UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

 WASHINGTON D.C. 20460

OFFICE OF THE ADMINISTRATOR

SCIENCE ADVISORY BOARD

January 11, 2011

EPA-SAB-11-002

The Honorable Lisa P. Jackson

Administrator

U.S. Environmental Protection Agency

1200 Pennsylvania Avenue, N.W.

Washington, DC 20460

Subject: Review of EPA’s Draft Assessment entitled “Toxicological Review of 

Trichloroethylene” (October 2009)

Dear Administrator Jackson:

EPA’s Office of Research and Development (ORD) requested the Science Advisory 

Board (SAB) to conduct a peer review of EPA’s draft Integrated Risk Information System (IRIS) 

assessment entitled, “Toxicological Review of Trichloroethylene” (October 2009). This draft 

document responded to the National Academy of Sciences (NAS) 2006 recommendations 

published in a report entitled, “Assessing the Human Health Risks of Trichloroethylene: Key 

Scientific Issues” (National Research Council, 2006). In response to ORD’s request, the SAB 

convened an expert panel to conduct this review. The SAB Panel was asked to comment on the 

scientific soundness of the hazard and dose-response assessments of trichloroethylene (TCE)-

induced cancer and non-cancer health effects. Specifically, the SAB was asked to comment on 

the use of a physiologically-based pharmacokinetic (PBPK) model for dose and route of 

exposure extrapolation within species and across species; TCE metabolism and mode of action; 

the derivation of an oral reference dose (RfD) and inhalation reference concentration (RfC) for 

non-cancer toxicity; the weight of evidence of potential human carcinogenicity; and the 

estimated cancer oral slope factor and inhalation unit risk for TCE.

The SAB commends EPA for its comprehensive approach and responsiveness to the NAS 

recommendations. Overall, the SAB Panel supported EPA’s scientific approaches to the risk 

assessment and found these to appropriately adhere to EPA’s risk assessment guidelines. The 

SAB Panel made a number of recommendations aimed at enhancing the transparency of the draft 

assessment and strengthening the scientific basis for the conclusions presented. The SAB 

responses to the EPA’s charge questions are detailed in the report. SAB major comments and 

recommendations are provided below:
ii

• EPA has made significant changes that improve the existing PBPK model for TCE. The 

Panel supported the use of this updated PBPK model for dose- response assessment for 

the extrapolation of doses within species, across species and route-to-route extrapolation. 

The Panel also supported the use of the Bayesian framework for estimation and 

characterization of the PBPK model parameter uncertainties. The Panel made a number 

of suggestions for better documentation of the model.

• The Panel found that the draft document adequately synthesizes the available scientific 

information to support a conclusion that TCE poses a potential human health hazard for 

non-cancer toxicity, including effects on the central nervous system, the kidney, the liver, 

the immune system, the male reproductive system, and the developing fetus. 

• The Panel supported the selection of an RfC and an RfD based on multiple candidate 

reference values that fell within a narrow range rather than reliance on a single most 

sensitive critical endpoint. Although recognizing the kidney hazards of TCE, the Panel 

was concerned about the use of three candidate RfD/RfCs based on kidney effects as the 

primary basis for the RfD and RfC because of uncertainties regarding the relative rate of 

formation of toxic metabolites in humans vs. animals. The Panel recommends that EPA 

derive RfD/RfC values based on immunological endpoints and cardiac malformations.

• The Panel found that the EPA’s meta-analyses for kidney cancer, lymphoma, and liver 

cancer were well-conducted, with results that bolster the weight of evidence for potential 

human carcinogenicity from TCE exposure. Accordingly, the Panel agreed with EPA’s 

conclusion that TCE is considered to be “Carcinogenic to Humans” by all routes of 

exposure, based on convincing epidemiological evidence of a causal association between 

TCE exposure and kidney cancer, compelling evidence for lymphoma, and limited 

evidence for liver cancer. This conclusion is further supported by consistent evidence 

from animal studies and pharmacokinetic and metabolism information.

• EPA concluded that a mutagenic mode of action (MOA) was operative in TCE-induced 

kidney tumorigenesis. However, the Panel concluded that the available evidence also 

supports MOAs involving cell death and compensatory cell proliferation. The Panel 

agreed with EPA’s conclusion that there is inadequate evidence for an MOA mediated by 

activation of peroxisome proliferator receptor-alpha for TCE-induced liver cancer in 

humans.

• Finally, the Panel supported EPA’s approaches for deriving cancer inhalation unit risk 

and oral slope factors, including the use of default age-dependent adjustment factors to 

address susceptible populations. The Panel supported the use of the French occupational 

study (Charbotel et al., 2006) as the basis for estimating cancer unit risks, and the use of a 

default linear extrapolation from the point of departure for cancer dose-response 

assessment. The Panel, however, recommended inclusion of a more detailed discussion 

of assumptions used in the analysis to support the calculation of the unit risks. 
iii

The SAB appreciates the opportunity to provide EPA with advice on this important subject. 

The SAB urges EPA to move expeditiously to finalize the IRIS document for trichloroethylene. 

We look forward to receiving the Agency’s response.

Sincerely,

/signed/ /signed/

Dr. Deborah L. Swackhamer, Chair Dr. Deborah Cory-Slechta, Chair

EPA Science Advisory Board SAB Trichloroethylene Review Panel
iv

 

NOTICE

This report has been written as part of the activities of the EPA Science Advisory Board, 

a public advisory committee providing extramural scientific information and advice to the 

Administrator and other officials of the Environmental Protection Agency. The Board is 

structured to provide balanced, expert assessment of scientific matters related to problems facing 

the Agency. This report has not been reviewed for approval by the Agency and, hence, the 

contents of this report do not necessarily represent the views and policies of the Environmental 

Protection Agency, nor of other agencies in the Executive Branch of the Federal government, nor 

does mention of trade names or commercial products constitute a recommendation for use.

Reports of the EPA Science Advisory Board are posted on the EPA Web site at:

http://www.epa.gov/sab
v

U.S. Environmental Protection Agency

Science Advisory Board

Trichloroethylene Review Panel

CHAIR

Dr. Deborah Cory-Slechta, Professor, Department of Environmental Medicine, School of 

Medicine and Dentistry, University of Rochester, Rochester, NY

(Member of the Board 2003 – 2010)

MEMBERS

Dr. Scott Bartell, Assistant Professor, Program in Public Health, University of California -

Irvine, Irvine, CA

Dr. Aaron Blair, Scientist Emeritus, National Cancer Institute, National Institutes of Health, 

Rockville, MD

Dr. Anneclaire De Roos, Associate Professor, Department of Epidemiology, University of 

Washington and Associate Member, Epidemiology Program, Fred Hutchinson Cancer Research 

Center, Seattle, WA

Dr. Rodney Dietert, Professor, Department of Microbiology and Immunology, College of 

Veterinary Medicine, Cornell University, Ithaca, NY

Dr. Claude Emond, Adjunct Clinical Professor , Department of Environmental and 

Occupational Health , Faculty of Medicine, University of Montreal, Montréal, QC, Canada

Dr. Montserrat Fuentes, Professor, Department of Statistics, North Carolina State University, 

Raleigh, NC

Dr. David G. Hoel, Distinguished University Professor, Department of Biometry and 

Epidemiology, Medical University of South Carolina, Charleston, SC

Dr. Gunnar Johanson, Professor and Deputy Director, Institute of Environmental Medicine, 

Karolinska Institutet, Stockholm, Sweden

Dr. Deborah Keil, Professor, Medical Laboratory Sciences, Department of Pathology, 

University of Utah School of Medicine, Salt Lake City, UT

Dr. Jose Manautou, Associate Professor & Marlene L. Cohen and Jerome H. Fleisch Scholar, 

Department of Pharmaceutical Sciences, School of Pharmacy, University of Connecticut, Storrs, 

CT

Dr David McMillan, Associate Professor, Department of Pharmacology and Experimental 

Neuroscience, College of Medicine, University of Nebraska Medical Center, Omaha, NE
vi

Dr. Michael Pennell, Assistant Professor, Division of Biostatistics, College of Public Health, 

The Ohio State University, Columbus, OH

Dr. Kenneth M. Portier, Director of Statistics, Department of Statistics and Evaluation, 

American Cancer Society, National Home Office, Atlanta, GA

Dr. Gloria Post, Research Scientist, Office of Science, New Jersey Department of 

Environmental Protection, Trenton, NJ

Dr Gary Rankin, Professor and Chair of Pharmacology, Physiology and Toxicology, 

Pharmacology, Physiology and Toxicology, Joan C. Edwards School of medicine, Marshall 

University, Huntington, WV

Dr. Ivan Rusyn, Associate Professor, Environmental Sciences and Engineering, Gillings School 

of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC

Dr. Ornella Selmin, Associate Research Scientist, Nutritional Sciences, Shantz Building 38, 

Room 309, University of Arizona, Tucson, AZ

Dr. Brian Thrall, Technical Group Leader, Cell Biology Group , Pacific Northwest National 

Laboratories, Richland, WA

Dr. John Vena, Professor and Department Head, Department of Epidemiology and 

Biostatistics, College of Public Health, University of Georgia, Athens, GA

Dr. Virginia Weaver, Associate Professor, Departments of Environmental Health Sciences & 

Medicine, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD

SCIENCE ADVISORY BOARD STAFF

Dr. Marc Rigas, EPA Science Advisory Board, Science Advisory Board Staff Office, 

Washington, DC (2009 – 2010)

Dr. Holly Stallworth, EPA Science Advisory Board, Science Advisory Board Staff Office, 

Washington, DC

Dr. Diana Wong, EPA Science Advisory Board, Science Advisory Board Staff Office, 

Washington, DC
vii

U.S. Environmental Protection Agency

Science Advisory Board (2011)

CHAIR

Dr. Deborah L. Swackhamer, Professor and Charles M. Denny, Jr., Chair in Science, 

Technology and Public Policy and Co-Director of the Water Resources Center, Hubert H. 

Humphrey Institute of Public Affairs, University of Minnesota, St. Paul, MN

SAB MEMBERS

Dr. David T. Allen, Professor, Department of Chemical Engineering, University of Texas, 

Austin, TX

Dr. Claudia Benitez-Nelson, Full Professor and Director of the Marine Science Program, 

Department of Earth and Ocean Sciences , University of South Carolina, Columbia, SC

Dr. Timothy Buckley, Associate Professor and Chair, Division of Environmental Health 

Sciences, College of Public Health, The Ohio State University, Columbus, OH

Dr. Patricia Buffler, Professor of Epidemiology and Dean Emerita, Department of 

Epidemiology, School of Public Health, University of California, Berkeley, CA

Dr. Ingrid Burke, Director, Haub School and Ruckelshaus Institute of Environment and Natural 

Resources, University of Wyoming , Laramie, WY

Dr. Thomas Burke, Professor, Department of Health Policy and Management, Johns Hopkins 

Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD

Dr. Terry Daniel, Professor of Psychology and Natural Resources, Department of Psychology, 

School of Natural Resources, University of Arizona, Tucson, AZ

Dr. George Daston, Victor Mills Society Research Fellow, Product Safety and Regulatory 

Affairs, Procter & Gamble, Cincinnati, OH

Dr. Costel Denson, Managing Member, Costech Technologies, LLC, Newark, DE

Dr. Otto C. Doering III, Professor, Department of Agricultural Economics, Purdue University, 

W. Lafayette, IN

Dr. David A. Dzombak, Walter J. Blenko Sr. Professor of Environmental Engineering , 

Department of Civil and Environmental Engineering, College of Engineering, Carnegie Mellon 

University, Pittsburgh, PA

Dr. T. Taylor Eighmy, Vice President for Research, Office of the Vice President for Research, 

Texas Tech University, Lubbock, TX
viii

Dr. Elaine Faustman, Professor, Department of Environmental and Occupational Health 

Sciences, School of Public Health and Community Medicine, University of Washington, Seattle, 

WA

Dr. John P. Giesy, Professor and Canada Research Chair, Veterinary Biomedical Sciences and 

Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

Dr. Jeffrey Griffiths, Associate Professor, Department of Public Health and Community 

Medicine, School of Medicine, Tufts University, Boston, MA

Dr. James K. Hammitt, Professor, Center for Risk Analysis, Harvard University, Boston, MA

Dr. Bernd Kahn, Professor Emeritus and Associate Director, Environmental Radiation Center, 

Georgia Institute of Technology, Atlanta, GA

Dr. Agnes Kane, Professor and Chair, Department of Pathology and Laboratory Medicine, 

Brown University, Providence, RI

Dr. Madhu Khanna, Professor, Department of Agricultural and Consumer Economics, 

University of Illinois at Urbana-Champaign, Urbana, IL

Dr. Nancy K. Kim, Senior Executive, Health Research, Inc., Troy, NY

Dr. Catherine Kling, Professor, Department of Economics, Iowa State University, Ames, IA

Dr. Kai Lee, Program Officer, Conservation and Science Program, David & Lucile Packard 

Foundation, Los Altos, CA

Dr. Cecil Lue-Hing, President, Cecil Lue-Hing & Assoc. Inc., Burr Ridge, IL

Dr. Floyd Malveaux, Executive Director, Merck Childhood Asthma Network, Inc., Washington, 

DC

Dr. Lee D. McMullen, Water Resources Practice Leader, Snyder & Associates, Inc., Ankeny, 

IA

Dr. Judith L. Meyer, Professor Emeritus, Odum School of Ecology, University of Georgia, 

Lopez Island, WA

Dr. James R. Mihelcic, Professor, Civil and Environmental Engineering, State of Florida 21st 

Century World Class Scholar, University of South Florida, Tampa, FL

Dr. Jana Milford, Professor, Department of Mechanical Engineering, University of Colorado, 

Boulder, CO
ix

Dr. Christine Moe, Eugene J. Gangarosa Professor, Hubert Department of Global Health, 

Rollins School of Public Health, Emory University, Atlanta, GA

Dr. Horace Moo-Young, Dean and Professor, College of Engineering, Computer Science, and 

Technology, California State University, Los Angeles, CA

Dr. Eileen Murphy, Grants Facilitator, Ernest Mario School of Pharmacy, Rutgers University, 

Piscataway, NJ

Dr. Duncan Patten, Research Professor, Hydroecology Research Program , Department of Land 

Resources and Environmental Sciences, Montana State University, Bozeman, MT

Dr. Stephen Polasky, Fesler-Lampert Professor of Ecological/Environmental Economics, 

Department of Applied Economics, University of Minnesota, St. Paul, MN

Dr. Arden Pope, Professor, Department of Economics, Brigham Young University , Provo, UT

Dr. Stephen M. Roberts, Professor, Department of Physiological Sciences, Director, Center for 

Environmental and Human Toxicology, University of Florida, Gainesville, FL

Dr. Amanda Rodewald, Professor of Wildlife Ecology, School of Environment and Natural 

Resources, The Ohio State University, Columbus, OH

Dr. Jonathan M. Samet, Professor and Flora L. Thornton Chair, Department of Preventive 

Medicine, University of Southern California, Los Angeles, CA

Dr. James Sanders, Director and Professor, Skidaway Institute of Oceanography, Savannah, 

GA

Dr. Jerald Schnoor, Allen S. Henry Chair Professor, Department of Civil and Environmental 

Engineering, Co-Director, Center for Global and Regional Environmental Research, University 

of Iowa, Iowa City, IA

Dr. Kathleen Segerson, Philip E. Austin Professor of Economics , Department of Economics, 

University of Connecticut, Storrs, CT

Dr. Herman Taylor, Director, Principal Investigator, Jackson Heart Study, University of 

Mississippi Medical Center, Jackson, MS

Dr. Barton H. (Buzz) Thompson, Jr., Robert E. Paradise Professor of Natural Resources Law 

at the Stanford Law School and Perry L. McCarty Director, Woods Institute for the 

Environment, Stanford University, Stanford, CA

Dr. Paige Tolbert, Professor and Chair, Department of Environmental Health, Rollins School of 

Public Health, Emory University, Atlanta, GA
x

Dr. John Vena, Professor and Department Head, Department of Epidemiology and 

Biostatistics, College of Public Health, University of Georgia, Athens, GA

Dr. Thomas S. Wallsten, Professor and Chair, Department of Psychology, University of 

Maryland, College Park, MD

Dr. Robert Watts, Professor of Mechanical Engineering Emeritus, Tulane University, 

Annapolis, MD

Dr. R. Thomas Zoeller, Professor, Department of Biology, University of Massachusetts, 

Amherst, MA

SCIENCE ADVISORY BOARD STAFF

Dr. Angela Nugent, Designated Federal Officer, U.S. Environmental Protection Agency, 

Science Advisory Board Staff Office, Washington, DC

Dr, Thomas Armitage, Designated Federal Officer, U.S. Environmental Protection Agency, 

Washington, DC
xi

TABLE OF CONTENTS

ABBREVIATIONS AND ACRONYMS............................................................xii

EXECUTIVE SUMMARY..............................................................................1

RESPONSES TO EPA’S CHARGE QUESTIONS.................................................6

1. PBPK Modeling.................................................................................................................... 6

2. Meta-analysis of cancer epidemiology ............................................................................. 10

3. Non-Cancer Hazard Assessment ...................................................................................... 14

4. Cancer Hazard Assessment........................................................................18

5. Role of Metabolism on TCE Toxicity............................................................22

6. Mode of Action ................................................................................................................... 27

