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nephrotoxicity appears to correlate with the known sex differences in metabolism rates, males 

having a higher metabolic rate than females (Lash et al., 2000b). Exposure to TCE was 

associated with a modest increase in lactate dehydrogenase release (a marker of cell injury) in 

male rat kidney cells, but not in cells from female rats at similar concentrations (Lash et al., 

2000b). 

Nephrotoxicity may result from oxidative stress, disturbances in calcium ion homeostasis, 

mitochondrial dysfunction, or protein alkylation. A number of studies demonstrate 

nephrotoxicity by these process with exposure to TCE or, more so, to DCVC. Both TCE and 

DCVC induce an oxidative stress response that includes GSH oxidation or depletion, lipid 

peroxidation, and oxidation or alkylation of protein sulfhydryl groups after exposure of renal 

cells to these agents. It is likely that oxidative stress plays some role in DCVC-induced 

nephrotoxicity, but lipid peroxidation is probably a consequence rather than a cause of cellular 

injury (Lash et al., 2000b). DCVC-induced changes in free calcium ion concentrations include 

inhibition of mitochondrial metabolism and function, severe mitochondrial damage, poly(ADP)­

ribosylation of nuclear proteins and DNA double-strand breaks, and changes in cytoskeletal 

protein structure. The cysteine conjugates, including DCVC, have been shown to covalently bind 

with proteins and other macromolecules in mitochondria. Cytosolic proteins are another target of 

the reactive metabolites of DCVC, and protein adducts have been observed in vitro in human 

proximal tubular cells with exposure to DCVG and DCVC and in rats exposed in vivo to DCVC. 

The finding of higher levels of covalent binding in TCE-exposed mice compared to rats suggests 

that other factors besides covalent adduct formation must contribute to the induction of renal 

carcinogenesis (Lash et al., 2000b). 

Recently, Green (Green et al., 1998) put forth another hypothesis that urinary excretion of 

formic acid leading to decreased pH after TCE exposure may partially explain the observed renal 

toxicity in the TCE studies. The excretion of increased levels of formic acid as observed with 

exposure to TCE appears to be related to folate deficiency (Dow and Green, 2000). Kidney 

toxicity has been reported in humans and rabbits with exposure to formic acid (Jacques, 1982; 

Liesivuori and Savolainen, 1986, 1987, 1991, 1992); however, data are lacking on whether 

formic acid induces kidney tumors. At the present time there is limited evidence that this 

pathway is involved with renal tumorigenesis. Moreover, demonstrating a role for this 

mechanism does not detract from a potential contribution to tumorigenesis by other pathways, 

e.g., glutathione conjugation and processing to DCVC and subsequent reactive metabolites. 

3.5.2.5. Contribution of Several Modes of Action 

It is likely that multiple modes of action may be important in TCE-induced kidney cancer 

(Lash et al., 2000b). As in the liver, level of exposure will be an important consideration. The 

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preponderance of the evidence suggests that the cysteine conjugates and reactive metabolites 

generated from their $-lyase metabolism are most likely responsible for kidney toxicity and 

tumorigenicity (Lash et al., 2000b). These metabolites have not been tested in a chronic bioassay 

and the lack of such information is an uncertainty associated with this inference. More 

information is also needed regarding any potential role of the oxidative metabolites of TCE and 

of formic acid. 

Separating the contribution of genotoxicity from that of nephrotoxicity and chronic 

cellular injury in relation to the development of renal tumors is important to a discussion of 

potential modes of action for kidney cancer and TCE. The cysteine metabolites appear to be 

inextricably intertwined such that the contribution of these events in tumor development cannot 

be evaluated on the basis of data that are available today. As DCVC has been demonstrated to be 

mutagenic in Salmonella, and mutation in the VHL gene seems to be associated with TCErelated tumors in epidemiological studies, it is reasonable to regard gene mutation as a potential 

mode of action for kidney tumors. 

3.5.3. Lung Cancer 

There has been much less investigation into potential modes of action for TCE-induced 

lung cancer than for the liver and kidney. Green (2000) suggests that mice may be more sensitive 

to the toxic effects of TCE exposure in the lung than are other species tested because of their 

increased amounts of CYP2E1 metabolism, specifically in Clara cells, compared to humans and 

rats. Lung tumors in mice are thought to arise through an accumulation of CH in the Clara cells 

of the lung, causing cell damage and compensatory cell replication, which in turn leads to tumor 

formation (Green, 2000). CH is believed to be the etiologic agent because independent studies 

have shown that CH when administered alone causes lung lesions identical to TCE-induced 

tumors. Neither TCA nor TCOH (metabolites of CH) causes lung lesions. 

This hypothesis suggests that such a mechanism in mice may not be extrapolated to 

humans, as there is little CYP2E1 activity in human lungs as a whole (Green, 2000). However, 

activity from whole lungs may give misleading results because of the variety of cell types. High 

activity for a few cell types may be diluted by other cells with low activity (Lash et al., 2000b). 

While thought to be relatively scarce in the human lung, recently Boers et al. (Boers et al., 1999) 

have characterized the number of Clara cells in the normal human airway and show that Clara 

cells contribute substantially to cell renewal in normal conducting airway epithelium in humans. 

Furthermore, Clara cells have been identified as important to the development of lung 

adenocarcinoma in humans (Boers et al., 1999). 

The hypothesis that CH is responsible for lung tumor induction requires further 

confirmation. It would be useful to develop strong connections between metabolism of TCE, the 

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actual tumor cell of origin, and the contribution of other possible mechanisms that CH might 

utilize to increase lung tumor incidence. CH is clearly clastogenic and mutagenic at high doses 

(Moore and Harrington-Brock, 2000), raising the possibility of genotoxic activity as well as a 

mode of action involving simple cytotoxicity and reparative hyperplasia. Moreover, it is not yet 

clearly demonstrated that CH is the metabolite responsible for lung tumor development. 

3.5.4. Toxicity in Other Organs 

Mode-of-action information for toxicity in other organs such as the endocrine and 

lymphopoietic system, and in the developing fetus, is more limited than that for the liver, kidney 

and lung. Data specific to TCE and its metabolites are few, and support for mode-of-action 

hypotheses are based on observations of common activities with other agents. In some cases, the 

roles of PPAR activation or cell signaling perturbations that are relevant to mode-of-action 

discussions for the liver and kidney are applicable to discussions of toxicity observed in these 

other organ systems. In other cases, such as lymphoma seen in the human epidemiologic studies, 

studies using animal models are lacking or are too few to support the human observations. 

Endocrine system effects associated with TCE exposure include the development of 

testicular (Leydig cell) cancer in rats (Maltoni et al., 1988; NTP, 1988) and hormone 

disturbances (SHBG, DHEAS, testosterone) in humans (Chia et al., 1997; Goh et al., 1998). 

TCE and its metabolites, TCA and TCOH, have been found to partition in the male reproductive 

organs of rats after 6 weeks of inhalation exposure (Zenick et al., 1984). A wide variety of 

agents that affect steroid hormone levels, such as testosterone, estradiol, and luteinizing 

hormone, will also induce Leydig cell tumors in the rat (Cook et al., 1999). Peroxisome 

proliferating chemicals have been shown to induce Leydig cell tumors via a modulation of 

growth factor expression by estradiol (Cook et al., 1999). Peroxisome proliferating chemicals 

induce hepatic aromatase activity, which can increase serum and testis estradiol levels. The 

increased interstitial fluid estradiol levels can modulate growth factors, including TGF", and 

stimulate Leydig cell proliferation (Cook et al., 1999). 

Steroid hormones such as testosterone, estradiol, and luteinizing hormone are regulated 

through the hypothalo-pituitary-testis (HPT) axis in both rats and humans, and agents that induce 

Leydig cell tumors in rats by disruption of the HPT axis are thought to pose a hazard to humans 

(Cook et al., 1999). Cook et al. (1999) further suggest that Leydig cell tumors induced through 

an aromatase activity mode-of-action such as that suggested for peroxisome proliferators are 

considered potentially relevant to humans. Although the risk of Leydig cell cancer in humans 

may be diminished compared to rats owing to the absence of SHBG, the observation of Leydig 

cell tumors in rats exposed to TCE may act as a signal for disturbance of the endocrine system 

and be indicative of potential endocrine disturbances in humans. Further, a potential target of 

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testosterone disruption may be the prostate, which in humans is sensitive to testosterone and 

other androgens (Shimada et al., 1999; Bosland et al., 1995) and is affected by Leydig cell 

function in the rat (Yount et al., 1982). The effects of endocrine disruption in human populations 

exposed to TCE are an area for more research. 

TCE exposure has been associated with effects on immune system functioning and 

includes both immune suppression and autoimmunity. Reports of both immune suppression and 

autoimmunity may appear paradoxical at first glance; however, these observations are plausible. 

Certain elements of the immune system may be differentially affected by a chemical, affecting 

the ability to mount well-regulated immune responses to both foreign and self-antigens and 

leading to conditions such as autoimmunity via an alteration in the balance between specific 

immune components. Immune suppression, furthermore, may play a role in the induction of 

cancer; many immune-suppressive agents are human carcinogens (Tomatis et al., 1989). 

Additionally, a common event or events may exist in mode-of-action pathways for tumorigenesis 

and for autoimmunity (e.g., dysregulation of apoptosis). The observation of an increased risk of 

multiple myeloma in patients with autoimmune diseases provides some support for this 

hypothesis (Cooper et al., 1999). 

Organic solvent exposure in general is associated with autoimmune disease such as 

scleroderma (systemic sclerosis) (Nietert et al., 1998). Mode-of-action hypotheses for 

autoimmunity resulting from environmental agents have been grouped into three broad 

categories: (1) a change in the hormonal milieu to favor estrogenic stimulation of the immune 

system; (2) suppression of one section of the immune system, which disrupts normal immune 

surveillance; and (3) chemical binding to a self-antigen forming a neoantigen, thus breaking 

tolerance by inducing immunity to the unmodified native molecule as well as to the modified 

antigen (Mayes, 1999). These mechanisms are currently speculative at best, but are supported by 

animal models (Mayes, 1999). TCE exposure, specifically, has been shown to induce 

autoantibody formation against dichloroacetyl chloride in genetically susceptible autoimmuneprone mice (Griffin et al., 2000a,b, Khan et al., 1995). Moreover, T-cells were shown to secrete 

more interferon-( and less interleukin-4 after TCE exposure (Griffin et al., 2000a). Griffin et al., 

(2000a) note that the pattern of response was consistent with a T-helper Type 1 immune or 

inflammatory response. Furthermore, these effects have been shown to contribute to the 

development of autoimmune disease in the liver (autoimmune hepatitis) (Griffin et al., 2000c). 

Oxidative metabolism is necessary for inducing the inflammatory response because blocking the 

CYP450 pathway was shown to primarily inhibit the response (Griffin et al., 2000b). One 

epidemiologic study also reported an strong association between TCE exposure and systemic 

sclerosis patients with autoantibodies (Nietert et al., 1998). The mode(s) of action for these 

observations is not known at this time. 

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The Kupffer cell may also play an important role in autoimmune response. Kupffer cells 

have been discussed previously in the liver mode-of-action section. Activated Kupffer cells 

release a number of chemicals, including hydrogen peroxide, superoxide ion, and hydrolytic 

enzymes, which aid in antigen destruction, and also cytokines, which have immunoregulatory 

and inflammatory activity (e.g., interleukins, TGF", and TGF$). Several of these chemicals 

have also been associated with apoptosis (see liver cancer discussion) (Laskin, 1996). Treatment 

of mice with TCE for 30 days has been shown to affect Kupffer cells and the production of 

inflammatory cells (Kjellstrand et al., 1983). Moreover, peroxisome-proliferating chemicals in 

general are also reported to affect Kupffer cell function (Youseff and Badr, 1998), and PPAR 

activation may play a role in autoimmune disease. 

Immune suppression and TCE exposure are areas needing more research. Immune 

suppression may play a significant role in the development of non-Hodgkin’s lymphoma, as seen 

in the epidemiologic studies (Wartenberg et al., 2000) and in the mouse bioassay (Henschler et 

al., 1980). Non-Hodgkin’s lymphoma is found with a higher frequency among individuals with 

compromised immune systems resulting from either viruses or past chemotherapy (Smith, 1996). 

Although this is not well studied, one report of TCE exposure to female mice via the drinking 

water shows exposure-related inhibition of humoral immunity with effects on bone marrow 

function, cell-mediated immune responses, and macrophage function (Sanders et al., 1982). 

Mode-of-action hypotheses for observed developmental effects seen with TCE, TCA, and 

DCA exposure are not well developed, and data specific to TCE exposure are few. 

Developmental effects that have been associated with TCE or TCE metabolite exposure include 

micropthalmia/anopthalmia (eye anomalies) in rats, cardiac defects in rats and humans and, 

more vaguely, neural tube defects in humans. Micropthalmia has been reported in human 

offspring with maternal alcohol and retinoic acid exposures. Both retinoic acid and ethanol have 

in common peroxisome receptor activity. PPAR" activation may be important to the 

development of eye anomalies, although no data currently support this hypothesis. In fact, 

another agent that activates PPAR, DEHP, induces pronounced anopthalmia and micropthalmia 

in rats (Narotsky et al., 1995a,b). 

Researchers at the University of Arizona are examining the cardiac teratogenicity of 

trichloroethylene. Research is focused on evaluating whether gene expression is affected during 

cardiogenesis by altering several molecules which are hypothesized to be critical for normal heart 

development. Recently Boyer et al. (2000) have reported that TCE treatment produces a dosedependent inhibition of mesenchymal cell transformation (a critical event in development of the 

heart) in progenitors of the valves and septa in the heart in vitro. Boyer et al. (2000) also note a 

concurrent study that shows alteration of gene expression in rat embryo hearts with maternal 

exposure in drinking water of 110 ppm TCE. Although questions may surround the experimental 

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evidence of whether cardiac anomalies observed in the developmental assays are related to TCE, 

Boyer et al. (2000) report that TCE can affect events important to the development of the heart, 

events which are consistent with an induction of cardiac anomalies. Moreover, the TCE 

metabolites, TCA and DCA, both produce cardiac anomalies in rats (Smith et al., 1989, 1992; 

Johnson et al., 1998a,b; Epstein et al., 1993). DCA also concentrates in rat myocardial 

mitochondria (Kerbey et al., 1976), freely crosses the placenta (Smith et al., 1992), and has 

known toxicity to tissues dependent on glycolysis as an energy source (e.g., the testes, lens, and 

nervous system in humans and dogs) (Yount et al., 1982; Stacpoole et al., 1979; Katz et al., 

1981; Cicmanec et al., 1991). Mammalian embryos also rely on glycolysis for energy, therefore 

suggesting a common vulnerability for toxicity (Smith et al., 1992). However, there is no direct 

evidence for DCA to affect glycolysis in the embryo. More research into TCE and its 

metabolites is needed to more fully elucidate possible modes of action for the effects observed in 

standard developmental protocols. 

3.6. HAZARD CHARACTERIZATION

3.6.1. Consideration of Causality in the Epidemiologic Evidence

Overall, the epidemiologic studies suggest an association between TCE exposure and 

excess risks of kidney cancer, liver cancer, lympho-hematopoietic cancer, cervical cancer, and 

prostate cancer (Wartenberg et al., 2000). Determining whether an association is one of cause 

and effect involves considering several aspects that are characteristic of cause-and-effect 

relationships (Hill, 1965; Rothman, 1986).45 No aspect is either necessary or sufficient; rather, 

they must be weighed to determine whether the most likely explanation of an observed 

association is cause and effect. 

(1) Strength of the observed association. The finding of large, precise risks increases 

confidence that the association is not likely due to chance, bias, or other factors. A 

modest risk, however, does not preclude a causal association and may reflect a lower 

level of exposure or an agent of lower potency. For TCE, the observed risks are generally 

modest (twofold or less) for cancers of the kidney, liver, and lympho-hematopoietic 

system. An exception is the large odds ratio for kidney cancer (OR=10.8, 95% CI= 

3.4-34.8) reported by Vamvakas et al. (1998). Differences between cohort and case45EPA’s proposed cancer guidelines (U.S. EPA, 1999) use this framework to determine whether a cause-and-effect 

interpretation of the epidemiologic evidence is credible. The proposed guidelines may extend this framework to the 

separate question of determining whether the mode-of-action information is sufficient to establish cause and effect. 

For this question, the key aspect of causality is whether experimental evidence in the laboratory demonstrates that a 

specific sequence of key events leads to the observed tumors and that intervening to prevent a key event will prevent 

tumor formation. 

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control studies, in part, may reflect exposure differences. The observed risks are not 

thought to be attributable to smoking (important in liver cancer) or socioeconomic status 

(important in cervical cancer). 

(2) Consistency of the observed association. An inference of causality is strengthened 

when a pattern of elevated risks is observed across several independent studies. Some 

degree of consistency was observed for associations of TCE exposure with cancers of the 

kidney, liver, lympho-hematopoietic system, cervix, and prostate (Wartenberg et al., 

2000). Consistency is strongest for kidney cancer, supported by tier-I, tier-II, and tier-III 

mortality (but not tier-III incidence) studies and case-control studies (see Section 3.4.2). 

For liver cancer, consistent results were observed in tier-I and tier-II but not tier-III 

studies (see Section 3.4.1). For lympho-hematopoietic cancer, associations observed in 

the tier-I studies were supported by the ecologic studies of residential drinking water 

exposure, though not by the tier-II or tier-III studies (see Section 3.4.4). Cervical cancer, 

though sparsely reported, is elevated in tier-I incidence studies and tier-III mortality 

studies (see Section 3.4.5). Prostate cancer is elevated in the tier-I studies (see Section 

3.4.5). 

(3) Specificity of the observed association. Traditionally, specificity has been defined in 

terms of one cause, one disease (Hill, 1965). TCE causes cancer at several sites in rats 

and mice; hence, there is no expectation that TCE would be associated with only one 

human cancer. Many agents cause cancer at multiple sites, and many cancers have 

multiple causes. Evidence of specificity may come from the recent observation of 

multiple mutations of the VHL tumor suppressor gene, primarily C>T changes including 

nucleotide 454, in renal cell carcinoma patients with high, prolonged TCE exposure 

(Brauch et al., 1999; Brüning et al., 1997b) (see Section 3.5.2). Key research related to 

specificity would include investigation of VHL gene mutations in other cohorts exposed 

to TCE (see Section 5). 