7. Susceptible Populations..................................................................................................... 30

8. Non-Cancer Dose-Response Assessment.......................................................................... 32

9. Cancer Dose-Response Assessment.................................................................................. 42

10. Age-Dependent Adjustment Factors.............................................................................. 45

11. Additional key studies...................................................................................................... 47

12. Research Needs................................................................................................................. 48

REFERENCES............................................................................................51

Appendix A: Editorial Comments..........................................................................................55
xii

ABBREVIATIONS AND ACRONYMS

AIC Akaike Information Criteria

ADAF age-dependent adjustment factor

BMD benchmark dose

BMDL benchmark dose lower bound

BW body weight

CI confidence interval

cRfCs candidate RfCs

cRfDs candidate RfDs

DCA dichloroacetic acid

DCVC dichlorovinyl cysteine

DCVG S-dichlorovinyl glutathione

DEHP di(2-ethylhexyl) phthalate

EPA Environmental Protection Agency

ESRD end stage renal disease

GC-MS gas chromatography-mass spectrometry

GSH gluthione

HEC human equivalent concentration

HED human equivalent dose

HPLC-UV high performance liquid chromatography-ultraviolet

idPOD internal dose points of departure

IQR interquartile range

IRIS Integrated Risk Information System

LOAEL Lowest Adverse Effect Level

MCMC Markov Chain Monte Carlo

MOA mode of action

NAG N-acetyl-β-D-glucosaminidase

NCI National Cancer Institute

NHL non-Hodgkin’s lymphoma

NOAEL No Adverse Effect Level

NRC National Research Council

NTP National Toxicology Program

OR odds ratio

ORD Office of Research and Development

PBPD physiologically-based pharmaodynamic

PBPK physiologically-based pharmacokinetic

p-cRfC PBPK model-based candidate RfCs

p-cRfD PBPK model-based candidate RfDs

PERC perchloroethylene

POD point of departure

PPARα peroxisome proliferator activated receptor alpha

RCC renal cell carcinoma

RfC reference concentration

RfD reference dose
xiii

RR relative risk

SIR standardized incidence ratio

SMR standardized mortality ratio

TCA trichloroacetic acid

TCE trichloroethylene

TCOH trichloroethanol

UF uncertainty factor

VSD ventricular defects
EXECUTIVE SUMMARY

This report was prepared by the Science Advisory Board (SAB) Trichloroethylene 

Review Panel (the “Panel”) in response to a request by EPA’s Office of Research and 

Development (ORD) to review the Draft IRIS Toxicological Review of Trichloroethylene (TCE) 

(hereafter referred to as the draft document). The Panel deliberated on the charge questions (see 

Appendix A) during a May 10 – 12, 2010 face-to-face meeting and subsequent conference calls 

on June 24, 2010 and September 13, 2010. The Panel’s draft report was considered and 

approved by the Chartered SAB in a public teleconference on December 15, 2010. There were 

12 charge questions that focused on: hazard assessment of non-cancer and cancer health effects, 

the use of a PBPK model for TCE and its metabolites for the derivation of a proposed oral 

reference dose (RfD), an inhalation reference concentration (RfC) for non-cancer endpoints, 

cancer weight of evidence classification, mode of action of TCE carcinogenicity, as well as 

inhalation and oral unit risks for TCE. This Executive Summary highlights the Panel’s major 

findings and recommendations.

The Panel commended the updated physiologically-based pharmacokinetic (PBPK)

model (Evans et al., 2009; Chiu et al., 2009) for dose-response assessment. The Panel found that 

while the PBPK model was generally well presented, its description was incomplete in that 

mass-balance equations were not presented. The Panel provided suggestions to improve model 

documentation and clarity, including clearer descriptions of the strategy behind the model 

structure and the biological relevance of each model equation. Model assumptions need to be 

more clearly described and the consequences of potential violations of these assumptions should 

be discussed. In addition, a more detailed justification was needed for the handling of betweenanimal variability in the model. The Panel agreed that use of the Bayesian framework for 

estimation and characterization of the PBPK model parameter uncertainties was appropriate. 

However, a more thorough description was needed for the choice of prior distributions, the 

Bayesian fitting methodology, and the fit of the posterior distribution for each model parameter. 

The Panel also generally endorsed the hierarchical calibration approach that uses the posterior 

results in mice to establish the rat priors, and the rat posterior results to set the human priors. 

The Panel also recommended performance of a local sensitivity analysis to identify key model 

parameters that drive changes in modeling results. 

PBPK Modeling

The Panel agreed that EPA’s updated meta-analyses for kidney cancer, lymphoma and 

liver cancer followed the National Research Council (NRC, 2006) recommendations. The Panel 

agreed with EPA’s conclusions that TCE increased the risk for the three cancers studied, based 

on appropriate inclusion criteria for studies, the methods of conducting the meta-analysis that 

included consideration of bias and confounding, and the robustness of the findings based on the 

tests for heterogeneity and sensitivity. The Panel also suggested performing a meta-analysis for 

lung cancer to further support the absence of smoking as a possible confounder. 

Meta-Analyses of Cancer Epidemiology
2

EPA has provided a comprehensive synthesis of the available evidence regarding the 

effects of TCE and its major metabolites on the central nervous system, the kidney, the liver, the 

immune system, the male reproductive system, and the developing fetus. One issue of concern 

was the inconsistencies between reported levels of glutathione conjugation pathway metabolites. 

The Panel recommended that the impact of these divergent levels be more transparently 

presented. The Panel recommended inclusion of the potential for TCE-induced immune 

dysfunctions (i.e., immunosuppression, autoimmunity, inappropriate and/or excessive 

inflammation) to mechanistically underlie other adverse health endpoints. 

Non-Cancer Hazard Assessment

The Panel agreed with EPA’s conclusion that TCE is “Carcinogenic to Humans” by all 

routes of exposure. This is based on convincing evidence of a causal association between TCE 

exposure and kidney cancer, compelling evidence for lymphoma, and more limited evidence for 

liver cancer as presented in the draft document. The epidemiologic data, in the aggregate, were 

quite strong. The summary risk estimates from the meta-analyses provided a clear indication of 

a cancer hazard from TCE. In addition, both animal data and toxicokinetic information provide 

biological plausibility and support the epidemiologic data. 

Carcinogenic Weight of Evidence

The Panel agreed with EPA’s conclusion that oxidative metabolites of TCE were likely 

responsible for mediating the liver effects. The Panel recommended that EPA examine studies 

that provided quantitative assessment of trichloroacetic acid (TCA) and dichloroacetic acid 

(DCA) formation after TCE exposure. Dose-response modeling, similar to that performed for 

tetrachloroethylene, may be considered by EPA to provide scientifically-based information on 

relative contribution, or lack thereof, of TCA and/or DCA to the liver carcinogenesis effect of 

TCE. 

EPA has provided a clear and comprehensive summary of the available evidence that 

metabolites derived from gluthione (GSH) conjugation of TCE mediate kidney effects. The 

Panel noted that uncertainties exist with regard to the extent of formation of the dichlorovinyl 

metabolites of TCE between humans and rodents. The issue of quantitative assessment of the 

metabolic flux of TCE through the GSH pathway vs. the oxidative metabolism pathway needs to 

be considered carefully. A more complete discussion of the strengths and limitations of the 

analytical methodologies used should be provided to address the large discrepancies in estimates 

of S-dichlorovinyl glutathione (DCVG) formation. 

Role of Metabolism

Mode of Action (MOA)

The Panel agreed that the weight of evidence supports a mutagenic MOA for TCEinduced kidney tumors. However, the Panel concluded that the weight of evidence also 
3

supported an MOA involving cytotoxicity and compensatory cell proliferation and including 

these may more accurately reflect kidney tumor formation than does a mutagenic mechanism 

alone. The combination of cytotoxicity, proliferation and DNA damage together may be a much 

stronger MOA than any individual components. 

The Panel agreed that the data are inadequate to conclude that any of the TCE-induced 

cancer and non-cancer effects in rodents are not relevant to humans. 

The Panel agreed that there is inadequate support for peroxisome proliferator activated 

receptor alpha (PPARα) agonism and its sequellae being key events in TCE-induced human liver 

carcinogenesis. Recent data from animal models (Yang et al., 2007) suggest that activation of 

PPARα is an important but not limiting factor for the development of mouse liver tumors, and 

additional molecular events may be involved. The Panel viewed the mode of action (MOA) for 

liver carcinogenicity in rodents as complex rather than unknown. It is likely that key events from 

several pathways may operate leading to acute, subchronic and chronic liver toxicity of TCE.

The Panel found EPA’s hazard assessment provided a good review of potentially 

susceptible populations, and identified factors (genetics, lifestage, background, co-exposures and 

pre-existing conditions) that may modulate susceptibility to TCE carcinogenicity and non-cancer 

effects. However, the Panel disagreed with EPA’s conclusion that toxicokinetic variability can 

be adequately quantified using existing data. The Panel recommended that exposure to solvent 

mixtures should be considered for potential co-exposures, since exposure to more than one 

chemical with the same target organ likely increases risk. 

Susceptible Populations

Selection of Critical Studies and Effects

The Panel supported the selection of a RfC and RfD based on multiple candidate 

reference values that lie within a narrow range at the low end of the full range of candidate 

reference values developed, rather than basing these values on the single most sensitive critical 

endpoint. The Panel expressed concerns about the use of several candidate critical studies and 

effects, specifically National Toxicology Program (NTP, 1988) [toxic nephropathy], National 

Cancer Institute (NCI, 1976) [toxic nephrosis], and Woolhiser et al. (2006) [increased kidney 

weights]. However, the Panel noted that uncertainties about the quantitative risk assessment 

based on kidney effects in NTP (1988), NCI (1976) and Woolhiser et al. (2006) did not indicate 

that there was uncertainty that TCE caused renal toxicity. As discussed previously, the three 

PBPK model-based candidate RfCs/RfDs (p-cRfCs/RfDs) for renal endpoints were based on an 

uncertain dose metric, especially in regard to the relative rate of formation of the toxic metabolite 

in humans and animals. Additional issues related to choice of toxic nephropathy in female 

Marshall rats from NTP (1988) included excessive mortality due to dosing errors and possibly 

other causes, and a high level of uncertainty in the extrapolation to the benchmark dose (BMD)

due to the use of very high doses and a high incidence (>60%) of toxic nephropathy at both dose 

levels used. With respect to toxic nephrosis in mice from NCI (1976), the BMD analysis was not 

supported because the effect occurred in nearly 100% of animals in both dose groups, and 

because a high level of uncertainty is associated with extrapolation from the Lowest Adverse 
4

Effect Level (LOAEL) at which nearly 100% animals were affected. Renal cytomegaly and 

toxic nephropathy, which were not selected as critical effects, occurred at high frequency in all 

treated groups. 

The Panel recommended that the two endpoints for immune effects from Keil et al. 

(2009) and the cardiac malformations from Johnson et al. (2003) be considered the principal 

studies supporting the RfC. The Panel also recommended that the endpoints for immune effects 

from Keil et al. (2009) and Peden-Adams et al. (2009) and the cardiac malformations from 

Johnson et al. (2003) be considered as the principal studies supporting the RfD. 

The screening, evaluation, and selection of candidate critical studies and effects used for 

the development of the RfC and RfD were sound. The derivation of the points of departure 

(PODs) was generally appropriate. However, the BMD modeling results were uncertain for 

some datasets. For example, the log-logistic BMD analysis for toxic nephropathy in female 

Marshall rats in NTP (1988), shown in Figure F-10 in Appendix F, may greatly overestimate the 

risks at low doses. As discussed above, this modeling involved extrapolation from a high 

LOAEL at which a high percentage of the animals were affected. 

EPA used PBPK-based dose metrics for interspecies, intraspecies, and route-to-route 

extrapolation. The Panel supported this approach for development of the RfC and RfD. The 

Panel noted that the candidate RfDs /RfCs for kidney endpoints were highly sensitive to the rate 

of renal bioactivation of the cysteine conjugate, S-dichlorovinyl glutathione (DCVC), in humans 

relative to rodents. Candidate RfDs/RfCs developed using this dose-metric were several 

hundred-fold lower than RfD/RfCs for the same endpoints based on applied dose with standard 

uncertainty factors. The Panel noted that the uncertainties about the in vitro and in vivo data 

used to estimate the rate of renal bioactivation of DCVC were much greater than for other dose 

metrics [e.g. there are large discrepancies in the rates of human glutathione conjugation reported 

by Lash et al. (1999a) and Green et al. (1997a)]. These uncertainties should be clarified and 

should be the basis of a sensitivity analysis in the next update of the TCE draft risk assessment. 

The Panel also recommended that the rationale for scaling the dose metric to body weight3/4, in 

conjunction with the interspecies extrapolation based on PBPK modeling, should be presented in 

a clearer and more transparent way. 

Derivation of RfD and RfC

The Panel agreed that, in general, the selection of uncertainty factors was clearly and 

transparently described and appropriate. EPA developed equivalent doses and concentrations for 

sensitive humans to replace standard uncertainty factors for inter- and intra-species 

toxicokinetics. The Panel concluded that the approach used, including the selections of PODs

and the extrapolations from rodent to human, followed by consideration of the 99th percentile 

human estimates, was acceptable to address the sensitive population. In future work, the 

variability and uncertainty could be better characterized by considering other quantiles of the 

distribution. 

Uncertainty Factors
5

In this assessment, EPA developed an inhalation unit risk and oral unit risk for the 

carcinogenic potency of TCE in accordance with the approach outlined in the U.S. EPA Cancer 

Guidelines (U.S. EPA, 2005a, b). The unit risks for renal cell carcinoma were based on a case 

control study published by Charbotel et al. (2006). The Panel found that the analysis of the 

Charbotel et al. (2006) data was well described and that the selection of this study to estimate 

unit risks was appropriate. However, more discussion is needed on whether or not it is necessary 

to adjust for exposure to cutting oils when computing an odds ratio or relative risk relating TCE 

exposure to kidney cancer. The Panel recommended that EPA take a closer look at the literature 

to determine if there are other studies which suggest that exposure to cutting oils is a risk factor 

for kidney cancer. EPA should also provide a more detailed discussion on the implication of 

assumptions made in their analysis. In addition, background kidney cancer rates in the United 

States were used in constructing the life table, although the Charbotel et al. (2006) data was 

based on a French cohort. A comparison of background cancer rates in France and the United 

States would be helpful in supporting their conclusions. The Panel supported the adjustment of 

the renal cell carcinoma unit risks to account for the added risk of other cancers, using the metaanalysis results and Raaschou-Nielsen et al. (2003). 

The Panel agreed that human data, when available, should be preferred over rodent data 

when estimating unit risk since within species uncertainty is easier to address than between 

species uncertainty. The Panel supported the use of linear extrapolation from the POD for cancer 

dose-response assessment of TCE as a default approach. The Panel agreed that characterization 

of uncertainty and variability was appropriate, and was exceptionally strong in the PBPK 

models. 

Inhalation Unit Risk and Oral Unit Risk

The Panel agreed that application of age-dependent adjustment factors (ADAFs) in the 

TCE analysis consistently followed recommendations in the U.S. EPA Cancer Guidelines (U.S. 

EPA, 2005a). All of the steps were clearly presented for inhalation exposure. However, the 

discussion for the oral exposure route was shortened and referred back to the inhalation section, 

making understanding of the example difficult to follow. Currently, EPA’s IRIS assessment 

provides lifetime cancer risk drinking water concentrations for adults only. The Panel 

recommended that drinking water concentrations for specified cancer risk levels should also be 

derived for various age groups. 

Age-Dependent Adjustment Factors (ADAFs)
6

RESPONSES TO EPA’S CHARGE QUESTIONS

1. PBPK Modeling

Is EPA’s updated PBPK model for TCE and its metabolites (also reported in Evans et al., 

2009, and Chiu et al., 2009) clearly and transparently described and technically and 

scientifically adequate for supporting EPA’s hazard characterization and dose-response 

assessment? Specifically, please address the PBPK model structure; Bayesian statistical 

approach; parameter calibration; model predictions of the available in vivo data; and 

characterization of PBPK model dose metric predictions, including those for the GSH 

conjugation pathway.

1a PBPK model structure

According to the TCE Review Document (page 3-64), the version of the PBPK model 

published by Hack et al. (2006) consisted of many parameter values that differed by study, 

particularly in the case of metabolism. In addition, according to the authors, DCA metabolism in 

the lung compartment remained highly uncertain. Subsequently, the EPA made efforts to 

improve the 2006 model using an extensive analysis with different datasets to produce the PBPK 

model used in this risk assessment. The Panel found that this PBPK model expansion seemed to 

accurately predict the internal dose in the target tissue. The Panel agreed that using a PBPK 

model improved the quality of the predictions for risk assessment and anticipated that the current 

model will reduce uncertainties that resulted from the use of previous PBPK models. 

The Panel found that, for the most part, the PBPK model was well presented in the TCE 

Review Document but also noted that improvement was still possible. For example, the 

conceptual representation of the PBPK model given in Figure 3-7 (page 3-69) was useful in 

understanding the changes made to the Hack (2006) model, but did not facilitate a full 

understanding of the model structure. Figure 3-7 could be expanded to also include the symbols 

used for the model parameters (e.g. blood flow and metabolic parameters along with the 

appropriate arrows and volumes in the compartments).

Response

The Panel agreed that the details provided in Appendix A fully explain how the 

population model was structured. However, the description of the PBPK model was incomplete 

in that the mass-balance equations are not presented. In parallel to presenting these equations, 

references should be given to Figure 3-7 (PBPK model structure) and Table A-4 (PBPK model 

parameters). A better description would facilitate a complete understanding of both the 

conceptual and mathematical structure of the model. The Panel suggested the following 

additions: 1) a more detailed explanation of how interspecies extrapolation was performed, 

especially the use of scaling equations, 2) graphical comparisons of prior vs. posterior 

distributions for all key parameters, and 3) fits and the graphs of the concentration-time profiles 

and the predictions of critical dose metrics. These additions can be made to either the master 
7

document or incorporated into Appendix A. Many of the desired graphics could be found in the 

“linked documents” but these were overlooked by many reviewers because they were not part of 

the formal documentation. Placing many of these graphics alongside the model descriptions will 

improve both clarity and transparency.

On the issue of PBPK model structure, the Panel had some difficulty in fully 

understanding its structure, and also noted deficiencies in the mathematical descriptions for each 

compartment. With enough work and persistence, the structure was understandable, but these 

deficiencies will be a bigger issue for users who are not experts in PBPK modeling. The Panel 

made recommendations regarding improvements to the documentation of the PBPK model.

The Panel believed that the model documentation should also highlight any questionable 

assumptions and discuss the potential implications of these assumptions being wrong. The Panel 

observed that there remained a significant amount of variability between animals that did not 

seem to be accounted for in the final model. Because the raw data sets were not available to the 

Panel, it was difficult to determine if this was indeed the case. In addition, some analyses 

discussed by the Panel would appear to be computationally unfeasible. The Panel initially 

discussed extensions of the model which would avoid some of these problems (e.g., inclusion of 

animal-specific parameters), but decided that these extensions are computationally unfeasible 

given current resources.

Recommendations: 

• Provide a better description of the final model structure and, in particular, provide a revised 

model structure diagram that identifies model parameters with model states and pathways 

(flows).

• Clarify the strategy behind the model structure and describe the biological relevance of each 

model equation. 

• Document model assumptions and discuss the consequences of potential violations of these 

assumptions, e.g. impacts on bias and accuracy.

• Provide a more detailed justification for how between animal variability is accounted for in 

the model.

1b Bayesian statistical approach

The Panel agreed with the EPA that use of the Bayesian framework for estimation and 

characterization of the PBPK model parameter uncertainties was appropriate. The general 

description of the Bayesian approach presented in the TCE review document was acceptable. 

The description of how uncertainty and variability are characterized was confusing mainly due to 

the inconsistent use of the terms “population” and “group.” The description of the Bayesian 

model fit suffered from a lack of sufficient detail to provide complete transparency. Several 

model parameters entered the Bayesian estimation method with wide and uniform prior 

distributions. The large number of such parameters made the Markov Chain Monte Carlo 

(MCMC) chains longer, resulting in long time to convergence and wide posterior distributions. 

The Panel noted high variability in the posterior distributions of many model inputs and the 

stated parameters. However the posterior distributions for many internal dose stated parameters 

were much less variable. 
8

The Panel would have liked to see the extent to which posterior parameter distributions 

are correlated. If rodent parameters were correlated as might be expected, how this correlation 

was accounted for in human-specific model parameter estimates should be discussed.

Recommendations: 

• Present better descriptions and/or details on the choice of prior distributions, the Bayesian 

fitting methodology and fit of the posterior distribution for each model parameter.

• Provide some information on correlations around posterior medians for species-specific 

parameters. 

• Supply more information on the model ordinary differential equations and on the likelihood 

function used in the Bayesian estimation.

1c Parameter Calibration

Parameter calibration as described in the draft Document was accomplished via a 

hierarchical fitting approach that used the posterior results in mice to establish the rat priors and 

the rat posterior results to set the human priors. The Panel generally endorsed this hierarchical 

fitting approach. 

Recommendation: 

• Improve the quality and the description of the assumptions underlying the use of the 

hierarchical approach to parameter calibration. Help the reader to understand the extent to 

which these assumptions are used consistently throughout the parameter calibration process.

1d Model Fit Assessment and Dose Metric Projections

There were a very large number of parameters in the PBPK model which made critical 

review of the whole model and in particular identifying the key issues around model fit a 

significant challenge. 

A review of Figures 3-9, 3-10, A-3 and A-4, suggested that the updated model has 

adequate fit. Table 3-45 was particularly useful, as were the graphs in the linked documents that 

provided detailed descriptions of how well the model fit for the individual in vivo studies. When 

evaluating the quality of each prior, the draft document focused on agreement of the interquartile 

ranges. In Figure 3.9 (page 3-107), the vertical axes changed from the Hack model fit to the 

updated model fit. This added a challenge to assessing model fit since the models were 

predicting two slightly different quantities [N-Ac(1,2-DCVC) excreted (ug) for the Hack model 

and N-Ac(1,2 or 2,2 -DCVC) excreted (ug) for the updated model]. 

As a measure of model goodness of fit, the draft document presented the residual error 

geometric standard deviations (Table 3-41, page 3-98). The Panel was not certain how to use this 

statistic. For example, what does it say about model fit when the residual error is GSD 2.7 for 

venous blood TCE? Does this indicate a good fit or poor fit? For people who are not familiar 

with the design of the PBPK model, it is hard to critically interpret the values in this table. 
9

The Panel pointed out other issues related to the evaluation of the posterior distributions. 

Some of the posteriors were flatter than their priors, which was an unexpected result. In 

addition, in Table 3-36, (section 3.5.6.2), pages 3-88 to 3-89, the Panel observed that prior and 

posterior distributions of model parameters were almost identical and only in a few cases were 

the distributions different. 

The Panel noted that a large number of studies were available to EPA for this review. 

Some of the rat studies were not used for parameter calibration and hence were used to assess the 

validity of the model; that is, to determine whether the fitted model was adequate to predict data 

from situations not specifically covered in the parameter estimation exercise. The Panel 

approved of this approach, finding that even a limited validation analysis improved the 

confidence of users in the final PBPK model and helped point to areas where the model may still 

be inadequate. 

Recommendations: 

• Move some graphical presentations from the linked graphics documents into the body of the 

report or into Appendix A. 

• Incorporate more discussion on model fit and in particular indicate areas where the model fits 

well and areas where it did not fit well. Tie this discussion somehow to Table 3-41.

• Include graphs that show predicted versus observed values for all data points used in the 

analysis (one graph per endpoint).

• To help readers identify which parameters are better specified than others, provide a table of 

model parameters listed in reverse order by the width of their posterior variability (width of 

the IQR or width of 95% CI). 

• Identify those parameters with very different prior and posterior distributions and discuss 

why this might be a reasonable result of the parameter calibration process. An alternative 

would be to provide a table where parameters are ranked based on the percent change of the 

posterior from the prior.

• Clarify which parameters are related to variability and which address parameter uncertainty. 

Separate the discussion of the two types of parameters. 

1e Lack of an adequate sensitivity analysis

The charge to the Panel did not specifically address parameter sensitivity but the Panel 

did discuss the lack of and need for some form of sensitivity analysis. A common feature of 

PBPK models is that the output is highly sensitive to a few parameters (key parameters) and far 

less sensitive to the remaining parameters.

Recommendation:

• Perform a local sensitivity analysis, starting from the final fitted PBPK model, to assess how 

small changes in model parameter estimates impact predictions. Provide graphical 

presentations of the sensitivity of the model to changes in key model parameters in the final 

documentation.
10

2. Meta-analysis of cancer epidemiology

NRC (2006) recommended that EPA develop updated meta-analyses of the epidemiologic 

data on TCE exposure and cancer, and provided advice as to how EPA should conduct 

such analyses. Is EPA’s updated meta-analysis of the epidemiologic data on TCE exposure 

and kidney cancer, lymphoma, and liver cancer clearly and transparently described and 

technically and scientifically adequate for supporting EPA’s hazard characterization and 

dose-response assessment? Specifically, please address the standards of epidemiologic 

study design and analysis as they were applied to select studies for inclusion in the metaanalysis; the rationales for study relative risk estimate selections; the meta-analysis 

methods; and the characterization of the conclusions of the meta-analyses. [Note: The 

scope of this charge question only includes the meta-analysis methods and results and not 

the overall weight of evidence for TCE carcinogenicity, which is addressed as part of a 

subsequent charge question.]

NRC recommended that EPA conduct a new meta-analysis and to (1) pay attention to 

essential design features; (2) include only studies where exposure is documented; (3) classify 

studies on objective characteristics; (4) assess study power for each; (5) combine cohort and 

case-control studies unless it introduces substantial heterogeneity; (6) test for heterogeneity; and 

(7) perform sensitive analyses. 

The Panel agreed that EPA followed these principles in their meta-analyses for 

lymphoma, and cancers of the kidney and liver. The EPA approach was clearly and transparently 

described and technically and scientific appropriate for supporting EPA’s hazard characterization 

and dose-response assessment. The Panel found EPA performed a thorough literature review and 

clearly developed a comprehensive listing of candidate studies for the meta-analyses. The 

strengths and weaknesses of each study were characterized and clearly presented in the draft 

document. Procedures for selection of studies for the meta-analyses were clearly described. 

Response

Studies selected for inclusion had clear indications of TCE exposure and included 

exposure assessments for each study participant. Exposure levels differed considerably among 

and within the studies, which was an advantage. Candidate studies were also evaluated based on 

study design, endpoints evaluated, TCE exposure assessment, follow-up procedures for cohort 

studies, interview type (for case-control studies), use of proxy respondents (for case-control 

studies), sample size, and statistical analysis. Information on these factors was clearly presented 

for each candidate study. Appropriate criteria for including and excluding studies from the metaanalysis were developed and carefully applied. Reasons for excluding studies were clearly 

stated. Studies included had cohort or case-control designs, appropriate evaluation of cancer 

incidence or mortality, adequate selection of study subjects, characterization of individual TCE 

exposure for each subject, and relative risk estimates for lymphoma or cancers of the kidney or 

liver adjusted for at least age, sex, and race. For example, studies where individual exposure to 

TCE could not be reasonably determined were excluded, even though some exposure to 

individuals in the group was a reasonable assumption. Although excluded studies likely included 

some individuals who had exposure to TCE, exclusion was appropriate because inclusion would 
11

likely result in classification of some unexposed individuals as exposed, which would increase 

exposure misclassification and bias estimates of relative risk downward. The Panel found EPA 

carefully considered and described overlap between different studies (because of slightly 

overlapping study populations and extended follow-up of individual cohorts) and made 

appropriate selection of the results to include in the meta-analyses. The strengths and weaknesses 

of the meta-analyses were appropriately considered in the evaluation and interpretation of the 

results in relation to hazard characterization. 

The Panel found that EPA discussed possible misclassification of exposure and disease 

for the studies included in the meta-analyses. EPA appropriately noted that most exposure 

assessment limitations would diminish relative risks and mute exposure-response gradients. 

EPA indicated that in only one study were the interviewers blinded with regard to 

case/control status. Although it is desirable to attempt blinding for case-control studies, it is 

usually not possible to fully accomplish this because subject responses during the interview 

provide clues as to subject status. The Panel thought this was not a serious limitation. 

The Panel found that EPA clearly described the statistical techniques used in the metaanalyses. Both random and fixed-effect models were used in the meta-analyses. This was useful 

to assess the accuracy of the underlying assumptions regarding study variation. The Panel 

agreed with EPA’s reliance upon the random effects models for interpretation. Use of several 

approaches to evaluate heterogeneity provided a fuller characterization than would be available 

from any single technique. The potential for publication bias was appropriately evaluated. The 

robustness of the findings was highlighted based on the tests for heterogeneity and sensitivity. 

Results from the meta-analyses were fully and clearly presented in tables and figures. 

Meta-analyses were performed only for lymphoma, and cancers of the kidney and liver. 

The text did not make clear why only these three were selected for the meta-analysis approach, 

although it was assumed this was because prior reviews of the literature had identified these 

cancers as possibly associated with TCE exposure. The Panel found it might be useful to have 

information on other cancers to provide evidence regarding possible confounding. For example, 

kidney cancer was associated with smoking. Most cohort studies lacked information on tobacco 

use. However, if there was confounding by smoking, there would have to be an excess of lung 

cancer and other tobacco-related diseases in the cohorts. Absence of an excess of lung cancer 

was very strong evidence that workers exposed to TCE did not smoke more than the unexposed, 

or comparison population. Although no studies had excess of lung cancer, a meta- analysis of 

lung cancer showing no association with TCE would document this conclusion regarding 

possible confounding. Smoking could not cause excesses of kidney cancer, liver cancer or 

lymphoma without also causing an excess of lung cancer. The lack of effect of TCE for lung 

cancer in individual studies provided convincing evidence that confounding by smoking is 

unlikely. 

The Panel agreed that EPA carefully evaluated the data from the studies included in their 

review and results from the meta-analyses against standard epidemiologic criteria for causality, 

i.e., consistency, strength of the association, specificity of the association, temporal relationship, 

exposure-response gradient, biologic plausibility, coherence, experimental evidence, and 

analogy. The document provided a full discussion of these issues.
12

Bias and confounding are concerns in epidemiologic studies. The Panel agreed that the 

draft document had a strong discussion on potential confounding. Age, gender and race were 

appropriate potential confounders to include in the meta-analyses and the meta-analyses included 

effect estimates that were adjusted. The potential for confounding was evaluated in a number of 

ways. Several of the case-control studies could directly adjust for potential confounding from 

important risk factors and provide directly adjusted relative risks. EPA also pointed out that 

many potential confounders, e.g., obesity, diabetes, tobacco, and hypertension in kidney cancer, 

were unlikely to be associated with the level of TCE exposure and, thus, were unlikely to 

confound. If these factors did confound, other cancers would be affected. Other occupational 

exposures were mentioned as possible confounders, e.g., other organic solvents, cutting fluids, 

and hydrazine. The link between most of these and the cancers of concern relative to TCE was 

weak or non-existent, so they were not strong candidates for confounding. Biases are also a 

concern in observational studies. In case-control studies, case-response bias and case or control 

selection bias are a concern, while in cohort studies biases associated with follow-up and 

exposure are a concern. No obvious bias that would occur across studies of different designs, in 

different countries, and with different exposure metrics falsely produced an association with 

TCE. The Panel did not think confounding or bias were likely explanations for the findings from 

the epidemiologic studies and meta-analyses. 

The Panel agreed that the findings of several community studies although intriguing, 

were appropriately omitted from the meta-analyses due to large misclassification errors and lack 

of control for confounding, which would tend to bias estimates from the meta-analysis. 

The Panel found that EPA appropriately discussed the changing classification of 

hematopoietic and lymphatic system tumors and selected lymphoma (predominately nonHodgkin’s lymphoma (NHL) as an outcome for meta-analysis. EPA specifically wanted to 

select studies with the best outcome definitions, rather than pick at studies where the 

hematopoietic cancers were grouped. (e.g. myeloid and lymphoid neoplasms together). EPA 

selected studies representing various groupings of NHLs (with some studies that included 

chronic lymphocytic leukemia) or focused on specific subtypes of NHL (including one study that 

focused on hairy cell leukemia), but did not include studies of Hodgkin lymphoma (if any such 

studies existed). Given that the EPA’s intent was to conduct a meta-analysis with NHL as the 

outcome, the Panel felt that the terminology should be changed to ‘non-Hodgkin lymphoma’ 

instead of ‘lymphoma’, throughout the document. The term ‘NHL’ more accurately describes 

the intent of the analysis as well as the overwhelming majority of cases in the analysis, despite 

changing classification schemes. The focus of the meta-analysis on NHL and any indication in 

the meta-analysis where cases definition may diverge from classical NHL (as in studies that 

included chronic lymphocytic leukemia) should be clearly explained in both Appendix C and in 

the Hazard Characterization section (section 4.6.1.2.2) in the main document. 

The Panel agreed that appropriate approaches were used in the meta-analysis. Effect size 

(the relative risks or odds ratios) included in the meta-analyses were selected appropriately using 

the most appropriate selection criteria. However the Panel had a few questions of clarification 

about the meta-analysis for kidney cancer.
13

There are a number of technical points that should be mentioned as footnotes to the meta 

analysis plots. First, the exact confidence intervals given in the original publications have been 

replaced with approximations. The Panel suggests that the explanation in Appendix C be 

reiterated in the main document. For reference, Appendix C, Table C-6 (pages C-26 to C-27)

shows the actual SE(logRR) used to calculate the weights. In addition, Appendix C, page C-3, 

lines 14-20 explains the discordant confidence intervals in the figures. A second example is that 

a 20 year lag was used for the Zhao study while lags were either not given or not used in the 

other studies. Clarify the rationale for selecting the “20 yr lag” result from Zhao et al. (2005) and 

not selecting the “20 yr lag” result from Raaschou- Nielsen et al. (2003).

The Panel agreed with EPA’s conclusions from the meta-analyses that TCE increased the 

risk for the three cancers studied. The Panel’s agreement with EPA’s conclusion was based on 

the strict and appropriate inclusion criteria, the methods of conducting the meta-analyses, 

including consideration of bias and confounding, and the robustness of the findings based on the 

tests for heterogeneity and sensitivity.