(4) Temporal relationship of the observed association. A causal interpretation is 

strengthened when exposure is known to precede development of the disease. 

Associations between TCE exposure and several forms of cancer are supported primarily 

by cohort and case-control studies, in which the temporal relationship is well described. 

All drinking water studies, except the case-control analysis by MA-DOH (1997), are 

ecologic or prevalence studies, in which knowledge of the temporal relationship is 

lacking. For this reason, the conclusions place greater weight on the cohort and casecontrol studies. 

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(5) Biologic gradient (exposure-response relationship). A clear exposure-response 

relationship strongly suggests cause and effect. For TCE, biologic gradients are only 

sporadically observed, though the number of site-specific cancers in a study is often too 

small to identify biologic gradients. For kidney cancer, only the studies by Morgan et al. 

(1998) and Vamvakas et al. (1998) observed exposure-response relationships. 

Both the urinary biomarker studies and the Blair et al. (1998) study observed liver cancer 

incidence to increase with exposure. Anttila et al. (1995) observed incidence to increase with 

time since first exposure, with the largest risk among those with the longest time since first 

exposure (SIR=6.1, 95% CI= 2.8-17.7). Similar findings were not observed for liver cancer 

mortality; however, liver cancer incidence is considered more definitive because liver cancer 

mortality is extensively miscoded on death certificates. 

Exposure-response analyses presented in the drinking water studies collectively suggest 

that greater exposure to drinking water contaminated with TCE and other chlorinated solvents is 

associated with lymphatic cancer, particularly leukemia. Studies by Cohn et al. (1994), Fagliano 

et al. (1990), and MA-DOH (1997) reported exposure-response relationships between drinking 

water exposure to TCE and other chlorinated solvents and the risk of lympho-hematopoietic 

cancer, particularly childhood leukemia and non-Hodgkin’s lymphoma. 

For cervical cancer, Anttila et al. (1995) and Blair et al. (1998) observed incidence to 

increase with exposure, with a significantly elevated risk among those with the highest urinary 

TCA levels in the Anttila study (SIR=4.4, 95% CI= 1.4-10.1). Comparison with internal controls 

in the Blair study suggests that TCE may be the etiologic agent rather than a confounder such as 

socioeconomic status. 

(6) Biologic plausibility. Section 3.5 discusses the many recent mechanistic studies 

investigating TCE’s carcinogenic effects in rats and mice and their relevance to humans. The 

mechanistic results to date indicate that TCE-induced carcinogenesis is complex, involving 

multiple carcinogenic metabolites acting through multiple mechanisms, all of which have 

relevance to humans (see Section 3.5). 

(7) Coherence. A cause-and-effect interpretation of the human data does not conflict 

with other lines of evidence. The strongest associations between TCE exposure and human 

cancer are for the kidney, liver, and lympho-hematopoietic system, sites where TCE causes 

cancer in either rats or mice. In both humans and laboratory animals, TCE can cause adverse 

effects other than cancer in the kidney, liver, and immune system, and it is biologically plausible 

that some of these effects may play a role in the development of cancer of the kidney, liver, and 

lympho-hematopoietic system, respectively (see Sections 3.5.2, 3.5.1, 3.5.4). The associations 

between human cervical and prostate cancer have no suitable animal models to compare. 

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1 (8) Experimental evidence (from human populations). Experimental evidence is

2 seldom available from human populations (Rothman, 1986) and exists only when conditions of

3 human exposure are altered to create a “natural experiment” at different levels of exposure. For

4 example, leukemia cases became more evenly distributed throughout the town of Woburn after

5 closure of two wells contaminated with TCE and other solvents (MA-DOH, 1997).

6 (9) Analogy. The pattern of effects associated with TCE, particularly in the liver, has

7 similarities to those of several other chlorinated solvents and to mixed-solvent exposures.

8

9 3.6.1.1. Synthesis of the Epidemiologic Evidence

10 Together, these nine aspects of causality suggest that a cause-and-effect association

11 between TCE exposure and cancer is credible. Among the different tumor sites, support is

12 strongest for kidney cancer, followed by liver and lympho-hematopoietic cancer and then cervical

13 and prostate cancer.

14 The strength of this conclusion is tempered by some still-open questions about TCE’s

15 cancer potential. The statistical significance of the results of the joint epidemiologic analysis

16 could change with omission of one study or another. Most studies lack quantitative exposure

17 information. More research is needed on mutations of the VHL gene, particularly in renal cell

18 carcinoma patients not exposed to TCE. In addition, a nested case-control study could help

19 resolve questions about causality, as this study design has the potential to examine the effects of

20 lifestyle factors and exposures to mixtures of solvents that may share common metabolites and

21 modes of action (see Section 5).

22

23 3.6.2. Weight of Evidence Under the Current and Proposed Cancer Guidelines

24 TCE has been extensively tested in animals, with mice developing liver tumors, lung

25 tumors, and lymphomas, and rats developing kidney tumors and testicular tumors. 

26 Epidemiologic studies, considered as a whole, have associated TCE exposure with excess risks

27 of kidney cancer, liver cancer, lympho-hematopoietic cancer, cervical cancer, and prostate

28 cancer. Weight-of-evidence characterizations have had to address issues concerning the strength

29 of the human evidence and the relevance of the animal tumors to humans.

30 With this assessment, the weight of the epidemiologic evidence of TCE’s potential

31 carcinogenicity has become stronger than before (see Section 3.4). To bridge the opposing views

32 of the past, this assessment commissioned a joint analysis of the epidemiologic studies

33 (Wartenberg et al., 2000). This joint analysis used a statistically based weight-of-evidence

34 approach that stratified the available studies into tiers according to how well each study’s results
46Tier I studies are those in which TCE exposure has been inferred for individual study subjects and in which it is

best characterized. Tier II studies are those in which there is putative TCE exposure, but individuals are not

identified as uniquely exposed to TCE. Tier III studies are the studies of dry cleaning and laundry workers in which

subjects are exposed to a variety of solvents including TCE (Wartenberg et al., 2000).

47Inverse variance gives greater weight to larger studies that yield more precise estimates. Study results are

averaged, with each study result weighted (i.e., multiplied) by a factor that is inversely proportional to the study

result’s variance.

48The VHL gene normally suppresses renal cell carcinomas. Mutations to this gene have been noted in kidney

cancers and may be an important risk factor and mode-of-action for chemically induced renal cell cancer (Lash et al.,

2000b). To illustrate the difference between “mode” of action and “mechanism” of action, knowledge that VHL

gene mutations are involved may be enough to identify a plausible “mode” of action, whereas knowing how such

modifications induce subsequent events leading to kidney cancer would be needed to identify the “mechanism” of

action.

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can be associated with TCE exposure specifically.46 1 Within each tier, study results were

weighted according to inverse variance.47 2 The joint analysis found that the most consistent and

3 compelling results are statistically significant increased incidences of kidney cancer and liver

4 cancer in workers exposed to TCE. These are followed by lympho-hematopoietic cancer,

5 cervical cancer, and prostate cancer. There are few studies of highly exposed populations,

6 consequently, the magnitude of response is generally less than a twofold increase. The joint

7 analysis leads to a conclusion that these increases are statistically significant, thus, TCE may be

8 capable of causing cancer in humans at multiple sites.

9 The epidemiologic studies have recently been augmented by molecular information, in

which multiple mutations of the VHL tumor suppressor gene,48 10 primarily C>T changes including

11 nucleotide 454, were found in renal cell carcinoma patients with high, prolonged TCE exposure

12 (Brüning et al., 1997b) (see Section 3.5.2). A followup study found mutations of the VHL gene

13 in 75% of TCE-exposed workers with renal cell carcinoma, with an association between number

14 of mutations and TCE exposure level (Brauch et al., 1999). Adding specificity to this

15 association, a C>T change at nucleotide 454 was found in 13 of 33 renal cell carcinoma patients

16 with TCE exposure; this specific mutation was not found in those without identifiable TCE

17 exposure.

18 The mechanistic research into the mode-of-action for each animal tumor site has begun to

19 link TCE with disturbances in cell signaling and carbohydrate metabolism, which can lead to

20 human cancer and other diseases (see Section 3.5). Subject to dose-response adjustments for

21 relative human-to-animal sensitivity (see Sections 4.5.2, 4.5.3), this research makes it plausible

22 that TCE acts through mechanisms that can cause cancer in humans.

23 EPA’s proposed cancer guidelines provide that molecular information can be used in

24 choosing a weight-of-evidence descriptor. TCE could be described as “carcinogenic to humans,”

25 supported by (1) association of TCE exposure with increased risk of human kidney cancer, liver
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1 cancer, lympho-hematopoietic cancer, cervical cancer, and prostate cancer; (2) induction of

2 kidney tumors and testicular tumors in rats and liver tumors, lung tumors, and lymphomas in

3 mice; and (3) induction of multiple VHL gene mutations, primarily C>T changes including

4 nucleotide 454, in a human population. At this time, however, fuller identification of the mode5 of-action and associated key events is lacking (see Section 5). It is not known whether the C>T

6 change at nucleotide 454 is a marker of exposure, a key event leading to kidney cancer, or

7 whether other factors selectively favor the growth of cells with this change. Functional alteration

8 of the VHL gene can be a mode of kidney cancer induction; whether this is how TCE induces

9 kidney cancer has not been established.

10 Alternatively, TCE could be described as “likely to be carcinogenic to humans,”

11 supported by evidence associating TCE exposure with human cancer and strong evidence of

12 carcinogenicity in animals involving processes that have relevance for humans. Among “likely”

13 carcinogens, the evidence for TCE is strong and is drawn from several types of information (see

14 Sections 3.2–3.5).

15 At this time, a strong characterization as “highly likely to be carcinogenic to humans” is

16 more appropriate, given the still-open questions about TCE’s cancer potential. The statistical

17 significance of the results of the joint epidemiologic analysis could change with omission of one

18 study or another. This does not decrease confidence in the results of the joint analysis, but

19 emphasizes that the TCE studies collectively have only recently accumulated enough power to

20 begin to detect associations between moderate TCE exposures and some relatively common

21 cancers. On the other hand, the new evidence associating TCE exposure with a transformation at

22 nucleotide 454 is striking evidence specific to TCE exposure, putting a “genetic fingerprint” that

23 associates such kidney tumors with TCE exposure. Replication of this result in another cohort

24 showing kidney tumors could warrant a future description of TCE as “carcinogenic to humans”

25 (see Section 5).

26 Under the current (1986) cancer guidelines, TCE would be classified as a “probable

27 human carcinogen” (group B1), with “limited” human evidence and “sufficient” animal evidence

28 of carcinogenicity. The principal changes from the controversy of the late 1980s are the stronger

29 weight of epidemiologic evidence (see Section 3.4) and the new mechanistic information

30 suggesting that TCE’s modes of action may be relevant to humans (see Section 3.5).
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Table 3-1. Liver tumors in mice exposed to TCE

Hepatocellular carcinomas and adenomas after 103 weeks gavage exposure, beginning at 8

weeks of age (NTP, 1990)

Administered dose (mg/kg-d) Vehicle ctl 1,000

Male B6C3F1 mice ** 14/48 (29%) 39/50 (78%)

Female B6C3F1 mice ** 6/48 (13%) 22/49 (45%)

Hepatocellular carcinomasa

 after 90 weeks gavage exposure, beginning at 5 weeks of age

(NCI, 1976)

Dose group Vehicle ctl Low doseb High dosec

Male B6C3F1 mice ** 1/20 (5%) 26/50 (52%) 31/48 (65%)

Female B6C3F1 mice ** 0/20 (0%) 4/50 (8%) 11/47 (23%)

Hepatomas after 78 weeks inhalation exposure, beginning at 11-12 weeks of age (Maltoni et

al., 1986)

Administered daily concentration

(mg/m3

)

d Control 112.5 337.5 675

Male Swiss micee

 ** 4/66 (6%) 2/53 (4%) 8/59 (14%) 13/61 (21%)

Female Swiss micee 0/84 (0%) 0/89 (0%) 0/86 (0%) 1/86 (1%)

Male B6C3F1 micef

 ** 1/59 (2%) 1/31 (3%) 3/38 (8%) 6/37 (16%)

Female B6C3F1 micef

 ** 3/88 (3%) 4/89 (4%) 4/88 (5%) 9/85 (11%)

Male B6C3F1 miceg 17/77 (22%) 19/47 (40%) 27/67 (40%) 21/63 (33%)

**Statistically significant by Cochran-Armitage trend test (p<0.05). a

Hepatocellular adenomas were not reported.

b

Low dose is 1,200 mg/kg-d for male mice, 900 mg/kg-d for female mice (5 d/wk).

c

High dose is 2,400 mg/kg-d for male mice, 1,800 mg/kg-d for female mice (5 d/wk).

d

Equivalent to 100, 300, 600 ppm (100 ppm=540 mg/m3), adjusted for 7 hr/d, 5 d/wk exposure. e

Mice alive at week 43, when first hepatoma was observed.

f

Mice alive at week 33, when first hepatoma was observed (expt BT306).

g

Mice alive at week 68, when first hepatoma was observed (expt BT306 bis).

Sources: NTP (1990) tables 8, 9; NCI (1976) table VIII; Maltoni et al (1986) IV/VI table 14,

IV/VII table 14, IV/VIII table 14.
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Table 3-2. Kidney tumors in rats, adjusted for reduced survivala

Tubular cell adenomas and adenocarcinomas after 103 weeks gavage exposure, beginning

at 6.5-8 weeks of age (NTP, 1988, 1990)

Administered dose (mg/kg-d) Untreated ctl Vehicle ctl 500 1,000

Male ACI rats 0/48 (0%) 0/46 (0%) 1/29 (3%) 0/22 (0%)

Male August rats 0/47 (0%) 0/47 (0%) 2/35 (6%) 1/33 (3%)

Male Marshall rats 2/43 (4%) 0/44 (0%) 1/25 (4%) 1/28 (4%)

Male Osborne-Mendel rats ** 0/46 (0%) 0/47 (0%) 6/44 (14%) 2/33 (6%)

Male F344/N rats ** 0/48 (0%) 0/46 (0%) 2/46 (4%) 3/33 (9%)

Pooled males ** 2/232 (1%) 0/230 (0%) 12/179 (7%) 7/149 (5%)

Female ACI rats 0/46 (0%) 0/43 (0%) 3/36 (6%) 1/31 (3%)

Female August rats 0/47 (0%) 1/47 (2%) 4/42 (10%) 0/34 (0%)

Female Marshall rats 1/48 (2%) 1/49 (2%) 2/44 (5%) 1/32 (3%)

Female Osborne-Mendel rats 1/47 (2%) 0/43 (0%) 0/44 (0%) 1/45 (2%)

Female F344/N rats 0/46 (0%) 0/46 (0%) 0/45 (0%) 1/44 (2%)

Pooled females ** 2/234 (1%) 2/228 (1%) 9/211 (4%) 4/186 (2%)

Renal tubuli adenocarcinomas after 104 weeks inhalation exposure, beginning at 12 weeks

of age (Maltoni et al., 1986)

Administered daily

concentration (mg/m3

)

b Control 112.5 337.5 675

M Sprague-Dawley rats ** 0/120 (0%) 0/118 (0%) 0/116 (0%) 4/122 (3%)

F Sprague-Dawley rats 0/139 (0%) 0/128 (0%) 0/127 (0%) 1/127 (1%)

**Statistically significant by Cochran-Armitage trend test (p<0.05). a

ACI, August, Marshall, Osborne-Mendel, and F344/N rats alive at week 57, Sprague-Dawley

rats at week 47 (expt BT304) or week 62 (expt BT304 bis).

b

Equivalent to 100, 300, 600 ppm (100 ppm=540 mg/m3), adjusted for 7 hr/d, 5 d/wk exposure.

Sources: NTP (1988) Tables A2, C2, E2, G2; NTP (1990) table A3; Maltoni et al. (1986) IV/IV

Table 19, IV/V Table 19.
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Table 3-3. Malignant lymphomas in mice exposed to TCE

Malignant lymphomas after 103 weeks gavage exposure, beginning at 8 weeks of age (NTP,

1990)

Administered dose (mg/kg-d) Vehicle ctl 1,000

Male B6C3F1 mice 11/50 (22%) 13/50 (26%)

Female B6C3F1 mice 7/48 (15%) 13/49 (27%)

Lymphosarcomas and reticulum cell sarcomas after 90 weeks gavage exposure, beginning

at 5 weeks of age (NCI, 1976)

Dose group Vehicle ctl Low dosea High doseb

Male B6C3F1 mice 1/20 (5%) 4/50 (8%) 2/48 (4%)

Female B6C3F1 mice 1/20 (5%) 5/50 (10%) 5/47 (11%)

Malignant lymphomas after 78 weeks inhalation exposure (Henschler et al., 1980)

Administered daily concentration

(mg/m3

)

c Control 96 480

Male Han:NMRI mice 7/30 (23%) 7/29 (24%) 6/30 (20%)

Female Han:NMRI mice ** 9/29 (31%) 17/30 (57%) 18/28 (64%)

**Statistically significant by Cochran-Armitage trend test (p<0.05). a

Low dose is 1,200 mg/kg-d for male mice, 900 mg/kg-d for female mice (5 d/wk).

b

High dose is 2,400 mg/kg-d for male mice, 1800 mg/kg-d for female mice (5 d/wk).

c

Equivalent to 100 and 500 ppm (100 ppm=540 mg/m3), adjusted for 6 hr/d, 5 d/wk exposure.

Sources: NTP (1990) tables 8, 9; NCI (1976) table XXXa; Henschler et al (1980) table 3a.
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Table 3-4. Testicular tumors in male rats exposed to TCE, adjusted for reduced survivala

Interstitial cell tumors after 103 weeks gavage exposure, beginning at 6.5-8 weeks of age

(NTP, 1988, 1990)

Administered dose (mg/kg-d) Untreated ctl Vehicle ctl 500 1,000

Male ACI rats 38/45 (84%) 36/44 (82%) 23/26 (88%) 17/19 (89%)

Male August rats 36/46 (78%) 34/46 (74%) 30/34 (88%) 26/30 (87%)

Male Marshall rats ** 16/46 (35%) 17/46 (37%) 21/33 (64%) 32/39 (82%)

Male Osborne-Mendel rats 1/30 (3%) 0/28 (0%) 0/25 (0%) 1/19 (5%)

Male F344/N rats 44/47 (94%) 47/48 (98%) 47/48 (98%) 32/44 (73%)

Leydig cell tumors after 104 weeks inhalation exposure, beginning at 12 weeks of age

(Maltoni et al., 1986)

Administered daily

concentration (mg/m3

)

b Control 112.5 337.5 675

M Sprague-Dawley rats ** 6/114 (5%) 16/105 (15%) 30/107 (28%) 31/113 (27%)

**Statistically significant by Cochran-Armitage trend test (p<0.05). a

ACI rats alive at week 70, August rats at week 65, Marshall rats at week 32, Osborne-Mendel

rats at week 97, F344/N rats at week 32, Sprague-Dawley rats at week 81 (expt BT304) or week

62 (expt BT304 bis).

b

Equivalent to 100, 300, 600 ppm (100 ppm=540 mg/m3), adjusted for 7 hr/d, 5 d/wk exposure.

Sources: NTP (1988) Tables A2, C2, E2, G2; NTP (1990) Table A3; Maltoni et al. (1986) IV/IV

Table 21, IV/V Table 21.
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Table 3-5. Lung tumors in mice exposed to TCE

Pulmonary adenomas and adenocarcinomas after 78 weeks inhalation exposure, beginning

at 11-12 weeks of age (Maltoni et al., 1986)

Administered daily concentration

(mg/m3) Control 112.5 337.5 675

Male Swiss mice ** 10/90 (11%) 11/90 (12%) 23/90 (26%) 27/90 (30%)

Female Swiss mice 15/90 (17%) 15/90 (17%) 13/90 (14%) 20/90 (22%)

Male B6C3F1 mice 2/90 ( 2%) 2/90 ( 2%) 3/90 ( 3%) 1/90 ( 1%)

Female B6C3F1 mice ** 4/90 ( 4%) 6/90 ( 7%) 7/90 ( 8%) 15/90 (17%)

Pulmonary adenomas and adenocarcinomas by inhalation (Fukuda et al., 1983)

Administered daily concentration

(mg/m3

) Control 56 168 504

Female ICR mice ** 6/49 (12%) 5/50 (10%) 13/50 (26%) 11/46 (24%)

Carcinomas, adenocarcinomas, and adenomas of the lung or alveoli after 90 weeks gavage

exposure, beginning at 5 weeks of age (NCI, 1976)

Dose group Vehicle ctl Low dosea High doseb

Male B6C3F1 mice 0/20 (0%) 5/50 (10%) 2/48 ( 4%)

Female B6C3F1 mice 1/20 (5%) 4/50 ( 8%) 7/47 (15%)

**Statistically significant by Cochran-Armitage trend test (p<0.05). a

Low dose is 1,200 mg/kg-d for male mice, 900 mg/kg-d for female mice (5 d/wk).

b

High dose is 2,400 mg/kg-d for male mice, 1,800 mg/kg-d for female mice (5 d/wk).

Sources: Maltoni et al., 1986, Tables 48, 51; Fukuda et al. (1983); NCI (1976) Table XXV.
8/1/01 DRAFT–DO NOT CITE OR QUOTE 3-55

Figure 3-1. Metabolism of Trichloroethylene (TCE).
49Pharmacokinetic models simulate the relationship between external exposure levels and the biologically effective

dose at a target tissue. Pharmacokinetic models take into account absorption, distribution, metabolism, and

elimination of the administered chemical and its metabolites. Pharmacodynamic models simulate the relationship

between a biologically effective dose and the occurrence of a disease response.

50Dose metrics are alternative ways of describing dose. They involve what is being measured (parent compound,

metabolite, or biomarker), where it is measured (whole body, blood, or specific tissue), and how it is measured

(cumulative, average, or peak exposure). For example, total administered TCE and average TCA in liver are dose

metrics.

51An empirical dose-response model is one based on fitting a curve to data. (They are sometimes called “curvefitting” models or “benchmark dose” models.) In contrast to mechanism-based dose-response models, an empirical

model is not based on specific knowledge about the biological mechanisms leading to disease.

52A mechanism-based dose-response model (also called a biologically based dose-response model) is a

pharmacodynamic model whose mathematical structure reflects the ascertained mode-of-action and whose

parameters are measured experimentally.

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4. DOSE-RESPONSE ASSESSMENT AND CHARACTERIZATION

1 4.1. GENERAL CONSIDERATIONS AND APPROACH

2 This dose-response assessment updates the cancer slope factor for TCE and develops an

3 oral RfD and an inhalation RfC for effects other than cancer. These effects include

4 neurotoxicity, immunotoxicity, developmental toxicity, liver toxicity, kidney toxicity, endocrine

5 effects, and several forms of cancer (see Section 3.4). The overall approach is to develop

6 alternative estimates using different studies and modeling approaches, then to choose values that

7 are supported by several sources of data and lines of reasoning. This provides a measure of

8 confidence that the risk estimates are robust and not likely to be substantially changed by a new

9 study or analysis.

10 This dose-response assessment draws from the state-of-the-science papers that appear in

volume 1. Pharmacokinetic models49 were developed to provide dose metrics50 11 for the RfD, RfC,

12 and cancer assessments (Fisher, 2000; Clewell et al., 2000). Statistical analyses improved these

13 models by calibrating them to fit more data sets, then quantifying the uncertainty in each dose

14 metric (Bois, 2000a,b). Analyses supporting the RfD and RfC applied pharmacokinetic models

and empirical dose-response models51 15 whenever suitable data were available (Barton and

16 Clewell, 2000). The cancer assessment pursued several lines of analysis and considered both

17 linear and nonlinear approaches, based on knowledge about mode-of-action at each tumor site

18 (see Section 3.5). Empirical dose-response models were fitted to data on liver tumors and lung

19 tumors in mice and kidney tumors and testicular tumors in rats (Rhomberg, 2000). A

mechanism-based dose-response model52 20 was developed for testing hypotheses about the mouse

21 liver tumor response (Chen, 2000). Slope factors for kidney tumors, liver tumors, and

22 non-Hodgkin’s lymphoma were also developed from the epidemiologic data.
53ATSDR calls these minimal risk levels (MRLs), defined as “an estimate of the daily human exposure to a hazardous

substance that is likely to be without appreciable risk of adverse noncancer health effects over a specified duration of

exposure” (ATDSR, 1997).

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1 The assessment for effects other than cancer follows EPA’s methods for developing RfDs

2 (Barnes and Dourson, 1988) and RfCs (U.S. EPA, 1994). Despite the existence of numerous

3 studies, however, the database for developing an RfD or RfC is problematic. ATSDR did not

derive analogous chronic-duration levels53 4 for TCE, viewing the chronic studies as limited by

5 inadequate characterization of exposure, inadequate quantification of results, or lack of endpoints

6 suitable for deriving chronic levels (ATSDR, 1997). This assessment, through modeling and

7 comparing results for different adverse effects, species, exposure durations, and exposure routes,

8 has overcome some data gaps and developed an RfD and RfC that are supported by multiple

9 lines of reasoning.

10 The cancer assessment is consistent with the approach of EPA’s proposed cancer

11 guidelines (U.S. EPA, 1996). These guidelines encourage use of models to incorporate a wider

12 range of experimental data and use of mode-of-action information to guide modeling and

13 extrapolation approaches. This assessment develops alternative risk values from many sources

14 before comparing and reconciling them. Because information about sensitive populations,

15 children, and cumulative risks indicates the potential for vast human variation in risk, a range of

16 cancer estimates is described, with guidance on how to choose a particular estimate based on risk

17 factors in the exposed population and exposure scenario.

18

19 4.2. DOSIMETRY MODELING

20 This dose-response assessment begins by expressing doses in common terms to facilitate

21 better comparisons across studies, health effects, species, exposure durations, and exposure

22 routes. Reflecting the importance of metabolism to TCE-induced toxicity, doses are estimated

23 from pharmacokinetic models whenever data are suitable. Otherwise, doses are scaled from

animals to humans based on equivalence of mg/kg3/4 24 -d (U.S. EPA, 1992). Pharmacokinetic

25 models can account for high- to low-dose nonlinearity and animal-to-human differences in

26 metabolism. The use of pharmacokinetic modeling and dose scaling in developing RfDs is

27 relatively new, representing a move toward harmonizing RfD methods with those already in use

28 for RfCs and cancer assessments.

29
54Area-under-the-curve refers to the tissue concentration of a chemical integrated over time. It is a measure of the

long-term average concentration of a chemical in a specific tissue.

55Pharmacokinetic models specify compartments to represent different tissues or groups of tissues, for example,

liver, fat, and the rest of the body.

56Allometric scaling is a method for calculating equivalent doses across species. It scales volumes according to body

weight, blood flow rates and metabolic capacities according to the 3/4 power of body weight, and rate constants

according to the inverse 1/4 power of body weight.

57Bayesian statistical methods regard a model’s parameters as random variables with a probability distribution. New

data can alter what is thought to be the best distribution for describing each parameter. The distribution based only

on prior information and assumptions is called the prior distribution. Analysis of new data yields a posterior

distribution that reconciles the prior information and assumptions with the new data.

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1 4.2.1. Pharmacokinetic Modeling

2 A pharmacokinetic model for ingested TCE in male mice is described in a state-of-thescience paper by Fisher (2000). The model predicts area-under-the-curve54 3 in the liver for TCA

and DCA. Compartments55 4 include TCE in liver, lung, kidney, fat, gut, rapidly perfused tissues,

5 and slowly perfused tissues. Metabolite submodels include CH, TCA, TCOH, TCOG, and DCA

6 in liver, lung, kidney, and the rest of the body; DCVC and DCVG were not modeled. The model

7 simplifies metabolism by modeling it only in the liver. Model parameters were taken from

8 published literature, measured experimentally, or fitted by running the model with data at 1,200

9 mg/kg. The model was validated against data at 300, 600, and 2,000 mg/kg, suggesting the

10 ability of the model to make predictions below the dose where it was developed. Parameters

11 were scaled according to body weight. This mouse model was then adapted for inhaled TCE in

12 human males and females. The human model does not include CH and DCA, because CH was

13 not detected in experimental subjects and DCA was detected only intermittently.

14 Another pharmacokinetic model for ingested and inhaled TCE in male mice, rats, and

15 humans is described in a state-of-the-science paper by Clewell et al. (2000). This model predicts

16 metabolite areas-under-the-curve in plasma. Compartments include TCE in liver, lung,

17 tracheobronchial region, fat, gut, rapidly perfused tissues, and slowly perfused tissues. One18 compartment metabolite submodels include TCA, TCOH, TCOG, and DCA. The model also

19 includes descriptions of GST metabolism in the kidney, CH metabolism and clearance in the

20 lung, GST metabolism of TCE to DCVC in the liver, activation of $-lyase and clearance by NAT

21 in the kidney, biliary excretion of TCOG, and enterohepatic recirculation of TCOH. Model

22 parameters were taken from published literature or fitted by running the model with selected data

sets. Parameters were scaled allometrically.56 23

24 Statistical analyses of these pharmacokinetic models are discussed in two additional stateof-the-science papers (Bois, 2000a,b). These analyses use a Bayesian57 25 statistical framework and
58Markov-chain Monte-Carlo simulation is an iterative process that can be used within a Bayesian statistical

framework to (1) sample each model’s parameters from their prior distributions, (2) fit that model with the sampled

parameters to several additional experimental data sets, and (3) compare the model’s predictions with the

experimental results to obtain posterior distributions for the model’s parameters that improve the model’s fit. These

steps are repeated thousands of times until each parameter’s posterior distribution converges to a more robust

distribution that reflects a wider database.

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Markov-chain Monte-Carlo58 1 simulation to refine the models by using more data sets to estimate

2 each model’s parameters. The result is a set of calibrated models that better fits a wider range of

3 experimental data. In some cases the calibrated parameters are quite different from the originals,

4 thus, substantial information has been gained by fitting the models to additional experimental

5 data sets. Dose estimates from the calibrated models were used in subsequent state-of-the6 science papers (Chen, 2000; Rhomberg, 2000) supporting the cancer assessment, whereas dose

7 estimates from the Clewell et al. (2000) pharmacokinetic model were used in the state-of-the8 science paper supporting the RfD and RfC (Barton and Clewell, 2000).

9 EPA’s National Exposure Research Laboratory is developing a Dose Estimating

10 Exposure Model, a model architecture that simulates internal doses for different chemicals and

11 species. As part of this assessment, this model has been configured for TCE, initially following

12 the structure of the Fisher and Clewell models. This new TCE model is being calibrated by

13 comparing its predictions to several experimental data sets. A notable feature of this model is its

14 capacity to simulate simultaneous exposure to TCE, TCA, and DCA. This will be particularly

15 useful in future site-specific applications (for example, at Superfund sites or in assessing the

16 safety of a drinking water supply) where people are exposed to TCE along with TCA and DCA

17 from other solvents or other sources.

18

19 4.2.2. Uncertainty Analyses

20 Another product of the statistical analyses is a quantitative description of uncertainty in

21 the dose estimates (Bois, 2000b). Table 4-1 summarizes distributions of potential dose metrics

22 for modeling liver, lung, and kidney tumors from the calibrated model (Bois, 2000b) on the basis

23 of the Clewell et al. (2000) pharmacokinetic model, the only model to calculate estimates outside

24 the liver. From Table 4-1 it is apparent that lung and kidney dose metrics are quite uncertain,

25 with 95% confidence intervals spanning more than a 5,000-fold range. This may be due to the

26 difficulty in measuring and modeling a transient metabolite such as CH, the possibility that

27 plasma CH may not be a good surrogate for CH accumulated in Clara cells of the lung, or the

28 poorer database for estimating GST metabolites in the kidney. Whatever the reason, this is more

29 uncertainty than is generally present in risk analyses supporting public health decisions. One

30 approach to addressing this uncertainty would be to take lung and kidney dose estimates from the

31 lower end, perhaps the first percentile, of their distributions; this would effectively increase risk
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1 estimates several hundred-fold above the median. Rather than make such a large adjustment, this

2 assessment uses default RfC dosimetry models (U.S. EPA, 1994) when modeling lung tumors by

3 inhalation and the default 3/4-power scaling factor when modeling kidney tumors by ingestion. 

4 Liver dose metrics, in contrast, are less uncertain (see Table 4-1); consequently, the cancer

5 assessment uses the calibrated model’s median dose estimates for the liver. In this way,

6 uncertainty analysis is used to distinguish between uncertain applications (lung and kidney) and

7 more robust applications (liver), so that pharmacokinetic modeling is used when the results are

8 robust and other methods are considered when there is too much uncertainty.

9 The statistical analyses also reveal several differences between males and females. 

10 Females have a significantly lower alveolar ventilation rate (beyond that explained by allometric

11 scaling), higher TCOH body-to-blood partition coefficient, lower TCA body-to-blood partition

coefficient, higher Vmax 12 /Km ratio for TCOH glucuronidation, higher conversion of TCOH to

13 TCA, and higher urinary excretion of TCA (Bois, 2000a). These insights are possible because

14 Fisher (2000) collected and modeled data from both males and females. The number of variables

15 with significant differences shows the complexity of overall human variation.

16

17 4.2.3. Route Extrapolation

18 Pharmacokinetic models can be used to investigate questions about extrapolation from

19 one exposure route to another. This can be done by determining oral and inhalation exposures

20 that yield equal internal doses of the active agent.

21 For this assessment, the pharmacokinetic model developed at EPA’s National Exposure

22 Research Laboratory calculated that chronic oral exposure to TCE at 1 mg/kg-d yields similar

TCA area-under-the-curve as chronic inhalation exposure to TCE at 75 mg/m3 23 . This relationship

24 was investigated at several dose levels near 1 mg/kg-d and found to be approximately linear at

25 this dose and below. The relationship is different, however, when matching DCA area-under26 the-curve as the internal dose. Chronic oral exposure to TCE at 1 mg/kg-d yields similar DCA

area-under-the-curve as chronic inhalation exposure to TCE at 3 mg/m3 (instead of 75 mg/m3 27 ). 

28 This 25-fold uncertainty highlights the importance of further research to identify the appropriate

29 internal dose metric for each toxic effect (see Section 5).

30

31 4.3. ORAL REFERENCE DOSE FOR EFFECTS OTHER THAN CANCER

32 For effects other than cancer, risk assessments have not attempted to describe dose33 response curves in the range of environmental exposures. Rather, the focus has been to estimate

34 an exposure level where there is little concern for adverse effects. This RfD is derived through a

35 process of (1) considering all studies and selecting the critical effects that occur at the lowest
59Point of departure denotes a dose at the lower end of the observed dose-response curve where extrapolation to

lower doses begins. For effects other than cancer, the point of departure is either a NOAEL (no-observed-adverseeffect level), a LOAEL (lowest-observed-adverse-effect level) if no NOAEL can be identified, or a modeled point

(for example, an LED10 or LED01) if the data are suitable for curve-fitting. For cancer, the point of departure is an

LED10 (or lower point if one can be reliably estimated) for tumors, or for a key tumor precursor when there is

information to describe the mode-of-action.

60A NOAEL (no-observed-adverse-effect level) is the highest experimental dose without a statistically or biologically

significant effect. Note that at this dose there may be effects that are not biologically significant (i.e., not judged

adverse), or there may be biologically significant effects that are not statistically significant. Statistical significance

is affected by study design and sample size, consequently, a NOAEL in one study may show a statistically significant

adverse effect in another study with a different design or larger sample.

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dose, (2) selecting a dose (or point of departure59 1 ) at which the critical effect either is not

2 observed or would occur at a relatively low incidence (for example, 10%), and (3) reducing this

3 dose by uncertainty factors to reflect differences between study conditions and conditions of

4 human environmental exposure.