Recommendations:

• Provide a rationale for the three cancer sites selected for the meta-analysis. The rationale 

could be nicely summarized in a table.

 Consider including meta-analysis for lung cancer for confounding purposes or other sites 

for comparison for which some association with TCE exposure has been reported in 

epidemiologic studies, such as childhood leukemia and cervical cancer. It might also be 

possible to provide this information without a formal meta-analysis.

 Provide measures of heterogeneity such as the I2 statistic for each meta-analysis. 

Although this information was provided and accurately explained in Appendix C, it was 

mischaracterized at several points in the primary document. For example, the summary 

of the kidney cancer meta-analysis on p. 4-167 of the primary document states that “there 

was no observable heterogeneity across the studies for any of the meta-analyses,” but 

Appendix C indicates “the I2 value of 38% suggested the extent of the heterogeneity was 

low-to-moderate.” Non-significant heterogeneity is indeed observed heterogeneity. 

 Evaluate the likely impact of converting odds ratios to relative risk estimates (i.e., using 

the method of Greenland (2004) or Zhang and Yu (1998), and decide if necessary to 

perform these conversions for the meta-analysis. 

 Change the terminology regarding the meta-analysis results for ‘lymphoma’ to ‘nonHodgkin lymphoma’ throughout the document. 
14

3. Non-Cancer Hazard Assessment

Does EPA’s hazard assessment of non-cancer human health effects of TCE logically, 

accurately, clearly, and objectively represent and synthesize the available scientific 

evidence to support its conclusions that TCE poses a potential human health hazard for 

non-cancer toxicity to the central nervous system; the kidney; the liver; the immune 

system; the male reproductive system; and the developing fetus, including the role of TCE 

in inducing fetal cardiac defects? 

Response: 

The Panel agreed that the EPA’s TCE hazard assessment has clearly, accurately, logically 

and objectively represented and synthesized the available scientific evidence to support its 

conclusions that TCE poses a potential human health hazard for non-cancer toxicity. 

Specifically, the EPA has provided a comprehensive and thorough synthesis of the available 

evidence regarding the effects of TCE and its major metabolites in each of the tissues addressed 

in the charge question. This includes human epidemiological studies, animal studies, in vitro 

studies using renal cell cultures, and in vivo and in vitro metabolism studies.

3a Central Nervous System

TCE-associated auditory impairment was discussed in this section (4.3.2.3.). It is noted 

that auditory impairment is commonly seen with various autoimmune conditions and 

inflammation-based diseases and these were among the immune dysfunctions observed with 

TCE exposure.

3b The Kidney

In regard to the effects of TCE in the kidney, EPA had provided a thorough and clear 

description of these effects. One issue of concern here was the quantitative aspect of the GSH 

pathway metabolites. Dr. Wolfgang Dekant, in his public comment, suggested that data obtained 

using the “Reed method” overestimated the amount of DCVG produced. This HPLC method is 

characterized by variability and overall decline in retention times over the life of the HPLC 

column due to derivatization of amine groups on the column (Lash et al., 1999b). Although data 

are limited, GSH pathway metabolite levels reported by methods that utilize

14C TCE and 

radiochemical detection followed by mass spectrometry identification of the metabolites (Green 

et al, 1997a) are lower than those from reports using the “Reed method”. In addition, studies

using HPLC-MS/MS techniques with stable isotope-labeled DCVG and DCVC standards have 

also been used to detect GSH pathway metabolite levels (Kim et al, 2009). Based on the in vitro 

work presented in Table 3-23 (page 3-44 of the draft EPA document) determining DCVG 

formation by the “Reed method” in human, rat and mouse liver, one would expect mouse serum 

DCVG levels to be ~4-6 times lower than humans. However, using the HPLC-MS/MS technique 

of Kim et al., the peak DCVG serum levels are ~1,000 times lower in mouse serum than 

determined by Lash et al. (1999a) in human serum. Although differences in exposure routes, 

exposure doses, etc. should be considered, this much larger than expected difference also 

suggests that the “Reed method” provides an overestimation of DCVG levels in humans. This 

could occur if the “Reed method” identifies non-specific derivatives as DCVG or other GSH 
15

pathway metabolites. Thus, interpretation of DCVG levels from the Lash et al. (1999a) paper 

should be made with caution.

It is noted that the focus on animal data in the EPA report is appropriate because human 

data on non-cancer kidney effects from TCE are limited by two factors. The first is outcome 

assessment. Due to the insensitivity of the clinical kidney outcomes such as glomerular filtration 

rate and end stage disease, human nephrotoxicant work often uses kidney early biological effect 

markers. Unfortunately, research to accurately determine the prognostic value of these 

biomarkers is fairly limited and data analysis in many of these studies is quite rudimentary often 

involving only a comparison of unadjusted mean values between an exposed and a control group. 

A range of biomarkers are used and results are frequently not entirely consistent as noted in 

Section 4.4. The second challenge is that human exposure often involves a mixture of solvents 

making determination of the impact of an individual solvent difficult. For example, the GNPROGRESS retrospective cohort study in Paris, France, which examined the impact of solvents 

on risk of end stage renal disease (ESRD) and progression of glomerulonephritis, included 

patients with a wide range of solvent exposures. Solvent exposure was assessed by industrial 

hygienists from lifetime occupational histories collected by interview and a list of the 30 most 

common solvents. These authors noted an elevated risk for progression of glomerulonephritis to 

ESRD from TCE although numbers were small and did not achieve statistical significance 

(adjusted hazard ratio [95% CI] 2.5 [0.9 to 6.5]) (Jacob et al, 2007). These authors also did not 

discuss how they addressed exposure to solvent mixtures as they attempted to focus on specific 

agents.

3c The Liver

The only criticism noted for this section was the (perhaps unavoidable) repetitive nature

of their coverage, as these issues appeared elsewhere in the document. Less repetition and better 

integration of these sections would improve the readability of the document.

3d The Immune System

It is noted that the children’s exposure data and adverse outcomes are consistent with the 

immunotoxicity reported in the animal developmental models. It is noted that while TCE 

exposure can produce a range of immune dysfunctions, including immunosuppression, elevated 

risk of autoimmunity and dysregulation of inflammation, it is possible that the doses of TCE 

producing each category of adverse immune outcomes may differ. For example, most studies 

reporting autoimmune dysregulation used higher doses of exposure compared with at least some 

studies where immunosuppression was observed.

3e The Male Reproductive System

It is noted that male potency/sterility issues can be associated with inflammatory 

dysfunction in the testes produced by some environmental pollutants (usually associated 

testicular macrophage dysfunction) (see Pace et al., 2005). Since inflammatory dysfunction is 

associated with TCE exposure, this is an additional possible mechanism that may be associated 

with adverse outcome for male potency. For in utero exposure studies in rodents using lower 

doses of TCE and metabolites, where effects (carcinogenic and non-carcinogenic) can be 
16

observed transgenerationally, attention should be directed to epigenetic changes as possible 

MOA for TCE-mediated effects on the reproductive systems.

3f The Developing Fetus, Including the Role of TCE in Inducing Fetal Cardiac Defects 

It is noted that the type of cytokine dysregulation seen with TCE exposure (e.g., 

involving IL-6) can play a role in cardiac dysfunction. The report explains logically why the 

Johnson et al. (2003) study was used to derive some reference points. Some recent publications 

confirm and reinforce the results obtained in the Johnson et al. (2003) study and could be cited 

to make a stronger argument. They are listed as follows:

• TCE effects on the cardiac system were specific for a narrow window of development 

corresponding to myocardial expansion and endocardial cushion formation, consistent 

with previous findings from Drake et al,2006a and b; Mishima 2006; Boyer et al. 2000,

and consistent with the definition of a teratogen.

• The types of defects and morphological changes (e.g cardiac hypertrophy and hypoplasia) 

were consistent with a mechanism of action involving disruption of calcium handling and 

cardiac contractility, observed by Caldwell et al, 2008 in rat cardiomyocytes. Numerous 

literature data (reviewed in Lehnart et al., 2008; Lebeche et al, 2008; Yano et al., 2008;

Gyorke et al., 2008) confirm the notion that alteration of calcium homeostasis is 

sufficient to induce alteration of contractility and in turn heart defects.

• A non-monotonic dose-response relationship was found that confirms several other 

studies (Caldwell et al., 2008; Drake et al., 2006) suggesting the presence of more than 

one MOA due to presence of metabolites, enzymatic sensitivity, etc.

Recommendations

• If additional endpoints of renal dysfunction (e.g. diuresis, increased glucose excretion) 

were present in the reported studies, they should be included in the report. Often only 

one or two parameters of renal function and histopathology were presented. A better 

overall description of renal dysfunction should be presented if available (especially for 

animal studies).

• There should be a better description of the location of the renal lesion, including nephron 

segment, if known. For example, TCE and DCVC appeared to affect the proximal tubule 

at the level of the outer stripe of the medulla (S3 segment of proximal tubule). Is this the 

site of lesions seen with other TCE metabolites? Explaining the role (or lack of a role) of 

any other TCE metabolites in TCE nephrotoxicity could be strengthened by comparing 

the sites of the renal lesion.

• On page 4-338, please clarify the use of the phrase, “subpopulation levels”, on lines 31 

and 33.
17

• A statement should be added that the spectrum of TCE-induced immune dysfunctions 

(immunosuppression, autoimmunity, inappropriate and/or excessive inflammation) 

included in this EPA draft report has the potential to produce adverse effects that are seen 

well beyond lymphoid organs and involving several other physiological tissues and 

systems. The types of immune-inflammatory dysfunctions described in this report have 

been observed to affect function and risk of disease in the nervous system (e.g., loss of 

hearing), the skin, the respiratory system, the liver, the kidney, the reproductive system 

(e.g., male sterility), and the cardiovascular system (e.g., heart disease, atherosclerosis). 

• A statement should be added to emphasize the cell-mediated immune effects of TCE as 

some of this has been supported by the human epidemiology data and the issue is 

pertinent to risk of cancer. 
18

4. Cancer Hazard Assessment

Using the approach outlined in the U.S. EPA Cancer Guidelines (U.S. EPA, 2005a), does 

EPA’s hazard assessment of carcinogenicity logically, accurately, clearly, and objectively 

represent and synthesize the available scientific evidence to support its conclusions that 

TCE is carcinogenic to humans by all routes of exposure? Specifically, please address the 

epidemiologic evidence for associations between TCE and kidney cancer, lymphoma, and 

liver and biliary tract cancer; the extent to which the results of the meta-analyses 

contribute to the overall weight of evidence for TCE carcinogenicity; the laboratory animal 

data for rat kidney tumors, mouse liver tumors, and lymphatic cancers in rats and mice; 

and the toxicokinetic and other data supporting TCE carcinogenicity by all routes of 

exposure.

Response

The Panel agreed that cancer hazard characterization hinges on the synthesis of the 

accumulated scientific evidence, especially the epidemiologic evidence supporting the 

carcinogenicity of TCE. Assessment of the causal association and weight of evidence supported

the conclusion that TCE is carcinogenic to humans by all routes of exposure as outlined in the 

US EPA cancer guidelines. Results from animal bioassays and toxico-kinetic data provide 

further support to the EPA conclusion. The report logically, accurately, clearly, and objectively 

presented the methodological review of the epidemiologic evidence, highlighted the criteria for 

study inclusion in meta-analyses and the meta-analysis methods (as noted in charge question 2) 

and appropriately assessed the weight of the evidence to conclude that TCE is causally related to 

lymphoma, and kidney and liver cancer. 

Epidemiological Data

The report appropriately highlighted the causal criteria in support of the conclusion. The 

consistency of the findings was notable given the rarity of the cancers, differences in latency and 

potential for exposure misclassification as described in the study assessments highlighted in the 

hazard characterization. Multiple explanations would be needed to account for the associations 

between TCE and several cancers from studies with differing designs, strengths and weaknesses. 

The summary risk estimates from the meta-analyses provided a clear indication of a 

cancer hazard from TCE. The pooled risk estimates from the meta-analyses for kidney cancer 

and liver cancer, although modest, were robust with no indication of publication bias or 

heterogeneity. Meta-analyses for both kidney cancer and lymphoma found higher increases in 

the risk estimates associated with higher TCE exposure than for any TCE exposure and no 

evidence of strong confounding, which further supported a causal association. 

:

EPA concluded TCE is carcinogenic to humans by all routes of exposure. This 

conclusion was based on convincing evidence of a causal association between TCE exposure and 

kidney cancer, compelling evidence for lymphoma, and more limited evidence for liver cancer. 

The epidemiologic data, in the aggregate, were quite strong. In addition, the epidemiologic data 

were supported by bioassays and toxicokinetic data. Although issues of concern could be raised 
19

about individual studies, the overall pattern and the results from the meta-analyses were quite 

compelling. Potential confounding from established risk factors for these cancers of concern 

could be directly assessed in some studies and indirectly evaluated by reviewing cancer excesses 

that did not occur in TCE exposed populations, e.g., the absence of an excess for lung cancer 

indicates confounding from smoking is not likely. 

Some studies had low power to evaluate the TCE-cancer relationship, but the metaanalysis provides a tool to combine underpowered studies and assess the overall effect. 

Exposure assessment in epidemiologic studies is difficult in the best of circumstances. EPA 

appropriately focused on studies with the stronger exposure assessment efforts to minimize the 

effects of exposure misclassification. However, misclassification of exposure undoubtedly 

occurred. In the cohort studies the effect of exposure misclassification on estimates of relative 

risk will be largely non-differential because factors used in exposure assessment were recorded 

before occurrence of the disease. Thus, it will tend to depress estimates of relative risk and mute 

exposure-response gradients and is not an explanation for any observed excesses. Nondifferential exposure misclassification would also occur in case-control studies. Differential 

misclassification is more of a concern in case-control studies. Differential misclassification can 

bias relative risks upward or downward, although the upward bias is usually raised in positive 

studies. However, no evidence is available to suggest that differential exposure bias occurs 

across all the case-control studies. The summary estimates from the meta-analysis provided a 

clear indication of a cancer hazard from TCE. EPA concluded the association between TCE and 

lymphoma and liver cancer were more limited than that for kidney cancer. These conclusions 

about the epidemiologic data were supported by the statistically significant excesses for these 

tumors in the meta-analyses, no statistically significant heterogeneity, and consistency of 

findings after exclusion of individual studies in sensitivity analyses. The consistency of the 

findings was remarkable given the rarity of the cancers, differences in latency and potential for 

exposure misclassification, as described in the study assessments highlighted in the hazard 

characterization.

EPA concluded that the epidemiology data were convincing for a causal association 

between TCE and kidney cancer, compelling for lymphoma, and positive but more limited for 

liver cancer. The Panel did not have strong disagreement with this statement, although some felt 

that the data for liver cancer were as compelling as that for lymphoma. Liver cancer has a much 

lower incidence than kidney cancer or lymphoma in Western countries (where most of the 

epidemiologic studies were conducted) and this requires more reliance on the meta-analysis for a 

summary effect estimate with adequate power. The meta-analysis found that the association of 

TCE exposure with liver cancer was elevated and statistically significant. Further grouping liver 

cancer cases by the level of exposure resulted in numbers that were too small to adequately 

evaluate risks among persons with higher exposures. Nevertheless, we considered these results 

for liver cancer to be strong because there was no evidence of heterogeneity or publication bias 

in the meta-analysis, and because the epidemiologic findings were supported by observations of 

liver cancer in animal models. Although potential confounding by other risk factors for liver 

cancer is possible, strong risk factors such as hepatitis are very rare in Western countries (where 

most of these studies were conducted), so this is unlikely to have caused such a degree of 

confounding. There were no studies to evaluate whether hepatitis might be a confounder in 

TCE studies, although this seemed unlikely.
20

The meta-analysis results were impressive for lymphoma, showing a significantly 

elevated relative risk for ever-exposure to TCE and an even higher effect estimate for high TCE 

exposure. However, it is important to note that there was weak evidence of publication bias in 

the lymphoma meta-analysis results, which means that studies showing no TCE effect or inverse 

associations may not have been published. In addition, there was significant heterogeneity in the 

meta-analysis results for lymphoma for ever-exposure to TCE, indicating that there is an 

unexplained factor causing heterogeneity that indicates it may be inappropriate to combine the 

estimates in a meta-analysis. This heterogeneity may reflect the complicated and changing 

definitions for lymphoma across studies and over time. It is also possible that effects from TCE 

may differ by type of lymphoma. The association with lymphoma was further supported by the 

larger relative risk in meta-analyses for the higher exposure categories compared to the overall 

relative risk. This was evidence for an exposure response gradient, even though no individual 

studies showed much evidence of this. 

Animal Data and Toxicokinetics

The Panel agreed that human data, when available, should be preferred over rodent data 

when estimating unit risk since within species uncertainty is easier to address than between 

species uncertainty. The Panel believed that the animal and toxicokinetic data were thoroughly 

reviewed and the biologic plausibility and coherence of the epidemiologic findings were 

supported by the laboratory animal data and the toxicokinetic data.

Recommendations:

• The immune effects as highlighted in the hazard assessment should be referred to in the 

conclusion especially in the criteria of biological plausibility and coherence because of the 

relationship between immune system dysfunction and cancer risk. 

• Although the summary evaluation focused on the scientific evidence and meta-analysis for 

kidney, lymphoma and liver cancers, there is also some suggestive evidence for TCE as a 

risk factor for cancer at other sites including bladder, esophagus, prostate, cervix, breast and 

childhood leukemia. This evidence that also supports the conclusion should be mentioned in 

the summary evaluation (section 4.11.2.1).

• Add a paragraph describing the definition of lymphoma as used in IRIS. Change the 

terminology regarding the meta-analysis to ‘non-Hodgkin lymphoma’ instead of 

‘lymphoma’, throughout the document. The term ‘NHL’ more accurately describes the 

intent of the analysis as well as the overwhelming majority of cases in the analysis, despite 

changing classification schemes. The focus of the meta-analysis on NHL and the exact 

classifications the meta-analysis includes where it may diverge from classical NHL (as in 

studies that included chronic lymphocytic leukemia) should be clearly explained in both 

Appendix C and in the Hazard Characterization document (section 4.6.1.2.2). 

• To assist the reader, please include references in the summary section (section 4.11.2). For 

example, “The other 13 high-quality studies [note: besides Hardell and Hansen] reported 
21

elevated Relative Risk estimates with overall TCE exposure that were not statistically 

significant.” References for statements like this would be helpful. The Panel counted 

fewer than 13 studies in the meta-analysis after subtracting out Hardell and Hansen, and not 

all of these showed elevated risk estimates, so it would be helpful for the reader to know 

which 13 studies this statement refers to.
22

5. Role of Metabolism on TCE Toxicity

Does EPA’s hazard assessment logically, accurately, clearly, and objectively represent and 

synthesize the available scientific evidence to support its conclusions regarding the role of 

metabolism in TCE carcinogenicity and non-cancer effects? Specifically, please address 

EPA’s conclusions that the liver effects induced by TCE are predominantly mediated by 

oxidative metabolism, but not adequately accounted for by the metabolite trichloroacetic 

acid (TCA) alone and that the kidney effects induced by TCE are predominantly mediated 

by metabolites formed from the GSH-conjugation pathway.

The Panel agreed that EPA’s hazard assessment in the draft document has produced a 

systematic, thorough, objective and clear summary of information on the role of metabolism in 

TCE-induced toxicity with regards to both cancer and non-cancer health effects. The Panel also 

found that EPA has presented a comprehensive review of metabolite formation in animals and 

humans, and has provided a clear, logical assessment of the role these metabolites play in 

mediating its carcinogenic and non-cancer effects.

5a Mediation of TCE-Induced Liver Effects by Oxidative Metabolism

The Panel found that EPA’s conclusion that oxidative metabolites of TCE are responsible for 

mediating the liver effects is sound and based on a wealth of supportive studies. 

The document was a thorough review of the extensive literature on the role of oxidative 

metabolism in TCE toxicity to the liver. Direct evidence that oxidative metabolism was required 

for liver toxicity, such as studies which modulated TCE toxicity by modulating P450 activity, 

was somewhat limited. One noted exception is the study by Ramdhan et al. (2008), that reported 

CYP2E1-deficient mice produced considerably less oxidative metabolites and showed reduced 

hepatoxicity, although due to a small number of animals studied, effects were significant only at 

the highest TCE dose. Nonetheless, the collective evidence, especially from studies with two 

major oxidative metabolites of TCE - TCA and DCA, was very strong that in rodents, at doses 

where metabolism is not saturated, the majority of TCE was metabolized and that metabolites 

from the oxidative pathway predominated over those of the glutathione conjugation pathway. 

Mice are the most susceptible species with respect to TCE-induced liver effects and the majority 

of studies support the conclusion the oxidative metabolites are playing the major role. 

5b Contribution of TCA to Adverse effects on the Liver

The Panel found the conclusion that “the adverse effects on the liver of one of the TCE 

metabolites, trichloroacetic acid, cannot adequately account for the liver effects of TCE” is 

sound and supported by several lines of experimental evidence. 

Response

TCA is the predominant oxidative metabolite of TCE and its effects are well known to be 

associated with liver toxicity and carcinogenicity. However, oxidative metabolism of TCE 

generates a number of molecules and the confidence in the ability to identify TCE’s oxidative 

metabolite(s) that may be responsible for hepatotoxicity and/or liver cancer in rodents or humans 

is much less than that for the overall role of oxidative metabolism. This uncertainty is due in 
23

part to the problems with quantitative assessment of DCA formation after TCE administration. 

There is sufficient evidence to implicate DCA in mediating carcinogenic effects of TCE that are 

not related to those produced by TCA. The EPA correctly stated that DCA was a minor 

metabolite of TCE in vivo, at least in rodents, and that some of the earlier reports on DCA 

dosimetry may have been erroneous due to the issues with the analytical methods. There are, 

however, several studies (Delinsky et al., 2005; Kim et al., 2009) which provide information on 

the blood levels of DCA after oral exposure to TCE in rats and mice. Such data, together with a 

large body of literature on TCA formation after treatment with TCE, should be carefully 

evaluated with regards to the estimation of the internal dose (or relative amounts) of each of 

these key metabolites. 

The Panel found that EPA has taken several approaches to determine whether liver 

tumors induced by TCE can be accounted for by TCA formation alone. The first approach was to 

compare dose-response profiles for non-cancer liver toxicity endpoints from TCE and TCA 

based on TCA dose equivalents, an internal dose metric. In contrast to DCA, the quantitative 

data available for TCA and TCOH, together with PBPK models relying on their measurements, 

are among the most consistent and allow for the assessment of the oxidative metabolite flux from 

TCE. Analysis of liver weight changes (Fig 4-7, 4-8) suggested that while total TCE oxidative 

metabolism was strongly correlated with liver weight changes (R2 = 0.89), the amount of TCA 

formed underestimated the degree of liver hypertrophy observed. The dose-response 

relationships for liver hypertrophy observed between TCE and TCA, based on TCA daily dose 

equivalents, were strikingly different in both slope of the dose-response and overall magnitude, 

suggesting that the mechanisms of hypertrophy, and/or the metabolites involved, were different. 

This analysis was compelling because TCA daily liver dose equivalents were used for 

comparison. The internal dose metrics, if accurately applied, should account for potential 

differences due to bioavailability and exposure route issues that have been previously raised for 

TCE and TCA. The Panel notes that the bioavailability of TCE, DCA and TCA in oral gavage 

studies was dependent, among many factors, on the type of the vehicle and the magnitude of the 

administered dose. It has been suggested [Sweeney et al., 2009; NRC review of the IRIS 

assessment of Tetrachloroethylene (Appendix B)] that the bioavailability of TCA (when 

administered directly) was highly non-linear with an increasing dose. Thus, the internal dose of 

each metabolite of interest, either through metabolism from TCE or following direct 

administration, was key for the comparison of health effects between the parent and its 

metabolites. 

The second approach used in the draft document to support the conclusion that multiple 

metabolites were involved in liver tumors induced by TCE included comparisons of liver 

phenotypic markers (glycogen staining, c-jun staining) and tumor-derived genetic markers 

(incidence of H-ras mutations). This analysis was interesting, yet qualitative in nature. The use 

of phenotypic markers such as H&E staining, glycogen staining, antibody reactivity, tumor 

tincture, etc., must be interpreted with caution since the underlying biochemistry/molecular 

biology of these descriptive attributes is often not well understood and may be highly dependent 

on the state of progression of the tumors The criteria used in each study for phenotypic 

classification (i.e., staining intensity, background staining) is not always clearly outlined in the 

original literature reports. The EPA has included adequate discussion noting the technical 

limitations for each of the studies, which increased the confidence that such evidence from a 
24

single study was not overly weighted in drawing conclusions about the role of TCA. While 

individual studies comparing phenotype/genotype of TCE-, TCA- and DCA- induced tumors 

have important limitations, the collective group of studies was consistent with the interpretation 

that TCE tumors displayed phenotypic and genotypic heterogeneity that was different than that 

of tumors induced by TCA alone. This was in agreement with the EPA conclusion that these 

data also did not support the hypothesis that TCA was a sole acting liver metabolite of TCE. 

However, since factors such as interactions among metabolites and tumor progression state may 

have unknown influences in the phenotype/genotypes observed, this type of qualitative evidence 

was not sufficient to invoke specific roles for other contributing metabolites, or to discount 

potential contributing roles of other metabolites.

The draft included little in terms of the comparative quantitative evaluation of the 

hepatocarcinogenic potency of TCE, TCA and DCA even though extensive information was 

available, especially in mice. A recent draft of the IRIS assessment of a highly related chemical, 

tetrachloroethylene (PERC), provided the evaluation of the consistencies between PERC and 

TCA with regards to the liver cancer endpoint (Appendix 4A of PERC IRIS draft document). 

TCA is a major metabolite of both TCE and PERC and it is debatable whether TCA toxicity can 

account for the majority (if not all) of the adverse liver effects of PERC. 

Given the controversy of DCA as a contributing metabolite in liver effects induced by 

TCE and the importance of this issue as it relates to understanding TCA’s role, it is somewhat 

surprising that there was relatively little analysis of the literature related to the use of DCA as a 

therapeutic agent in humans as an integrated part of this section of the review. Although these 

studies obviously involved high doses, they are relevant to the potential spectrum of effects 

observed in humans.

Recommendations:

• The EPA should examine studies that provide quantitative assessment of TCA and DCA 

formation after TCE exposure in vivo and draw conclusions with regards to the relative 

amount and kinetics of the oxidative metabolites of interest for liver toxicity.

• A careful evaluation of the concentration-time kinetics is needed to achieve certainty in the 

comparisons of liver effects and the conclusions drawn by the EPA which suggest that 

TCA-induced adverse liver effects do not explain those observed with TCE. Equally

important is to fully consider the bioavailability of TCE itself with regards to the vehicle 

effects between studies.

• The body of the document could be further strengthened by reporting EPA’s evaluation on 

the strength of the specific criteria used for phenotypic classification described in each study 

discussed, and noting where specific criteria were not reported. While most of this 

information was included in the appendix, the EPA may consider constructing a summary 

table for Section 4.5.6.

• Dose-response modeling, similar to that performed for PERC, may be considered by the 

EPA to provide science-based information on relative contribution, or lack thereof, of TCA 

and/or DCA to the apical liver carcinogenesis effect of TCE. While data gaps exist and there 

are limitations in the comparisons between independent cancer bioassays, the document 

should clearly state what the limitations are should such analysis be deemed futile.
25

• The draft assessment may be strengthened by including information from human use of 

DCA in clinical practice.

5c Role of GSH-Conjugation Pathway on TCE-Induced Kidney Effects

The Panel concluded that EPA has provided a clear and comprehensive summary of the 

available evidence that metabolites derived from GSH conjugation of TCE are responsible for 

mediating kidney effects.

The Panel found the integration of the data from human epidemiological, animal and in 

vitro mechanistic studies produced a clear and transparent weight-of-evidence assessment 

supportive of TCE GSH conjugation metabolites’ role in kidney toxicity and cancer. Whereas 

sufficient amounts of oxidative metabolites of TCE (i.e., TCOH) may be formed which could 

contribute to kidney effects, potentially through formic acid, the literature indicated the 

pathological effects on the kidney induced by oxidative metabolites were not consistent with 

those observed with TCE. In contrast, the pathological effects on the kidney induced by 

DCVC/DCVG were similar to TCE. Thus, a reasonable conclusion was that the glutathione 

conjugation pathway played a more important role in driving these effects. The primary 

challenge was to determine the true flux through the glutathione conjugation pathway. 

Many uncertainties exist in PBPK model estimates for the GSH pathway. This issue is 

critical, since these uncertainties can result in orders of magnitude differences in flux between 

rodents and humans. The argument that mercapturates of the glutathione conjugates, as 

detoxication pathway products, are not quantitative markers of flux through the GSH pathway is 

rational and supported by in vivo human and rodent data. The level of urinary mercapturates, as 

deactivation products, is evidence that the pathway operates in humans, but does not necessarily 

reflect the amount of DCVC formed. Direct data on DCVG/DCVC formation, or its reactive 

metabolites, are the more appropriate measures of flux for this pathway. This was clearly and 

adequately discussed in the review. 

The quantitative analysis of the species differences in GSH metabolism was somewhat 

narrow. Specifically, the issue of vast differences in human vs rodent metabolism of TCE to 

GSH conjugates hinged on the very limited experimental evidence. Only one human in vivo

study was available that directly quantified DCVG in urine in a few subjects (Lash et al. 1998). 

The rodent in vivo data (Kim et al. 2009) was limited to only one isogenic (hybrid) mouse strain. 

Other important differences between these studies were that they utilized different exposure 

routes, doses, and analytical methods. The uncertainties associated with the potential several 

orders of magnitude difference in TCE metabolism through GSH pathway between species 

should be considered more carefully. 

In addition, multiple in vitro studies have been published in the peer reviewed literature. For 

example, in vitro GSH conjugation data were used to develop prior distributions for GSH 

conjugation rates, something which was not done for previous PBPK models of TCE. Ample 

discussion was given to the data generated by the Lash laboratory, which was clearly the most 

extensive set of data relative to DCVG and DCVC levels in humans. These data indicated

DCVG may be formed at levels similar to that of oxidative metabolites in humans. Based on 
26

these data, the conclusion that the GSH conjugation pathway plays an important role in kidney 

tumors/toxicity in both rodents and likely in humans is logical. 

However the discussion of additional published in vitro studies that show disparately lower 

results for DCVG formation (beyond mercapturates) was not given a comparable level of 

attention. For example, the documents pointed out discrepancies between in vitro studies of 

DCVG formation conducted by the Green and Lash laboratories that report results differing by 

orders of magnitude. The studies from these labs reported very similar assay conditions using 

the same strain of rats, but differed in the analytical techniques used (HPLC-UV versus GC-MS). 

The analysis of these disparate results provided in the review was limited to nondescript 

statements that the differences may be “related to the different analytical methods employed such 

as detection of radiolabeled substrate vs. derivatized analytes” (section 3.3.2.7). Unfortunately, 

the authors of the original studies do not really provide technical explanations for the disparities 

either. Given such disparate results, the EPA has chosen to use the geometric mean of these 

two studies in estimating DCVG formation. This decision process and its impacts on the final 

rates for DCVG formation need to be more clearly spelled out in the discussion of these studies. 

The discrepancies in estimates of DCVG formation are among the most contentious issues 

associated with TCE risk analysis. Given the difficult task of drawing conclusions from such 

different results, the conservative approach the EPA has taken is defensible from a public safety 

policy perspective. From a strictly scientific perspective however, at a minimum, such large 

literature disparities call for a more complete discussion of the strengths and limitations of the 

analytical methodologies used than what is described in the review. 

Recommendations:

• The issue of quantitative assessment of the metabolic flux of TCE through the GSH pathway 

vs. the oxidative metabolism pathway should be considered carefully since uncertainties 

exist with regard to the extent of formation of the dichlorovinyl metabolites of TCE between 

humans and rodents. EPA may need to provide appropriate reservations to the conclusions 

based on the limited data for GSH metabolites.

• The discussion of how each of the in vitro and in vivo data sets were used to estimate 

DCVG formation parameters for the PBPK model should be more transparent indicating 

strengths and weaknesses in the database. 
27

6. Mode of Action

Using the approach outlined in the U.S. EPA Cancer Guidelines (U.S. EPA, 2005a), does 

EPA’s hazard assessment logically, accurately, clearly, and objectively represent and 

synthesize the available scientific evidence to support its conclusions regarding the mode(s) 

of action [MOA(s)] of TCE carcinogenicity and non-cancer effects? Specifically, please 

address the conclusions that the weight of evidence supports a mutagenic MOA for TCEinduced kidney tumors; that a MOA for TCE-induced kidney tumors involving cytotoxicity 

and compensatory cell proliferation, possibly in combination with a mutagenic MOA, is 

inadequately supported by available data; that there is inadequate support for PPARα 

agonism and its sequellae being key events in TCE-induced liver carcinogenesis; that there 

are inadequate data to specify the key events and MOAs involved in other TCE-induced 

cancer and non-cancer effects; and that the available data are inadequate to conclude that 

any of the TCE-induced cancer and non-cancer effects in rodents are not relevant to 

humans.

6a Hazard Assessment and Mode of Action

The Panel agreed that the IRIS TCE hazard assessment logically, accurately, clearly, and 

objectively represented and synthesized the available scientific evidence to support its 

conclusions regarding the mode(s) of action [MOA(s)] of TCE carcinogenicity and non-cancer 

effects. For each end point, the hazard assessment described the possible MOA and underlying 

mechanisms. In general, the assessment provided explanations for inconsistent data or lack of 

results. For example, Section 4.8.3.3.2 provided a comprehensive, detailed, and very useful 

discussion of potential reasons for inconsistencies in the body of literature on TCE exposure in 

utero and heart defects. 

The Panel agreed that the MOA for TCE nephrotoxicity involves conversion of TCE to 

GSH derived metabolites followed by conversion of the glutathione conjugate (DCVG) to the 

cysteine conjugate (DCVC) and activation by β-lyase in the kidney to the ultimate nephrotoxic 

species. Thus, the EPA’s hazard assessment logically, accurately, clearly, and objectively 

represents and synthesizes the available scientific evidence to support the conclusion regarding 

the MOA for TCE kidney non-cancer toxicity. However, as discussed in the response to charge 

question 3, the Panel noted that uncertainties remain with regards to quantity of metabolites 

formed in humans and rodents. The panel concluded that the narrative presentation of the data, 

along with the evaluation of the strengths and weaknesses of each study, was appropriate with

supplemental information.