5

6 4.3.1. Critical Effects

7 Many different toxic effects are associated with oral TCE exposure, as TCE can disrupt

8 fundamental cellular processes through multiple metabolites and mechanisms (see Section 3.4). 

9 At higher doses (above approximately 100 mg/kg-d), targets of oral TCE toxicity include the

10 liver, kidney, nervous system, reproductive system, and developing fetus (ATSDR, 1997). 

11 Effects have been observed at these sites in acute studies (2 weeks or less), intermediate studies,

12 or chronic studies (1 year or more). At lower doses (below approximately 100 mg/kg-d), there

13 are fewer studies, but effects continue to be observed in several systems, often in subchronic

14 studies. At the lowest doses tested (approximately 1–10 mg/kg-d), effects are observed in the

15 liver, kidney, and developing fetus. These are considered to be the critical effects of oral TCE

16 exposure.

17

18 4.3.2. Point of Departure

19 Table 4-2 summarizes the results of oral studies conducted at the lower doses. To

20 compare studies, doses are expressed in human-equivalent terms. For liver effects,

21 pharmacokinetic modeling (Clewell et al., 2000) was used to estimate plasma TCA as the dose

22 metric (Barton and Clewell, 2000). For kidney effects, statistical analyses (Bois, 2000b) revealed

23 substantial parameter uncertainty in the pharmacokinetic modeling (see Table 4-1), consequently,

human-equivalent doses were based on equivalence of mg/kg3/4 24 -d (U.S. EPA, 1992). Response

levels are presented as either a NOAEL,60 25 a LOAEL if the study did not identify a NOAEL, or a

modeled LED10 26 if the study results were suitable for modeling (Barton and Clewell, 2000).

27 Adverse effects have been observed in several studies at a human-equivalent dose range

28 of 1–10 mg/kg-d. For the rat kidney there is a chronic NOAEL at 10 mg/kg-d (Maltoni et al.,
61RfDs apply to lifetime human environmental exposure, including sensitive subgroups. Differences between study

conditions and conditions of human environmental exposure may make a dose that appears safe in an experiment not

be safe in the environment. Uncertainty factors account for differences between study conditions and conditions of

human environmental exposure. These include:

(a) Variation from average humans to sensitive humans: RfDs apply to the human population, including

sensitive subgroups, but studies rarely target sensitive humans. Sensitive humans could be adversely

affected at lower doses than a general study population; consequently, general-population NOAELs are

reduced to cover sensitive humans.

(b) Uncertainty in extrapolating from animals to humans: If an RfD is developed from animal studies, the

animal NOAEL is reduced to reflect pharmacokinetic and pharmacodynamic factors that may make humans

more sensitive than animals.

(c) Uncertainty in extrapolating from subchronic NOAELs to chronic NOAELs: RfDs apply to lifetime

exposure, but sometimes the best data come from shorter studies. Lifetime exposure can have effects that

do not appear in a shorter study; consequently, a safe dose for lifetime exposure can be less than the safe

dose for a shorter period. If an RfD is developed from less-than-lifetime studies, the less-than-lifetime

NOAEL is adjusted to estimate a lifetime NOAEL.

(d) Uncertainty in extrapolating from LOAELs to NOAELs: RfDs estimate a dose without appreciable risks,

but sometimes adverse effects are observed at all study doses. If an RfD is developed from a dose where

there are adverse effects, that dose is adjusted to estimate a NOAEL.

(e) Other factors to reflect professional assessment of scientific uncertainties not explicitly treated above,

including completeness of the overall database, minimal sample size, or poor exposure characterization.

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1 1986). For the liver there is a subchronic NOAEL at 1 mg/kg-d in mice (Tucker et al., 1982), a

2 subchronic LOAEL at 1 mg/kg-d in mice (Buben and O’Flaherty, 1985), and a subchronic LED10

3 of 0.6 mg/kg-d in rats (Berman et al., 1995). Among these liver values, 1 mg/kg-d is chosen as

4 the point of departure, supported by these three studies as a dose where liver toxicity can begin to

5 be observed in two species after subchronic dosing. For cardiac anomalies in developing rats,

6 there is a LOAEL at 34 mg/kg-d and a NOAEL at 0.05 mg/kg-d (Dawson et al., 1993). The 700-

7 fold difference between this LOAEL and NOAEL reflects the wide dose spacing in this study,

8 which makes use of this endpoint as a point of departure highly uncertain.

9

10 4.3.3. Uncertainty Factors

11 To address differences between study conditions and conditions of lifetime human

environmental exposure, the point of departure is reduced by uncertainty factors61 12 that reflect

13 different areas of uncertainty.

14 Several studies have identified similar points of departure for adverse liver effects,

15 indicating some degree of confidence in using this effect as a basis for calculating an RfD. There

16 are several sources of uncertainty in using these studies.

(a) Human variation: The NOAELs, LOAELs, and LED10 17 s for adverse liver effects (see

18 Section 4.3.2, Table 4-2) were estimated using a pharmacokinetic model (Barton and

19 Clewell, 2000; Clewell et al., 2000). The parameter uncertainty in these modeled dose

20 estimates (estimated between the 50th and 99th percentiles, see Section 4.2.2, Table 4-1)

21 is 15-fold if plasma TCA is used as the dose metric and 20-fold if plasma DCA is used
62101/2 denotes the square root of 10. On a logarithmic scale, this represents half of a factor of 10. Numerically, 101/2

is approximately equal to 3.1, which is usually rounded to 3 in a final calculation.

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1 (Bois, 2000b, Rhomberg, 2000). Not known is whether either of these dose metrics is an

2 appropriate indicator of liver toxicity. This pharmacokinetic variation is complemented

3 by pharmacodynamic variation (Renwick, 1998). In the absence of data to quantify

human pharmacodynamic variation, a default factor of 101/2 62 4 has been suggested

(Renwick, 1998). Multiplying 15- to 20-fold pharmacokinetic variation by 101/2 5 -fold

6 pharmacodynamic variation indicates overall human variation of about 50-fold (rounded

7 to one significant digit).

8 (b) Animal-to-human uncertainty: This aspect of uncertainty can also be viewed in terms of

9 pharmacokinetic and pharmacodynamic components. Animal-to-human pharmacokinetic

10 uncertainty is reflected in the 15- to 20-fold factor just discussed. Animal-to-human

pharmacodynamic uncertainty is covered by a default factor of 101/2 11 , similar to the

12 practice used for deriving RfCs (U.S. EPA, 1994).

(c) Subchronic-to-chronic uncertainty: The NOAEL, LOAEL, and LED10 13 for adverse liver

14 effects (see Section 4.3.2, Table 4-2) were based on subchronic exposures. When

15 subchronic studies are used to derive an RfD for lifetime exposure, an uncertainty factor

16 of up to 10-fold is generally applied. Although duration-response trends are not evident

17 in the animal studies, recent human studies have found statistically significant duration18 response trends for decreased testosterone, decreased FSH, decreased SHBG, and

19 increased DHEA (Chia et al., 1997; Goh et al., 1998). Duration-response trends have

20 also been reported for central nervous system toxicity (Rasmussen et al., 1993; Ruitjen et

21 al., 1991). These studies suggest that prolonged exposure to TCE can increase the

severity of effects, prompting the use of a partial factor of 101/2 22 until duration-response

23 relationships are better characterized in humans.

24 (d) LOAEL-to-NOAEL uncertainty: The 1 mg/kg-d point of departure is a subchronic

LOAEL in one study, a NOAEL in another, and an LED10 25 in a third. When adverse

26 effects are observed at the point of departure, an uncertainty factor of up to 10-fold is

generally applied. In this case, a reduced factor of 101/2 27 is appropriate because 1 mg/kg-d

28 appears to be at the boundary where effects can begin to be observed.

29 (e) Other factors: The general population is routinely exposed to many agents that induce

30 CYP2E1, for example, ethanol, acetaminophen, and many ubiquitous environmental

31 contaminants. Moreover, several metabolites of TCE are major environmental

32 contaminants in their own right: for example, TCA and DCA are two principal toxic

33 byproducts of drinking water disinfection. Thus, humans generally have higher

34 background exposures to TCE and its metabolites compared to background exposures in
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1 test animals; in other words, humans start higher on the dose-response curve than the test

2 animals. To account for this difference between humans and laboratory animals, a

modifying factor of 101/2 3 will be applied. This factor appears reasonable in view of the

4 large numbers of people who are at increased risk because of certain medications (for

5 example, acetaminophen and barbiturates) or diseases (for example, diabetes and

6 alcoholism) that affect metabolism and may exacerbate the effects of TCE exposure

7 (Pastino et al., 2000).

8 This modifying factor to reflect background exposures to TCE and its metabolites is new. 

9 It is meant to address the issue of cumulative risks, thus responding to calls by the risk

10 assessment and public health communities that risk assessments would be more realistic and

11 relevant if they address how exposure to other chemicals and stressors can alter a chemical’s

12 toxicity. More than for most other chemicals, the general population is exposed to TCE and its

13 metabolites from multiple sources (see Section 3). Consideration of a preexisting background

14 exposure to toxic metabolites is important when these chemicals are assumed to have a sublinear

15 dose-response curve (see Section 1.8, footnote 26). This modifying factor applies only to

16 populations that have background exposure to the metabolites that are involved in TCE’s

17 toxicity.

18

19 4.3.4. Calculation and Characterization of the Oral Reference Dose

20 For TCE, an RfD can be based on critical effects in the liver, kidney, and developing

21 fetus. The point of departure is 1 mg/kg-d, a dose where adverse liver effects can begin to be

22 observed in two species after subchronic dosing. A composite uncertainty factor of 5,000 is

obtained by multiplying factors of 50 for average-to-sensitive human variation, 101/2 23 for animalto-human uncertainty, 101/2 for using subchronic instead of lifetime studies, 101/2 24 for using a point

of departure where adverse effects have been observed, and a modifying factor of 101/2 25 to reflect

26 background exposures to TCE and its metabolites, thus beginning to address the issue of

27 cumulative risks involving TCE. Dividing the 1 mg/kg-d point of departure by a composite

uncertainty factor of 5,000 would yield an RfD of 2×10–4 28 mg/kg-d.

29 When RfDs are calculated using conventional 10-fold uncertainty factors, EPA limits the

30 composite factor to 3,000 when human-equivalent doses are used (U.S. EPA, 1994). With this

limitation, the RfD would be 3×10–4 31 . The TCE uncertainty factors are not, for the most part,

32 conventional 10-fold factors: the 50-fold factor for human variation is based on an uncertainty

33 analysis, and the other factors are reduced below 10 based on the available data. Each factor is

34 appropriate at some level, as the RfD is otherwise derived from a dose where adverse effects are

35 observed in subchronic animal studies, but the size of each factor is an open question.
63Barton and Clewell (2000) suggest an oral RfD in the range of 0.06–0.12 mg/kg-d based on liver effects (increased

liver-weight-to-body-weight ratio) in rats (Berman et al., 1995) and mice (Buben and O’Flaherty, 1985), obtaining

Weibull-model ED10s of 308–515 mg/kg-d using modeled TCA area-under-the-curve (Clewell et al., 2000) as the

dose metric. The corresponding human-equivalent doses were divided by a composite uncertainty factor of 10 (for

human variation).

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1 Another perspective on the size of the uncertainty factors is provided by research in

2 progress at ATSDR (El Masri, 2000), in which a data-derived factor covering human variation

and animal-to-human uncertainty for TCE was 625 (compared with 50×101/2 3 =150 used in this

4 assessment). ATSDR’s analysis showed more uncertainty for TCE than for the three other

5 chlorinated solvents that were modeled. This research suggests that an RfD based on a

6 composite uncertainty factor of 3,000 would leave only a five-fold margin (3,000/625=5) to

7 cover subchronic-to-chronic uncertainty, LOAEL-to-NOAEL uncertainty, and higher cumulative

8 background exposures in humans. This further indicates the high level of uncertainty inherent in

9 any RfD currently derivable for TCE.

10 Barton and Clewell (2000) suggest an RfD of 0.06–0.12 mg/kg-d based on subchronic

liver effects in rats and mice.63 11 Their RfD is based on increased liver-weight-to-body-weight

12 ratio, one of the critical effects used here. Their point of departure is equal to the 1 mg/kg-d used

13 here. The difference is their use of an uncertainty factor only for average-to-sensitive human

14 variation. This indicates the potential for discussion of alternative RfDs using fewer uncertainty

15 factors.

16 The argument for omitting the animal-to-human factor would rest on an assertion that

17 humans are no more sensitive than rats and mice in terms of pharmacodynamic response to TCE. 

18 Although some mechanisms of liver toxicity may be quantitatively less in humans (for example,

19 PPAR activation), others might not (for example, cell signaling with DCA). TCE’s modes of

20 action are not known, nor is the potential for interaction between modes of action. In addition,

21 critical effects in the kidney and developing fetus occur at doses only slightly above those

22 causing liver toxicity. It has not been established that humans are more sensitive than rats and

23 mice to these other effects.

24 The argument for omitting the subchronic-to-chronic factor would rest on an assertion

25 that TCE’s subchronic effects would not increase over longer durations (the principal studies

26 supporting liver effects involved exposure for only 14 and 30 days). Such short durations usually

27 indicate application of a 10-fold factor. In addition, recent human and animal studies have found

28 statistically significant duration-response trends for several hormone levels, providing further

29 support for using a subchronic-to-chronic factor.

30 The argument for omitting the LOAEL-to-NOAEL factor would rest on an assertion that

31 the LOAELs for increased liver-weight-to-body-weight ratio represent a minimal effect level. 

32 This assertion receives some support from the use of “benchmark dose” modeling, which
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-11

indicates that the LOAELs are comparable to LED10 1 s. Benchmark dose modeling, however, does

not mandate rote treatment of LED10 2 s as if they were NOAELs. In this case, the observation of

3 adverse effects at the lowest doses tested after only 14 and 30 days exposure warrants some

4 concern for effects at still lower doses.

This assessment uses partial uncertainty factors of 101/2 5 (approximately threefold) in each

6 of these areas. This is commensurate with the limited nature of the principal studies, in which

7 rats and mice dosed for 14 or 30 days, respectively, showed adverse effects at the lowest tested

8 doses. Further, ATSDR’s decision that the database is not strong enough to support a chronic

9 value indicates the presence of much uncertainty in these areas.

10 Limitations of the database are many. As noted by ATSDR (1997), the chronic-duration

11 studies are problematic. Epidemiologic studies of TCE-contaminated drinking water are limited

12 in their ability to quantify dose-response relationships because of difficulties in estimating past

13 contamination levels and the presence of other solvents with similar metabolic profiles. Most

14 animal studies focus on liver and kidney, organs where TCE induces tumors; studies assessing

15 alteration of organ function or enzyme levels are less common. Finally, neither the occupational

16 studies nor the animal studies provide information about sensitive individuals. In view of these

17 limitations, the use of several uncertainty factors appears reasonable.

18

19 4.4. INHALATION REFERENCE CONCENTRATION FOR EFFECTS OTHER

20 THAN CANCER

21 4.4.1. Critical Effects

22 For acute inhalation exposure to high concentrations (above approximately 1,000 ppm, or

5,400 mg/m3 23 ), targets of TCE toxicity include the central nervous system, cardiovascular system,

24 kidney, and developing fetus (ATSDR, 1997). At lower concentrations (approximately

100–1,000 ppm, or 540–5,400 mg/m3 25 ), acute exposure still affects these systems, and there are

26 also intermediate and chronic studies (1 year or more) that show effects on the central nervous

27 system, kidney, liver, and endocrine system (see Section 3.4). At the lowest concentrations,

28 effects are observed in the central nervous system, liver, and endocrine system. These are

29 considered to be the critical effects of inhaled TCE.

30

31 4.4.2. Point of Departure

32 Table 4-3 summarizes the results of inhalation studies of lower concentrations. 

33 Concentrations in occupational studies were converted to continuous (24 hr/d, 7 d/wk)

34 concentrations. Concentrations in animal studies were taken from the analysis of Barton and

35 Clewell (2000). If pharmacokinetic-adjusted doses were not available, the RfC methodology

36 (U.S. EPA, 1994) for systemic effects for a water-soluble, perfusion-limited gas was used
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-12

1 instead. Response levels are presented as either a NOAEL, a LOAEL if the study did not identify

a NOAEL, or a modeled LEC10 2 if the study results were suitable for modeling.

3 Adverse effects have been observed in several studies at a concentration range of 5–16

ppm (27–86 mg/m3). These include LOAELs of 7–16 ppm (38–86 mg/m3 4 ) for signs of central

5 nervous system toxicity in several occupational studies (Rasmussen et al., 1993; Ruitjen et al.,

1991; Vandervort et al., 1973; Okawa and Bodner, 1973). Also, an LEC10 of 5 ppm (27 mg/m3 6 )

7 was identified for heart rate and electroencephalographic changes in rats (Arito et al., 1994). For

endocrine effects, a LOAEL of 11 ppm (59 mg/m3 8 ) was identified for decreased mean levels of

9 testosterone, decreased FSH, decreased SHBG, and increased DHEA in an occupational study

(Chia et al., 1997; Goh et al., 1998). A pharmacokinetic-model-derived LEC10 10 of 5 ppm (27

mg/m3 11 ) was identified for increased liver-weight-to-body-weight ratio in mice (Kjellstrand et al.,

12 1983).

Together, these studies indicate 5–16 ppm (27–86 mg/m3 13 ) as the lower end of the range of

concentrations at which adverse effects have been observed. From this range, 7 ppm (38 mg/m3 14 )

15 is chosen as the point of departure. This concentration was identified as a subchronic LOAEL

16 for central nervous system effects in two occupational studies and is supported by central nervous

system effects in rats at 5 ppm (27 mg/m3 17 ). This latter dose is also associated with a 10% 

18 increased incidence of liver effects in mice.

19

20 4.4.3. Uncertainty Factors

21 To address differences between study conditions and conditions of human environmental

22 exposure, the point of departure is reduced by several uncertainty factors.

23 (a) Human variation: The occupational studies observed healthy adult workers, who do not

24 reflect the potential for effects in children, the elderly, or those with disease or other

25 conditions that can increase susceptibility. This supports use of the standard 10-fold

26 factor for human variation. If human variation for this effect were as large as that for

27 liver effects (see Section 4.3.3), then this factor would be an underestimate.

28 (b) Animal-to-human uncertainty: Because the point of departure is supported by human

29 studies, this factor is not needed.

30 (c) Subchronic-to-chronic uncertainty: The point of departure is based on subchronic

31 studies. Duration-response trends have been reported for central nervous system toxicity

32 (Rasmussen et al., 1993; Ruitjen et al., 1991). These observations suggest that continuing

33 exposure to TCE can increase the severity of effects, supporting a 10-fold factor to

34 address the potential for more severe toxicity from lifetime exposure to TCE. 

35 Statistically significant duration-response trends have also been observed for decreased

36 mean levels of testosterone, decreased follicle-stimulating hormone, decreased SHBG,
64Barton and Clewell (2000) suggest an inhalation RfC in the range of 0.4–1 ppm (2–5 mg/m3

) based on neurologic

effects (changes in sleep and wakefulness) in rats (Arito et al., 1994), obtaining an ED10 of 0.57 ppm using modeled

TCOH in blood (Clewell et al., 2000) as the dose metric. The corresponding human-equivalent concentration was

divided by a composite uncertainty factor of 10 (for human variation).

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1 and increased DHEA in humans (Chia et al., 1997; Goh et al., 1998), plus decreased

2 testosterone in rats (Kumar et al., 2000).

3 (d) LOAEL-to-NOAEL uncertainty: The central nervous system effects and the endocrine

4 effects observed in occupational studies are LOAELs, supporting a 10-fold factor to

5 approach the range where a NOAEL could be expected.

6 (e) Other factors: These are not being used.

7

8 4.4.4. Calculation and Characterization of the Inhalation Reference Concentration

9 For TCE, an RfC can be based on critical effects in the central nervous system, liver, and

endocrine system. The point of departure is 7 ppm (38 mg/m3 10 ). This concentration was

11 identified as a subchronic LOAEL for central nervous system effects in two occupational studies

12 and is further supported by central nervous system effects in rats and liver effects in mice at 5

ppm (27 mg/m3 13 ). A composite uncertainty factor of 1,000 is obtained by multiplying factors of

14 10 for average-to-sensitive human variation, 10 for starting from subchronic instead of lifetime

studies, and 10 for starting from effect levels instead of NOAELs. Dividing the 38 mg/m3 15 point

of departure by a composite uncertainty factor of 1,000 yields an RfC of 4×10–2 mg/m3 16 .

Barton and Clewell (2000) suggest an RfC of 0.4–1 ppm (2–5 mg/m3 17 ) based on

subchronic neurological effects in rats.64 18 Their RfC is based on one of the studies supporting the

19 point of departure. The difference is their use of an uncertainty factor for only average-to20 sensitive human variation. This indicates the potential for discussion of alternative RfCs that do

21 not incorporate uncertainty factors for using a subchronic instead of a lifetime study or for using

22 an effect level instead of a NOAEL.

23

24 4.4.5. Comparison of Reference Dose and Reference Concentration

25 The similarity of effects by oral or inhalation exposure to TCE suggests using route

26 extrapolation (see Section 4.2.3):

27 (a) to compare effect levels for oral and inhalation exposure, and

28 (b) to compare the RfD and RfC.

29 The results will depend on the equivalence between oral dose and inhaled concentration

30 that yields similar internal levels of the active agent causing a particular toxic effect. If TCA

31 area-under-the-curve is the appropriate dose metric, then ingesting TCE at 1 mg/kg-d yields

similar internal TCA as inhaling TCE at 75 mg/m3 32 . On the other hand, if DCA area-under-the-
65The calculations are:

(2×10–4 mg/kg-d) × (75 mg/m3

)/(mg/kg-d) = 1.5×10–2 :g/m3

(2×10–4 mg/kg-d) × (3 mg/m3)/(mg/kg-d) = 6×10–4 :g/m3

66The calculations are:

(162 mg/m3

) × (8 hr/24 hr) × (1 mg/kg-d)/(75 mg/m3) = 0.71 mg/kg-d

(0.71 mg/kg-d) × (70 kg) / (2 L/d) = 25 mg/L

8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-14

1 curve is the appropriate dose metric, then ingesting TCE at 1 mg/kg-d yields similar internal

DCA as inhaling TCE at 3 mg/m3 2 (see Section 4.2.3).

3 The oral point of departure is an equivalent human dose of 1 mg/kg-d (see Section 4.3.2). 

This corresponds to an inhaled concentration of 75 mg/m3 4 if TCA is the appropriate dose metric

for route extrapolation, and 3 mg/m3 5 if DCA is the appropriate dose metric. The 25-fold range

between these two concentrations contains the inhalation point of departure of 38 mg/m3 6 (see

7 Section 4.4.2). This suggests a convergence of effect levels for these two exposure routes.

Similarly, the oral RfD is 3×10–4 8 mg/kg-d (see Section 4.4.4). This corresponds to an

inhaled concentration of 2×10–2 mg/m3 if TCA is the appropriate dose metric and 9×10–4 mg/m3 9 if

DCA is the appropriate dose metric.65 The inhalation RfC of 4×10–2 mg/m3 10 is close to the

11 concentration that results when TCA is used as the dose metric.

12 Another useful route extrapolation compares TCE exposures in the occupational and

13 drinking water studies. In the Singapore cohort (Chia et al., 1997; Goh et al., 1998), hormone

disruption was observed in male workers exposed to an average of 30 ppm (162 mg/m3 14 ) for 8

15 hr/d. This inhalation exposure yields similar internal TCA as ingesting TCE at 0.71 mg/kg-d,

16 and this dose corresponds to a drinking water concentration of 25 mg/L for a 70-kg adult

drinking 2 L/d.66 17 The corresponding ingested dose (0.71 mg/kg-d) is close to the oral point of

18 departure (1 mg/kg-d), reiterating the convergence between the oral and inhalation databases and

19 giving them greater confidence than they would have if analyzed separately. At the same time,

20 the corresponding drinking water concentration (25 mg/L) greatly exceeds the Safe Drinking

21 Water Act maximum contaminant level of 5 :g/L for TCE, suggesting that there is much value

22 in attempting to use drinking water studies to estimate risk and avoid some of the problems of

23 low-dose extrapolation (see Section 4.5.1).

24

25 4.5. SLOPE FACTOR AND INHALATION UNIT RISK FOR CANCER

26 Cancer risk assessments have described dose-response curves in the range of

27 environmental exposures, conveying an appreciation of how risk decreases as dose decreases. 

28 Past risk assessments have used a dose-response curve that is linear at low doses, implying that
67Low-dose-linear models are appropriate for extrapolation to lower doses when a carcinogen acts in concert with

other exposures and processes that cause a background incidence of cancer (Crump et al., 1976; Lutz, 1990). 

Further, even when the mode-of-action indicates a nonlinear dose-response curve in homogeneous animal

populations, the presence of genetic and lifestyle factors in a heterogeneous human population tends to make the

dose-response curve more linear (Lutz, 1990). This is because genetic and lifestyle factors contribute to a wider

spread of human sensitivity, which extends and straightens the dose-response curve over a wider range. Although

these considerations provide a reasonable argument for a model that is linear at low doses, the relation of the lowdose slope to one from the experimental range is uncertain; this uncertainty increases with the distance from the

experimental range.

8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-15

risk decreases proportionately with dose.67 1 The slope of the dose-response curve at low doses is

2 called a slope factor.

3 EPA’s proposed cancer guideline revisions (U.S. EPA, 1996) describe a two-step process

4 of modeling the observed data followed by extrapolation to lower doses. Extrapolation to lower

5 doses follows either a linear or a nonlinear approach, depending on what is known about mode6 of-action. Extrapolation begins from a point of departure near the lowest doses. When linear

7 extrapolation is used, the slope factor is determined by the line from the point of departure

8 toward zero. When the mode-of-action is understood well enough to support nonlinear

9 extrapolation, a dose-response curve is not estimated below the point of departure. Instead, a

10 discussion provides information about the distance from the point of departure to a dose where

11 there would be little concern for cancer.

12 The use of pharmacokinetic and dosimetry models carries an implicit assumption of

13 tumor site concordance across species; therefore, it is important to assess and consider each

14 tumor site that may be relevant to human environmental exposure. In this regard, it is important

15 to note that lack of suitable dose-response data has precluded development of slope factors for

16 some sites where there might be a cancer risk, notably for cervical cancer and prostate cancer.

17

18 4.5.1. Risk Estimates From Cancer Epidemiology Studies

19 Quantitative risk methods for human studies must be tailored to the type of data available

20 in each particular case. Of utmost importance is the availability of quantitative exposure data,

21 which can be the principal determinant of the quality of the risk estimates.

22

23 4.5.1.1. Finnish Cohort 

24 One set of risk estimates was derived from a cohort of 2,050 male and 1,924 female

25 Finnish workers exposed to TCE and other solvents (Anttila et al., 1995). This cohort had

26 statistically significant increases in non-Hodgkin’s lymphoma and, among workers exposed for

27 more than 20 years, liver cancer. Although kidney cancer was not statistically significantly

28 increased in this cohort, the observed incidence was used to bound the potential risk identified

29 qualitatively from the epidemiologic studies as a whole (Wartenberg et al., 2000). For this
68The data for each cancer case are as follows:

Liver cancer Kidney cancer Non-Hodgkin’s lymphoma 

Average urinary TCA (:mol/L) 15 257 17 112 92 23 77 363 2,217 3 3 48 207 312 422 13 44 21

Age at first TCA measurement 64 50 29 15 52 48 41 40 32 54 54 36 63 34 33 33 18 21

Age at last TCA measurement 65 52 30 25 54 49 42 41 33 55 55 37 64 35 34 34 19 27

Age at end of followup 78 79 56 41 68 62 62 63 49 80 74 61 81 58 62 57 39 43

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1 assessment, Anttila made available to EPA urinary TCA measurements and the cancer status for

each worker.68 2 A separate study of 51 male workers (Ikeda, 1972) was used to derive a

3 relationship between urinary TCA and air concentrations of TCE:

4 Urinary TCA (mg/L) = 2.956 × Ambient TCE (ppm, 8 hr/d, 6 d/wk)

5 Occupational exposure duration, which was not available, was assumed to be 15 years,

6 and each worker’s mean urinary TCA measurement was taken as representative for this duration. 

7 The resulting inhalation unit risk estimates, adjusted for lifetime exposure to an average of 1

ug/m3 8 of TCE in air, are:

Liver cancer: 1×10–7; upper bound, 9×10–7 9

Kidney cancer: 2×10–5; upper bound, 3×10–5 10

Non-Hodgkin’s lymphoma: 5×10–5; upper bound, 9×10–5 11

Overall: 7×10–5; upper bound, >9×10–5 12

13 These estimates are based on a small number of cancer cases. As with other occupational

14 studies, they are based on healthy adult workers and may not be representative of children or

15 other groups. Moreover, the exposure duration is uncertain. In addition, the workers were

16 exposed to other solvents (albeit solvents with similar metabolic pathways, potential modes of

17 action, and toxic effects), so that attributing all risk to TCE can overestimate the risk from TCE

18 (though not, perhaps, for the combined solvent exposure). On the other hand, the exposure

19 metric is based on biological measurements of a major metabolite of TCE.

20

21 4.5.1.2. Hill Air Force Base Cohort

22 Another analysis considered the cohort of Hill Air Force Base workers exposed to TCE

23 and other chemicals (Blair et al., 1998). This cohort showed nonsignificant increases in mortality

24 from liver cancer, kidney cancer, and non-Hodgkin’s lymphoma, cancers that are elevated in the

25 epidemiologic studies overall (Wartenberg et al., 2000). Blair made available to EPA a

26 qualitative exposure index and the cancer mortality status for each worker. The exposure index

27 did not quantify exposure, instead, it classified each job as either high or low peak exposure. 

28 Difficulties in converting this exposure index into a credible quantitative estimate include

29 differences between peak exposure and cumulative exposure and the diversity of jobs and

30 exposure levels in the cohort; indeed, Blair (1998) cautioned that the exposure index was not
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-17

1 convertible into ppm. Without a quantitative estimate of exposure, it is not possible to derive

2 risk estimates.

3

4 4.5.1.3. New Jersey Drinking Water Study

5 Risk estimates were derived from a study of drinking water contamination in a 75-town

6 area of New Jersey (Cohn et al., 1994). Female residents had increased incidences of leukemia

7 (RR=1.4, 95% CI=1.1–1.9, N=56) and non-Hodgkin’s lymphoma (RR=1.4, 95% CI=1.1–1.7,

8 N=87). For homes exceeding the Safe Drinking Water Act maximum contaminant level of 

9 5 μg/L, the average concentration was 23 :g/L and the highest concentration was 67 μg/L. 

10 Using this average from the most-exposed homes, a drinking water unit risk estimate for non11 Hodgkin’s lymphoma from exposure to 1 :g/L of TCE in drinking water is:

12 (RR – 1) × Background risk / Average concentration =

(1.4 – 1) × (6×10–4) / (23 :g/L) = 1×10–5 13 per :g/L

where 6×10–4 14 is the background risk of non-Hodgkin’s lymphoma in the United States. This unit

15 risk can be converted to a slope factor by multiplying by 35,000 (based on a 70-kg adult drinking

2 L/d). The resulting slope factor is 3.5×10–1 16 per mg/kg-d average lifetime exposure to TCE. 

17 The residents were exposed to other drinking water contaminants, so that attributing all risk to

18 TCE can overestimate the risk from TCE. On the other hand, this risk estimate is derived from

19 human environmental exposure, avoiding the uncertainties of animal-to-human extrapolation and

20 high- to low-dose extrapolation. Moreover, using the average concentration from the most21 exposed homes would tend to overestimate exposure and, hence, underestimate unit risks.

22

23 4.5.1.4. German Cohort

24 The California Environmental Protection Agency (Cal/U.S. EPA, 1999) derived a slope

25 factor from the increased incidence of kidney cancer in German cardboard workers exposed to

26 TCE (Henschler et al., 1995). In the absence of exposure data, the German threshold limit value

of 50 ppm (270 mg/m3 27 ) was used as a surrogate for the average workplace concentration. This

28 was converted to a lifetime average daily dose, assuming 50% absorption of inhaled TCE:

50 ppm × (8 hr/24 hr) × (5 d/7 d) × (15.2 yr/70 yr) × 0.50 × 5.37 (mg/m3 29 )/ppm 

 × 20 m3 30 /d / 70 kg = 1.98 mg/kg-d

31 From the reported kidney cancer incidence (7/169, SIR=13.53 based on East German

32 background rates), a slope factor was obtained by the following calculation, based on an additive

33 risk model that is linear in dose:

(7/169) × (1–1/13.53) / 1.98 mg/kg-d = 1.9 ×10-2 34 per mg/kg-d
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-18

1 By this calculation, Cal/EPA converted the air concentration into mg/kg-d by figuring

gross amount inhaled by a 70-kg adult breathing 20 m3 2 /d. Therefore, to convert the slope factor

to an inhalation unit risk, 20 m3 3 /d and 70 kg would be removed from the preceding calculations. 

The resulting inhalation unit risk is 5×10–6 per :g/m3 4 .

5 To the extent that the workers were exposed to mixed solvents and other risk factors, this

6 slope factor derived from the Henschler study would tend to overestimate risks from TCE,

7 because it attributes all kidney cancers to TCE and not to the other risk factors. On the other

8 hand, to the extent that the threshold limit value of 50 ppm overestimates average workplace

9 concentration, this slope factor would tend to underestimate risks. Further uncertainty arises

10 from using an unadjusted incidence (7/169) instead of one based on life-table methods, from

11 comparing this incidence with East German background rates (the plant was in West Germany,

12 but West German rates were unavailable), and from treating exposure concentration and

13 exposure duration as constant across all workers.

14 Variation in human susceptibility was explored by further analyses based on observed

15 differences in kidney cancer risk attributed to GST polymorphisms (Brüning et al., 1997). The

16 odds ratio for the combined genotype GSTM/GSTT (found in 40% of Caucasians) was 3.75. 

This corresponds to a higher slope factor (3.5×10–2 17 per mg/kg-d) for those with the combined

genotype and a lower slope factor (9×10–3 18 per mg/kg-d) for the rest of the population (Cal/U.S.

19 EPA, 1999).

20

21 4.5.2. Risk Estimates From Liver Tumors in Mice

22 TCE causes hepatocellular carcinomas and adenomas in male and female mice exposed

23 by ingestion or inhalation (see Table 3-1). TCE is also associated with liver cancer in humans

24 (Wartenberg et al., 2000). Several approaches are explored for estimating these risks.

25

26 4.5.2.1. Mechanism-Based Modeling

27 Chen (2000) developed a mechanism-based dose-response model for mouse liver tumors,

28 based on mechanistic hypotheses discussed by Bull (2000). The model is a stochastic form of the

29 two-stage model, in which normal cells can become initiated (the first stage); initiated cells either

30 die, proliferate into a clone of initiated cells, or are converted to malignancy (the second stage);

31 and malignant cells either die or progress to a detectable tumor. Dose metrics were area-under32 the-curve of TCA or DCA in the liver, taken from Bois’s calibration of Fisher’s pharmacokinetic

33 model (Bois, 2000a). Chen’s model assumes that TCE induces liver tumors through TCA and

34 DCA acting by clonal expansion of preexisting initiated cells. This assumption of action on only
69The harmonic mean is computed as N/(3 1/Xi

). For this application, this is equivalent to averaging slope factors

(which are inversely proportional to LED10s), then taking the reciprocal to convert the average slope factor back to a

dose.

8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-19

1 preexisting initiated cells implies that TCE-induced liver tumor incidences are proportional to

2 background liver tumor incidences.

3 In a sequence of applications of the mechanism-based model, (1) a dose-response curve

4 for DCA-induced liver tumors was developed from bioassays of DCA in mice, (2) this curve was

5 found to be consistent with bioassays of TCA in mice, considering the rate at which some TCA is

6 metabolized to DCA, and (3) these curves were found to be consistent with the bioassays of TCE

7 in mice, considering the rate at which some TCE is metabolized to TCA and DCA (Chen, 2000). 