Response

Recommendations:

• The impact of the inconsistencies in data on the quantity of GSH pathway metabolites 

formed in humans and rodents should be presented more transparently. 

• In the body of the document, MOA information should be systematized and broken down 

into key events for each proposed MOA. The EPA may consider using a tabular format to 

facilitate the ease of evaluation. Information on supporting/refuting (if any) evidence (with 
28

appropriate references indicated), human relevance (if available), and “strength” of each line 

of evidence/study should be included. 

• EPA should consider tabular summaries by specific metabolites when studies used 

metabolite exposure rather than the parent compound. 

• Data gaps should be clearly identified to help guide future research. 

• Key conclusions supporting/refuting each key event should be presented in bullet form 

indicating where in the document a more detailed narrative/tables can be found.

6b MOA for TCE-Induced Kidney Tumors

The Panel agreed that the weight of evidence supported a mutagenic MOA for TCEinduced kidney tumors. However, the Panel concluded that the weight of evidence did not 

exclude the MOA for TCE-induced kidney tumors involving cytotoxicity and compensatory cell 

proliferation and including this MOA may more accurately reflect kidney tumor formation than a 

mutagenic mechanism alone. Furthermore, the combination of cytotoxicity, proliferation and 

DNA damage together may be a much stronger MOA than the individual components. 

Recommendations:

• Modify the relevant text to reflect that the available data do, in fact, provide support for 

TCE-induced kidney tumors involving cytotoxicity and compensatory cell proliferation, 

possibly in combination with a mutagenic MOA, although not to the extent that support for 

a mutagenic MOA was provided.

6c Inadequate Support for PPARα agonism and its sequellae being key events in TCEinduced liver carcinogenesis 

The Panel agreed that there was inadequate support for PPARα agonism and its sequellae 

being key events in TCE-induced human liver carcinogenesis. The Panel noted that PPARα

agonists do not elicit peroxisomal proliferation in humans, a pathological change which is a 

hallmark effect of TCE and other peroxisome proliferators in rodents.

The Panel noted that a number of studies important for consideration of the relevance of 

PPARα mode of action to human liver carcinogenesis have been completed recently. These 

include, but are not limited to, studies in PPARα-null mice (Ito et al. 2007; Takashima et al. 

2008; Eveillard et al. 2009), PPARα humanized transgenic mice (Morimura et al. 2006), and 

hepatocyte-specific constitutively-activated PPARα transgenic mice (Yang et al. 2007). The data 

from these animal models suggest that activation of PPARα is an important but not limiting 

factor for the development of mouse liver tumors and that additional molecular events may be 

involved.

The Panel noted the quantitative differences in the affinity of the various isoforms of 

PPARs to TCA, DCA and other model peroxisome proliferators are well established. Likewise, 

the quantitative differences in affinity between species are also known. 
29

Recommendations:

• Graphical or tabular presentation of these data to strengthen the comparative analysis 

between metabolites and chemicals. 

• Including some of the analyses which compare the receptor transactivation potency and the 

carcinogenic potential of TCA, DCA and other model peroxisome proliferators from Guyton 

et al (2009) to strengthen the arguments.

6d Inadequate Data to specify Key Events and MOAs involved in other TCE-Induced Cancer 

and Non-Cancer Effects 

The Panel agreed that the data are inadequate to specify the key events and MOAs 

involved in other TCE-induced cancer (lung, lymphoma) and non-cancer effects (central nervous 

system, immune system, respiratory tract toxicity, reproductive effects, developmental effects). 

6e Human Relevance of TCE-Induced Cancer and Non-Cancer Effects in Rodents

The Panel agreed that the data are inadequate to conclude that any of the TCE-induced 

cancer and non-cancer effects in rodents are not relevant to humans. 

Recommendations:

• The impact of potential overestimation of the extent of the GSH pathway in humans in 

Section 4.4.7 (Kidney) must be transparent

• The MOA for carcinogenicity should be described as complex rather than unknown in 

Section 4.5.7.4. Mode of Action (MOA). With respect to conclusions regarding the liver, 

while the complete MOA in animals may not be clear at this time, complex is a more 

appropriate descriptor since it is likely that key events from several pathways may operate

leading to acute, sub-chronic and chronic liver toxicity of TCE. 
30

7. Susceptible Populations

Does EPA’s hazard assessment logically, accurately, clearly, and objectively represent 

and synthesize the available scientific evidence to support its conclusions that the 

factors that could modulate susceptibility to TCE carcinogenicity and non-cancer 

effects include genetics, lifestage, background and co-exposures, and pre-existing 

conditions, but that only toxicokinetic variability in adults can be quantified given the 

available data? 

The Panel agreed that Section 4.10 of the Hazard Assessment provided a good review of 

potentially susceptible populations, and that the identified factors (genetics, lifestage, 

background, co-exposures and pre-existing conditions) may modulate susceptibility to TCE 

carcinogenicity and non-cancer effects. The review included adequate data to support factors 

that modulate exposure and pharmacokinetics in both animals and humans, but few data to 

demonstrate differing susceptibility to health effects from TCE exposure in either animals or 

humans. The Panel agreed with the conclusion that the existing data are inadequate to form a 

conclusion about whether the potentially modulating factors do or do not impact risk 

estimates for TCE and human health effects. The Panel agreed with the use of standard agedependent adjustment factors in the protection of children. 

Response

Recommendations:

• The Panel disagreed with the statement that “toxicokinetic variability in adults can be 

quantified given the existing data,” as the main study characterizing toxicokinetic 

variability in adults was small (n<100) and was composed of subjects selected nonrandomly. The Hazard Assessment document should note the limitations of the adult 

data for toxicokinetic modeling in terms of uncertainty and possible bias in section 

4.10.3, and elsewhere in the document where these data are used for hazard 

characterization modeling. 

• Section 4.10 of the Hazard Assessment should discuss explicitly the lack of data 

demonstrating modulation of health effects from TCE by the identified factors (genetics, 

lifestage, background, co-exposures, and pre-existing conditions), and the need for such 

data in risk assessment.

• EPA should make specific recommendations for studies that would fill the data gap for 

susceptible groups. For example, epidemiologic studies in which TCE exposure is wellcharacterized and in which internal comparisons can be made to determine whether there 

is effect modification, and animal studies comparing subgroups (e.g., based on genetics, 

obesity, multiple solvent exposures). 

• Modulation of TCE exposure-related hypersensitivity dermatitis by genetic variation may 

be relevant for future study, given results of the study of hypersensitivity dermatitis in 

Asian workers reported in Li et al. (2007) and increasing industrial chemical exposures in 

China.
31

• The wording in Section 4.10 was often not clear about whether it was describing results 

for a study that looked at effect modification of the TCE effect or not, as opposed to 

direct effects of age, gender, etc. Also, the draft document needs to state explicitly where 

effects of TCE within one subgroup were stated, whether the other subgroup was also 

examined in the same study. 

• The Panel recommended that exposure to solvent mixtures should be added as a potential 

susceptibility factor (co-exposures) to Section 4.10, since exposure to more than one 

chemical to the same target organ likely increases risk.

• Section 4.10.2.4.1 (page 4-585) should be more accurately titled ‘Obesity’, rather than 

‘Obesity and metabolic syndrome’. As presently written, Section 4.10.2.4.1 gives no 

clear message as to how obesity affected the kinetics of TCE, and the section should be 

revised to provide clarification. 
32

8. Non-Cancer Dose-Response Assessment

EPA’s dose-response assessment includes the development of a chronic inhalation 

Reference Concentration (RfC) and chronic oral Reference Dose (RfD) for non-cancer 

effects. Please address the following methods and results from EPA’s non-cancer doseresponse assessment in terms of the extent to which they are clearly and transparently 

described and technically/scientifically adequate to support EPA’s draft RfC and RfD:

a. The screening, evaluation, and selection of candidate critical studies and 

effects;

b. The points of departure, including those derived from benchmark dose 

modeling (e.g., selection of dose-response models, benchmark response 

levels);

c. The selected PBPK-based dose metrics for inter-species, intra-species, and 

route-to-route extrapolation, including the use of body weight to the 3⁄4 power 

scaling for some dose metrics;

d. The selected uncertainty factors;

e. The equivalent doses and concentrations for sensitive humans developed 

from PBPK modeling to replace standard uncertainty factors for inter- and 

intra-species toxicokinetics, including selection of the 99th percentile for 

overall uncertainty and variability to represent the toxicokinetically-sensitive 

individual;

f. The qualitative and quantitative characterization of uncertainty and 

variability;

g. The selection of NTP (1988) [toxic nephropathy], NCI (1976) [toxic 

nephrosis], Woolhiser et al. (2006) [increased kidney weights], Keil et al. 

(2009) [decreased thymus weights and increased anti-dsDNA and anti-ssDNA 

antibodies], Peden-Adams et al. (2006 [developmental immunotoxicity], and 

Johnson et al. (2003) [fetal heart malformations] as the critical studies and 

effects for non-cancer dose-response assessment;

h. The selection of the draft RfC and RfD on the basis of multiple critical effects 

for which candidate reference values are in a narrow range at the low end of 

the full range of candidate critical effects, rather than on the basis of the 

single most sensitive critical effect. 

8a The screening, evaluation, and selection of candidate critical studies and effects

Response

The Panel agreed that the screening, evaluation, and selection of candidate critical studies 

and effects were generally adequate to support EPA’s draft RfC and RfD. The Panel noted that a 

very large number of studies were considered and included in the tables, and agreed that it was 

appropriate to evaluate all studies showing dose-response for neurological, kidney, liver, 

immunologic, respiratory system, reproductive, and developmental effects, and body weight 
33

change. The Panel’s comments on sub-question (a) related primarily to making the information 

presented in the document more clear and transparent to the reader, rather than to the screening, 

evaluation, and selection process itself.

The Panel believed that it was important that the reader easily be able to find the details 

of the studies included in the Chapter 5 tables. 

For instance, four different studies with different durations were cited as “Crofton and 

Zhao (1997)” in Table 4-23, and it was not clear which duration was the basis for the cRfD in 

Table 5-1. In other cases, it was not stated whether the cRfD or cRfC was based on males or 

females when both were included in the study, or which strain was the basis when multiple 

strains were used. For example, from Table 5-2 and the text on p. 5-15 to 5-16, it was not clear 

which strain, gender, or exposure duration was used for the RfC for increased liver weight based 

on Kjellstrand et al. (1983b) (discussed in Chapter 4 and Appendix E). Another example for 

which cross-referencing the different sections of the document would be helpful is the 

information on the doses in the drinking water study of Keil et al. (2009). In the description of 

the study on p. 4-395, the doses were given as drinking water concentrations (ppb), but in Table 

5-3, the LOAELs for this study were given in mg/kg/day, and the conversion from ppb in 

drinking water to mg/kg/day is found in Appendix E (p. E-34). A final example of where crossreferencing would be helpful relates to the studies of Carney et al. (2006) and Schwetz et al. 

(1975). These studies were listed in Table 5-4 (Reproductive Toxicity) because the key effect, 

decreased maternal body weight gain in a developmental study, was considered a “reproductive” 

effect. However, these studies were discussed under developmental toxicity in Chapter 4, 

making it difficult to locate them while reading the section on reproductive toxicity in Chapter 5. 

Finally, it was stated on p. 5-1, point (1) that studies with “quantitative dose-response 

data” were considered. Some of the studies which were considered as the basis for RfCs and 

RfDs used only one dose of TCE and a control group (for example, Barrett et al., 1992). If a 

control group and a single treated group were considered adequate “quantitative dose-response 

data,” this should be stated. 

Recommendations:

• Chapter 5 should include a list of all non-cancer health effects and studies discussed in 

Chapter 4, noting those which were considered candidate critical effects and studies. 

• Tables 5.1-5.5 should provide cross-references to the table or page in Chapter 4 and/or to the 

Appendices (such as Appendix E for hepatic studies) where the listed study was discussed, 

and should include more details (e.g. gender, strain, duration) of the studies selected as the 

basis for cRfDs and cRfCs when these details were needed to prevent ambiguity.

• Consistent dose units should be used in discussing the same study in different places in the 

document.
34

8b The points of departure, including those derived from benchmark dose modeling (e.g., 

selection of dose-response models, benchmark response levels)

The Panel agreed that the derivation of the points of departure (PODs) was generally 

technically/scientifically adequate to support EPA’s draft RfC and RfD. The Panel noted that the 

graphics in Appendix F provided a good presentation of the BMD analyses.

The Panel noted that, although BMD modeling was generally an appropriate approach for 

POD determination, the results of BMD modeling were very uncertain with some datasets. For 

example the log-logistic BMD analysis for toxic nephropathy in female Marshall rats in the NTP 

(1988) study, shown in Figure F-10, may greatly overestimate the risks at low doses. This 

modeling involved extrapolation from a high LOAEL at which a high percentage of the animals 

were affected. 

Recommendation:

• Chapter 5 should include the information on POD derivation from Table F-13 of Appendix 

F, including approach, selection criterion and decision points.

8c The selected PBPK-based dose metrics for inter-species, intra-species, and route-to-route 

extrapolation, including the use of body weight to the 3⁄4 power scaling for some dose metrics

The Panel agreed that the use of PBPK-based dose metrics for inter-species, intra-species, 

and route-to-route extrapolation modeling were, for the most part, technically and scientifically 

adequate to support EPA’s draft RfC and RfD. 

However, it was noted by the Panel that the RfDs and RfCs for kidney endpoints were 

highly sensitive to the rate of renal bioactivation of DCVC (ABioactDCVCBW34) in human 

versus rodents. Specifically, it was noted that p-cRfDs/RfCs based on this dose-metric were 

several hundred-fold lower than RfDs/RfCs for the same endpoints based on applied dose with 

standard uncertainty factors, while p-cRfDs/RfCs for endpoints based on other dose metrics were 

much closer to RfDs/RfCs based on applied dose and standard uncertainty factors.

In addition to the strong dependence of the p-cRfDs and p-cRfCs on the rate of renal 

bioactivation of DCVC, the Panel noted that the uncertainties about the in vitro and in vivo data 

used to estimate this dose metric were much greater than for other dose metrics. For example, 

there were very large discrepancies in the rates of human glutathione conjugation reported by 

Lash et al. (1999a) and Green et al. (1997a). 

The Panel understood that the rationale for scaling the dose metric to body weight3/4, in 

conjunction with the interspecies extrapolation, is that the PBPK model predicted the dose rate to 

the target tissue rather than the internal concentration of TCE. However, this distinction and the 

associated rationale would likely not be readily apparent to most readers of the document as 

currently written. Confusion might arise because, for other contaminants, PBPK models were 

used to estimate serum levels or other metrics of internal concentration, rather than delivered 

doses, and in such case, scaling of body weight3⁄4 would not be used.
35

The discussion of “empirical dosimetry” vs. “concentration equivalence dosimetry”as 

presented in the draft document would likely not be readily understandable to many readers. 

Furthermore, since body weight3/4 scaling was used for all of the dose metrics discussed in 

sections 5.1.3.1.1-5.1.3.1.5, it may not be necessary to include the extensive discussion of the 

two dosimetry approaches in each of these sections. 

Recommendations:

• The uncertainty about the rate of human glutathione conjugation found in Lash et al. (1999a) 

versus Green et al. (1997a) should be highlighted in the current assessment.

• The basis for the renal bioactivation dose metric should be more clearly and transparently 

presented and discussed in Chapter 3 and other appropriate sections. If this dose metric was 

derived indirectly from data on other metabolic pathways leading to and/or competing with 

bioactivation, this should be more clearly discussed.

• The rationale for scaling the dose metric to body weight3/4, in conjunction with the 

interspecies extrapolation based on PBPK modeling, should be presented in a clearer and 

more transparent way (e.g. on pp. 5-33 – 5-36).

• The discussion of “empirical dosimetry” vs. “concentration equivalence dosimetry” should 

be made clearer and more transparent (pp. 5-33 – 5-36).

8d Uncertainty factors

The Panel agreed that, in general, the selection of uncertainty factors was clearly and 

transparently described and technically/scientifically adequate to support EPA’s draft RfC and 

RfD. The uncertainty factors were consistently applied in Tables 5-8 to 5-13. 

However it was noted that the uncertainty factors were appropriately applied only if the BMDPBPK 99th percentile (HEC99 and HED99) dose metrics were correctly derived.

The Panel recognized that EPA guidance defines the duration of subchronic rodent 

studies as 4 weeks to 90 days, and chronic rodent studies as 90 days to 2 years, and noted that 

some of the subchronic studies considered as the basis for risk assessment were of duration as 

short as 4 weeks (e.g. Isaacson, 1990). Also, some studies of duration only slightly greater than 

90 days (e.g. 18 weeks for Kulig et al., 1987) were classified as chronic, as appropriate under the 

EPA definition of chronic as longer than 90 days. However, exposures for 18 weeks may not 

always accurately predict effects for lifetime duration, since 18 weeks is only a small percentage 

of a two year (104 week) rodent lifespan (less than 18%). 

Recommendations:

• The definitions of chronic and subchronic studies should be provided in the document and a 

citation given. 

• The discussion of the subchronic to chronic uncertainty factor on p. 5-6 should be clarified as 

far as durations of studies considered suitable as the basis of a chronic risk assessment.

• The draft document should include discussion of whether studies in the lower end of the 

range defined as subchronic (e.g. 4 weeks) are of sufficient duration to be used as the basis 

for a chronic (lifetime) risk assessment. 
36

• Studies only slightly longer than the minimum needed to be considered chronic should be 

noted as such, and the use of an uncertainty factor to account for less than lifetime exposure 

(of less than the full uncertainty factor of 10) could be considered for studies of such 

durations, especially for endpoints thought to progress in incidence or severity with time.

8e The equivalent doses and concentrations for sensitive humans developed from PBPK 

modeling to replace standard uncertainty factors for inter- and intra-species toxicokinetics, 

including selection of the 99th percentile for overall uncertainty and variability to represent the 

toxicokinetically-sensitive individual

The Panel generally agreed that this information is clearly and transparently described 

and technically/scientifically adequate to support EPA’s draft RfC and RfD. It was noted that 

the 99th percentile estimates may be very sensitive to modeling assumptions, such as the choice 

of prior distribution and the shape of the distribution for population variability in the 

toxicokinetic parameters. The Panel concluded that the approach used, including the selections 

of idPODs and the extrapolations from rodent to human followed by consideration of the 99th 

percentile human estimates, was acceptable to address the sensitive population. It was also 

concluded that the approach used to simulate a large range of exposure doses in order to obtain 

the distribution for the relationship between human exposure and internal dose (page 5-68) was 

appropriate.

Recommendations:

• The Panel noted variability/uncertainty for the toxicokinetically-sensitive individual could be 

quantified in future work by considering distributions in addition to the distribution of the 

99th percentile, such as the 95th percentile. 

• A quantile regression looking simultaneously at several quantiles could be developed in the 

future and presented in future refinements of this assessment.

Additional issue related to sub-questions (c), (d), and (e) discussed by the Panel:

The question arose as to whether the general approach used in the draft document to

develop p-RfDs and p-RfCs was appropriately protective, as opposed to being overly 

conservative. Specifically, the Panel noted that the PODs identified through BMD analysis were 

based on most sensitive species, strain, and sex, and that the idPODs based on lower bound 

estimates of the 1% or 5% response in animals were used as a central dose estimate in humans. 

It was also noted that uncertainty factors for interspecies and intra-human pharmacodynamic 

variability were applied to the 99th percentile estimates (i.e. the doses for the 1% most 

pharmacokinetically sensitive humans) of the internal dose (HEC99 and HED99).

The Panel endorsed the use of BMD modeling instead of an approach based on an 

uncertainty factor for LOAEL-to-NOAEL extrapolation, and the use of PBPK modeling instead 

of default uncertainty factors for inter- and intra-species pharmacokinetic differences, when these 

approaches were supported by the data. The Panel recognized that these approaches were not 
37

intended to introduce greater conservatism, but rather to incorporate data to replace default 

assumptions when appropriate. 

There was consensus among the Panel members that the general approach described 

above was consistent with accepted EPA methodology for RfD/RfC development. It was 

specifically noted that the uncertainty factors for interspecies and intra-human pharmacodynamic 

variability were intended to account for variability as well as uncertainty, and that some pRfDs/p-RfCs based on PBPK modeling were higher than RfDs/RfCs for the same endpoints 

based on the default methodology. The Panel recommended that HEC50 and HED50 values be 

included in Tables 5-8 to 5-13 for informational purposes. 

Finally, as discussed further under sub-question (h), the Panel concluded that the 

consistency of RfDs and RfCs, and that selected endpoints utilized relatively certain dose 

metrics, gave confidence in the PBPK approach. Dose metrics used for selected endpoinds and 

their levels of certainty are summarized as follows: 

Dose Metric Level of Certainty Dose Metric Use

DCVC activation uncertain Renal endpoints

Total metabolism Relatively certain Decreased thymus weight, 

anti-ss and ds DNA antibodies

Total oxidative metabolism Relatively certain Cardiac malformations

Applied Dose (dose metric 

based on PBPK modeling not 

used

Developmental 

immunotoxicity

8f The qualitative and quantitative characterization of uncertainty and variability;

The Panel generally agreed that the uncertainties related to the RfC and RfD were clearly 

and transparently described and technically/scientifically adequate to support EPA's draft RfC 

and RfD. 

It was noted that in the PBPK model, the uncertainty and variability were quantified with 

the posterior distributions, as appropriate for any Bayesian framework, while in the more general 

dose-response framework, the uncertainty is characterized with uncertainty factors which 

account for the main sources of variability and uncertainty. One Panel member commented that 

it was inconsistent to use a Bayesian approach in the PBPK modeling but not in the doseresponse analysis, which uses numeric uncertainty factors, rather than distributions, which 

represent variability and uncertainty as a fixed effect.

The Panel recognized that the use of uncertainty factors in the TCE assessment followed 

the currently accepted EPA approach. 
38

Recommendations:

• The quantitative uncertainty analysis of PBPK model-based dose metrics for LOAEL or 

NOAEL based PODs (Section 5.1.4.2) should be revised to clarify the objective of this 2-D 

type analysis, as well as the methodology used. 

• In future work, EPA could develop an approach using distribution to characterize uncertainty 

in a Bayesian framework.

8g The selection of NTP (1988) [toxic nephropathy], NCI (1976) [toxic nephrosis], Woolhiser 

et al. (2006) [increased kidney weights], Keil et al. (2009) [decreased thymus weights and 

increased anti-dsDNA and anti-ssDNA antibodies], Peden-Adams et al. (2006 [developmental 

immunotoxicity], and Johnson et al. (2003) [fetal heart malformations] as the critical studies 

and effects for non-cancer dose-response assessment

The Panel concluded that the choices of Keil et al. (2009) [decreased thymus weights and 

increased anti-dsDNA and anti-ssDNA antibodies], Peden-Adams et al. (2006) [developmental 

immunotoxicity], and Johnson et al. (2003) [fetal heart malformations] as critical studies and 

effects were technically/scientifically adequate to support EPA’s draft RfC and RfD. The Panel 

noted that questions related to the use of cardiac malformations from Johnson et al. (2003) as a 

critical endpoint were adequately addressed in the response to Charge Question 3. It was noted 

that BMD modeling for the data from Johnson et al. (2003) was highly sensitive to model choice. 

It was also noted that, although a tremendous amount of information was available on liver 

toxicity, hepatic effects were not a critical endpoint because they were less sensitive than other 

endpoints.

The Panel expressed concerns about the use of NTP (1988) [toxic nephropathy], NCI 

(1976) [toxic nephrosis], and Woolhiser et al. (2006) [increased kidney weights] as critical 

studies and effects. For all three of these studies, uncertainties exist for the PBPK modeling 

based on renal bioactivation of DCVC, as discussed in sub-question (c) above. 

Additional issues related to choice of toxic nephropathy in female Marshall rats from 

NTP (1988) as a critical effect and study include excessive mortality due to dosing errors and 

possibly other causes, and a high level of uncertainty in the extrapolation to the BMD due to the 

use of very high doses and a high incidence (>60%) of toxic nephropathy at both dose levels 

used. It was also noted that the incidence of this effect was lower in this study in other strains of 

rats and in male Marshall rats, suggesting that the sensitivity for this effect was highest in the 

female Marshall rats. 

It should be noted that the uncertainties noted by the Panel about the quantitative risk 

assessment based on toxic nephropathy in NTP (1988) did not indicate that there was uncertainty 

that TCE caused renal toxicity in this study. The Panel noted that renal cytomegaly, which was 

not selected as a critical effect, occurred at a very high frequency in both sexes of all four strains 

used in this study, with 90-100% incidence in almost all dosed groups, and toxic nephropathy 

also occurred in all treated groups. In contrast, neither renal cytomegaly nor toxic nephropathy 

was seen in any of 396 control animals in study, which included groups of 50 males and females 

of the four different rat strains. 
39

Additional issues related to the choice of toxic nephrosis in mice from NCI (1976) were 

that BMD analysis was not supported because the effect occurred in nearly 100% of animals in 

both dose groups, and that a high level of uncertainty was associated with extrapolation from the 

LOAEL at which nearly 100% animals were affected. It was noted by the Panel that toxic 

nephrosis did not occur in any control animals of either sex in this study.

Thus, although the numerical values for the RfD and RfC based on the renal endpoints 

were highly uncertain, TCE could clearly cause renal toxicity in both sexes of the four strains of 

rats tested, as well as in both sexes of mice, when administered in sufficient doses.

8h The selection of the draft RfC and RfD on the basis of multiple critical effects for which 

candidate reference values are in a narrow range at the low end of the full range of candidate 

critical effects, rather than on the basis of the single most sensitive critical effect. 

The Panel supported the selection of a draft RfC and a draft RfD based on multiple 

candidate reference values in a narrow range which was at the low end of the full range of 

candidate reference values developed, rather than basing these values on the single most 

sensitive critical endpoint. This approach was supported by the Panel because it was a very 

robust approach that increases confidence in the final RfC and RfD.

Reference Concentration

As noted in the draft assessment, the proposed RfC, 0.001 ppm (5 ug/m3

), was within a 

factor of 3 of the p-cRfCs for the six critical endpoints selected. The Panel agreed with the use of 

PBPK modeling for route-to-route extrapolation for the five p-cRfCs which were based on oral 

studies. 

EPA stated in the draft document (p. 5-83) that there was high confidence in the three pcRfCs based on renal endpoints [increased kidney weight (Woolhiser et al., 2006), toxic 

nephrosis (NCI, 1976), and toxic nephropathy, (NTP,1988)] because of the clearly adverse 

nature of the effects, the fact that two of them were based on chronic studies, and high 

confidence in its estimate of the dose metric which was clearly related to toxicity, while there 

was somewhat less confidence in the three p-cRfCs based on other endpoints [decreased thymus 

weight and anti-DNA antibodies (Keil et al., 2009) and cardiac malformation (Johnson et al., 

2003)]. As stated in the response to (g), TCE can clearly cause significant renal toxicity when 

administered in sufficient doses. Thus, the Panel agreed that kidney toxicity was indisputably a 

key effect of TCE from a hazard identification perspective. However, as discussed above, the 

Panel concluded that the three p-cRfCs for renal endpoints were based on an uncertain dose 

metric, especially in regard to the relative rate of formation of the toxic metabolite in humans 

and animals. Although there was somewhat less confidence in the immune and cardiac 

malformation endpoints from a hazard identification perspective, for reasons discussed 

extensively in other sections of this response, there was sufficient confidence in them to consider 

them critical endpoints to support the RfC. While the confidence in these three endpoints was 

less than for the kidney endpoints as far as hazard identification, the three p-cRfCs for these 

endpoints were based on relatively certain dose metrics. 
40

Although there was much greater pharmacokinetic uncertainty for the RfCs based on the 

three studies with renal endpoints [(Woolhiser et al., NCI (1976), and NTP (1988)], they 

provided additional support for the RfC.

The Panel noted that the same final RfC, 0.001 ppm, was supported by the p-cRfCs based 

on both the three principal studies (0.0003 ppm, 0.0004 ppm, and 0.003 ppm) and the supporting 

(kidney) studies (0.0006 ppm, 0.001 ppm, and 0.002 ppm), and concluded that the use of pcRfCs for multiple critical effects to derive the final recommended RfC reduced uncertainty and 

better characterizes variability. It was noted that, in general, this approach may create more 

work for the risk assessors and the users of the risk assessment than use of the single most 

sensitive endpoint. However, it was recognized that, even if the RfC were to be based on the 

single most sensitive endpoint, it would be necessary to develop p-cRfCs for multiple endpoints 

in order to rigorously determine which study and endpoint provides the most sensitive RfC. It 

was also noted that a single RfC value was provided to users of the risk assessment.

The Panel noted that the same final RfD, 0.0004 mg/kg/day was supported by the p-cRfCs 

based on both the three principal studies (0.0004 mg/kg/day, 0.0005 mg/kg/day, and 0.0005 

mg/kg/day) and the supporting (kidney) study (0.0003 mg/kg/day), and concluded that the use of 

Reference Dose

As discussed in the draft document, the proposed RfD, 0.0004 mg/kg/day, was within 25% of 

the p-cRfDs for the four critical endpoints selected (toxic nephropathy (NTP, 1988), decreased 

thymus weight [(Keil et al, 2009), developmental immunotoxicity (Peden-Adams et al., 2006), 

and cardiac malformations (Johnson et al., 2003)]. All four p-cRfDs were based on oral 

exposure, and three of them were based on drinking water exposure, a route relevant to 

environmental exposures. EPA stated in the draft document (p. 5-83) that there was high 

confidence in the p-cRfD based on a renal endpoint (toxic nephropathy, (NTP, 1988)) because of 

the clearly adverse nature of the effects in a chronic study and the high confidence in the 

estimate of the dose metric which was clearly related to toxicity, while there was somewhat less 

confidence in the three p-cRfCs based on other endpoints [decreased thymus weight (Keil et al., 

2009), developmental immunotoxicity (Peden-Adams et al., 2006), and cardiac malformations 

(Johnson et al., 2003)]. As stated in the response to (g), TCE could clearly cause significant renal 

toxicity when administered in sufficient doses. Thus, as in the RfC discussion above, the Panel 

agreed that kidney toxicity was indisputably a key effect of TCE from a hazard identification

perspective. However, as discussed above, the Panel concluded that the p-cRfD for the kidney 

endpoint was based on an uncertain dose metric in regard to the relative rate of formation of the 

toxic metabolite in humans and animals. Although there was somewhat less confidence in the 

immune and cardiac malformation endpoints from a hazard identification perspective, for 

reasons discussed extensively in other sections of this response, there was sufficient confidence 

in them to consider them critical endpoints to support the RfC. While the confidence in these 

three endpoints was less than for the kidney endpoints as far as hazard identification, the three pcRfCs for these endpoints were based on relatively certain dose metrics. 

 

Although there was greater pharmacokinetic uncertainty for the p-cRfD based on the renal 

endpoint (NTP, 1988), it provided additional support for the final RfD.
41

p-cRfDs for multiple critical effects to derive the final recommended RfD reduced uncertainty 

and better characterizes variability. As discussed above for the RfC, it was noted that, in general, 

this approach may create more work for the risk assessors and the users of the risk assessment 

than use of the single most sensitive endpoint. However, it was recognized that, even if the RfD 

were to be based on the single most sensitive endpoint, it would be necessary to develop p-cRfCs 

for multiple endpoints in order to rigorously determine which study and endpoint would give the 

most sensitive RfD. It was also noted that a single RfD value was provided to users of the risk 

assessment. 

Recommendations:

• The two endpoints for immune effects from Keil et al. (2009) and the cardiac malformations 

from Johnson et al. (2003) should be considered the principal studies supporting the RfC.

• The endpoints for immune effects from Keil et al. (2009) and Peden-Adams et al. (2009) and 

the cardiac malformations from Johnson et al. (2003) should be considered as the principal 

studies supporting the RfD.
42

9. Cancer Dose-Response Assessment

In accordance with the approach outlined in the U.S. EPA Cancer Guidelines and 

Supplemental Guidance (U.S. EPA, 2005a; U.S. EPA, 2005b), EPA’s dose-response 

assessment includes the development of an inhalation unit risk and oral unit risk for the 

carcinogenic potency of TCE. Please address the following methods, results, and 

conclusions from EPA’s cancer dose-response assessment in terms of the extent to 

which they are clearly and transparently described and technically/scientifically 

adequate to support EPA’s draft inhalation and oral unit risks:

a. the estimation of unit risks for renal cell carcinoma from the Charbotel et 

al. (2006) case-control study;

b. the adjustments of renal cell carcinoma unit risks to account for the 

added risk of other cancers using the meta-analysis results and 

Raaschou-Nielsen et al. (2003);

c. the estimation of human unit risks from rodent bioassays; 

d. in accordance with the approach in the U.S. EPA Cancer Guidelines (U.S. 

EPA, 2005a) and the conclusions as to MOA (above), the use of linear 

extrapolation from the point of departure (POD) for the cancer doseresponse assessment of TCE; 

e. the applications of PBPK modeling, including the selection of dose 

metrics and the use of PBPK model predictions for inter-species, intraspecies, and route-to-route extrapolation based on internal dose, and 

their preference over default approaches based on applied dose;

f. the qualitative and quantitative characterization of uncertainty and 

variability;

g. the conclusion that the unit risk estimates for TCE based on human 

epidemiologic data and those based on rodent bioassay data are 

consistent overall; and,

h. the preference for the unit risk estimates for TCE based on human 

epidemiologic data over those based on rodent bioassay data.