8 This suggests that TCE produces enough DCA to theoretically explain the mouse liver tumors

9 induced in the TCE bioassays, though it cannot rule out a role for TCA or other metabolites. 

10 Thus, the relative importance of DCA or TCA in TCE-induced mouse liver tumors is not

11 resolved by mechanism-based modeling.

12 The mechanism-based model also explored extrapolation to lower doses. Following the

model below the observed range yields an (upper bound) unit risk of 2.4×10–8 13 for oral exposure

14 to 1 ug/L of TCE in drinking water (Chen, 2000).

15

16 4.5.2.2. Empirical Modeling

17 Rhomberg (2000) fitted empirical dose-response models to the liver tumor data. Dose

18 metrics were area-under-the-curve of TCA or DCA in the liver or plasma, taken from Bois’s

19 calibrations of Fisher’s and Clewell’s pharmacokinetic models (Bois, 2000a,b). Table 4-4

presents the modeled LED10 20 s that provide a point of departure for low-dose extrapolation. These

LED10 21 s reveal substantial model uncertainty between Fisher’s and Clewell’s models. They also

reveal that Bois’s calibrations bring some convergence: harmonic means69 of LED10 22 s from the

23 four experiments span 0.16–25.82 mg/kg-d when dose estimates come from Fisher’s and

24 Clewell’s original models, but narrow to 0.5–3.1 mg/kg-d when dose estimates come from Bois’s

25 calibrations of those models. (Bois did not calibrate Fisher’s model with DCA as the dose

26 metric; in this case, only Fisher’s original model is considered.) This latter range (0.5–3.1

27 mg/kg-d) is used as the point of departure for low-dose extrapolation. Although both endpoints

28 of this range use TCA as the dose metric, this does not imply a preference for TCA over DCA. 

29 The DCA range (1.7–2.5 mg/kg-d) simply falls within the TCA range, moreover, Section 4.5.1

30 discusses why neither is established as the appropriate dose metric.

31 It is tempting to note that the point-of-departure range for liver tumors (0.5–3.1 mg/kg-d)

32 includes the point of departure for liver toxicity (a subchronic LOAEL of 1 mg/kg-d for increased

33 liver-weight-to-body-weight ratio; see Section 4.3.2). This concordance, however, is likely to be
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-20

1 only coincidental, as liver weight change is a nonspecific effect observed in both rats and mice,

2 but rats have not developed liver tumors from TCE exposure. The liver weight changes,

3 therefore, are not considered a key event in liver tumor development.

4

5 4.5.2.3. Extending the Observed Range Using Subchronic-dosing Studies

6 A key determinant of risk is the shape of the dose-response curve below the point of

7 departure. One source of information about lower doses is the comprehensive study by Maltoni

8 et al. (1986), who had the foresight to investigate different exposure durations in parallel

experiments. Groups of Swiss mice inhaled TCE at 100–600 ppm (540–3,240 mg/m3 9 ), 7 hours

10 daily, 5 days weekly, for either 8 weeks or 78 weeks; then the experiments continued past 2 years

11 to ensure that the latent period for tumor development had passed.

12 Table 4-5 and Figure 4-1 show the incidence of hepatomas in male mice (females did not

13 develop hepatomas). If response is considered as a function of cumulative exposure

14 (concentration times duration), the 8-week exposure groups can be regarded as extending the

15 observed range to lower cumulative exposures. Overall, response decreases as cumulative

16 exposure decreases, but the curve does not appear to be sublinear anywhere in this extended

17 range. This suggests that exposures must be far below the observable range before the risk drops

18 appreciably.

19 This finding may be pertinent for risk assessments of less-than-lifetime exposure

20 durations. If the lifetime average dose is low because the exposure duration is relatively short, it

21 may be important to consider that risk may not fall proportionately as exposure duration

22 decreases. That is, cumulative exposure (concentration times duration, or area-under-the-curve)

23 may not be a good dose metric to use to predict liver tumor risk.

24

25 4.5.2.4. Extending the Observed Range Using DCA and TCA Studies

26 Another source of information at lower doses are the studies of DCA- or TCA-induced

27 liver cancer. Bioassays of these TCE metabolites cover a 100-fold dose range, wider than the

28 dose range of the TCE bioassays. Chen (2000) analyzed these studies jointly, using area-under29 the-curve of DCA in the liver as a common dose metric.

30 Table 4-6 and Figure 4-2 bring together the dose-response information from the DCA,

31 TCA, and TCE bioassays. The DCA and TCA studies extend the dose range as much as 100-fold

32 below the TCE studies. Overall, the responses appear to decrease as dose decreases, but the

33 dose-response curve does not appear to be sublinear anywhere in this extended range, again

suggesting that exposures must be far below an LED10 34 before the risk drops appreciably.

35 Looking at the multiple-dose TCA and DCA bioassays individually, the dose-response

36 curve for TCA appears linear, whereas the curve for DCA appears sublinear. If we consider
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-21

1 tumor multiplicity along with tumor incidence, however, the DCA dose-response curve, too,

2 becomes linear (DeAngelo et al., 1999). Tumor multiplicity may more closely reflect rates of

3 underlying carcinogenic processes.

4

5 4.5.2.5. Extrapolation to Lower Doses

6 Under EPA’s proposed cancer guideline revisions (U.S. EPA, 1996), choice of linear or

7 nonlinear extrapolation to lower doses is based on what is known about the mode-of-action. The

8 evidence indicates that TCE-induced liver tumors arise through multiple metabolites and

9 multiple modes of action (see Section 3.5.1). The preponderance of evidence suggests that the

10 CYP450 metabolites are sufficient to explain the liver tumors (Bull, 2000; Chen, 2000). CH and

11 TCA cause liver tumors in mice, and DCA causes liver tumors in mice and rats. A plausible

12 mode-of-action is that TCE induces liver tumors through TCA and DCA modifying cell signaling

13 systems that control rates of cell division and cell death (Bull, 2000). Tumors from the TCA and

14 DCA bioassays arise from different mechanisms and can be distinguished: TCA-induced tumors

15 are associated with peroxisome proliferation, while DCA-induced tumors are associated with

16 selectively inhibiting growth of normal hepatocytes and suppressing apoptosis (Bull, 2000). 

17 DCA may also have different modes of action at different doses, possibly involving differential

18 accumulation of glycogen in normal and initiated liver cells (Bull, 2000). Moreover, DCA is

19 also weakly mutagenic (Moore and Harrington-Brock, 2000), although this property is not

20 needed to explain the bioassay results. Characterizing the tumors from the TCE bioassays is a

21 critical research need that presently limits understanding of the respective roles of TCA and DCA

22 in TCE-induced tumors (see Section 5). At present, however, the extensive mode-of-action

23 information still lacks identification of the sequence of key events and a quantitative description

24 of the doses at which those key events begin to occur. In such cases, EPA’s proposed cancer

25 guideline revisions (U.S. EPA, 1996) support consideration of both linear and nonlinear

26 extrapolation to lower doses.

27 Linear extrapolation follows a straight line toward the origin, the slope of this line (or

slope factor) is 0.10/LED10. The LED10 range of 0.5–3.1 mg/kg-d yields slope factors of 3×10-2 28

to 2×10-1 29 per mg/kg-d. Because the pharmacokinetic models allow for a nonlinear relationship

30 between (external) exposure and (internal) dose, this “straight-line” extrapolation can reflect

31 nonlinearity in the exposure-dose-response data.

32 Nonlinear extrapolation under the proposed cancer guideline revisions (U.S. EPA, 1996)

33 does not attempt to describe dose-response curves in the range of environmental exposures,

34 instead, it examines whether an exposure is small enough to pose little risk of cancer. The goal is

35 that exposures be small enough that “the key events in tumor development would not occur

36 among sensitive individuals in a heterogeneous human population, thus representing an actual
70A nonlinear analysis considers the following factors (U.S. EPA, 1999):

(a) Nature of the response: The point of departure is ideally based on key precursor events, not tumors, just as

RfDs and RfCs are based on critical (precursor) effects occurring at lower doses than those associated with

frank toxicity. Precursor events are likely to occur at doses below those where tumors are observable. If a

point of departure is based on tumors, then the point of departure is adjusted to reflect the dose at which key

precursor events would begin to occur.

(b) Shape of the observed dose-response curve: If the dose-response curve is steep at the point of departure,

then the occurrence of key events declines rapidly in this range. On the other hand, if the dose-response

curve is relatively shallow, then the point where the key event no longer occurs may lie far below this range.

(c) Human sensitivity compared with experimental animals: If the point of departure is developed from animal

studies, this dose is adjusted to reflect pharmacokinetic and pharmacodynamic factors that may make

humans and animals differ in sensitivity.

(d) Nature and extent of human variability in sensitivity: Pertinent information would come from human

studies, because animal studies, particularly those using homogeneous animal strains, do not provide

information about human variation.

(e) Human exposure: Cancer response can depend on the expected pattern of human exposure, including the

magnitude, frequency, and duration of exposure.

8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-22

‘no effect level’” (U.S. EPA, 1999). A nonlinear analysis considers several factors70 1 where the

2 available data may fall short of this ideal.

3 (a) Nature of the response: The point of departure is ideally based on key precursor events. 

4 If tumors are used instead, then the dose is adjusted to bring it into the range where the

5 key precursor events would not occur (U.S. EPA, 1999). For TCE-induced liver cancer,

6 further research is needed to identify the key events and the doses at which the key events

begin to occur (see Section 5). These doses will be below the LED10 7 s where tumors are

8 clearly observable, how far below cannot now be determined.

9 (b) Shape of the observed dose-response curve: The shape of the dose-response curve can

10 indicate how fast risk decreases below the point of departure. For TCE-induced liver

11 cancer, dose-response curves are ambiguous, consistent with either low-dose linearity or

12 nonlinearity (Rhomberg, 2000). Extending the dose-response curve using information

13 from subchronic-dosing studies or from DCA and TCA bioassays suggests that the dose14 response curve may remain linear quite far below, perhaps two orders of magnitude

below, the LED10 15 s. At still lower doses, the mode-of-action may ultimately be nonlinear;

nonetheless, current data do not identify how far below the LED10 16 s sublinearity begins to

17 prevail. Further research is needed to identify the key precursor events and describe their

18 dose-response curves (see Section 5).

19 (c) Human sensitivity compared with experimental animals: This factor has been viewed as

20 having pharmacokinetic and pharmacodynamic components (U.S. EPA, 1994). For TCE,

21 the pharmacokinetic component is addressed by the pharmacokinetic models, which

22 predict higher internal doses of TCA and DCA in humans than in mice. Little

23 information is typically available on the pharmacodynamic component, and the proposed

24 cancer guideline revisions (U.S. EPA, 1996) recommend that humans be considered
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-23

potentially more sensitive by a default factor of 101/2 1 . For TCE, however, the prevailing

2 view is that humans would be quantitatively less sensitive than mice to the PPAR activity

3 and other processes leading to the mouse liver tumors (Bull, 2000). In this case, an

inverse factor of 10–1/2 4 could be used to reflect the potential lesser sensitivity of humans

5 (U.S. EPA, 1996).

6 (d) Nature and extent of human variability in sensitivity: For TCE, this factor can be based

7 on the statistical uncertainty analyses (Bois, 2000b). A factor of 15–20 reflects the

8 pharmacokinetic uncertainty in the liver between the 50th and 99th percentiles (see Table

4-1). Coupled with a factor of 101/2 9 to cover pharmacodynamic differences between

10 humans, a factor of 50–60 would appear appropriate to reflect human variation in

11 sensitivity.

12 This discussion of data-derived factors indicates several large uncertainties: 50–60-fold

13 to reflect human variation, approximately 100-fold to reach a range where the dose-response

14 curve does not appear linear in some bioassay, an as-yet-undeterminable factor to reflect the

difference in dose between tumors and key precursor events, and a factor of 10–1/2 15 to reflect the

16 potential for humans to be less sensitive than mice. Overall, then, the composite factor would

17 appear comparable to the 3000–10,000 that is the maximum for developing a credible RfC or

18 RfD (U.S. EPA, 1994; Barnes and Dourson, 1988).

19

20 4.5.2.6. Comparison of Empirical and Mechanism-Based Modeling Results

Mechanism-based modeling yields a unit risk of 2.4×10–8 21 for oral exposure to 1 :g/L in

22 water (Chen, 2000). This unit risk can be converted to a slope factor by multiplying by 35,000

(based on a 70-kg adult drinking 2 L/d). The resulting slope factor is 8.4×10–4 23 per mg/kg-d. This

result lies below the empirically based slope factor range of 3×10–2 to 2×10–1 24 .

25 The mechanism-based slope factor includes a 100-fold reduction attributable to an

26 assumption that chemically induced tumor rates scale across species in proportion to background

27 tumor rates, the rate in mice being 100 times that of humans. This difference is not based on any

28 data for TCE, rather, it is a corollary of the model’s assumptions that TCE acts by clonal

29 expansion of preexisting initiated cells and that there is strict tumor site concordance across

30 species. If these assumptions, which generally are not used in cross-species extrapolations for

31 other chemicals, were not used here, then the mechanism-based slope factor would be 100-fold

higher, or 8×10–2 32 per mg/kg-d. This latter result lies within the slope factor range based on

33 empirical modeling.

34

35 4.5.3. Risk Estimates From Lung Tumors in Mice
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-24

1 4.5.3.1. Modeling

2 TCE causes lung adenomas and adenocarcinomas in mice exposed by inhalation (see

3 Table 3-5). Pharmacokinetic models (Clewell et al., 2000; Bois, 2000b) were used to estimate

4 area-under-the-curve and peak concentration of CH in plasma, plasma CH being considered as a

5 surrogate for CH accumulation in Clara cells of the lung. Bois’s uncertainty analysis (Bois,

6 2000b), however, revealed the presence of substantial parameter uncertainty in the lung dose

7 estimates (see Table 4-1, Section 4.2.2). Consequently, dosimetry models for deriving inhalation

8 RfCs were used instead (U.S. EPA, 1994). Rhomberg (Rhomberg, 2000) fitted empirical doseresponse models to the lung data (see Table 4-7). The resulting LEC10 9 s are quite similar, ranging

from 1.3×105 to 3.6×105 :g/m3 10 .

11

12 4.5.3.2. Extrapolation to Lower Doses

13 The proposed mode-of-action for TCE-induced lung tumors is that rapid CYP2E1

14 metabolism of TCE in the Clara cells of the mouse lung leads to an accumulation of CH and

15 causes cell damage and compensatory cell replication that leads to tumor formation (Green,

16 2000). Because humans have fewer Clara cells and little CYP2E1 activity in the lung as a whole,

17 humans may be less sensitive to this particular mode-of-action. (This argument, however,

18 pertains more to the relative sensitivity of mice and humans than to nonlinearity in either mice or

19 humans.) Although this hypothesis is consistent with the higher level of CYP2E1 in mouse lung,

20 it has not yet been determined whether accumulation of CH is a key event causing tumors or a

21 coincidental event unrelated to tumors. Moreover, several questions about this hypothesis

22 remain unresolved (see Section 3.5.3). In addition, CH is clearly clastogenic and mutagenic at

23 high doses (Moore and Harrington-Brock, 2000), raising the possibility of multiple modes of

24 action. For these reasons, this assessment will pursue both linear and nonlinear extrapolation to

25 lower doses.

26 Linear extrapolation takes the form of a straight line to the origin; the slope of this line

(or inhalation unit risk) is 0.10/LEC10. The LEC10 range of 1.3×105 to 3.6×105 :g/m3 27 yields unit

risks of 3×10–7 to 8×10–7 per :g/m3 28 .

29 Nonlinear extrapolation under the proposed cancer guidelines (U.S. EPA, 1996) considers

30 several factors in identifying a dose where there would be little concern for cancer.

31 (a) Nature of the response: The proposed key event, CH accumulation in Clara cells, has

been studied only at high doses (450 ppm and above, equivalent to 2,430 :g/m3 32 ) (Green,

33 2000) relative to those where lung tumors have been observed (150–300 ppm, or

810–1,620 mg/m3 34 ). This makes determination of cause-and-effect premature, and it does

35 not identify doses at which CH accumulation would not occur. Further research is needed
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-25

1 to establish the quantitative relationship between CH accumulation and lung tumors so

2 that a point of departure can be based on the key precursor event.

3 (b) Shape of the observed dose-response curve: Dose-response curves for TCE-induced lung

4 cancer are ambiguous, consistent with either linearity or nonlinearity at low doses

(Rhomberg, 2000). Current data do not identify how far below the LEC10 5 s sublinearity

6 begins to prevail. Further research is needed to link CH accumulation to lung tumors and

7 describe the dose-response curve for CH accumulation.

8 (c) Human sensitivity compared with experimental animals: The pharmacokinetic

9 component is addressed by inhalation dosimetry models (U.S. EPA, 1994). For the

10 pharmacodynamic component, if rapid CYP2E1 metabolism leading to CH accumulation

11 in Clara cells is the key event, humans may be quantitatively less sensitive than mice to

this effect (Green, 2000). In this case, an inverse factor of 10–1/2 12 could be used to reflect

13 the potential lesser sensitivity of humans (U.S. EPA, 1996). Nonetheless, humans may

14 not necessarily be less sensitive if other CYP450 enzymes play a role or if other lung cell

15 types are involved.

16 (d) Nature and extent of human variability in sensitivity: For TCE in the lung, this factor is

17 problematic in light of the substantial variation revealed by the uncertainty analysis of the

18 pharmacokinetic modeling (Bois, 2000b). A factor of more than 300 would be needed to

19 reflect just the pharmacokinetic uncertainty in the lung between the 50th and 99th

20 percentiles (see Table 4-1). A further factor would be needed to reflect

21 pharmacodynamic differences. Clearly, more research is needed in the area of human

22 variation.

23 In light of these research needs (see Section 5), it is difficult to proceed with nonlinear

24 extrapolation to establish a dose at which lung tumors would not be of concern.

25

26 4.5.4. Risk Estimates From Kidney Tumors in Rats

27 4.5.4.1. Modeling

28 TCE causes kidney adenocarcinomas and adenomas in male and female rats by oral

29 gavage and in male rats by inhalation (see Table 3-2). TCE is also associated with kidney cancer

30 in humans (Wartenberg et al., 2000). Pharmacokinetic models (Clewell et al., 2000; Bois,

31 2000b) were used to estimate metabolites in the kidney. Bois’s uncertainty analysis (Bois,

32 2000b), however, revealed the presence of substantial parameter uncertainty in the kidney dose

33 estimates (see Table 4-1, Section 4.2.2). Consequently, dose estimates for the kidney were based

on equivalence of mg/kg3/4 34 -d instead (U.S. EPA, 1992). Rhomberg (2000) fitted empirical dose35 response models to the kidney data (see Table 4-8). Following NTP guidance to combine kidney

36 adenocarcinomas and adenomas (McConnell et al., 1986), the resulting human-equivalent LED01
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-26

for kidney tumors is 33 mg/kg-d. LED01s were calculated instead of LED10 1 s because kidney

2 tumor incidences can be observed below the 10% level.

3

4 4.5.4.2. Extrapolation to Lower Doses

5 TCE-induced kidney tumors may arise through multiple modes of action (see Section

6 3.5.2). The preponderance of evidence suggests that TCE’s GST metabolites are responsible for

7 kidney toxicity and kidney tumors (Lash et al., 2000b). Although these metabolites have not

8 been tested in cancer bioassays, the GST metabolite DCVC is mutagenic (Moore and

9 Harrington-Brock, 2000). Involvement of a mutagenic metabolite would indicate use of linear

10 extrapolation to lower doses (U.S. EPA, 1996). Cytotoxicity may also be involved, as

11 cytotoxicity is observed in both rats and humans exposed to high levels of TCE.

Linear extrapolation from the LED01 of 33 mg/kg-d yields a slope factor of 3×10–4 12 per

mg/kg-d (0.01/33 = 3×10–4 13 ).

14

15 4.5.5. Risk Estimates From Testicular Tumors in Rats

16 4.5.5.1. Modeling

17 Independent studies provide some evidence that TCE causes testicular tumors in rats by

18 oral gavage and by inhalation (see Table 3-4). Health Canada derived a cancer potency estimate

(a TD05 19 , the dose associated with an increased tumor risk of 5%) from the increased incidence of

20 testicular interstitial cell tumors in Marshall rats exposed to TCE (see Table 3-4). Health

Canada’s dosimetry assumed cross-species equivalence of mg/kg-d. Their resulting TD05 21 was

22 200 mg/kg-d.

Using EPA’s methodology with the same study yields an LED10 23 of 25 mg/kg-d. The

eightfold ratio of Health Canada’s TD05 to EPA’s LED10 24 is attributable to a factor of 3.8 for

25 EPA’s use of 3/4-power scaling and a factor of 2.2 between the lower-bound dose at 10%

26 response and the central-estimate dose at 5% response.

27

28 4.5.5.2. Extrapolation to Lower Doses

29 TCE-induced testicular tumors would likely result from endocrine disturbances (see

30 Section 3.5.4). There is evidence of endocrine disturbance in both rats (Kumar et al., 2000) and

31 humans (Chia et al., 1997; Goh et al., 1998). Endocrine disturbance in humans is one of the

critical effects used in developing the RfC, where a subchronic LOAEL of 11 ppm (59 mg/m3 32 )

33 was observed for decreased testosterone, decreased follicle-stimulating hormone, decreased

34 SHBG, and increased DHEA (see Section 4.4.2). Route extrapolation yields equivalent oral

35 doses of 0.8 mg/kg-d to 20 mg/kg-d, depending on whether TCA or DCA is used as the dose
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-27

1 metric (see Section 4.2.3). As a check for consistency with the tumor data, these precursor doses

lie below the LED10 2 for tumors of 25 mg/kg-d.

3 EPA’s proposed cancer guidelines (U.S. EPA, 1996) prefer to extrapolate from precursor

4 data instead of tumor data, using an RfD or RfC for the precursor if the tumors were

5 demonstrated to occur only subsequently to the precursor. If this is the case, then an RfD for

6 endocrine-related tumors would be calculated by dividing the point of departure for endocrine

7 disturbance (0.8 mg/kg-d) by the composite uncertainty factor (1,000, comprising 10 for human

8 variation, 10 for a subchronic study, 10 for the LOAEL) previously applied to endocrine

disturbance as a critical effect (see Section 4.4.3). The resulting RfD is 8×10–4 9 mg/kg-d.

10

11 4.5.6. Discussion of Alternative Cancer Risk Estimates

12 4.5.6.1. Slope Factors

13 Table 4-9 and Figure 4-3 compile the alternative points of departure and cancer risk

14 estimates for TCE. Inhalation-to-oral extrapolation of estimates from the Anttila study was

15 based on internal TCA (see Section 4.2.3), because that study quantified exposure by measuring

16 urinary TCA. Route extrapolation is also supported by the general similarity of tumor sites from

17 oral or inhalation exposure (see Section 3.4). Nonetheless, the best approach for route

18 extrapolation remains a topic for further research (see Section 5).

19 Two sets of estimates appear to lie apart from all others. On the low end, rats appear to

20 be less sensitive than mice or humans. On the high end, estimates from the Anttila study are

21 rather uncertain, based on a small number of cancer cases and an assumed uniform exposure

22 duration of 15 years. Setting aside these lowest and highest estimates, there appears to be

23 convergence of the other estimates, even though they are derived from different sources. The

remaining slope factors, per mg/kg-d, are 2×10–2 24 (derived from occupational inhalation

exposure), 3×10–2 to 2×10–1 (derived from mice), and 4×10–1 25 (derived from residential drinking

26 water exposure). Because they are supported by diverse studies and do not reflect the highest

27 estimates (from the Anttila study) or the lowest estimates (from the rat studies), these remaining

28 estimates constitute a middle range of risk estimates where confidence is greatest. This middle

29 range is robust in the sense that it is not likely to be dramatically altered by a new study or by

30 minor changes in the analysis of existing studies.

This new slope factor range, 2×10-2 to 4×10-1 31 per mg/kg-d, lies just above EPA’s previous

slope factor for TCE, 1.1×10-2 32 per mg/kg-d. The increase can be attributed principally to two

33 factors: (1) the pharmacokinetic models predict that humans would experience higher internal

34 doses of TCA and DCA than would mice from similar exposures (Rhomberg, 2000), perhaps due

35 to more extensive enterohepatic recirculation (Barton and Clewell, 2000); and (2) the generation

36 of TCA and DCA would be more efficient at the lower doses expected from environmental
71Cal/EPA’s cancer estimates are based on liver tumors in mice (NCI, 1976; Maltoni et al., 1986), modeled to an

LED10 of 7.9 mg/kg-d using total amount of TCE metabolized as the dose metric. Using linear extrapolation to

lower doses, their slope factor is 0.10/LED10 = 1.3×10–2. Cal/EPA considered nonlinear extrapolation to be

insufficiently supported by the data.

72A risk-specific dose is calculated by dividing a risk level by a slope factor. For example, if the slope factor is 2 per

mg/kg-d, then the risk-specific dose associated with a risk of 1 in 1 million is 10–6/(2 per mg/kg-d) = 5×10–7 mg/kg-d.

8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-28

1 exposure than from doses used in the bioassays (Rhomberg, 2000). The new slope factor range

also lies just above the recent slope factor used by Cal/EPA, 1.3×10-2 2 per mg/kg-d (Cal/U.S.

EPA, 1999).71 3

4

5 4.5.6.2. Nonlinear Analyses

6 Nonlinear extrapolations were attempted for liver tumors, lung tumors, and testicular

7 tumors. For liver tumors, the point of departure was in the range of 0.5–3.1 mg/kg-d, with a dose

8 reduction of 3,000–10,000 needed to reach a dose where there may be little concern for cancer. 

9 For lung tumors, it was problematic to determine the reduction needed to reach such a dose. For

testicular tumors, an RfD of 8×10–4 10 mg/kg-d was developed.

11

12 4.5.6.3. Joint Consideration of Slope Factors and Nonlinear Analyses

13 Joint consideration of results from linear and nonlinear extrapolations is an area for which

14 there has been no guidance. This may prove to be a complex question, as the purpose of linear

15 extrapolation is to estimate a bound on risk whereas nonlinear extrapolation attempts to make a

16 qualitative assurance of safety. It would not be appropriate to settle on a single result by

17 choosing one analysis and ignoring the other.

18 In the meantime, linear and nonlinear analyses can be harmonized somewhat by being

19 expressed in common units. For example, slope factors can be inverted to obtain risk-specific

doses.

72 For an increased risk of 1 in 1 million, the slope factor range (2×10–2 to 4×10–1 20 per

mg/kg-d) yields 2.5×10–6 to 5×10–5 21 mg/kg-d as a range of risk-specific doses. Similarly, for a

risk of 1 in 10,000, the range is 2.5×10–4 to 5×10–3 22 mg/kg-d. Nonlinear extrapolation can be

23 expressed as a dose where there may be little concern for cancer. For example, for a point of

24 departure of 1.8 mg/kg-d (the midpoint of 0.5–3.1 mg/kg-d, the point-of-departure range for liver

25 tumors) and a dose reduction of 6500 (the midpoint of 3000–10,000, the dose-reduction range

26 developed for mouse liver tumors), a dose where there may be little concern for liver cancer is

roughly 3×10–4 mg/kg-d (1.8/6500=3×10–4 27 ). Higher or lower results could be obtained by using

28 other choices from these two ranges, but the midpoints serve to illustrate the comparison between

29 linear and nonlinear health benchmarks.

30 Figure 4-3 shows that for TCE the health benchmarks under nonlinear extrapolation are

31 approximately 10–100 times the doses associated with an increased cancer risk of 1 in 1 million
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-29

1 under linear extrapolation. In other words, nonlinear extrapolation leads to doses that would be

2 associated with increased cancer risks of 1 in 100,000 to 1 in 10,000 under linear extrapolation. 

3 This suggests that if confidence increases in the appropriateness of nonlinear extrapolation for

4 TCE, then levels that we now associate with risks approaching 1 in 10,000 may be considered

5 virtually safe in the future.

6

7 4.5.6.4. Sensitive Populations and Cumulative Risks

8 Information is beginning to emerge about differences in metabolism, disease, and other

9 factors that make humans vary in their response to TCE, as well as the potential for other

10 chemicals to alter TCE’s metabolism and toxicity. This information indicates that a single risk

11 value is not appropriate to describe the differential effects of TCE. For this reason, the

12 alternative slope factors have not been consolidated into a single estimate. Rather, a range of

13 slope factors has been described. Depending on the characteristics of the exposed population and

14 the exposure scenario, each risk assessment should select an appropriate slope factor from this

15 range. Risk assessments involving the presence of risk factors such as diabetes or alcohol

16 consumption, or high background exposure to TCE or its metabolites, would more appropriately

17 choose a higher slope factor. Conversely, lower slope factors may be appropriate when these risk

18 factors and background exposures are not involved.

19 It will be a challenge for future research to quantify the differential risk indicated by each

20 risk factor or exposure scenario (see Section 5). In this way, nonlinear cancer assessments will

21 stop resembling single-point RfDs and will move toward risk ranges with the flexibility to

22 quantitatively accommodate population-specific and site-specific considerations.

23

24 4.5.7. Sources of Uncertainty in the Dose-Response Assessment

25 All risk assessments involve uncertainty, as study data are extrapolated to make

26 inferences about potential effects from human environmental exposure. One type of uncertainty

27 stems from different interpretations of the available data (known as model uncertainty), another

28 from estimation errors due to incomplete or imprecise data (parameter uncertainty). An

29 understanding of uncertainty can be used:

30 < To make more informed choices during the conduct of a risk assessment, and

31 < To better characterize the range of plausible risk estimates for different populations.

32

33 4.5.7.1. Model Uncertainty

34 This assessment’s exploration of multiple modeling approaches affords an opportunity to

35 understand and address different sources of model uncertainty. The full extent of model
73Exposure patterns refer to magnitude, frequency, duration, and timing of exposure. For example, exposure to 30

ppm for 10 hr at age 30 and exposure to 300 ppm for 1 hr at age 1 are two different exposure patterns with a total

exposure of 300 ppm-hr.

8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-30

1 uncertainty cannot be quantified, only the models that have been analyzed. In this assessment,

2 several sources of model uncertainty have been discussed:

3 < Two pharmacokinetic models initially led to risk estimates that differed by 15-fold (see

4 Table 4-4). To reduce this uncertainty, the models were fitted to additional data sets

5 (Bois, 2000a,b). This calibration improved the models and made the models’ results

6 more compatible, reducing this source of model uncertainty (see Section 4.2.1).

7 < A mechanism-based model assumption that TCE-induced liver tumor incidences are

8 proportional to a species’ background liver tumor incidence results in 100-fold lower

9 human risk estimates (see Section 4.5.2). Because current mechanistic understanding

10 suggests that cross-species extrapolation is far more complex (see Section 3.5.1), this

11 assessment chose estimates not based on this assumption.

12 < Different exposure routes yield different proportions of metabolite formation. Depending

13 on whether TCA formation or DCA formation is used as the basis of comparison, route

14 extrapolations can differ by 25-fold (see Section 4.2.3). To convey an appreciation for

15 this unresolved uncertainty, this assessment has developed estimates using both

16 approaches.

 < Different exposure patterns73 17 may yield different risks, but this has not been well

18 investigated. In the absence of TCE-specific data, this assessment has followed the

19 common, neutral practice of assuming that equal risks result from equal cumulative

20 doses. There are some data, however, to suggest that this approach may underestimate

21 risks from short-duration exposures (see Section 4.5.2).

22 < The choice between linear and nonlinear extrapolation to environmental doses creates

23 two distinct classes of estimates (see Sections 4.5.2, 4.5.3). This dichotomous uncertainty

24 stems from different interpretations of the information on TCE’s mode-of-action (see

25 Section 3.5). Some toxicologists maintain that cross-species differences would make

26 humans less sensitive to TCE according to the modes of action proposed for mice and

27 rats, suggesting less concern for human cancer. Other scientists maintain that such

28 conclusions are premature without more specific definition and experimental

29 confirmation of the active agents and sequences of key events leading to cancer. In

30 addition, some of TCE’s effects on cell signaling may augment ongoing processes and

31 conditions in some groups of humans, leading to an increased cancer risk for some. 

32 EPA’s proposed cancer guidelines take the latter view, that the active agents and key

33 events be identified in order to establish the mode-of-action. Consequently, this
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-31

1 assessment has prepared estimates using both linear and nonlinear approaches, the former

2 to bound the risks and the latter to demonstrate the degree of uncertainty and value of

3 further mechanistic research. In addition, this assessment has made efforts to develop

4 risk estimates from human studies as an alternative to extrapolating from animal models.

5

6 4.5.7.2. Parameter Uncertainty

7 Uncertainty analyses and confidence intervals were developed for some of the key

8 parameters used in this assessment. Each description of parameter uncertainty assumes that the

9 underlying model is valid.

10 < Uncertainty in the pharmacokinetic data is reflected by 95% confidence intervals

11 spanning approximately 100-fold for the liver, 14,000-fold for the lung, and 5,000-fold

12 for the kidney (see Table 4-1, Section 4.2.2). Because of this high uncertainty for lung

13 and kidney, this assessment reverted to default animal-to-human dose-scaling models for

14 these organs. In all cases, median dose estimates were used in subsequent calculations.

15 < Uncertainty in the animal dose-response data is reflected by the ratio of ED10s to

16 LED10s. These generally do not exceed a factor of 2 (Rhomberg, 2000).
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-32

Table 4-1. Distributions of potential dose metrics from the uncertainty analysis of the

pharmacokinetic models

Ratio of dose estimate at each percentile to the

mediana

Dose metric GSD 1 2.5 10 50 90 97.5 99

Span of

95% CIb

Mouse liver TCA AUC 3.2 1/15 1/10 1/4 1 4 10 15 100

Mouse liver DCA AUC 3.6 1/20 1/12 1/5 1 5 12 20 144

Mouse lung CH AUC 11.7 1/300 1/120 1/23 1 23 120 300 14,400

Mouse lung CH max 12.5 1/360 1/140 1/25 1 25 140 360 19,600

Rat kidney thiol 9.0 1/170 1/74 1/17 1 17 74 170 5,476

a

Assuming lognormally distributed errors.

b

Ratio of estimate at 97.5 percentile to estimate at 2.5 percentile.

Source: Adapted from Bois, 2000b, Table 5–7; Rhomberg, 2000, Table 15.
8/1/01 4-33 DRAFT–DO NOT CITE OR QUOTE Table 4-2. Oral studies supporting development of an RfD Study Species Duration; exposure route Exoerimental doses (mg/kg/day)1,2 Organ; effect Human-equival

Tucker et al. (1982) and Sanders et al. (1982) Mouse Subchronic (6 months) Oral - drinking water 0, 184, 2175, 393, 660 (m) 0, 184, 1935, 437, 793 (f) Liver: liver weight- body weight ratio Immune system: antibody (plaque)- forming cell assay NOAEL, PK - ad

NOAEL 

Buben and O’Flaherty (1985) Mouse Subchronic (6 weeks) Oral - gavage 0, 100, 200, 400, 800, 1600, 2400, 3200 Liver: liver weight- body weight ratio LOAEL, PK - ad

Berman et al. (1995) and Moser et al. (1995) Rat 14 days Oral - gavage 0, 506, 1507, 500, 1500 Liver: liver weight- body weight ratio Neurobehavioral LOAEL, PK - ad

BMD10, PK - adj

 (Weibull)  (Power) NOAEL 

Maltoni et al. (1986) Rat Chronic bioassay (52 weeks exposure, followed until natural death) Oral - gavage 0, 50, 250 Kidney: renal megalonucleocytosis NOAEL 

Dawson et al. (1993) Rat Developmental Oral - drinking water 0, 1.58, 1100 ppm 

•0.18, 129 mg/kg/day

Developmental:cardiac anomalies

LOAEL NOAEL 

Narotsky et al. (1995) Rat Developmental 

Oral - gavage

0, 10, 32, 101, 320,

475, 633, 844, 1,125

Developmental:anopthalmia/micropthalmia

NOAEL BMD10 

Griffin et al. (2000c) Mouse Subchronic

(4 weeks and 32weeks)Oral - drinkingwater

0, 21, 100, 400 Immune system/liver:

serum antinuclearantibodies (ANA),histopathologcialchanges, serumalanineaminotransferase

LOAEL 

1 NOAELs or LOAELs are underlined.2 From Barton and Clewell (1999).3 Human equivalent doses calculated using RfD methodology (Barnes and

Dourson, 1988) and scaling to the ratio of body weights to the 0.75 power. 