9a Estimation of Unit Risks for Renal Cell Carcinoma

The Panel agreed that the analysis of the Charbotel et al. (2006) data was well 

described and scientifically appropriate and that the study should be used to estimate unit 

risks. The Panel did, however, agree that some more discussion was needed on cutting oils 

and whether or not it was necessary to adjust for exposure to cutting oils when computing an 

odds ratio or relative risk relating TCE exposure to kidney cancer. As noted in the document 

(p. 5-136), Charbotel et al. (2006) found a marginally significant relationship between cutting 

and petroleum oils and RCC (p-value < 0.1) though the relationship disappeared after 

adjustment for other variables. Given that there was some suggestion of a relationship, the 

Panel recommended that the EPA take a closer look at the literature to determine if there 

were other studies which suggested that exposure to cutting oils was a risk factor for kidney 

cancer. 
43

Recommendations:

• The Panel believed that the EPA should provide a more detailed discussion of the 

limitations of their analysis. In particular, the model described on p. 5-131 made some 

very restrictive assumptions: linear dose-response and exposure was measured without 

error. In addition, the life table analysis applied the same estimated RR to each age 

interval; another restrictive assumption. While the Panel understood that these 

assumptions were necessary due to limited data, there was inadequate discussion of how 

violations of these assumptions may affect the results. 

• Finally, in constructing the life table, the EPA used background kidney cancer rates in 

the US though the Charbotel et al. (2006) data were based on a French cohort. Hence, a 

comparison of background cancer rates in France and the U.S. would be helpful in 

supporting their conclusions.

9b Adjustment of Renal Cell Carcinoma Unit Risks 

The Panel agreed that the analysis and presentation should be accepted in its current 

form.

9c Estimation of Human Unit Risks from Rodent Bioassays

EPA also calculated cancer unit risk estimates based on chronic bioassays on rats and 

mice. Five inhalation bioassays and 7 oral bioassays were selected for dose-response 

analyses. Dose-response modeling using the linearized multistage model was performed 

using applied doses as well as PBPK model-based internal doses. Bioassays for which timeto-tumor data were available were analyzed using a Multistage Weibull model. A cancer 

potency estimate for different tumor types combined were derived from bioassays in which 

there was more than one type of tumor response in the same sex and species. Unit risk 

estimates based on PBPK model-estimated internal doses were then extrapolated to human 

population unit risk estimates using the human PBPK model. Based on these results, the 

most sensitive bioassay (i.e. the one with the greatest unit risk estimate) was considered as a 

candidate unit risk estimates for TCE. 

Recommendations:

• The Panel agreed that the analysis and results were appropriate but recommended that 

the EPA providemore details about their implementation and potential biases. For 

instance, in bioassays in which mortality occurred before time to first tumor, the authors 

simply adjusted their denominators to equal the number alive at time to first tumor. This 

approach assumed that drop-out prior to time to first tumor was unrelated to future risk 

of a tumor which could result in biased estimates. 

• In addition, more information was needed on the priors used in their Bayesian analysis 

of combined risk across tumor types.

9d Use of Linear Extrapolation for Cancer Dose-Response Assessment

The Panel agreed that the analysis was consistent with current cancer guidelines. 

There was sufficient evidence to conclude that a mutagenic MOA was operative for TCE-
44

induced kidney tumors, so linear extrapolation was used to derive unit risk estimates for this 

site. For all other tumor types, linear extrapolation was used as the default approach, in 

accordance with EPA’s cancer guidelines. Hence, the Panel recommended accepting the 

analysis and presentation of the results in its present form.

9e Application of PBPK Modeling

The Panel agreed that the PBPK models provided valuable information to the risk 

assessment and agreed that the internal dose should be preferred over applied dose as it was 

the only way one could, at the mechanistic level, combine information about 

pharmacokinetics and pharmacodynamics.

9f Qualitative and Quantitative Characterization of Uncertainty and Variability

The Panel agreed that consideration of uncertainty and variability was adequate. The 

Panel believed that the characterization of uncertainty and variability in the PBPK models 

was exceptionally strong. Use of AIC to select the best fit model was an adequate way to 

address model uncertainty. However, the authors’ use of a 0.05 significance level for 

goodness of fit tests was inappropriate; typically, larger type-I error rates are used in such 

tests (e.g., values between 0.1 and 0.2) since one usually does not want to reject the null 

hypothesis that the model fits the data.

9g Conclusion on the Consistency of Unit Risk Estimates Based on Human 

Epidemiologic Data and Rodent Bioassay Data

The Panel agreed with this conclusion. For inhalation, the most sensitive rodent 

bioassay responses based on the preferred dose metrics ranged from 2.6 x 10-3 per ppm to 8.3 

x 10-2 per ppm across the sex/species combinations. For oral exposure, the most sensitive 

bioassay responses based on the preferred dose metrics ranged from 2.3 x 10-3 per mg/kg/d to 

2.5 x 10-1 per mg/kg/d across the sex/species combination. For both routes of exposure, the 

most sensitive sex/species response was male rat kidney cancer based on the preferred dose 

metric. When the human epidemiologic data were considered, a cancer inhalation unit risk 

estimate of 2.2 x 10-2 per ppm and oral unit risk estimate of 5 x 10-2 per mg/kg/d were 

obtained, which are both within the ranges reported in the aforementioned animal studies.

9h Preference for the Unit Risk Estimates based on Human Epidemiologic Data

The Panel agreed that human data, when available, should be preferred over rodent 

data when estimating unit risk, since within-species uncertainty was easier to address than 

between-species uncertainty.
45

10. Age-Dependent Adjustment Factors

Based on the conclusions that the weight of evidence supports a mutagenic MOA for 

TCE-induced kidney cancer and that the MOAs for TCE-induced liver cancer and 

lymphomas are not known, the Age-Dependent Adjustment Factors (ADAFs) are only 

applied to the kidney cancer component of the unit risk estimates. Please address the 

extent to which the recommended approach to applying the ADAFs in this situation is 

clearly, transparently, and accurately described.

The Panel concluded that EPA has done an excellent job of describing and presenting 

the ADAF computations for both oral and inhalation situations. Application of ADAFs in 

the TCE analysis consistently followed recommendations in U.S. EPA Cancer Guidelines 

(U.S. EPA, 2005a) and Supplemental Guidance (U.S. EPA, 2005b). All of the steps were 

clearly presented for inhalation exposure. However, the discussion for the oral exposure 

route was shortened and referred back to the inhalation section, making understanding of the 

example less easy to follow. 

EPA supplemental guidance recommends adjustment for children based on the 

presumption that children <16 years of age are intrinsically more susceptible than adults to 

mutagenic carcinogens because of biochemical and physiological factors related to the 

development of many organs and tissues during this time period; the rationale for the 

application of an ADAF is not based on the assumption that children have greater exposure 

on a per body weight basis than adults. 

The Panel recognized that EPA wished to maximize utility in its IRIS database for TCE and 

other chemicals for which ADAFs were applied by providing slope factors and unit risk 

factors that allow users to compute risks for situation-specific drinking water intake values 

and for exposures to different age groups. Drinking water concentrations for specified 

lifetime cancer risk levels (10-4

, 10-5, 10-6) are routinely included in IRIS assessments in

which ADAFs are not applied; this information is very helpful to public health professionals 

who use the IRIS database to evaluate situations of water contamination. For IRIS 

assessments in which ADAFs are applied, as in TCE, it would be useful to users to include 

this information, using representative drinking water intakes for various age groups. Other 

drinking water estimates may be used if determined to be more applicable.

Response

The Panel was somewhat concerned that the use of ADAFs was in conflict with the 

assumptions that underlie the life-table analysis described in Section 5.2.2.1.2 and Appendix 

H. As indicated on p. 5-131, lines 25-28, the life-table method used to calculate lifetime 

extra risks from the Charbotel et al. (2006) study assumed that relative risk (RR) was 

independent of age; as seen in Table H-1, the same estimate of RR was used in each age 

interval of the life-table to compute the exposed RCC hazard rate (column L). However, 

ADAFs were applied under the assumption that children were more susceptible to the 

mutagenic effects which implied that RRs were age-dependent. The Panel recommended 

that EPA clarify whether this conflict in assumptions truly exists and if so, what impact it 
46

might have on risk estimation and how it may be resolved in the future. For example, it 

might make more sense to apply ADAFs during the life-table analysis instead of at the end of 

the analysis, following estimation of the unit risk.

Recommendations:

• The Panel recommended that the statement on page 5-151, lines 14-18, be expanded to 

better explain why age-dependent adjustment factors were used for <16 years of age, but 

not for the elderly, and why EPA did not directly produce age dependent unit risks per 

mg/kg/d.

• Include all details presented for the inhalation sample calculations as was done for the 

oral exposure sample calculations.

• IRIS assessments in which ADAFs are applied, such as TCE, should include estimated 

drinking water concentrations for specified lifetime cancer risk levels (10-4

, 10-5, 10-6), 

using representative drinking water intakes for various age groups, while noting that 

other drinking water estimates may be used if preferred.

• Include in the documentation a discussion of the perceived conflict between the use of 

ADAFs and the assumptions underlying the life table analysis of the Charbotel et al. 

(2006) data.
47

11. Additional key studies

Please identify any additional studies that would make a significant impact on the 

conclusions of the Toxicological Review and should therefore be considered in the 

assessment of the noncancer and cancer health effects of TCE. 

The Panel has identified additional studies to be considered in the assessment:

11a Fetal Cardiac Effects

Some recent publications confirm and reinforce the results obtained in the Johnson et al. 

(2003) study, so maybe they could be cited to make a stronger argument. They are listed as 

follows:

Caldwell, PT; Thorne, PA; Johnson, PD et al. (2008) Trichloroethylene disrupts cardiac gene 

expression and calcium homeostasis in rat myocytes. Toxicol Sci 104: 135-143.

Györke S, Terentyev D. (2008) Modulation of ryanodine receptor by luminal calcium and

accessory proteins in health and cardiac disease. Cardiovasc Res. 77(2):245-55. Epub 2007 

Oct 15. Review. PubMed PMID: 18006456.

Lehnart SE, Mongillo M, Bellinger A,et al.(2008) Leaky Ca2+ release channel/ryanodine 

receptor 2 causes seizures and sudden cardiac death in mice. J Clin Invest. 118(6):2230-45. 

PubMed PMID: 18483626; PubMed Central PMCID: PMC2381750.

Lebeche D, Davidoff AJ, Hajjar RJ. (2008) Interplay between impaired calcium

regulation and insulin signaling abnormalities in diabetic cardiomyopathy. Nat

Clin Pract Cardiovasc Med. 5(11):715-24. Epub 2008 Sep 23. Review.

PubMed PMID: 18813212.

Pace, BM; Lawrence, DA; Behr, MJ; et al. (2005) Neonatal lead exposure changes quality 

of sperm and number of macrophages in testes of BALB/c mice. Toxicology 210: 247-

256.

Yano M, Yamamoto T, Kobayashi S. et al. (2008) Defective Ca2+ cycling 

as a key pathogenic mechanism of heart failure. Circ J. 72 Suppl A:A22-30.

Epub Sep 4. Review. PubMed PMID: 18772523.

11b Kidney Effects

Jacob, S; Héry, M ; Protois, JC ; et al. (2007) New insight into solvent-related end-stage renal 

disease : occupations, products and types of solvents at risk. Occup Environ Med 64: 843-

848.

Response
48

12. Research Needs

Please discuss research likely to substantially increase confidence in the database for future

assessments of TCE. 

12a PBPK Model

The Panel concluded the analysis presented in the TCE Review Document defined how 

EPA expects to use PBPK models to integrate what is known about animal and human biology 

with TCE mode of action information and available animal and human study data to improve the 

transparency and accuracy of chemical risk assessments. This is a substantial piece of research 

and the EPA is to be applauded for this effort. The Panel discussed additional research, which 

could further improve the TCE risk assessment as well as influence the broader use of PBPD 

models in risk assessment. 

The current model does not account for the temporal variability of the inputs and outputs 

in humans. Future development of the trichloroethylene PBPK model requires accommodation 

in the model for inter-individual temporal variability in the population. This is particularly 

important for modeling both sub-chronic and chronic exposures. If anything, the model should

be most accurate in modeling the effects of human exposure over an extended period. Support 

for adding an inter-individual temporal component to the model can be found in a number of 

places in the report. For example on page 3-108 (lines 14-16) the text reads: “However, data 

from Chiu et al. (2007) indicated substantial interoccasion variability, as the same individual 

exposed to the same concentration on different occasions sometimes had substantial differences 

in urinary excretion.’’ In this paper Chiu et al. (2007), found that there was variability in urinary 

excretion from the same individual exposed to the same concentration on different occasions. 

Also, Fisher et al. (1998) (see Table 3-45, page 3-111) documents an occasion in which a female 

was exposed to both 50 and 100 ppm. Assuming the same subject-specific estimates across the 

two occasions at different doses resulted in over-prediction at the higher exposure. 

To substantially improve the PBPK model for trichloroethylene, EPA should perform a 

global sensitivity analysis. A formal Bayesian sensitivity analysis is one approach available, but 

even a more traditional approach to model sensitivity would provide useful information. In 

addition, the impact of changing priors and/or incorporating correlations among parameters 

should be examined. Because key dose metrics include upper tails from the predicted posterior 

distribution, future work should evaluate the sensitivity of the predictions to distributional 

assumptions for the random effects, for example by replacing uniform priors with normal or 

lognormal priors or by modifying the bounds on the priors. In future studies, the EPA should 

perform at least a limited analysis of sensitivity of results to model form (especially sensitivity to 

different assumed GSH pathways).

Response

However, the hierarchical approach formulated in this report also made important 

assumptions about the relationship between the PBPK model parameters across the different 
49

species. These assumptions should be used consistently throughout the model development and 

not just in the case where there is limited prior information about a particular species. 

Recommendations:

• Continue to look for data to support further refinement of priors, especially improving noninformative priors to informative priors and wide priors to narrower priors.

• Develop more efficient sophisticated model algorithms/environments to improve the 

simulation and reduce run time.

• Incorporate inter-individual temporal variability in future enhancements of the PBPK model 

for TCE.

• Perform a sensitivity analysis that ranges from the traditional assessment of the impact of 

parameter changes on final model predictions to an examination of the effect of changing 

prior distributions.

12b Derivation of RfD and RfC

Recommendations:

• The uncertainty about the rate of human glutathione conjugation found in Lash et al. (1999a) 

versus Green et al. (1997a) should be highlighted in the current assessment and addressed by 

sensitivity analysis in future refinements of this assessment. 

• The variability/uncertainty for the toxicokinetically-sensitive individual could be quantified 

in future work by considering distributions in addition to the distribution of the 99th 

percentile, such as the 95th percentile. A quantile regression looking simultaneously at 

several quantiles could be developed in the future and presented in future refinements of this 

assessment.

• In future work, EPA could develop an approach using distribution to characterize uncertainty 

in a Bayesian framework. 

12c Susceptibility Factors

Recommendations:

• There is a need for data examining potential modulation of health effects of TCE by t factors 

such as genetics, lifestage, background, co-exposures, and pre-existing conditions). 

• Modulation of TCE exposure-related hypersensitivity dermatitis by genetic variation may be 

relevant for future study, given results of the study of hypersensitivity dermatitis in Asian 

workers reported in Li et al. (2007) and increasing industrial chemical exposures in China. 
50

12d Male Reproductive System

Recommendations:

• For in utero exposure studies in rodents using lower doses of TCE and metabolites, where 

effects (carcinogenic and non-carcinogenic) can be observed trans-generationally, attention 

should be directed to epigenetic changes as possible MOA for TCE-mediated effects on the 

reproductive systems.
51

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55

Appendix A: Editorial Comments

Chapter 4

Typographical corrections - In the section on vestibular function – (headaches, dizziness, nausea) 

there is a typo on p 4-101 that should be corrected. LOAEL 1000 ppm human study (Kylin et 

al., 1967); 2700 ppm in rats (Tham et al 1984, Niklasson et al., 1993) and rabbits (Tham et al, 

1983).

In the kidney section, there needs to be added mention of the 18% increase in kidney weight (in 

male mice only) seen in the largely immunotoxicity study conducted by Peden-Adams (2008).

Editorial Footnote #1 on page 146: “Elevation of NAG in urine is a sign of proteinuria, and 

proteinuria is both a sign and a cause of kidney malfunction (Zandi-Nejad et al., 2004). “ Beta –

N-acetylglucosaminidase (NAG) is an enzyme released by the proximal tubules. Usually total 

NAG is measured, however, this is comprised of NAG B, which reflects necrosis, and NAG A, 

which reflects milder forms of proximal tubule perturbation.

The last sentence on p4-173 line 32, 33 needs to be reworded as it is unclear. Additionally, there 

is a double period on line 23, p4-199.

Chapter 5

p. 5-33, line 25. Does “delivered dose” mean “administered dose”? If so, the term 

“administered dose” would be clearer.

p. 5-37, line 17. Should “kidney tumors” be changed to “kidney toxicity”, since this section 

discusses non-cancer effects?

p. 5-10, line 9, Barrett et al., 1992, was referred to as an “acute study”. On p.4-91, Table 4-21, it 

was shown that Barrett et al., 1991, was acute and Barrett et al., 1992, was subchronic (10 

weeks). This should be corrected.

p. 5-2, point (7), the use of the 99th percentile HEC and HED estimates was discussed. The 

reason for choosing 99th percentile instead of 95th percentile was explained later in the chapter 

(p. 5-45). A reference to this discussion (p. 5-48) here would be helpful for clarification, since 

the 95th percentile was more commonly used in other risk assessments.

Table 5-23, NCI (1976), last bullet. 0.9 ug/m3 should be corrected to 9 ug/m3

.

p. 5-24, lines 31-32. Change to “within 2-fold of each other” (1.1-1.9 mg/kg/day).