4 Tucker et al. (1982). 5 Saunder et al. (1982). 6 Berman et al. (1995).

7 Moser et al. (1995).8 NOAEL.
8/1/01 4-34 DRAFT–DO NOT CITE OR QUOTE Table 4-3. Inhalation studies supporting development of an RfC Study Species Duration; exposure route Concentration1 Organ; effect Human-equ

concentrati

Kjellstrand et al. (1983) Mouse Subchronic (30 days) inhalation 0, 37, 75, 150, 300 ppm Liver: liver weight- body weight ratio LOAEL,PK

BMD10, PK 

 

Chia et al. (1997) Goh et al. (1998) Chia et al. (1996) Human Subchronic (mean =5 years) inhalation 30 ppm (mean) Endocrine/Liver: FSH, testosterone, DHEAS4

,

FAI4, SHBG4, insulin

LOAEL 

Maltoni et al. (1986) Rat Chronic bioassay 

(104 weeks)inhalation

0, 100, 300, 600 ppm Kidney: 

megalonucleocytosis

NOAEL, PK 

Ruijten et al. (1991) Human Subchronic

(mean =16 years)inhalation

44 ppm (calculated

from mean cumulativeexposure)

CNS:massiter reflex latency

LOAEL 

Rasmussen et al. (1993) Human Subchronic 

(mean = 7 years)inhalation

40 - 60 mg/l urinaryTCA- • 20 ppm5

CNS:motor dyscoordination

LOAEL 

Vandervort andPelakoff (1973)

Human Subchronic

(8 years) inhalation

32 ppm CNS:

drowiness, fatigue,dizziness, eye irritation

LOAEL 

Okawa and Bodner(1973)

Human Subchronic 

inhalation

71 mg/l urinary TCA(average)- •20 - 30 ppm5

CNS:nausea, headache, dizziness,fatigue, upper respiratoryirritation 

LOAEL 

Arito et al. (1994) Rat Subchronic 

(6 weeks)inhalation

0, 50, 100, 600 ppm CNS:

heart rate,electroencephalographicchanges

LOAEL,PKBMD10, PK 

 (w

Kumar et al. (2000) Rat Subchronic 

(12 weeks and 24weeks)inhalation

0, 376 ppm Endocrine/Reproductive:

testosterone, testicularcholesterol, 17 B-hydrokysteroid dehydrogenase,glucose 6-P- dehydrogenase,sperm number and motility

LOAEL 

1 NOAELs or LOAELs are underlined.2 Human equivalent doses calculated using RfC methodology (U.S. EPA,

1994) for a category 3 gas, extrathoracic effects. NOAEL* [HEC] (mg/m3) = NOAEL* [ADJ] (mg/m3) x [(H b/g ) A / (H b/g ) H ]. 

The value of 1.0 is used if (H b/g ) A > (H b/g ) H . (H b/g ) A = 15.9, mouse

(Fisher, 2000); 14, mouse, 18.5, rat (Clewell et al., 2000). (H b/g ) H = 9.13,

male, 11.15, female (Fisher, 2000); 9.2 (Clewell et al., 2000).

3 HECs for mice and rats are from Barton and Clewell (1999).

4 DHEAS - dihydroepiandrosterone sulphate, FAI 

SHBG - sex hormone binding globin.

5 U-TCA interpolated to atmospheric concentratio

TCA is equivalent to 20 ppm (Axelson et al., 1994
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-35

Table 4-4. Human-equivalent LED10s (mg/kg-d) derived from liver tumor incidences in

four mouse studies (NTP males or females, NCI males or females), using two dose metrics

(TCA area-under-the-curve, DCA area-under-the-curve) and four pharmacokinetic models

(Fisher 2000; Bois, 2000a; Clewell et al., 2000; Bois, 2000b)

Based on TCA area-under-the-curve Based on DCA area-under-the-curve

Fisher

Bois/

Fisher Clewell

Bois/

Clewell Fisher

Bois/

Fisher Clewell Bois/ Clewell

Male mice (NTP) 0.19 1.47 0.077 0.23 0.77 NA 12.05 1.15

Female mice

(NTP) 0.33 3.03 0.15 0.45 1.39 NA 24.39 2.33

Male mice (NCI) 0.83 4.76 0.24 0.83 3.45 NA 40.00 4.35

Female mice

(NCI) 2.63 18.52 1.01 3.57 10.75 NA 169.49 18.18

Mean1

 of 4 exp’ts 0.40 3.14 0.16 0.50 1.67 NA 25.82 2.52

NA = not available.

1

Harmonic mean = N/(3 1/Xi); equivalent to averaging slope factors (which are inversely

proportional to LED10s), then taking the reciprocal to convert back to a dose.

Source: Adapted from Rhomberg (2000) (LED10s are computed as 0.10 divided by slope factors

in Rhomberg, 2000, Table 4).
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-36

Table 4-5. Extending the observed range with a study of less-than-lifetime dosing of TCE

Concentration

(ppm)

Duration

(wk)

Exposure index

(conc × dur) Hepatoma incidence

0 8 0 1/41 ( 2%)

100 8 800 3/36 ( 8%)

600 8 4,800 4/23 (17%)

0 78 0 4/66 ( 6%)

100 78 7,800 2/53 ( 4%)

300 78 23,400 8/59 (14%)

600 78 46,800 13/61 (21%)

Source: Adapted from Maltoni et al., 1986.
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-37

Table 4-6. Extending the observed range with studies of TCE’s metabolites

Compound

Dose

(DCA AUC) Liver tumor incidence

DCA 1.38 3/21 (14%)

14.01 1/18 (5%)

63.66 8/12 (67%)

88.4 25/30 (83%)

TCA 0.07 2/9 (22%)

7.12 8/21 (38%)

40.8 21/24 (88%)

TCE 67 (61%)

71 (86%)

67 (65%)

Source: Adapted from Chen, 2000.
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Table 4-7. Human-equivalent LEC10s (ug/m3) derived from

lung tumor incidences in four mouse studies, using RfC

dosimetry

LEC10

Female ICR mice (Fukuda) 1.4×105

Male Swiss mice (Maltoni) 1.3×105

Female Swiss mice (Maltoni) 3.6×105

Male B6C3F1 mice (Maltoni) 2.8×105

Mean of 4 experiments 1.9×105

Source: Adapted from Rhomberg, 2000. (LEC10s are computed as

0.10 divided by unit risks in Rhomberg, 2000, Table 9).
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-39

Table 4-8. Human-equivalent LED01s (mg/kg-d) derived from kidney tumor incidences in

rat studies, using 3/4-power scaling

Based on cross-species scaling

factor of 3/4-power of relative

body weight

Male and female rats, 5 strains pooled (NTP), gavage,

adenocarcinomas only 6.7×101

Male and female rats, 5 strains pooled (NTP), gavage,

adenocarcinomas and adenomas1 3.3×101

Male Sprague-Dawley rats (Maltoni), inhalation,

adenocarcinomas only N/A

1

NTP guidance (McConnell et al., 1986) recommends combining adenocarcinomas and

adenomas; Rhomberg (2000) Table 12 states that including adenomas yields values about half

those for adenocarcinomas alone.

Source: Adapted from Rhomberg, 2000. (LED01s are computed as 0.01 divided by slope factors

in Rhomberg, 2000, Table 13.)
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-40

Table 4-9. Compilation of cancer estimates

Point of departure

(mg/kg-d)

Slope factor

(mg/kg-d)–1

Risk-specific dosea

(mg/kg-d)

Cancer estimates based on human studies

Liver cancer

 Finnish cohortb 1.4c 7×10–2 1.4×10–5

Kidney cancer

 Finnish cohortb

 German cohort

0.05c

5c

2×100

2×10–2

5×10–7

5×10–5

Non-Hodgkin’s lymphoma

 Finnish cohortb

 New Jersey cohort

0.014c

0.25c

7×100

4×10–1

1.4×10–7

2.5×10–6

Cancer estimates based on mouse studies

Liver cancer

 Mechanism-based modeld

 Mechanism-based modele

 Linear extrapolation

 Nonlinear extrapolation

Not applicable

Not applicable

0.5–3.1

0.5–3.1

8×10–4

8×10–2

3×10–2–2×10–1

Not applicable

1.25×10–3

1.25×10–5

0.5–3.1×10–5

(3×10–4)

f

Lung cancerg 1.7–4.8 Not applicable (Not calculable)f

Cancer estimates based on rat studies

Kidney cancer 33h 3×10–4 3.3×10–3

Testicular cancer 25 Not indicated (8×10–4)

f

a

Dose associated with an upper-bound increased cancer risk of 1 in 1 million, calculated as 10–6/slope factor. When

nonlinear low-dose extrapolation is used, this is a dose considered to pose minimal cancer risks, calculated as the

point of departure divided by the composite risk reduction factor from a nonlinear dose-response analysis.

b

For the Finnish cohort, the slope factors are based on route extrapolation from air unit risks, using a correspondence

between inhaled and oral doses giving equal TCA area-under-the-curve (75 mg/m3

 ~ 1 mg/kg-d, see Section 5.2.3). 

Air unit risks for liver cancer, kidney cancer, and non-Hodgkin’s lymphoma are 9×10–7, 3×10–5, and 9×10–5 per

μg/m3

, respectively. c

The point of departure (an LED10) is calculated from the slope factor (LED10=0.10/slope factor). d

From a mechanism-based model, assuming that tumor response is proportional to background liver tumor incidence

(Chen, 2000).

e

From a mechanism-based model (see note m), without assuming that liver tumor response is proportional to

background liver tumor incidence.

f

This is a dose considered to pose minimal cancer risks, calculated as the point of departure divided by the composite

risk reduction factor from a nonlinear dose-response analysis. This is not a dose associated with a 1-in-1 million

cancer risk.

g

The point of departure is based on route extrapolation from the point of departure for an inhalation study in mice

using a correspondence between inhaled and oral doses giving equal TCA area-under-the-curve [75 mg/m3

 ~ 1

mg/kg-d, see Section 5.2.3]. The point of departure for mouse lung tumors (an LEC10) spans a range of 1.3×105

 to

3.6×105

 μg/m3. h

The point of departure is an LED01 rather than an LED10 (see Section 5.5.3).
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-41

0 10 20 30 40 50

Exposure index(concentration x duration)

0%

10%

20%

30%

40%

50%

Hepatoma incidence

8 wks 78 wks

Figure 4-1. Extending the observed range with less-than-lifetime dosing
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-42

0 20 40 60 80 100

AUC DCA

0%

20%

40%

60%

80%

100%

Liver tumor incidence

DCA TCA TCE

Figure 4-2. Extending the observed range with studies of TCE's metabolites
8/1/01 DRAFT–DO NOT CITE OR QUOTE 4-43

Mid-range of cancer risk-specific doses (excluding highest 

 RfD

 RfC

Liver cancer (Finnish workers)

Kidney cancer (Finnish workers)

Kidney cancer (polymorphism in German workers)

Non-Hodgkin's lymphoma (Finnish workers)

Non-Hodgkin's lymphoma (N Jersey drinking water exposure)

Liver cancer (range in mice, linear extrapolation)

Liver cancer (range in mice, nonlinear extrapolation)

Lung cancer (range in mice, no extrapolation)

Kidney cancer (rats, linear extrapolation)

Testicular cancer (rats, nonlinear extrapolation)

1E-8 1E-6 1E-4 1E-2 1E+0 1E+2 1E+4 1E+6 1E+8

TCE dose (mg/kg-d)

Risk-specific dose Point of departure

Figure 4-3. TCE health benchmarks
8/1/01 DRAFT–DO NOT CITE OR QUOTE 5-1

5. RESEARCH NEEDS

1 Although the database for TCE is rather extensive and much has been learned in recent

2 years, this is a time when new research could significantly increase understanding and lead to

3 later refinement of this health risk assessment. Many research needs were discussed in the state4 of-the-science papers that comprise volume 1 of this assessment, and this research promises to

5 increase understanding of TCE’s adverse health effects, the active agents and modes of action for

6 each effect, and their relevance to human environmental exposure. This section focuses on those

7 research needs that left this assessment with open questions that were filled by default

8 assumptions and methods. Further research into these areas could, therefore, have direct bearing

9 on specific sections of this assessment. The practical importance of describing these research

10 needs is not to decrease confidence in this assessment, as all assessments have areas that can be

11 improved by further research, but to be explicit about what specific research could help resolve

12 the major open questions in this assessment.

13

14 5.1. RESEARCH NEEDS FOR HAZARD ASSESSMENT

15 (a) TCE’s effects other than cancer have not been adequately identified or studied. There is a

16 lack of definitive chronic studies of effects other than cancer (see Section 3.4). 

17 Investigation into the modes of action for effects other than cancer lags behind what has

18 been developed for TCE-induced cancer (see Section 3.5).

19 (b) With EPA’s proposed cancer guidelines becoming more specific (U.S. EPA, 1999), it is

20 now apparent that what is needed is a description of the sequence of key events for each

21 tumor type and the dose levels at which those events begin to occur. Although there has

22 been much progress in developing and investigating several plausible modes of action,

23 the active agents and key events have not yet been identified (see Section 3.5).

24 (c) For TCE-induced liver cancer, further research is needed to identify the key events and

25 the doses at which they begin to occur. Characterizing biological markers of the liver

26 tumors from the TCE bioassays would promote understanding of the respective roles and

27 interaction of TCA and DCA in the TCE-induced tumors (see Section 3.5.1).

28 (d) Fuller identification of the mode-of-action and associated key events for kidney tumors,

29 especially with respect to the potential role of VHL mutations in human kidney cancer,

30 could affect the description of TCE as either “carcinogenic to humans” or “likely to be

31 carcinogenic to humans.” Replication of recent findings in another cohort showing

32 kidney tumors could warrant a future description of TCE as “carcinogenic to humans”

33 (see Section 1.3).

34
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1 5.2. RESEARCH NEEDS FOR DOSE-RESPONSE ASSESSMENT

2 (a) Route extrapolations can differ by 25-fold, depending on whether internal TCA or DCA

3 is used as the dose metric. Further research could identify the appropriate internal dose

4 metric for each toxic effect (see Section 4.2.3).

5 (b) There is a need for consensus on a preferred empirical model to be used for each type of

6 data set. This assessment’s experience with benchmark dose modeling revealed

7 differences of more than an order of magnitude depending on which empirical model was

8 used. This invites “model shopping” and introduces too much uncertainty for use in risk

9 assessments that support public health decisions (see Section 4.3.2, Table 4-2).

10 (c) The RfD includes a data-derived factor of 50 for human variation and default factors of

11 100 overall for uncertainty in extrapolating from animals to humans, from subchronic

12 studies to lifetime exposure, from effect levels to NOAELs, and from single-chemical

13 toxicity tests to complex exposures involving multiple chemicals. This latter 100-fold

14 factor indicates the potential for future research to reduce uncertainty and improve the

15 assessment’s accuracy (see Section 4.3.3. 4.3.4).

16 (d) There is considerable pharmacokinetic and pharmacodynamic uncertainty for children. 

17 An approach is needed for evaluating the need for and size of an additional factor to

18 ensure that the RfD and RfC are protective of children, while also providing incentives

19 for research to quantify the potential difference in sensitivity between children and adults.

20 (e) At low doses, the mode-of-action for liver tumors may be nonlinear; nonetheless, current

data do not identify how far below the LED10 21 s sublinearity begins to prevail. Further

22 research is needed to identify the key precursor events and describe their dose-response

23 curves (see Section 4.5.2).

24 (f) The mode-of-action for lung tumors is undeveloped with respect to developing dose25 response curves for the key events. In addition, there is substantial parameter uncertainty

26 in current pharmacokinetic estimates of a dose metric in the lung (see Section 4.5.3).

27 (g) Similarly, there is substantial parameter uncertainty in current pharmacokinetic estimates

28 of a dose metric in the kidney (see Section 4.5.4).

29 (h) This assessment identifies several risk factors that would make a population more

30 sensitive. Further research is needed to estimate the magnitude of the increased risk. 

31 Understanding the functional relationship between a risk factor and the associated disease

32 is a critical research need that presently prevents estimating the differential risk faced by

33 sensitive populations (see Sections 2.4, 2.5, 2.6, 4.5.6).

34 (i) It may be important to understand the joint effect of several risk factors together. If the

35 effects of several factors are multiplicative, the combined effect could be quite large (see

36 Section 4.5.6).
8/1/01 DRAFT–DO NOT CITE OR QUOTE 5-3

1 (j) Risk assessment guidelines and methods will need to explicitly consider the effect of

2 background exposures as risk assessments consider the cumulative effect of multiple

3 chemicals and stressors on an exposed population. Finding a safe dose in an otherwise

4 unexposed population does not mean that that dose is safe when background exposures

5 are considered (see Section 1.6).

6

7 5.3. RESEARCH NEEDS FOR EXPOSURE ASSESSMENT

8 (a) Limited data indicate that TCE has been detected in all of eight samples of human milk

9 from four U.S. urban areas. Quantification of levels of TCE and its metabolites in milk,

10 in both highly exposed and relatively unexposed populations, is a critical research need

11 that presently prevents comparing levels in milk with levels allowed in drinking water

12 (see Section 1.5).

13 (b) As TCE’s metabolites have a large role in TCE-induced toxicity, there is a need to

14 estimate cumulative exposure to these metabolites from all parent compounds, sources,

15 and pathways (see Section 1.6).
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